diff --git a/.github/workflows/codeql.yml b/.github/workflows/codeql.yml
new file mode 100644
index 0000000..e74a129
--- /dev/null
+++ b/.github/workflows/codeql.yml
@@ -0,0 +1,93 @@
+# For most projects, this workflow file will not need changing; you simply need
+# to commit it to your repository.
+#
+# You may wish to alter this file to override the set of languages analyzed,
+# or to provide custom queries or build logic.
+#
+# ******** NOTE ********
+# We have attempted to detect the languages in your repository. Please check
+# the `language` matrix defined below to confirm you have the correct set of
+# supported CodeQL languages.
+#
+name: "CodeQL"
+
+on:
+  push:
+    branches: [ "activ_dev", "main*" ]
+  pull_request:
+    branches: [ "activ_dev", "main*" ]
+  schedule:
+    - cron: '24 12 * * 2'
+
+jobs:
+  analyze:
+    name: Analyze (${{ matrix.language }})
+    # Runner size impacts CodeQL analysis time. To learn more, please see:
+    #   - https://gh.io/recommended-hardware-resources-for-running-codeql
+    #   - https://gh.io/supported-runners-and-hardware-resources
+    #   - https://gh.io/using-larger-runners (GitHub.com only)
+    # Consider using larger runners or machines with greater resources for possible analysis time improvements.
+    runs-on: ${{ (matrix.language == 'swift' && 'macos-latest') || 'ubuntu-latest' }}
+    timeout-minutes: ${{ (matrix.language == 'swift' && 120) || 360 }}
+    permissions:
+      # required for all workflows
+      security-events: write
+
+      # required to fetch internal or private CodeQL packs
+      packages: read
+
+      # only required for workflows in private repositories
+      actions: read
+      contents: read
+
+    strategy:
+      fail-fast: false
+      matrix:
+        include:
+        - language: python
+          build-mode: none
+        # CodeQL supports the following values keywords for 'language': 'c-cpp', 'csharp', 'go', 'java-kotlin', 'javascript-typescript', 'python', 'ruby', 'swift'
+        # Use `c-cpp` to analyze code written in C, C++ or both
+        # Use 'java-kotlin' to analyze code written in Java, Kotlin or both
+        # Use 'javascript-typescript' to analyze code written in JavaScript, TypeScript or both
+        # To learn more about changing the languages that are analyzed or customizing the build mode for your analysis,
+        # see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/customizing-your-advanced-setup-for-code-scanning.
+        # If you are analyzing a compiled language, you can modify the 'build-mode' for that language to customize how
+        # your codebase is analyzed, see https://docs.github.com/en/code-security/code-scanning/creating-an-advanced-setup-for-code-scanning/codeql-code-scanning-for-compiled-languages
+    steps:
+    - name: Checkout repository
+      uses: actions/checkout@v4
+
+    # Initializes the CodeQL tools for scanning.
+    - name: Initialize CodeQL
+      uses: github/codeql-action/init@v3
+      with:
+        languages: ${{ matrix.language }}
+        build-mode: ${{ matrix.build-mode }}
+        # If you wish to specify custom queries, you can do so here or in a config file.
+        # By default, queries listed here will override any specified in a config file.
+        # Prefix the list here with "+" to use these queries and those in the config file.
+
+        # For more details on CodeQL's query packs, refer to: https://docs.github.com/en/code-security/code-scanning/automatically-scanning-your-code-for-vulnerabilities-and-errors/configuring-code-scanning#using-queries-in-ql-packs
+        # queries: security-extended,security-and-quality
+
+    # If the analyze step fails for one of the languages you are analyzing with
+    # "We were unable to automatically build your code", modify the matrix above
+    # to set the build mode to "manual" for that language. Then modify this step
+    # to build your code.
+    # ℹ️ Command-line programs to run using the OS shell.
+    # 📚 See https://docs.github.com/en/actions/using-workflows/workflow-syntax-for-github-actions#jobsjob_idstepsrun
+    - if: matrix.build-mode == 'manual'
+      shell: bash
+      run: |
+        echo 'If you are using a "manual" build mode for one or more of the' \
+          'languages you are analyzing, replace this with the commands to build' \
+          'your code, for example:'
+        echo '  make bootstrap'
+        echo '  make release'
+        exit 1
+
+    - name: Perform CodeQL Analysis
+      uses: github/codeql-action/analyze@v3
+      with:
+        category: "/language:${{matrix.language}}"
diff --git a/readthedocs.yml b/.readthedocs.yaml
similarity index 50%
rename from readthedocs.yml
rename to .readthedocs.yaml
index 36acb0a..d27f814 100644
--- a/readthedocs.yml
+++ b/.readthedocs.yaml
@@ -8,13 +8,26 @@ version: 2
 
 # Build documentation in the docs/ directory with Sphinx
 sphinx:
-   configuration: docs/source/conf.py
+   configuration: docs/conf.py
+
+# Set the OS, Python version and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+    # You can also specify other tool versions:
+    # nodejs: "20"
+    # rust: "1.70"
+    # golang: "1.20"
+
+
 
 # Optionally set the version of Python and requirements required to build your docs
 python:
    install:
    - requirements: requirements.txt
-   system_packages: True
-   
-submodules:
-   include: all
\ No newline at end of file
+   - requirements: docs/requirements.txt
+#   system_packages: True
+#   
+#submodules:
+#   include: all
\ No newline at end of file
diff --git a/ForMoSA.egg-info/PKG-INFO b/ForMoSA.egg-info/PKG-INFO
index 4a56e94..fdedc95 100644
--- a/ForMoSA.egg-info/PKG-INFO
+++ b/ForMoSA.egg-info/PKG-INFO
@@ -1,11 +1,11 @@
 Metadata-Version: 2.1
 Name: ForMoSA
-Version: 2.0.0
+Version: 1.1.3
 Summary: ForMoSA: Forward Modeling Tool for Spectral Analysis
 Home-page: https://github.com/exoAtmospheres/ForMoSA
 Author: P. Palma-Bifani, S. Petrus, M. Ravet, A. Denis, M. Bonnefoy, G. Chauvin
 Author-email: paulina.palma-bifani@oca.eu
-License: BSD 2-Clause License
+License: BSD 3-Clause License
 License-File: LICENSE
 Requires-Dist: numpy
 Requires-Dist: matplotlib
@@ -18,5 +18,11 @@ Requires-Dist: nestle
 Requires-Dist: PyAstronomy
 Requires-Dist: spectres
 Requires-Dist: pyyaml
+Requires-Dist: tqdm
 Requires-Dist: importlib-metadata==4.13.0
 Requires-Dist: xarray==2023.10.1
+
+Welcome to ForMoSA, an open-source Python package. 
+We designed this tool to model exoplanetary atmospheres with a forward modeling approach. 
+
+We encourage the community to exploit its capabilities!
diff --git a/ForMoSA.egg-info/requires.txt b/ForMoSA.egg-info/requires.txt
index e29281c..6c4410b 100644
--- a/ForMoSA.egg-info/requires.txt
+++ b/ForMoSA.egg-info/requires.txt
@@ -9,5 +9,6 @@ nestle
 PyAstronomy
 spectres
 pyyaml
+tqdm
 importlib-metadata==4.13.0
 xarray==2023.10.1
diff --git a/ForMoSA/__init__.py b/ForMoSA/__init__.py
index fd9425a..c9a7955 100644
--- a/ForMoSA/__init__.py
+++ b/ForMoSA/__init__.py
@@ -1,5 +1,5 @@
 import os
 
-__version__ = "2.0.0"
+__version__ = "1.1.3"
 
 __all__ = ['adapt', 'nested_sampling','plotting']
\ No newline at end of file
diff --git a/ForMoSA/adapt/adapt_grid.py b/ForMoSA/adapt/adapt_grid.py
index 53e807a..b35cc36 100755
--- a/ForMoSA/adapt/adapt_grid.py
+++ b/ForMoSA/adapt/adapt_grid.py
@@ -2,7 +2,9 @@
 import numpy as np
 import xarray as xr
 import time
-import os
+import os, sys
+
+sys.path.insert(0, os.path.abspath('../'))
 
 from adapt.extraction_functions import adapt_model, decoupe
 
@@ -15,7 +17,7 @@ def adapt_grid(global_params, wav_obs_spectro, wav_obs_photo, res_mod_obs_merge,
     Adapt the synthetic spectra of a grid to make them comparable with the data.
     
     Args:
-        global_params (object): Class containing each parameter
+        global_params    (object): Class containing each parameter
         wav_obs_spectro   (array): Merged wavelength grid of the data
         wav_obs_photo     (array): Wavelengths of the photometry points
         obs_name            (str): Name of the current observation looping
@@ -23,7 +25,7 @@ def adapt_grid(global_params, wav_obs_spectro, wav_obs_photo, res_mod_obs_merge,
     Returns:
         None
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet & Paulina Palma-Bifani
+    Author: Simon Petrus, Matthieu Ravet and Paulina Palma-Bifani
     """
 
     ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4")
diff --git a/ForMoSA/adapt/adapt_obs_mod.py b/ForMoSA/adapt/adapt_obs_mod.py
index fdf4bcd..ba0002b 100755
--- a/ForMoSA/adapt/adapt_obs_mod.py
+++ b/ForMoSA/adapt/adapt_obs_mod.py
@@ -1,14 +1,15 @@
 from __future__ import print_function, division
 import numpy as np
-import os
+import os,sys
 import xarray as xr
 from scipy.interpolate import interp1d
 
+sys.path.insert(0, os.path.abspath('../'))
+
 from adapt.extraction_functions import extract_observation
 from adapt.adapt_grid import adapt_grid
 from main_utilities import diag_mat
 import glob
-# import matplotlib.pyplot as plt
 # ----------------------------------------------------------------------------------------------------------------------
 
 
@@ -22,7 +23,7 @@ def launch_adapt(global_params, justobs='no'):
     Returns:
         None
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet, Paulina Palma-Bifani and Allan Denis
+    Author: Simon Petrus, Matthieu Ravet, Paulina Palma-Bifani and Allan Denis
     """
 
     # Get back the grid information from the config file
diff --git a/ForMoSA/adapt/extraction_functions.py b/ForMoSA/adapt/extraction_functions.py
index 993ded1..eae8173 100755
--- a/ForMoSA/adapt/extraction_functions.py
+++ b/ForMoSA/adapt/extraction_functions.py
@@ -5,7 +5,6 @@
 from scipy.interpolate import interp1d
 from spectres import spectres
 import os
-import matplotlib.pyplot as plt
 # ----------------------------------------------------------------------------------------------------------------------
 
 
@@ -16,7 +15,9 @@ def decoupe(second):
     Args:
         second (float): number of second
     Returns:
-        hour, minute, second (float, float, float): hours-minutes-seconds format
+        - float     : hours
+        - float     : minutes
+        - float     : seconds
 
     Author: Simon Petrus
     """
@@ -39,7 +40,7 @@ def find_nearest(array, value):
         array (array): Array to explore
         value (float): Desire value
     Returns:
-        idx     (int): Indice of the closest values from the desire value
+        - idx (int)          : Indice of the closest values from the desire value
 
     Author: Simon Petrus
     """
@@ -62,19 +63,15 @@ def extract_observation(global_params, wav_mod_nativ, res_mod_nativ, cont='no',
         cont                 (str): Boolean string. If the function is used to estimate the continuum cont='yes'
         obs_name             (str): Name of the current observation looping
         indobs               (int): Index of the current observation looping
+
     Returns:
-        obs_spectro      (n-array): List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution
-                                    [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
-        obs_photo          (array): List containing the photometry (0 replace the spectral resolution here).
-                                    [wav_phot, flx_phot, err_phot, 0]
-        obs_spectro_ins    (array): List containing different instruments used for the data (1 per wavelength).
-                                    [[instru_range_1], ..., [instru_range_n]]
-        obs_photo_ins      (array): List containing different filters used for the data (1 per photometric point).
-                                    [filter_phot_1, filter_phot_2, ..., filter_phot_n]
-        obs_opt          (n-array): List containing the optional sub-arrays defined by the parameter "wav_for_adapt".
-                                    [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]  
-
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
+        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0]
+        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]
+        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]
+        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]  
+
+    Author: Simon Petrus, Matthieu Ravet
     """
 
     # Extract the wavelengths, flux, errors, spectral resolution, and instrument/filter names from the observation file.
@@ -93,10 +90,10 @@ def extract_observation(global_params, wav_mod_nativ, res_mod_nativ, cont='no',
             # If we want to decrease the resolution of the data: (if by_sample, the data don't need to be adapted)
             if global_params.adapt_method[indobs] == 'by_reso':
                 obs_spectro[c][1] = resolution_decreasing(global_params, cut[0], cut[1], cut[3], wav_mod_nativ, [], res_mod_obs,
-                                                    'obs', obs_name=obs_name, indobs=indobs)
+                                                    'obs', indobs=indobs)
             if cont == 'yes':
             # If we want to estimate and substract the continuum of the data:
-                obs_spectro[c][1] -= continuum_estimate(global_params, cut[0], cut[1], cut[3], obs_name=obs_name, indobs=indobs)
+                obs_spectro[c][1] -= continuum_estimate(global_params, cut[0], cut[1], cut[3], indobs=indobs)
                 
     return obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt
 
@@ -112,17 +109,13 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
         global_params  (object): Class containing each parameter
         obs_name          (str): Name of the current observation looping
         indobs            (int): Index of the current observation looping
+
     Returns:
-        obs_spectro   (n-array): List containing the sub-spectra defined by the parameter "wav_for_adapt".
-                                [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
-        obs_photo       (array): List containing the photometry (0 replace the spectral resolution here).
-                                [wav_phot, flx_phot, err_phot, 0]
-        obs_spectro_ins (array): List containing different instruments used for the data (1 per wavelength).
-                                [[instru_1], ..., [instru_n]]
-        obs_photo_ins   (array): List containing different filters used for the data (1 per photometric point).
-                                [filter_phot_1, filter_phot_2, ..., filter_phot_n]
-        obs_opt       (n-array): List containing the optional sub-arrays defined by the parameter "wav_for_adapt".
-                                [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]
+        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution  [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
+        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here).  [wav_phot, flx_phot, err_phot, 0]
+        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]
+        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]
+        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]] 
 
     Author: Simon Petrus, Matthieu Ravet and Allan Denis
     """
@@ -316,16 +309,16 @@ def adapt_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge,
         obs_name          (str): Name of the current observation looping
         indobs            (int): Index of the current observation looping
     Returns:
-        flx_mod_extract (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
-        mod_photo       (array): List containing the photometry ('0' replace the spectral resolution here).
+        - mod_spectro (array)     : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
+        - mod_photo (array)       : List containing the photometry ('0' replace the spectral resolution here).
 
     Author: Simon Petrus
     """
     # Estimate and subtract the continuum (if needed)
     if global_params.continuum_sub[indobs] != 'NA':
-        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='yes', obs_name=obs_name)
+        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='yes', obs_name=obs_name, indobs=indobs)
     else:
-        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name=obs_name)
+        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
 
     return mod_spectro, mod_photo
 
@@ -346,8 +339,8 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge
         obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        mod_spectro    (array): List containing the sub-spectra defined by the parameter "wav_for_adapt".
-        mod_photo      (array): List containing the photometry ('0' replace the spectral resolution here).
+        - mod_spectro (array) : List containing the sub-spectra defined by the parameter "wav_for_adapt".
+        - mod (array)         : List containing the photometry ('0' replace the spectral resolution here).
 
     Author: Simon Petrus
     """
@@ -362,13 +355,13 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge
             # If we want to decrease the resolution of the data:
             if global_params.adapt_method[indobs] == 'by_reso':
                 mod_cut_flx = resolution_decreasing(global_params, cut[0], [], cut[3], wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge[c],
-                                                    'mod', obs_name=obs_name, indobs=indobs)
+                                                    'mod', indobs=indobs)
             else:
                 mod_cut_flx = spectres(cut[0], wav_mod_nativ, flx_mod_nativ)
 
             # If we want to estimate the continuum of the data:
             if cont == 'yes':     
-                continuum = continuum_estimate(global_params, cut[0], mod_cut_flx, res_mod_obs_merge[c], obs_name=obs_name, indobs=indobs)
+                continuum = continuum_estimate(global_params, cut[0], mod_cut_flx, res_mod_obs_merge[c], indobs=indobs)
                 mod_cut_flx -= continuum
 
             # Concatenate to speed up the code
@@ -411,7 +404,7 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si
         model_fluxes       (array): the fluxes of the model 
         num_sigma          (float): number of +/- sigmas to evaluate the LSF to.
     Returns:
-        output_model       (array): the fluxes in each of the wavelength channels 
+        - output_model (array)      : the fluxes in each of the wavelength channels 
 
     Author: Jason Wang
     """
@@ -442,7 +435,7 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nativ, flx_mod_nativ, res_mod_obs, obs_or_mod, obs_name='', indobs=0):
+def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nativ, flx_mod_nativ, res_mod_obs, obs_or_mod, indobs=0):
     """
     Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
     wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one
@@ -459,12 +452,11 @@ def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nati
         flx_mod_nativ  (array): Flux of the model
         res_mod_obs    (array): Spectral resolution of the model as a function of the wavelength grid of the data
         obs_or_mod       (str): Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod'
-        obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        flx_obs_final  (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
+        - flx_obs_final (array) : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+    Author: Simon Petrus, Matthieu Ravet
     """
     # Estimate of the FWHM of the data as a function of the wavelength
     fwhm_obs = 2 * wav_obs / res_obs
@@ -494,7 +486,7 @@ def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nati
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def continuum_estimate(global_params, wav, flx, res, obs_name='', indobs=0):
+def continuum_estimate(global_params, wav, flx, res, indobs=0):
     """
     Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
     wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease
@@ -506,12 +498,11 @@ def continuum_estimate(global_params, wav, flx, res, obs_name='', indobs=0):
         wav            (array): Wavelength grid of the spectrum for which you want to estimate the continuum
         flx            (array): Flux of the spectrum for which you want to estimate the continuum
         res              (int): Spectral resolution of the spectrum for which you want to estimate the continuum
-        obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        continuum      (array): Estimated continuum of the spectrum re-sampled on the data wavelength grid
+        - continuum (array)   : Estimated continuum of the spectrum re-sampled on the data wavelength grid
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+    Author: Simon Petrus, Matthieu Ravet
 
     """
 
diff --git a/ForMoSA/main_utilities.py b/ForMoSA/main_utilities.py
index e430279..7562bc2 100755
--- a/ForMoSA/main_utilities.py
+++ b/ForMoSA/main_utilities.py
@@ -3,12 +3,15 @@
 
 # ----------------------------------------------------------------------------------------------------------------------
 def yesno(text):
-    ''' Function to interact with the terminal and decide for different options when running ForMoSA
+    '''
+    Function to interact with the terminal and decide for different options when running ForMoSA (Loop to repeat question if answer is different to 'y' or 'n).
+
     Args:
         text    (str): (y/n) answer in the terminall in interactive mode
     Returns:
         asw     (str): answer y or n
-        (Loop to repeat question if answer is different to 'y' or 'n)
+
+    Author: Simon Petrus
     '''
     print(text)
     asw = input()
@@ -19,15 +22,17 @@ def yesno(text):
 
 # ----------------------------------------------------------------------------------------------------------------------
 def diag_mat(rem=[], result=np.empty((0, 0))):
-    ''' Function to concatenate and align iterativly block matrices (usefull during the extraction and the inversion)
-    Parameters:
+    '''
+    Function to concatenate and align iterativly block matrices (usefull during the extraction and the inversion).
+
+    Args:
         rem        (list): matrices to be add iterativly (use diag([mat1, mat2]))
         result    (array): final array with each sub-matrices aligned allong the diagonal
     Returns:
         diag_mat (matrix): Generated diagonal matrix
         (If rem input is empty, it wull return an empy array)
 
-    Credits : ishigoya, Stack-overflow : https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array
+    Author : Ishigoya, Stack-overflow : https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array
     '''
     if not rem:
         return result
@@ -45,7 +50,7 @@ def diag_mat(rem=[], result=np.empty((0, 0))):
 
 class GlobFile:
     '''
-    Import all the parameters from the config file and make them GLOBAL FORMOSA VARIABLES
+    Class that import all the parameters from the config file and make them GLOBAL FORMOSA VARIABLES.
     
     Author: Paulina Palma-Bifani
     '''
@@ -143,7 +148,7 @@ def __init__(self, config_file_path):
         # self.p_context = eval(config['config_pymultinest']['context'])
         # self.p_write_output = config['config_pymultinest']['write_output']
         # self.p_log_zero = eval(config['config_pymultinest']['log_zero'])
-        # self.p_max_iter = eval(config['config_pymultinest']['max_iter'])
+        # self.p_max_iter = eval(config['config_pymultinest']['max_iter'])
         # self.p_init_MPI = config['config_pymultinest']['init_MPI']
         # self.p_dump_callback = config['config_pymultinest']['dump_callback']
         # self.p_use_MPI = config['config_pymultinest']['use_MPI']
@@ -172,7 +177,7 @@ def __init__(self, config_file_path):
         # print()
         #
         # config_current = self.result_path + '/past_config.ini'
-        config.filename = ' '
+        # config.filename = ' '
         # config['config_path']['stock_interp_grid'] = stock_interp_grid
         # config['config_path']['stock_result'] = stock_result_subsub_dir
         # config.write()
diff --git a/ForMoSA/nested_sampling/nested_logL_functions.py b/ForMoSA/nested_sampling/nested_logL_functions.py
index 3dab1d9..6e581b3 100755
--- a/ForMoSA/nested_sampling/nested_logL_functions.py
+++ b/ForMoSA/nested_sampling/nested_logL_functions.py
@@ -3,13 +3,13 @@
 def logL_chi2_classic(delta_flx, err):
     """
     Function to compute logL based on the classical chi2
-    under the assumption of gaussian and spectrally uncorrelated noise
+    under the assumption of gaussian and spectrally uncorrelated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        err         : error (=standard deviation) of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -23,13 +23,13 @@ def logL_chi2_classic(delta_flx, err):
 def logL_chi2_covariance(delta_flx, inv_cov):
     """
     Function to compute logL based on the generalized chi2
-    under the assumption of gaussian and spectrally correlated noise
+    under the assumption of gaussian and spectrally correlated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        inv_cov     : inverse of the covariance matrix of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)       : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -43,13 +43,13 @@ def logL_chi2_covariance(delta_flx, inv_cov):
 def logL_chi2_extended(delta_flx, err):
     """
     Function to compute logL based on the extended chi2
-    under the assumption of gaussian and spectrally uncorrelated noise
+    under the assumption of gaussian and spectrally uncorrelated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        err         : error (=standard deviation) of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Allan Denis
     """
@@ -65,13 +65,13 @@ def logL_chi2_extended(delta_flx, err):
 def logL_chi2_extended_covariance(delta_flx, inv_cov):
     """
     Function to compute logL based on the extended chi2
-    under the assumption of gaussian and spectrally uncorrelated noise
+    under the assumption of gaussian and spectrally uncorrelated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        err         : error (=standard deviation) of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Allan Denis
     """
@@ -89,13 +89,13 @@ def logL_full_covariance(delta_flx, inv_cov):
     """
     Function to compute logL under the assumption of gaussian and spectrally correlated noise.
     This function is a generalized version of the logL_chi2_covariance and is to be used when dealing
-    with GP extimation of the covariance matrix
+    with GP extimation of the covariance matrix.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        inv_cov     : inverse of the covariance matrix of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)      : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -108,13 +108,13 @@ def logL_full_covariance(delta_flx, inv_cov):
 def logL_CCF_Brogi(flx_obs, flx_mod):
     """
     Function to compute logL based on the CCF mapping from Brogi et al. 2019
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs     : flux of the observation as a function of wavelength
-        flx_mod     : flux of the model as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -132,13 +132,13 @@ def logL_CCF_Brogi(flx_obs, flx_mod):
 def logL_CCF_Zucker(flx_obs, flx_mod):
     """
     Function to compute logL based on the CCF mapping from Zucker 2003
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs : flux of the observation as a function of wavelength
-        flx_mod : flux of the model as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
     Returns:
-        logL    : the loglikelihood value
+        - logL (float)      : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -157,14 +157,14 @@ def logL_CCF_Zucker(flx_obs, flx_mod):
 def logL_CCF_custom(flx_obs, flx_mod, err_obs):
     """
     Function to compute logL based on the custom CCF mapping from Me
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs : flux of the observation as a function of wavelength
-        flx_mod : flux of the model as a function of wavelength
-        err_obs : errors of the observation as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
+        err_obs     (array): errors of the observation as a function of wavelength
     Returns:
-        logL    : the loglikelihood value
+        - logL (float)       : the loglikelihood value
         
     Author: Matthieu Ravet
     """
diff --git a/ForMoSA/nested_sampling/nested_modif_spec.py b/ForMoSA/nested_sampling/nested_modif_spec.py
index d849599..cea9c65 100755
--- a/ForMoSA/nested_sampling/nested_modif_spec.py
+++ b/ForMoSA/nested_sampling/nested_modif_spec.py
@@ -1,12 +1,9 @@
 import numpy as np
-import xarray as xr
 import extinction
 from scipy.interpolate import interp1d
 import astropy.units as u
 import astropy.constants as const
-from PyAstronomy.pyasl import dopplerShift, rotBroad, fastRotBroad
-from adapt.extraction_functions import resolution_decreasing, convolve_and_sample
-import scipy.ndimage as ndi
+from PyAstronomy.pyasl import rotBroad, fastRotBroad
 import scipy.signal as sg
 import scipy.optimize as optimize
 import matplotlib.pyplot as plt
@@ -20,19 +17,22 @@ def lsq_fct(global_params, wave, indobs, flx_obs_spectro, err_obs_spectro, star_
     Estimation of the contribution of the planet and of the star to a spectrum (Used for HiRISE data)
 
     Args:
-        flx_obs_spectro  : Flux of the data (spectroscopy)
-        err_obs_spectro  : Error of the data (spectroscopy)
-        star_flx_obs     : Flux of star observation data (spectroscopy)
-        transm_obs       : Transmission (Atmospheric + Instrumental)
-        system_obs       : Systematics of the data (spectroscopy)
-        flx_mod_spectro  : Flux of interpolated synthetic spectrum (spectroscopy)
-        
+        flx_obs_spectro    (array): Flux of the data (spectroscopy)
+        err_obs_spectro    (array): Error of the data (spectroscopy)
+        star_flx_obs     (n-array): Flux of star observation data (spectroscopy)
+        transm_obs         (array): Transmission (Atmospheric + Instrumental)
+        system_obs       (n-array): Systematics of the data (spectroscopy)
+        flx_mod_spectro    (array): Flux of interpolated synthetic spectrum (spectroscopy)
     Returns:
-        cp               : Planetary contribution to the data (Spectroscopy)
-        cs               : Stellar contribution to the data (Spectroscopy)
-        flx_mod_spectro  : New model of the companion 
-        flx_obs_spectro  : New flux of the data
-        star_flx_obs     : New star flux of the data
+        - cp (array)                : Planetary contribution to the data (Spectroscopy)
+        - cs (array)                : Stellar contribution to the data (Spectroscopy)
+        - flx_mod_spectro (array)   : New model of the companion 
+        - flx_obs_spectro (array)   : New flux of the data
+        - star_flx_obs (n-array)    : New star flux of the data
+        - systematics (array)       : The systematics
+
+    Author : Allan Denis
+
     """
     
     wave_final, cp_final, cs_final, flx_mod_spectro_final, flx_obs_spectro_final, star_flx_obs_final, systematics_final, flx_mod_spectro_nativ, err_obs_spectro_final = np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([]), np.array([])
@@ -141,28 +141,28 @@ def calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro, flx_obs_photo, er
     and distance or analytically).
 
     Args:
-        flx_obs_spectro  : Flux of the data (spectroscopy)
-        err_obs_spectro  : Error of the data (spectroscopy)
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_obs_photo    : Flux of the data (photometry)
-        err_obs_photo    : Error of the data (photometry)
-        flx_mod_photo    : Flux of the interpolated synthetic spectrum (photometry)
-        r_picked         : Radius randomly picked by the nested sampling (in RJup)
-        d_picked         : Distance randomly picked by the nested sampling (in pc)
-        alpha            : Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²
-        analytic         : = 'yes' if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)
+        flx_obs_spectro  (array): Flux of the data (spectroscopy)
+        err_obs_spectro  (array): Error of the data (spectroscopy)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_obs_photo    (array): Flux of the data (photometry)
+        err_obs_photo    (array): Error of the data (photometry)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        r_picked         (float): Radius randomly picked by the nested sampling (in RJup)
+        d_picked         (float): Distance randomly picked by the nested sampling (in pc)
+        alpha            (float): Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²
+        analytic           (str): = 'yes' if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)
     Returns:
-        flx_mod_spectro  : Re-normalysed model spectrum
-        flx_mod_photo    : Re-normalysed model photometry
-        ck               : Ck calculated
+        - flx_mod_spectro (array)   : Re-normalysed model spectrum
+        - flx_mod_photo (array)     : Re-normalysed model photometry
+        - ck (float)                : Ck calculated
 
     Author: Simon Petrus
     """
     # Calculation of the dilution factor ck as a function of the radius and distance
     if analytic == 'no':
-        r_picked *= 69911
-        d_picked *= 3.086e+13
-        ck = alpha * (r_picked/d_picked)**2
+        r_picked *= u.Rjup
+        d_picked *= u.pc
+        ck = alpha * (r_picked.value/d_picked.value)**2
     # Calculation of the dilution factor ck analytically
     else:
         if len(flx_obs_spectro) != 0:
@@ -197,37 +197,25 @@ def doppler_fct(wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spect
     Note: Observation can change due to side effects of the shifting.
 
     Args:
-        wav_obs_spectro      : Wavelength grid of the data
-        flx_obs_spectro      : Flux of the data
-        err_obs_spectro      : Error of the data
-        flx_mod_spectro      : Flux of the interpolated synthetic spectrum
-        rv_picked            : Radial velocity randomly picked by the nested sampling (in km.s-1)
+        wav_obs_spectro      (array): Wavelength grid of the data
+        flx_obs_spectro      (array): Flux of the data
+        err_obs_spectro      (array): Error of the data
+        flx_mod_spectro      (array): Flux of the interpolated synthetic spectrum
+        rv_picked            (float): Radial velocity randomly picked by the nested sampling (in km.s-1)
     Returns:
-        wav_obs_spectro      : New wavelength grid of the data
-        flx_obs_spectro      : New flux of the data
-        err_obs_spectro      : New error of the data
-        flx_post_doppler     : New flux of the interpolated synthetic spectrum
+        - wav_obs_spectro (array)       : New wavelength grid of the data
+        - flx_obs_spectro (array)       : New flux of the data
+        - err_obs_spectro (array)       : New error of the data
+        - flx_post_doppler (array)      : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
-    # wav_doppler = wav_obs_spectro*10000
-    # flx_post_doppler, wav_post_doppler = dopplerShift(wav_doppler, flx_mod_spectro, rv_picked)
-    new_wav = wav_obs_spectro * ((rv_picked / 299792.458) + 1)
+    new_wav = wav_obs_spectro * ((rv_picked / const.c.to(u.km/u.s).value) + 1)
     rv_interp = interp1d(new_wav, flx_mod_spectro, fill_value="extrapolate")
     flx_post_doppler = rv_interp(wav_obs_spectro)
 
     return wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_post_doppler
 
-    # return Spectrum1d.from_array(new_wavelength, new_flux)
-    # # Side effects
-    # ind_nonan = np.argwhere(~np.isnan(flx_post_doppler))
-    # wav_obs_spectro = wav_obs_spectro[ind_nonan[:, 0]]
-    # flx_obs_spectro = flx_obs_spectro[ind_nonan[:, 0]]
-    # err_obs_spectro = err_obs_spectro[ind_nonan[:, 0]]
-    # flx_post_doppler = flx_post_doppler[ind_nonan[:, 0]]
-
-    # return wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_post_doppler
-
 # ----------------------------------------------------------------------------------------------------------------------
 
 
@@ -237,14 +225,14 @@ def reddening_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo
     extinction.fm07.
 
     Args:
-        wav_obs_spectro  : Wavelength grid of the data (spectroscopy)
-        wav_obs_photo    : Wavelength of the data (photometry)
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_mod_photo    : Flux of the interpolated synthetic spectrum (photometry)
-        av_picked        : Extinction randomly picked by the nested sampling (in mag)
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        wav_obs_photo    (array): Wavelength of the data (photometry)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        av_picked        (float): Extinction randomly picked by the nested sampling (in mag)
     Returns:
-        flx_mod_spectro  : New flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_mod_photo    : New flux of the interpolated synthetic spectrum (photometry)
+        - flx_mod_spectro (array) : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - flx_mod_photo (array)   : New flux of the interpolated synthetic spectrum (photometry)
 
     Author: Simon Petrus
     """
@@ -266,12 +254,12 @@ def vsini_fct_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picke
     extinction.fm07.
 
     Args:
-        wav_obs_spectro  : Wavelength grid of the data
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum
-        ld_picked        : Limd darkening randomly picked by the nested sampling
-        vsini_picked     : v.sin(i) randomly picked by the nested sampling (in km.s-1)
+        wav_obs_spectro  (array): Wavelength grid of the data
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum
+        ld_picked        (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
     Returns:
-        flx_mod_spectro  : New flux of the interpolated synthetic spectrum
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
@@ -297,12 +285,12 @@ def vsini_fct_fast_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_
     extinction.fm07.
 
     Args:
-        wav_obs_spectro  : Wavelength grid of the data
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum
-        ld_picked        : Limd darkening randomly picked by the nested sampling
-        vsini_picked     : v.sin(i) randomly picked by the nested sampling (in km.s-1)
+        wav_obs_spectro  (array): Wavelength grid of the data
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum
+        ld_picked        (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
     Returns:
-        flx_mod_spectro  : New flux of the interpolated synthetic spectrum
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
@@ -324,31 +312,25 @@ def vsini_fct_fast_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_
 def vsini_fct_accurate(wave_obs_merge, flx_mod_spectro, ld_picked, vsini_picked, nr=50, ntheta=100, dif=0.0):
     '''
     A routine to quickly rotationally broaden a spectrum in linear time.
+    Adapted from Carvalho & Johns-Krull 2023 https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract
 
-    Carvalho & Johns-Krull 2023
-    https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract
-
-    ARGS:
-        wav_obs_spectro   : Wavelength grid of the data
-        flx_mod_spectro   : Flux of the interpolated synthetic spectrum
-        ld_picked         : Limd darkening randomly picked by the nested sampling
-        vsini_picked      : v.sin(i) randomly picked by the nested sampling (in km.s-1)
-    
+    Args:
+        wav_obs_spectro   (array): Wavelength grid of the data
+        flx_mod_spectro   (array): Flux of the interpolated synthetic spectrum
+        ld_picked         (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked      (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
+        nr                  (int): (default = 10) The number of radial bins on the projected disk
+        ntheta              (int): (default = 100) The number of azimuthal bins in the largest radial annulus
+                                   note: the number of bins at each r is int(r*ntheta) where r < 1
+        dif               (float): (default = 0) The differential rotation coefficient, applied according to the law Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). 
+                                    Dif = .675 nicely reproduces the law proposed by Smith, 1994, A&A, Vol. 287, p. 523-534, to unify WTTS and CTTS. 
+                                    Dif = .23 is similar to observed solar differential rotation. Note: the th in the above expression is the stellar co-latitude, not the same as the integration variable used below. 
+                                    This is a disk integration routine.
     Returns:
-        flx_mod_spectro   : New flux of the interpolated synthetic spectrum
+        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum
 
-    OPTIONAL ARGS:
-        nr (default = 10)       : The number of radial bins on the projected disk
-        ntheta (default = 100)  : The number of azimuthal bins in the largest radial annulus
-                                note: the number of bins at each r is int(r*ntheta) where r < 1
-        
-        dif (default = 0)       : The differential rotation coefficient, applied according to the law
-        Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). Dif = .675 nicely reproduces the law 
-        proposed by Smith, 1994, A&A, Vol. 287, p. 523-534, to unify WTTS and CTTS. Dif = .23 is 
-        similar to observed solar differential rotation. Note: the th in the above expression is 
-        the stellar co-latitude, not the same as the integration variable used below. This is a 
-        disk integration routine.
-    '''
+    Author: Allan Denis
+    ''' 
 
     ns = np.copy(flx_mod_spectro)*0.0
     tarea = 0.0
@@ -360,11 +342,11 @@ def vsini_fct_accurate(wave_obs_merge, flx_mod_spectro, ld_picked, vsini_picked,
             th = np.pi/int(ntheta*r) + k * 2.0*np.pi/int(ntheta*r)
             if dif != 0:
                 vl = vsini_picked * r * np.sin(th) * (1.0 - dif/2.0 - dif/2.0*np.cos(2.0*np.arccos(r*np.cos(th))))
-                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/2.9979e5, wave_obs_merge, flx_mod_spectro)
+                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/const.c.to(u.km/u.s).value, wave_obs_merge, flx_mod_spectro)
                 tarea += area
             else:
                 vl = r * vsini_picked * np.sin(th)
-                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/2.9979e5, wave_obs_merge, flx_mod_spectro)
+                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/const.c.to(u.km/u.s).value, wave_obs_merge, flx_mod_spectro)
                 tarea += area
     
     flx_mod_spectro = ns / tarea
@@ -373,20 +355,21 @@ def vsini_fct_accurate(wave_obs_merge, flx_mod_spectro, ld_picked, vsini_picked,
 # ----------------------------------------------------------------------------------------------------------------------
 
 def bb_cpd_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo, distance, bb_T_picked, bb_R_picked):
-    ''' Function to add the effect of a cpd (circum planetary disc) to the models
-    Args:  
-        wav_obs_spectro  : Wavelength grid of the data (spectroscopy)
-        wav_obs_photo    : Wavelength of the data (photometry)
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_mod_photo    : Flux of the interpolated synthetic spectrum (photometry)
-        bb_temp          : Temperature value randomly picked by the nested sampling in K units
-        bb_rad           : Radius randomly picked by the nested sampling in units of planetary radius
+    ''' 
+    Function to add the effect of a cpd (circum planetary disc) to the models.
     
+    Args:  
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        wav_obs_photo    (array): Wavelength of the data (photometry)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        bb_temp          (float): Temperature value randomly picked by the nested sampling in K units
+        bb_rad           (float): Radius randomly picked by the nested sampling in units of planetary radius
     Returns:
-        flx_mod_spectro  : New flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_mod_photo    : New flux of the interpolated synthetic spectrum (photometry)
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - flx_mod_photo (array)     : New flux of the interpolated synthetic spectrum (photometry)
 
-    Author: P. Palma-Bifani
+    Author: Paulina Palma-Bifani
     '''
 
     bb_T_picked *= u.K
@@ -402,12 +385,8 @@ def planck(wav, T):
     bb_intensity    = planck(wav_obs_spectro*u.um, bb_T_picked)
     bb_intensity_f    = planck(wav_obs_photo*u.um, bb_T_picked)
 
-    #flux_bb_lambda   = ( np.pi * (bb_R_picked)**2 / ( ck*u.km **2) * bb_intensity ).to(u.W/u.m**2/u.micron)
-    flux_bb_lambda   = ( 4*np.pi*bb_R_picked**2/(distance**2) * bb_intensity ).to(u.W/u.m**2/u.micron)
-
-    #flux_bb_lambda_f = ( np.pi * (bb_R_picked)**2 / ( ck*u.km **2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
-    flux_bb_lambda_f = ( 4*np.pi*bb_R_picked**2/(distance**2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
-
+    flux_bb_lambda   = ( np.pi*bb_R_picked**2/(distance**2) * bb_intensity ).to(u.W/u.m**2/u.micron)
+    flux_bb_lambda_f = ( np.pi*bb_R_picked**2/(distance**2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
 
     # add to model flux of the atmosphere
     flx_mod_spectro  += flux_bb_lambda.value
@@ -419,82 +398,41 @@ def planck(wav, T):
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def reso_fct(global_params, theta, theta_index, wav_obs_spectro, flx_mod_spectro, reso_picked):
-    """
-    WORKING!
-    Function to scale the spectral resolution of the synthetic spectra. This option is currently in test and make use
-    of the functions defined in the 'adapt' section of ForMoSA, meaning that they will significantly decrease the speed of
-    your inversion as the grid needs to be re-interpolated
-
-    Args:
-        global_params    : Class containing each parameter
-        theta            : Parameter values randomly picked by the nested sampling
-        theta_index      : Parameter index identificator
-        wav_obs_spectro  : Wavelength grid of the data
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum
-        reso_picked      : Spectral resolution randomly picked by the nested sampling
-    Returns:
-        None
-
-    Author: Matthieu Ravet
-    """
-
-    # Import the grid and set it with the right parameters
-    ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4")
-    wav_mod_nativ = ds["wavelength"].values
-    grid = ds['grid']
-    attr = ds.attrs
-    grid_np = grid.to_numpy()
-    model_to_adapt = grid_np[:, theta]
-
-    # Modify the spectrum with the wanted spectral resolution
-    flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, model_to_adapt, attr['res'], obs_name=obs_name,
-                                        indobs=indobs)
-
-    return 
-
-
-# ----------------------------------------------------------------------------------------------------------------------
-
-
 def modif_spec(global_params, theta, theta_index,
                wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
                wav_obs_photo, flx_obs_photo, err_obs_photo, flx_mod_photo, transm_obs = [], star_flx_obs = [], system_obs = [], indobs=0):
     """
-    Modification of the interpolated synthetic spectra with the different extra-grid parameters:
-        - Re-calibration on the data
-        - Doppler shifting
-        - Application of a substellar extinction
-        - Application of a rotational velocity
-        - Application of a circumplanetary disk (CPD)
+    Modification of the interpolated synthetic spectra with the different extra-grid parameters.
+    It can perform : Re-calibration on the data, Doppler shifting, Application of a substellar extinction, Application of a rotational velocity, 
+    Application of a circumplanetary disk (CPD).
     
     Args:
-        global_params    : Class containing each parameter
-        theta            : Parameter values randomly picked by the nested sampling
-        theta_index      : Parameter index identificator
-        wav_obs_spectro  : Wavelength grid of the data (spectroscopy)
-        flx_obs_spectro  : Flux of the data (spectroscopy)
-        err_obs_spectro  : Error of the data (spectroscopy)
-        flx_mod_spectro  : Flux of the interpolated synthetic spectrum (spectroscopy)
-        wav_obs_photo    : Wavelength grid of the data (photometry)
-        flx_obs_photo    : Flux of the data (photometry)
-        err_obs_photo    : Error of the data (photometry)
-        flx_mod_photo    : Flux of the interpolated synthetic spectrum (photometry)
-        transm_obs       : Transmission (Atmospheric + Instrumental)
-        star_flx_obs     : Flux of star observation data (spectroscopy)
-        system_obs       : Systematics of the data (spectroscopy)
-        indobs      (int): Index of the current observation looping
+        global_params   (object): Class containing each parameter
+        theta             (list): Parameter values randomly picked by the nested sampling
+        theta_index       (list): Parameter index identificator
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        flx_obs_spectro  (array): Flux of the data (spectroscopy)
+        err_obs_spectro  (array): Error of the data (spectroscopy)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        wav_obs_photo    (array): Wavelength grid of the data (photometry)
+        flx_obs_photo    (array): Flux of the data (photometry)
+        err_obs_photo    (array): Error of the data (photometry)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        transm_obs       (array): Transmission (Atmospheric + Instrumental)
+        star_flx_obs   (n-array): Flux of star observation data (spectroscopy)
+        system_obs     (n-array): Systematics of the data (spectroscopy)
+        indobs             (int): Index of the current observation looping
     Returns:
-        wav_obs_spectro  : New wavelength grid of the data (may change with the Doppler shift)
-        flx_obs_spectro  : New flux of the data (may change with the Doppler shift)
-        err_obs_spectro  : New error of the data (may change with the Doppler shift)
-        flx_mod_spectro  : New flux of the interpolated synthetic spectrum (spectroscopy)
-        wav_obs_photo    : Wavelength grid of the data (photometry)
-        flx_obs_photo    : Flux of the data (photometry)
-        err_obs_photo    : Error of the data (photometry)
-        flx_mod_photo    : New flux of the interpolated synthetic spectrum (photometry)
+        - wav_obs_spectro (array)  : New wavelength grid of the data (may change with the Doppler shift)
+        - flx_obs_spectro (array)  : New flux of the data (may change with the Doppler shift)
+        - err_obs_spectro (array)  : New error of the data (may change with the Doppler shift)
+        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - wav_obs_photo (array)    : Wavelength grid of the data (photometry)
+        - flx_obs_photo (array)    : Flux of the data (photometry)
+        - err_obs_photo (array)    : Error of the data (photometry)
+        - flx_mod_photo (array)    : New flux of the interpolated synthetic spectrum (photometry)
     
-    Author: Simon Petrus and Paulina Palma-Bifani
+    Author: Simon Petrus, Paulina Palma-Bifani, Allan Denis and Matthieu Ravet
     """
     # Correction of the radial velocity of the interpolated synthetic spectrum.
     if len(flx_obs_spectro) != 0:
diff --git a/ForMoSA/nested_sampling/nested_prior_function.py b/ForMoSA/nested_sampling/nested_prior_function.py
index d782458..a107ff3 100755
--- a/ForMoSA/nested_sampling/nested_prior_function.py
+++ b/ForMoSA/nested_sampling/nested_prior_function.py
@@ -3,14 +3,15 @@
 
 def uniform_prior(prior_fct_arg, theta):
     '''
-    Uniform prior for nested sampling
+    Uniform prior for nested sampling.
 
     Args:
         prior_fct_arg   (list): Two-values list with uniform prior boundaries.
         theta           (list): Parameter values randomly picked by the nested sampling
-    
     Returns:
-        Evaluated      (float): Evaluated prior
+        - Evaluated      (float): Evaluated prior
+
+    Author: Simon Petrus
     '''
     arg1 = float(prior_fct_arg[0])
     arg2 = float(prior_fct_arg[1])
@@ -19,13 +20,15 @@ def uniform_prior(prior_fct_arg, theta):
 
 def gaussian_prior(prior_fct_arg, theta):
     '''
-    Gaussian prior for nested sampling
+    Gaussian prior for nested sampling.
 
     Args:
         prior_fct_arg   (list): Two-values list with uniform prior boundaries.
         theta           (list): Parameter values randomly picked by the nested sampling
     Returns:
-        Evaluated      (float): Evaluated prior
+        - Evaluated      (float): Evaluated prior
+
+    Author: Simon Petrus
     '''
     arg1 = float(prior_fct_arg[0])
     arg2 = float(prior_fct_arg[1])
diff --git a/ForMoSA/nested_sampling/nested_sampling.py b/ForMoSA/nested_sampling/nested_sampling.py
index 9915229..72f3f32 100755
--- a/ForMoSA/nested_sampling/nested_sampling.py
+++ b/ForMoSA/nested_sampling/nested_sampling.py
@@ -1,18 +1,17 @@
 import numpy as np
-import os
+import os, sys
 import glob
 import nestle
 import time
 import xarray as xr
 import pickle
 
+sys.path.insert(0, os.path.abspath('../'))
+
 from nested_sampling.nested_modif_spec import modif_spec
 from nested_sampling.nested_prior_function import uniform_prior, gaussian_prior
 from nested_sampling.nested_logL_functions import *
 from main_utilities import diag_mat
-import matplotlib.pyplot as plt
-
-c = 299792.458 # Speed of light in km/s
 
 
 def import_obsmod(global_params):
@@ -23,7 +22,7 @@ def import_obsmod(global_params):
         global_params  (object): Class containing every input from the .ini file.
         
     Returns:
-        main_file (list(array)): return a list of lists with the wavelengths, flux, errors, covariance matrix,
+        - main_file (list(array)): Return a list of lists with the wavelengths, flux, errors, covariance matrix,
                                 transmission, star flux, systematics and the grids for both spectroscopic and photometric data. 
 
     Authors: Simon Petrus, Matthieu Ravet and Allan Denis
@@ -76,6 +75,7 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
     """
     Function that calculates the logarithm of the likelihood. 
     The evaluation depends on the choice of likelihood.
+    (If this function is used on the plotting module, it returns the outputs of the modif_spec function)
     
     Args:
         theta           (list): Parameter values randomly picked by the nested sampling
@@ -85,8 +85,7 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
         for_plot         (str): Default is 'no'. When this function is called from the plotting functions module, we use 'yes'
         
     Returns:
-        FINAL_logL     (float): Final evaluated loglikelihood for both spectra and photometry. 
-        (If this function is used on the plotting module, it returns the outputs of the modif_spec function)
+        - FINAL_logL     (float): Final evaluated loglikelihood for both spectra and photometry. 
 
     Authors: Simon Petrus, Matthieu Ravet and Allan Denis
     """
@@ -301,9 +300,9 @@ def prior_transform(theta, theta_index, lim_param_grid, global_params):
         global_params (object): Class containing every input from the .ini file.
         
     Returns:
-        prior           (list): List containing all the prior information
+        - prior           (list): List containing all the prior information
         
-    Author: Simon Petrus
+    Author: Simon Petrus, Matthieu Ravet, Allan Denis
     """
     prior = []
     if global_params.par1 != 'NA':
@@ -752,46 +751,46 @@ def launch_nested_sampling(global_params):
             print('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))
         print('\n')
 
-    if global_params.ns_algo == 'ultranest':
-        import ultranest, ultranest.stepsampler
+    # if global_params.ns_algo == 'ultranest':
+    #     import ultranest, ultranest.stepsampler
 
-        tmpstot1 = time.time()
+    #     tmpstot1 = time.time()
         
-        loglike_gp = lambda theta: loglike(theta, theta_index, global_params)
-
-        prior_transform_gp = lambda theta: prior_transform(theta, theta_index, lim_param_grid, global_params)
-
-        sampler = ultranest.ReactiveNestedSampler(theta_index,loglike=loglike_gp, transform=prior_transform_gp,
-                                                  wrapped_params=[False, False, False, False])#,
-                                                #log_dir=global_params.result_path, resume=True)
-        #result = sampler.run(min_num_live_points=100, max_ncalls=100000)
-
-        # have to choose the number of steps the slice sampler should take
-        # after first results, this should be increased and checked for consistency.
-        nsteps = 2 * len(theta_index)
-        # create step sampler:
-        sampler.stepsampler = ultranest.stepsampler.SliceSampler(nsteps=nsteps,
-                                                                 generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
-                                                                 # adaptive_nsteps=False,
-                                                                 # max_nsteps=40
-                                                                 )
-        sampler.print_results()
-        #sampler.plot_corner()
-
-        tmpstot2 = time.time()-tmpstot1
-        print(' ')
-        print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
-        print('-> Ultranest  ')
-        print(' ')
-        print('The code spent ' + str(tmpstot2) + ' sec to run.')
-        print(result.summary())
-        print('\n')
+    #     loglike_gp = lambda theta: loglike(theta, theta_index, global_params)
+
+    #     prior_transform_gp = lambda theta: prior_transform(theta, theta_index, lim_param_grid, global_params)
+
+    #     sampler = ultranest.ReactiveNestedSampler(theta_index,loglike=loglike_gp, transform=prior_transform_gp,
+    #                                               wrapped_params=[False, False, False, False])#,
+    #                                             #log_dir=global_params.result_path, resume=True)
+    #     #result = sampler.run(min_num_live_points=100, max_ncalls=100000)
+
+    #     # have to choose the number of steps the slice sampler should take
+    #     # after first results, this should be increased and checked for consistency.
+    #     nsteps = 2 * len(theta_index)
+    #     # create step sampler:
+    #     sampler.stepsampler = ultranest.stepsampler.SliceSampler(nsteps=nsteps,
+    #                                                              generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
+    #                                                              # adaptive_nsteps=False,
+    #                                                              # max_nsteps=40
+    #                                                              )
+    #     sampler.print_results()
+    #     #sampler.plot_corner()
+
+    #     tmpstot2 = time.time()-tmpstot1
+    #     print(' ')
+    #     print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
+    #     print('-> Ultranest  ')
+    #     print(' ')
+    #     print('The code spent ' + str(tmpstot2) + ' sec to run.')
+    #     print(result.summary())
+    #     print('\n')
     
-    if global_params.ns_algo == 'dynesty':
-        from dynesty import NestedSampler
+    # if global_params.ns_algo == 'dynesty':
+    #     from dynesty import NestedSampler
 
-        # initialize our nested sampler
-        #sampler = NestedSampler(loglike, ptform, ndim)
+    #     initialize our nested sampler
+    #     sampler = NestedSampler(loglike, ptform, ndim)
 
     result_reformat = {"samples": samples,
                        "weights": weights,
diff --git a/ForMoSA/plotting/plotting_class.py b/ForMoSA/plotting/plotting_class.py
index acc73f1..a717e0a 100644
--- a/ForMoSA/plotting/plotting_class.py
+++ b/ForMoSA/plotting/plotting_class.py
@@ -1,22 +1,16 @@
 
 from __future__ import print_function, division
-import sys, os, yaml, time
+import os, glob, sys
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib
-from matplotlib.figure import Figure
-import astropy.constants as const
-import astropy.units as u
+from matplotlib.backends.backend_pdf import PdfPages
+from scipy.interpolate import interp1d
 import corner
 import xarray as xr
 import pickle
-import scipy.signal as sg
-from scipy.interpolate import interp1d
-import extinction
-from PyAstronomy.pyasl import dopplerShift, rotBroad
-from spectres import spectres
 from tqdm import tqdm
-import glob
+
+sys.path.insert(0, os.path.abspath('../'))
 import scipy.signal as sg
 
 # Import ForMoSA
@@ -27,7 +21,7 @@
 from nested_sampling.nested_modif_spec import vsini_fct_accurate
 from adapt.extraction_functions import resolution_decreasing, adapt_model, decoupe
 from adapt.extraction_functions import adapt_observation_range
-from matplotlib.backends.backend_pdf import PdfPages
+
 
 
 
@@ -35,16 +29,16 @@ def bin_data(wave, data, bin_size):
     '''
     Function to bin data given a bin size
 
-    Parameters
-    ----------
-    wave : wavelength of the data
-    data : data
-    bin_size : size of the bin to apply
+    Args:
+        wave         (array): wavelength of the data
+        data         (array): data
+        bin_size       (int): size of the bin to apply
+
+    Returns:
+        - wave_binned  (array): binned wavelength
+        - data_binned  (array): binned data
 
-    Returns
-    -------
-    wave_binned : binned wavelength
-    data_binned : binned data
+    Author: Allan Denis
     '''
     # First quick check that len of data is a multpiple of bin_size
     while(len(data)%bin_size != 0):
@@ -62,12 +56,25 @@ def bin_data(wave, data, bin_size):
 # ----------------------------------------------------------------------------------------------------------------------
 class ComplexRadar():
     '''
-    Original from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb
-    
-    Adapted by: P. Palma-Bifani
+    Class to create Radar plots with asymmetric error bars.
+
+    Author: Paulina Palma-Bifani
+            Adapted from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb
+   
     '''
 
     def __init__(self, fig, variables, ranges, n_ordinate_levels=6):
+        '''
+        Initialize class.
+        
+        Args:
+            fig               (object): matplotlib figure object
+            variables           (list): list of parameters to plot
+            ranges       (list(tuple)): upper and lower limits for each parameters
+            n_ordinate_levels    (int): (default = 6) number of gridlines in the plot
+        Returns:
+            None   
+        '''
         angles = np.arange(0, 360, 360./len(variables))
 
         axes = [fig.add_axes([0.1,0.1,0.9,0.9], polar=True, label = "axes{}".format(i)) for i in range(len(variables))]
@@ -98,20 +105,61 @@ def __init__(self, fig, variables, ranges, n_ordinate_levels=6):
         self.ax = axes[0]
 
     def plot(self, data, *args, **kw):
+        '''
+        Function to display the plot.
+        
+        Args:
+            data       (list): best value for each parameter
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata = self.scale_data(data, self.ranges)
         self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
 
     def fill(self, data, *args, **kw):
+        '''
+        Add symmetric error bars to the plot.
+        
+        Args:
+            data       (list): best value for each parameter
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata = self.scale_data(data, self.ranges)
         self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
     
     def fill_between(self, list_down, list_up, *args, **kw):
+        '''
+        Add asymmetric error bars to the plot.
+        
+        Args:
+            list_down (list): list of lower error bars
+            list_up   (list): list of upper error bars
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata_down = self.scale_data(list_down, self.ranges)
         sdata_up = self.scale_data(list_up, self.ranges)
         self.ax.fill_between(self.angle,np.r_[sdata_down,sdata_down[0]], np.r_[sdata_up,sdata_up[0]], *args, **kw)
 
     def scale_data(self, data, ranges):
-        """scales data[1:] to ranges[0]"""
+        '''
+        Function to check that lower and upper limits are correctly ordered. It scales data[1:] to ranges[0]
+
+        Args:
+            data              (list): best value for each parameter
+            ranges     (list(tuple)): upper and lower limits for each parameters
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         for d, (y1, y2) in zip(data[1:], ranges[1:]):
             assert (y1 <= d <= y2) or (y2 <= d <= y1)
         x1, x2 = ranges[0]
@@ -130,45 +178,47 @@ def scale_data(self, data, ranges):
 # ----------------------------------------------------------------------------------------------------------------------
 class PlottingForMoSA():
     '''
-    Here all the plotting functionalities of ForMoSA to see the 
+    Class containing all the plotting functionalities of ForMoSA.
 
-    Author: Paulina Palma-Bifani 
+    Author: Paulina Palma-Bifani, Simon Petrus, Matthieu Ravet and Allan Denis
     '''
 
     def __init__(self, config_file_path, color_out):
         '''
-        Plotting class initializer
+        Initialize class by inheriting the global parameter class of ForMoSA.
+
+        Args:
+            config_file_path   (str): path to the config.ini file currently used
+            color_out          (str): color to use for the model
+        Returns:
+            None
         '''
         
         self.global_params = GlobFile(config_file_path)  
         self.color_out     = color_out
 
 
-
     def _get_posteriors(self):
         '''
-        Function to get the posteriors, including luminosity derivation and corvengence parameters logz
+        Function to get the posteriors, including luminosity derivation and Bayesian evidence logz.
 
-        (Adapted from Simon Petrus plotting functions)
+        Args:
+            None
+        Returns:
+            None
         '''
         with open(self.global_params.result_path + '/result_' + self.global_params.ns_algo + '.pic', 'rb') as open_pic:
             result = pickle.load(open_pic)
-        # self.samples = result.samples
         self.samples = result['samples']
-        # self.weights = result.weights
         self.weights = result['weights']
 
         # To test the quality of the fit
-        # self.logl=result.logl
         self.logl=result['logl']
         ind = np.where(self.logl==max(self.logl))
         self.theta_best = self.samples[ind][0]
 
-        # self.sample_logz    = round(result['logz'],1)
-        # self.sample_logzerr = round(result['logzerr'],1)
         self.sample_logz    = round(result['logz'][0],1)
         self.sample_logzerr = round(result['logz'][1],1)
-        # self.outputs_string = 'logz = '+ str(self.sample_logz)+' ± '+str(self.sample_logzerr)+ ' ; h = '+str(self.sample_h)
         self.outputs_string = 'logz = '+ str(self.sample_logz)+' ± '+str(self.sample_logzerr)
 
         ds = xr.open_dataset(self.global_params.model_path, decode_cf=False, engine='netcdf4')
@@ -291,7 +341,15 @@ def _get_posteriors(self):
 
     def plot_corner(self, levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16, 0.5, 0.84), burn_in=0):
         '''
-        See the corner plots
+        Function to display the corner plot
+
+        Args:
+            levels_sig    (list): (default = [0.997, 0.95, 0.68]) 1, 2 and 3 sigma contour levels of the corner plot
+            bins           (int): (default = 100) number of bins for the posteriors
+            quantiles     (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values
+            burn_in        (int): (default = 0) number of steps to remove from the plot
+        Returns:
+            - fig         (object): matplotlib figure object
         '''
         print('ForMoSA - Corner plot')
 
@@ -324,10 +382,16 @@ def plot_corner(self, levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16,
         return fig
 
 
-    def plot_chains(self,figsize=(7,15)):
+    def plot_chains(self, figsize=(7,15)):
         '''
-        To check the convergence of the chains
+        Plot to check the convergence of the posterior chains.
+        Multiple (sub-)axis plot.
 
+        Args:
+            figsize     (tuple): (default = (7, 15)) size of the plot
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Posteriors chains for each parameter')
 
@@ -353,12 +417,19 @@ def plot_chains(self,figsize=(7,15)):
         return fig, axs
 
 
-    def plot_radar(self,ranges,label='',quantiles=[0.16, 0.5, 0.84],chiffres=[0,2,2,2]):
+    def plot_radar(self, ranges, label='', quantiles=[0.16, 0.5, 0.84]):
         '''
-        To check overall the distribution of the parameters 
+        Radar plot to check the distribution of the parameters.
+        Useful to compare different models.
+
+        Args:
+            ranges     (list(tuple)): upper and lower limits for each parameters
+            label              (str): (default = '') label of the plot
+            quantiles         (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - radar.ax   (object) : matplotlib radar class axes object    
 
-        Inputs:
-        ranges
         '''
         print('ForMoSA - Radar plot')
 
@@ -374,23 +445,30 @@ def plot_radar(self,ranges,label='',quantiles=[0.16, 0.5, 0.84],chiffres=[0,2,2,
             list_uncert_down.append(q16)
             list_uncert_up.append(q84)
 
-        fig1 = plt.figure(figsize=(6, 6))
-        radar = ComplexRadar(fig1, self.posteriors_names, ranges)
+        fig = plt.figure(figsize=(6, 6))
+        radar = ComplexRadar(fig, self.posteriors_names, ranges)
 
         radar.plot(list_posteriors, 'o-', color=self.color_out, label=label)
         radar.fill_between(list_uncert_down,list_uncert_up, color=self.color_out, alpha=0.2)
 
         radar.ax.legend(loc='center', bbox_to_anchor=(0.5, -0.20),frameon=False, ncol=2)
 
-        return fig1, radar.ax
+        return fig, radar.ax
 
 
     def _get_spectra(self,theta,return_model=False):
         '''
-        To get the data and best model asociated 
-        Use numba: https://numba.pydata.org/
+        Function to get the data and best model asociated.
 
-        (Adapted from Simon Petrus)
+        Args:
+            theta                   (list): best parameter values
+        Returns:
+            - modif_spec_chi2  list(n-array): list containing the spectroscopic wavelength, spectroscopic fluxes of the data, 
+                                            spectroscopic errors of the data, spectroscopic fluxes of the model, 
+                                            photometric wavelength, photometric fluxes of the data, photometric errors of the data, 
+                                            spectroscopic fluxes of the model,
+                                            planet transmission, star fluxes, systematics
+            - ck                list(floats): list scaling factor(s)
         '''
         # Get the posteriors
         self._get_posteriors()
@@ -498,24 +576,21 @@ def _get_spectra(self,theta,return_model=False):
             return modif_spec_chi2, ck
     
 
-    def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], res_out=1000, re_interp=False, int_method="linear"):
+    def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], N_points=1000, re_interp=False, int_method="linear"):
         '''
-        To get the data and best model asociated 
-        Use numba: https://numba.pydata.org/.
+        Extract a model spectrum from another grid.
 
         Args:
-            theta:          List of model and extra-model parameters.
-            grid_used:      Default 'original' will use the raw grid. Else, input the path to your desired grid.
-            wavelengths:    Default [] will use max values of the wav_for_adapt range to create a model spectrum.
-                            Else, input the desired wavelength range.
-            res_out:        Default 1000 will be the resolution of your model spectrum. Else, input the desired resolution.
-            re_interp:      Default False. If true, will re-interpolate the grid's hole (WARNING, time consumming...).
-            int_method:     Default "linear" will be the interpolation method use for the grid. Else, input the desired interpolation method.
+            theta:                       (list): best parameter values
+            grid_used:                    (str): (default = 'original') Path to the grid from where to extract the spectrum. If 'original', the current grid will be used.
+            wavelengths:                 (list): (default = []) Desired wavelength range. If [] max and min values of wav_for_adapt range will be use to create the wavelength range.
+            N_points:                     (int): (default = 1000) Number of points.
+            re_interp:                (boolean): (default = False). Option to reinterpolate or not the grid.
+            int_method:                   (str): (default = "linear") Interpolation method for the grid (if reinterpolated).
         Returns:   
-            fig, ax, axr, axr2
-
-
-        Authors: Paulina Palma-Bifani and Matthieu Ravet
+            - wav_final                   (array): Wavelength array of the full model
+            - flx_final                   (array): Flux array of the full model
+            - ck                          (float): Scaling factor of the full model
         '''
         self._get_posteriors()
 
@@ -529,9 +604,9 @@ def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], res_ou
                 else:
                     wav = np.concatenate((wav, wav_ind))
             wav = np.sort(wav)
-            wavelengths = np.linspace(wav[0],wav[-1],res_out)
+            wavelengths = np.linspace(wav[0],wav[-1],N_points)
         else:
-            wavelengths = np.linspace(wavelengths[0],wavelengths[-1],res_out)
+            wavelengths = np.linspace(wavelengths[0],wavelengths[-1],N_points)
 
         # Recover the original grid
         if grid_used == 'original':
@@ -590,16 +665,17 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
         Plot the best fit comparing with the data.
 
         Args:
-            figsize:    x/y size of the plot
-            uncert:     'yes' or 'no' to plot spectra with associated error bars
-            trans:      'yes' or 'no' to plot transmision curves for photometry
-            logx:       'yes' or 'no' to plot the wavelength in log scale
-            logy:       'yes' or 'no' to plot the flux in log scale
-            norm:       'yes' or 'no' to plot the normalized spectra
+            figsize    (tuple): (default = (10, 5)) Size of the plot
+            uncert     (str): (default = no) 'yes' or 'no' to plot spectra with associated error bars
+            trans      (str): (default = no) 'yes' or 'no' to plot transmision curves for photometry
+            logx       (str): (default = no) 'yes' or 'no' to plot the wavelength in log scale
+            logy       (str): (default = no) 'yes' or 'no' to plot the flux in log scale
+            norm       (str): (default = no) 'yes' or 'no' to plot the normalized spectra
         Returns:
-            fig, ax, axr, axr2
-
-        Author: Paulina Palma-Bifani and Matthieu Ravet
+            - fig    (object) : matplotlib figure object
+            - ax     (object) : matplotlib axes objects, main spectra plot
+            - axr    (object) : matplotlib axes objects, residuals
+            - axr2   (object) : matplotlib axes objects, right side density histogram
         '''
         print('ForMoSA - Best fit and residuals plot')
 
@@ -695,8 +771,6 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
                     
                     iobs_photo = -1
                     
-                    
-
         # Set xlog-scale
         if logx == 'yes':
             ax.set_xscale('log')
@@ -720,26 +794,24 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
             
         # define the data as global
         self.spectra = spectra
-        #self.residuals = residuals
 
         return fig, ax, axr, axr2
     
     
     def plot_fit_HiRes(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no', norm='no'):
         '''
-        Plot the best fit comparing with the data.
+        Same as plot_fit but with the stellar and planetary models for high-resolution spectroscopy. Does not include residuals in a sub-axis.
 
         Args:
-            figsize:    x/y size of the plot
-            uncert:     'yes' or 'no' to plot spectra with associated error bars
-            trans:      'yes' or 'no' to plot transmision curves for photometry
-            logx:       'yes' or 'no' to plot the wavelength in log scale
-            logy:       'yes' or 'no' to plot the flux in log scale
-            norm:       'yes' or 'no' to plot the normalized spectra
+            figsize    (tuple): (default = (10, 5)) Size of the plot
+            uncert     (str): (default = no) 'yes' or 'no' to plot spectra with associated error bars
+            trans      (str): (default = no) 'yes' or 'no' to plot transmision curves for photometry
+            logx       (str): (default = no) 'yes' or 'no' to plot the wavelength in log scale
+            logy       (str): (default = no) 'yes' or 'no' to plot the flux in log scale
+            norm       (str): (default = no) 'yes' or 'no' to plot the normalized spectra
         Returns:
-            fig, ax, axr, axr2
-
-        Author: Paulina Palma-Bifani and Matthieu Ravet
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Best fit and residuals plot')
 
@@ -825,35 +897,29 @@ def plot_fit_HiRes(self, figsize=(10, 5), uncert='no', trans='no', logx='no', lo
         else:
             ax1.set_ylabel(r'Normalised flux (W m-2 µm-1)')
             
-        ax1.set_xlabel('wavelength ($ \mu $m)')
+        ax1.set_xlabel(r'wavelength ($ \mu $m)')
         
-        fig1.legend(fontsize=18)
-        #ax.legend(frameon=False)
+        fig1.legend()
         plt.figure(fig1)
         plt.savefig(self.global_params.result_path + 'full_data.pdf')
 
         # define the data as global
         self.spectra = spectra
-        #self.residuals = residuals
 
         return fig1, ax1
     
     
-    def plot_HiRes_comp_model(self, figsize=(10, 5), trans='no', logx='no', logy='no', norm='no', data_resolution = 0):
+    def plot_HiRes_comp_model(self, figsize=(10, 5), norm='no', data_resolution = 0):
         '''
-        Plot the best fit comparing with the data.
+        Specific function to plot the best fit comparing with the data for high-resolution spectroscopy.
 
         Args:
-            figsize:    x/y size of the plot
-            uncert:     'yes' or 'no' to plot spectra with associated error bars
-            trans:      'yes' or 'no' to plot transmision curves for photometry
-            logx:       'yes' or 'no' to plot the wavelength in log scale
-            logy:       'yes' or 'no' to plot the flux in log scale
-            norm:       'yes' or 'no' to plot the normalized spectra
+            figsize             (tuple): (default = (10, 5)) Size of the plot
+            norm                  (str): (default = no) 'yes' or 'no' to plot the normalized spectra
+            data_resolution       (int): (default = 0) Custom resolution to broadened data
         Returns:
-            fig, ax, axr, axr2
-
-        Author: Paulina Palma-Bifani and Matthieu Ravet
+            - fig1  (object) : matplotlib figure object
+            - ax1   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Planet model and data')
        
@@ -906,8 +972,8 @@ def plot_HiRes_comp_model(self, figsize=(10, 5), trans='no', logx='no', logy='no
                 ax.plot(wave, data_broadened, c='k')
                 ax.plot(wave, planet_model_broadened, c='r')
                 
-                ax.set_xlabel('wavelength ($\mu$m)', fontsize=18)
-                ax.set_ylabel('Flux (ADU)', fontsize=18)
+                ax.set_xlabel(r'wavelength ($\mu$m)')
+                ax.set_ylabel('Flux (ADU)')
                 
                 ax1.plot(wave, data, c='k')
                 ax1.plot(wave, planet_model, c = 'r')
@@ -917,21 +983,38 @@ def plot_HiRes_comp_model(self, figsize=(10, 5), trans='no', logx='no', logy='no
                 else:
                     legend_data = 'data'
                     
-                ax.legend([legend_data, 'planet model'], fontsize=18)
-                ax.tick_params(axis='both', labelsize=18)
+                ax.legend([legend_data, 'planet model'])
+                ax.tick_params(axis='both')
                 
         
-        ax1.legend([legend_data, "planet model"], fontsize = 18)
-        ax1.set_xlabel('wavelength ($ \mu $m)', fontsize=18)
-        ax1.tick_params(axis='both', labelsize=18)
+        ax1.legend([legend_data, "planet model"])
+        ax1.set_xlabel('wavelength ($ \mu $m)')
+        ax1.tick_params(axis='both')
                             
         return fig1, ax1, fig, ax
     
     
     def plot_ccf(self, rv_grid = [-300,300], rv_step = 0.5, figsize = (10,5), norm = 'no', window_normalisation = 100, model_spectra = [], model_wavelength = [], model_resolution = [], data_resolution = 0, model_name = 'Full', rv_cor=0, data_ccf = [], wave_ccf = [], star_ccf = [], transm_ccf = [], system_ccf = []):
         '''
-        Plot the ccf (used for high resolution data such as CRIRES+ / HiRISE)
-        
+        Plot the cross-correlation function. It is used for high resolution spectroscopy.
+
+        Args:
+            figsize                   (tuple): (default = (10, 5)) Size of the plot
+            rv_grid                    (list): (default = [-300,300]) Maximum and minumum values of the radial velocity shift (in km/s)
+            rv_step                   (float): (default = 0.5) Radial velocity shift steps (in km/s)
+            window_normalisation        (int): (default = 100) ?
+            model_wavelength           (list): (default = []) ?
+            model_spectra =            (list): (default = []) ?
+            model_resolution           (list): (default = []) ?
+            model_name                  (str): (default = 'Full') ?
+            rv_cor                      (int): (default = 0) ?
+        Returns:
+            - fig1                    (object) : matplotlib figure object
+            - ax1                     (object) : matplotlib axes objects
+            - rv_grid                    (list): Radial velocity grid
+            - ccf                        (list): Cross-correlation function
+            - acf                        (list): Auto-correlation function  
+
         Author: Allan Denis
         '''
         print('ForMoSA - CCF plot')
@@ -1071,17 +1154,24 @@ def plot_ccf(self, rv_grid = [-300,300], rv_step = 0.5, figsize = (10,5), norm =
         #ax1.set_title(f'SNR = {np.nanmax(ccf_norm):.1f}, RV = {rv_grid[np.argmax(ccf_norm)]:.1f} km/s')
         plt.figure(fig1)
         plt.savefig(self.global_params.result_path + 'ccf_' + model_name + '.pdf')
-        
-        return rv_grid, ccf_norm, acf_norm
+
+        return fig1, ax1, rv_grid, ccf, acf
     
-    def plot_PT(self,path_temp_profile, figsize=(6,5), model = 'ExoREM'):
-        '''
-        Plot the Pressure-Temperature profiles 
-        Calculates the most probable temperature profile
 
-        Return: fig, ax
 
-        Author: Nathan Zimniak and Paulina Palma-Bifani
+    
+    def plot_PT(self, path_temp_profile, figsize=(6,5), model = 'ExoREM'):
+        '''
+        Function to plot the Pressure-Temperature profiles.
+        Adpated from Nathan Zimniak.
+
+        Args:
+            path_temp_profile    (str): Path to the temperature profile grid
+            figsize            (tuple): (default = (6, 5)) Size of the plot
+            model                (str): (default = 'ExoREM') Name of the model grid
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Pressure-Temperature profile')
 
@@ -1176,34 +1266,34 @@ def plot_PT(self,path_temp_profile, figsize=(6,5), model = 'ExoREM'):
 
     def plot_Clouds(self, cloud_prop, path_cloud_profile, figsize=(6,5)):
         ''' 
-        Cloud profiles calculations
-
-        Inputs: 
-        - cloud_prop (str) : choose the cloud 
-               'eddy_diffusion_coefficient',
-               'vmr_CH4',
-               'vmr_CO',
-               'vmr_CO2',
-               'vmr_FeH',
-               'vmr_H2O',
-               'vmr_H2S',
-               'vmr_HCN',
-               'vmr_K',
-               'vmr_Na',
-               'vmr_NH3',
-               'vmr_PH3',
-               'vmr_TiO',
-               'vmr_VO',
-               'cloud_opacity_Fe',
-               'cloud_opacity_Mg2SiO4',
-               'cloud_particle_radius_Fe',
-               'cloud_particle_radius_Mg2SiO4',
-               'cloud_vmr_Fe',
-               'cloud_vmr_Mg2SiO4'
-        
-        Return: fig, ax
-
-        Author: Nathan Zimniak and Paulina Palma-Bifani
+        Function to plot cloud profiles.
+        Adapted from Nathan Zimniak
+
+        Args: 
+            cloud_prop (str) : Choose the cloud species. The options are
+                                ['eddy_diffusion_coefficient',
+                                'vmr_CH4',
+                                'vmr_CO',
+                                'vmr_CO2',
+                                'vmr_FeH',
+                                'vmr_H2O',
+                                'vmr_H2S',
+                                'vmr_HCN',
+                                'vmr_K',
+                                'vmr_Na',
+                                'vmr_NH3',
+                                'vmr_PH3',
+                                'vmr_TiO',
+                                'vmr_VO',
+                                'cloud_opacity_Fe',
+                                'cloud_opacity_Mg2SiO4',
+                                'cloud_particle_radius_Fe',
+                                'cloud_particle_radius_Mg2SiO4',
+                                'cloud_vmr_Fe',
+                                'cloud_vmr_Mg2SiO4']
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Cloud profile')
         
@@ -1254,7 +1344,7 @@ def plot_Clouds(self, cloud_prop, path_cloud_profile, figsize=(6,5)):
             propsup95.append(np.percentile(cloud_prop_profiles[:, i], 98))
 
         # Plot le profil le plus probable et les percentiles associés
-        fig = plt.figure()
+        fig = plt.figure(figsize=figsize)
         ax = plt.axes()
 
         ax.fill_betweenx(P, propinf95, propsup95, color=self.color_out, alpha=0.1, label=r'2 $\sigma$')
diff --git a/LICENSE b/LICENSE
index c932307..2394e35 100644
--- a/LICENSE
+++ b/LICENSE
@@ -1,6 +1,6 @@
-BSD 2-Clause License
+BSD 3-Clause License
 
-Copyright (c) 2022, exoAtmospheres
+Copyright (c) 2022 - 2024, Paulina Palma-Bifani, Simon Petrus, Mickaël Bonnefoy, and Gaël Chauvin
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without
@@ -13,6 +13,10 @@ modification, are permitted provided that the following conditions are met:
    this list of conditions and the following disclaimer in the documentation
    and/or other materials provided with the distribution.
 
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
 THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
 AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
diff --git a/README.md b/README.md
index 61d7763..8e7cfe3 100644
--- a/README.md
+++ b/README.md
@@ -1,84 +1,18 @@
-<p align="left"><img src="docs/source/ForMoSA.png" alt="ForMoSA" width="250"/></p>
+<p align="left"><img src="docs/ForMoSA.png" alt="ForMoSA" width="250"/></p>
 
 
-***
-Installation
-===
-
-We strongly recommend using a ``conda`` environment ([learn more here](https://conda.io/docs/user-guide/tasks/manage-environments.html)).
-
-To install and use our package in macOS with an M1 chip, proceed with the following sequence of commands:
-
-1/ To run parallelization without warnings, make sure you are building your environment under an OSX-ARM64 architecture ([learn more here](https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple)).
-
-    $ CONDA_SUBDIR=osx-arm64 conda create -n env_formosa python=3.11 numpy -c conda-forge
-
-    $ conda activate env_formosa 
-    
-    $ conda config --env --set subdir osx-arm64
-
-2/ Install the following packages in your environment with pip install: 
-    
-    $ pip install numpy matplotlib corner astropy scipy configobj extinction nestle PyAstronomy spectres pyyaml importlib-metadata==4.13.0 xarray==2023.10.1
-
-
-3/ To solve a standard error, run the following line in your environment:
-
-    $ conda install xarray dask netCDF4 bottleneck
-
-4/ If you want to use Pymultinest to run your inversion, follow the installation instructions from [PyMultinest](https://johannesbuchner.github.io/PyMultiNest/install.html), detailed below. 
-
-If you don't need PyMultinest, you can directly go to point 5/.
-
-First. Clone PyMultinest from GitHub and install it.
-
-    $ git clone https://github.com/JohannesBuchner/PyMultiNest/
-    $ cd PyMultiNest
-    $ python setup.py install
-
-Second. Make sure your system has a C++ and a Fortran interpreter. If you need to install brew, follow [these instructions](https://brew.sh/).
-
-    $ brew install cmake
-    $ brew install gcc
-    $ brew install open-mpi
+Welcome to ForMoSA, an open-source Python package. 
 
-Third. In your ForMoSA environment, install mpi4pi as:
-    
-    $ pip install mpi4py
+We designed this tool to model exoplanetary atmospheres with a forward modeling approach. 
+We encourage you to exploit its capabilities!
 
-Fourth. Install MultiNest by cloning the GitHub repository and building it. Make sure you empty the build folder if you run this step more than once.
-    
-    $ git clone https://github.com/JohannesBuchner/MultiNest
-    $ cd MultiNest/build
-    $ cmake ..
-    $ make
-
-Finally, copy the files that were generated by building MultiNest onto your conda (may be miniconda) environment by doing:
-
-    $ cp -v ~/YOUR_PATH/MultiNest/lib/* /YOUR_PATH/opt/anaconda3/envs/env_formosa/lib/
-	
-
-5/ The last instalation step is to actually get ForMoSA. ForMoSA can be installed throgh pip, however, to work on the final released version, please clone the main branch from our GitHub repository. You can do it by moving to the desired directory on your terminal and writing:
-
-    $ git clone https://github.com/exoAtmospheres/ForMoSA.git
-
-
-***
-Running the code
-===
-
-Follow the instructions in ForMoSA/DEMO
+Get started at [https://formosa.readthedocs.io](https://formosa.readthedocs.io)
 
 ***
-Issues?
-===
-
-If you encounter any other problem, please create an issue on GitHub ([https://github.com/exoAtmospheres/ForMoSA/issues](https://github.com/exoAtmospheres/ForMoSA/issues/new)).
-
-***
-
-[![Documentation Status](https://readthedocs.org/projects/formosa/badge/?version=latest)](https://formosa.readthedocs.io/en/latest/?badge=latest)
+ 
 [![PyPI version](https://badge.fury.io/py/formosa.svg)](https://badge.fury.io/py/formosa)
 [![PyPI downloads](https://img.shields.io/pypi/dm/formosa.svg)](https://pypistats.org/packages/formosa)
-[![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
+[![Documentation Status](https://readthedocs.org/projects/formosa/badge/?version=latest)](https://formosa.readthedocs.io/en/latest/?badge=latest)
+[![License](https://img.shields.io/badge/License-BSD%203--Clause-blue.svg)](https://opensource.org/licenses/BSD-3-Clause)
 [![A rectangular badge, half black half purple containing the text made at Code Astro](https://img.shields.io/badge/Made%20at-Code/Astro-blueviolet.svg)](https://semaphorep.github.io/codeastro/)
+
diff --git a/RELEASE_NOTES.txt b/RELEASE_NOTES.txt
index 9666866..59f2c0a 100644
--- a/RELEASE_NOTES.txt
+++ b/RELEASE_NOTES.txt
@@ -331,13 +331,63 @@ Tests that have been done to checkup the changes:
     - Different high resolution datasets
     - Datasets combining photometric and spectroscopic data points 
      
-    
+
+
+
+- - - 
+
+28/07/2024
+
+Paulina Palma-Bifani, Matthieu Ravet 
+
+Comments:
+     - Commenting all functions
+     - Replacing physical constants with astropy ones
+
+Tests:
+    None
+
+
+- - - 
+
+02/08/2024
+
+Paulina Palma-Bifani
+
+Comments:
+     - Adapted docstrings returns in some functions (not all)
+     - Generated documentation for the webpage 
+     - This is a test. I will do a second commit afterwards with the complete updated docs.
+
+Tests:
+    Webpage API is generating without issues locally (make html)
+
+
+
+- - - 
+
+08/08/2024
+
+Matthieu Ravet
+
+Comments:
+     - Correcting for the HiRISE type data merging when using cuts (i.e. remplacing axis=0 by axis=1). In the future, it would be
+     nice to create a more simple version of the star and systematic matrix (for now its (1, lambda, sys/star) it would be best to
+     have (lambda, sys/star) to avoid keeping large matrices accross the inversion)
+
+Tests:
+    - Test with CRIRES+ datasets with cuts in the adapt and in the fit
+
+
+- - -
 
 29/08/2024
 
+Allan Denis 
+
 Comments:
       - New approach in the adaptation of the data in the case where the user defined separated windows in 'wave_for_adapt' (eg : '1.4, 1.5 / 1.6, 1.7 / 1.8, 1.9 / ... '). Instead of making a loop on each sub-interval, we now combine the data in one array and do the adaptation on one array
       - New approach in the lsq function, we can now use the lsq in each sub wavelength separatly (eg. '1.4, 1.5', '1.6, 1.7', '1.8, 1.9' ...) and merge the results into a single array to compute the loglikelihood on one array only. This gives the advantage of limiting flux error calibration that can arrise between order for data such as CRIRES, HiRISE ... and being fast at the same time. Previously, the use had to define separated windows in 'wave_fit' (eg : '1.4, 1.5', '1.6, 1.7', '1.8, 1.9' ...) and one loglikelihood one computed for each of these windows. Now the user has to define separated windows in the format '1.4, 1.5 / 1.6, 1.7 / 1.8, 1.9 / ...' 
       
 Tests tha have been done to checkup the changes:
-     - High resolution datasets (HiRISE, CRIRES)
+     - High resolution datasets (HiRISE, CRIRES)
\ No newline at end of file
diff --git a/build/lib/ForMoSA/__init__.py b/build/lib/ForMoSA/__init__.py
index fd9425a..c9a7955 100644
--- a/build/lib/ForMoSA/__init__.py
+++ b/build/lib/ForMoSA/__init__.py
@@ -1,5 +1,5 @@
 import os
 
-__version__ = "2.0.0"
+__version__ = "1.1.3"
 
 __all__ = ['adapt', 'nested_sampling','plotting']
\ No newline at end of file
diff --git a/build/lib/ForMoSA/adapt/adapt_grid.py b/build/lib/ForMoSA/adapt/adapt_grid.py
index f083b2e..b35cc36 100644
--- a/build/lib/ForMoSA/adapt/adapt_grid.py
+++ b/build/lib/ForMoSA/adapt/adapt_grid.py
@@ -2,7 +2,9 @@
 import numpy as np
 import xarray as xr
 import time
-import os
+import os, sys
+
+sys.path.insert(0, os.path.abspath('../'))
 
 from adapt.extraction_functions import adapt_model, decoupe
 
@@ -10,20 +12,20 @@
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0):
+def adapt_grid(global_params, wav_obs_spectro, wav_obs_photo, res_mod_obs_merge, obs_name='', indobs=0):
     """
     Adapt the synthetic spectra of a grid to make them comparable with the data.
     
     Args:
-        global_params (object): Class containing each parameter
-        wav_obs_spec   (array): Merged wavelength grid of the data
-        wav_obs_phot   (array): Wavelengths of the photometry points
-        obs_name         (str): Name of the current observation looping
-        indobs           (int): Index of the current observation looping
+        global_params    (object): Class containing each parameter
+        wav_obs_spectro   (array): Merged wavelength grid of the data
+        wav_obs_photo     (array): Wavelengths of the photometry points
+        obs_name            (str): Name of the current observation looping
+        indobs              (int): Index of the current observation looping
     Returns:
         None
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet & Paulina Palma-Bifani
+    Author: Simon Petrus, Matthieu Ravet and Paulina Palma-Bifani
     """
 
     ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4")
@@ -31,68 +33,46 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
     grid = ds['grid']
     attr = ds.attrs
     grid_np = grid.to_numpy()
-    
-    # Prepare the low resolution wavelength array if the continuum is to be estimated
-    if global_params.continuum_sub[indobs] != 'NA':
-        for w_ind, wav_for_cont in enumerate(global_params.wav_for_continuum[indobs].split('/')):
-            wav_mod_for_cont_ind = np.where((float(wav_for_cont.split(',')[0]) < wav_mod_nativ) &
-                                            (wav_mod_nativ < float(wav_for_cont.split(',')[1])))
-            if w_ind == 0:
-                wav_mod_for_cont = wav_mod_nativ[wav_mod_for_cont_ind]
-            else:
-                wav_mod_for_cont = np.concatenate((wav_mod_for_cont, wav_mod_nativ[wav_mod_for_cont_ind]))
-                
-        wav_reso = min(wav_mod_for_cont)
-        n = 0
-        while wav_reso < max(wav_mod_for_cont):
-            last_wav_reso = wav_reso
-            wav_reso += wav_reso / float(global_params.continuum_sub[indobs])
-            n += 1
-            
-        wav_min = min(wav_mod_for_cont)
-        wav_reso_tab = np.logspace(np.log10(wav_min), np.log10(last_wav_reso), num = n)
-    else:
-        wav_reso_tab = []
 
     
     if len(attr['par']) == 2:
-        grid_new_np = np.full((len(wav_obs_spec),
+        grid_spectro_np = np.full((len(wav_obs_spectro),
                                len(grid["par1"].values),
                                len(grid["par2"].values)), np.nan)
-        grid_phot_new_np = np.full((len(wav_obs_phot),
+        grid_photo_np = np.full((len(wav_obs_photo),
                                     len(grid["par1"].values),
                                     len(grid["par2"].values)), np.nan)
         tot_par = len(grid["par1"].values) * len(grid["par2"].values)
     if len(attr['par']) == 3:
-        grid_new_np = np.full((len(wav_obs_spec),
+        grid_spectro_np = np.full((len(wav_obs_spectro),
                                len(grid["par1"].values),
                                len(grid["par2"].values),
                                len(grid["par3"].values)), np.nan)
-        grid_phot_new_np = np.full((len(wav_obs_phot),
+        grid_photo_np = np.full((len(wav_obs_photo),
                                     len(grid["par1"].values),
                                     len(grid["par2"].values),
                                     len(grid["par3"].values)), np.nan)
         tot_par = len(grid["par1"].values) * len(grid["par2"].values) * len(grid["par3"].values)
     if len(attr['par']) == 4:
-        grid_new_np = np.full((len(wav_obs_spec),
+        grid_spectro_np = np.full((len(wav_obs_spectro),
                                len(grid["par1"].values),
                                len(grid["par2"].values),
                                len(grid["par3"].values),
                                len(grid["par4"].values)), np.nan)
-        grid_phot_new_np = np.full((len(wav_obs_phot),
+        grid_photo_np = np.full((len(wav_obs_photo),
                                     len(grid["par1"].values),
                                     len(grid["par2"].values),
                                     len(grid["par3"].values),
                                     len(grid["par4"].values)), np.nan)
         tot_par = len(grid["par1"].values) * len(grid["par2"].values) * len(grid["par3"].values) * len(grid["par4"].values)
     if len(attr['par']) == 5:
-        grid_new_np = np.full((len(wav_obs_spec),
+        grid_spectro_np = np.full((len(wav_obs_spectro),
                                len(grid["par1"].values),
                                len(grid["par2"].values),
                                len(grid["par3"].values),
                                len(grid["par4"].values),
                                len(grid["par5"].values)), np.nan)
-        grid_phot_new_np = np.full((len(wav_obs_phot),
+        grid_photo_np = np.full((len(wav_obs_photo),
                                     len(grid["par1"].values),
                                     len(grid["par2"].values),
                                     len(grid["par3"].values),
@@ -115,10 +95,10 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                                     model_to_adapt = grid_np[:, p1_i, p2_i, p3_i, p4_i, p5_i]
                                     nan_mod_ind = ~np.isnan(model_to_adapt)
                                     if len(np.where(nan_mod_ind is False)[0]) == 0:
-                                        flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, wav_reso_tab,
-                                                                               model_to_adapt, attr['res'], obs_name=obs_name, indobs=indobs)
-                                        grid_new_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = flx_mod_extract
-                                        grid_phot_new_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = mod_pho
+                                        mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt,
+                                                                                 res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
+                                        grid_spectro_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = mod_spectro
+                                        grid_photo_np[:, p1_i, p2_i, p3_i, p4_i, p5_i] = mod_photo
                                     else:
 
                                         print('The extraction of the model : '+attr['title'][0]+'=' + str(p1) +
@@ -146,11 +126,11 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                                 model_to_adapt = grid_np[:, p1_i, p2_i, p3_i, p4_i]
                                 nan_mod_ind = ~np.isnan(model_to_adapt)
                                 if len(np.where(nan_mod_ind is False)[0]) == 0:
-                                    flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, wav_reso_tab, model_to_adapt, attr['res'], obs_name=obs_name,
-                                                                           indobs=indobs)
+                                    mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt,
+                                                                             res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
                                     
-                                    grid_new_np[:, p1_i, p2_i, p3_i, p4_i] = flx_mod_extract
-                                    grid_phot_new_np[:, p1_i, p2_i, p3_i, p4_i] = mod_pho
+                                    grid_spectro_np[:, p1_i, p2_i, p3_i, p4_i] = mod_spectro
+                                    grid_photo_np[:, p1_i, p2_i, p3_i, p4_i] = mod_photo
                                 else:
 
                                     print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) +
@@ -178,11 +158,11 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                         model_to_adapt = grid_np[:, p1_i, p2_i, p3_i]
                         nan_mod_ind = ~np.isnan(model_to_adapt)
                         if len(np.where(nan_mod_ind is False)[0]) == 0:
-                            flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, wav_reso_tab, model_to_adapt,
-                                                                   attr['res'], obs_name=obs_name, indobs=indobs)
+                            mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt,
+                                                                   res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
 
-                            grid_new_np[:, p1_i, p2_i, p3_i] = flx_mod_extract
-                            grid_phot_new_np[:, p1_i, p2_i, p3_i] = mod_pho
+                            grid_spectro_np[:, p1_i, p2_i, p3_i] = mod_spectro
+                            grid_photo_np[:, p1_i, p2_i, p3_i] = mod_photo
                         else:
 
                             print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) +
@@ -208,9 +188,10 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                 model_to_adapt = grid_np[:, p1_i, p2_i]
                 nan_mod_ind = ~np.isnan(model_to_adapt)
                 if len(np.where(nan_mod_ind is False)[0]) == 0:
-                    flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, wav_reso_tab, model_to_adapt, attr['res'], obs_name=obs_name, indobs=indobs)
-                    grid_new_np[:, p1_i, p2_i] = flx_mod_extract
-                    grid_phot_new_np[:, p1_i, p2_i] = mod_pho
+                    mod_spectro, mod_photo = adapt_model(global_params, wav_mod_nativ, model_to_adapt,
+                                                             res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
+                    grid_spectro_np[:, p1_i, p2_i] = mod_spectro
+                    grid_photo_np[:, p1_i, p2_i] = mod_photo
                 else:
 
                     print('The extraction of the model : ' + attr['title'][0] + '=' + str(p1) +
@@ -231,40 +212,40 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                 i_tot += 1
 
     if len(attr['par']) == 2:
-        ds_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_new_np)),
-                            coords={"wavelength": wav_obs_spec,
+        ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_spectro_np)),
+                            coords={"wavelength": wav_obs_spectro,
                                     "par1": grid["par1"].values,
                                     "par2": grid["par2"].values},
                             attrs=attr)
-        ds_new_phot = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_phot_new_np)),
-                                 coords={"wavelength": wav_obs_phot,
+        ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2"], grid_photo_np)),
+                                 coords={"wavelength": wav_obs_photo,
                                          "par1": grid["par1"].values,
                                          "par2": grid["par2"].values},
                                  attrs=attr)
     if len(attr['par']) == 3:
-        ds_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_new_np)),
-                            coords={"wavelength": wav_obs_spec,
+        ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_spectro_np)),
+                            coords={"wavelength": wav_obs_spectro,
                                     "par1": grid["par1"].values,
                                     "par2": grid["par2"].values,
                                     "par3": grid["par3"].values},
                             attrs=attr)
-        ds_new_phot = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_phot_new_np)),
-                                 coords={"wavelength": wav_obs_phot,
+        ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3"], grid_photo_np)),
+                                 coords={"wavelength": wav_obs_photo,
                                          "par1": grid["par1"].values,
                                          "par2": grid["par2"].values,
                                          "par3": grid["par3"].values},
                                  attrs=attr)
     if len(attr['par']) == 4:
-        ds_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], grid_new_np)),
-                            coords={"wavelength": wav_obs_spec,
+        ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], grid_spectro_np)),
+                            coords={"wavelength": wav_obs_spectro,
                                     "par1": grid["par1"].values,
                                     "par2": grid["par2"].values,
                                     "par3": grid["par3"].values,
                                     "par4": grid["par4"].values},
                             attrs=attr)
-        ds_new_phot = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], 
-                                                      grid_phot_new_np)),
-                                 coords={"wavelength": wav_obs_phot,
+        ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4"], 
+                                                      grid_photo_np)),
+                                 coords={"wavelength": wav_obs_photo,
                                          "par1": grid["par1"].values,
                                          "par2": grid["par2"].values,
                                          "par3": grid["par3"].values,
@@ -272,17 +253,17 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
                                  attrs=attr)
 
     if len(attr['par']) == 5:
-        ds_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], grid_new_np)),
-                            coords={"wavelength": wav_obs_spec,
+        ds_spectro_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], grid_spectro_np)),
+                            coords={"wavelength": wav_obs_spectro,
                                     "par1": grid["par1"].values,
                                     "par2": grid["par2"].values,
                                     "par3": grid["par3"].values,
                                     "par4": grid["par4"].values,
                                     "par5": grid["par5"].values},
                             attrs=attr)
-        ds_new_phot = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], 
-                                                      grid_phot_new_np)),
-                                 coords={"wavelength": wav_obs_phot,
+        ds_photo_new = xr.Dataset(data_vars=dict(grid=(["wavelength", "par1", "par2", "par3", "par4", "par5"], 
+                                                      grid_photo_np)),
+                                 coords={"wavelength": wav_obs_photo,
                                          "par1": grid["par1"].values,
                                          "par2": grid["par2"].values,
                                          "par3": grid["par3"].values,
@@ -295,16 +276,16 @@ def adapt_grid(global_params, wav_obs_spec, wav_obs_phot, obs_name='', indobs=0)
     print()
     for key_ind, key in enumerate(attr['key']):
         print(str(key_ind+1) + '/' + str(len(attr['key'])))
-        ds_new = ds_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None,
+        ds_spectro_new = ds_spectro_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None,
                                        max_gap=None)
-        ds_new_phot = ds_new_phot.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None,
+        ds_photo_new = ds_photo_new.interpolate_na(dim=key, method="linear", fill_value="extrapolate", limit=None,
                                                  max_gap=None)
  
-    ds_new.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_spectro_{global_params.grid_name}_{obs_name}_nonan.nc'),
+    ds_spectro_new.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_spectro_{global_params.grid_name}_{obs_name}_nonan.nc'),
                     format='NETCDF4',
                     engine='netcdf4',
                     mode='w')
-    ds_new_phot.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_photo_{global_params.grid_name}_{obs_name}_nonan.nc'),
+    ds_photo_new.to_netcdf(os.path.join(global_params.adapt_store_path, f'adapted_grid_photo_{global_params.grid_name}_{obs_name}_nonan.nc'),
                         format='NETCDF4',
                         engine='netcdf4',
                         mode='w')       
diff --git a/build/lib/ForMoSA/adapt/adapt_obs_mod.py b/build/lib/ForMoSA/adapt/adapt_obs_mod.py
index 8be38f1..016af49 100644
--- a/build/lib/ForMoSA/adapt/adapt_obs_mod.py
+++ b/build/lib/ForMoSA/adapt/adapt_obs_mod.py
@@ -1,8 +1,10 @@
 from __future__ import print_function, division
 import numpy as np
-import os
+import os,sys
 import xarray as xr
-# import matplotlib.pyplot as plt
+from scipy.interpolate import interp1d
+
+sys.path.insert(0, os.path.abspath('../'))
 
 from adapt.extraction_functions import extract_observation
 from adapt.adapt_grid import adapt_grid
@@ -17,17 +19,18 @@ def launch_adapt(global_params, justobs='no'):
     
     Args:
         global_params  (object): Class containing each parameter
-        justobs ('yes'/'no'): 'no' by default to also adapt the grid
+        justobs    ('yes'/'no'): 'no' by default to also adapt the grid
     Returns:
         None
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet, Paulina Palma-Bifani and Allan Denis
+    Author: Simon Petrus, Matthieu Ravet, Paulina Palma-Bifani and Allan Denis
     """
 
-    # Get back information from the config file
+    # Get back the grid information from the config file
     ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4")
     wav_mod_nativ = ds["wavelength"].values
     attr = ds.attrs
+    res_mod_nativ = attr['res']
     ds.close()
 
     # Extract the data from the observation files
@@ -38,68 +41,90 @@ def launch_adapt(global_params, justobs='no'):
         
         global_params.observation_path = obs
         obs_name = os.path.splitext(os.path.basename(global_params.observation_path))[0]
-        
-        obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, attr['res'], obs_name=obs_name,
-                                                                                      indobs=indobs)
 
-        # Estimate and subtraction of the continuum (if needed) + check-ups
+        # Estimate and subtract the continuum (if needed) + check-ups
         if global_params.continuum_sub[indobs] != 'NA':
             print()
             print(obs_name + ' will have a R=' + global_params.continuum_sub[indobs] + ' continuum removed using a ' 
                 + global_params.wav_for_continuum[indobs] + ' wavelength range')
             print()
-        if global_params.continuum_sub[indobs] != 'NA':
-            obs_spectro_c, obs_photo_c, obs_spectro_ins_c, obs_photo_ins_c, obs_opt_c = extract_observation(global_params, wav_mod_nativ,
-                                                                                    attr['res'], 'yes', obs_name=obs_name, indobs=indobs)
-            for c, cut in enumerate(obs_spectro):
-                obs_spectro[c][1] -= obs_spectro_c[c][1]
+            obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ, 'yes', 
+                                                                                                  obs_name=obs_name, indobs=indobs)
+        else:
+            obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt = extract_observation(global_params, wav_mod_nativ, res_mod_nativ,
+                                                                                                   obs_name=obs_name, indobs=indobs)
+
 
-        # Merging of each sub-spectrum
+        # Merging of each sub-spectrum and interpolating the grid
         for c, cut in enumerate(obs_spectro):
-            if c == 0:
-                wav_obs_extract = obs_spectro[c][0]
-                flx_obs_extract = obs_spectro[c][1]
-                err_obs_extract = obs_spectro[c][2]
-                res_obs_extract = obs_spectro[c][3]
-                cov_obs_extract = obs_opt[c][0]
-                transm_obs_extract = obs_opt[c][1]
-                star_flx_obs_extract = obs_opt[c][2]
 
-            else:
-                wav_obs_extract = np.concatenate((wav_obs_extract, obs_spectro[c][0]))
-                flx_obs_extract = np.concatenate((flx_obs_extract, obs_spectro[c][1]))
-                err_obs_extract = np.concatenate((err_obs_extract, obs_spectro[c][2]))
-                res_obs_extract = np.concatenate((res_obs_extract, obs_spectro[c][3]))
+            if len(cut[0]) > 0:
+                # Interpolate the resolution onto the wavelength of the data
+                ind_mod_obs = np.where((wav_mod_nativ <= cut[0][-1]) & (wav_mod_nativ > cut[0][0]))
+                wav_mod_cut = wav_mod_nativ[ind_mod_obs]
+                res_mod_cut = res_mod_nativ[ind_mod_obs]
+                interp_mod_to_obs = interp1d(wav_mod_cut, res_mod_cut, fill_value='extrapolate')
+                res_mod_cut = interp_mod_to_obs(cut[0])
+    
+                if c == 0:
+                    wav_obs_extract = obs_spectro[c][0]
+                    flx_obs_extract = obs_spectro[c][1]
+                    err_obs_extract = obs_spectro[c][2]
+                    res_obs_extract = obs_spectro[c][3]
+                    cov_obs_extract = obs_opt[c][0]
+                    transm_obs_extract = obs_opt[c][1]
+                    star_flx_obs_extract = obs_opt[c][2]
+                    system_obs_extract = obs_opt[c][3]
+                    # Save the interpolated resolution of the grid
+                    res_mod_obs_merge = [res_mod_cut]
+    
+                else:
+                    wav_obs_extract = np.concatenate((wav_obs_extract, obs_spectro[c][0]))
+                    flx_obs_extract = np.concatenate((flx_obs_extract, obs_spectro[c][1]))
+                    err_obs_extract = np.concatenate((err_obs_extract, obs_spectro[c][2]))
+                    res_obs_extract = np.concatenate((res_obs_extract, obs_spectro[c][3]))
+                    if len(cov_obs_extract) != 0:
+                        cov_obs_extract = diag_mat([cov_obs_extract, obs_opt[c][0]])
+                    if len(transm_obs_extract) != 0:
+                        transm_obs_extract = np.concatenate((transm_obs_extract, obs_opt[c][1]))
+                    if len(star_flx_obs_extract) != 0:
+                        star_flx_obs_extract = np.concatenate((star_flx_obs_extract, obs_opt[c][2]))
+                    if len(system_obs_extract) != 0:
+                        system_obs_extract = np.concatenate((system_obs_extract, obs_opt[c][3]), axis=0)
+                    # Save the interpolated resolution of the grid
+                    res_mod_obs_merge.append(res_mod_cut)
+    
+    
+                # Compute the inverse of the merged covariance matrix (note: inv(C1, C2) = (in(C1), in(C2)) if C1 and C2 are block matrix on the diagonal)
+                # if necessary
                 if len(cov_obs_extract) != 0:
-                    cov_obs_extract = diag_mat([cov_obs_extract, obs_opt[c][0]])
-                if len(transm_obs_extract) != 0:
-                    transm_obs_extract = np.concatenate((transm_obs_extract, obs_opt[c][1]))
-                if len(star_flx_obs_extract) != 0:
-                    star_flx_obs_extract = np.concatenate((star_flx_obs_extract, obs_opt[c][2]))
-
-
-            # Compute the inverse of the merged covariance matrix (note: inv(C1, C2) = (in(C1), in(C2)) if C1 and C2 are block matrix on the diagonal)
-            # if necessary
-            if len(cov_obs_extract) != 0:
-                inv_cov_obs_extract = np.linalg.inv(cov_obs_extract)
+                    inv_cov_obs_extract = np.linalg.inv(cov_obs_extract)
+                else:
+                    inv_cov_obs_extract = np.asarray([])
+
+                # Check-ups and warnings for negative values in the diagonal of the covariance matrix
+                if len(cov_obs_extract) != 0 and any(np.diag(cov_obs_extract) < 0):
+                    print()
+                    print("WARNING: Negative value(s) is(are) present on the diagonal of the covariance matrix.") 
+                    print("Operation aborted.")
+                    print()
+                    exit()
+                    
             else:
-                inv_cov_obs_extract = np.asarray([])
-
-            # Compile everything and changing data type to object to allow for different array sizes
-            obs_spectro_merge = np.asarray([wav_obs_extract, flx_obs_extract, err_obs_extract, res_obs_extract])
-            obs_spectro = np.asarray(obs_spectro, dtype=object)
-            obs_spectro_ins = np.asarray(obs_spectro_ins, dtype=object)
-            obs_photo = np.asarray(obs_photo, dtype=object)
-            obs_photo_ins = np.asarray(obs_photo_ins, dtype=object)
-            obs_opt_merge = np.asarray([inv_cov_obs_extract, transm_obs_extract, star_flx_obs_extract], dtype=object)
-
-            # Check-ups and warnings for negative values in the diagonal of the covariance matrix
-            if len(cov_obs_extract) != 0 and any(np.diag(cov_obs_extract) < 0):
-                print()
-                print("WARNING: Negative value(s) is(are) present on the diagonal of the covariance matrix.") 
-                print("Operation aborted.")
-                print()
-                exit()
+                wav_obs_extract, flx_obs_extract, err_obs_extract, res_obs_extract = [], [], [], []
+                inv_cov_obs_extract, transm_obs_extract, star_flx_obs_extract, system_obs_extract = [], [], [], []
+                res_mod_obs_merge = res_mod_nativ
+
+        # Compile everything and changing data type to object to allow for different array sizes
+        obs_spectro_merge = np.asarray([wav_obs_extract, flx_obs_extract, err_obs_extract, res_obs_extract])
+        obs_spectro = np.asarray(obs_spectro, dtype=object)
+        obs_spectro_ins = np.asarray(obs_spectro_ins, dtype=object)
+        obs_photo = np.asarray(obs_photo, dtype=object)
+        obs_photo_ins = np.asarray(obs_photo_ins, dtype=object)
+        obs_opt_merge = np.asarray([inv_cov_obs_extract, transm_obs_extract, star_flx_obs_extract, system_obs_extract], dtype=object)
+
+    
+            
 
         # Save the new data spectrum
         np.savez(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'),
@@ -129,8 +154,8 @@ def launch_adapt(global_params, justobs='no'):
             print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
             print(f"-> Sarting the adaptation of {obs_name}")
 
-            adapt_grid(global_params, obs_spectro_merge[0], obs_photo[0], obs_name=obs_name, indobs=indobs)
-
+            adapt_grid(global_params, obs_spectro_merge[0], obs_photo[0], res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
+        
 
 # ----------------------------------------------------------------------------------------------------------------------
 
diff --git a/build/lib/ForMoSA/adapt/extraction_functions.py b/build/lib/ForMoSA/adapt/extraction_functions.py
index c118a37..4964568 100644
--- a/build/lib/ForMoSA/adapt/extraction_functions.py
+++ b/build/lib/ForMoSA/adapt/extraction_functions.py
@@ -5,7 +5,6 @@
 from scipy.interpolate import interp1d
 from spectres import spectres
 import os
-
 # ----------------------------------------------------------------------------------------------------------------------
 
 
@@ -16,7 +15,9 @@ def decoupe(second):
     Args:
         second (float): number of second
     Returns:
-        hour, minute, second (float, float, float): hours-minutes-seconds format
+        - float     : hours
+        - float     : minutes
+        - float     : seconds
 
     Author: Simon Petrus
     """
@@ -39,7 +40,7 @@ def find_nearest(array, value):
         array (array): Array to explore
         value (float): Desire value
     Returns:
-        idx     (int): Indice of the closest values from the desire value
+        - idx (int)          : Indice of the closest values from the desire value
 
     Author: Simon Petrus
     """
@@ -62,19 +63,15 @@ def extract_observation(global_params, wav_mod_nativ, res_mod_nativ, cont='no',
         cont                 (str): Boolean string. If the function is used to estimate the continuum cont='yes'
         obs_name             (str): Name of the current observation looping
         indobs               (int): Index of the current observation looping
+
     Returns:
-        obs_spectro      (n-array): List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution
-                                    [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
-        obs_photo          (array): List containing the photometry (0 replace the spectral resolution here).
-                                    [wav_phot, flx_phot, err_phot, 0]
-        obs_spectro_ins    (array): List containing different instruments used for the data (1 per wavelength).
-                                    [[instru_range_1], ..., [instru_range_n]]
-        obs_photo_ins      (array): List containing different filters used for the data (1 per photometric point).
-                                    [filter_phot_1, filter_phot_2, ..., filter_phot_n]
-        obs_opt          (n-array): List containing the optional sub-arrays defined by the parameter "wav_for_adapt".
-                                    [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]  
-
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
+        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0]
+        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]
+        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]
+        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]  
+
+    Author: Simon Petrus, Matthieu Ravet
     """
 
     # Extract the wavelengths, flux, errors, spectral resolution, and instrument/filter names from the observation file.
@@ -83,46 +80,21 @@ def extract_observation(global_params, wav_mod_nativ, res_mod_nativ, cont='no',
 
     # Reduce the spectral resolution for each sub-spectrum.
     for c, cut in enumerate(obs_spectro):
-        # If we want to decrease the resolution of the data:
         if len(cut[0]) != 0:
-            if cont == 'no':
-                if global_params.adapt_method[indobs] == 'by_reso':
-                    obs_spectro[c][1] = resolution_decreasing(global_params, cut, wav_mod_nativ, [], res_mod_nativ,
-                                                        'obs', obs_name=obs_name, indobs=indobs)
-            # If we want to estimate the continuum of the data:
-            else:
-                for w_ind, wav_for_cont in enumerate(global_params.wav_for_continuum[indobs].split('/')):
-                    wav_obs_for_cont_ind = np.where((float(wav_for_cont.split(',')[0]) < cut[0]) &
-                                                    (cut[0] < float(wav_for_cont.split(',')[1])))
-                    if w_ind == 0:
-                        wav_obs_for_cont = cut[0][wav_obs_for_cont_ind]
-                        flx_obs_for_cont = cut[1][wav_obs_for_cont_ind]
-                    else:
-                        wav_obs_for_cont = np.concatenate((wav_obs_for_cont, cut[0][wav_obs_for_cont_ind]))
-                        flx_obs_for_cont = np.concatenate((flx_obs_for_cont, cut[1][wav_obs_for_cont_ind]))
-                wav_reso_tab = []
-                wav_reso = min(wav_obs_for_cont)
-                while wav_reso < max(wav_obs_for_cont):
-                    wav_reso_tab.append(wav_reso)
-                    wav_reso += wav_reso / float(global_params.continuum_sub[indobs])
-                wav_reso_tab = np.asarray(wav_reso_tab)
-
-                flx_obs_cont = spectres(wav_reso_tab, wav_obs_for_cont, flx_obs_for_cont, fill=np.nan, verbose=False)
-                for w_ind, wav_for_cont in enumerate(global_params.wav_for_continuum[indobs].split('/')):
-                    wav_final_cont_ind = np.where((float(wav_for_cont.split(',')[0]) < wav_reso_tab) &
-                                                    (wav_reso_tab < float(wav_for_cont.split(',')[1])))
-                    if w_ind == 0:
-                        wav_final_cont = wav_reso_tab[wav_final_cont_ind]
-                        flx_final_cont = flx_obs_cont[wav_final_cont_ind]
-                    else:
-                        wav_final_cont = np.concatenate((wav_final_cont, wav_reso_tab[wav_final_cont_ind]))
-                        flx_final_cont = np.concatenate((flx_final_cont, flx_obs_cont[wav_final_cont_ind]))
+            # Interpolate the resolution of the model onto the wavelength of the data to properly decrease the resolution if necessary
+            ind_mod_obs = np.where((wav_mod_nativ <= cut[0][-1]) & (wav_mod_nativ > cut[0][0]))
+            wav_mod_obs = wav_mod_nativ[ind_mod_obs]
+            res_mod_obs = res_mod_nativ[ind_mod_obs]
+            interp_mod_to_obs = interp1d(wav_mod_obs, res_mod_obs, fill_value='extrapolate')
+            res_mod_obs = interp_mod_to_obs(cut[0])
+            # If we want to decrease the resolution of the data: (if by_sample, the data don't need to be adapted)
+            if global_params.adapt_method[indobs] == 'by_reso':
+                obs_spectro[c][1] = resolution_decreasing(global_params, cut[0], cut[1], cut[3], wav_mod_nativ, [], res_mod_obs,
+                                                    'obs', indobs=indobs)
+            if cont == 'yes':
+            # If we want to estimate and substract the continuum of the data:
+                obs_spectro[c][1] -= continuum_estimate(global_params, cut[0], cut[1], cut[3], indobs=indobs)
                 
-                interp_reso = interp1d(wav_final_cont[~np.isnan(flx_final_cont)], flx_final_cont[~np.isnan(flx_final_cont)], fill_value="extrapolate")
-                flx_obs_cont = interp_reso(cut[0])
-                obs_spectro[c][1] = flx_obs_cont
-
-
     return obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt
 
 # ----------------------------------------------------------------------------------------------------------------------
@@ -137,17 +109,13 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
         global_params  (object): Class containing each parameter
         obs_name          (str): Name of the current observation looping
         indobs            (int): Index of the current observation looping
+
     Returns:
-        obs_spectro   (n-array): List containing the sub-spectra defined by the parameter "wav_for_adapt".
-                                [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
-        obs_photo       (array): List containing the photometry (0 replace the spectral resolution here).
-                                [wav_phot, flx_phot, err_phot, 0]
-        obs_spectro_ins (array): List containing different instruments used for the data (1 per wavelength).
-                                [[instru_1], ..., [instru_n]]
-        obs_photo_ins   (array): List containing different filters used for the data (1 per photometric point).
-                                [filter_phot_1, filter_phot_2, ..., filter_phot_n]
-        obs_opt       (n-array): List containing the optional sub-arrays defined by the parameter "wav_for_adapt".
-                                [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]
+        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter "wav_for_adapt" with decreased resolution  [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
+        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here).  [wav_phot, flx_phot, err_phot, 0]
+        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]
+        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]
+        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter "wav_for_adapt". [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]] 
 
     Author: Simon Petrus, Matthieu Ravet and Allan Denis
     """
@@ -167,21 +135,59 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
             err = np.sqrt(np.diag(np.abs(cov)))
         try:
             transm = hdul[1].data['TRANSM']
-            star_flx = hdul[1].data['STAR FLX']
         except:
             transm = np.asarray([])
-            star_flx = np.asarray([])            
-
+        try:
+            star_flx = hdul[1].data['STAR_FLX1'][:,np.newaxis]
+            is_star = True
+        except:
+            star_flx = np.asarray([])   
+            is_star = False
+        try:
+            star_flx = hdul[1].data['STAR FLX'][:,np.newaxis]
+        except:
+            pass
+        try:
+            is_system = True
+            system = hdul[1].data['SYSTEMATICS1'][:,np.newaxis]
+        except:
+            is_system = False
+            system = np.asarray([])
+            
+        if is_system:
+            i = 2
+            while True:  # In case there is multiple systematics
+                try:
+                    system = np.concatenate((system, hdul[1].data['SYSTEMATICS' + str(i)][:,np.newaxis]),axis=1)
+                    i += 1
+                except:
+                    break
+                
+        if is_star:
+            i = 2
+            while True:
+                try:
+                    star_flx = np.concatenate((star_flx, hdul[1].data['STAR_FLX' + str(i)][:,np.newaxis]),axis=1)
+                    i += 1
+                except:
+                    break
         # Only take the covariance if you use the chi2_covariance likelihood function (will need to be change when new likelihood functions using the
         # covariance matrix will come)
         if global_params.logL_type[indobs] != 'chi2_covariance':
             cov = np.asarray([])
 
-        # Filter the NaN values
-        if len(transm) != 0 and len(star_flx) != 0:
-            nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(transm)) & (~np.isnan(star_flx)) & (~np.isnan(err))
+        # Filter the NaN and inf values
+        if len(transm) != 0:
+            nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(transm)) & (~np.isnan(err)) & (np.isfinite(flx)) & (np.isfinite(transm)) & (np.isfinite(err))
         else:
-            nan_mod_ind = (~np.isnan(flx)) 
+            nan_mod_ind = (~np.isnan(flx)) & (~np.isnan(err)) & (np.isfinite(flx)) & (np.isfinite(err))
+        if len(star_flx) != 0:
+            for i in range(len(star_flx[0])):
+                nan_mod_ind = (nan_mod_ind) & (~np.isnan(star_flx.T[i])) & (np.isfinite(star_flx.T[i]))
+        if len(system) != 0:
+            for i in range(len(system[0])):
+                nan_mod_ind = (nan_mod_ind) & (~np.isnan(system.T[i])) & (np.isfinite(system.T[i]))
+                
         wav = wav[nan_mod_ind]
         flx = flx[nan_mod_ind]
         res = res[nan_mod_ind]
@@ -191,8 +197,11 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
             cov = np.transpose(np.transpose(cov[nan_mod_ind])[nan_mod_ind])
         if len(transm) != 0 and len(star_flx) != 0:
             transm = transm[nan_mod_ind]
-            star_flx = star_flx[nan_mod_ind]
-
+        if len(star_flx) != 0:
+            star_flx = np.delete(star_flx, np.where(~nan_mod_ind), axis=0)
+        if len(system) != 0:
+            system = np.delete(system, np.where(~nan_mod_ind), axis=0)
+            
         # Select the wavelength range(s) for the extraction
         if global_params.wav_for_adapt == '':
             wav_for_adapt_tab = [str(min(wav)) + ',' + str(max(wav))]
@@ -235,22 +244,19 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
                 transm_spectro = np.asarray([])
             
             if len(star_flx) != 0:
-                star_flx_spectro = np.delete(star_flx[ind], ind_photometry)
+                star_flx_spectro = np.delete(star_flx[ind,:], ind_photometry, axis=0)
             else:
                 star_flx_spectro = np.asarray([])
+                
+            if len(system) != 0:
+                system_spectro = np.delete(system[ind,:], ind_photometry, axis=0)
+            else:
+                system_spectro = np.asarray([])
 
             # Merge spectroscopic data
             obs_spectro[range_ind] = [wav_spectro, flx_spectro, err_spectro, res_spectro]
-            obs_opt[range_ind] = [cov_spectro, transm_spectro, star_flx_spectro]
+            obs_opt[range_ind] = [cov_spectro, transm_spectro, star_flx_spectro, system_spectro]
             obs_spectro_ins[range_ind] = ins_spectro
-            # if range_ind == 0:
-            #     obs_spectro = [[wav_spectro, flx_spectro, err_spectro, res_spectro]]
-            #     obs_opt = [[cov_spectro, transm_spectro, star_flx_spectro]] # Optional arrays
-            #     obs_spectro_ins = [[ins_spectro]]
-            # else:
-            #     obs_spectro.append([wav_spectro, flx_spectro, err_spectro, res_spectro])
-            #     obs_opt.append([cov_spectro, transm_spectro, star_flx_spectro]) # Optional arrays
-            #     obs_spectro_ins.append([ins_spectro])
             
         return obs_spectro, obs_photo, obs_spectro_ins, obs_photo_ins, obs_opt   
 
@@ -258,7 +264,7 @@ def adapt_observation_range(global_params, obs_name='', indobs=0):
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def adapt_model(global_params, wav_mod_nativ, wave_reso_tab, flx_mod_nativ, res_mod_nativ, obs_name='', indobs=0):
+def adapt_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name='', indobs=0):
     """
     Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
     calculated too. Then each sub-spectrum are merged.
@@ -272,59 +278,23 @@ def adapt_model(global_params, wav_mod_nativ, wave_reso_tab, flx_mod_nativ, res_
         obs_name          (str): Name of the current observation looping
         indobs            (int): Index of the current observation looping
     Returns:
-        flx_mod_extract (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
-        mod_photo       (array): List containing the photometry ('0' replace the spectral resolution here).
+        - mod_spectro (array)     : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
+        - mod_photo (array)       : List containing the photometry ('0' replace the spectral resolution here).
 
     Author: Simon Petrus
     """
-    # Extract the synthetic spectra from the model grid
-    mod_spectro, mod_photo, obs_spectro = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, obs_name=obs_name)
-    # Estimate and subtraction of the continuum (if needed)
+    # Estimate and subtract the continuum (if needed)
     if global_params.continuum_sub[indobs] != 'NA':
-        for c, cut in enumerate(obs_spectro):
-            #mod_cut_c, mod_pho_c = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, 'yes')
-            for w_ind, wav_for_cont in enumerate(global_params.wav_for_continuum[indobs].split('/')):
-                wav_mod_for_cont_ind = np.where((float(wav_for_cont.split(',')[0]) < wav_mod_nativ) &
-                                                (wav_mod_nativ < float(wav_for_cont.split(',')[1])))
-                if w_ind == 0:
-                    wav_mod_for_cont = wav_mod_nativ[wav_mod_for_cont_ind]
-                    flx_mod_for_cont = flx_mod_nativ[wav_mod_for_cont_ind]
-                else:
-                    wav_mod_for_cont = np.concatenate((wav_mod_for_cont, wav_mod_nativ[wav_mod_for_cont_ind]))
-                    flx_mod_for_cont = np.concatenate((flx_mod_for_cont, flx_mod_nativ[wav_mod_for_cont_ind]))
-
-            flx_obs_cont = spectres(wave_reso_tab, wav_mod_for_cont, flx_mod_for_cont, fill=np.nan, verbose=False)
-            for w_ind, wav_for_cont in enumerate(global_params.wav_for_continuum[indobs].split('/')):
-                wav_final_cont_ind = np.where((float(wav_for_cont.split(',')[0]) < wave_reso_tab) &
-                                            (wave_reso_tab < float(wav_for_cont.split(',')[1])))
-                if w_ind == 0:
-                    wav_final_cont = wave_reso_tab[wav_final_cont_ind]
-                    flx_final_cont = flx_obs_cont[wav_final_cont_ind]
-                else:
-                    wav_final_cont = np.concatenate((wav_final_cont, wave_reso_tab[wav_final_cont_ind]))
-                    flx_final_cont = np.concatenate((flx_final_cont, flx_obs_cont[wav_final_cont_ind]))
-            if len(flx_final_cont[~np.isnan(flx_final_cont)]) != 0:
-                interp_reso = interp1d(wav_final_cont[~np.isnan(flx_final_cont)], flx_final_cont[~np.isnan(flx_final_cont)],
-                                    fill_value="extrapolate")
-                flx_obs_cont = interp_reso(obs_spectro[c][0])
-            else:
-                flx_obs_cont = obs_spectro[c][0]*np.nan
-                
-            mod_spectro[c] -= flx_obs_cont
-
-    # Merging of each sub-spectrum
-    for c, cut in enumerate(mod_spectro):
-        if c == 0:
-            flx_mod_extract = mod_spectro[c]
-        else:
-            flx_mod_extract = np.concatenate((flx_mod_extract, mod_spectro[c]))
+        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='yes', obs_name=obs_name, indobs=indobs)
+    else:
+        mod_spectro, mod_photo = extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, obs_name=obs_name, indobs=indobs)
 
-    return flx_mod_extract, mod_photo
+    return mod_spectro, mod_photo
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, cont='no', obs_name='', indobs=0):
+def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge, cont='no', obs_name='', indobs=0):
     """
     Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
     calculated too.
@@ -338,8 +308,8 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, co
         obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        mod_spectro    (array): List containing the sub-spectra defined by the parameter "wav_for_adapt".
-        mod_photo      (array): List containing the photometry ('0' replace the spectral resolution here).
+        - mod_spectro (array) : List containing the sub-spectra defined by the parameter "wav_for_adapt".
+        - mod (array)         : List containing the photometry ('0' replace the spectral resolution here).
 
     Author: Simon Petrus
     """
@@ -347,37 +317,30 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, co
     spectrum_obs = np.load(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True)
     obs_spectro = spectrum_obs['obs_spectro']
     obs_photo_ins = spectrum_obs['obs_photo_ins']
-
+    mod_spectro, mod_photo = [], []
     # Reduce the spectral resolution for each sub-spectrum.
-    mod_spectro = []
     for c, cut in enumerate(obs_spectro):
-        # If we want to decrease the resolution of the data:
         if len(cut[0]) != 0:
-            if cont == 'no':
-                if global_params.adapt_method[indobs] == 'by_reso':
-                    mod_cut_flx = resolution_decreasing(global_params, cut, wav_mod_nativ, flx_mod_nativ, res_mod_nativ,
-                                                        'mod', obs_name=obs_name, indobs=indobs) # Conversion of the cuts to floats for the np computations
-                else:
-                    res_tab = cut[3]
-                    wave_reso_tab = []
-                    wav_reso = min(cut[0])
-                    while wav_reso < max(cut[0]):
-                        wave_reso_tab.append(wav_reso)
-                        wav_reso += wav_reso/res_tab[find_nearest(cut[0], wav_reso)]
-                    wave_reso_tab = np.asarray(wave_reso_tab)
-                    mod_cut_flx = spectres(wave_reso_tab, wav_mod_nativ, flx_mod_nativ)
-                    interp_reso = interp1d(wave_reso_tab, mod_cut_flx, fill_value="extrapolate")
-                    mod_cut_flx = interp_reso(cut[0])        
+            # If we want to decrease the resolution of the data:
+            if global_params.adapt_method[indobs] == 'by_reso':
+                mod_cut_flx = resolution_decreasing(global_params, cut[0], [], cut[3], wav_mod_nativ, flx_mod_nativ, res_mod_obs_merge[c],
+                                                    'mod', indobs=indobs)
+            else:
+                mod_cut_flx = spectres(cut[0], wav_mod_nativ, flx_mod_nativ)
+
             # If we want to estimate the continuum of the data:
+            if cont == 'yes':     
+                continuum = continuum_estimate(global_params, cut[0], mod_cut_flx, res_mod_obs_merge[c], indobs=indobs)
+                mod_cut_flx -= continuum
+
+            # Concatenate to speed up the code
+            if c==0:
+                mod_spectro = mod_cut_flx
             else:
-                mod_cut_flx = continuum_estimate(global_params, cut[0], wav_mod_nativ, flx_mod_nativ, res_mod_nativ,
-                                                'mod', obs_name=obs_name, indobs=indobs)
-        else:
-            mod_cut_flx = []
-        mod_spectro.append(mod_cut_flx)
+                mod_spectro = np.concatenate((mod_spectro, mod_cut_flx))
+
 
     # Calculate each photometry point.
-    mod_photo = []
     for pho_ind, pho in enumerate(obs_photo_ins):
         path_list = __file__.split("/")[:-2]
         separator = '/'
@@ -393,7 +356,7 @@ def extract_model(global_params, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, co
         flx_filt = flx_filt / y_filt_tot
         mod_photo.append(flx_filt)
 
-    return mod_spectro, mod_photo, obs_spectro
+    return mod_spectro, mod_photo
 
 # ----------------------------------------------------------------------------------------------------------------------
 
@@ -410,7 +373,7 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si
         model_fluxes       (array): the fluxes of the model 
         num_sigma          (float): number of +/- sigmas to evaluate the LSF to.
     Returns:
-        output_model       (array): the fluxes in each of the wavelength channels 
+        - output_model (array)      : the fluxes in each of the wavelength channels 
 
     Author: Jason Wang
     """
@@ -422,9 +385,12 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si
     filter_coords = np.tile(filter_coords, [wv_channels.shape[0], 1])  # shape of (N_output, filter_size)
 
     filter_wv_coords = filter_coords * sigmas_wvs[:, None] + wv_channels[:, None]  # model wavelengths we want
+
     lsf = np.exp(-filter_coords ** 2 / 2) / np.sqrt(2 * np.pi)
-    if np.sum(lsf) != 0:
 
+
+    if np.sum(lsf) != 0:
+      
         model_interp = interp1d(model_wvs, model_fluxes, kind='cubic', bounds_error=False)
         filter_model = model_interp(filter_wv_coords)
 
@@ -438,7 +404,7 @@ def convolve_and_sample(wv_channels, sigmas_wvs, model_wvs, model_fluxes, num_si
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def resolution_decreasing(global_params, cut, wav_mod_nativ, flx_mod_nativ, res_mod_nativ, obs_or_mod, obs_name='', indobs=0):
+def resolution_decreasing(global_params, wav_obs, flx_obs, res_obs, wav_mod_nativ, flx_mod_nativ, res_mod_obs, obs_or_mod, indobs=0):
     """
     Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
     wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one
@@ -448,58 +414,49 @@ def resolution_decreasing(global_params, cut, wav_mod_nativ, flx_mod_nativ, res_
 
     Args:
         global_params (object): Class containing each parameter used in ForMoSA
-        cut             (list): Sub-spectra defined by the parameter "wav_for_adapt".
-                                [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]
+        wav_obs        (array): Wavelength grid of the data
+        flx_obs        (array): Flux of the data
+        res_obs        (array): Spectral resolution of the data
         wav_mod_nativ  (array): Wavelength grid of the model
         flx_mod_nativ  (array): Flux of the model
-        res_mod_nativ  (array): Spectral resolution of the model as a function of the wavelength grid
+        res_mod_obs    (array): Spectral resolution of the model as a function of the wavelength grid of the data
         obs_or_mod       (str): Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod'
-        obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        flx_obs_final  (array): Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
+        - flx_obs_final (array) : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+    Author: Simon Petrus, Matthieu Ravet
     """
     # Estimate of the FWHM of the data as a function of the wavelength
-    fwhm_obs = 2 * cut[0] / cut[3]
-
+    fwhm_obs = 2 * wav_obs / res_obs
     # Estimate of the FWHM of the model as a function of the wavelength
-    ind_mod_obs = np.where((wav_mod_nativ <= cut[0][-1]) & (wav_mod_nativ > cut[0][0]))
-    wav_mod_obs = wav_mod_nativ[ind_mod_obs]
-    res_mod_obs = res_mod_nativ[ind_mod_obs]
-    interp_mod_to_obs = interp1d(wav_mod_obs, res_mod_obs, fill_value='extrapolate')
-    res_mod_obs = interp_mod_to_obs(cut[0])
-    fwhm_mod = 2 * cut[0] / res_mod_obs
+    fwhm_mod = 2 * wav_obs / res_mod_obs
 
     # Estimate of the FWHM of the custom resolution (if defined) as a function of the wavelength
     if global_params.custom_reso[indobs] != 'NA':
-        fwhm_custom = 2 * cut[0] / float(global_params.custom_reso[indobs])
+        fwhm_custom = 2 * wav_obs / float(global_params.custom_reso[indobs])
     else:
-        fwhm_custom = cut[0] * np.nan
+        fwhm_custom = wav_obs * np.nan
+
 
     # Estimate of the sigma for the convolution as a function of the wavelength and decrease the resolution
     max_fwhm = np.nanmax([fwhm_obs, fwhm_mod, fwhm_custom], axis=0)
     if obs_or_mod == 'obs':
         fwhm_conv = np.sqrt(max_fwhm ** 2 - fwhm_obs ** 2)
         sigma_conv = fwhm_conv / 2.355
-        flx_obs_final = convolve_and_sample(cut[0], sigma_conv, cut[0], cut[1])
+        flx_obs_final = convolve_and_sample(wav_obs, sigma_conv, wav_obs, flx_obs)
     else:
         fwhm_conv = np.sqrt(max_fwhm ** 2 - fwhm_mod ** 2)
         sigma_conv = fwhm_conv / 2.355
-        flx_obs_final = convolve_and_sample(cut[0], sigma_conv, wav_mod_nativ, flx_mod_nativ)
+        flx_obs_final = convolve_and_sample(wav_obs, sigma_conv, wav_mod_nativ, flx_mod_nativ)
 
     return flx_obs_final
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def continuum_estimate(global_params, wav_cut, wav, flx, res, obs_or_mod, obs_name='', indobs=0):
+def continuum_estimate(global_params, wav, flx, res, indobs=0):
     """
-
-    NOT USED ANYMORE !!!!!!
-
-
     Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
     wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease
     the resolution of the spectrum to this custom FWHM for each wavelength using a gaussian filter and resample it on
@@ -507,38 +464,22 @@ def continuum_estimate(global_params, wav_cut, wav, flx, res, obs_or_mod, obs_na
 
     Args:
         global_params (object): Class containing each parameter used in ForMoSA
-        wav_cut        (array): Wavelength grid of the sub-spectrum data
         wav            (array): Wavelength grid of the spectrum for which you want to estimate the continuum
         flx            (array): Flux of the spectrum for which you want to estimate the continuum
         res              (int): Spectral resolution of the spectrum for which you want to estimate the continuum
-        obs_or_mod       (str): Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod'
-        obs_name         (str): Name of the current observation looping
         indobs           (int): Index of the current observation looping
     Returns:
-        continuum      (array): Estimated continuum of the spectrum re-sampled on the data wavelength grid
+        - continuum (array)   : Estimated continuum of the spectrum re-sampled on the data wavelength grid
 
-    Author: Simon Petrus / Adapted: Matthieu Ravet
+    Author: Simon Petrus, Matthieu Ravet
 
     """
-    # Adapt the model to the data grid wavelength
-    if obs_or_mod == 'mod':
-        interp_mod_to_obs = interp1d(wav, flx, kind='cubic', bounds_error=False)
-        flx = interp_mod_to_obs(wav_cut)
-        interp_mod_to_obs = interp1d(wav, res, kind='cubic', bounds_error=False)
-        res = interp_mod_to_obs(wav_cut)
-        wav = wav_cut
 
     # Redifined a spectrum only composed by the wavelength ranges used to estimate the continuum
     wav_for_continuum = global_params.wav_for_continuum[indobs].split('/')
     for wav_for_cont_cut_ind, wav_for_cont_cut in enumerate(wav_for_continuum):
         wav_for_cont_cut = wav_for_cont_cut.split(',')
-        if wav_for_cont_cut_ind == 0:
-            ind_cont_cut = np.where(wav <= float(wav_for_cont_cut[1]))
-        elif wav_for_cont_cut_ind == len(wav_for_continuum)-1:
-            ind_cont_cut = np.where(float(wav_for_cont_cut[0]) <= wav)
-        else:
-            ind_cont_cut = np.where((float(wav_for_cont_cut[0]) <= wav) &
-                                    (wav <= float(wav_for_cont_cut[1])))
+        ind_cont_cut = np.where((float(wav_for_cont_cut[0]) <= wav) & (wav <= float(wav_for_cont_cut[1])))
         if wav_for_cont_cut_ind == 0:
             wav_for_cont_final = wav[ind_cont_cut]
             flx_for_cont_final = flx[ind_cont_cut]
@@ -549,19 +490,20 @@ def continuum_estimate(global_params, wav_cut, wav, flx, res, obs_or_mod, obs_na
     model_interp = interp1d(wav_for_cont_final, flx_for_cont_final, kind='linear', bounds_error=False)
     flx = model_interp(wav)
 
-    # To limit the computing time, the sigma for the convolution is not as a function of the wavelength but calculated
+    # # To limit the computing time, the convolution is not as a function of the wavelength but calculated
     # from the median wavelength. We just want an estimate of the continuum here.
     wav_median = np.median(wav)
-    ind_wav_median_obs = find_nearest(wav, wav_median)
-    dwav = wav[ind_wav_median_obs+1] - wav[ind_wav_median_obs]
+    dwav_median = np.median(np.abs(wav - np.roll(wav, 1))) # Estimated the median wavelength separation instead of taking wav_median - (wav_median+1) that could be on a border
 
     fwhm = 2 * wav_median / np.median(res)
     fwhm_continuum = 2 * wav_median / float(global_params.continuum_sub[indobs])
 
 
     fwhm_conv = np.sqrt(fwhm_continuum**2 - fwhm**2)
-    sigma = fwhm_conv / (dwav * 2.355)
+    sigma = fwhm_conv / (dwav_median * 2.355)
     continuum = gaussian_filter(flx, sigma)
+    # import scipy.signal as sg
+    # continuum = sg.savgol_filter(flx, 3001, 2)
 
     return continuum
 
diff --git a/build/lib/ForMoSA/nested_sampling/nested_logL_functions.py b/build/lib/ForMoSA/nested_sampling/nested_logL_functions.py
index 4e93b16..6e581b3 100644
--- a/build/lib/ForMoSA/nested_sampling/nested_logL_functions.py
+++ b/build/lib/ForMoSA/nested_sampling/nested_logL_functions.py
@@ -3,13 +3,13 @@
 def logL_chi2_classic(delta_flx, err):
     """
     Function to compute logL based on the classical chi2
-    under the assumption of gaussian and spectrally uncorrelated noise
+    under the assumption of gaussian and spectrally uncorrelated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        err         : error (=standard deviation) of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -23,13 +23,13 @@ def logL_chi2_classic(delta_flx, err):
 def logL_chi2_covariance(delta_flx, inv_cov):
     """
     Function to compute logL based on the generalized chi2
-    under the assumption of gaussian and spectrally correlated noise
+    under the assumption of gaussian and spectrally correlated noise.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        inv_cov     : inverse of the covariance matrix of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)       : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -39,17 +39,63 @@ def logL_chi2_covariance(delta_flx, inv_cov):
 
     return logL
 
+
+def logL_chi2_extended(delta_flx, err):
+    """
+    Function to compute logL based on the extended chi2
+    under the assumption of gaussian and spectrally uncorrelated noise.
+    
+    Args:
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
+    Returns:
+        - logL (float)     : the loglikelihood value
+        
+    Author: Allan Denis
+    """
+    
+    N = len(delta_flx)
+    chi2 = np.nansum((delta_flx / err) ** 2)
+    s2 = 1/N * chi2
+    logL = -(chi2 / (2*s2) + N/2 * np.log(2*np.pi*s2) + 1/2 * np.log(np.dot(err,err)))
+    
+    return logL
+
+
+def logL_chi2_extended_covariance(delta_flx, inv_cov):
+    """
+    Function to compute logL based on the extended chi2
+    under the assumption of gaussian and spectrally uncorrelated noise.
+    
+    Args:
+        delta_flx   (array): residual data-model as a function of wavelength
+        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength
+    Returns:
+        - logL (float)     : the loglikelihood value
+        
+    Author: Allan Denis
+    """
+    
+    cov = np.linalg.inv(inv_cov)
+    N = len(delta_flx)
+    chi2 = np.dot(delta_flx, np.dot(inv_cov, delta_flx))
+    s2 = 1/N * chi2
+    logL = -chi2 / (2*s2) - 1/2 * np.log(np.linalg.det(cov))
+    
+    return logL
+
+
 def logL_full_covariance(delta_flx, inv_cov):
     """
     Function to compute logL under the assumption of gaussian and spectrally correlated noise.
     This function is a generalized version of the logL_chi2_covariance and is to be used when dealing
-    with GP extimation of the covariance matrix
+    with GP extimation of the covariance matrix.
     
     Args:
-        delta_flx   : residual data-model as a function of wavelength
-        inv_cov     : inverse of the covariance matrix of the observed spectrum as a function of wavelength
+        delta_flx   (array): residual data-model as a function of wavelength
+        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)      : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -62,13 +108,13 @@ def logL_full_covariance(delta_flx, inv_cov):
 def logL_CCF_Brogi(flx_obs, flx_mod):
     """
     Function to compute logL based on the CCF mapping from Brogi et al. 2019
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs     : flux of the observation as a function of wavelength
-        flx_mod     : flux of the model as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
     Returns:
-        logL        : the loglikelihood value
+        - logL (float)     : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -86,13 +132,13 @@ def logL_CCF_Brogi(flx_obs, flx_mod):
 def logL_CCF_Zucker(flx_obs, flx_mod):
     """
     Function to compute logL based on the CCF mapping from Zucker 2003
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs : flux of the observation as a function of wavelength
-        flx_mod : flux of the model as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
     Returns:
-        logL    : the loglikelihood value
+        - logL (float)      : the loglikelihood value
         
     Author: Matthieu Ravet
     """
@@ -111,14 +157,14 @@ def logL_CCF_Zucker(flx_obs, flx_mod):
 def logL_CCF_custom(flx_obs, flx_mod, err_obs):
     """
     Function to compute logL based on the custom CCF mapping from Me
-    under the assumption of gaussian and spectrally constant noise
+    under the assumption of gaussian and spectrally constant noise.
     
     Args:
-        flx_obs : flux of the observation as a function of wavelength
-        flx_mod : flux of the model as a function of wavelength
-        err_obs : errors of the observation as a function of wavelength
+        flx_obs     (array): flux of the observation as a function of wavelength
+        flx_mod     (array): flux of the model as a function of wavelength
+        err_obs     (array): errors of the observation as a function of wavelength
     Returns:
-        logL    : the loglikelihood value
+        - logL (float)       : the loglikelihood value
         
     Author: Matthieu Ravet
     """
diff --git a/build/lib/ForMoSA/nested_sampling/nested_modif_spec.py b/build/lib/ForMoSA/nested_sampling/nested_modif_spec.py
index 95bb39a..a5157a2 100644
--- a/build/lib/ForMoSA/nested_sampling/nested_modif_spec.py
+++ b/build/lib/ForMoSA/nested_sampling/nested_modif_spec.py
@@ -1,115 +1,135 @@
 import numpy as np
-import xarray as xr
 import extinction
 from scipy.interpolate import interp1d
 import astropy.units as u
 import astropy.constants as const
-from PyAstronomy.pyasl import dopplerShift, rotBroad
-from adapt.extraction_functions import resolution_decreasing, convolve_and_sample
-import scipy.ndimage as ndi
+from PyAstronomy.pyasl import rotBroad, fastRotBroad
+import scipy.signal as sg
 import scipy.optimize as optimize
-import matplotlib.pyplot as plt
-import time 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def lsq_fct(flx_obs_merge, err_obs_merge, star_flx_obs_merge, transm_obs_merge, new_flx_merge, ccf_method = 'continuum_unfiltered'):
+def lsq_fct(flx_obs_spectro, err_obs_spectro, star_flx_obs, transm_obs, flx_mod_spectro, system_obs, ccf_method = 'continuum_unfiltered'):
     """
     Estimation of the contribution of the planet and of the star to a spectrum (Used for HiRISE data)
 
     Args:
-        flx_obs_merge: Flux of the data (spectroscopy)
-        err_obs_merge: Error of the data (spectroscopy)
-        star_flx_obs_merge: Flux of star observation data (spectroscopy)
-        transm_obs_merge: Transmission (Atmospheric + Instrumental)
-        new_flx_merge: Flux of interpolated synthetic spectrum (spectroscopy)
-        wav_obs_merge : Wavelength grid of the data (spectroscopy)
-        
+        flx_obs_spectro    (array): Flux of the data (spectroscopy)
+        err_obs_spectro    (array): Error of the data (spectroscopy)
+        star_flx_obs     (n-array): Flux of star observation data (spectroscopy)
+        transm_obs         (array): Transmission (Atmospheric + Instrumental)
+        system_obs       (n-array): Systematics of the data (spectroscopy)
+        flx_mod_spectro    (array): Flux of interpolated synthetic spectrum (spectroscopy)
     Returns:
-        cp : Planetary contribution to the data (Spectroscopy)
-        cs : Stellar contribution to the data (Spectroscopy)
-        new_flx_merge : New model of the companion 
-        flx_obs_merge : New flux of the data
-        star_flx_obs_merge : New star flux of the data
+        - cp (array)                : Planetary contribution to the data (Spectroscopy)
+        - cs (array)                : Stellar contribution to the data (Spectroscopy)
+        - flx_mod_spectro (array)   : New model of the companion 
+        - flx_obs_spectro (array)   : New flux of the data
+        - star_flx_obs (n-array)    : New star flux of the data
+        - systematics (array)       : The systematics
+
+    Author : Allan Denis
+
     """
-    new_flx_merge = new_flx_merge * transm_obs_merge
-    
+    flx_mod_spectro *= transm_obs
+    star_flx_0 = star_flx_obs[0,:,len(star_flx_obs[0][0]) // 2]
+
     # # # # # Continuum estimation with lowpass filtering
     #
     # Low-pass filtering
-    flx_obs_merge_continuum = ndi.gaussian_filter(flx_obs_merge, 300)
-    star_flx_obs_merge_continuum = ndi.gaussian_filter(star_flx_obs_merge, 300)
-    new_flx_merge_continuum = ndi.gaussian_filter(new_flx_merge, 300)
+    flx_obs_spectro_continuum = sg.savgol_filter(flx_obs_spectro, 301, 2)
+    star_flx_obs_continuum = sg.savgol_filter(star_flx_0, 301, 2)
+    flx_mod_spectro_continuum = sg.savgol_filter(flx_mod_spectro, 301, 2)
     #
     # # # # #
         
     if ccf_method == 'continuum_filtered':
         # Removal of low-pass filtered data
-        flx_obs_merge = flx_obs_merge - flx_obs_merge_continuum + np.nanmedian(flx_obs_merge)
-        star_flx_obs_merge = star_flx_obs_merge - star_flx_obs_merge_continuum + np.nanmedian(star_flx_obs_merge)
-        new_flx_merge = new_flx_merge - new_flx_merge_continuum + np.nanmedian(new_flx_merge)
+        flx_obs_spectro = flx_obs_spectro - flx_obs_spectro_continuum + np.nanmedian(flx_obs_spectro)
+        star_flx_obs = star_flx_obs - star_flx_obs_continuum + np.nanmedian(star_flx_obs)
+        flx_mod_spectro = flx_mod_spectro - flx_mod_spectro_continuum + np.nanmedian(flx_mod_spectro)
     elif ccf_method == 'continuum_unfiltered':
-        star_flx_obs_merge = star_flx_obs_merge * flx_obs_merge_continuum / star_flx_obs_merge_continuum
-        new_flx_merge = new_flx_merge - new_flx_merge_continuum * star_flx_obs_merge / star_flx_obs_merge_continuum
+        flx_mod_spectro = flx_mod_spectro - flx_mod_spectro_continuum * star_flx_0 / star_flx_obs_continuum
+        for i in range(len(star_flx_obs[0][0])):
+            star_flx_obs[0,:,i] = star_flx_obs[0,:,i] * flx_obs_spectro_continuum / star_flx_obs_continuum
     
     # # # # # Least squares estimation
     #    
     # Construction of the matrix
-    
-    A_matrix = np.zeros([np.size(flx_obs_merge), 2])
-    A_matrix[:,0] = new_flx_merge * err_obs_merge
-    A_matrix[:,1] = star_flx_obs_merge * err_obs_merge
+    if len(system_obs) > 0:
+        A_matrix = np.zeros([np.size(flx_obs_spectro), 1 + len(star_flx_obs[0][0]) + len(system_obs[0][0])])
+        for j in range(len(system_obs[0][0])):
+            A_matrix[:,1+len(star_flx_obs[0][0])+j] = system_obs[0][:,j] * err_obs_spectro
+    else:
+        A_matrix = np.zeros([np.size(flx_obs_spectro), 1 + len(star_flx_obs[0][0])])
+        
+    for j in range(len(star_flx_obs[0][0])):
+        A_matrix[:,1+j] = star_flx_obs[0][:,j] * err_obs_spectro
+        
+    A_matrix[:,0] = flx_mod_spectro * err_obs_spectro
     
     # Least square 
-    res = optimize.lsq_linear(A_matrix, flx_obs_merge * err_obs_merge)
-    cp, cs = res.x[0], res.x[1]
+    res = optimize.lsq_linear(A_matrix, flx_obs_spectro * err_obs_spectro, bounds=([0]*len(A_matrix[0]), [1]*len(A_matrix[0])))
+    cp = res.x[0]
+    
+    cs = np.array([])
+    for i in range(len(star_flx_obs[0][0])):
+        cs = np.append(cs, res.x[i+1])
+        
+    systematics_c = np.array([])
+    systematics = np.asarray([])
+    if len(system_obs) > 0:
+        for i in range(len(system_obs[0][0])):
+            systematics_c = np.append(systematics_c, res.x[1+len(star_flx_obs[0][0])+i])
+            
+        systematics = np.dot(systematics_c, system_obs[0].T)
     
     #
     # # # # #
     
-    return cp, cs, new_flx_merge, flx_obs_merge, star_flx_obs_merge
+    return cp, cs, flx_mod_spectro, flx_obs_spectro, star_flx_obs, systematics
 
 
-def calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge, flx_obs_phot, err_obs_phot, new_flx_phot, r_picked, d_picked,
+def calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro, flx_obs_photo, err_obs_photo, flx_mod_photo, r_picked, d_picked,
             alpha=1, analytic='no'):
     """
     Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum (from the radius
     and distance or analytically).
 
     Args:
-        flx_obs_merge   : Flux of the data (spectroscopy)
-        err_obs_merge   : Error of the data (spectroscopy)
-        new_flx_merge   : Flux of the interpolated synthetic spectrum (spectroscopy)
-        flx_obs_phot    : Flux of the data (photometry)
-        err_obs_phot    : Error of the data (photometry)
-        new_flx_phot    : Flux of the interpolated synthetic spectrum (photometry)
-        r_picked        : Radius randomly picked by the nested sampling (in RJup)
-        d_picked        : Distance randomly picked by the nested sampling (in pc)
-        alpha           : Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²
-        analytic        : = 'yes' if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)
+        flx_obs_spectro  (array): Flux of the data (spectroscopy)
+        err_obs_spectro  (array): Error of the data (spectroscopy)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_obs_photo    (array): Flux of the data (photometry)
+        err_obs_photo    (array): Error of the data (photometry)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        r_picked         (float): Radius randomly picked by the nested sampling (in RJup)
+        d_picked         (float): Distance randomly picked by the nested sampling (in pc)
+        alpha            (float): Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²
+        analytic           (str): = 'yes' if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)
     Returns:
-        new_flx_merge   : Re-normalysed model spectrum
-        new_flx_phot    : Re-normalysed model photometry
-        ck              : Ck calculated
+        - flx_mod_spectro (array)   : Re-normalysed model spectrum
+        - flx_mod_photo (array)     : Re-normalysed model photometry
+        - ck (float)                : Ck calculated
 
     Author: Simon Petrus
     """
     # Calculation of the dilution factor ck as a function of the radius and distance
     if analytic == 'no':
-        r_picked *= 69911
-        d_picked *= 3.086e+13
-        ck = alpha * (r_picked/d_picked)**2
+        r_picked *= u.Rjup
+        d_picked *= u.pc
+        ck = alpha * (r_picked.value/d_picked.value)**2
     # Calculation of the dilution factor ck analytically
     else:
-        if len(flx_obs_merge) != 0:
-            ck_top_merge = np.sum((new_flx_merge * flx_obs_merge) / (err_obs_merge * err_obs_merge))
-            ck_bot_merge = np.sum((new_flx_merge / err_obs_merge)**2)
+        if len(flx_obs_spectro) != 0:
+            ck_top_merge = np.sum((flx_mod_spectro * flx_obs_spectro) / (err_obs_spectro * err_obs_spectro))
+            ck_bot_merge = np.sum((flx_mod_spectro / err_obs_spectro)**2)
         else:
             ck_top_merge = 0
             ck_bot_merge = 0
-        if len(flx_obs_phot) != 0:
-            ck_top_phot = np.sum((new_flx_phot * flx_obs_phot) / (err_obs_phot * err_obs_phot))
-            ck_bot_phot = np.sum((new_flx_phot / err_obs_phot)**2)
+        if len(flx_obs_photo) != 0:
+            ck_top_phot = np.sum((flx_mod_photo * flx_obs_photo) / (err_obs_photo * err_obs_photo))
+            ck_bot_phot = np.sum((flx_mod_photo / err_obs_photo)**2)
         else:
             ck_top_phot = 0
             ck_bot_phot = 0
@@ -117,146 +137,158 @@ def calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge, flx_obs_phot, err_obs_p
         ck = (ck_top_merge + ck_top_phot) / (ck_bot_merge + ck_bot_phot)
 
     # Re-normalization of the interpolated synthetic spectra with ck
-    if len(new_flx_merge) != 0:
-        new_flx_merge *= ck
-    if len(new_flx_phot) != 0:
-        new_flx_phot *= ck
+    if len(flx_mod_spectro) != 0:
+        flx_mod_spectro *= ck
+    if len(flx_mod_photo) != 0:
+        flx_mod_photo *= ck
 
-    return new_flx_merge, new_flx_phot, ck
+    return flx_mod_spectro, flx_mod_photo, ck
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def doppler_fct(wav_obs_merge, flx_obs_merge, err_obs_merge, new_flx_merge, rv_picked):
+def doppler_fct(wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro, rv_picked):
     """
     Application of a Doppler shifting to the interpolated synthetic spectrum using the function pyasl.dopplerShift.
     Note: Observation can change due to side effects of the shifting.
 
     Args:
-        wav_obs_merge       : Wavelength grid of the data
-        flx_obs_merge       : Flux of the data
-        err_obs_merge       : Error of the data
-        new_flx_merge       : Flux of the interpolated synthetic spectrum
-        rv_picked           : Radial velocity randomly picked by the nested sampling (in km.s-1)
+        wav_obs_spectro      (array): Wavelength grid of the data
+        flx_obs_spectro      (array): Flux of the data
+        err_obs_spectro      (array): Error of the data
+        flx_mod_spectro      (array): Flux of the interpolated synthetic spectrum
+        rv_picked            (float): Radial velocity randomly picked by the nested sampling (in km.s-1)
     Returns:
-        wav_obs_merge       : New wavelength grid of the data
-        flx_obs_merge       : New flux of the data
-        err_obs_merge       : New error of the data
-        flx_post_doppler    : New flux of the interpolated synthetic spectrum
+        - wav_obs_spectro (array)       : New wavelength grid of the data
+        - flx_obs_spectro (array)       : New flux of the data
+        - err_obs_spectro (array)       : New error of the data
+        - flx_post_doppler (array)      : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
-    # wav_doppler = wav_obs_merge*10000
-    # flx_post_doppler, wav_post_doppler = dopplerShift(wav_doppler, new_flx_merge, rv_picked)
-    new_wav = wav_obs_merge * ((rv_picked / 299792.458) + 1)
+    new_wav = wav_obs_spectro * ((rv_picked / const.c.to(u.km/u.s).value) + 1)
+    rv_interp = interp1d(new_wav, flx_mod_spectro, fill_value="extrapolate")
+    flx_post_doppler = rv_interp(wav_obs_spectro)
+
+    return wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_post_doppler
 
-    rv_interp = interp1d(new_wav, new_flx_merge, fill_value="extrapolate")
-    flx_post_doppler = rv_interp(wav_obs_merge)
+# ----------------------------------------------------------------------------------------------------------------------
+
+
+def reddening_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo, av_picked):
+    """
+    Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function
+    extinction.fm07.
 
-    return wav_obs_merge, flx_obs_merge, err_obs_merge, flx_post_doppler
+    Args:
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        wav_obs_photo    (array): Wavelength of the data (photometry)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        av_picked        (float): Extinction randomly picked by the nested sampling (in mag)
+    Returns:
+        - flx_mod_spectro (array) : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - flx_mod_photo (array)   : New flux of the interpolated synthetic spectrum (photometry)
 
-    # return Spectrum1d.from_array(new_wavelength, new_flux)
-    # # Side effects
-    # ind_nonan = np.argwhere(~np.isnan(flx_post_doppler))
-    # wav_obs_merge = wav_obs_merge[ind_nonan[:, 0]]
-    # flx_obs_merge = flx_obs_merge[ind_nonan[:, 0]]
-    # err_obs_merge = err_obs_merge[ind_nonan[:, 0]]
-    # flx_post_doppler = flx_post_doppler[ind_nonan[:, 0]]
+    Author: Simon Petrus
+    """
+    if len(wav_obs_spectro) != 0:
+        dered_merge = extinction.fm07(wav_obs_spectro * 10000, av_picked, unit='aa')
+        flx_mod_spectro *= 10**(-0.4*dered_merge)
+    if len(wav_obs_photo) != 0:
+        dered_phot = extinction.fm07(wav_obs_photo * 10000, av_picked, unit='aa')
+        flx_mod_photo *= 10**(-0.4*dered_phot)
 
-    # return wav_obs_merge, flx_obs_merge, err_obs_merge, flx_post_doppler
+    return flx_mod_spectro, flx_mod_photo
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def reddening_fct(wav_obs_merge, wav_obs_phot, new_flx_merge, new_flx_phot, av_picked):
+def vsini_fct_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked):
     """
-    Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function
+    Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function
     extinction.fm07.
 
     Args:
-        wav_obs_merge   : Wavelength grid of the data (spectroscopy)
-        wav_obs_phot    : Wavelength of the data (photometry)
-        new_flx_merge   : Flux of the interpolated synthetic spectrum (spectroscopy)
-        new_flx_phot    : Flux of the interpolated synthetic spectrum (photometry)
-        av_picked       : Extinction randomly picked by the nested sampling (in mag)
+        wav_obs_spectro  (array): Wavelength grid of the data
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum
+        ld_picked        (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
     Returns:
-        new_flx_merge   : New flux of the interpolated synthetic spectrum (spectroscopy)
-        new_flx_phot    : New flux of the interpolated synthetic spectrum (photometry)
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
-    if len(wav_obs_merge) != 0:
-        dered_merge = extinction.fm07(wav_obs_merge * 10000, av_picked, unit='aa')
-        new_flx_merge *= 10**(-0.4*dered_merge)
-    if len(wav_obs_phot) != 0:
-        dered_phot = extinction.fm07(wav_obs_phot * 10000, av_picked, unit='aa')
-        new_flx_phot *= 10**(-0.4*dered_phot)
+    # Correct irregulatities in the wavelength grid
+    wav_interval = wav_obs_spectro[1:] - wav_obs_spectro[:-1]
+    wav_to_vsini = np.arange(min(wav_obs_spectro), max(wav_obs_spectro), min(wav_interval) * 2/3)
+    vsini_interp = interp1d(wav_obs_spectro, flx_mod_spectro, fill_value="extrapolate")
+    flx_to_vsini = vsini_interp(wav_to_vsini)
+    # Apply the v.sin(i)
+    new_flx = rotBroad(wav_to_vsini, flx_to_vsini, ld_picked, vsini_picked)
+    vsini_interp = interp1d(wav_to_vsini, new_flx, fill_value="extrapolate")
+    flx_mod_spectro = vsini_interp(wav_obs_spectro)
+
+    return flx_mod_spectro
 
-    return new_flx_merge, new_flx_phot
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def vsini_fct(wav_obs_merge, new_flx_merge, ld_picked, vsini_picked):
+def vsini_fct_fast_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked):
     """
     Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function
     extinction.fm07.
 
     Args:
-        wav_obs_merge   : Wavelength grid of the data
-        new_flx_merge   : Flux of the interpolated synthetic spectrum
-        ld_picked       : Limd darkening randomly picked by the nested sampling
-        vsini_picked    : v.sin(i) randomly picked by the nested sampling (in km.s-1)
+        wav_obs_spectro  (array): Wavelength grid of the data
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum
+        ld_picked        (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
     Returns:
-        new_flx_merge   : New flux of the interpolated synthetic spectrum
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum
 
     Author: Simon Petrus
     """
     # Correct irregulatities in the wavelength grid
-    wav_interval = wav_obs_merge[1:] - wav_obs_merge[:-1]
-    wav_to_vsini = np.arange(min(wav_obs_merge), max(wav_obs_merge), min(wav_interval) * 2/3)
-    vsini_interp = interp1d(wav_obs_merge, new_flx_merge, fill_value="extrapolate")
+    wav_interval = wav_obs_spectro[1:] - wav_obs_spectro[:-1]
+    wav_to_vsini = np.arange(min(wav_obs_spectro), max(wav_obs_spectro), min(wav_interval) * 2/3)
+    vsini_interp = interp1d(wav_obs_spectro, flx_mod_spectro, fill_value="extrapolate")
     flx_to_vsini = vsini_interp(wav_to_vsini)
     # Apply the v.sin(i)
-    new_flx = rotBroad(wav_to_vsini, flx_to_vsini, ld_picked, vsini_picked)
+    new_flx = fastRotBroad(wav_to_vsini, flx_to_vsini, ld_picked, vsini_picked)
     vsini_interp = interp1d(wav_to_vsini, new_flx, fill_value="extrapolate")
-    new_flx_merge = vsini_interp(wav_obs_merge)
+    flx_mod_spectro = vsini_interp(wav_obs_spectro)
 
-    return new_flx_merge
+    return flx_mod_spectro
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
-def vsini_fct_new(wave_obs_merge, new_flx_merge, ld_picked, vsini_picked, nr=50, ntheta=100, dif=0.0):
+def vsini_fct_accurate(wave_obs_merge, flx_mod_spectro, ld_picked, vsini_picked, nr=50, ntheta=100, dif=0.0):
     '''
     A routine to quickly rotationally broaden a spectrum in linear time.
+    Adapted from Carvalho & Johns-Krull 2023 https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract
 
-    Carvalho & Johns-Krull 2023
-    https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract
-
-    ARGS:
-        wav_obs_merge   : Wavelength grid of the data
-        new_flx_merge   : Flux of the interpolated synthetic spectrum
-        ld_picked       : Limd darkening randomly picked by the nested sampling
-        vsini_picked    : v.sin(i) randomly picked by the nested sampling (in km.s-1)
-    
+    Args:
+        wav_obs_spectro   (array): Wavelength grid of the data
+        flx_mod_spectro   (array): Flux of the interpolated synthetic spectrum
+        ld_picked         (float): Limd darkening randomly picked by the nested sampling
+        vsini_picked      (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)
+        nr                  (int): (default = 10) The number of radial bins on the projected disk
+        ntheta              (int): (default = 100) The number of azimuthal bins in the largest radial annulus
+                                   note: the number of bins at each r is int(r*ntheta) where r < 1
+        dif               (float): (default = 0) The differential rotation coefficient, applied according to the law Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). 
+                                    Dif = .675 nicely reproduces the law proposed by Smith, 1994, A&A, Vol. 287, p. 523-534, to unify WTTS and CTTS. 
+                                    Dif = .23 is similar to observed solar differential rotation. Note: the th in the above expression is the stellar co-latitude, not the same as the integration variable used below. 
+                                    This is a disk integration routine.
     Returns:
-        new_flx_merge   : New flux of the interpolated synthetic spectrum
+        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum
 
-    OPTIONAL ARGS:
-        nr (default = 10) - the number of radial bins on the projected disk
-        ntheta (default = 100) - the number of azimuthal bins in the largest radial annulus
-                                note: the number of bins at each r is int(r*ntheta) where r < 1
-        
-        dif (default = 0) - the differential rotation coefficient, applied according to the law
-        Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). Dif = .675 nicely reproduces the law 
-        proposed by Smith, 1994, A&A, Vol. 287, p. 523-534, to unify WTTS and CTTS. Dif = .23 is 
-        similar to observed solar differential rotation. Note: the th in the above expression is 
-        the stellar co-latitude, not the same as the integration variable used below. This is a 
-        disk integration routine.
-    '''
+    Author: Allan Denis
+    ''' 
 
-    ns = np.copy(new_flx_merge)*0.0
+    ns = np.copy(flx_mod_spectro)*0.0
     tarea = 0.0
     dr = 1./nr
     for j in range(0, nr):
@@ -266,33 +298,34 @@ def vsini_fct_new(wave_obs_merge, new_flx_merge, ld_picked, vsini_picked, nr=50,
             th = np.pi/int(ntheta*r) + k * 2.0*np.pi/int(ntheta*r)
             if dif != 0:
                 vl = vsini_picked * r * np.sin(th) * (1.0 - dif/2.0 - dif/2.0*np.cos(2.0*np.arccos(r*np.cos(th))))
-                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/2.9979e5, wave_obs_merge, new_flx_merge)
+                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/const.c.to(u.km/u.s).value, wave_obs_merge, flx_mod_spectro)
                 tarea += area
             else:
                 vl = r * vsini_picked * np.sin(th)
-                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/2.9979e5, wave_obs_merge, new_flx_merge)
+                ns += area * np.interp(wave_obs_merge + wave_obs_merge*vl/const.c.to(u.km/u.s).value, wave_obs_merge, flx_mod_spectro)
                 tarea += area
     
-    new_flx_merge = ns / tarea
-    return new_flx_merge
+    flx_mod_spectro = ns / tarea
+    return flx_mod_spectro
 
 # ----------------------------------------------------------------------------------------------------------------------
 
-def bb_cpd_fct(wav_obs_merge, wav_obs_phot, new_flx_merge, new_flx_phot, distance, bb_T_picked, bb_R_picked):
-    ''' Function to add the effect of a cpd (circum planetary disc) to the models
-    Args:  
-        wav_obs_merge   : Wavelength grid of the data (spectroscopy)
-        wav_obs_phot    : Wavelength of the data (photometry)
-        new_flx_merge   : Flux of the interpolated synthetic spectrum (spectroscopy)
-        new_flx_phot    : Flux of the interpolated synthetic spectrum (photometry)
-        bb_temp         : Temperature value randomly picked by the nested sampling in K units
-        bb_rad          : Radius randomly picked by the nested sampling in units of planetary radius
+def bb_cpd_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo, distance, bb_T_picked, bb_R_picked):
+    ''' 
+    Function to add the effect of a cpd (circum planetary disc) to the models.
     
+    Args:  
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        wav_obs_photo    (array): Wavelength of the data (photometry)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        bb_temp          (float): Temperature value randomly picked by the nested sampling in K units
+        bb_rad           (float): Radius randomly picked by the nested sampling in units of planetary radius
     Returns:
-        new_flx_merge   : New flux of the interpolated synthetic spectrum (spectroscopy)
-        new_flx_phot    : New flux of the interpolated synthetic spectrum (photometry)
+        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - flx_mod_photo (array)     : New flux of the interpolated synthetic spectrum (photometry)
 
-    Author: P. Palma-Bifani
+    Author: Paulina Palma-Bifani
     '''
 
     bb_T_picked *= u.K
@@ -305,104 +338,60 @@ def planck(wav, T):
         intensity = a/ ( (wav**5) * (np.exp(b) - 1.0) )
         return intensity
     
-    bb_intensity    = planck(wav_obs_merge*u.um, bb_T_picked)
-    bb_intensity_f    = planck(wav_obs_phot*u.um, bb_T_picked)
-
-    #flux_bb_lambda   = ( np.pi * (bb_R_picked)**2 / ( ck*u.km **2) * bb_intensity ).to(u.W/u.m**2/u.micron)
-    flux_bb_lambda   = ( 4*np.pi*bb_R_picked**2/(distance**2) * bb_intensity ).to(u.W/u.m**2/u.micron)
-
-    #flux_bb_lambda_f = ( np.pi * (bb_R_picked)**2 / ( ck*u.km **2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
-    flux_bb_lambda_f = ( 4*np.pi*bb_R_picked**2/(distance**2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
+    bb_intensity    = planck(wav_obs_spectro*u.um, bb_T_picked)
+    bb_intensity_f    = planck(wav_obs_photo*u.um, bb_T_picked)
 
+    flux_bb_lambda   = ( np.pi*bb_R_picked**2/(distance**2) * bb_intensity ).to(u.W/u.m**2/u.micron)
+    flux_bb_lambda_f = ( np.pi*bb_R_picked**2/(distance**2) * bb_intensity_f ).to(u.W/u.m**2/u.micron)
 
     # add to model flux of the atmosphere
-    new_flx_merge  += flux_bb_lambda.value
-    new_flx_phot   += flux_bb_lambda_f.value
+    flx_mod_spectro  += flux_bb_lambda.value
+    flx_mod_photo   += flux_bb_lambda_f.value
     # 
-    return new_flx_merge, new_flx_phot
-
-
-# ----------------------------------------------------------------------------------------------------------------------
-
-
-def reso_fct(global_params, theta, theta_index, wav_obs_merge, new_flx_merge, reso_picked):
-    """
-    WORKING!
-    Function to scale the spectral resolution of the synthetic spectra. This option is currently in test and make use
-    of the functions defined in the 'adapt' section of ForMoSA, meaning that they will significantly decrease the speed of
-    your inversion as the grid needs to be re-interpolated
-
-    Args:
-        global_params   : Class containing each parameter
-        theta           : Parameter values randomly picked by the nested sampling
-        theta_index     : Parameter index identificator
-        wav_obs_merge   : Wavelength grid of the data
-        new_flx_merge   : Flux of the interpolated synthetic spectrum
-        reso_picked     : Spectral resolution randomly picked by the nested sampling
-    Returns:
-        None
-
-    Author: Matthieu Ravet
-    """
-
-    # Import the grid and set it with the right parameters
-    ds = xr.open_dataset(global_params.model_path, decode_cf=False, engine="netcdf4")
-    wav_mod_nativ = ds["wavelength"].values
-    grid = ds['grid']
-    attr = ds.attrs
-    grid_np = grid.to_numpy()
-    model_to_adapt = grid_np[:, theta]
-
-    # Modify the spectrum with the wanted spectral resolution
-    flx_mod_extract, mod_pho = adapt_model(global_params, wav_mod_nativ, model_to_adapt, attr['res'], obs_name=obs_name,
-                                        indobs=indobs)
-
-    return 
+    return flx_mod_spectro, flx_mod_photo
 
 
 # ----------------------------------------------------------------------------------------------------------------------
 
 
 def modif_spec(global_params, theta, theta_index,
-               wav_obs_merge, flx_obs_merge, err_obs_merge, new_flx_merge,
-               wav_obs_phot, flx_obs_phot, err_obs_phot, new_flx_phot, transm_obs_merge = [], star_flx_obs_merge = [], indobs=0):
+               wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+               wav_obs_photo, flx_obs_photo, err_obs_photo, flx_mod_photo, transm_obs = [], star_flx_obs = [], system_obs = [], indobs=0):
     """
-    Modification of the interpolated synthetic spectra with the different extra-grid parameters:
-        - Re-calibration on the data
-        - Doppler shifting
-        - Application of a substellar extinction
-        - Application of a rotational velocity
-        - Application of a circumplanetary disk (CPD)
+    Modification of the interpolated synthetic spectra with the different extra-grid parameters.
+    It can perform : Re-calibration on the data, Doppler shifting, Application of a substellar extinction, Application of a rotational velocity, 
+    Application of a circumplanetary disk (CPD).
     
     Args:
-        global_params   : Class containing each parameter
-        theta           : Parameter values randomly picked by the nested sampling
-        theta_index     : Parameter index identificator
-        wav_obs_merge   : Wavelength grid of the data (spectroscopy)
-        flx_obs_merge   : Flux of the data (spectroscopy)
-        err_obs_merge   : Error of the data (spectroscopy)
-        new_flx_merge   : Flux of the interpolated synthetic spectrum (spectroscopy)
-        wav_obs_phot    : Wavelength grid of the data (photometry)
-        flx_obs_phot    : Flux of the data (photometry)
-        err_obs_phot    : Error of the data (photometry)
-        new_flx_phot    : Flux of the interpolated synthetic spectrum (photometry)
-        transm_obs_merge: Transmission (Atmospheric + Instrumental)
-        star_flx_obs_merge: Flux of star observation data (spectroscopy)
-        indobs     (int): Index of the current observation looping
+        global_params   (object): Class containing each parameter
+        theta             (list): Parameter values randomly picked by the nested sampling
+        theta_index       (list): Parameter index identificator
+        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)
+        flx_obs_spectro  (array): Flux of the data (spectroscopy)
+        err_obs_spectro  (array): Error of the data (spectroscopy)
+        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)
+        wav_obs_photo    (array): Wavelength grid of the data (photometry)
+        flx_obs_photo    (array): Flux of the data (photometry)
+        err_obs_photo    (array): Error of the data (photometry)
+        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)
+        transm_obs       (array): Transmission (Atmospheric + Instrumental)
+        star_flx_obs   (n-array): Flux of star observation data (spectroscopy)
+        system_obs     (n-array): Systematics of the data (spectroscopy)
+        indobs             (int): Index of the current observation looping
     Returns:
-        wav_obs_merge   : New wavelength grid of the data (may change with the Doppler shift)
-        flx_obs_merge   : New flux of the data (may change with the Doppler shift)
-        err_obs_merge   : New error of the data (may change with the Doppler shift)
-        new_flx_merge   : New flux of the interpolated synthetic spectrum (spectroscopy)
-        wav_obs_phot    : Wavelength grid of the data (photometry)
-        flx_obs_phot    : Flux of the data (photometry)
-        err_obs_phot    : Error of the data (photometry)
-        new_flx_phot    : New flux of the interpolated synthetic spectrum (photometry)
+        - wav_obs_spectro (array)  : New wavelength grid of the data (may change with the Doppler shift)
+        - flx_obs_spectro (array)  : New flux of the data (may change with the Doppler shift)
+        - err_obs_spectro (array)  : New error of the data (may change with the Doppler shift)
+        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum (spectroscopy)
+        - wav_obs_photo (array)    : Wavelength grid of the data (photometry)
+        - flx_obs_photo (array)    : Flux of the data (photometry)
+        - err_obs_photo (array)    : Error of the data (photometry)
+        - flx_mod_photo (array)    : New flux of the interpolated synthetic spectrum (photometry)
     
-    Author: Simon Petrus and Paulina Palma-Bifani
+    Author: Simon Petrus, Paulina Palma-Bifani, Allan Denis and Matthieu Ravet
     """
     # Correction of the radial velocity of the interpolated synthetic spectrum.
-    if len(flx_obs_merge) != 0:
+    if len(flx_obs_spectro) != 0:
         if len(global_params.rv) > 3: # If you want separate rv for each observations
             if global_params.rv[indobs*3] != "NA":
                 if global_params.rv[indobs*3] == "constant":
@@ -410,8 +399,8 @@ def modif_spec(global_params, theta, theta_index,
                 else:
                     ind_theta_rv = np.where(theta_index == f'rv_{indobs}')
                     rv_picked = theta[ind_theta_rv[0][0]]
-                wav_obs_merge, flx_obs_merge, err_obs_merge, new_flx_merge = doppler_fct(wav_obs_merge, flx_obs_merge,
-                                                                                    err_obs_merge, new_flx_merge,
+                wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro = doppler_fct(wav_obs_spectro, flx_obs_spectro,
+                                                                                    err_obs_spectro, flx_mod_spectro,
                                                                                     rv_picked)
         else: # If you want 1 common rv for all observations
             if global_params.rv != "NA":
@@ -420,8 +409,8 @@ def modif_spec(global_params, theta, theta_index,
                 else:
                     ind_theta_rv = np.where(theta_index == 'rv')
                     rv_picked = theta[ind_theta_rv[0][0]]
-                wav_obs_merge, flx_obs_merge, err_obs_merge, new_flx_merge = doppler_fct(wav_obs_merge, flx_obs_merge,
-                                                                                        err_obs_merge, new_flx_merge,
+                wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro = doppler_fct(wav_obs_spectro, flx_obs_spectro,
+                                                                                        err_obs_spectro, flx_mod_spectro,
                                                                                         rv_picked)
 
     # Application of a synthetic interstellar extinction to the interpolated synthetic spectrum.
@@ -431,30 +420,63 @@ def modif_spec(global_params, theta, theta_index,
         else:
             ind_theta_av = np.where(theta_index == 'av')
             av_picked = theta[ind_theta_av[0][0]]
-        new_flx_merge, new_flx_phot = reddening_fct(wav_obs_merge, wav_obs_phot, new_flx_merge, new_flx_phot, av_picked)
+        flx_mod_spectro, flx_mod_photo = reddening_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo, av_picked)
         
     # Correction of the rotational velocity of the interpolated synthetic spectrum.
-    if len(flx_obs_merge) != 0:
-        if global_params.vsini != "NA" and global_params.ld != "NA":
-            if global_params.vsini[0] == 'constant':
-                vsini_picked = float(global_params.vsini[1])
-            else:
-                ind_theta_vsini = np.where(theta_index == 'vsini')
-                vsini_picked = theta[ind_theta_vsini[0][0]]
-            if global_params.ld[0] == 'constant':
-                ld_picked = float(global_params.ld[1])
+    if len(flx_obs_spectro) != 0:
+        if len(global_params.vsini) > 4 and len(global_params.ld) > 3: # If you want separate vsini/ld for each observations
+            if global_params.vsini[indobs*4] != "NA" and global_params.ld[indobs*3] != "NA":
+                if global_params.vsini[indobs*4] == 'constant':
+                    vsini_picked = float(global_params.vsini[indobs*3+1])
+                else:
+                    ind_theta_vsini = np.where(theta_index == f'vsini_{indobs}')
+                    vsini_picked = theta[ind_theta_vsini[0][0]]
+                if global_params.ld[indobs*3] == 'constant':
+                    ld_picked = float(global_params.ld[indobs*3+1])
+                else:
+                    ind_theta_ld = np.where(theta_index == f'ld_{indobs}')
+                    ld_picked = theta[ind_theta_ld[0][0]]
+
+                if global_params.vsini[indobs*4 + 3] == 'RotBroad':
+                    flx_mod_spectro = vsini_fct_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
+                if global_params.vsini[indobs*4 + 3] == 'FastRotBroad':
+                    flx_mod_spectro = vsini_fct_fast_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
+                if global_params.vsini[indobs*4 + 3] == 'Accurate':
+                    flx_mod_spectro = vsini_fct_accurate(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
+
+            elif global_params.vsini[indobs*4] == "NA" and global_params.ld[indobs*3] == "NA":
+                pass
+
             else:
-                ind_theta_ld = np.where(theta_index == 'ld')
-                ld_picked = theta[ind_theta_ld[0][0]]
+                print(f'WARNING: You need to define a v.sin(i) AND a limb darkening, or set them both to NA for observation {indobs}')
+                exit()
+
+        else:# If you want 1 common vsini/ld for all observations
+            if global_params.vsini != "NA" and global_params.ld != "NA":
+                if global_params.vsini[0] == 'constant':
+                    vsini_picked = float(global_params.vsini[1])
+                else:
+                    ind_theta_vsini = np.where(theta_index == 'vsini')
+                    vsini_picked = theta[ind_theta_vsini[0][0]]
+                if global_params.ld[0] == 'constant':
+                    ld_picked = float(global_params.ld[1])
+                else:
+                    ind_theta_ld = np.where(theta_index == 'ld')
+                    ld_picked = theta[ind_theta_ld[0][0]]
 
-            new_flx_merge = vsini_fct_new(wav_obs_merge, new_flx_merge, ld_picked, vsini_picked)
+                if global_params.vsini[3] == 'RotBroad':
+                    flx_mod_spectro = vsini_fct_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
+                if global_params.vsini[3] == 'FastRotBroad':
+                    flx_mod_spectro = vsini_fct_fast_rot_broad(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
+                if global_params.vsini[3] == 'Accurate':
+                    flx_mod_spectro = vsini_fct_accurate(wav_obs_spectro, flx_mod_spectro, ld_picked, vsini_picked)
 
-        elif global_params.vsini == "NA" and global_params.ld == "NA":
-            pass
+            elif global_params.vsini == "NA" and global_params.ld == "NA":
+                pass
 
-        else:
-            print('WARNING: You need to define a v.sin(i) AND a limb darkening, or set them both to NA')
-            exit()
+            else:
+                print('WARNING: You need to define a v.sin(i) AND a limb darkening, or set them both to NA')
+                exit()  
     
     # Adding a CPD
     if global_params.bb_T != "NA" and global_params.bb_R != "NA":
@@ -474,7 +496,7 @@ def modif_spec(global_params, theta, theta_index,
             ind_theta_d = np.where(theta_index == 'd')
             d_picked = theta[ind_theta_d[0][0]]
 
-        new_flx_merge, new_flx_phot = bb_cpd_fct(wav_obs_merge, wav_obs_phot, new_flx_merge, new_flx_phot, d_picked, bb_T_picked, bb_R_picked)
+        flx_mod_spectro, flx_mod_photo = bb_cpd_fct(wav_obs_spectro, wav_obs_photo, flx_mod_spectro, flx_mod_photo, d_picked, bb_T_picked, bb_R_picked)
 
     elif global_params.bb_T == "NA" and global_params.bb_R == "NA":
         pass
@@ -485,8 +507,8 @@ def modif_spec(global_params, theta, theta_index,
 
     # Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum.
     # From the radius and the distance.
+    
     if global_params.r != "NA" and global_params.d != "NA":
-        planet_contribution, stellar_contribution = 1, 1
         if global_params.r[0] == "constant":
             r_picked = float(global_params.r[1])
         else:
@@ -506,12 +528,12 @@ def modif_spec(global_params, theta, theta_index,
                 else:
                     ind_theta_alpha = np.where(theta_index == f'alpha_{indobs}')
                     alpha_picked = theta[ind_theta_alpha[0][0]]
-                new_flx_merge, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge,
-                                                        flx_obs_phot, err_obs_phot, new_flx_phot, r_picked, d_picked,
+                flx_mod_spectro, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                                        flx_obs_photo, err_obs_photo, flx_mod_photo, r_picked, d_picked,
                                                         alpha=alpha_picked)
             else: # Without the extra alpha scaling
-                new_flx_merge, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge,
-                                                  flx_obs_phot, err_obs_phot, new_flx_phot, r_picked, d_picked)
+                flx_mod_spectro, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                                  flx_obs_photo, err_obs_photo, flx_mod_photo, r_picked, d_picked)
         else: # If you want 1 common alpha for all observations
             if global_params.alpha != "NA":
                 if global_params.alpha[0] == "constant":
@@ -519,38 +541,39 @@ def modif_spec(global_params, theta, theta_index,
                 else:
                     ind_theta_alpha = np.where(theta_index == 'alpha')
                     alpha_picked = theta[ind_theta_alpha[0][0]]
-                new_flx_merge, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge,
-                                                        flx_obs_phot, err_obs_phot, new_flx_phot, r_picked, d_picked,
+                flx_mod_spectro, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                                        flx_obs_photo, err_obs_photo, flx_mod_photo, r_picked, d_picked,
                                                         alpha=alpha_picked)   
             else: # Without the extra alpha scaling
-                new_flx_merge, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge,
-                                                    flx_obs_phot, err_obs_phot, new_flx_phot, r_picked, d_picked)
-                
+                flx_mod_spectro, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                                    flx_obs_photo, err_obs_photo, flx_mod_photo, r_picked, d_picked)
+        # Set HiRES contribution to 1 if not used
+        planet_contribution, stellar_contribution, systematics = 1, 1, 1                
     # Analytically
     # If MOSAIC
     elif global_params.r == "NA" and global_params.d == "NA" and global_params.use_lsqr[indobs] == 'False':
-        new_flx_merge, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, new_flx_merge,
-                                                flx_obs_phot, err_obs_phot, new_flx_phot, 0, 0, 0,
+        flx_mod_spectro, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                                flx_obs_photo, err_obs_photo, flx_mod_photo, 0, 0, 0,
                                                 analytic='yes')
             
-        planet_contribution, stellar_contribution = 1, 1
+        planet_contribution, stellar_contribution, systematics = 1, 1, 1
 
 
     elif global_params.r == "NA" and global_params.d == "NA" and global_params.use_lsqr[indobs] == 'True':   
         # global_params.use_lsqr = 'True', so no need to re-normalize the interpolated sythetic spectrum because the least squares automatically does it
             
-        _, new_flx_phot, ck = calc_ck(flx_obs_merge, err_obs_merge, np.copy(new_flx_merge), 
-                        flx_obs_phot, err_obs_phot, new_flx_phot, 0, 0, 0,
+        _, flx_mod_photo, ck = calc_ck(flx_obs_spectro, err_obs_spectro, np.copy(flx_mod_spectro), 
+                        flx_obs_photo, err_obs_photo, flx_mod_photo, 0, 0, 0,
                         analytic='yes')
         
-        planet_contribution, stellar_contribution, new_flx_merge, flx_obs_merge, star_flx_obs_merge = lsq_fct(flx_obs_merge, err_obs_merge, star_flx_obs_merge, transm_obs_merge, new_flx_merge)
+        planet_contribution, stellar_contribution, flx_mod_spectro, flx_obs_spectro, star_flx_obs, systematics = lsq_fct(flx_obs_spectro, err_obs_spectro, star_flx_obs, transm_obs, flx_mod_spectro, system_obs)
 
 
     else:   # either global_params.r or global_params.d is set to 'NA' 
         print('WARNING: You need to define a radius AND a distance, or set them both to "NA"')
         exit()
 
-    return wav_obs_merge, flx_obs_merge, err_obs_merge, new_flx_merge, wav_obs_phot, flx_obs_phot, err_obs_phot, new_flx_phot, ck, planet_contribution, stellar_contribution, star_flx_obs_merge
+    return wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro, wav_obs_photo, flx_obs_photo, err_obs_photo, flx_mod_photo, ck, planet_contribution, stellar_contribution, star_flx_obs, systematics, transm_obs
 
 
 
diff --git a/build/lib/ForMoSA/nested_sampling/nested_prior_function.py b/build/lib/ForMoSA/nested_sampling/nested_prior_function.py
index d782458..a107ff3 100644
--- a/build/lib/ForMoSA/nested_sampling/nested_prior_function.py
+++ b/build/lib/ForMoSA/nested_sampling/nested_prior_function.py
@@ -3,14 +3,15 @@
 
 def uniform_prior(prior_fct_arg, theta):
     '''
-    Uniform prior for nested sampling
+    Uniform prior for nested sampling.
 
     Args:
         prior_fct_arg   (list): Two-values list with uniform prior boundaries.
         theta           (list): Parameter values randomly picked by the nested sampling
-    
     Returns:
-        Evaluated      (float): Evaluated prior
+        - Evaluated      (float): Evaluated prior
+
+    Author: Simon Petrus
     '''
     arg1 = float(prior_fct_arg[0])
     arg2 = float(prior_fct_arg[1])
@@ -19,13 +20,15 @@ def uniform_prior(prior_fct_arg, theta):
 
 def gaussian_prior(prior_fct_arg, theta):
     '''
-    Gaussian prior for nested sampling
+    Gaussian prior for nested sampling.
 
     Args:
         prior_fct_arg   (list): Two-values list with uniform prior boundaries.
         theta           (list): Parameter values randomly picked by the nested sampling
     Returns:
-        Evaluated      (float): Evaluated prior
+        - Evaluated      (float): Evaluated prior
+
+    Author: Simon Petrus
     '''
     arg1 = float(prior_fct_arg[0])
     arg2 = float(prior_fct_arg[1])
diff --git a/build/lib/ForMoSA/nested_sampling/nested_sampling.py b/build/lib/ForMoSA/nested_sampling/nested_sampling.py
index 4e5702e..b2c1232 100644
--- a/build/lib/ForMoSA/nested_sampling/nested_sampling.py
+++ b/build/lib/ForMoSA/nested_sampling/nested_sampling.py
@@ -1,18 +1,17 @@
 import numpy as np
-import os
+import os, sys
 import glob
 import nestle
 import time
 import xarray as xr
 import pickle
 
+sys.path.insert(0, os.path.abspath('../'))
+
 from nested_sampling.nested_modif_spec import modif_spec
 from nested_sampling.nested_prior_function import uniform_prior, gaussian_prior
 from nested_sampling.nested_logL_functions import *
 from main_utilities import diag_mat
-import matplotlib.pyplot as plt
-
-c = 299792.458 # Speed of light in km/s
 
 
 def import_obsmod(global_params):
@@ -23,8 +22,8 @@ def import_obsmod(global_params):
         global_params  (object): Class containing every input from the .ini file.
         
     Returns:
-        main_file (list(array)): return a list of lists with the wavelengths, flux, errors, covariance matrix,
-                                transmission, star flux and the grids for both spectroscopic and photometric data. 
+        - main_file (list(array)): Return a list of lists with the wavelengths, flux, errors, covariance matrix,
+                                transmission, star flux, systematics and the grids for both spectroscopic and photometric data. 
 
     Authors: Simon Petrus, Matthieu Ravet and Allan Denis
     """
@@ -38,34 +37,36 @@ def import_obsmod(global_params):
         obs_name = os.path.splitext(os.path.basename(global_params.observation_path))[0]
         spectrum_obs = np.load(os.path.join(global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True)
 
-        wav_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float)
-        flx_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float)
-        err_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float)
+        wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float)
+        flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float)
+        err_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float)
         # Optional arrays
-        inv_cov_obs_merge = np.asarray(spectrum_obs['obs_opt_merge'][0], dtype=float)
-        transm_obs_merge = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float)
-        star_flx_obs_merge = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float)
+        inv_cov_obs = np.asarray(spectrum_obs['obs_opt_merge'][0], dtype=float)
+        transm_obs = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float)
+        star_flx_obs = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float)
+        system_obs = np.asarray(spectrum_obs['obs_opt_merge'][3], dtype=float)
 
         if 'obs_photo' in spectrum_obs.keys():
-            wav_obs_phot = np.asarray(spectrum_obs['obs_photo'][0], dtype=float)
-            flx_obs_phot = np.asarray(spectrum_obs['obs_photo'][1], dtype=float)
-            err_obs_phot = np.asarray(spectrum_obs['obs_photo'][2], dtype=float)
+            wav_obs_photo = np.asarray(spectrum_obs['obs_photo'][0], dtype=float)
+            flx_obs_photo = np.asarray(spectrum_obs['obs_photo'][1], dtype=float)
+            err_obs_photo = np.asarray(spectrum_obs['obs_photo'][2], dtype=float)
         else:
-            wav_obs_phot = np.asarray([], dtype=float)
-            flx_obs_phot = np.asarray([], dtype=float)
-            err_obs_phot = np.asarray([], dtype=float)
+            wav_obs_photo = np.asarray([], dtype=float)
+            flx_obs_photo = np.asarray([], dtype=float)
+            err_obs_photo = np.asarray([], dtype=float)
 
         # Recovery of the spectroscopy and photometry model
-        path_grid_m = os.path.join(global_params.adapt_store_path, f'adapted_grid_spectro_{global_params.grid_name}_{obs_name}_nonan.nc')
-        path_grid_p = os.path.join(global_params.adapt_store_path, f'adapted_grid_photo_{global_params.grid_name}_{obs_name}_nonan.nc')
-        ds = xr.open_dataset(path_grid_m, decode_cf=False, engine='netcdf4')
-        grid_merge = ds['grid']
+        path_grid_spectro = os.path.join(global_params.adapt_store_path, f'adapted_grid_spectro_{global_params.grid_name}_{obs_name}_nonan.nc')
+        path_grid_photo = os.path.join(global_params.adapt_store_path, f'adapted_grid_photo_{global_params.grid_name}_{obs_name}_nonan.nc')
+        ds = xr.open_dataset(path_grid_spectro, decode_cf=False, engine='netcdf4')
+        grid_spectro = ds['grid']
         ds.close()
-        ds = xr.open_dataset(path_grid_p, decode_cf=False, engine='netcdf4')
-        grid_phot = ds['grid']
+        ds = xr.open_dataset(path_grid_photo, decode_cf=False, engine='netcdf4')
+        grid_photo = ds['grid']
         ds.close()
+        
 
-        main_file.append([[wav_obs_merge, wav_obs_phot], [flx_obs_merge, flx_obs_phot], [err_obs_merge, err_obs_phot], inv_cov_obs_merge, transm_obs_merge, star_flx_obs_merge, grid_merge, grid_phot])
+        main_file.append([[wav_obs_spectro, wav_obs_photo], [flx_obs_spectro, flx_obs_photo], [err_obs_spectro, err_obs_photo], inv_cov_obs, transm_obs, star_flx_obs, system_obs, grid_spectro, grid_photo])
 
     return main_file
 
@@ -74,6 +75,7 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
     """
     Function that calculates the logarithm of the likelihood. 
     The evaluation depends on the choice of likelihood.
+    (If this function is used on the plotting module, it returns the outputs of the modif_spec function)
     
     Args:
         theta           (list): Parameter values randomly picked by the nested sampling
@@ -83,8 +85,7 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
         for_plot         (str): Default is 'no'. When this function is called from the plotting functions module, we use 'yes'
         
     Returns:
-        FINAL_logL     (float): Final evaluated loglikelihood for both spectra and photometry. 
-        (If this function is used on the plotting module, it returns the outputs of the modif_spec function)
+        - FINAL_logL     (float): Final evaluated loglikelihood for both spectra and photometry. 
 
     Authors: Simon Petrus, Matthieu Ravet and Allan Denis
     """
@@ -97,157 +98,171 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
     for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
         
         # Recovery of spectroscopy and photometry data
-        wav_obs_merge = main_file[indobs][0][0]
-        wav_obs_phot = main_file[indobs][0][1]
-        flx_obs_merge = main_file[indobs][1][0]
-        flx_obs_phot = main_file[indobs][1][1]
-        err_obs_merge = main_file[indobs][2][0]
-        err_obs_phot = main_file[indobs][2][1]
-        inv_cov_obs_merge = main_file[indobs][3]
-        transm_obs_merge = main_file[indobs][4]
-        star_flx_obs_merge = main_file[indobs][5]
-
+        wav_obs_spectro = main_file[indobs][0][0]
+        wav_obs_photo = main_file[indobs][0][1]
+        flx_obs_spectro = main_file[indobs][1][0]
+        flx_obs_photo = main_file[indobs][1][1]
+        err_obs_spectro = main_file[indobs][2][0]
+        err_obs_photo = main_file[indobs][2][1]
+        inv_cov_obs = main_file[indobs][3]
+        transm_obs = main_file[indobs][4]
+        star_flx_obs = main_file[indobs][5]
+        system_obs = main_file[indobs][6]
+        
+        
         # Recovery of the spectroscopy and photometry model
-        grid_merge = main_file[indobs][6]
-        grid_phot = main_file[indobs][7]
+        grid_spectro = main_file[indobs][7]
+        grid_photo = main_file[indobs][8]
 
         # Calculation of the likelihood for each sub-spectrum defined by the parameter 'wav_fit'
         for ns_u_ind, ns_u in enumerate(global_params.wav_fit[indobs].split('/')):
             
             min_ns_u = float(ns_u.split(',')[0])
             max_ns_u = float(ns_u.split(',')[1])
-            ind_grid_merge_sel = np.where((grid_merge['wavelength'] >= min_ns_u) & (grid_merge['wavelength'] <= max_ns_u))
-            ind_grid_phot_sel = np.where((grid_phot['wavelength'] >= min_ns_u) & (grid_phot['wavelength'] <= max_ns_u))
+            ind_grid_spectro_sel = np.where((grid_spectro['wavelength'] >= min_ns_u) & (grid_spectro['wavelength'] <= max_ns_u))
+            ind_grid_photo_sel = np.where((grid_photo['wavelength'] >= min_ns_u) & (grid_photo['wavelength'] <= max_ns_u))
 
             # Cutting of the grid on the wavelength grid defined by the parameter 'wav_fit'
-            grid_merge_cut = grid_merge.sel(wavelength=grid_merge['wavelength'][ind_grid_merge_sel])
-            grid_phot_cut = grid_phot.sel(wavelength=grid_phot['wavelength'][ind_grid_phot_sel])
+            grid_spectro_cut = grid_spectro.sel(wavelength=grid_spectro['wavelength'][ind_grid_spectro_sel])
+            grid_photo_cut = grid_photo.sel(wavelength=grid_photo['wavelength'][ind_grid_photo_sel])
 
             # Interpolation of the grid at the theta parameters set
             if global_params.par3 == 'NA':
-                if len(grid_merge_cut['wavelength']) != 0:
-                    flx_mod_merge_cut = np.asarray(grid_merge_cut.interp(par1=theta[0], par2=theta[1],
+                if len(grid_spectro_cut['wavelength']) != 0:
+                    flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge_cut = np.asarray([])
-                if len(grid_phot_cut['wavelength']) != 0:
-                    flx_mod_phot_cut = np.asarray(grid_phot_cut.interp(par1=theta[0], par2=theta[1],
+                    flx_mod_spectro_cut = np.asarray([])
+                if len(grid_photo_cut['wavelength']) != 0:
+                    flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot_cut = np.asarray([])
+                    flx_mod_photo_cut = np.asarray([])
             elif global_params.par4 == 'NA':
-                if len(grid_merge_cut['wavelength']) != 0:
-                    flx_mod_merge_cut = np.asarray(grid_merge_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2],
+                if len(grid_spectro_cut['wavelength']) != 0:
+                    flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge_cut = np.asarray([])
-                if len(grid_phot_cut['wavelength']) != 0:
-                    flx_mod_phot_cut = np.asarray(grid_phot_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2],
+                    flx_mod_spectro_cut = np.asarray([])
+                if len(grid_photo_cut['wavelength']) != 0:
+                    flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot_cut = np.asarray([])
+                    flx_mod_photo_cut = np.asarray([])
             elif global_params.par5 == 'NA':
-                if len(grid_merge_cut['wavelength']) != 0:
-                    flx_mod_merge_cut = np.asarray(grid_merge_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                if len(grid_spectro_cut['wavelength']) != 0:
+                    flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge_cut = np.asarray([])
-                if len(grid_phot_cut['wavelength']) != 0:
-                    flx_mod_phot_cut = np.asarray(grid_phot_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                    flx_mod_spectro_cut = np.asarray([])
+                if len(grid_photo_cut['wavelength']) != 0:
+                    flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot_cut = np.asarray([])
+                    flx_mod_photo_cut = np.asarray([])
             else:
-                if len(grid_merge_cut['wavelength']) != 0:
-                    flx_mod_merge_cut = np.asarray(grid_merge_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                if len(grid_spectro_cut['wavelength']) != 0:
+                    flx_mod_spectro_cut = np.asarray(grid_spectro_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             par5=theta[4],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge_cut = np.asarray([])
-                if len(grid_phot_cut['wavelength']) != 0:
-                    flx_mod_phot_cut = np.asarray(grid_phot_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                    flx_mod_spectro_cut = np.asarray([])
+                if len(grid_photo_cut['wavelength']) != 0:
+                    flx_mod_photo_cut = np.asarray(grid_photo_cut.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             par5=theta[4],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot_cut = np.asarray([])
+                    flx_mod_photo_cut = np.asarray([])
 
 
             # Re-merging of the data and interpolated synthetic spectrum to a wavelength grid defined by the parameter 'wav_fit'
-            ind_merge = np.where((wav_obs_merge >= min_ns_u) & (wav_obs_merge <= max_ns_u))
-            ind_phot = np.where((wav_obs_phot >= min_ns_u) & (wav_obs_phot <= max_ns_u))
+            ind_spectro = np.where((wav_obs_spectro >= min_ns_u) & (wav_obs_spectro <= max_ns_u))
+            ind_photo = np.where((wav_obs_photo >= min_ns_u) & (wav_obs_photo <= max_ns_u))
             if ns_u_ind == 0:
-                wav_obs_merge_ns_u = wav_obs_merge[ind_merge]
-                flx_obs_merge_ns_u = flx_obs_merge[ind_merge]
-                err_obs_merge_ns_u = err_obs_merge[ind_merge]
-                flx_mod_merge_ns_u = flx_mod_merge_cut
-                if len(inv_cov_obs_merge) != 0:  # Add covariance in the loop (if necessary)
-                    inv_cov_obs_merge_ns_u = inv_cov_obs_merge[np.ix_(ind_merge[0],ind_merge[0])]
+                wav_obs_spectro_ns_u = wav_obs_spectro[ind_spectro]
+                flx_obs_spectro_ns_u = flx_obs_spectro[ind_spectro]
+                err_obs_spectro_ns_u = err_obs_spectro[ind_spectro]
+                flx_mod_spectro_ns_u = flx_mod_spectro_cut
+                if len(inv_cov_obs) != 0:  # Add covariance in the loop (if necessary)
+                    inv_cov_obs_ns_u = inv_cov_obs[np.ix_(ind_spectro[0],ind_spectro[0])]
+                else:
+                    inv_cov_obs_ns_u = np.asarray([])
+                if len(transm_obs) != 0: # Add the transmission (if necessary)
+                    transm_obs_ns_u = transm_obs[ind_spectro]
                 else:
-                    inv_cov_obs_merge_ns_u = np.asarray([])
-                if len(transm_obs_merge) != 0: # Add the transmission (if necessary)
-                    transm_obs_merge_ns_u = transm_obs_merge[ind_merge]
+                    transm_obs_ns_u = np.asarray([])
+                if len(star_flx_obs) != 0: # Add star flux (if necessary)
+                    star_flx_obs_ns_u = star_flx_obs[0,ind_spectro]
                 else:
-                    transm_obs_merge_ns_u = np.asarray([])
-                if len(star_flx_obs_merge) != 0: # Add star flux (if necessary)
-                    star_flx_obs_merge_ns_u = star_flx_obs_merge[ind_merge]
+                    star_flx_obs_ns_u = np.asarray([])
+                if len(system_obs) != 0: # Add systematics model (if necessary)
+                    system_obs_ns_u = system_obs[0,ind_spectro]
                 else:
-                    star_flx_obs_merge_ns_u = np.asarray([])
-                wav_obs_phot_ns_u = wav_obs_phot[ind_phot]
-                flx_obs_phot_ns_u = flx_obs_phot[ind_phot]
-                err_obs_phot_ns_u = err_obs_phot[ind_phot]
-                flx_mod_phot_ns_u = flx_mod_phot_cut
+                    system_obs_ns_u = np.asarray([])
+                wav_obs_photo_ns_u = wav_obs_photo[ind_photo]
+                flx_obs_photo_ns_u = flx_obs_photo[ind_photo]
+                err_obs_photo_ns_u = err_obs_photo[ind_photo]
+                flx_mod_photo_ns_u = flx_mod_photo_cut
             else:
-                wav_obs_merge_ns_u = np.concatenate((wav_obs_merge_ns_u, wav_obs_merge[ind_merge]))
-                flx_obs_merge_ns_u = np.concatenate((flx_obs_merge_ns_u, flx_obs_merge[ind_merge]))
-                err_obs_merge_ns_u = np.concatenate((err_obs_merge_ns_u, err_obs_merge[ind_merge]))
-                flx_mod_merge_ns_u = np.concatenate((flx_mod_merge_ns_u, flx_mod_merge_cut))
-                if len(inv_cov_obs_merge_ns_u) != 0: # Merge the covariance matrices (if necessary)
-                    inv_cov_obs_merge_ns_u = diag_mat([inv_cov_obs_merge_ns_u, inv_cov_obs_merge[np.ix_(ind_merge[0],ind_merge[0])]])
-                if len(transm_obs_merge_ns_u) != 0: # Merge the transmissions (if necessary)
-                    transm_obs_merge_ns_u = np.concatenate((transm_obs_merge_ns_u, transm_obs_merge[ind_merge]))
-                if len(star_flx_obs_merge_ns_u) != 0: # Merge star fluxes (if necessary)
-                    star_flx_obs_merge_ns_u = np.concatenate((star_flx_obs_merge_ns_u, star_flx_obs_merge[ind_grid_merge_sel]))
-                wav_obs_phot_ns_u = np.concatenate((wav_obs_phot_ns_u, wav_obs_phot[ind_phot]))
-                flx_obs_phot_ns_u = np.concatenate((flx_obs_phot_ns_u, flx_obs_phot[ind_phot]))
-                err_obs_phot_ns_u = np.concatenate((err_obs_phot_ns_u, err_obs_phot[ind_phot]))
-                flx_mod_phot_ns_u = np.concatenate((flx_mod_phot_ns_u, flx_mod_phot_cut))
+                wav_obs_spectro_ns_u = np.concatenate((wav_obs_spectro_ns_u, wav_obs_spectro[ind_spectro]))
+                flx_obs_spectro_ns_u = np.concatenate((flx_obs_spectro_ns_u, flx_obs_spectro[ind_spectro]))
+                err_obs_spectro_ns_u = np.concatenate((err_obs_spectro_ns_u, err_obs_spectro[ind_spectro]))
+                flx_mod_spectro_ns_u = np.concatenate((flx_mod_spectro_ns_u, flx_mod_spectro_cut))
+                if len(inv_cov_obs_ns_u) != 0: # Merge the covariance matrices (if necessary)
+                    inv_cov_obs_ns_u = diag_mat([inv_cov_obs_ns_u, inv_cov_obs[np.ix_(ind_spectro[0],ind_spectro[0])]])
+                if len(transm_obs_ns_u) != 0: # Merge the transmissions (if necessary)
+                    transm_obs_ns_u = np.concatenate((transm_obs_ns_u, transm_obs[ind_spectro]))
+                if len(star_flx_obs_ns_u) != 0: # Merge star fluxes (if necessary)
+                    star_flx_obs_ns_u = np.concatenate((star_flx_obs_ns_u, star_flx_obs[0,ind_grid_spectro_sel]),axis=0)
+                if len(system_obs) != 0: # Merge systematics model (if necessary)
+                    system_obs_ns_u = np.concatenate((system_obs_ns_u, system_obs[0,ind_grid_spectro_sel]), axis=0)
+                wav_obs_photo_ns_u = np.concatenate((wav_obs_photo_ns_u, wav_obs_photo[ind_photo]))
+                flx_obs_photo_ns_u = np.concatenate((flx_obs_photo_ns_u, flx_obs_photo[ind_photo]))
+                err_obs_photo_ns_u = np.concatenate((err_obs_photo_ns_u, err_obs_photo[ind_photo]))
+                flx_mod_photo_ns_u = np.concatenate((flx_mod_photo_ns_u, flx_mod_photo_cut))
 
         # Modification of the synthetic spectrum with the extra-grid parameters
         modif_spec_LL = modif_spec(global_params, theta, theta_index,
-                                    wav_obs_merge_ns_u,  flx_obs_merge_ns_u,  err_obs_merge_ns_u,  flx_mod_merge_ns_u,
-                                    wav_obs_phot_ns_u,  flx_obs_phot_ns_u, err_obs_phot_ns_u,  flx_mod_phot_ns_u, 
-                                    transm_obs_merge_ns_u, star_flx_obs_merge_ns_u, indobs=indobs)
+                                    wav_obs_spectro_ns_u,  flx_obs_spectro_ns_u,  err_obs_spectro_ns_u,  flx_mod_spectro_ns_u,
+                                    wav_obs_photo_ns_u,  flx_obs_photo_ns_u, err_obs_photo_ns_u,  flx_mod_photo_ns_u, 
+                                    transm_obs_ns_u, star_flx_obs_ns_u, system_obs_ns_u, indobs=indobs)
         
-        flx_obs, flx_obs_phot = modif_spec_LL[1], modif_spec_LL[5]
-        flx_mod, flx_mod_phot = modif_spec_LL[3], modif_spec_LL[7]
-        err, err_phot = modif_spec_LL[2], modif_spec_LL[6]
-        inv_cov = inv_cov_obs_merge_ns_u
+        flx_obs_spectro_modif, flx_obs_photo_modif = modif_spec_LL[1], modif_spec_LL[5]
+        flx_mod_spectro_modif, flx_mod_photo_modif = modif_spec_LL[3], modif_spec_LL[7]
+        err_obs_spectro_modif, err_obs_photo_modif = modif_spec_LL[2], modif_spec_LL[6]
+        inv_cov_obs_modif = inv_cov_obs_ns_u
         ck = modif_spec_LL[8]
-        planet_contribution, stellar_contribution, star_flx_obs_merge = modif_spec_LL[9], modif_spec_LL[10], modif_spec_LL[11]
+        planet_contribution, stellar_contribution, star_flx_obs, systematics = modif_spec_LL[9], modif_spec_LL[10], modif_spec_LL[11], modif_spec_LL[12]
         
         if global_params.use_lsqr[indobs] == 'True':
             # If our data is contaminated by starlight difraction, the model is the sum of the estimated stellar contribution + planet model
-            flx_mod = planet_contribution * flx_mod + stellar_contribution * star_flx_obs_merge
-
+            flx_mod_spectro_modif = planet_contribution * flx_mod_spectro_modif + np.dot(stellar_contribution, star_flx_obs[0].T)
+            if len(systematics) > 0:
+                flx_mod_spectro_modif += systematics
+                
 
         # Computation of the photometry logL
-        if len(flx_mod_phot) != 0:
-            logL_phot = logL_chi2_classic(flx_obs_phot-flx_mod_phot, err_phot)
+        if len(flx_obs_photo_modif) != 0:
+            logL_photo = logL_chi2_classic(flx_obs_photo_modif-flx_mod_photo_modif, err_obs_photo_modif)
         else:
-            logL_phot = 0
+            logL_photo = 0
 
         # Computation of the spectroscopy logL
-        if len(flx_obs) != 0:
+        if len(flx_obs_spectro_modif) != 0:
             if global_params.logL_type[indobs] == 'chi2_classic':
-                logL_spec = logL_chi2_classic(flx_obs-flx_mod, err)
-            elif global_params.logL_type[indobs] == 'chi2_covariance' and len(inv_cov) != 0:
-                logL_spec = logL_chi2_covariance(flx_obs-flx_mod, inv_cov)
+                logL_spectro = logL_chi2_classic(flx_obs_spectro_modif-flx_mod_spectro_modif, err_obs_spectro_modif)
+            elif global_params.logL_type[indobs] == 'chi2_covariance' and len(inv_cov_obs_modif) != 0:
+                logL_spectro = logL_chi2_covariance(flx_obs_spectro_modif-flx_mod_spectro_modif, inv_cov_obs_modif)
             elif global_params.logL_type[indobs] == 'CCF_Brogi':
-                logL_spec = logL_CCF_Brogi(flx_obs, flx_mod)
+                logL_spectro = logL_CCF_Brogi(flx_obs_spectro_modif, flx_mod_spectro_modif)
             elif global_params.logL_type[indobs] == 'CCF_Zucker':
-                logL_spec = logL_CCF_Zucker(flx_obs, flx_mod)
+                logL_spectro = logL_CCF_Zucker(flx_obs_spectro_modif, flx_mod_spectro_modif)
             elif global_params.logL_type[indobs] == 'CCF_custom':
-                logL_spec = logL_CCF_custom(flx_obs, flx_mod, err)
+                logL_spectro = logL_CCF_custom(flx_obs_spectro_modif, flx_mod_spectro_modif, err_obs_spectro_modif)
+            elif global_params.logL_type[indobs] == 'chi2_extended':
+                logL_spectro = logL_chi2_extended(flx_obs_spectro_modif-flx_mod_spectro_modif, err_obs_spectro_modif)
+            elif global_params.logL_type[indobs] == 'chi2_extended_covariance' and len(inv_cov_obs_modif) != 0:
+                logL_spectro = logL_chi2_extended_covariance(flx_obs_spectro_modif-flx_mod_spectro_modif, inv_cov_obs_modif)
             else:
                 print()
                 print('WARNING: One or more dataset are not included when performing the inversion.')
@@ -255,10 +270,10 @@ def loglike(theta, theta_index, global_params, main_file, for_plot='no'):
                 print()
                 exit()
         else:
-            logL_spec = 0
+            logL_spectro = 0
 
         # Compute the final logL (sum of all likelihood under the hypothesis of independent instruments)
-        FINAL_logL = logL_phot + logL_spec + FINAL_logL
+        FINAL_logL = logL_photo + logL_spectro + FINAL_logL
         
     if for_plot == 'no':
         return FINAL_logL
@@ -278,9 +293,9 @@ def prior_transform(theta, theta_index, lim_param_grid, global_params):
         global_params (object): Class containing every input from the .ini file.
         
     Returns:
-        prior           (list): List containing all the prior information
+        - prior           (list): List containing all the prior information
         
-    Author: Simon Petrus
+    Author: Simon Petrus, Matthieu Ravet, Allan Denis
     """
     prior = []
     if global_params.par1 != 'NA':
@@ -412,6 +427,50 @@ def prior_transform(theta, theta_index, lim_param_grid, global_params):
                 if prior_law == 'gaussian':
                     prior_rv = gaussian_prior([float(global_params.rv[1]), float(global_params.rv[2])], theta[ind_theta_rv[0][0]])
                 prior.append(prior_rv)
+    if len(global_params.vsini) > 4: # If you want separate vsini for each observations
+        main_obs_path = global_params.main_observation_path
+        for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+            if global_params.vsini[indobs*4] != 'NA':
+                prior_law = global_params.vsini[indobs*4] # Prior laws should be separeted by 2 values (need to be upgraded)
+                if prior_law != 'constant':
+                    ind_theta_vsini = np.where(theta_index == f'vsini_{indobs}')
+                    if prior_law == 'uniform':
+                        prior_vsini = uniform_prior([float(global_params.vsini[indobs*4+1]), float(global_params.vsini[indobs*4+2])], theta[ind_theta_vsini[0][0]]) # Prior values should be by two
+                    if prior_law == 'gaussian':
+                        prior_vsini = gaussian_prior([float(global_params.vsini[indobs*4+1]), float(global_params.vsini[indobs*4+2])], theta[ind_theta_vsini[0][0]])
+                    prior.append(prior_vsini)
+    else: # If you want 1 common vsini for all observations
+        if global_params.vsini != 'NA':
+            prior_law = global_params.vsini[0]
+            if prior_law != 'constant':
+                ind_theta_vsini = np.where(theta_index == 'vsini')
+                if prior_law == 'uniform':
+                    prior_vsini = uniform_prior([float(global_params.vsini[1]), float(global_params.vsini[2])], theta[ind_theta_vsini[0][0]])
+                if prior_law == 'gaussian':
+                    prior_vsini = gaussian_prior([float(global_params.vsini[1]), float(global_params.vsini[2])], theta[ind_theta_vsini[0][0]])
+                prior.append(prior_vsini)
+    if len(global_params.ld) > 3: # If you want separate ld for each observations
+        main_obs_path = global_params.main_observation_path
+        for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+            if global_params.ld[indobs*3] != 'NA':
+                prior_law = global_params.ld[indobs*3] # Prior laws should be separeted by 2 values (need to be upgraded)
+                if prior_law != 'constant':
+                    ind_theta_ld = np.where(theta_index == f'ld_{indobs}')
+                    if prior_law == 'uniform':
+                        prior_ld = uniform_prior([float(global_params.ld[indobs*3+1]), float(global_params.ld[indobs*3+2])], theta[ind_theta_ld[0][0]]) # Prior values should be by two
+                    if prior_law == 'gaussian':
+                        prior_ld = gaussian_prior([float(global_params.ld[indobs*3+1]), float(global_params.ld[indobs*3+2])], theta[ind_theta_ld[0][0]])
+                    prior.append(prior_ld)
+    else: # If you want 1 common ld for all observations
+        if global_params.ld != 'NA':
+            prior_law = global_params.ld[0]
+            if prior_law != 'constant':
+                ind_theta_ld = np.where(theta_index == 'ld')
+                if prior_law == 'uniform':
+                    prior_ld = uniform_prior([float(global_params.ld[1]), float(global_params.ld[2])], theta[ind_theta_ld[0][0]])
+                if prior_law == 'gaussian':
+                    prior_ld = gaussian_prior([float(global_params.ld[1]), float(global_params.ld[2])], theta[ind_theta_ld[0][0]])
+                prior.append(prior_ld)
 
     # - - - - - - - - - - - - - - - - - - - - -
 
@@ -424,24 +483,6 @@ def prior_transform(theta, theta_index, lim_param_grid, global_params):
             if prior_law == 'gaussian':
                 prior_av = gaussian_prior([float(global_params.av[1]), float(global_params.av[2])], theta[ind_theta_av[0][0]])
             prior.append(prior_av)
-    if global_params.vsini != 'NA':
-        prior_law = global_params.vsini[0]
-        if prior_law != 'constant':
-            ind_theta_vsini = np.where(theta_index == 'vsini')
-            if prior_law == 'uniform':
-                prior_vsini = uniform_prior([float(global_params.vsini[1]), float(global_params.vsini[2])], theta[ind_theta_vsini[0][0]])
-            if prior_law == 'gaussian':
-                prior_vsini = gaussian_prior([float(global_params.vsini[1]), float(global_params.vsini[2])], theta[ind_theta_vsini[0][0]])
-            prior.append(prior_vsini)
-    if global_params.ld != 'NA':
-        prior_law = global_params.ld[0]
-        if prior_law != 'constant':
-            ind_theta_ld = np.where(theta_index == 'ld')
-            if prior_law == 'uniform':
-                prior_ld = uniform_prior([float(global_params.ld[1]), float(global_params.ld[2])], theta[ind_theta_ld[0][0]])
-            if prior_law == 'gaussian':
-                prior_ld = gaussian_prior([float(global_params.ld[1]), float(global_params.ld[2])], theta[ind_theta_ld[0][0]])
-            prior.append(prior_ld)
     ## adding the CPD params, bb_T and bb_R
     if global_params.bb_T != 'NA':
         prior_law = global_params.bb_T[0]
@@ -564,18 +605,32 @@ def launch_nested_sampling(global_params):
         if global_params.rv != 'NA' and global_params.rv[0] != 'constant':
             n_free_parameters += 1
             theta_index.append(f'rv')
+    if len(global_params.vsini) > 4: 
+        main_obs_path = global_params.main_observation_path
+        for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+            if global_params.vsini[indobs*4] != 'NA' and global_params.vsini[indobs*4] != 'constant': # Check if the idobs is different from constant
+                n_free_parameters += 1
+                theta_index.append(f'vsini_{indobs}')
+    else:
+        if global_params.vsini != 'NA' and global_params.vsini[0] != 'constant':
+            n_free_parameters += 1
+            theta_index.append('vsini')
+    if len(global_params.ld) > 3: 
+        main_obs_path = global_params.main_observation_path
+        for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+            if global_params.ld[indobs*3] != 'NA' and global_params.ld[indobs*3] != 'constant': # Check if the idobs is different from constant
+                n_free_parameters += 1
+                theta_index.append(f'ld_{indobs}')
+    else:
+        if global_params.ld != 'NA' and global_params.ld[0] != 'constant':
+            n_free_parameters += 1
+            theta_index.append('ld')
 
     # - - - - - - - - - - - - - - - - - - - - -
             
     if global_params.av != 'NA' and global_params.av[0] != 'constant':
         n_free_parameters += 1
         theta_index.append('av')
-    if global_params.vsini != 'NA' and global_params.vsini[0] != 'constant':
-        n_free_parameters += 1
-        theta_index.append('vsini')
-    if global_params.ld != 'NA' and global_params.ld[0] != 'constant':
-        n_free_parameters += 1
-        theta_index.append('ld')
     ## adding cpd
     if global_params.bb_T != 'NA' and global_params.bb_T[0] != 'constant':
         n_free_parameters += 1
@@ -689,46 +744,46 @@ def launch_nested_sampling(global_params):
             print('%15s : %.3f +- %.3f' % (name, col.mean(), col.std()))
         print('\n')
 
-    if global_params.ns_algo == 'ultranest':
-        import ultranest, ultranest.stepsampler
+    # if global_params.ns_algo == 'ultranest':
+    #     import ultranest, ultranest.stepsampler
 
-        tmpstot1 = time.time()
+    #     tmpstot1 = time.time()
         
-        loglike_gp = lambda theta: loglike(theta, theta_index, global_params)
-
-        prior_transform_gp = lambda theta: prior_transform(theta, theta_index, lim_param_grid, global_params)
-
-        sampler = ultranest.ReactiveNestedSampler(theta_index,loglike=loglike_gp, transform=prior_transform_gp,
-                                                  wrapped_params=[False, False, False, False])#,
-                                                #log_dir=global_params.result_path, resume=True)
-        #result = sampler.run(min_num_live_points=100, max_ncalls=100000)
-
-        # have to choose the number of steps the slice sampler should take
-        # after first results, this should be increased and checked for consistency.
-        nsteps = 2 * len(theta_index)
-        # create step sampler:
-        sampler.stepsampler = ultranest.stepsampler.SliceSampler(nsteps=nsteps,
-                                                                 generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
-                                                                 # adaptive_nsteps=False,
-                                                                 # max_nsteps=40
-                                                                 )
-        sampler.print_results()
-        #sampler.plot_corner()
-
-        tmpstot2 = time.time()-tmpstot1
-        print(' ')
-        print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
-        print('-> Ultranest  ')
-        print(' ')
-        print('The code spent ' + str(tmpstot2) + ' sec to run.')
-        print(result.summary())
-        print('\n')
+    #     loglike_gp = lambda theta: loglike(theta, theta_index, global_params)
+
+    #     prior_transform_gp = lambda theta: prior_transform(theta, theta_index, lim_param_grid, global_params)
+
+    #     sampler = ultranest.ReactiveNestedSampler(theta_index,loglike=loglike_gp, transform=prior_transform_gp,
+    #                                               wrapped_params=[False, False, False, False])#,
+    #                                             #log_dir=global_params.result_path, resume=True)
+    #     #result = sampler.run(min_num_live_points=100, max_ncalls=100000)
+
+    #     # have to choose the number of steps the slice sampler should take
+    #     # after first results, this should be increased and checked for consistency.
+    #     nsteps = 2 * len(theta_index)
+    #     # create step sampler:
+    #     sampler.stepsampler = ultranest.stepsampler.SliceSampler(nsteps=nsteps,
+    #                                                              generate_direction=ultranest.stepsampler.generate_mixture_random_direction,
+    #                                                              # adaptive_nsteps=False,
+    #                                                              # max_nsteps=40
+    #                                                              )
+    #     sampler.print_results()
+    #     #sampler.plot_corner()
+
+    #     tmpstot2 = time.time()-tmpstot1
+    #     print(' ')
+    #     print('- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -')
+    #     print('-> Ultranest  ')
+    #     print(' ')
+    #     print('The code spent ' + str(tmpstot2) + ' sec to run.')
+    #     print(result.summary())
+    #     print('\n')
     
-    if global_params.ns_algo == 'dynesty':
-        from dynesty import NestedSampler
+    # if global_params.ns_algo == 'dynesty':
+    #     from dynesty import NestedSampler
 
-        # initialize our nested sampler
-        #sampler = NestedSampler(loglike, ptform, ndim)
+    #     initialize our nested sampler
+    #     sampler = NestedSampler(loglike, ptform, ndim)
 
     result_reformat = {"samples": samples,
                        "weights": weights,
diff --git a/build/lib/ForMoSA/plotting/plotting_class.py b/build/lib/ForMoSA/plotting/plotting_class.py
index 23259fe..19248d9 100644
--- a/build/lib/ForMoSA/plotting/plotting_class.py
+++ b/build/lib/ForMoSA/plotting/plotting_class.py
@@ -1,38 +1,79 @@
 
 from __future__ import print_function, division
-import sys, os, yaml, time
+import os, glob, sys
 import numpy as np
 import matplotlib.pyplot as plt
-import matplotlib
-from matplotlib.figure import Figure
-import astropy.constants as const
-import astropy.units as u
+from matplotlib.backends.backend_pdf import PdfPages
+from scipy.interpolate import interp1d
 import corner
 import xarray as xr
 import pickle
-from scipy.interpolate import interp1d
-import extinction
-from PyAstronomy.pyasl import dopplerShift, rotBroad
-from spectres import spectres
 from tqdm import tqdm
-import glob
+
+sys.path.insert(0, os.path.abspath('../'))
 
 # Import ForMoSA
 from main_utilities import GlobFile
 from nested_sampling.nested_modif_spec import modif_spec
-from adapt.extraction_functions import resolution_decreasing,adapt_model, decoupe
+from nested_sampling.nested_modif_spec import doppler_fct
+from nested_sampling.nested_modif_spec import lsq_fct
+from nested_sampling.nested_modif_spec import vsini_fct_accurate
+from adapt.extraction_functions import resolution_decreasing, adapt_model, decoupe
+from adapt.extraction_functions import adapt_observation_range
+
+
+
+
+def bin_data(wave, data, bin_size):
+    '''
+    Function to bin data given a bin size
+
+    Args:
+        wave         (array): wavelength of the data
+        data         (array): data
+        bin_size       (int): size of the bin to apply
 
+    Returns:
+        - wave_binned  (array): binned wavelength
+        - data_binned  (array): binned data
+
+    Author: Allan Denis
+    '''
+    # First quick check that len of data is a multpiple of bin_size
+    while(len(data)%bin_size != 0):
+        wave, data = wave[:-1], data[:-1]
+        
+    bins = np.arange(0, len(wave), bin_size)
+    wave_binned = np.add.reduceat(wave, bins) / bin_size
+    data_binned = np.add.reduceat(data, bins) 
+    
+    return wave_binned, data_binned
+    
+    
 
 # ----------------------------------------------------------------------------------------------------------------------
 # ----------------------------------------------------------------------------------------------------------------------
 class ComplexRadar():
     '''
-    Original from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb
-    
-    Adapted by: P. Palma-Bifani
+    Class to create Radar plots with asymmetric error bars.
+
+    Author: Paulina Palma-Bifani
+            Adapted from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb
+   
     '''
 
     def __init__(self, fig, variables, ranges, n_ordinate_levels=6):
+        '''
+        Initialize class.
+        
+        Args:
+            fig               (object): matplotlib figure object
+            variables           (list): list of parameters to plot
+            ranges       (list(tuple)): upper and lower limits for each parameters
+            n_ordinate_levels    (int): (default = 6) number of gridlines in the plot
+        Returns:
+            None   
+        '''
         angles = np.arange(0, 360, 360./len(variables))
 
         axes = [fig.add_axes([0.1,0.1,0.9,0.9], polar=True, label = "axes{}".format(i)) for i in range(len(variables))]
@@ -63,20 +104,61 @@ def __init__(self, fig, variables, ranges, n_ordinate_levels=6):
         self.ax = axes[0]
 
     def plot(self, data, *args, **kw):
+        '''
+        Function to display the plot.
+        
+        Args:
+            data       (list): best value for each parameter
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata = self.scale_data(data, self.ranges)
         self.ax.plot(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
 
     def fill(self, data, *args, **kw):
+        '''
+        Add symmetric error bars to the plot.
+        
+        Args:
+            data       (list): best value for each parameter
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata = self.scale_data(data, self.ranges)
         self.ax.fill(self.angle, np.r_[sdata, sdata[0]], *args, **kw)
     
     def fill_between(self, list_down, list_up, *args, **kw):
+        '''
+        Add asymmetric error bars to the plot.
+        
+        Args:
+            list_down (list): list of lower error bars
+            list_up   (list): list of upper error bars
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         sdata_down = self.scale_data(list_down, self.ranges)
         sdata_up = self.scale_data(list_up, self.ranges)
         self.ax.fill_between(self.angle,np.r_[sdata_down,sdata_down[0]], np.r_[sdata_up,sdata_up[0]], *args, **kw)
 
     def scale_data(self, data, ranges):
-        """scales data[1:] to ranges[0]"""
+        '''
+        Function to check that lower and upper limits are correctly ordered. It scales data[1:] to ranges[0]
+
+        Args:
+            data              (list): best value for each parameter
+            ranges     (list(tuple)): upper and lower limits for each parameters
+            *args           : Variable length argument list.
+            **kw            : Arbitrary keyword arguments.
+        Returns:
+            None
+        '''
         for d, (y1, y2) in zip(data[1:], ranges[1:]):
             assert (y1 <= d <= y2) or (y2 <= d <= y1)
         x1, x2 = ranges[0]
@@ -95,46 +177,47 @@ def scale_data(self, data, ranges):
 # ----------------------------------------------------------------------------------------------------------------------
 class PlottingForMoSA():
     '''
-    Here all the plotting functionalities of ForMoSA to see the 
+    Class containing all the plotting functionalities of ForMoSA.
 
-    Author: Paulina Palma-Bifani 
+    Author: Paulina Palma-Bifani, Simon Petrus, Matthieu Ravet and Allan Denis
     '''
 
     def __init__(self, config_file_path, color_out):
         '''
-        Plotting class initializer
+        Initialize class by inheriting the global parameter class of ForMoSA.
+
+        Args:
+            config_file_path   (str): path to the config.ini file currently used
+            color_out          (str): color to use for the model
+        Returns:
+            None
         '''
         
         self.global_params = GlobFile(config_file_path)  
         self.color_out     = color_out
 
 
-
     def _get_posteriors(self):
         '''
-        Function to get the posteriors, including luminosity derivation and corvengence parameters logz
+        Function to get the posteriors, including luminosity derivation and Bayesian evidence logz.
 
-        (Adapted from Simon Petrus plotting functions)
+        Args:
+            None
+        Returns:
+            None
         '''
         with open(self.global_params.result_path + '/result_' + self.global_params.ns_algo + '.pic', 'rb') as open_pic:
             result = pickle.load(open_pic)
-        # self.samples = result.samples
         self.samples = result['samples']
-        # self.weights = result.weights
         self.weights = result['weights']
 
         # To test the quality of the fit
-        # self.logl=result.logl
         self.logl=result['logl']
         ind = np.where(self.logl==max(self.logl))
         self.theta_best = self.samples[ind][0]
 
-        # self.sample_logz    = round(result['logz'],1)
-        # self.sample_logzerr = round(result['logzerr'],1)
         self.sample_logz    = round(result['logz'][0],1)
         self.sample_logzerr = round(result['logz'][1],1)
-        # self.sample_h       = round(result['h'],1)
-        # self.outputs_string = 'logz = '+ str(self.sample_logz)+' ± '+str(self.sample_logzerr)+ ' ; h = '+str(self.sample_h)
         self.outputs_string = 'logz = '+ str(self.sample_logz)+' ± '+str(self.sample_logzerr)
 
         ds = xr.open_dataset(self.global_params.model_path, decode_cf=False, engine='netcdf4')
@@ -201,18 +284,32 @@ def _get_posteriors(self):
             if self.global_params.rv != 'NA' and self.global_params.rv != 'constant':
                 tot_list_param_title.append(extra_parameters[3][1] + ' ' + extra_parameters[3][2])
                 theta_index.append('rv')
+        if len(self.global_params.vsini) > 4: # If you want separate vsini for each observations
+            main_obs_path = self.global_params.main_observation_path
+            for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+                if self.global_params.vsini[indobs*4] != 'NA' and self.global_params.vsini[indobs*4] != 'constant': # Check if the idobs is different from constant
+                    tot_list_param_title.append(extra_parameters[5][1] + fr'$_{indobs}$' + ' ' + extra_parameters[5][2])
+                    theta_index.append(f'vsini_{indobs}')
+        else: # If you want 1 common vsini for all observations
+            if self.global_params.vsini != 'NA' and self.global_params.vsini != 'constant':
+                tot_list_param_title.append(extra_parameters[5][1] + ' ' + extra_parameters[5][2])
+                theta_index.append('vsini')
+        if len(self.global_params.ld) > 3: # If you want separate ld for each observations
+            main_obs_path = self.global_params.main_observation_path
+            for indobs, obs in enumerate(sorted(glob.glob(main_obs_path))):
+                if self.global_params.ld[indobs*3] != 'NA' and self.global_params.ld[indobs*3] != 'constant': # Check if the idobs is different from constant
+                    tot_list_param_title.append(extra_parameters[6][1] + fr'$_{indobs}$' + ' ' + extra_parameters[6][2])
+                    theta_index.append(f'ld_{indobs}')
+        else: # If you want 1 common vsini for all observations
+            if self.global_params.ld != 'NA' and self.global_params.ld != 'constant':
+                tot_list_param_title.append(extra_parameters[6][1] + ' ' + extra_parameters[6][2])
+                theta_index.append('ld')
 
         # - - - - - - - - - - - - - - - - - - - - -
 
         if self.global_params.av != 'NA' and self.global_params.av[0] != 'constant':
             tot_list_param_title.append(extra_parameters[4][1] + ' ' + extra_parameters[4][2])
             theta_index.append('av')
-        if self.global_params.vsini != 'NA' and self.global_params.vsini[0] != 'constant':
-            tot_list_param_title.append(extra_parameters[5][1] + ' ' + extra_parameters[5][2])
-            theta_index.append('vsini')
-        if self.global_params.ld != 'NA' and self.global_params.ld[0] != 'constant':
-            tot_list_param_title.append(extra_parameters[6][1] + ' ' + extra_parameters[6][2])
-            theta_index.append('ld')
         ## cpd bb
         if self.global_params.bb_T != 'NA' and self.global_params.bb_T[0] != 'constant':
             tot_list_param_title.append(extra_parameters[7][1] + ' ' + extra_parameters[7][2])
@@ -243,7 +340,15 @@ def _get_posteriors(self):
 
     def plot_corner(self, levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16, 0.5, 0.84), burn_in=0):
         '''
-        See the corner plots
+        Function to display the corner plot
+
+        Args:
+            levels_sig    (list): (default = [0.997, 0.95, 0.68]) 1, 2 and 3 sigma contour levels of the corner plot
+            bins           (int): (default = 100) number of bins for the posteriors
+            quantiles     (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values
+            burn_in        (int): (default = 0) number of steps to remove from the plot
+        Returns:
+            - fig         (object): matplotlib figure object
         '''
         print('ForMoSA - Corner plot')
 
@@ -264,20 +369,27 @@ def plot_corner(self, levels_sig=[0.997, 0.95, 0.68], bins=100, quantiles=(0.16,
                             fill_contours=True,
                             show_titles=True,
                             title_fmt='.2f',
-                            title_kwargs=dict(fontsize=10),
+                            title_kwargs=dict(fontsize=14),
                             contour_kwargs=dict(colors=self.color_out, linewidths=0.7),
                             pcolor_kwargs=dict(color='red'),
-                            label_kwargs=dict(fontsize=10))
+                            label_kwargs=dict(fontsize=14))
+    
 
         fig.supxlabel(self.outputs_string, va='top')
 
         return fig
 
 
-    def plot_chains(self,figsize=(7,15)):
+    def plot_chains(self, figsize=(7,15)):
         '''
-        To check the convergence of the chains
+        Plot to check the convergence of the posterior chains.
+        Multiple (sub-)axis plot.
 
+        Args:
+            figsize     (tuple): (default = (7, 15)) size of the plot
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Posteriors chains for each parameter')
 
@@ -303,12 +415,19 @@ def plot_chains(self,figsize=(7,15)):
         return fig, axs
 
 
-    def plot_radar(self,ranges,label='',quantiles=[0.16, 0.5, 0.84],chiffres=[0,2,2,2]):
+    def plot_radar(self, ranges, label='', quantiles=[0.16, 0.5, 0.84]):
         '''
-        To check overall the distribution of the parameters 
+        Radar plot to check the distribution of the parameters.
+        Useful to compare different models.
+
+        Args:
+            ranges     (list(tuple)): upper and lower limits for each parameters
+            label              (str): (default = '') label of the plot
+            quantiles         (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - radar.ax   (object) : matplotlib radar class axes object    
 
-        Inputs:
-        ranges
         '''
         print('ForMoSA - Radar plot')
 
@@ -324,23 +443,30 @@ def plot_radar(self,ranges,label='',quantiles=[0.16, 0.5, 0.84],chiffres=[0,2,2,
             list_uncert_down.append(q16)
             list_uncert_up.append(q84)
 
-        fig1 = plt.figure(figsize=(6, 6))
-        radar = ComplexRadar(fig1, self.posteriors_names, ranges)
+        fig = plt.figure(figsize=(6, 6))
+        radar = ComplexRadar(fig, self.posteriors_names, ranges)
 
         radar.plot(list_posteriors, 'o-', color=self.color_out, label=label)
         radar.fill_between(list_uncert_down,list_uncert_up, color=self.color_out, alpha=0.2)
 
         radar.ax.legend(loc='center', bbox_to_anchor=(0.5, -0.20),frameon=False, ncol=2)
 
-        return fig1, radar.ax
+        return fig, radar.ax
 
 
     def _get_spectra(self,theta):
         '''
-        To get the data and best model asociated 
-        Use numba: https://numba.pydata.org/
+        Function to get the data and best model asociated.
 
-        (Adapted from Simon Petrus)
+        Args:
+            theta                   (list): best parameter values
+        Returns:
+            - modif_spec_chi2  list(n-array): list containing the spectroscopic wavelength, spectroscopic fluxes of the data, 
+                                            spectroscopic errors of the data, spectroscopic fluxes of the model, 
+                                            photometric wavelength, photometric fluxes of the data, photometric errors of the data, 
+                                            spectroscopic fluxes of the model,
+                                            planet transmission, star fluxes, systematics
+            - ck                list(floats): list scaling factor(s)
         '''
         # Get the posteriors
         self._get_posteriors()
@@ -355,82 +481,83 @@ def _get_spectra(self,theta):
             obs_name = os.path.splitext(os.path.basename(self.global_params.observation_path))[0]
 
             spectrum_obs = np.load(os.path.join(self.global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True)
-            wav_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float)
-            flx_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float)
-            err_obs_merge = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float)
-            transm_obs_merge = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float)
-            star_flx_obs_merge = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float)
+            wav_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][0], dtype=float)
+            flx_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][1], dtype=float)
+            err_obs_spectro = np.asarray(spectrum_obs['obs_spectro_merge'][2], dtype=float)
+            transm_obs = np.asarray(spectrum_obs['obs_opt_merge'][1], dtype=float)
+            star_flx_obs = np.asarray(spectrum_obs['obs_opt_merge'][2], dtype=float)
+            system_obs = np.asarray(spectrum_obs['obs_opt_merge'][3], dtype=float)
             if 'obs_photo' in spectrum_obs.keys():
-                wav_obs_phot = np.asarray(spectrum_obs['obs_photo'][0], dtype=float)
-                flx_obs_phot = np.asarray(spectrum_obs['obs_photo'][1], dtype=float)
-                err_obs_phot = np.asarray(spectrum_obs['obs_photo'][2], dtype=float)
+                wav_obs_photo = np.asarray(spectrum_obs['obs_photo'][0], dtype=float)
+                flx_obs_photo = np.asarray(spectrum_obs['obs_photo'][1], dtype=float)
+                err_obs_photo = np.asarray(spectrum_obs['obs_photo'][2], dtype=float)
             else:
-                wav_obs_phot = np.asarray([], dtype=float)
-                flx_obs_phot = np.asarray([], dtype=float)
-                err_obs_phot = np.asarray([], dtype=float)
+                wav_obs_photo = np.asarray([], dtype=float)
+                flx_obs_photo = np.asarray([], dtype=float)
+                err_obs_photo = np.asarray([], dtype=float)
 
             # Recovery of the spectroscopy and photometry model
-            path_grid_m = os.path.join(self.global_params.adapt_store_path, f'adapted_grid_spectro_{self.global_params.grid_name}_{obs_name}_nonan.nc')
-            path_grid_p = os.path.join(self.global_params.adapt_store_path, f'adapted_grid_photo_{self.global_params.grid_name}_{obs_name}_nonan.nc')
-            ds = xr.open_dataset(path_grid_m, decode_cf=False, engine='netcdf4')
-            grid_merge = ds['grid']
+            path_grid_spectro = os.path.join(self.global_params.adapt_store_path, f'adapted_grid_spectro_{self.global_params.grid_name}_{obs_name}_nonan.nc')
+            path_grid_photo = os.path.join(self.global_params.adapt_store_path, f'adapted_grid_photo_{self.global_params.grid_name}_{obs_name}_nonan.nc')
+            ds = xr.open_dataset(path_grid_spectro, decode_cf=False, engine='netcdf4')
+            grid_spectro = ds['grid']
             ds.close()
-            ds = xr.open_dataset(path_grid_p, decode_cf=False, engine='netcdf4')
-            grid_phot = ds['grid']
+            ds = xr.open_dataset(path_grid_photo, decode_cf=False, engine='netcdf4')
+            grid_photo = ds['grid']
             ds.close()
 
             if self.global_params.par3 == 'NA':
-                if len(grid_merge['wavelength']) != 0:
-                    flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1],
+                if len(grid_spectro['wavelength']) != 0:
+                    flx_mod_spectro = np.asarray(grid_spectro.interp(par1=theta[0], par2=theta[1],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge = np.asarray([])
-                if len(grid_phot['wavelength']) != 0:
-                    flx_mod_phot = np.asarray(grid_phot.interp(par1=theta[0], par2=theta[1],
+                    flx_mod_spectro = np.asarray([])
+                if len(grid_photo['wavelength']) != 0:
+                    flx_mod_photo = np.asarray(grid_photo.interp(par1=theta[0], par2=theta[1],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot = np.asarray([])
+                    flx_mod_photo = np.asarray([])
             elif self.global_params.par4 == 'NA':
-                if len(grid_merge['wavelength']) != 0:
-                    flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2],
+                if len(grid_spectro['wavelength']) != 0:
+                    flx_mod_spectro = np.asarray(grid_spectro.interp(par1=theta[0], par2=theta[1], par3=theta[2],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge = np.asarray([])
-                if len(grid_phot['wavelength']) != 0:
-                    flx_mod_phot = np.asarray(grid_phot.interp(par1=theta[0], par2=theta[1], par3=theta[2],
+                    flx_mod_spectro = np.asarray([])
+                if len(grid_photo['wavelength']) != 0:
+                    flx_mod_photo = np.asarray(grid_photo.interp(par1=theta[0], par2=theta[1], par3=theta[2],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot = np.asarray([])
+                    flx_mod_photo = np.asarray([])
             elif self.global_params.par5 == 'NA':
-                if len(grid_merge['wavelength']) != 0:
-                    flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                if len(grid_spectro['wavelength']) != 0:
+                    flx_mod_spectro = np.asarray(grid_spectro.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge = np.asarray([])
-                if len(grid_phot['wavelength']) != 0:
-                    flx_mod_phot = np.asarray(grid_phot.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                    flx_mod_spectro = np.asarray([])
+                if len(grid_photo['wavelength']) != 0:
+                    flx_mod_photo = np.asarray(grid_photo.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot = np.asarray([])
+                    flx_mod_photo = np.asarray([])
             else:
-                if len(grid_merge['wavelength']) != 0:
-                    flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                if len(grid_spectro['wavelength']) != 0:
+                    flx_mod_spectro = np.asarray(grid_spectro.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             par5=theta[4],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_merge = np.asarray([])
-                if len(grid_phot['wavelength']) != 0:
-                    flx_mod_phot = np.asarray(grid_phot.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
+                    flx_mod_spectro = np.asarray([])
+                if len(grid_photo['wavelength']) != 0:
+                    flx_mod_photo = np.asarray(grid_photo.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
                                                             par5=theta[4],
                                                             method="linear", kwargs={"fill_value": "extrapolate"}))
                 else:
-                    flx_mod_phot = np.asarray([])
+                    flx_mod_photo = np.asarray([])
 
             # Modification of the synthetic spectrum with the extra-grid parameters
             modif_spec_chi2 = modif_spec(self.global_params, theta, self.theta_index,
-                                        wav_obs_merge, flx_obs_merge, err_obs_merge, flx_mod_merge,
-                                        wav_obs_phot, flx_obs_phot, err_obs_phot, flx_mod_phot,
-                                        transm_obs_merge, star_flx_obs_merge, indobs=indobs)
+                                        wav_obs_spectro, flx_obs_spectro, err_obs_spectro, flx_mod_spectro,
+                                        wav_obs_photo, flx_obs_photo, err_obs_photo, flx_mod_photo,
+                                        transm_obs, star_flx_obs, system_obs, indobs=indobs)
             ck = modif_spec_chi2[8]
 
             modif_spec_MOSAIC.append(modif_spec_chi2)
@@ -442,24 +569,21 @@ def _get_spectra(self,theta):
         return modif_spec_chi2, ck
     
 
-    def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], res_out=1000, re_interp=False, int_method="linear"):
+    def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], N_points=1000, re_interp=False, int_method="linear"):
         '''
-        To get the data and best model asociated 
-        Use numba: https://numba.pydata.org/.
+        Extract a model spectrum from another grid.
 
         Args:
-            theta:          List of model and extra-model parameters.
-            grid_used:      Default 'original' will use the raw grid. Else, input the path to your desired grid.
-            wavelengths:    Default [] will use max values of the wav_for_adapt range to create a model spectrum.
-                            Else, input the desired wavelength range.
-            res_out:        Default 1000 will be the resolution of your model spectrum. Else, input the desired resolution.
-            re_interp:      Default False. If true, will re-interpolate the grid's hole (WARNING, time consumming...).
-            int_method:     Default "linear" will be the interpolation method use for the grid. Else, input the desired interpolation method.
+            theta:                       (list): best parameter values
+            grid_used:                    (str): (default = 'original') Path to the grid from where to extract the spectrum. If 'original', the current grid will be used.
+            wavelengths:                 (list): (default = []) Desired wavelength range. If [] max and min values of wav_for_adapt range will be use to create the wavelength range.
+            N_points:                     (int): (default = 1000) Number of points.
+            re_interp:                (boolean): (default = False). Option to reinterpolate or not the grid.
+            int_method:                   (str): (default = "linear") Interpolation method for the grid (if reinterpolated).
         Returns:   
-            fig, ax, axr, axr2
-
-
-        Authors: Paulina Palma-Bifani and Matthieu Ravet
+            - wav_final                   (array): Wavelength array of the full model
+            - flx_final                   (array): Flux array of the full model
+            - ck                          (float): Scaling factor of the full model
         '''
         self._get_posteriors()
 
@@ -473,9 +597,9 @@ def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], res_ou
                 else:
                     wav = np.concatenate((wav, wav_ind))
             wav = np.sort(wav)
-            wavelengths = np.linspace(wav[0],wav[-1],res_out)
+            wavelengths = np.linspace(wav[0],wav[-1],N_points)
         else:
-            wavelengths = np.linspace(wavelengths[0],wavelengths[-1],res_out)
+            wavelengths = np.linspace(wavelengths[0],wavelengths[-1],N_points)
 
         # Recover the original grid
         if grid_used == 'original':
@@ -521,7 +645,7 @@ def get_FULL_spectra(self, theta, grid_used = 'original', wavelengths=[], res_ou
         #print(flx_mod_final[100],ck)
         err_mod_final_calib = flx_mod_final_calib*0.1
 
-        wav_final, _, _, flx_final, _, _, _, _, _ = modif_spec(self.global_params, theta, self.theta_index,
+        wav_final, _, _, flx_final, _, _, _, _, _, _, _, _, _ = modif_spec(self.global_params, theta, self.theta_index,
                                                                                     wavelengths, flx_mod_final_calib, err_mod_final_calib, flx_mod_final_calib/ck,
                                                                                     [], [], [], [], [], [])
     
@@ -534,16 +658,17 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
         Plot the best fit comparing with the data.
 
         Args:
-            figsize:    x/y size of the plot
-            uncert:     'yes' or 'no' to plot spectra with associated error bars
-            trans:      'yes' or 'no' to plot transmision curves for photometry
-            logx:       'yes' or 'no' to plot the wavelength in log scale
-            logy:       'yes' or 'no' to plot the flux in log scale
-            norm:       'yes' or 'no' to plot the normalized spectra
+            figsize    (tuple): (default = (10, 5)) Size of the plot
+            uncert     (str): (default = no) 'yes' or 'no' to plot spectra with associated error bars
+            trans      (str): (default = no) 'yes' or 'no' to plot transmision curves for photometry
+            logx       (str): (default = no) 'yes' or 'no' to plot the wavelength in log scale
+            logy       (str): (default = no) 'yes' or 'no' to plot the flux in log scale
+            norm       (str): (default = no) 'yes' or 'no' to plot the normalized spectra
         Returns:
-            fig, ax, axr, axr2
-
-        Author: Paulina Palma-Bifani and Matthieu Ravet
+            - fig    (object) : matplotlib figure object
+            - ax     (object) : matplotlib axes objects, main spectra plot
+            - axr    (object) : matplotlib axes objects, residuals
+            - axr2   (object) : matplotlib axes objects, right side density histogram
         '''
         print('ForMoSA - Best fit and residuals plot')
 
@@ -556,11 +681,17 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
 
        
         spectra, ck = self._get_spectra(self.theta_best)
+        iobs_spectro = 0
+        iobs_photo = 0
         
 
         # Scale or not in absolute flux
         if norm != 'yes': 
-            ck = np.full(len(spectra[0][0]), 1)
+            if len(spectra[0][0]) != 0:
+                ck = np.full(len(spectra[0][0]), 1)
+            else:
+                ck = np.full(len(spectra[0][4]), 1)
+
 
         for indobs, obs in enumerate(sorted(glob.glob(self.global_params.main_observation_path))):
             
@@ -570,9 +701,14 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
                 spectra = list(spectra) # Transform spectra to a list so that we can modify its values
                 spectra[indobs] = list(spectra[indobs])
                 model, planet_contribution, stellar_contribution, star_flx = spectra[indobs][3], spectra[indobs][9], spectra[indobs][10], spectra[indobs][11]
-                spectra[indobs][3] = planet_contribution * model + stellar_contribution * star_flx
+                spectra[indobs][3] = planet_contribution * model + np.dot(stellar_contribution, star_flx[0].T)
+                systematics = spectra[indobs][12]
+                if len(systematics) > 0:
+                    spectra[indobs][3] += systematics
 
             if len(spectra[indobs][0]) != 0:
+                iobs_spectro += 1
+                iobs_photo += 1
                 if uncert=='yes':
                     ax.errorbar(spectra[indobs][0], spectra[indobs][1]/ck[indobs], yerr=spectra[indobs][2]/ck[indobs], c='k', alpha=0.2)
                 ax.plot(spectra[indobs][0], spectra[indobs][1]/ck[indobs], c='k')
@@ -586,14 +722,19 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
                 axr2.hist(residuals/sigma_res, bins=100 ,color=self.color_out, alpha=0.5, density=True, orientation='horizontal')
                 axr2.legend(frameon=False,handlelength=0)
 
-                if indobs == 0:
+                if indobs == iobs_spectro-1:
                     # Add labels out of the loops
-                    ax.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='data')
-                    ax.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c=self.color_out, label='model')
-                    axr.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c=self.color_out, label='model-data')
+                    ax.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='Spectroscopic data')
+                    ax.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c=self.color_out, label='Spectroscopic model')
+                    axr.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c=self.color_out, label='Spectroscopic model-data')
                     axr2.hist(residuals/sigma_res, bins=100 ,color=self.color_out, alpha=0.5, density=True, orientation='horizontal', label='density')
+                    
+                    iobs_spectro = -1
+                    
 
             if len(spectra[indobs][4]) != 0:
+                iobs_photo += 1
+                iobs_spectro += 1
                 # If you want to plot the transmission filters
                 if trans == 'yes':
                     self.global_params.observation_path = obs
@@ -606,8 +747,8 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
                         filter_pho = np.load(separator.join(path_list) + '/phototeque/' + pho + '.npz')
                         ax.fill_between(filter_pho['x_filt'], filter_pho['y_filt']*0.8*min(spectra[indobs][5]/ck[indobs]),color=self.color_out, alpha=0.3)
                         ax.text(np.mean(filter_pho['x_filt']), np.mean(filter_pho['y_filt']*0.4*min(spectra[indobs][5]/ck[indobs])), pho, horizontalalignment='center', c='gray')
-                ax.plot(spectra[indobs][4], spectra[indobs][5]/ck[indobs], 'ko', alpha=0.7)
-                ax.plot(spectra[indobs][4], spectra[indobs][7]/ck[indobs], 'o', color=self.color_out)
+                ax.plot(spectra[indobs][4], spectra[indobs][5] / ck[indobs], 'ko', alpha=0.7)
+                ax.plot(spectra[indobs][4], spectra[indobs][7] / ck[indobs], 'o', color=self.color_out)
                 
 
                 residuals_phot = spectra[indobs][7]-spectra[indobs][5]
@@ -615,12 +756,14 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
                 axr.plot(spectra[indobs][4], residuals_phot/sigma_res, 'o', c=self.color_out, alpha=0.8)
                 axr.axhline(y=0, color='k', alpha=0.5, linestyle='--')
 
-                if indobs == 0:
+                if indobs == iobs_photo-1:
                     # Add labels out of the loops
                     ax.plot(spectra[0][4], np.empty(len(spectra[0][4]))*np.nan, 'ko', label='Photometry data')
                     ax.plot(spectra[0][4], np.empty(len(spectra[0][4]))*np.nan, 'o', c=self.color_out, label='Photometry model')
                     axr.plot(spectra[0][4], np.empty(len(spectra[0][4]))*np.nan, 'o', c=self.color_out, label='Photometry model-data')
-
+                    
+                    iobs_photo = -1
+                    
         # Set xlog-scale
         if logx == 'yes':
             ax.set_xscale('log')
@@ -644,127 +787,357 @@ def plot_fit(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no
             
         # define the data as global
         self.spectra = spectra
-        #self.residuals = residuals
 
-        return spectra
+        return fig, ax, axr, axr2
+    
+    
+    def plot_fit_HiRes(self, figsize=(10, 5), uncert='no', trans='no', logx='no', logy='no', norm='no'):
+        '''
+        Same as plot_fit but with the stellar and planetary models for high-resolution spectroscopy. Does not include residuals in a sub-axis.
+
+        Args:
+            figsize    (tuple): (default = (10, 5)) Size of the plot
+            uncert     (str): (default = no) 'yes' or 'no' to plot spectra with associated error bars
+            trans      (str): (default = no) 'yes' or 'no' to plot transmision curves for photometry
+            logx       (str): (default = no) 'yes' or 'no' to plot the wavelength in log scale
+            logy       (str): (default = no) 'yes' or 'no' to plot the flux in log scale
+            norm       (str): (default = no) 'yes' or 'no' to plot the normalized spectra
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
+        '''
+        print('ForMoSA - Best fit and residuals plot')
+
+        fig1, ax1 = plt.subplots(1,1, figsize=figsize)
+       
+        spectra, ck = self._get_spectra(self.theta_best)
+        iobs_spectro = 0
+        
+
+        # Scale or not in absolute flux
+        if norm != 'yes': 
+            ck = np.full(len(spectra[0][0]), 1)
+
+        for indobs, obs in enumerate(sorted(glob.glob(self.global_params.main_observation_path))):
+            
+            if self.global_params.use_lsqr[indobs] == 'True':
+                # If we used the lsq function, it means that our data is contaminated by the starlight difraction
+                # so the model is the sum of the planet model + the estimated stellar contribution
+                spectra = list(spectra) # Transform spectra to a list so that we can modify its values
+                spectra[indobs] = list(spectra[indobs])
+                model, planet_contribution, stellar_contribution, star_flx, systematics = spectra[indobs][3], spectra[indobs][9], spectra[indobs][10], spectra[indobs][11], spectra[indobs][12]
+                transm = spectra[indobs][13]
+                spectra[indobs][3] = planet_contribution * model + np.dot(stellar_contribution, star_flx[0].T)
+                if len(systematics) > 0:
+                    spectra[indobs][3] += systematics
+
+            if len(spectra[indobs][0]) != 0:
+                iobs_spectro += 1
+                if uncert=='yes':
+                    ax1.errorbar(spectra[indobs][0], spectra[indobs][1]/ck[indobs], yerr=spectra[indobs][2]/ck[indobs], c='k', alpha=0.2)
+                ax1.plot(spectra[indobs][0], spectra[indobs][1] - spectra[indobs][3], 'o', alpha = 0.2, color='g')
+                ax1.plot(spectra[indobs][0], spectra[indobs][1]/ck[indobs], c='k')
+                ax1.plot(spectra[indobs][0], spectra[indobs][3]/ck[indobs], c='r')
+                ax1.plot(spectra[indobs][0], np.dot(stellar_contribution, star_flx[0].T), c='b')
+                ax1.plot(spectra[indobs][0], planet_contribution * model, c='purple')
+
+ 
+                if indobs == iobs_spectro - 1:
+                    # Add labels out of the loops
+                    ax1.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='residuals')
+                    ax1.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='data')
+                    ax1.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c=self.color_out, label='full model')
+                    ax1.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='stellar model')
+                    ax1.plot(spectra[0][0], np.empty(len(spectra[0][0]))*np.nan, c='k', label='planetary model')
+                    
+                    iobs_spectro = -1
+
+            if len(spectra[indobs][4]) != 0:
+                # If you want to plot the transmission filters
+                if trans == 'yes':
+                    self.global_params.observation_path = obs
+                    obs_name = os.path.splitext(os.path.basename(self.global_params.observation_path))[0]
+                    spectrum_obs = np.load(os.path.join(self.global_params.result_path, f'spectrum_obs_{obs_name}.npz'), allow_pickle=True)
+                    obs_photo_ins = spectrum_obs['obs_photo_ins']
+                    for pho_ind, pho in enumerate(obs_photo_ins):
+                        path_list = __file__.split("/")[:-2]
+                        separator = '/'
+                        filter_pho = np.load(separator.join(path_list) + '/phototeque/' + pho + '.npz')
+                        ax1.fill_between(filter_pho['x_filt'], filter_pho['y_filt']*0.8*min(spectra[indobs][5]/ck[indobs]),color=self.color_out, alpha=0.3)
+                        ax1.text(np.mean(filter_pho['x_filt']), np.mean(filter_pho['y_filt']*0.4*min(spectra[indobs][5]/ck[indobs])), pho, horizontalalignment='center', c='gray')
+                ax1.plot(spectra[indobs][4], spectra[indobs][5]/ck[indobs], 'ko', alpha=0.7)
+                ax1.plot(spectra[indobs][4], spectra[indobs][7]/ck[indobs], 'o', color=self.color_out)
+                
+
+                residuals_phot = spectra[indobs][7]-spectra[indobs][5]
+                sigma_res = np.std(residuals_phot)
+
+
+                if indobs == 0:
+                    # Add labels out of the loops
+                    ax1.plot(spectra[0][4], np.empty(len(spectra[0][4]))*np.nan, 'ko', label='Photometry data')
+                    ax1.plot(spectra[0][4], np.empty(len(spectra[0][4]))*np.nan, 'o', c=self.color_out, label='Photometry model')
+
+        # Set xlog-scale
+        if logx == 'yes':
+            ax1.set_xscale('log')
+        # Set xlog-scale
+        if logy == 'yes':
+            ax1.set_yscale('log')
+        # Remove the xticks from the first ax
+        ax1.set_xticks([])
+        # Labels
+        if norm != 'yes': 
+            ax1.set_ylabel(r'Flux (ADU)')
+        else:
+            ax1.set_ylabel(r'Normalised flux (W m-2 µm-1)')
+            
+        ax1.set_xlabel(r'wavelength ($ \mu $m)')
+        
+        fig1.legend()
+        plt.figure(fig1)
+        plt.savefig(self.global_params.result_path + 'full_data.pdf')
+
+        # define the data as global
+        self.spectra = spectra
+
+        return fig1, ax1
     
     
-    def plot_ccf(self, rv_grid = [-300,300], rv_step = 0.5, figsize = (10,5)):
+    def plot_HiRes_comp_model(self, figsize=(10, 5), norm='no', data_resolution = 0):
         '''
-        Plot the ccf (used for high resolution data such as CRIRES+ / HiRISE)
+        Specific function to plot the best fit comparing with the data for high-resolution spectroscopy.
+
+        Args:
+            figsize             (tuple): (default = (10, 5)) Size of the plot
+            norm                  (str): (default = no) 'yes' or 'no' to plot the normalized spectra
+            data_resolution       (int): (default = 0) Custom resolution to broadened data
+        Returns:
+            - fig1  (object) : matplotlib figure object
+            - ax1   (object) : matplotlib axes objects
+        '''
+        print('ForMoSA - Planet model and data')
+       
+        spectra, ck = self._get_spectra(self.theta_best)
+        fig1, ax1 = plt.subplots(1, 1, figsize = figsize)
+        fig, ax = plt.subplots(1, 1, figsize = figsize)
+
+        # Scale or not in absolute flux
+        if norm != 'yes': 
+            ck = np.full(len(spectra[0][0]), 1)
+            
+        pdf = PdfPages(self.global_params.result_path + 'PLanet_model_and_data_resolution_degraded.pdf')
+        plt.ioff()
         
+
+        for indobs, obs in enumerate(sorted(glob.glob(self.global_params.main_observation_path))):
+            
+            
+            if self.global_params.use_lsqr[indobs] == 'True':
+                # If we used the lsq function, it means that our data is contaminated by the starlight difraction
+                # so the model is the sum of the planet model + the estimated stellar contribution
+                spectra = list(spectra) # Transform spectra to a list so that we can modify its values
+                spectra[indobs] = list(spectra[indobs])
+                model, planet_contribution, stellar_contribution, star_flx, systematics, transm = spectra[indobs][3], spectra[indobs][9], spectra[indobs][10], spectra[indobs][11], spectra[indobs][12], spectra[indobs][13]
+
+            if len(spectra[indobs][0]) != 0:
+
+                if (len(systematics) > 0) and (len(star_flx) > 0):
+                    data = spectra[indobs][1] - np.dot(stellar_contribution, star_flx[0].T) - systematics
+                elif (len(star_flx) > 0):     # if len(systematics) = 0 but len(star_flx) > 0
+                    data = spectra[indobs][1] - np.dot(stellar_contribution, star_flx[0].T)
+                elif (len(systematics) > 0):  # if len(star_flx) = 0 but len(systematics) > 0
+                    data = spectra[indobs][1] - systematics
+                else:                         # if len(star_flx) = 0 and len(systematics) = 0
+                    data = spectra[indobs][1]
+
+                wave = spectra[indobs][0]
+                planet_model = planet_contribution * model 
+                
+                # Compute intrinsic resolution of the data because of the v.sini
+                resolution = 3.0*1e5 / (self.theta_best[self.theta_index == 'vsini'])
+                resolution = resolution * np.ones(len(wave))
+                
+                if data_resolution > 0:
+                    self.global_params.custom_reso[indobs] = 'NA'
+                    resolution_data = data_resolution * np.ones(len(wave))
+                    data_broadened = vsini_fct_accurate(wave, data, 0.6, self.theta_best[self.theta_index == 'vsini'])
+                    data_broadened = resolution_decreasing(self.global_params, wave, [], resolution, wave, data, resolution_data, 'mod')
+                    planet_model_broadened = resolution_decreasing(self.global_params, wave, [], resolution, wave, planet_model, resolution_data, 'mod')
+       
+                
+                fig = plt.figure('comp_model', figsize=figsize)
+                fig.clf()
+                ax = fig.add_subplot(111)
+                
+                ax.plot(wave, data_broadened, c='k')
+                ax.plot(wave, planet_model_broadened, c='r')
+                
+                ax.set_xlabel(r'wavelength ($\mu$m)')
+                ax.set_ylabel('Flux (ADU)')
+                
+                ax1.plot(wave, data, c='k')
+                ax1.plot(wave, planet_model, c = 'r')
+                
+                if self.global_params.use_lsqr[indobs] == 'True':
+                    legend_data = 'data - star'
+                else:
+                    legend_data = 'data'
+                    
+                ax.legend([legend_data, 'planet model'])
+                
+                pdf.savefig()
+                
+        pdf.close()
+        
+        ax1.legend([legend_data, "planet model"], fontsize = 18)
+        ax1.set_xlabel(r'wavelength ($ \mu $m)', fontsize=18)
+        ax1.set_ylabel('Flux (ADU)', fontsize=18)
+        plt.figure(fig1)
+        plt.savefig(self.global_params.result_path + 'Planet_model_and_data.pdf')
+                            
+        return fig1, ax1
+    
+    
+    def plot_ccf(self, figsize = (10,5), rv_grid = [-300,300], rv_step = 0.5, window_normalisation = 100, model_wavelength = [], model_spectra = [], model_resolution = [], model_name = 'Full', rv_cor=0):
+        '''
+        Plot the cross-correlation function. It is used for high resolution spectroscopy.
+
+        Args:
+            figsize                   (tuple): (default = (10, 5)) Size of the plot
+            rv_grid                    (list): (default = [-300,300]) Maximum and minumum values of the radial velocity shift (in km/s)
+            rv_step                   (float): (default = 0.5) Radial velocity shift steps (in km/s)
+            window_normalisation        (int): (default = 100) ?
+            model_wavelength           (list): (default = []) ?
+            model_spectra =            (list): (default = []) ?
+            model_resolution           (list): (default = []) ?
+            model_name                  (str): (default = 'Full') ?
+            rv_cor                      (int): (default = 0) ?
+        Returns:
+            - fig1                    (object) : matplotlib figure object
+            - ax1                     (object) : matplotlib axes objects
+            - rv_grid                    (list): Radial velocity grid
+            - ccf                        (list): Cross-correlation function
+            - acf                        (list): Auto-correlation function  
+
         Author: Allan Denis
         '''
         print('ForMoSA - CCF plot')
         
         
-        self._get_posteriors()
-        theta = self.theta_best
-        theta_index = self.theta_index
-        # Recovery of the spectroscopy data
-        spectrum_obs = np.load(self.global_params.result_path + '/spectrum_obs.npz', allow_pickle=True)
-        wav_obs_merge = spectrum_obs['obs_spectro_merge'][0]
-        flx_obs_merge = spectrum_obs['obs_spectro_merge'][1]
-        err_obs_merge = spectrum_obs['obs_spectro_merge'][2]
-        transm_obs_merge = spectrum_obs['obs_opt_merge'][1]
-        star_flx_obs_merge = spectrum_obs['obs_opt_merge'][2]
-
-        # Recovery of the spectroscopy and photometry model
-        path_grid_m = self.global_params.adapt_store_path + '/adapted_grid_merge_' + self.global_params.grid_name + '_nonan.nc'
-        ds = xr.open_dataset(path_grid_m, decode_cf=False, engine='netcdf4')
-        grid_merge = ds['grid']
-        ds.close()
-
-        if self.global_params.par3 == 'NA':
-            if len(grid_merge['wavelength']) != 0:
-                flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1],
-                                                          method="linear", kwargs={"fill_value": "extrapolate"}))
-            else:
-                flx_mod_merge = np.asarray([])
-        elif self.global_params.par4 == 'NA':
-            if len(grid_merge['wavelength']) != 0:
-                flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2],
-                                                          method="linear", kwargs={"fill_value": "extrapolate"}))
-            else:
-                flx_mod_merge = np.asarray([])
-        elif self.global_params.par5 == 'NA':
-            if len(grid_merge['wavelength']) != 0:
-                flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
-                                                          method="linear", kwargs={"fill_value": "extrapolate"}))
-            else:
-                flx_mod_merge = np.asarray([])
-        else:
-            if len(grid_merge['wavelength']) != 0:
-                flx_mod_merge = np.asarray(grid_merge.interp(par1=theta[0], par2=theta[1], par3=theta[2], par4=theta[3],
-                                                          par5=theta[4],
-                                                          method="linear", kwargs={"fill_value": "extrapolate"}))
-            else:
-                flx_mod_merge = np.asarray([])
+        fig1, ax1 = plt.subplots(1,1, figsize=figsize)
         
         rv_grid = np.arange(rv_grid[0], rv_grid[1], rv_step)
-        cp, cs = np.ones(len(rv_grid)), np.ones(len(rv_grid))
-        i = 0
+        ccf = np.array([])
+        acf = np.array([])
         
-        self.global_params.rv = "NA"
-        self.global_params.use_lsqr = "False"
-        modif_spec_chi2 = modif_spec(self.global_params, theta, self.theta_index,
-                                  wav_obs_merge, flx_obs_merge, err_obs_merge, flx_mod_merge,
-                                  [], [], [], [],
-                                  transm_obs_merge, star_flx_obs_merge)
-        
-        flx_mod_merge = modif_spec_chi2[3] / modif_spec_chi2[8]
+        spectra, ck = self._get_spectra(self.theta_best)
         
-        # # Making sure we only call the doppler correction function and the lsq function
-        # # because we want to plot the cross correlation as a function of the radial velocity
-        self.global_params.rv = "uniform"
-        self.global_params.av = "NA"
-        self.global_params.vsini, self.global_params.ld = "NA", "NA"
-        self.global_params.bb_T, self.global_params.bb_R = "NA", "NA"
-        self.global_params.r, self.global_params.T = "NA", "NA"
-        self.global_params.ccf_method = 'continuum_unfiltered'
-        self.global_params.use_lsqr = 'True'
+        model_array = np.array([])
+        wave_array = np.array([])
+        err_array = np.array([])
+        transm_array = np.array([])
+        data_array = np.array([])
         
-        for rv_i in tqdm(rv_grid):
-            theta[theta_index == 'rv'] = rv_i
-            
-            modif_spec_chi2 = modif_spec(self.global_params, theta, self.theta_index,
-                                      np.copy(wav_obs_merge), np.copy(flx_obs_merge), np.copy(err_obs_merge), np.copy(flx_mod_merge),
-                                      [], [], [], [],
-                                      transm_obs_merge, np.copy(star_flx_obs_merge))
+        for indobs, obs in enumerate(sorted(glob.glob(self.global_params.main_observation_path))):
             
-            cp[i] = modif_spec_chi2[9]
-            cs[i] = modif_spec_chi2[10]
+            if self.global_params.use_lsqr[indobs] == 'True':
+                # If we used the lsq function, it means that our data is contaminated by the starlight difraction
+                # so the model is the sum of the planet model + the estimated stellar contribution
+                spectra = list(spectra) # Transform spectra to a list so that we can modify its values
+                spectra[indobs] = list(spectra[indobs])
+                wavelength, err, model, stellar_contribution, star_flx, systematics = spectra[indobs][0], spectra[indobs][2], spectra[indobs][3], spectra[indobs][10], spectra[indobs][11], spectra[indobs][12]
+                transm = spectra[indobs][13]
+     
 
-            i += 1
+            if len(spectra[indobs][0]) != 0:
+
+                if (len(systematics) > 0) and (len(star_flx) > 0):
+                    data = spectra[indobs][1] - np.dot(stellar_contribution, star_flx[0].T) - systematics
+                elif (len(star_flx) > 0):     # if len(systematics) = 0 but len(star_flx) > 0
+                    data = spectra[indobs][1] - np.dot(stellar_contribution, star_flx[0].T)
+                elif (len(systematics) > 0):  # if len(star_flx) = 0 but len(systematics) > 0
+                    data = spectra[indobs][1] - systematics
+                else:                         # if len(star_flx) = 0 and len(systematics) = 0
+                    data = spectra[indobs][1]
+
+                _, _, _, model = doppler_fct(wavelength, data, err, model, -self.theta_best[self.theta_index == 'rv'])
+                    
+                if len(model_spectra) == 0:
+                    model_array = np.append(model_array, model)
+                else:
+                    obs_spectro, _, _, _, _ = adapt_observation_range(self.global_params, indobs=indobs)
+                    res_obs = obs_spectro[0][3]
+                    res_obs_interp = interp1d(obs_spectro[0][0], res_obs, fill_value = 'extrapolate')
+                    res_obs = res_obs_interp(wavelength)
+                    
+                    ind = np.where((model_wavelength >= wavelength[0]) & (model_wavelength <= wavelength[-1]))
+                    model_wavelength_adapt, model_resolution_adapt, model_spectra_adapt = model_wavelength[ind], model_resolution[ind], model_spectra[ind]
+                    model_resolution_interp = interp1d(model_wavelength_adapt, model_resolution_adapt, fill_value = 'extrapolate')
+                    model_resolution_adapt = model_resolution_interp(wavelength)
+                    model_adapted = resolution_decreasing(self.global_params, wavelength, [], res_obs, model_wavelength_adapt, model_spectra_adapt, model_resolution_adapt,
+                                                        'mod', indobs=indobs)
+                    
+                    
+                    if len(transm) > 0:
+                        _, _, model_adapted, _, _, _ = lsq_fct(spectra[indobs][1], err, star_flx, transm, model_adapted, systematics)
+                        
+                    model_adapted = vsini_fct_accurate(wavelength, model_adapted, 0.6, self.theta_best[self.theta_index=='vsini'])
+                    
+                    model_array = np.append(model_array, model_adapted)
+                
+                wave_array = np.append(wave_array, wavelength)
+                data_array = np.append(data_array, data)
+                transm_array = np.append(transm_array, transm)
+                err_array = np.append(err_array, err)
+
+        max_ccf = 0
+        for rv in tqdm(rv_grid):
+            _, _, _, model_doppler = doppler_fct(wave_array, data_array, err_array, model_array, rv+rv_cor)
+            ccf = np.append(ccf, np.nansum(model_doppler * data_array))
+            acf = np.append(acf, np.nansum(model_doppler * model_array))
             
+            if np.nansum(model_doppler * data_array) > max_ccf:
+                max_ccf = np.nansum(model_doppler * data_array)
+                model_max_ccf = model_doppler
             
-        normalization_limit = 100
+        # Rescaling cross-correlation function to estimate a SNR
+        acf_norm = acf - np.median(acf[(np.abs(rv_grid) > window_normalisation)])
+        ccf_norm = ccf - np.median(ccf[(np.abs(rv_grid-rv_grid[np.argmax(ccf)]) > window_normalisation)])
+        ccf_noise = np.std(ccf_norm[(np.abs(rv_grid-rv_grid[np.argmax(ccf)]) > window_normalisation)])
+        ccf_norm = ccf_norm / ccf_noise
         
+        # Rescaling autocorrelation function to make comparable with cross-correlation function
+        acf_norm = acf_norm / np.max(acf_norm) * np.max(ccf_norm)
         
-        fig, ax = plt.subplots(figsize = figsize)
-        fig.tight_layout()
-        
-        cp_norm = cp - np.median(cp[(np.abs(rv_grid-rv_grid[np.argmax(cp)]) > normalization_limit)])
-        cp_noise = np.std(cp_norm[(np.abs(rv_grid-rv_grid[np.argmax(cp)]) > normalization_limit)])
-        ccf_norm = cp_norm / cp_noise
-            
-        ax.plot(rv_grid, ccf_norm)
-        ax.set_xlabel('rv (km/s)')
-        ax.set_ylabel('ccf signal')
-        
-        return 
-            
-            
+        ax1.plot(rv_grid, ccf_norm, label = 'ccf')
+        ax1.plot(rv_grid + rv_grid[np.argmax(ccf_norm)] + rv_cor, acf_norm)
+        ax1.axvline(x = rv_grid[np.argmax(ccf_norm)], linestyle = '--', c='C3')
+        ax1.set_xlabel('RV (km/s)')
+        ax1.set_ylabel('S/N')
+        ax1.legend(['ccf', 'acf'])
+        print(f'SNR = {np.nanmax(ccf_norm):.1f}, RV = {rv_grid[np.argmax(ccf_norm)]:.1f} km/s')
+        #ax1.set_title(f'SNR = {np.nanmax(ccf_norm):.1f}, RV = {rv_grid[np.argmax(ccf_norm)]:.1f} km/s')
+        plt.figure(fig1)
+        plt.savefig(self.global_params.result_path + 'ccf_' + model_name + '.pdf')
+
+        return fig1, ax1, rv_grid, ccf, acf
+    
 
 
-    def plot_PT(self,path_temp_profile, figsize=(6,5), model = 'ExoREM'):
+    
+    def plot_PT(self, path_temp_profile, figsize=(6,5), model = 'ExoREM'):
         '''
-        Plot the Pressure-Temperature profiles 
-        Calculates the most probable temperature profile
-
-        Return: fig, ax
+        Function to plot the Pressure-Temperature profiles.
+        Adpated from Nathan Zimniak.
 
-        Author: Nathan Zimniak and Paulina Palma-Bifani
+        Args:
+            path_temp_profile    (str): Path to the temperature profile grid
+            figsize            (tuple): (default = (6, 5)) Size of the plot
+            model                (str): (default = 'ExoREM') Name of the model grid
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Pressure-Temperature profile')
 
@@ -859,34 +1232,34 @@ def plot_PT(self,path_temp_profile, figsize=(6,5), model = 'ExoREM'):
 
     def plot_Clouds(self, cloud_prop, path_cloud_profile, figsize=(6,5)):
         ''' 
-        Cloud profiles calculations
-
-        Inputs: 
-        - cloud_prop (str) : choose the cloud 
-               'eddy_diffusion_coefficient',
-               'vmr_CH4',
-               'vmr_CO',
-               'vmr_CO2',
-               'vmr_FeH',
-               'vmr_H2O',
-               'vmr_H2S',
-               'vmr_HCN',
-               'vmr_K',
-               'vmr_Na',
-               'vmr_NH3',
-               'vmr_PH3',
-               'vmr_TiO',
-               'vmr_VO',
-               'cloud_opacity_Fe',
-               'cloud_opacity_Mg2SiO4',
-               'cloud_particle_radius_Fe',
-               'cloud_particle_radius_Mg2SiO4',
-               'cloud_vmr_Fe',
-               'cloud_vmr_Mg2SiO4'
-        
-        Return: fig, ax
-
-        Author: Nathan Zimniak and Paulina Palma-Bifani
+        Function to plot cloud profiles.
+        Adapted from Nathan Zimniak
+
+        Args: 
+            cloud_prop (str) : Choose the cloud species. The options are
+                                ['eddy_diffusion_coefficient',
+                                'vmr_CH4',
+                                'vmr_CO',
+                                'vmr_CO2',
+                                'vmr_FeH',
+                                'vmr_H2O',
+                                'vmr_H2S',
+                                'vmr_HCN',
+                                'vmr_K',
+                                'vmr_Na',
+                                'vmr_NH3',
+                                'vmr_PH3',
+                                'vmr_TiO',
+                                'vmr_VO',
+                                'cloud_opacity_Fe',
+                                'cloud_opacity_Mg2SiO4',
+                                'cloud_particle_radius_Fe',
+                                'cloud_particle_radius_Mg2SiO4',
+                                'cloud_vmr_Fe',
+                                'cloud_vmr_Mg2SiO4']
+        Returns:
+            - fig  (object) : matplotlib figure object
+            - ax   (object) : matplotlib axes objects
         '''
         print('ForMoSA - Cloud profile')
         
@@ -937,7 +1310,7 @@ def plot_Clouds(self, cloud_prop, path_cloud_profile, figsize=(6,5)):
             propsup95.append(np.percentile(cloud_prop_profiles[:, i], 98))
 
         # Plot le profil le plus probable et les percentiles associés
-        fig = plt.figure()
+        fig = plt.figure(figsize=figsize)
         ax = plt.axes()
 
         ax.fill_betweenx(P, propinf95, propsup95, color=self.color_out, alpha=0.1, label=r'2 $\sigma$')
@@ -996,5 +1369,6 @@ def plot_Clouds(self, cloud_prop, path_cloud_profile, figsize=(6,5)):
         ax.legend(frameon=False)
 
         return fig, ax
+    
 
 
diff --git a/dist/ForMoSA-1.1.3-py3-none-any.whl b/dist/ForMoSA-1.1.3-py3-none-any.whl
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diff --git a/docs/build/html/_images/ForMoSA.png b/docs/ForMoSA.png
similarity index 100%
rename from docs/build/html/_images/ForMoSA.png
rename to docs/ForMoSA.png
diff --git a/docs/Makefile b/docs/Makefile
index d0c3cbf..d4bb2cb 100644
--- a/docs/Makefile
+++ b/docs/Makefile
@@ -5,8 +5,8 @@
 # from the environment for the first two.
 SPHINXOPTS    ?=
 SPHINXBUILD   ?= sphinx-build
-SOURCEDIR     = source
-BUILDDIR      = build
+SOURCEDIR     = .
+BUILDDIR      = _build
 
 # Put it first so that "make" without argument is like "make help".
 help:
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diff --git a/docs/_build/doctrees/main_utilities.doctree b/docs/_build/doctrees/main_utilities.doctree
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diff --git a/docs/_build/doctrees/nbsphinx/demoabpic.ipynb b/docs/_build/doctrees/nbsphinx/demoabpic.ipynb
new file mode 100644
index 0000000..72a10ab
--- /dev/null
+++ b/docs/_build/doctrees/nbsphinx/demoabpic.ipynb
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB Pic demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ".. toctree::\n",
+    "   :maxdepth: 2\n",
+    "   :titlesonly:\n",
+    "\n",
+    "   demo_basic.rst\n",
+    "\n",
+    "   tutorials/demo_basic.ipynb\n",
+    "\n",
+    "   :doc:`tutorials/demo_basic.ipynb`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/doctrees/nbsphinx/tutorials/atmospheric_grid_info.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/atmospheric_grid_info.ipynb
new file mode 100644
index 0000000..5e240b0
--- /dev/null
+++ b/docs/_build/doctrees/nbsphinx/tutorials/atmospheric_grid_info.ipynb
@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here Exo-REM grid is used as example\n",
+    "grid_path = '/Users/ppalmabifani/Desktop/exoAtm/c0_ForMoSA/INPUTS/atm_grids/atm_grids_native/EXOREM_native.nc'\n",
+    "\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "    \n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb
new file mode 100644
index 0000000..bfffa13
--- /dev/null
+++ b/docs/_build/doctrees/nbsphinx/tutorials/demoabpic.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# demo AB Pic b\n",
+    "\n",
+    "\n",
+    "This tutorial is intended as a quick start. \n",
+    "\n",
+    "\n",
+    "We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in [P. Palma-Bifani et al (2023)](https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf).\n",
+    "\n",
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic packages\n",
+    "import sys, time, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# ForMoSA modules\n",
+    "sys.path.insert(0, os.path.abspath('/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/'))\n",
+    "# For the interpolation & sampling\n",
+    "from ForMoSA.main_utilities import GlobFile\n",
+    "from ForMoSA.adapt.adapt_obs_mod import launch_adapt\n",
+    "from ForMoSA.nested_sampling.nested_sampling import launch_nested_sampling\n",
+    "# For the plots\n",
+    "from ForMoSA.plotting.plotting_class import PlottingForMoSA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0. Setup\n",
+    "\n",
+    "You need to create a config file with extension <.ini> and modify the parameters. Learn more about our config files in it's specific tutorial.\n",
+    "\n",
+    "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = 'data_abpic/'\n",
+    "\n",
+    "# CONFIG_FILE \n",
+    "# reading and defining global parameters\n",
+    "config_file_path = base_path + 'config_ABPicb.ini'\n",
+    "global_params = GlobFile(config_file_path)    \n",
+    "\n",
+    "# Optional: Add \"time_now\" and \"save_name\" to avoid overwriting results\n",
+    "time_now = time.strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_name = 'test'\n",
+    "\n",
+    "# Create directory to save the outputs \n",
+    "global_params.result_path = global_params.result_path+ save_name+'_t' + time_now+'/'\n",
+    "os.makedirs(global_params.result_path)\n",
+    "\n",
+    "# Overwrite some parameters\n",
+    "global_params.config.filename = global_params.result_path + 'config_used.ini'\n",
+    "global_params.config['config_path']['result_path']=global_params.result_path\n",
+    "global_params.config.write()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Interpolate the grid\n",
+    "\n",
+    "Once everything is setup, we start by adapting the models and observations. \n",
+    "\n",
+    "The grid of models is interpolated for this, but you don't need to repeat this step once you've adapted the grid for a specific dataset. \n",
+    "\n",
+    "(Answer 'no' only the first time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Have you already interpolated the grids for this data? \n",
+    "y_n_par = 'yes'\n",
+    "#y_n_par = 'no' # Only answer no the first time, then comment to save time\n",
+    "\n",
+    "launch_adapt(global_params, justobs=y_n_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Lunch Nested Sampling \n",
+    "\n",
+    "Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Likelihood functions check-ups\n",
+      "\n",
+      "ABPicb_SINFONI_K will be computed with chi2_classic\n",
+      "\n",
+      "Done !\n",
+      "\n",
+      "\u001b[Kit=  1330 logz=-1071.92517042 \n",
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Nestle  \n",
+      " \n",
+      "The code spent 234.33751487731934 sec to run.\n",
+      "niter: 1331\n",
+      "ncall: 13109\n",
+      "nsamples: 1431\n",
+      "logz: -1071.101 +/-  0.347\n",
+      "h: 12.010\n",
+      "\n",
+      "\n",
+      " \n",
+      "-> Voilà, on est prêt\n"
+     ]
+    }
+   ],
+   "source": [
+    "launch_nested_sampling(global_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting the outcomes\n",
+    "\n",
+    "ForMoSA has been designed with a plotting class. Bellow we show 4 main features: \n",
+    "\n",
+    "- Plotting corner-plots\n",
+    "- Plotting spectra and residuals\n",
+    "- Plotting chains \n",
+    "- Accessing the different parameters\n",
+    "\n",
+    "All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc...\n",
+    "\n",
+    "We need to start by initializing the plotting class as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to output file created in the first step\n",
+    "config_file_path_pl = 'data_abpic/outputs/test_t20240807_005025'\n",
+    "\n",
+    "# Initialize the plotting class and set the color\n",
+    "plotForMoSA = PlottingForMoSA(config_file_path_pl+'/config_used.ini', 'red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Corner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Corner plot\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 970x970 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plotForMoSA.plot_corner(levels_sig=[0.997, 0.95, 0.68], bins=20, quantiles=(0.16, 0.5, 0.84), burn_in=1100)\n",
+    "#plt.savefig('') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Spectrum and Residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Best fit and residuals plot\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/plotting/plotting_class.py:723: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  axr2.legend(frameon=False,handlelength=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax, axr, axr2 = plotForMoSA.plot_fit(figsize=(10, 5), uncert='no')\n",
+    "\n",
+    "# You can modify the different axes and includ further plotting features\n",
+    "axr.set_ylim(-5,5)\n",
+    "\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Chains of posteriors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Posteriors chains for each parameter\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plotForMoSA.plot_chains(figsize=(10,6))\n",
+    "#axs[i, j] #i=cols, j=0,1\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access information\n",
+    "\n",
+    "You can access different parametes since we are working with a class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "posteriors_chains = plotForMoSA.posterior_to_plot\n",
+    "posteriors_names = plotForMoSA.posteriors_names"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/doctrees/nbsphinx/tutorials/exorem_info.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/exorem_info.ipynb
new file mode 100644
index 0000000..4e848cc
--- /dev/null
+++ b/docs/_build/doctrees/nbsphinx/tutorials/exorem_info.ipynb
@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exo-REM\n",
+    "\n",
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges.\n",
+    "\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Open the grid\n",
+    "\n",
+    "You can check the free parameters names, ranges and units together with the wavelength range and spectral resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_path = '/EXOREM_native.nc'\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a spectrum for specific values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open the grid \n",
+    "grid = ds['grid']\n",
+    "\n",
+    "# get wavelength \n",
+    "wave = grid['wavelength'].values\n",
+    "\n",
+    "# for Exo-REM par1=Teff, par2=logg, par3=[M/H], par4=C/O\n",
+    "# get flux by selecting a value for each parameter\n",
+    "flux = grid.sel(par1=1700,par2=4.0, par3=0., par4=0.55, method=\"nearest\")\n",
+    "\n",
+    "# Plot the spectrum\n",
+    "plt.figure(figsize=(7,3))\n",
+    "plt.plot(wave, flux*wave, 'k')\n",
+    "plt.xlabel('Wavelength (µm)')\n",
+    "plt.ylabel('Flux')\n",
+    "plt.xlim(0.5,5) \n",
+    "plt.yticks([])\n",
+    "plt.title('Exo-REM at Teff=1700K, logg=4, and solar [M/H] and C/O')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/doctrees/nbsphinx/tutorials/inputs.ipynb b/docs/_build/doctrees/nbsphinx/tutorials/inputs.ipynb
new file mode 100644
index 0000000..6162c18
--- /dev/null
+++ b/docs/_build/doctrees/nbsphinx/tutorials/inputs.ipynb
@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INPUTS\n",
+    "\n",
+    "## Observations (.fits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Config file (.ini)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/doctrees/nbsphinx/tutorials_demoabpic_11_1.png b/docs/_build/doctrees/nbsphinx/tutorials_demoabpic_11_1.png
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diff --git a/docs/_build/doctrees/tutorials/inputs.doctree b/docs/_build/doctrees/tutorials/inputs.doctree
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diff --git a/docs/build/html/.buildinfo b/docs/_build/html/.buildinfo
similarity index 82%
rename from docs/build/html/.buildinfo
rename to docs/_build/html/.buildinfo
index 5fdb911..2da4d42 100644
--- a/docs/build/html/.buildinfo
+++ b/docs/_build/html/.buildinfo
@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 806cb0a2c1bc29933a5079b8fcb3af9f
+config: 3d69bd81622fe4392ef94600d12c6fdb
 tags: 645f666f9bcd5a90fca523b33c5a78b7
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similarity index 100%
rename from docs/source/ForMoSA.png
rename to docs/_build/html/_images/ForMoSA.png
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diff --git a/docs/build/html/_modules/ForMoSA/adapt/adapt_grid.html b/docs/_build/html/_modules/ForMoSA/adapt/adapt_grid.html
similarity index 77%
rename from docs/build/html/_modules/ForMoSA/adapt/adapt_grid.html
rename to docs/_build/html/_modules/ForMoSA/adapt/adapt_grid.html
index f3cbafa..984614e 100644
--- a/docs/build/html/_modules/ForMoSA/adapt/adapt_grid.html
+++ b/docs/_build/html/_modules/ForMoSA/adapt/adapt_grid.html
@@ -1,22 +1,24 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="../../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>ForMoSA.adapt.adapt_grid &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
+  <title>ForMoSA.adapt.adapt_grid &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
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+  
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   <!--[if lt IE 9]>
     <script src="../../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
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-        <script src="../../../_static/jquery.js"></script>
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+        <script src="../../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../../_static/documentation_options.js?v=51b770b3"></script>
+        <script src="../../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -33,9 +35,6 @@
           <a href="../../../index.html" class="icon icon-home">
             ForMoSA
           </a>
-              <div class="version">
-                1.0.3
-              </div>
 <div role="search">
   <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
     <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
@@ -44,9 +43,10 @@
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-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
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+<li class="toctree-l1"><a class="reference internal" href="../../../demo.html">Tutorials</a></li>
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 </ul>
 
@@ -79,67 +79,79 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
 <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
 <span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
 <span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;../&#39;</span><span class="p">))</span>
 
 <span class="kn">from</span> <span class="nn">adapt.extraction_functions</span> <span class="kn">import</span> <span class="n">adapt_model</span><span class="p">,</span> <span class="n">decoupe</span>
 
+
 <span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
 
 
-<div class="viewcode-block" id="adapt_grid"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.adapt_grid.adapt_grid">[docs]</a><span class="k">def</span> <span class="nf">adapt_grid</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_obs_spec</span><span class="p">,</span> <span class="n">wav_obs_phot</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Adapt the synthetic spectra of a grid to make them comparable with data.</span>
+<div class="viewcode-block" id="adapt_grid">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.adapt_grid.adapt_grid">[docs]</a>
+<span class="k">def</span> <span class="nf">adapt_grid</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adapt the synthetic spectra of a grid to make them comparable with the data.</span>
 <span class="sd">    </span>
 <span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter</span>
-<span class="sd">        wav_obs_spec: Merged wavelength grid of the data</span>
-<span class="sd">        wav_obs_phot: Wavelengths of the photometry points</span>
+<span class="sd">        global_params    (object): Class containing each parameter</span>
+<span class="sd">        wav_obs_spectro   (array): Merged wavelength grid of the data</span>
+<span class="sd">        wav_obs_photo     (array): Wavelengths of the photometry points</span>
+<span class="sd">        obs_name            (str): Name of the current observation looping</span>
+<span class="sd">        indobs              (int): Index of the current observation looping</span>
 <span class="sd">    Returns:</span>
+<span class="sd">        None</span>
 
-<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet and Paulina Palma-Bifani</span>
 <span class="sd">    &quot;&quot;&quot;</span>
+
     <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">)</span>
     <span class="n">wav_mod_nativ</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
     <span class="n">grid</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
     <span class="n">attr</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">attrs</span>
     <span class="n">grid_np</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">to_numpy</span><span class="p">()</span>
+
+    
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">grid_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spec</span><span class="p">),</span>
+        <span class="n">grid_spectro_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">grid_phot_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">),</span>
+        <span class="n">grid_photo_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
         <span class="n">tot_par</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">grid_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spec</span><span class="p">),</span>
+        <span class="n">grid_spectro_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">grid_phot_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">),</span>
+        <span class="n">grid_photo_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
         <span class="n">tot_par</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="n">grid_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spec</span><span class="p">),</span>
+        <span class="n">grid_spectro_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">grid_phot_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">),</span>
+        <span class="n">grid_photo_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
         <span class="n">tot_par</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span> <span class="o">*</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
-        <span class="n">grid_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spec</span><span class="p">),</span>
+        <span class="n">grid_spectro_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par5&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="n">grid_phot_new_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">),</span>
+        <span class="n">grid_photo_np</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
                                     <span class="nb">len</span><span class="p">(</span><span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span>
@@ -162,10 +174,10 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                                     <span class="n">model_to_adapt</span> <span class="o">=</span> <span class="n">grid_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">,</span> <span class="n">p5_i</span><span class="p">]</span>
                                     <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">model_to_adapt</span><span class="p">)</span>
                                     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">nan_mod_ind</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                        <span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_pho</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span>
-                                                                               <span class="n">model_to_adapt</span><span class="p">,</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">])</span>
-                                        <span class="n">grid_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">,</span> <span class="n">p5_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">flx_mod_extract</span>
-                                        <span class="n">grid_phot_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">,</span> <span class="n">p5_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_pho</span>
+                                        <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span>
+                                                                                 <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                                        <span class="n">grid_spectro_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">,</span> <span class="n">p5_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_spectro</span>
+                                        <span class="n">grid_photo_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">,</span> <span class="n">p5_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_photo</span>
                                     <span class="k">else</span><span class="p">:</span>
 
                                         <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The extraction of the model : &#39;</span><span class="o">+</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span><span class="o">+</span><span class="s1">&#39;=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span> <span class="o">+</span>
@@ -193,9 +205,11 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                                 <span class="n">model_to_adapt</span> <span class="o">=</span> <span class="n">grid_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">]</span>
                                 <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">model_to_adapt</span><span class="p">)</span>
                                 <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">nan_mod_ind</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                                    <span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_pho</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">])</span>
-                                    <span class="n">grid_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">flx_mod_extract</span>
-                                    <span class="n">grid_phot_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_pho</span>
+                                    <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span>
+                                                                             <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                                    
+                                    <span class="n">grid_spectro_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_spectro</span>
+                                    <span class="n">grid_photo_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">,</span> <span class="n">p4_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_photo</span>
                                 <span class="k">else</span><span class="p">:</span>
 
                                     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The extraction of the model : &#39;</span> <span class="o">+</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span> <span class="o">+</span>
@@ -223,10 +237,11 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                         <span class="n">model_to_adapt</span> <span class="o">=</span> <span class="n">grid_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">]</span>
                         <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">model_to_adapt</span><span class="p">)</span>
                         <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">nan_mod_ind</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                            <span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_pho</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span>
-                                                                   <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">])</span>
-                            <span class="n">grid_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">flx_mod_extract</span>
-                            <span class="n">grid_phot_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_pho</span>
+                            <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span>
+                                                                   <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+
+                            <span class="n">grid_spectro_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_spectro</span>
+                            <span class="n">grid_photo_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">,</span> <span class="n">p3_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_photo</span>
                         <span class="k">else</span><span class="p">:</span>
 
                             <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The extraction of the model : &#39;</span> <span class="o">+</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span> <span class="o">+</span>
@@ -252,9 +267,10 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                 <span class="n">model_to_adapt</span> <span class="o">=</span> <span class="n">grid_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">]</span>
                 <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">model_to_adapt</span><span class="p">)</span>
                 <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">nan_mod_ind</span> <span class="ow">is</span> <span class="kc">False</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                    <span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_pho</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">])</span>
-                    <span class="n">grid_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">flx_mod_extract</span>
-                    <span class="n">grid_phot_new_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_pho</span>
+                    <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">model_to_adapt</span><span class="p">,</span>
+                                                             <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                    <span class="n">grid_spectro_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_spectro</span>
+                    <span class="n">grid_photo_np</span><span class="p">[:,</span> <span class="n">p1_i</span><span class="p">,</span> <span class="n">p2_i</span><span class="p">]</span> <span class="o">=</span> <span class="n">mod_photo</span>
                 <span class="k">else</span><span class="p">:</span>
 
                     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The extraction of the model : &#39;</span> <span class="o">+</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;=&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">p1</span><span class="p">)</span> <span class="o">+</span>
@@ -275,40 +291,40 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                 <span class="n">i_tot</span> <span class="o">+=</span> <span class="mi">1</span>
 
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">2</span><span class="p">:</span>
-        <span class="n">ds_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">],</span> <span class="n">grid_new_np</span><span class="p">)),</span>
-                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spec</span><span class="p">,</span>
+        <span class="n">ds_spectro_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">],</span> <span class="n">grid_spectro_np</span><span class="p">)),</span>
+                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spectro</span><span class="p">,</span>
                                     <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                             <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
-        <span class="n">ds_new_phot</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">],</span> <span class="n">grid_phot_new_np</span><span class="p">)),</span>
-                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_phot</span><span class="p">,</span>
+        <span class="n">ds_photo_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">],</span> <span class="n">grid_photo_np</span><span class="p">)),</span>
+                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_photo</span><span class="p">,</span>
                                          <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                                  <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">3</span><span class="p">:</span>
-        <span class="n">ds_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">],</span> <span class="n">grid_new_np</span><span class="p">)),</span>
-                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spec</span><span class="p">,</span>
+        <span class="n">ds_spectro_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">],</span> <span class="n">grid_spectro_np</span><span class="p">)),</span>
+                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spectro</span><span class="p">,</span>
                                     <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                             <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
-        <span class="n">ds_new_phot</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">],</span> <span class="n">grid_phot_new_np</span><span class="p">)),</span>
-                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_phot</span><span class="p">,</span>
+        <span class="n">ds_photo_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">],</span> <span class="n">grid_photo_np</span><span class="p">)),</span>
+                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_photo</span><span class="p">,</span>
                                          <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                                  <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">4</span><span class="p">:</span>
-        <span class="n">ds_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">],</span> <span class="n">grid_new_np</span><span class="p">)),</span>
-                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spec</span><span class="p">,</span>
+        <span class="n">ds_spectro_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">],</span> <span class="n">grid_spectro_np</span><span class="p">)),</span>
+                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spectro</span><span class="p">,</span>
                                     <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par4&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                             <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
-        <span class="n">ds_new_phot</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">],</span> 
-                                                      <span class="n">grid_phot_new_np</span><span class="p">)),</span>
-                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_phot</span><span class="p">,</span>
+        <span class="n">ds_photo_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">],</span> 
+                                                      <span class="n">grid_photo_np</span><span class="p">)),</span>
+                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_photo</span><span class="p">,</span>
                                          <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
@@ -316,48 +332,47 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
                                  <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
 
     <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;par&#39;</span><span class="p">])</span> <span class="o">==</span> <span class="mi">5</span><span class="p">:</span>
-        <span class="n">ds_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">,</span> <span class="s2">&quot;par5&quot;</span><span class="p">],</span> <span class="n">grid_new_np</span><span class="p">)),</span>
-                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spec</span><span class="p">,</span>
+        <span class="n">ds_spectro_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">,</span> <span class="s2">&quot;par5&quot;</span><span class="p">],</span> <span class="n">grid_spectro_np</span><span class="p">)),</span>
+                            <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_spectro</span><span class="p">,</span>
                                     <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par4&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                     <span class="s2">&quot;par5&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par5&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                             <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
-        <span class="n">ds_new_phot</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">,</span> <span class="s2">&quot;par5&quot;</span><span class="p">],</span> 
-                                                      <span class="n">grid_phot_new_np</span><span class="p">)),</span>
-                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_phot</span><span class="p">,</span>
+        <span class="n">ds_photo_new</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">Dataset</span><span class="p">(</span><span class="n">data_vars</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">grid</span><span class="o">=</span><span class="p">([</span><span class="s2">&quot;wavelength&quot;</span><span class="p">,</span> <span class="s2">&quot;par1&quot;</span><span class="p">,</span> <span class="s2">&quot;par2&quot;</span><span class="p">,</span> <span class="s2">&quot;par3&quot;</span><span class="p">,</span> <span class="s2">&quot;par4&quot;</span><span class="p">,</span> <span class="s2">&quot;par5&quot;</span><span class="p">],</span> 
+                                                      <span class="n">grid_photo_np</span><span class="p">)),</span>
+                                 <span class="n">coords</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;wavelength&quot;</span><span class="p">:</span> <span class="n">wav_obs_photo</span><span class="p">,</span>
                                          <span class="s2">&quot;par1&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par1&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par2&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par2&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par3&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par3&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par4&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par4&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">,</span>
                                          <span class="s2">&quot;par5&quot;</span><span class="p">:</span> <span class="n">grid</span><span class="p">[</span><span class="s2">&quot;par5&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">},</span>
                                  <span class="n">attrs</span><span class="o">=</span><span class="n">attr</span><span class="p">)</span>
-
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">()</span>
     <span class="nb">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; The possible holes in the grid are interpolated: &#39;</span><span class="p">)</span>
     <span class="nb">print</span><span class="p">()</span>
     <span class="k">for</span> <span class="n">key_ind</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]):</span>
         <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key_ind</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">attr</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">])))</span>
-        <span class="n">ds_new</span> <span class="o">=</span> <span class="n">ds_new</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">ds_spectro_new</span> <span class="o">=</span> <span class="n">ds_spectro_new</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                        <span class="n">max_gap</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
-        <span class="n">ds_new_phot</span> <span class="o">=</span> <span class="n">ds_new_phot</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+        <span class="n">ds_photo_new</span> <span class="o">=</span> <span class="n">ds_photo_new</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
                                                  <span class="n">max_gap</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+ 
+    <span class="n">ds_spectro_new</span><span class="o">.</span><span class="n">to_netcdf</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_spectro_</span><span class="si">{</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">),</span>
+                    <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;NETCDF4&#39;</span><span class="p">,</span>
+                    <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">,</span>
+                    <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
+    <span class="n">ds_photo_new</span><span class="o">.</span><span class="n">to_netcdf</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_photo_</span><span class="si">{</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">),</span>
+                        <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;NETCDF4&#39;</span><span class="p">,</span>
+                        <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">,</span>
+                        <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>       
 
-    <span class="n">ds_new</span><span class="o">.</span><span class="n">to_netcdf</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;/adapted_grid_merge_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span><span class="p">,</span>
-                     <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;NETCDF4&#39;</span><span class="p">,</span>
-                     <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">,</span>
-                     <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-    <span class="n">ds_new_phot</span><span class="o">.</span><span class="n">to_netcdf</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;/adapted_grid_phot_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span><span class="p">,</span>
-                          <span class="nb">format</span><span class="o">=</span><span class="s1">&#39;NETCDF4&#39;</span><span class="p">,</span>
-                          <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">,</span>
-                          <span class="n">mode</span><span class="o">=</span><span class="s1">&#39;w&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; The possible holes have been interpolated.&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The possible holes have been interpolated!&#39;</span><span class="p">)</span>
 
     <span class="k">return</span> <span class="kc">None</span></div>
+
 </pre></div>
 
            </div>
@@ -367,7 +382,7 @@ <h1>Source code for ForMoSA.adapt.adapt_grid</h1><div class="highlight"><pre>
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html b/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html
new file mode 100644
index 0000000..56a8fd6
--- /dev/null
+++ b/docs/_build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html
@@ -0,0 +1,287 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for ForMoSA.adapt.adapt_obs_mod</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span><span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;../&#39;</span><span class="p">))</span>
+
+<span class="kn">from</span> <span class="nn">adapt.extraction_functions</span> <span class="kn">import</span> <span class="n">extract_observation</span>
+<span class="kn">from</span> <span class="nn">adapt.adapt_grid</span> <span class="kn">import</span> <span class="n">adapt_grid</span>
+<span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">diag_mat</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="launch_adapt">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.adapt_obs_mod.launch_adapt">[docs]</a>
+<span class="k">def</span> <span class="nf">launch_adapt</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">justobs</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Adapt the synthetic spectra of a grid to make them comparable with the data.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        global_params  (object): Class containing each parameter</span>
+<span class="sd">        justobs    (&#39;yes&#39;/&#39;no&#39;): &#39;no&#39; by default to also adapt the grid</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        None</span>
+
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet, Paulina Palma-Bifani and Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Get back the grid information from the config file</span>
+    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">)</span>
+    <span class="n">wav_mod_nativ</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+    <span class="n">attr</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">attrs</span>
+    <span class="n">res_mod_nativ</span> <span class="o">=</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">]</span>
+    <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+    <span class="c1"># Extract the data from the observation files</span>
+    <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+
+
+    <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+        
+        <span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+        <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="c1"># Estimate and subtract the continuum (if needed) + check-ups</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="n">obs_name</span> <span class="o">+</span> <span class="s1">&#39; will have a R=&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; continuum removed using a &#39;</span> 
+                <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_continuum</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; wavelength range&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">obs_photo</span><span class="p">,</span> <span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">obs_opt</span> <span class="o">=</span> <span class="n">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="s1">&#39;yes&#39;</span><span class="p">,</span> 
+                                                                                                  <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">obs_photo</span><span class="p">,</span> <span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">obs_opt</span> <span class="o">=</span> <span class="n">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span>
+                                                                                                   <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+
+
+        <span class="c1"># Merging of each sub-spectrum and interpolating the grid</span>
+        <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_spectro</span><span class="p">):</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># Interpolate the resolution onto the wavelength of the data</span>
+                <span class="n">ind_mod_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_mod_nativ</span> <span class="o">&lt;=</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_mod_nativ</span> <span class="o">&gt;</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+                <span class="n">wav_mod_cut</span> <span class="o">=</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
+                <span class="n">res_mod_cut</span> <span class="o">=</span> <span class="n">res_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
+                <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_mod_cut</span><span class="p">,</span> <span class="n">res_mod_cut</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+                <span class="n">res_mod_cut</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+    
+                <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">wav_obs_extract</span> <span class="o">=</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">flx_obs_extract</span> <span class="o">=</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">err_obs_extract</span> <span class="o">=</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+                    <span class="n">res_obs_extract</span> <span class="o">=</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
+                    <span class="n">cov_obs_extract</span> <span class="o">=</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">transm_obs_extract</span> <span class="o">=</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                    <span class="n">star_flx_obs_extract</span> <span class="o">=</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
+                    <span class="n">system_obs_extract</span> <span class="o">=</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
+                    <span class="c1"># Save the interpolated resolution of the grid</span>
+                    <span class="n">res_mod_obs_merge</span> <span class="o">=</span> <span class="p">[</span><span class="n">res_mod_cut</span><span class="p">]</span>
+    
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">wav_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_extract</span><span class="p">,</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+                    <span class="n">flx_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_extract</span><span class="p">,</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
+                    <span class="n">err_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_extract</span><span class="p">,</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+                    <span class="n">res_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">res_obs_extract</span><span class="p">,</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">]))</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cov_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">cov_obs_extract</span> <span class="o">=</span> <span class="n">diag_mat</span><span class="p">([</span><span class="n">cov_obs_extract</span><span class="p">,</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">transm_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">transm_obs_extract</span><span class="p">,</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">star_flx_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">star_flx_obs_extract</span><span class="p">,</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">system_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">system_obs_extract</span><span class="p">,</span> <span class="n">obs_opt</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                    <span class="c1"># Save the interpolated resolution of the grid</span>
+                    <span class="n">res_mod_obs_merge</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">res_mod_cut</span><span class="p">)</span>
+    
+    
+                <span class="c1"># Compute the inverse of the merged covariance matrix (note: inv(C1, C2) = (in(C1), in(C2)) if C1 and C2 are block matrix on the diagonal)</span>
+                <span class="c1"># if necessary</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cov_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">inv_cov_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">cov_obs_extract</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">inv_cov_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+                <span class="c1"># Check-ups and warnings for negative values in the diagonal of the covariance matrix</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cov_obs_extract</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">any</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">cov_obs_extract</span><span class="p">)</span> <span class="o">&lt;</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="nb">print</span><span class="p">()</span>
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;WARNING: Negative value(s) is(are) present on the diagonal of the covariance matrix.&quot;</span><span class="p">)</span> 
+                    <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;Operation aborted.&quot;</span><span class="p">)</span>
+                    <span class="nb">print</span><span class="p">()</span>
+                    <span class="n">exit</span><span class="p">()</span>
+                    
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">wav_obs_extract</span><span class="p">,</span> <span class="n">flx_obs_extract</span><span class="p">,</span> <span class="n">err_obs_extract</span><span class="p">,</span> <span class="n">res_obs_extract</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+                <span class="n">inv_cov_obs_extract</span><span class="p">,</span> <span class="n">transm_obs_extract</span><span class="p">,</span> <span class="n">star_flx_obs_extract</span><span class="p">,</span> <span class="n">system_obs_extract</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+                <span class="n">res_mod_obs_merge</span> <span class="o">=</span> <span class="n">res_mod_nativ</span>
+
+        <span class="c1"># Compile everything and changing data type to object to allow for different array sizes</span>
+        <span class="n">obs_spectro_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">wav_obs_extract</span><span class="p">,</span> <span class="n">flx_obs_extract</span><span class="p">,</span> <span class="n">err_obs_extract</span><span class="p">,</span> <span class="n">res_obs_extract</span><span class="p">])</span>
+        <span class="n">obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">obs_spectro</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_spectro_ins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">obs_photo</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_photo_ins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_opt_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">inv_cov_obs_extract</span><span class="p">,</span> <span class="n">transm_obs_extract</span><span class="p">,</span> <span class="n">star_flx_obs_extract</span><span class="p">,</span> <span class="n">system_obs_extract</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+
+    
+            
+
+        <span class="c1"># Save the new data spectrum</span>
+        <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span>
+                    <span class="n">obs_spectro_merge</span><span class="o">=</span><span class="n">obs_spectro_merge</span><span class="p">,</span>
+                    <span class="n">obs_spectro</span><span class="o">=</span><span class="n">obs_spectro</span><span class="p">,</span>
+                    <span class="n">obs_spectro_ins</span><span class="o">=</span><span class="n">obs_spectro_ins</span><span class="p">,</span>
+                    <span class="n">obs_photo</span><span class="o">=</span><span class="n">obs_photo</span><span class="p">,</span>
+                    <span class="n">obs_photo_ins</span><span class="o">=</span><span class="n">obs_photo_ins</span><span class="p">,</span>
+                    <span class="n">obs_opt_merge</span><span class="o">=</span><span class="n">obs_opt_merge</span><span class="p">)</span> <span class="c1"># Optional arrays kept separatly</span>
+        
+        <span class="c1"># Adaptation of the model grid</span>
+        <span class="k">if</span> <span class="n">justobs</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
+            <span class="c1"># Creation of the repertory to store the adapted grid (if needed)</span>
+            <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">):</span>
+                <span class="k">pass</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">)</span>
+
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&gt; To compare synthetic spectra with the observation we need to manage them. The following actions are performed:&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- extraction -&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- resizing on the observation&#39;s wavelength range -&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- adjustement of the spectral resolution -&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- substraction of the continuum (if needed) -&quot;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">&quot;-&gt; Sarting the adaptation of </span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s2">&quot;</span><span class="p">)</span>
+
+            <span class="n">adapt_grid</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">obs_spectro_merge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">obs_photo</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span></div>
+
+        
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
+
+    <span class="c1"># USER configuration path</span>
+    <span class="nb">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Configuration of environment&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Where is your configuration file?&#39;</span><span class="p">)</span>
+    <span class="n">config_file_path</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">()</span>
+
+    <span class="c1"># CONFIG_FILE reading and defining global parameters</span>
+    <span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  <span class="c1"># To access any param.: global_params.parameter_name</span>
+
+    <span class="n">launch_adapt</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
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+             
+  <h1>Source code for ForMoSA.adapt.extraction_functions</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">from</span> <span class="nn">astropy.io</span> <span class="kn">import</span> <span class="n">fits</span>
+<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+<span class="kn">from</span> <span class="nn">spectres</span> <span class="kn">import</span> <span class="n">spectres</span>
+<span class="kn">import</span> <span class="nn">os</span>
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="decoupe">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.decoupe">[docs]</a>
+<span class="k">def</span> <span class="nf">decoupe</span><span class="p">(</span><span class="n">second</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Re-arranged a number of seconds in the hours-minutes-seconds format.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        second (float): number of second</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - float     : hours</span>
+<span class="sd">        - float     : minutes</span>
+<span class="sd">        - float     : seconds</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">hour</span> <span class="o">=</span> <span class="n">second</span> <span class="o">/</span> <span class="mi">3600</span>
+    <span class="n">second</span> <span class="o">%=</span> <span class="mi">3600</span>
+    <span class="n">minute</span> <span class="o">=</span> <span class="n">second</span> <span class="o">/</span> <span class="mi">60</span>
+    <span class="n">second</span> <span class="o">%=</span> <span class="mi">60</span>
+
+    <span class="k">return</span> <span class="n">hour</span><span class="p">,</span> <span class="n">minute</span><span class="p">,</span> <span class="n">second</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="find_nearest">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.find_nearest">[docs]</a>
+<span class="k">def</span> <span class="nf">find_nearest</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Return the indice of the closest values from a desire value in an array.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        array (array): Array to explore</span>
+<span class="sd">        value (float): Desire value</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - idx (int)          : Indice of the closest values from the desire value</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">array</span> <span class="o">-</span> <span class="n">value</span><span class="p">))</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">idx</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="extract_observation">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.extract_observation">[docs]</a>
+<span class="k">def</span> <span class="nf">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="n">cont</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Take back the extracted data spectrum from the function &#39;adapt_observation_range&#39; and decrease its spectral</span>
+<span class="sd">    resolution.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params     (object): Class containing each parameter</span>
+<span class="sd">        wav_mod_nativ      (array): Wavelength grid of the model</span>
+<span class="sd">        res_mod_nativ (array(int)): Spectral resolution of the model</span>
+<span class="sd">        cont                 (str): Boolean string. If the function is used to estimate the continuum cont=&#39;yes&#39;</span>
+<span class="sd">        obs_name             (str): Name of the current observation looping</span>
+<span class="sd">        indobs               (int): Index of the current observation looping</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot; with decreased resolution [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</span>
+<span class="sd">        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0]</span>
+<span class="sd">        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]</span>
+<span class="sd">        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]</span>
+<span class="sd">        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter &quot;wav_for_adapt&quot;. [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]]  </span>
+
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Extract the wavelengths, flux, errors, spectral resolution, and instrument/filter names from the observation file.</span>
+
+    <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">obs_photo</span><span class="p">,</span> <span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">obs_opt</span> <span class="o">=</span> <span class="n">adapt_observation_range</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+
+    <span class="c1"># Reduce the spectral resolution for each sub-spectrum.</span>
+    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_spectro</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># Interpolate the resolution of the model onto the wavelength of the data to properly decrease the resolution if necessary</span>
+            <span class="n">ind_mod_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_mod_nativ</span> <span class="o">&lt;=</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_mod_nativ</span> <span class="o">&gt;</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+            <span class="n">wav_mod_obs</span> <span class="o">=</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
+            <span class="n">res_mod_obs</span> <span class="o">=</span> <span class="n">res_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
+            <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_mod_obs</span><span class="p">,</span> <span class="n">res_mod_obs</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+            <span class="n">res_mod_obs</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="c1"># If we want to decrease the resolution of the data: (if by_sample, the data don&#39;t need to be adapted)</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_method</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;by_reso&#39;</span><span class="p">:</span>
+                <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="p">[],</span> <span class="n">res_mod_obs</span><span class="p">,</span>
+                                                    <span class="s1">&#39;obs&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">cont</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+            <span class="c1"># If we want to estimate and substract the continuum of the data:</span>
+                <span class="n">obs_spectro</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-=</span> <span class="n">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                
+    <span class="k">return</span> <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">obs_photo</span><span class="p">,</span> <span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">obs_opt</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="adapt_observation_range">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.adapt_observation_range">[docs]</a>
+<span class="k">def</span> <span class="nf">adapt_observation_range</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extract the information from the observation file, including the wavelengths (um - vacuum), flux (W.m-2.um.1), errors (W.m-2.um.1), covariance (W.m-2.um.1)**2, spectral resolution, </span>
+<span class="sd">    instrument/filter name, transmission (Atmo+inst) and star flux (W.m-2.um.1). The wavelength range is define by the parameter &quot;wav_for_adapt&quot;.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params  (object): Class containing each parameter</span>
+<span class="sd">        obs_name          (str): Name of the current observation looping</span>
+<span class="sd">        indobs            (int): Index of the current observation looping</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        - obs_spectro (n-array)   : List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot; with decreased resolution  [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</span>
+<span class="sd">        - obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here).  [wav_phot, flx_phot, err_phot, 0]</span>
+<span class="sd">        - obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], ..., [instru_range_n]]</span>
+<span class="sd">        - obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, ..., filter_phot_n]</span>
+<span class="sd">        - obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter &quot;wav_for_adapt&quot;. [[cov_1, tran_1, star_1], ..., [cov_n, tran_n, star_n]] </span>
+
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet and Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Extraction</span>
+    <span class="k">with</span> <span class="n">fits</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">hdul</span><span class="p">:</span>
+
+        <span class="c1"># Check the format of the file and extract data accordingly</span>
+        <span class="n">wav</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;WAV&#39;</span><span class="p">]</span>
+        <span class="n">flx</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;FLX&#39;</span><span class="p">]</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;RES&#39;</span><span class="p">]</span>
+        <span class="n">ins</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;INS&#39;</span><span class="p">]</span>
+        <span class="k">try</span><span class="p">:</span> <span class="c1"># Check for spectral covariances</span>
+            <span class="n">err</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;ERR&#39;</span><span class="p">]</span>
+            <span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span> <span class="c1"># Create an empty covariance matrix if not already present in the data (to not slow the inversion)</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">cov</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;COV&#39;</span><span class="p">]</span>
+            <span class="n">err</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">diag</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">cov</span><span class="p">)))</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">transm</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;TRANSM&#39;</span><span class="p">]</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">transm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">star_flx</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;STAR_FLX1&#39;</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+            <span class="n">is_star</span> <span class="o">=</span> <span class="kc">True</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">star_flx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>   
+            <span class="n">is_star</span> <span class="o">=</span> <span class="kc">False</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">star_flx</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;STAR FLX&#39;</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="k">pass</span>
+        <span class="k">try</span><span class="p">:</span>
+            <span class="n">is_system</span> <span class="o">=</span> <span class="kc">True</span>
+            <span class="n">system</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;SYSTEMATICS1&#39;</span><span class="p">][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
+        <span class="k">except</span><span class="p">:</span>
+            <span class="n">is_system</span> <span class="o">=</span> <span class="kc">False</span>
+            <span class="n">system</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            
+        <span class="k">if</span> <span class="n">is_system</span><span class="p">:</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="mi">2</span>
+            <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>  <span class="c1"># In case there is multiple systematics</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">system</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">system</span><span class="p">,</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;SYSTEMATICS&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">break</span>
+                
+        <span class="k">if</span> <span class="n">is_star</span><span class="p">:</span>
+            <span class="n">i</span> <span class="o">=</span> <span class="mi">2</span>
+            <span class="k">while</span> <span class="kc">True</span><span class="p">:</span>
+                <span class="k">try</span><span class="p">:</span>
+                    <span class="n">star_flx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">star_flx</span><span class="p">,</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;STAR_FLX&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">i</span><span class="p">)][:,</span><span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]),</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+                    <span class="n">i</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="k">except</span><span class="p">:</span>
+                    <span class="k">break</span>
+        <span class="c1"># Only take the covariance if you use the chi2_covariance likelihood function (will need to be change when new likelihood functions using the</span>
+        <span class="c1"># covariance matrix will come)</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;chi2_covariance&#39;</span><span class="p">:</span>
+            <span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+        <span class="c1"># Filter the NaN and inf values</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">flx</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">transm</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">err</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">flx</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">transm</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">err</span><span class="p">))</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">flx</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">err</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">flx</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">err</span><span class="p">))</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+                <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">nan_mod_ind</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">star_flx</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">star_flx</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">system</span><span class="p">[</span><span class="mi">0</span><span class="p">])):</span>
+                <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="p">(</span><span class="n">nan_mod_ind</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">system</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">isfinite</span><span class="p">(</span><span class="n">system</span><span class="o">.</span><span class="n">T</span><span class="p">[</span><span class="n">i</span><span class="p">]))</span>
+                
+        <span class="n">wav</span> <span class="o">=</span> <span class="n">wav</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="n">flx</span> <span class="o">=</span> <span class="n">flx</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="n">res</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="n">ins</span> <span class="o">=</span> <span class="n">ins</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="n">err</span> <span class="o">=</span> <span class="n">err</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">cov</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">])[</span><span class="n">nan_mod_ind</span><span class="p">])</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">transm</span> <span class="o">=</span> <span class="n">transm</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">star_flx</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">star_flx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="o">~</span><span class="n">nan_mod_ind</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">system</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">system</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="o">~</span><span class="n">nan_mod_ind</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            
+        <span class="c1"># Select the wavelength range(s) for the extraction</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_adapt</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
+            <span class="n">wav_for_adapt_tab</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">wav</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;,&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">wav</span><span class="p">))]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wav_for_adapt_tab</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_adapt</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
+
+
+        <span class="c1"># Photometry part of the data (OUT OF THE WINDOW LOOP)</span>
+        <span class="n">ind_photometry</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">res</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">)</span>
+        <span class="n">obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([</span><span class="n">wav</span><span class="p">[</span><span class="n">ind_photometry</span><span class="p">],</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind_photometry</span><span class="p">],</span> <span class="n">err</span><span class="p">[</span><span class="n">ind_photometry</span><span class="p">],</span>
+                <span class="n">res</span><span class="p">[</span><span class="n">ind_photometry</span><span class="p">]])</span>
+        <span class="n">obs_photo_ins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">ins</span><span class="p">[</span><span class="n">ind_photometry</span><span class="p">])</span>
+
+        <span class="c1"># Initiate spectroscopy data numpy arrays</span>
+        <span class="n">obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_for_adapt_tab</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_opt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_for_adapt_tab</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+        <span class="n">obs_spectro_ins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wav_for_adapt_tab</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">object</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">range_ind</span><span class="p">,</span> <span class="n">rangee</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">wav_for_adapt_tab</span><span class="p">):</span>
+            <span class="n">rangee</span> <span class="o">=</span> <span class="n">rangee</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+            <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="nb">float</span><span class="p">(</span><span class="n">rangee</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">wav</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav</span> <span class="o">&lt;=</span> <span class="nb">float</span><span class="p">(</span><span class="n">rangee</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
+            <span class="n">ind_photometry</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">)</span>
+
+            <span class="c1"># Spectroscopy part of the data</span>
+            <span class="n">wav_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">wav</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="n">flx_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">flx</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="n">err_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">err</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="n">res_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="n">ins_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">ins</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cov</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Check if the covariance exists</span>
+                <span class="n">cov_spectro</span> <span class="o">=</span> <span class="n">cov</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
+                <span class="n">cov_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">cov_spectro</span><span class="p">,</span> <span class="n">ind_photometry</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">cov_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">cov_spectro</span><span class="p">,</span> <span class="n">ind_photometry</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">cov_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">transm_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">transm</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">transm_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">star_flx_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">star_flx</span><span class="p">[</span><span class="n">ind</span><span class="p">,:],</span> <span class="n">ind_photometry</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">star_flx_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">system_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">system</span><span class="p">[</span><span class="n">ind</span><span class="p">,:],</span> <span class="n">ind_photometry</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">system_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+            <span class="c1"># Merge spectroscopic data</span>
+            <span class="n">obs_spectro</span><span class="p">[</span><span class="n">range_ind</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">wav_spectro</span><span class="p">,</span> <span class="n">flx_spectro</span><span class="p">,</span> <span class="n">err_spectro</span><span class="p">,</span> <span class="n">res_spectro</span><span class="p">]</span>
+            <span class="n">obs_opt</span><span class="p">[</span><span class="n">range_ind</span><span class="p">]</span> <span class="o">=</span> <span class="p">[</span><span class="n">cov_spectro</span><span class="p">,</span> <span class="n">transm_spectro</span><span class="p">,</span> <span class="n">star_flx_spectro</span><span class="p">,</span> <span class="n">system_spectro</span><span class="p">]</span>
+            <span class="n">obs_spectro_ins</span><span class="p">[</span><span class="n">range_ind</span><span class="p">]</span> <span class="o">=</span> <span class="n">ins_spectro</span>
+            
+        <span class="k">return</span> <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">obs_photo</span><span class="p">,</span> <span class="n">obs_spectro_ins</span><span class="p">,</span> <span class="n">obs_photo_ins</span><span class="p">,</span> <span class="n">obs_opt</span>   </div>
+
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="adapt_model">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.adapt_model">[docs]</a>
+<span class="k">def</span> <span class="nf">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are</span>
+<span class="sd">    calculated too. Then each sub-spectrum are merged.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params  (object): Class containing each parameter used in ForMoSA</span>
+<span class="sd">        wav_mod_nativ   (array): Wavelength grid of the model</span>
+<span class="sd">        wave_reso_tab   (array): Wavelength grid of the model at specified resolution</span>
+<span class="sd">        flx_mod_nativ   (array): Flux of the model</span>
+<span class="sd">        res_mod_nativ   (array): Spectral resolution of the model as a function of the wavelength grid</span>
+<span class="sd">        obs_name          (str): Name of the current observation looping</span>
+<span class="sd">        indobs            (int): Index of the current observation looping</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - mod_spectro (array)     : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</span>
+<span class="sd">        - mod_photo (array)       : List containing the photometry (&#39;0&#39; replace the spectral resolution here).</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Estimate and subtract the continuum (if needed)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">cont</span><span class="o">=</span><span class="s1">&#39;yes&#39;</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="n">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="n">obs_name</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="extract_model">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.extract_model">[docs]</a>
+<span class="k">def</span> <span class="nf">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">,</span> <span class="n">cont</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">obs_name</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are</span>
+<span class="sd">    calculated too.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params (object): Class containing each parameter used in ForMoSA</span>
+<span class="sd">        wav_mod_nativ  (array): Wavelength grid of the model</span>
+<span class="sd">        flx_mod_nativ  (array): Flux of the model</span>
+<span class="sd">        res_mod_nativ  (array): Spectral resolution of the model as a function of the wavelength grid</span>
+<span class="sd">        cont             (str): Boolean string. If the function is used to estimate the continuum cont=&#39;yes&#39;</span>
+<span class="sd">        obs_name         (str): Name of the current observation looping</span>
+<span class="sd">        indobs           (int): Index of the current observation looping</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - mod_spectro (array) : List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</span>
+<span class="sd">        - mod (array)         : List containing the photometry (&#39;0&#39; replace the spectral resolution here).</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Take back the extracted data.</span>
+    <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+    <span class="n">obs_spectro</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro&#39;</span><span class="p">]</span>
+    <span class="n">obs_photo_ins</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo_ins&#39;</span><span class="p">]</span>
+    <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[]</span>
+    <span class="c1"># Reduce the spectral resolution for each sub-spectrum.</span>
+    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_spectro</span><span class="p">):</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># If we want to decrease the resolution of the data:</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_method</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;by_reso&#39;</span><span class="p">:</span>
+                <span class="n">mod_cut_flx</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="p">[],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">[</span><span class="n">c</span><span class="p">],</span>
+                                                    <span class="s1">&#39;mod&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mod_cut_flx</span> <span class="o">=</span> <span class="n">spectres</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">)</span>
+
+            <span class="c1"># If we want to estimate the continuum of the data:</span>
+            <span class="k">if</span> <span class="n">cont</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>     
+                <span class="n">continuum</span> <span class="o">=</span> <span class="n">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">mod_cut_flx</span><span class="p">,</span> <span class="n">res_mod_obs_merge</span><span class="p">[</span><span class="n">c</span><span class="p">],</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                <span class="n">mod_cut_flx</span> <span class="o">-=</span> <span class="n">continuum</span>
+
+            <span class="c1"># Concatenate to speed up the code</span>
+            <span class="k">if</span> <span class="n">c</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
+                <span class="n">mod_spectro</span> <span class="o">=</span> <span class="n">mod_cut_flx</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_cut_flx</span><span class="p">))</span>
+
+
+    <span class="c1"># Calculate each photometry point.</span>
+    <span class="k">for</span> <span class="n">pho_ind</span><span class="p">,</span> <span class="n">pho</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_photo_ins</span><span class="p">):</span>
+        <span class="n">path_list</span> <span class="o">=</span> <span class="vm">__file__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+        <span class="n">separator</span> <span class="o">=</span> <span class="s1">&#39;/&#39;</span>
+        <span class="n">filter_pho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">separator</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_list</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/phototeque/&#39;</span> <span class="o">+</span> <span class="n">pho</span> <span class="o">+</span> <span class="s1">&#39;.npz&#39;</span><span class="p">)</span>
+        <span class="n">x_filt</span> <span class="o">=</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">]</span>
+        <span class="n">y_filt</span> <span class="o">=</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span>
+        <span class="n">filter_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">x_filt</span><span class="p">,</span> <span class="n">y_filt</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+        <span class="n">y_filt</span> <span class="o">=</span> <span class="n">filter_interp</span><span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">)</span>
+
+        <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">wav_mod_nativ</span> <span class="o">&gt;</span> <span class="nb">min</span><span class="p">(</span><span class="n">x_filt</span><span class="p">),</span> <span class="n">wav_mod_nativ</span> <span class="o">&lt;</span> <span class="nb">max</span><span class="p">(</span><span class="n">x_filt</span><span class="p">)))</span>
+        <span class="n">flx_filt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">flx_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="n">y_filt</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">y_filt_tot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_filt</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
+        <span class="n">flx_filt</span> <span class="o">=</span> <span class="n">flx_filt</span> <span class="o">/</span> <span class="n">y_filt_tot</span>
+        <span class="n">mod_photo</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flx_filt</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">mod_spectro</span><span class="p">,</span> <span class="n">mod_photo</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="convolve_and_sample">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.convolve_and_sample">[docs]</a>
+<span class="k">def</span> <span class="nf">convolve_and_sample</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">,</span> <span class="n">sigmas_wvs</span><span class="p">,</span> <span class="n">model_wvs</span><span class="p">,</span> <span class="n">model_fluxes</span><span class="p">,</span> <span class="n">num_sigma</span><span class="o">=</span><span class="mi">1</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Simulate the observations of a model. Convolves the model with a variable Gaussian LSF, sampled at each desired</span>
+<span class="sd">    spectral channel.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wv_channels (list(floats)): the wavelengths values desired</span>
+<span class="sd">        sigmas_wvs  (list(floats)): the LSF gaussian standard deviation of each wv_channels [IN UNITS OF model_wvs] </span>
+<span class="sd">        model_wvs          (array): the wavelengths of the model </span>
+<span class="sd">        model_fluxes       (array): the fluxes of the model </span>
+<span class="sd">        num_sigma          (float): number of +/- sigmas to evaluate the LSF to.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - output_model (array)      : the fluxes in each of the wavelength channels </span>
+
+<span class="sd">    Author: Jason Wang</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">model_in_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">model_wvs</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">model_wvs</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">)))</span>
+    <span class="n">dwv_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">[</span><span class="n">model_in_range</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">[</span><span class="n">model_in_range</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
+    <span class="n">dwv_model</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">dwv_model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">filter_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="n">num_sigma</span> <span class="o">*</span> <span class="n">sigmas_wvs</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">dwv_model</span><span class="p">))))</span>
+    <span class="n">filter_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">num_sigma</span><span class="p">,</span> <span class="n">num_sigma</span><span class="p">,</span> <span class="n">filter_size</span><span class="p">)</span>
+    <span class="n">filter_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">filter_coords</span><span class="p">,</span> <span class="p">[</span><span class="n">wv_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">])</span>  <span class="c1"># shape of (N_output, filter_size)</span>
+
+    <span class="n">filter_wv_coords</span> <span class="o">=</span> <span class="n">filter_coords</span> <span class="o">*</span> <span class="n">sigmas_wvs</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">wv_channels</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>  <span class="c1"># model wavelengths we want</span>
+
+    <span class="n">lsf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">filter_coords</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
+
+
+    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">lsf</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+      
+        <span class="n">model_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">,</span> <span class="n">model_fluxes</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">filter_model</span> <span class="o">=</span> <span class="n">model_interp</span><span class="p">(</span><span class="n">filter_wv_coords</span><span class="p">)</span>
+
+        <span class="n">output_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">filter_model</span> <span class="o">*</span> <span class="n">lsf</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">lsf</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+
+        <span class="n">output_model</span> <span class="o">=</span> <span class="n">model_fluxes</span>
+
+    <span class="k">return</span> <span class="n">output_model</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="resolution_decreasing">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.resolution_decreasing">[docs]</a>
+<span class="k">def</span> <span class="nf">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_obs</span><span class="p">,</span> <span class="n">flx_obs</span><span class="p">,</span> <span class="n">res_obs</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_obs</span><span class="p">,</span> <span class="n">obs_or_mod</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the</span>
+<span class="sd">    wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one</span>
+<span class="sd">    for each wavelength (the lowest spectral resolution). It then calculates a sigma to decrease the resolution of the</span>
+<span class="sd">    spectrum to this lowest FWHM for each wavelength and resample it on the wavelength grid of the data using the</span>
+<span class="sd">    function &#39;convolve_and_sample&#39;.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params (object): Class containing each parameter used in ForMoSA</span>
+<span class="sd">        wav_obs        (array): Wavelength grid of the data</span>
+<span class="sd">        flx_obs        (array): Flux of the data</span>
+<span class="sd">        res_obs        (array): Spectral resolution of the data</span>
+<span class="sd">        wav_mod_nativ  (array): Wavelength grid of the model</span>
+<span class="sd">        flx_mod_nativ  (array): Flux of the model</span>
+<span class="sd">        res_mod_obs    (array): Spectral resolution of the model as a function of the wavelength grid of the data</span>
+<span class="sd">        obs_or_mod       (str): Parameter to identify if you want to manage a data or a model spectrum. &#39;obs&#39; or &#39;mod&#39;</span>
+<span class="sd">        indobs           (int): Index of the current observation looping</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_obs_final (array) : Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</span>
+
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Estimate of the FWHM of the data as a function of the wavelength</span>
+    <span class="n">fwhm_obs</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_obs</span> <span class="o">/</span> <span class="n">res_obs</span>
+    <span class="c1"># Estimate of the FWHM of the model as a function of the wavelength</span>
+    <span class="n">fwhm_mod</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_obs</span> <span class="o">/</span> <span class="n">res_mod_obs</span>
+
+    <span class="c1"># Estimate of the FWHM of the custom resolution (if defined) as a function of the wavelength</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">custom_reso</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">fwhm_custom</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_obs</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">custom_reso</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fwhm_custom</span> <span class="o">=</span> <span class="n">wav_obs</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+
+
+    <span class="c1"># Estimate of the sigma for the convolution as a function of the wavelength and decrease the resolution</span>
+    <span class="n">max_fwhm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">([</span><span class="n">fwhm_obs</span><span class="p">,</span> <span class="n">fwhm_mod</span><span class="p">,</span> <span class="n">fwhm_custom</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">obs_or_mod</span> <span class="o">==</span> <span class="s1">&#39;obs&#39;</span><span class="p">:</span>
+        <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">max_fwhm</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm_obs</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">sigma_conv</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="mf">2.355</span>
+        <span class="n">flx_obs_final</span> <span class="o">=</span> <span class="n">convolve_and_sample</span><span class="p">(</span><span class="n">wav_obs</span><span class="p">,</span> <span class="n">sigma_conv</span><span class="p">,</span> <span class="n">wav_obs</span><span class="p">,</span> <span class="n">flx_obs</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">max_fwhm</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm_mod</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+        <span class="n">sigma_conv</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="mf">2.355</span>
+        <span class="n">flx_obs_final</span> <span class="o">=</span> <span class="n">convolve_and_sample</span><span class="p">(</span><span class="n">wav_obs</span><span class="p">,</span> <span class="n">sigma_conv</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">flx_obs_final</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="continuum_estimate">
+<a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.continuum_estimate">[docs]</a>
+<span class="k">def</span> <span class="nf">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav</span><span class="p">,</span> <span class="n">flx</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the</span>
+<span class="sd">    wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease</span>
+<span class="sd">    the resolution of the spectrum to this custom FWHM for each wavelength using a gaussian filter and resample it on</span>
+<span class="sd">    the wavelength grid of the data.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params (object): Class containing each parameter used in ForMoSA</span>
+<span class="sd">        wav            (array): Wavelength grid of the spectrum for which you want to estimate the continuum</span>
+<span class="sd">        flx            (array): Flux of the spectrum for which you want to estimate the continuum</span>
+<span class="sd">        res              (int): Spectral resolution of the spectrum for which you want to estimate the continuum</span>
+<span class="sd">        indobs           (int): Index of the current observation looping</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - continuum (array)   : Estimated continuum of the spectrum re-sampled on the data wavelength grid</span>
+
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Redifined a spectrum only composed by the wavelength ranges used to estimate the continuum</span>
+    <span class="n">wav_for_continuum</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_continuum</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
+    <span class="k">for</span> <span class="n">wav_for_cont_cut_ind</span><span class="p">,</span> <span class="n">wav_for_cont_cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">wav_for_continuum</span><span class="p">):</span>
+        <span class="n">wav_for_cont_cut</span> <span class="o">=</span> <span class="n">wav_for_cont_cut</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+        <span class="n">ind_cont_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">wav</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav</span> <span class="o">&lt;=</span> <span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
+        <span class="k">if</span> <span class="n">wav_for_cont_cut_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">wav_for_cont_final</span> <span class="o">=</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]</span>
+            <span class="n">flx_for_cont_final</span> <span class="o">=</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wav_for_cont_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_for_cont_final</span><span class="p">,</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]))</span>
+            <span class="n">flx_for_cont_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_for_cont_final</span><span class="p">,</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]))</span>
+
+    <span class="n">model_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_for_cont_final</span><span class="p">,</span> <span class="n">flx_for_cont_final</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+    <span class="n">flx</span> <span class="o">=</span> <span class="n">model_interp</span><span class="p">(</span><span class="n">wav</span><span class="p">)</span>
+
+    <span class="c1"># # To limit the computing time, the convolution is not as a function of the wavelength but calculated</span>
+    <span class="c1"># from the median wavelength. We just want an estimate of the continuum here.</span>
+    <span class="n">wav_median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">wav</span><span class="p">)</span>
+    <span class="n">dwav_median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">wav</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">wav</span><span class="p">,</span> <span class="mi">1</span><span class="p">)))</span> <span class="c1"># Estimated the median wavelength separation instead of taking wav_median - (wav_median+1) that could be on a border</span>
+
+    <span class="n">fwhm</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_median</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
+    <span class="n">fwhm_continuum</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_median</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+
+
+    <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">fwhm_continuum</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+    <span class="n">sigma</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="p">(</span><span class="n">dwav_median</span> <span class="o">*</span> <span class="mf">2.355</span><span class="p">)</span>
+    <span class="n">continuum</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">flx</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
+    <span class="c1"># import scipy.signal as sg</span>
+    <span class="c1"># continuum = sg.savgol_filter(flx, 3001, 2)</span>
+
+    <span class="k">return</span> <span class="n">continuum</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
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diff --git a/docs/_build/html/_modules/ForMoSA/main_utilities.html b/docs/_build/html/_modules/ForMoSA/main_utilities.html
new file mode 100644
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for ForMoSA.main_utilities</h1><div class="highlight"><pre>
+<span></span><span class="kn">from</span> <span class="nn">configobj</span> <span class="kn">import</span> <span class="n">ConfigObj</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<div class="viewcode-block" id="yesno">
+<a class="viewcode-back" href="../../main_utilities.html#ForMoSA.main_utilities.yesno">[docs]</a>
+<span class="k">def</span> <span class="nf">yesno</span><span class="p">(</span><span class="n">text</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Function to interact with the terminal and decide for different options when running ForMoSA (Loop to repeat question if answer is different to &#39;y&#39; or &#39;n).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        text    (str): (y/n) answer in the terminall in interactive mode</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        asw     (str): answer y or n</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="nb">print</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
+    <span class="n">asw</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
+    <span class="k">if</span> <span class="n">asw</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]:</span>
+        <span class="k">return</span> <span class="n">asw</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">yesno</span><span class="p">()</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<div class="viewcode-block" id="diag_mat">
+<a class="viewcode-back" href="../../main_utilities.html#ForMoSA.main_utilities.diag_mat">[docs]</a>
+<span class="k">def</span> <span class="nf">diag_mat</span><span class="p">(</span><span class="n">rem</span><span class="o">=</span><span class="p">[],</span> <span class="n">result</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">))):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Function to concatenate and align iterativly block matrices (usefull during the extraction and the inversion).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        rem        (list): matrices to be add iterativly (use diag([mat1, mat2]))</span>
+<span class="sd">        result    (array): final array with each sub-matrices aligned allong the diagonal</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        diag_mat (matrix): Generated diagonal matrix</span>
+<span class="sd">        (If rem input is empty, it wull return an empy array)</span>
+
+<span class="sd">    Author : Ishigoya, Stack-overflow : https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="k">if</span> <span class="ow">not</span> <span class="n">rem</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">result</span>
+    <span class="n">m</span> <span class="o">=</span> <span class="n">rem</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="mi">0</span><span class="p">)</span>
+    <span class="n">result</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">block</span><span class="p">(</span>
+        <span class="p">[</span>
+            <span class="p">[</span><span class="n">result</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">result</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">]))],</span>
+            <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">m</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">result</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">])),</span> <span class="n">m</span><span class="p">],</span>
+        <span class="p">]</span>
+    <span class="p">)</span>
+    <span class="k">return</span> <span class="n">diag_mat</span><span class="p">(</span><span class="n">rem</span><span class="p">,</span> <span class="n">result</span><span class="p">)</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="GlobFile">
+<a class="viewcode-back" href="../../main_utilities.html#ForMoSA.main_utilities.GlobFile">[docs]</a>
+<span class="k">class</span> <span class="nc">GlobFile</span><span class="p">:</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Class that import all the parameters from the config file and make them GLOBAL FORMOSA VARIABLES.</span>
+<span class="sd">    </span>
+<span class="sd">    Author: Paulina Palma-Bifani</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config_file_path</span><span class="p">):</span>
+        <span class="c1"># Generate the confog object</span>
+        <span class="n">config</span> <span class="o">=</span> <span class="n">ConfigObj</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">=</span><span class="n">config</span>
+
+        <span class="c1">## Read CONFIG:</span>
+        <span class="c1"># [config_path] (4)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;observation_path&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;*&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">main_observation_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;observation_path&#39;</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39;*&#39;</span> <span class="c1"># Needs to be changed</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;adapt_store_path&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">result_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;result_path&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;model_path&#39;</span><span class="p">]</span>
+        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;model_path&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
+        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.nc&#39;</span><span class="p">)</span>
+        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span>
+        <span class="n">model_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span>
+        <span class="n">model_name</span> <span class="o">=</span> <span class="n">model_name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">model_name</span> <span class="o">=</span> <span class="n">model_name</span>
+
+        <span class="k">if</span> <span class="nb">type</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_adapt&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="nb">list</span><span class="p">:</span> <span class="c1"># Create lists if only one obs in the loop </span>
+            <span class="c1"># [config_adapt] (5)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_adapt</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_adapt&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">adapt_method</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;adapt_method&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">custom_reso</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;custom_reso&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">continuum_sub</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;continuum_sub&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_continuum</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_continuum&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">use_lsqr</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;use_lsqr&#39;</span><span class="p">]]</span>
+
+            <span class="c1"># [config_inversion] (4)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">logL_type</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;logL_type&#39;</span><span class="p">]]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_fit</span> <span class="o">=</span> <span class="p">[</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;wav_fit&#39;</span><span class="p">]]</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="c1"># [config_adapt] (5)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_adapt</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_adapt&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">adapt_method</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;adapt_method&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">custom_reso</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;custom_reso&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">continuum_sub</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;continuum_sub&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_continuum</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_continuum&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">use_lsqr</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;use_lsqr&#39;</span><span class="p">]</span>
+
+            <span class="c1"># [config_inversion] (4)</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">logL_type</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;logL_type&#39;</span><span class="p">]</span>
+            <span class="bp">self</span><span class="o">.</span><span class="n">wav_fit</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;wav_fit&#39;</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;ns_algo&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">npoint</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;npoint&#39;</span><span class="p">]</span>
+
+        <span class="c1"># [config_parameter] (11)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">par1</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">par2</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">par3</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">par4</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">par5</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;r&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;d&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">alpha</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;alpha&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">rv</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;rv&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">av</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;av&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">vsini</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;vsini&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ld</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;ld&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;bb_T&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;bb_R&#39;</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">ck</span> <span class="o">=</span> <span class="kc">None</span>
+        
+        <span class="c1"># [config_nestle] (5, some mutually exclusive)  (n_ prefix for params)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_method</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_nestle&#39;</span><span class="p">][</span><span class="s1">&#39;method&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_maxiter</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_nestle&#39;</span><span class="p">][</span><span class="s1">&#39;maxiter&#39;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_maxcall</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_nestle&#39;</span><span class="p">][</span><span class="s1">&#39;maxcall&#39;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_dlogz</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_nestle&#39;</span><span class="p">][</span><span class="s1">&#39;dlogz&#39;</span><span class="p">])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">n_decline_factor</span> <span class="o">=</span> <span class="nb">eval</span><span class="p">(</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_nestle&#39;</span><span class="p">][</span><span class="s1">&#39;decline_factor&#39;</span><span class="p">])</span></div>
+
+
+        <span class="c1"># [config_pymultinest]</span>
+        <span class="c1"># self.p_n_params = config[&#39;config_pymultinest&#39;][&#39;n_params&#39;]</span>
+        <span class="c1"># self.p_n_clustering_params = config[&#39;config_pymultinest&#39;][&#39;n_clustering_params&#39;]</span>
+        <span class="c1"># self.p_wrapped_params = config[&#39;config_pymultinest&#39;][&#39;wrapped_params&#39;]</span>
+        <span class="c1"># self.p_importance_nested_sampling = config[&#39;config_pymultinest&#39;][&#39;importance_nested_sampling&#39;]</span>
+        <span class="c1"># self.p_multimodal = config[&#39;config_pymultinest&#39;][&#39;multimodal&#39;]</span>
+        <span class="c1"># self.p_const_efficiency_mode = config[&#39;config_pymultinest&#39;][&#39;const_efficiency_mode&#39;]</span>
+        <span class="c1"># self.p_evidence_tolerance = eval(config[&#39;config_pymultinest&#39;][&#39;evidence_tolerance&#39;])</span>
+        <span class="c1"># self.p_sampling_efficiency = eval(config[&#39;config_pymultinest&#39;][&#39;sampling_efficiency&#39;])</span>
+        <span class="c1"># self.p_n_iter_before_update = eval(config[&#39;config_pymultinest&#39;][&#39;n_iter_before_update&#39;])</span>
+        <span class="c1"># self.p_null_log_evidence = eval(config[&#39;config_pymultinest&#39;][&#39;null_log_evidence&#39;])</span>
+        <span class="c1"># self.p_max_modes = eval(config[&#39;config_pymultinest&#39;][&#39;max_modes&#39;])</span>
+        <span class="c1"># self.p_mode_tolerance = eval(config[&#39;config_pymultinest&#39;][&#39;mode_tolerance&#39;])</span>
+        <span class="c1"># self.p_seed = eval(config[&#39;config_pymultinest&#39;][&#39;seed&#39;])</span>
+        <span class="c1"># self.p_verbose = config[&#39;config_pymultinest&#39;][&#39;verbose&#39;]</span>
+        <span class="c1"># self.p_resume = config[&#39;config_pymultinest&#39;][&#39;resume&#39;]</span>
+        <span class="c1"># self.p_context = eval(config[&#39;config_pymultinest&#39;][&#39;context&#39;])</span>
+        <span class="c1"># self.p_write_output = config[&#39;config_pymultinest&#39;][&#39;write_output&#39;]</span>
+        <span class="c1"># self.p_log_zero = eval(config[&#39;config_pymultinest&#39;][&#39;log_zero&#39;])</span>
+        <span class="c1"># self.p_max_iter = eval(config[&#39;config_pymultinest&#39;][&#39;max_iter&#39;])</span>
+        <span class="c1"># self.p_init_MPI = config[&#39;config_pymultinest&#39;][&#39;init_MPI&#39;]</span>
+        <span class="c1"># self.p_dump_callback = config[&#39;config_pymultinest&#39;][&#39;dump_callback&#39;]</span>
+        <span class="c1"># self.p_use_MPI = config[&#39;config_pymultinest&#39;][&#39;use_MPI&#39;]</span>
+        
+        <span class="c1"># [config_dinesty] &amp; [config_ultranest] CHECK THIS</span>
+
+        <span class="c1"># ## create OUTPUTS Sub-Directories: interpolated grids and results</span>
+        <span class="c1"># stock_result_sub_dir = self.stock_result_raw + self.name_obs + &#39;_&#39; + self.model_name[</span>
+        <span class="c1">#                                                                      :-4] + self.data_type  # sub_directory: obsname_grid_datatype</span>
+        <span class="c1"># self.stock_interp_grid = stock_result_sub_dir + &#39;/interp_grid&#39;  # sub_sub directory to save interp grid (one interpolation for grid and data type, full wavelength covarage)</span>
+        <span class="c1">#</span>
+        <span class="c1"># self.path_grid_management = self.base_path + stock_result_sub_dir + &#39;/interp_grid/grid_management&#39;</span>
+        <span class="c1"># os.makedirs(self.base_path + stock_result_sub_dir + &#39;/interp_grid&#39; + &#39;/grid_management&#39;, exist_ok=True)</span>
+        <span class="c1">#</span>
+        <span class="c1"># subsub_dir_name = self.nest_samp_algo + &#39;_&#39; + self.NS_using_band + &#39;_Res&#39; + str(</span>
+        <span class="c1">#     self.R_by_wl[2]) + &#39;_&#39;  # subsub_directory: nestle_band_ResOBS_params_date</span>
+        <span class="c1"># subsub_dir_name = subsub_dir_name + self.free_params + &#39;_t&#39; + time.strftime(&quot;%Y%m%d_%H%M%S&quot;)</span>
+        <span class="c1"># stock_result_subsub_dir = stock_result_sub_dir + &#39;/&#39; + subsub_dir_name</span>
+        <span class="c1"># self.stock_result = self.base_path + stock_result_subsub_dir</span>
+        <span class="c1"># os.makedirs(self.base_path + stock_result_subsub_dir)</span>
+
+        <span class="c1"># ## Save CONFIG file with updated params for current run in OUTPUT subsub directory</span>
+        <span class="c1">#</span>
+        <span class="c1"># print(&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;)</span>
+        <span class="c1"># print(&#39;-&gt; Saving new configuration&#39;)</span>
+        <span class="c1"># print()</span>
+        <span class="c1">#</span>
+        <span class="c1"># config_current = self.result_path + &#39;/past_config.ini&#39;</span>
+        <span class="c1"># config.filename = &#39; &#39;</span>
+        <span class="c1"># config[&#39;config_path&#39;][&#39;stock_interp_grid&#39;] = stock_interp_grid</span>
+        <span class="c1"># config[&#39;config_path&#39;][&#39;stock_result&#39;] = stock_result_subsub_dir</span>
+        <span class="c1"># config.write()</span>
+        <span class="c1">#</span>
+        <span class="c1"># print(&#39;Saved config: --- &#39; + config_current + &#39; ---&#39;)</span>
+        <span class="c1"># print()</span>
+</pre></div>
+
+           </div>
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diff --git a/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_logL_functions.html b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_logL_functions.html
new file mode 100644
index 0000000..abcbd54
--- /dev/null
+++ b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_logL_functions.html
@@ -0,0 +1,310 @@
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+           <div itemprop="articleBody">
+             
+  <h1>Source code for ForMoSA.nested_sampling.nested_logL_functions</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+
+<div class="viewcode-block" id="logL_chi2_classic">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_classic">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_chi2_classic</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">err</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the classical chi2</span>
+<span class="sd">    under the assumption of gaussian and spectrally uncorrelated noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        delta_flx   (array): residual data-model as a function of wavelength</span>
+<span class="sd">        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)     : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">((</span><span class="n">delta_flx</span> <span class="o">/</span> <span class="n">err</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span> <span class="n">chi2</span> <span class="o">/</span> <span class="mi">2</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_chi2_covariance">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_covariance">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_chi2_covariance</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">inv_cov</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the generalized chi2</span>
+<span class="sd">    under the assumption of gaussian and spectrally correlated noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        delta_flx   (array): residual data-model as a function of wavelength</span>
+<span class="sd">        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)       : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">,</span> <span class="n">delta_flx</span><span class="p">))</span>
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span> <span class="n">chi2</span> <span class="o">/</span> <span class="mi">2</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_chi2_extended">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_chi2_extended</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">err</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the extended chi2</span>
+<span class="sd">    under the assumption of gaussian and spectrally uncorrelated noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        delta_flx   (array): residual data-model as a function of wavelength</span>
+<span class="sd">        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)     : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">)</span>
+    <span class="n">chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">((</span><span class="n">delta_flx</span> <span class="o">/</span> <span class="n">err</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">s2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">chi2</span>
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span><span class="p">(</span><span class="n">chi2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">s2</span><span class="p">)</span> <span class="o">+</span> <span class="n">N</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">s2</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">err</span><span class="p">,</span><span class="n">err</span><span class="p">)))</span>
+    
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_chi2_extended_covariance">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended_covariance">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_chi2_extended_covariance</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">inv_cov</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the extended chi2</span>
+<span class="sd">    under the assumption of gaussian and spectrally uncorrelated noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        delta_flx   (array): residual data-model as a function of wavelength</span>
+<span class="sd">        err         (array): error (=standard deviation) of the observed spectrum as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)     : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    
+    <span class="n">cov</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">)</span>
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">)</span>
+    <span class="n">chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">,</span> <span class="n">delta_flx</span><span class="p">))</span>
+    <span class="n">s2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">chi2</span>
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span><span class="n">chi2</span> <span class="o">/</span> <span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">s2</span><span class="p">)</span> <span class="o">-</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">cov</span><span class="p">))</span>
+    
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_full_covariance">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_full_covariance">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_full_covariance</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">inv_cov</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL under the assumption of gaussian and spectrally correlated noise.</span>
+<span class="sd">    This function is a generalized version of the logL_chi2_covariance and is to be used when dealing</span>
+<span class="sd">    with GP extimation of the covariance matrix.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        delta_flx   (array): residual data-model as a function of wavelength</span>
+<span class="sd">        inv_cov     (n-array): inverse of the covariance matrix of the observed spectrum as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)      : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span> <span class="mi">1</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">,</span> <span class="n">delta_flx</span><span class="p">))</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">det</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">linalg</span><span class="o">.</span><span class="n">inv</span><span class="p">(</span><span class="n">inv_cov</span><span class="p">)))</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">delta_flx</span><span class="p">)</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span> <span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_CCF_Brogi">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Brogi">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_CCF_Brogi</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">,</span> <span class="n">flx_mod</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the CCF mapping from Brogi et al. 2019</span>
+<span class="sd">    under the assumption of gaussian and spectrally constant noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        flx_obs     (array): flux of the observation as a function of wavelength</span>
+<span class="sd">        flx_mod     (array): flux of the model as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)     : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">)</span>
+    <span class="n">Sf2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">))</span>
+    <span class="n">Sg2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">))</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">flx_obs</span> <span class="o">*</span> <span class="n">flx_mod</span><span class="p">)</span>
+
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span><span class="n">N</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="n">Sf2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">R</span> <span class="o">+</span> <span class="n">Sg2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_CCF_Zucker">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Zucker">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_CCF_Zucker</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">,</span> <span class="n">flx_mod</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the CCF mapping from Zucker 2003</span>
+<span class="sd">    under the assumption of gaussian and spectrally constant noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        flx_obs     (array): flux of the observation as a function of wavelength</span>
+<span class="sd">        flx_mod     (array): flux of the model as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)      : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">)</span>
+    <span class="n">Sf2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">))</span>
+    <span class="n">Sg2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">))</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">flx_obs</span> <span class="o">*</span> <span class="n">flx_mod</span><span class="p">)</span>
+    <span class="n">C2</span> <span class="o">=</span> <span class="p">(</span><span class="n">R</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="p">(</span><span class="n">Sf2</span> <span class="o">*</span> <span class="n">Sg2</span><span class="p">)</span>
+
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span><span class="n">N</span><span class="o">/</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">log</span><span class="p">(</span><span class="mi">1</span><span class="o">-</span><span class="n">C2</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+
+
+<div class="viewcode-block" id="logL_CCF_custom">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_custom">[docs]</a>
+<span class="k">def</span> <span class="nf">logL_CCF_custom</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">,</span> <span class="n">flx_mod</span><span class="p">,</span> <span class="n">err_obs</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to compute logL based on the custom CCF mapping from Me</span>
+<span class="sd">    under the assumption of gaussian and spectrally constant noise.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        flx_obs     (array): flux of the observation as a function of wavelength</span>
+<span class="sd">        flx_mod     (array): flux of the model as a function of wavelength</span>
+<span class="sd">        err_obs     (array): errors of the observation as a function of wavelength</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - logL (float)       : the loglikelihood value</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="n">N</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">)</span>
+    <span class="n">Sf2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_obs</span><span class="p">))</span>
+    <span class="n">Sg2</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">square</span><span class="p">(</span><span class="n">flx_mod</span><span class="p">))</span>
+    <span class="n">R</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">flx_obs</span> <span class="o">*</span> <span class="n">flx_mod</span><span class="p">)</span>
+    <span class="n">sigma2_weight</span> <span class="o">=</span> <span class="mi">1</span><span class="o">/</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">N</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="mi">1</span><span class="o">/</span><span class="n">err_obs</span><span class="o">**</span><span class="mi">2</span><span class="p">))</span>
+
+    <span class="n">logL</span> <span class="o">=</span> <span class="o">-</span><span class="n">N</span><span class="o">/</span><span class="p">(</span><span class="mi">2</span><span class="o">*</span><span class="n">sigma2_weight</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">Sf2</span> <span class="o">+</span> <span class="n">Sg2</span> <span class="o">-</span> <span class="mi">2</span><span class="o">*</span><span class="n">R</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">logL</span></div>
+
+</pre></div>
+
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+             
+  <h1>Source code for ForMoSA.nested_sampling.nested_modif_spec</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">extinction</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+<span class="kn">import</span> <span class="nn">astropy.units</span> <span class="k">as</span> <span class="nn">u</span>
+<span class="kn">import</span> <span class="nn">astropy.constants</span> <span class="k">as</span> <span class="nn">const</span>
+<span class="kn">from</span> <span class="nn">PyAstronomy.pyasl</span> <span class="kn">import</span> <span class="n">rotBroad</span><span class="p">,</span> <span class="n">fastRotBroad</span>
+<span class="kn">import</span> <span class="nn">scipy.signal</span> <span class="k">as</span> <span class="nn">sg</span>
+<span class="kn">import</span> <span class="nn">scipy.optimize</span> <span class="k">as</span> <span class="nn">optimize</span>
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="lsq_fct">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.lsq_fct">[docs]</a>
+<span class="k">def</span> <span class="nf">lsq_fct</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">transm_obs</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">,</span> <span class="n">ccf_method</span> <span class="o">=</span> <span class="s1">&#39;continuum_unfiltered&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Estimation of the contribution of the planet and of the star to a spectrum (Used for HiRISE data)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        flx_obs_spectro    (array): Flux of the data (spectroscopy)</span>
+<span class="sd">        err_obs_spectro    (array): Error of the data (spectroscopy)</span>
+<span class="sd">        star_flx_obs     (n-array): Flux of star observation data (spectroscopy)</span>
+<span class="sd">        transm_obs         (array): Transmission (Atmospheric + Instrumental)</span>
+<span class="sd">        system_obs       (n-array): Systematics of the data (spectroscopy)</span>
+<span class="sd">        flx_mod_spectro    (array): Flux of interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - cp (array)                : Planetary contribution to the data (Spectroscopy)</span>
+<span class="sd">        - cs (array)                : Stellar contribution to the data (Spectroscopy)</span>
+<span class="sd">        - flx_mod_spectro (array)   : New model of the companion </span>
+<span class="sd">        - flx_obs_spectro (array)   : New flux of the data</span>
+<span class="sd">        - star_flx_obs (n-array)    : New star flux of the data</span>
+<span class="sd">        - systematics (array)       : The systematics</span>
+
+<span class="sd">    Author : Allan Denis</span>
+
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">flx_mod_spectro</span> <span class="o">*=</span> <span class="n">transm_obs</span>
+    <span class="n">star_flx_0</span> <span class="o">=</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">//</span> <span class="mi">2</span><span class="p">]</span>
+
+    <span class="c1"># # # # # Continuum estimation with lowpass filtering</span>
+    <span class="c1">#</span>
+    <span class="c1"># Low-pass filtering</span>
+    <span class="n">flx_obs_spectro_continuum</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">savgol_filter</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="mi">301</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">star_flx_obs_continuum</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">savgol_filter</span><span class="p">(</span><span class="n">star_flx_0</span><span class="p">,</span> <span class="mi">301</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="n">flx_mod_spectro_continuum</span> <span class="o">=</span> <span class="n">sg</span><span class="o">.</span><span class="n">savgol_filter</span><span class="p">(</span><span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="mi">301</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
+    <span class="c1">#</span>
+    <span class="c1"># # # # #</span>
+        
+    <span class="k">if</span> <span class="n">ccf_method</span> <span class="o">==</span> <span class="s1">&#39;continuum_filtered&#39;</span><span class="p">:</span>
+        <span class="c1"># Removal of low-pass filtered data</span>
+        <span class="n">flx_obs_spectro</span> <span class="o">=</span> <span class="n">flx_obs_spectro</span> <span class="o">-</span> <span class="n">flx_obs_spectro_continuum</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">)</span>
+        <span class="n">star_flx_obs</span> <span class="o">=</span> <span class="n">star_flx_obs</span> <span class="o">-</span> <span class="n">star_flx_obs_continuum</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">)</span>
+        <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">flx_mod_spectro</span> <span class="o">-</span> <span class="n">flx_mod_spectro_continuum</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmedian</span><span class="p">(</span><span class="n">flx_mod_spectro</span><span class="p">)</span>
+    <span class="k">elif</span> <span class="n">ccf_method</span> <span class="o">==</span> <span class="s1">&#39;continuum_unfiltered&#39;</span><span class="p">:</span>
+        <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">flx_mod_spectro</span> <span class="o">-</span> <span class="n">flx_mod_spectro_continuum</span> <span class="o">*</span> <span class="n">star_flx_0</span> <span class="o">/</span> <span class="n">star_flx_obs_continuum</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])):</span>
+            <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,:,</span><span class="n">i</span><span class="p">]</span> <span class="o">*</span> <span class="n">flx_obs_spectro_continuum</span> <span class="o">/</span> <span class="n">star_flx_obs_continuum</span>
+    
+    <span class="c1"># # # # # Least squares estimation</span>
+    <span class="c1">#    </span>
+    <span class="c1"># Construction of the matrix</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">A_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">),</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])])</span>
+        <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])):</span>
+            <span class="n">A_matrix</span><span class="p">[:,</span><span class="mi">1</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">err_obs_spectro</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">A_matrix</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="n">np</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">),</span> <span class="mi">1</span> <span class="o">+</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])])</span>
+        
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">A_matrix</span><span class="p">[:,</span><span class="mi">1</span><span class="o">+</span><span class="n">j</span><span class="p">]</span> <span class="o">=</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span><span class="n">j</span><span class="p">]</span> <span class="o">*</span> <span class="n">err_obs_spectro</span>
+        
+    <span class="n">A_matrix</span><span class="p">[:,</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">flx_mod_spectro</span> <span class="o">*</span> <span class="n">err_obs_spectro</span>
+    
+    <span class="c1"># Least square </span>
+    <span class="n">res</span> <span class="o">=</span> <span class="n">optimize</span><span class="o">.</span><span class="n">lsq_linear</span><span class="p">(</span><span class="n">A_matrix</span><span class="p">,</span> <span class="n">flx_obs_spectro</span> <span class="o">*</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">bounds</span><span class="o">=</span><span class="p">([</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">A_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">A_matrix</span><span class="p">[</span><span class="mi">0</span><span class="p">])))</span>
+    <span class="n">cp</span> <span class="o">=</span> <span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+    
+    <span class="n">cs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+    <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])):</span>
+        <span class="n">cs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">cs</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="n">i</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+        
+    <span class="n">systematics_c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+    <span class="n">systematics</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])):</span>
+            <span class="n">systematics_c</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">systematics_c</span><span class="p">,</span> <span class="n">res</span><span class="o">.</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="o">+</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span><span class="o">+</span><span class="n">i</span><span class="p">])</span>
+            
+        <span class="n">systematics</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">systematics_c</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+    
+    <span class="c1">#</span>
+    <span class="c1"># # # # #</span>
+    
+    <span class="k">return</span> <span class="n">cp</span><span class="p">,</span> <span class="n">cs</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">systematics</span></div>
+
+
+
+<div class="viewcode-block" id="calc_ck">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.calc_ck">[docs]</a>
+<span class="k">def</span> <span class="nf">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">,</span>
+            <span class="n">alpha</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">analytic</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum (from the radius</span>
+<span class="sd">    and distance or analytically).</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        flx_obs_spectro  (array): Flux of the data (spectroscopy)</span>
+<span class="sd">        err_obs_spectro  (array): Error of the data (spectroscopy)</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        flx_obs_photo    (array): Flux of the data (photometry)</span>
+<span class="sd">        err_obs_photo    (array): Error of the data (photometry)</span>
+<span class="sd">        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)</span>
+<span class="sd">        r_picked         (float): Radius randomly picked by the nested sampling (in RJup)</span>
+<span class="sd">        d_picked         (float): Distance randomly picked by the nested sampling (in pc)</span>
+<span class="sd">        alpha            (float): Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²</span>
+<span class="sd">        analytic           (str): = &#39;yes&#39; if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array)   : Re-normalysed model spectrum</span>
+<span class="sd">        - flx_mod_photo (array)     : Re-normalysed model photometry</span>
+<span class="sd">        - ck (float)                : Ck calculated</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Calculation of the dilution factor ck as a function of the radius and distance</span>
+    <span class="k">if</span> <span class="n">analytic</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
+        <span class="n">r_picked</span> <span class="o">*=</span> <span class="n">u</span><span class="o">.</span><span class="n">Rjup</span>
+        <span class="n">d_picked</span> <span class="o">*=</span> <span class="n">u</span><span class="o">.</span><span class="n">pc</span>
+        <span class="n">ck</span> <span class="o">=</span> <span class="n">alpha</span> <span class="o">*</span> <span class="p">(</span><span class="n">r_picked</span><span class="o">.</span><span class="n">value</span><span class="o">/</span><span class="n">d_picked</span><span class="o">.</span><span class="n">value</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
+    <span class="c1"># Calculation of the dilution factor ck analytically</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ck_top_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">flx_mod_spectro</span> <span class="o">*</span> <span class="n">flx_obs_spectro</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">err_obs_spectro</span> <span class="o">*</span> <span class="n">err_obs_spectro</span><span class="p">))</span>
+            <span class="n">ck_bot_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">flx_mod_spectro</span> <span class="o">/</span> <span class="n">err_obs_spectro</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ck_top_merge</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">ck_bot_merge</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_photo</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">ck_top_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">flx_mod_photo</span> <span class="o">*</span> <span class="n">flx_obs_photo</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">err_obs_photo</span> <span class="o">*</span> <span class="n">err_obs_photo</span><span class="p">))</span>
+            <span class="n">ck_bot_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">flx_mod_photo</span> <span class="o">/</span> <span class="n">err_obs_photo</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ck_top_phot</span> <span class="o">=</span> <span class="mi">0</span>
+            <span class="n">ck_bot_phot</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="n">ck</span> <span class="o">=</span> <span class="p">(</span><span class="n">ck_top_merge</span> <span class="o">+</span> <span class="n">ck_top_phot</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">ck_bot_merge</span> <span class="o">+</span> <span class="n">ck_bot_phot</span><span class="p">)</span>
+
+    <span class="c1"># Re-normalization of the interpolated synthetic spectra with ck</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_mod_spectro</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">flx_mod_spectro</span> <span class="o">*=</span> <span class="n">ck</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_mod_photo</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">flx_mod_photo</span> <span class="o">*=</span> <span class="n">ck</span>
+
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="doppler_fct">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct">[docs]</a>
+<span class="k">def</span> <span class="nf">doppler_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">rv_picked</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Application of a Doppler shifting to the interpolated synthetic spectrum using the function pyasl.dopplerShift.</span>
+<span class="sd">    Note: Observation can change due to side effects of the shifting.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wav_obs_spectro      (array): Wavelength grid of the data</span>
+<span class="sd">        flx_obs_spectro      (array): Flux of the data</span>
+<span class="sd">        err_obs_spectro      (array): Error of the data</span>
+<span class="sd">        flx_mod_spectro      (array): Flux of the interpolated synthetic spectrum</span>
+<span class="sd">        rv_picked            (float): Radial velocity randomly picked by the nested sampling (in km.s-1)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - wav_obs_spectro (array)       : New wavelength grid of the data</span>
+<span class="sd">        - flx_obs_spectro (array)       : New flux of the data</span>
+<span class="sd">        - err_obs_spectro (array)       : New error of the data</span>
+<span class="sd">        - flx_post_doppler (array)      : New flux of the interpolated synthetic spectrum</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">new_wav</span> <span class="o">=</span> <span class="n">wav_obs_spectro</span> <span class="o">*</span> <span class="p">((</span><span class="n">rv_picked</span> <span class="o">/</span> <span class="n">const</span><span class="o">.</span><span class="n">c</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">km</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">value</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
+    <span class="n">rv_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">new_wav</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+    <span class="n">flx_post_doppler</span> <span class="o">=</span> <span class="n">rv_interp</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_post_doppler</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="reddening_fct">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct">[docs]</a>
+<span class="k">def</span> <span class="nf">reddening_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function</span>
+<span class="sd">    extinction.fm07.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)</span>
+<span class="sd">        wav_obs_photo    (array): Wavelength of the data (photometry)</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)</span>
+<span class="sd">        av_picked        (float): Extinction randomly picked by the nested sampling (in mag)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array) : New flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        - flx_mod_photo (array)   : New flux of the interpolated synthetic spectrum (photometry)</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">dered_merge</span> <span class="o">=</span> <span class="n">extinction</span><span class="o">.</span><span class="n">fm07</span><span class="p">(</span><span class="n">wav_obs_spectro</span> <span class="o">*</span> <span class="mi">10000</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;aa&#39;</span><span class="p">)</span>
+        <span class="n">flx_mod_spectro</span> <span class="o">*=</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">0.4</span><span class="o">*</span><span class="n">dered_merge</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="n">dered_phot</span> <span class="o">=</span> <span class="n">extinction</span><span class="o">.</span><span class="n">fm07</span><span class="p">(</span><span class="n">wav_obs_photo</span> <span class="o">*</span> <span class="mi">10000</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;aa&#39;</span><span class="p">)</span>
+        <span class="n">flx_mod_photo</span> <span class="o">*=</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">0.4</span><span class="o">*</span><span class="n">dered_phot</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="vsini_fct_rot_broad">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_rot_broad">[docs]</a>
+<span class="k">def</span> <span class="nf">vsini_fct_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function</span>
+<span class="sd">    extinction.fm07.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wav_obs_spectro  (array): Wavelength grid of the data</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum</span>
+<span class="sd">        ld_picked        (float): Limd darkening randomly picked by the nested sampling</span>
+<span class="sd">        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Correct irregulatities in the wavelength grid</span>
+    <span class="n">wav_interval</span> <span class="o">=</span> <span class="n">wav_obs_spectro</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">wav_obs_spectro</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">wav_to_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">wav_interval</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+    <span class="n">flx_to_vsini</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">)</span>
+    <span class="c1"># Apply the v.sin(i)</span>
+    <span class="n">new_flx</span> <span class="o">=</span> <span class="n">rotBroad</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">flx_to_vsini</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">new_flx</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span></div>
+
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="vsini_fct_fast_rot_broad">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_fast_rot_broad">[docs]</a>
+<span class="k">def</span> <span class="nf">vsini_fct_fast_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function</span>
+<span class="sd">    extinction.fm07.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wav_obs_spectro  (array): Wavelength grid of the data</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum</span>
+<span class="sd">        ld_picked        (float): Limd darkening randomly picked by the nested sampling</span>
+<span class="sd">        vsini_picked     (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum</span>
+
+<span class="sd">    Author: Simon Petrus</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Correct irregulatities in the wavelength grid</span>
+    <span class="n">wav_interval</span> <span class="o">=</span> <span class="n">wav_obs_spectro</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">wav_obs_spectro</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+    <span class="n">wav_to_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">wav_interval</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
+    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+    <span class="n">flx_to_vsini</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">)</span>
+    <span class="c1"># Apply the v.sin(i)</span>
+    <span class="n">new_flx</span> <span class="o">=</span> <span class="n">fastRotBroad</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">flx_to_vsini</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">new_flx</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
+    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">)</span>
+
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="vsini_fct_accurate">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_accurate">[docs]</a>
+<span class="k">def</span> <span class="nf">vsini_fct_accurate</span><span class="p">(</span><span class="n">wave_obs_merge</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">,</span> <span class="n">nr</span><span class="o">=</span><span class="mi">50</span><span class="p">,</span> <span class="n">ntheta</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">dif</span><span class="o">=</span><span class="mf">0.0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    A routine to quickly rotationally broaden a spectrum in linear time.</span>
+<span class="sd">    Adapted from Carvalho &amp; Johns-Krull 2023 https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wav_obs_spectro   (array): Wavelength grid of the data</span>
+<span class="sd">        flx_mod_spectro   (array): Flux of the interpolated synthetic spectrum</span>
+<span class="sd">        ld_picked         (float): Limd darkening randomly picked by the nested sampling</span>
+<span class="sd">        vsini_picked      (float): v.sin(i) randomly picked by the nested sampling (in km.s-1)</span>
+<span class="sd">        nr                  (int): (default = 10) The number of radial bins on the projected disk</span>
+<span class="sd">        ntheta              (int): (default = 100) The number of azimuthal bins in the largest radial annulus</span>
+<span class="sd">                                   note: the number of bins at each r is int(r*ntheta) where r &lt; 1</span>
+<span class="sd">        dif               (float): (default = 0) The differential rotation coefficient, applied according to the law Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). </span>
+<span class="sd">                                    Dif = .675 nicely reproduces the law proposed by Smith, 1994, A&amp;A, Vol. 287, p. 523-534, to unify WTTS and CTTS. </span>
+<span class="sd">                                    Dif = .23 is similar to observed solar differential rotation. Note: the th in the above expression is the stellar co-latitude, not the same as the integration variable used below. </span>
+<span class="sd">                                    This is a disk integration routine.</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum</span>
+
+<span class="sd">    Author: Allan Denis</span>
+<span class="sd">    &#39;&#39;&#39;</span> 
+
+    <span class="n">ns</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">flx_mod_spectro</span><span class="p">)</span><span class="o">*</span><span class="mf">0.0</span>
+    <span class="n">tarea</span> <span class="o">=</span> <span class="mf">0.0</span>
+    <span class="n">dr</span> <span class="o">=</span> <span class="mf">1.</span><span class="o">/</span><span class="n">nr</span>
+    <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">nr</span><span class="p">):</span>
+        <span class="n">r</span> <span class="o">=</span> <span class="n">dr</span><span class="o">/</span><span class="mf">2.0</span> <span class="o">+</span> <span class="n">j</span><span class="o">*</span><span class="n">dr</span>
+        <span class="n">area</span> <span class="o">=</span> <span class="p">((</span><span class="n">r</span> <span class="o">+</span> <span class="n">dr</span><span class="o">/</span><span class="mf">2.0</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="p">(</span><span class="n">r</span> <span class="o">-</span> <span class="n">dr</span><span class="o">/</span><span class="mf">2.0</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span><span class="o">/</span><span class="nb">int</span><span class="p">(</span><span class="n">ntheta</span><span class="o">*</span><span class="n">r</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">ld_picked</span> <span class="o">+</span> <span class="n">ld_picked</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arcsin</span><span class="p">(</span><span class="n">r</span><span class="p">)))</span>
+        <span class="k">for</span> <span class="n">k</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="nb">int</span><span class="p">(</span><span class="n">ntheta</span><span class="o">*</span><span class="n">r</span><span class="p">)):</span>
+            <span class="n">th</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="nb">int</span><span class="p">(</span><span class="n">ntheta</span><span class="o">*</span><span class="n">r</span><span class="p">)</span> <span class="o">+</span> <span class="n">k</span> <span class="o">*</span> <span class="mf">2.0</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">/</span><span class="nb">int</span><span class="p">(</span><span class="n">ntheta</span><span class="o">*</span><span class="n">r</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">dif</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">vl</span> <span class="o">=</span> <span class="n">vsini_picked</span> <span class="o">*</span> <span class="n">r</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">th</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="mf">1.0</span> <span class="o">-</span> <span class="n">dif</span><span class="o">/</span><span class="mf">2.0</span> <span class="o">-</span> <span class="n">dif</span><span class="o">/</span><span class="mf">2.0</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="mf">2.0</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">arccos</span><span class="p">(</span><span class="n">r</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">th</span><span class="p">))))</span>
+                <span class="n">ns</span> <span class="o">+=</span> <span class="n">area</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">wave_obs_merge</span> <span class="o">+</span> <span class="n">wave_obs_merge</span><span class="o">*</span><span class="n">vl</span><span class="o">/</span><span class="n">const</span><span class="o">.</span><span class="n">c</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">km</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">wave_obs_merge</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">)</span>
+                <span class="n">tarea</span> <span class="o">+=</span> <span class="n">area</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">vl</span> <span class="o">=</span> <span class="n">r</span> <span class="o">*</span> <span class="n">vsini_picked</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">th</span><span class="p">)</span>
+                <span class="n">ns</span> <span class="o">+=</span> <span class="n">area</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">wave_obs_merge</span> <span class="o">+</span> <span class="n">wave_obs_merge</span><span class="o">*</span><span class="n">vl</span><span class="o">/</span><span class="n">const</span><span class="o">.</span><span class="n">c</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">km</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">s</span><span class="p">)</span><span class="o">.</span><span class="n">value</span><span class="p">,</span> <span class="n">wave_obs_merge</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">)</span>
+                <span class="n">tarea</span> <span class="o">+=</span> <span class="n">area</span>
+    
+    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">ns</span> <span class="o">/</span> <span class="n">tarea</span>
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span></div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+<div class="viewcode-block" id="bb_cpd_fct">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.bb_cpd_fct">[docs]</a>
+<span class="k">def</span> <span class="nf">bb_cpd_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">distance</span><span class="p">,</span> <span class="n">bb_T_picked</span><span class="p">,</span> <span class="n">bb_R_picked</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39; </span>
+<span class="sd">    Function to add the effect of a cpd (circum planetary disc) to the models.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:  </span>
+<span class="sd">        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)</span>
+<span class="sd">        wav_obs_photo    (array): Wavelength of the data (photometry)</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)</span>
+<span class="sd">        bb_temp          (float): Temperature value randomly picked by the nested sampling in K units</span>
+<span class="sd">        bb_rad           (float): Radius randomly picked by the nested sampling in units of planetary radius</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - flx_mod_spectro (array)   : New flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        - flx_mod_photo (array)     : New flux of the interpolated synthetic spectrum (photometry)</span>
+
+<span class="sd">    Author: Paulina Palma-Bifani</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="n">bb_T_picked</span> <span class="o">*=</span> <span class="n">u</span><span class="o">.</span><span class="n">K</span>
+    <span class="n">bb_R_picked</span> <span class="o">*=</span> <span class="n">u</span><span class="o">.</span><span class="n">Rjup</span>
+    <span class="n">distance</span> <span class="o">*=</span> <span class="n">u</span><span class="o">.</span><span class="n">pc</span>
+
+    <span class="k">def</span> <span class="nf">planck</span><span class="p">(</span><span class="n">wav</span><span class="p">,</span> <span class="n">T</span><span class="p">):</span>
+        <span class="n">a</span> <span class="o">=</span> <span class="mf">2.0</span><span class="o">*</span><span class="n">const</span><span class="o">.</span><span class="n">h</span><span class="o">*</span><span class="n">const</span><span class="o">.</span><span class="n">c</span><span class="o">**</span><span class="mi">2</span>
+        <span class="n">b</span> <span class="o">=</span> <span class="n">const</span><span class="o">.</span><span class="n">h</span><span class="o">*</span><span class="n">const</span><span class="o">.</span><span class="n">c</span><span class="o">/</span><span class="p">(</span><span class="n">wav</span><span class="o">*</span><span class="n">const</span><span class="o">.</span><span class="n">k_B</span><span class="o">*</span><span class="n">T</span><span class="p">)</span>
+        <span class="n">intensity</span> <span class="o">=</span> <span class="n">a</span><span class="o">/</span> <span class="p">(</span> <span class="p">(</span><span class="n">wav</span><span class="o">**</span><span class="mi">5</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">b</span><span class="p">)</span> <span class="o">-</span> <span class="mf">1.0</span><span class="p">)</span> <span class="p">)</span>
+        <span class="k">return</span> <span class="n">intensity</span>
+    
+    <span class="n">bb_intensity</span>    <span class="o">=</span> <span class="n">planck</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">um</span><span class="p">,</span> <span class="n">bb_T_picked</span><span class="p">)</span>
+    <span class="n">bb_intensity_f</span>    <span class="o">=</span> <span class="n">planck</span><span class="p">(</span><span class="n">wav_obs_photo</span><span class="o">*</span><span class="n">u</span><span class="o">.</span><span class="n">um</span><span class="p">,</span> <span class="n">bb_T_picked</span><span class="p">)</span>
+
+    <span class="n">flux_bb_lambda</span>   <span class="o">=</span> <span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">bb_R_picked</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="n">distance</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">bb_intensity</span> <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">W</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">micron</span><span class="p">)</span>
+    <span class="n">flux_bb_lambda_f</span> <span class="o">=</span> <span class="p">(</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="o">*</span><span class="n">bb_R_picked</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="p">(</span><span class="n">distance</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span> <span class="o">*</span> <span class="n">bb_intensity_f</span> <span class="p">)</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">u</span><span class="o">.</span><span class="n">W</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">m</span><span class="o">**</span><span class="mi">2</span><span class="o">/</span><span class="n">u</span><span class="o">.</span><span class="n">micron</span><span class="p">)</span>
+
+    <span class="c1"># add to model flux of the atmosphere</span>
+    <span class="n">flx_mod_spectro</span>  <span class="o">+=</span> <span class="n">flux_bb_lambda</span><span class="o">.</span><span class="n">value</span>
+    <span class="n">flx_mod_photo</span>   <span class="o">+=</span> <span class="n">flux_bb_lambda_f</span><span class="o">.</span><span class="n">value</span>
+    <span class="c1"># </span>
+    <span class="k">return</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span></div>
+
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<div class="viewcode-block" id="modif_spec">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.modif_spec">[docs]</a>
+<span class="k">def</span> <span class="nf">modif_spec</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span>
+               <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+               <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">transm_obs</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">star_flx_obs</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">system_obs</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">indobs</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Modification of the interpolated synthetic spectra with the different extra-grid parameters.</span>
+<span class="sd">    It can perform : Re-calibration on the data, Doppler shifting, Application of a substellar extinction, Application of a rotational velocity, </span>
+<span class="sd">    Application of a circumplanetary disk (CPD).</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        global_params   (object): Class containing each parameter</span>
+<span class="sd">        theta             (list): Parameter values randomly picked by the nested sampling</span>
+<span class="sd">        theta_index       (list): Parameter index identificator</span>
+<span class="sd">        wav_obs_spectro  (array): Wavelength grid of the data (spectroscopy)</span>
+<span class="sd">        flx_obs_spectro  (array): Flux of the data (spectroscopy)</span>
+<span class="sd">        err_obs_spectro  (array): Error of the data (spectroscopy)</span>
+<span class="sd">        flx_mod_spectro  (array): Flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        wav_obs_photo    (array): Wavelength grid of the data (photometry)</span>
+<span class="sd">        flx_obs_photo    (array): Flux of the data (photometry)</span>
+<span class="sd">        err_obs_photo    (array): Error of the data (photometry)</span>
+<span class="sd">        flx_mod_photo    (array): Flux of the interpolated synthetic spectrum (photometry)</span>
+<span class="sd">        transm_obs       (array): Transmission (Atmospheric + Instrumental)</span>
+<span class="sd">        star_flx_obs   (n-array): Flux of star observation data (spectroscopy)</span>
+<span class="sd">        system_obs     (n-array): Systematics of the data (spectroscopy)</span>
+<span class="sd">        indobs             (int): Index of the current observation looping</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - wav_obs_spectro (array)  : New wavelength grid of the data (may change with the Doppler shift)</span>
+<span class="sd">        - flx_obs_spectro (array)  : New flux of the data (may change with the Doppler shift)</span>
+<span class="sd">        - err_obs_spectro (array)  : New error of the data (may change with the Doppler shift)</span>
+<span class="sd">        - flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum (spectroscopy)</span>
+<span class="sd">        - wav_obs_photo (array)    : Wavelength grid of the data (photometry)</span>
+<span class="sd">        - flx_obs_photo (array)    : Flux of the data (photometry)</span>
+<span class="sd">        - err_obs_photo (array)    : Error of the data (photometry)</span>
+<span class="sd">        - flx_mod_photo (array)    : New flux of the interpolated synthetic spectrum (photometry)</span>
+<span class="sd">    </span>
+<span class="sd">    Author: Simon Petrus, Paulina Palma-Bifani, Allan Denis and Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="c1"># Correction of the radial velocity of the interpolated synthetic spectrum.</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate rv for each observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+                    <span class="n">rv_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;rv_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">rv_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">doppler_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span>
+                                                                                    <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                                                    <span class="n">rv_picked</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common rv for all observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+                    <span class="n">alpha_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;rv&#39;</span><span class="p">)</span>
+                    <span class="n">rv_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">doppler_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span>
+                                                                                        <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                                                        <span class="n">rv_picked</span><span class="p">)</span>
+
+    <span class="c1"># Application of a synthetic interstellar extinction to the interpolated synthetic spectrum.</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">av_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind_theta_av</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;av&#39;</span><span class="p">)</span>
+            <span class="n">av_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">reddening_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">)</span>
+        
+    <span class="c1"># Correction of the rotational velocity of the interpolated synthetic spectrum.</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate vsini/ld for each observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">vsini_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;vsini_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">vsini_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ld_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;ld_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">ld_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;RotBroad&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;FastRotBroad&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_fast_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span> <span class="o">+</span> <span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;Accurate&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_accurate</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;WARNING: You need to define a v.sin(i) AND a limb darkening, or set them both to NA for observation </span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                <span class="n">exit</span><span class="p">()</span>
+
+        <span class="k">else</span><span class="p">:</span><span class="c1"># If you want 1 common vsini/ld for all observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">vsini_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
+                    <span class="n">vsini_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ld_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;ld&#39;</span><span class="p">)</span>
+                    <span class="n">ld_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;RotBroad&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;FastRotBroad&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_fast_rot_broad</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;Accurate&#39;</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">vsini_fct_accurate</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
+
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">pass</span>
+
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: You need to define a v.sin(i) AND a limb darkening, or set them both to NA&#39;</span><span class="p">)</span>
+                <span class="n">exit</span><span class="p">()</span>  
+    
+    <span class="c1"># Adding a CPD</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+        <span class="c1"># posteriors T_eff, R_disk</span>
+        <span class="c1"># Enter 1 or 2 bb</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">bb_T_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">bb_R_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind_theta_bb_T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;bb_T&#39;</span><span class="p">)</span>
+            <span class="n">ind_theta_bb_R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;bb_R&#39;</span><span class="p">)</span>
+            <span class="n">bb_T_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+            <span class="n">bb_R_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+            <span class="n">d_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind_theta_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span>
+            <span class="n">d_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+
+        <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">bb_cpd_fct</span><span class="p">(</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">,</span> <span class="n">bb_T_picked</span><span class="p">,</span> <span class="n">bb_R_picked</span><span class="p">)</span>
+
+    <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+        <span class="k">pass</span>
+
+    <span class="k">else</span><span class="p">:</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: You need to define a blackbody radius and blackbody temperature, or set them to &quot;NA&quot;&#39;</span><span class="p">)</span>
+        <span class="n">exit</span><span class="p">()</span>
+
+    <span class="c1"># Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum.</span>
+    <span class="c1"># From the radius and the distance.</span>
+    
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+            <span class="n">r_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+            <span class="n">r_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+            <span class="n">d_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ind_theta_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span>
+            <span class="n">d_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+
+        <span class="c1"># With the extra alpha scaling</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate alpha for each observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+                    <span class="n">alpha_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;alpha_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="n">alpha_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                        <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">,</span>
+                                                        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha_picked</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span> <span class="c1"># Without the extra alpha scaling</span>
+                <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                  <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common alpha for all observations</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
+                    <span class="n">alpha_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">ind_theta_alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+                    <span class="n">alpha_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
+                <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                        <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">,</span>
+                                                        <span class="n">alpha</span><span class="o">=</span><span class="n">alpha_picked</span><span class="p">)</span>   
+            <span class="k">else</span><span class="p">:</span> <span class="c1"># Without the extra alpha scaling</span>
+                <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                    <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">)</span>
+        <span class="c1"># Set HiRES contribution to 1 if not used</span>
+        <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>                
+    <span class="c1"># Analytically</span>
+    <span class="c1"># If MOSAIC</span>
+    <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;False&#39;</span><span class="p">:</span>
+        <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                                <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
+                                                <span class="n">analytic</span><span class="o">=</span><span class="s1">&#39;yes&#39;</span><span class="p">)</span>
+            
+        <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span>
+
+
+    <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>   
+        <span class="c1"># global_params.use_lsqr = &#39;True&#39;, so no need to re-normalize the interpolated sythetic spectrum because the least squares automatically does it</span>
+            
+        <span class="n">_</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">copy</span><span class="p">(</span><span class="n">flx_mod_spectro</span><span class="p">),</span> 
+                        <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
+                        <span class="n">analytic</span><span class="o">=</span><span class="s1">&#39;yes&#39;</span><span class="p">)</span>
+        
+        <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="n">lsq_fct</span><span class="p">(</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">transm_obs</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">)</span>
+
+
+    <span class="k">else</span><span class="p">:</span>   <span class="c1"># either global_params.r or global_params.d is set to &#39;NA&#39; </span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: You need to define a radius AND a distance, or set them both to &quot;NA&quot;&#39;</span><span class="p">)</span>
+        <span class="n">exit</span><span class="p">()</span>
+
+    <span class="k">return</span> <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span> <span class="n">ck</span><span class="p">,</span> <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">systematics</span><span class="p">,</span> <span class="n">transm_obs</span></div>
+
+
+
+
+
+
+
+
+
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html
similarity index 64%
rename from docs/build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html
rename to docs/_build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html
index 73ba075..ec3a3eb 100644
--- a/docs/build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html
+++ b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_prior_function.html
@@ -1,23 +1,23 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="en" >
+<html class="writer-html5" lang="en" data-content_root="../../../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>ForMoSA.nested_sampling.nested_prior_function &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
+  <title>ForMoSA.nested_sampling.nested_prior_function &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="../../../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-        <script src="../../../_static/jquery.js"></script>
-        <script src="../../../_static/underscore.js"></script>
-        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../../../_static/doctools.js"></script>
-        <script src="../../../_static/sphinx_highlight.js"></script>
-        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script src="../../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../../_static/documentation_options.js?v=51b770b3"></script>
+        <script src="../../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="../../../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../../../genindex.html" />
     <link rel="search" title="Search" href="../../../search.html" /> 
@@ -42,7 +42,8 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../demo.html">DEMO</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../../../api.html">ForMoSA API</a></li>
@@ -76,23 +77,45 @@ <h1>Source code for ForMoSA.nested_sampling.nested_prior_function</h1><div class
 <span></span><span class="kn">from</span> <span class="nn">scipy.special</span> <span class="kn">import</span> <span class="n">ndtri</span>
 <span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
 
-<div class="viewcode-block" id="uniform_prior"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_prior_function.uniform_prior">[docs]</a><span class="k">def</span> <span class="nf">uniform_prior</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
-    <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    Uniform prior for nested sampling</span>
+<div class="viewcode-block" id="uniform_prior">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_prior_function.uniform_prior">[docs]</a>
+<span class="k">def</span> <span class="nf">uniform_prior</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Uniform prior for nested sampling.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        prior_fct_arg   (list): Two-values list with uniform prior boundaries.</span>
+<span class="sd">        theta           (list): Parameter values randomly picked by the nested sampling</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - Evaluated      (float): Evaluated prior</span>
+
+<span class="sd">    Author: Simon Petrus</span>
 <span class="sd">    &#39;&#39;&#39;</span>
     <span class="n">arg1</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
     <span class="n">arg2</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 
     <span class="k">return</span> <span class="p">(</span><span class="n">arg2</span> <span class="o">-</span> <span class="n">arg1</span><span class="p">)</span> <span class="o">*</span> <span class="n">theta</span> <span class="o">+</span> <span class="n">arg1</span></div>
 
-<div class="viewcode-block" id="gaussian_prior"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior">[docs]</a><span class="k">def</span> <span class="nf">gaussian_prior</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
-    <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    Gaussian prior for nested sampling</span>
+
+<div class="viewcode-block" id="gaussian_prior">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior">[docs]</a>
+<span class="k">def</span> <span class="nf">gaussian_prior</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">,</span> <span class="n">theta</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Gaussian prior for nested sampling.</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        prior_fct_arg   (list): Two-values list with uniform prior boundaries.</span>
+<span class="sd">        theta           (list): Parameter values randomly picked by the nested sampling</span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - Evaluated      (float): Evaluated prior</span>
+
+<span class="sd">    Author: Simon Petrus</span>
 <span class="sd">    &#39;&#39;&#39;</span>
     <span class="n">arg1</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
     <span class="n">arg2</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">prior_fct_arg</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
 
     <span class="k">return</span> <span class="n">arg1</span> <span class="o">+</span> <span class="n">arg2</span> <span class="o">*</span> <span class="n">ndtri</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span></div>
+
 </pre></div>
 
            </div>
@@ -102,7 +125,7 @@ <h1>Source code for ForMoSA.nested_sampling.nested_prior_function</h1><div class
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_sampling.html b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_sampling.html
new file mode 100644
index 0000000..b999279
--- /dev/null
+++ b/docs/_build/html/_modules/ForMoSA/nested_sampling/nested_sampling.html
@@ -0,0 +1,941 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>ForMoSA.nested_sampling.nested_sampling &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../../../_static/css/theme.css?v=19f00094" />
+
+  
+    <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
+  <!--[if lt IE 9]>
+    <script src="../../../_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="../../../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../../../_static/documentation_options.js?v=51b770b3"></script>
+        <script src="../../../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../../../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="../../../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../../../genindex.html" />
+    <link rel="search" title="Search" href="../../../search.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../../../index.html" class="icon icon-home">
+            ForMoSA
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../demo.html">Tutorials</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="../../../index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">ForMoSA.nested_sampling.nested_sampling</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for ForMoSA.nested_sampling.nested_sampling</h1><div class="highlight"><pre>
+<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">glob</span>
+<span class="kn">import</span> <span class="nn">nestle</span>
+<span class="kn">import</span> <span class="nn">time</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;../&#39;</span><span class="p">))</span>
+
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">modif_spec</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_prior_function</span> <span class="kn">import</span> <span class="n">uniform_prior</span><span class="p">,</span> <span class="n">gaussian_prior</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_logL_functions</span> <span class="kn">import</span> <span class="o">*</span>
+<span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">diag_mat</span>
+
+
+<div class="viewcode-block" id="import_obsmod">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.import_obsmod">[docs]</a>
+<span class="k">def</span> <span class="nf">import_obsmod</span><span class="p">(</span><span class="n">global_params</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to import spectra (model and data) before the inversion</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params  (object): Class containing every input from the .ini file.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - main_file (list(array)): Return a list of lists with the wavelengths, flux, errors, covariance matrix,</span>
+<span class="sd">                                transmission, star flux, systematics and the grids for both spectroscopic and photometric data. </span>
+
+<span class="sd">    Authors: Simon Petrus, Matthieu Ravet and Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+
+    <span class="n">main_file</span> <span class="o">=</span> <span class="p">[]</span>
+
+    <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+        
+        <span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+        <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+
+        <span class="n">wav_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">flx_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">err_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="c1"># Optional arrays</span>
+        <span class="n">inv_cov_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">transm_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">star_flx_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">system_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+        <span class="k">if</span> <span class="s1">&#39;obs_photo&#39;</span> <span class="ow">in</span> <span class="n">spectrum_obs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+            <span class="n">wav_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">flx_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">err_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wav_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">flx_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">err_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+        <span class="c1"># Recovery of the spectroscopy and photometry model</span>
+        <span class="n">path_grid_spectro</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_spectro_</span><span class="si">{</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">)</span>
+        <span class="n">path_grid_photo</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_photo_</span><span class="si">{</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">)</span>
+        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_spectro</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+        <span class="n">grid_spectro</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+        <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_photo</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+        <span class="n">grid_photo</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+        <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        
+
+        <span class="n">main_file</span><span class="o">.</span><span class="n">append</span><span class="p">([[</span><span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">],</span> <span class="p">[</span><span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">],</span> <span class="p">[</span><span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">],</span> <span class="n">inv_cov_obs</span><span class="p">,</span> <span class="n">transm_obs</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">,</span> <span class="n">grid_spectro</span><span class="p">,</span> <span class="n">grid_photo</span><span class="p">])</span>
+
+    <span class="k">return</span> <span class="n">main_file</span></div>
+
+
+
+<div class="viewcode-block" id="loglike">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.loglike">[docs]</a>
+<span class="k">def</span> <span class="nf">loglike</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">global_params</span><span class="p">,</span> <span class="n">main_file</span><span class="p">,</span> <span class="n">for_plot</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function that calculates the logarithm of the likelihood. </span>
+<span class="sd">    The evaluation depends on the choice of likelihood.</span>
+<span class="sd">    (If this function is used on the plotting module, it returns the outputs of the modif_spec function)</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        theta           (list): Parameter values randomly picked by the nested sampling</span>
+<span class="sd">        theta_index     (list): Index for the parameter values randomly picked</span>
+<span class="sd">        global_params (object): Class containing every input from the .ini file.</span>
+<span class="sd">        main_file (list(list)): List containing the wavelengths, flux, errors, covariance, and grid information</span>
+<span class="sd">        for_plot         (str): Default is &#39;no&#39;. When this function is called from the plotting functions module, we use &#39;yes&#39;</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - FINAL_logL     (float): Final evaluated loglikelihood for both spectra and photometry. </span>
+
+<span class="sd">    Authors: Simon Petrus, Matthieu Ravet and Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># Recovery of each observation spectroscopy and photometry data</span>
+    <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+    <span class="n">FINAL_logL</span> <span class="o">=</span> <span class="mi">0</span>
+
+
+    <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+        
+        <span class="c1"># Recovery of spectroscopy and photometry data</span>
+        <span class="n">wav_obs_spectro</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">wav_obs_photo</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">flx_obs_spectro</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">flx_obs_photo</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">err_obs_spectro</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">err_obs_photo</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+        <span class="n">inv_cov_obs</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
+        <span class="n">transm_obs</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">]</span>
+        <span class="n">star_flx_obs</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">system_obs</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">6</span><span class="p">]</span>
+        
+        
+        <span class="c1"># Recovery of the spectroscopy and photometry model</span>
+        <span class="n">grid_spectro</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">7</span><span class="p">]</span>
+        <span class="n">grid_photo</span> <span class="o">=</span> <span class="n">main_file</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">8</span><span class="p">]</span>
+
+        <span class="c1"># Calculation of the likelihood for each sub-spectrum defined by the parameter &#39;wav_fit&#39;</span>
+        <span class="k">for</span> <span class="n">ns_u_ind</span><span class="p">,</span> <span class="n">ns_u</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">wav_fit</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)):</span>
+            
+            <span class="n">min_ns_u</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ns_u</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">max_ns_u</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ns_u</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">ind_grid_spectro_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
+            <span class="n">ind_grid_photo_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
+
+            <span class="c1"># Cutting of the grid on the wavelength grid defined by the parameter &#39;wav_fit&#39;</span>
+            <span class="n">grid_spectro_cut</span> <span class="o">=</span> <span class="n">grid_spectro</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">][</span><span class="n">ind_grid_spectro_sel</span><span class="p">])</span>
+            <span class="n">grid_photo_cut</span> <span class="o">=</span> <span class="n">grid_photo</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">][</span><span class="n">ind_grid_photo_sel</span><span class="p">])</span>
+
+            <span class="c1"># Interpolation of the grid at the theta parameters set</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+
+            <span class="c1"># Re-merging of the data and interpolated synthetic spectrum to a wavelength grid defined by the parameter &#39;wav_fit&#39;</span>
+            <span class="n">ind_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_obs_spectro</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_obs_spectro</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
+            <span class="n">ind_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_obs_photo</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_obs_photo</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
+            <span class="k">if</span> <span class="n">ns_u_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">wav_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">wav_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="n">flx_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">flx_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="n">err_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">err_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="n">flx_mod_spectro_ns_u</span> <span class="o">=</span> <span class="n">flx_mod_spectro_cut</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inv_cov_obs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>  <span class="c1"># Add covariance in the loop (if necessary)</span>
+                    <span class="n">inv_cov_obs_ns_u</span> <span class="o">=</span> <span class="n">inv_cov_obs</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="n">ind_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ind_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">])]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">inv_cov_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm_obs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Add the transmission (if necessary)</span>
+                    <span class="n">transm_obs_ns_u</span> <span class="o">=</span> <span class="n">transm_obs</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">transm_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Add star flux (if necessary)</span>
+                    <span class="n">star_flx_obs_ns_u</span> <span class="o">=</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">star_flx_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Add systematics model (if necessary)</span>
+                    <span class="n">system_obs_ns_u</span> <span class="o">=</span> <span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ind_spectro</span><span class="p">]</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">system_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="n">wav_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">wav_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]</span>
+                <span class="n">flx_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">flx_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]</span>
+                <span class="n">err_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">err_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]</span>
+                <span class="n">flx_mod_photo_ns_u</span> <span class="o">=</span> <span class="n">flx_mod_photo_cut</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">wav_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_spectro_ns_u</span><span class="p">,</span> <span class="n">wav_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]))</span>
+                <span class="n">flx_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_spectro_ns_u</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]))</span>
+                <span class="n">err_obs_spectro_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_spectro_ns_u</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]))</span>
+                <span class="n">flx_mod_spectro_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_mod_spectro_ns_u</span><span class="p">,</span> <span class="n">flx_mod_spectro_cut</span><span class="p">))</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">inv_cov_obs_ns_u</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Merge the covariance matrices (if necessary)</span>
+                    <span class="n">inv_cov_obs_ns_u</span> <span class="o">=</span> <span class="n">diag_mat</span><span class="p">([</span><span class="n">inv_cov_obs_ns_u</span><span class="p">,</span> <span class="n">inv_cov_obs</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ix_</span><span class="p">(</span><span class="n">ind_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">ind_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">])]])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm_obs_ns_u</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Merge the transmissions (if necessary)</span>
+                    <span class="n">transm_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">transm_obs_ns_u</span><span class="p">,</span> <span class="n">transm_obs</span><span class="p">[</span><span class="n">ind_spectro</span><span class="p">]))</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">star_flx_obs_ns_u</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Merge star fluxes (if necessary)</span>
+                    <span class="n">star_flx_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">star_flx_obs_ns_u</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ind_grid_spectro_sel</span><span class="p">]),</span><span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">system_obs</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span> <span class="c1"># Merge systematics model (if necessary)</span>
+                    <span class="n">system_obs_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">system_obs_ns_u</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="n">ind_grid_spectro_sel</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+                <span class="n">wav_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_photo_ns_u</span><span class="p">,</span> <span class="n">wav_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]))</span>
+                <span class="n">flx_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_photo_ns_u</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]))</span>
+                <span class="n">err_obs_photo_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_photo_ns_u</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">[</span><span class="n">ind_photo</span><span class="p">]))</span>
+                <span class="n">flx_mod_photo_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_mod_photo_ns_u</span><span class="p">,</span> <span class="n">flx_mod_photo_cut</span><span class="p">))</span>
+
+        <span class="c1"># Modification of the synthetic spectrum with the extra-grid parameters</span>
+        <span class="n">modif_spec_LL</span> <span class="o">=</span> <span class="n">modif_spec</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span>
+                                    <span class="n">wav_obs_spectro_ns_u</span><span class="p">,</span>  <span class="n">flx_obs_spectro_ns_u</span><span class="p">,</span>  <span class="n">err_obs_spectro_ns_u</span><span class="p">,</span>  <span class="n">flx_mod_spectro_ns_u</span><span class="p">,</span>
+                                    <span class="n">wav_obs_photo_ns_u</span><span class="p">,</span>  <span class="n">flx_obs_photo_ns_u</span><span class="p">,</span> <span class="n">err_obs_photo_ns_u</span><span class="p">,</span>  <span class="n">flx_mod_photo_ns_u</span><span class="p">,</span> 
+                                    <span class="n">transm_obs_ns_u</span><span class="p">,</span> <span class="n">star_flx_obs_ns_u</span><span class="p">,</span> <span class="n">system_obs_ns_u</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+        
+        <span class="n">flx_obs_spectro_modif</span><span class="p">,</span> <span class="n">flx_obs_photo_modif</span> <span class="o">=</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
+        <span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">flx_mod_photo_modif</span> <span class="o">=</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span>
+        <span class="n">err_obs_spectro_modif</span><span class="p">,</span> <span class="n">err_obs_photo_modif</span> <span class="o">=</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">6</span><span class="p">]</span>
+        <span class="n">inv_cov_obs_modif</span> <span class="o">=</span> <span class="n">inv_cov_obs_ns_u</span>
+        <span class="n">ck</span> <span class="o">=</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+        <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">9</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">10</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">11</span><span class="p">],</span> <span class="n">modif_spec_LL</span><span class="p">[</span><span class="mi">12</span><span class="p">]</span>
+        
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+            <span class="c1"># If our data is contaminated by starlight difraction, the model is the sum of the estimated stellar contribution + planet model</span>
+            <span class="n">flx_mod_spectro_modif</span> <span class="o">=</span> <span class="n">planet_contribution</span> <span class="o">*</span> <span class="n">flx_mod_spectro_modif</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">flx_mod_spectro_modif</span> <span class="o">+=</span> <span class="n">systematics</span>
+                
+
+        <span class="c1"># Computation of the photometry logL</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_photo_modif</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="n">logL_photo</span> <span class="o">=</span> <span class="n">logL_chi2_classic</span><span class="p">(</span><span class="n">flx_obs_photo_modif</span><span class="o">-</span><span class="n">flx_mod_photo_modif</span><span class="p">,</span> <span class="n">err_obs_photo_modif</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">logL_photo</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># Computation of the spectroscopy logL</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;chi2_classic&#39;</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_chi2_classic</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="o">-</span><span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">err_obs_spectro_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;chi2_covariance&#39;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">inv_cov_obs_modif</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_chi2_covariance</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="o">-</span><span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">inv_cov_obs_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_Brogi&#39;</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_CCF_Brogi</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="p">,</span> <span class="n">flx_mod_spectro_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_Zucker&#39;</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_CCF_Zucker</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="p">,</span> <span class="n">flx_mod_spectro_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_custom&#39;</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_CCF_custom</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="p">,</span> <span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">err_obs_spectro_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;chi2_extended&#39;</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_chi2_extended</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="o">-</span><span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">err_obs_spectro_modif</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;chi2_extended_covariance&#39;</span> <span class="ow">and</span> <span class="nb">len</span><span class="p">(</span><span class="n">inv_cov_obs_modif</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">logL_spectro</span> <span class="o">=</span> <span class="n">logL_chi2_extended_covariance</span><span class="p">(</span><span class="n">flx_obs_spectro_modif</span><span class="o">-</span><span class="n">flx_mod_spectro_modif</span><span class="p">,</span> <span class="n">inv_cov_obs_modif</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="nb">print</span><span class="p">()</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING: One or more dataset are not included when performing the inversion.&#39;</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Please adapt your likelihood function choice.&#39;</span><span class="p">)</span>
+                <span class="nb">print</span><span class="p">()</span>
+                <span class="n">exit</span><span class="p">()</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">logL_spectro</span> <span class="o">=</span> <span class="mi">0</span>
+
+        <span class="c1"># Compute the final logL (sum of all likelihood under the hypothesis of independent instruments)</span>
+        <span class="n">FINAL_logL</span> <span class="o">=</span> <span class="n">logL_photo</span> <span class="o">+</span> <span class="n">logL_spectro</span> <span class="o">+</span> <span class="n">FINAL_logL</span>
+        
+    <span class="k">if</span> <span class="n">for_plot</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">FINAL_logL</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">return</span> <span class="n">modif_spec</span></div>
+
+
+
+<div class="viewcode-block" id="prior_transform">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.prior_transform">[docs]</a>
+<span class="k">def</span> <span class="nf">prior_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">lim_param_grid</span><span class="p">,</span> <span class="n">global_params</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function that define the priors to be used for the inversion. </span>
+<span class="sd">    We check that the boundaries are consistent with the grid extension.</span>
+<span class="sd">    </span>
+<span class="sd">    Args:</span>
+<span class="sd">        theta           (list): Parameter values randomly picked by the nested sampling</span>
+<span class="sd">        theta_index     (list): Index for the parameter values randomly picked</span>
+<span class="sd">        lim_param_grid  (list): Boundaries for the parameters explored</span>
+<span class="sd">        global_params (object): Class containing every input from the .ini file.</span>
+<span class="sd">        </span>
+<span class="sd">    Returns:</span>
+<span class="sd">        - prior           (list): List containing all the prior information</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Simon Petrus, Matthieu Ravet, Allan Denis</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+    <span class="n">prior</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_par1</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">prior_par1</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par1</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_par2</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">prior_par2</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par2</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_par3</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">prior_par3</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par3</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_par4</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">prior_par4</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par4</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">prior_par5</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
+                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">elif</span> <span class="n">prior_par5</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
+                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par5</span><span class="p">)</span>
+
+    <span class="c1"># Extra-grid parameters</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_r</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_r</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_r</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">ind_theta_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_d</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_d</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_d</span><span class="p">)</span>
+
+    <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+            
+    <span class="c1"># Individual parameters / observation</span>
+            
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate alpha for each observations</span>
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># Prior laws should be separeted by 2 values (need to be upgraded)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ind_theta_alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;alpha_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                        <span class="n">prior_alpha</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span> <span class="c1"># Prior values should be by two</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                        <span class="n">prior_alpha</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_alpha</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common alpha for all observations</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">ind_theta_alpha</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                    <span class="n">prior_alpha</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                    <span class="n">prior_alpha</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_alpha</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate rv for each observations</span>
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># Prior laws should be separeted by 2 values (need to be upgraded)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;rv_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                        <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span> <span class="c1"># Prior values should be by two</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                        <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_rv</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common rv for all observations</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;rv&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                    <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                    <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_rv</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span> <span class="c1"># If you want separate vsini for each observations</span>
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="c1"># Prior laws should be separeted by 2 values (need to be upgraded)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;vsini_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                        <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span> <span class="c1"># Prior values should be by two</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                        <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_vsini</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common vsini for all observations</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                    <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                    <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_vsini</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate ld for each observations</span>
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="c1"># Prior laws should be separeted by 2 values (need to be upgraded)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                    <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="sa">f</span><span class="s1">&#39;ld_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                        <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span> <span class="c1"># Prior values should be by two</span>
+                    <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                        <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="o">+</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_ld</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common ld for all observations</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;ld&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                    <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                    <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_ld</span><span class="p">)</span>
+
+    <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">ind_theta_av</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;av&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_av</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_av</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_av</span><span class="p">)</span>
+    <span class="c1">## adding the CPD params, bb_T and bb_R</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">ind_theta_bb_T</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;bb_T&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_bb_T</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_bb_T</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_bb_T</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">ind_theta_bb_R</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;bb_R&#39;</span><span class="p">)</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
+                <span class="n">prior_bb_R</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
+                <span class="n">prior_bb_R</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_bb_R</span><span class="p">)</span>
+    <span class="k">return</span> <span class="n">prior</span></div>
+
+
+
+<div class="viewcode-block" id="launch_nested_sampling">
+<a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">[docs]</a>
+<span class="k">def</span> <span class="nf">launch_nested_sampling</span><span class="p">(</span><span class="n">global_params</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&quot;&quot;&quot;</span>
+<span class="sd">    Function to launch the nested sampling. </span>
+<span class="sd">    We first perform LogL function check-ups. </span>
+<span class="sd">    Then the free parameters are counted and the data imported. </span>
+<span class="sd">    Finally, depending on the nested sampling methode chosen in the config file, we perform the inversion.</span>
+<span class="sd">    (Methods succesfully implemented are Nestle and PyMultinest)</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        global_params (object): Class containing every input from the .ini file.</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        None</span>
+<span class="sd">        </span>
+<span class="sd">    Author: Simon Petrus and Matthieu Ravet</span>
+<span class="sd">    &quot;&quot;&quot;</span>
+
+    <span class="c1"># LogL functions check-ups</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Likelihood functions check-ups&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">()</span>
+
+    <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+    <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>      
+        <span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+        <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="c1"># Check the choice of likelihood (only for MOSAIC)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">obs_name</span> <span class="o">+</span> <span class="s1">&#39; will be computed with &#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_Brogi&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING. You cannot use CCF mappings without substracting the continuum&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="n">exit</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_Zucker&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING. You cannot use CCF mappings without substracting the continuum&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="n">exit</span><span class="p">()</span>
+        <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">logL_type</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;CCF_custom&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;WARNING. You cannot use CCF mappings without substracting the continuum&#39;</span><span class="p">)</span>
+            <span class="nb">print</span><span class="p">()</span>
+            <span class="n">exit</span><span class="p">()</span>
+            
+        <span class="nb">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Done !&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">()</span>
+
+    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+
+    <span class="c1"># Count the number of free parameters and identify the parameter position in theta</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span>
+        <span class="n">lim_param_grid</span> <span class="o">=</span> <span class="p">[[</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)]]</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">lim_param_grid</span> <span class="o">=</span> <span class="p">[]</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par2&#39;</span><span class="p">)</span>
+        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par3&#39;</span><span class="p">)</span>
+        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par4&#39;</span><span class="p">)</span>
+        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par5&#39;</span><span class="p">)</span>
+        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
+    <span class="n">n_free_parameters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">])</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">)</span>
+
+    <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+            
+    <span class="c1"># Individual parameters / observation</span>
+        
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> 
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;alpha_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> 
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;rv_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;rv&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span> 
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;vsini_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> 
+        <span class="n">main_obs_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;ld_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+    <span class="k">else</span><span class="p">:</span>
+        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;ld&#39;</span><span class="p">)</span>
+
+    <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+            
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;av&#39;</span><span class="p">)</span>
+    <span class="c1">## adding cpd</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;bb_T&#39;</span><span class="p">)</span>
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
+        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;bb_R&#39;</span><span class="p">)</span>
+    <span class="n">theta_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">theta_index</span><span class="p">)</span>
+
+    <span class="c1"># Import all the data (only done once)</span>
+    <span class="n">main_file</span> <span class="o">=</span> <span class="n">import_obsmod</span><span class="p">(</span><span class="n">global_params</span><span class="p">)</span>
+    
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">==</span> <span class="s1">&#39;nestle&#39;</span><span class="p">:</span>
+        <span class="n">tmpstot1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">loglike_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">loglike</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">global_params</span><span class="p">,</span> <span class="n">main_file</span><span class="o">=</span><span class="n">main_file</span><span class="p">)</span>
+        <span class="n">prior_transform_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">prior_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">lim_param_grid</span><span class="p">,</span> <span class="n">global_params</span><span class="p">)</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">nestle</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">loglike_gp</span><span class="p">,</span> <span class="n">prior_transform_gp</span><span class="p">,</span> <span class="n">n_free_parameters</span><span class="p">,</span> 
+                               <span class="n">callback</span><span class="o">=</span><span class="n">nestle</span><span class="o">.</span><span class="n">print_progress</span><span class="p">,</span>
+                               <span class="n">npoints</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">npoint</span><span class="p">)),</span> 
+                               <span class="n">method</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">n_method</span><span class="p">,</span>
+                               <span class="n">maxiter</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">n_maxiter</span><span class="p">,</span>
+                               <span class="n">maxcall</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">n_maxcall</span><span class="p">,</span>
+                               <span class="n">dlogz</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">n_dlogz</span><span class="p">,</span>
+                               <span class="n">decline_factor</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">n_decline_factor</span><span class="p">)</span>
+        <span class="c1"># Reformat the result file</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;_RAW.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
+            <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">f1</span><span class="p">)</span>
+        <span class="n">logz</span> <span class="o">=</span> <span class="p">[</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logz&#39;</span><span class="p">],</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;logzerr&#39;</span><span class="p">]]</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;weights&#39;</span><span class="p">]</span>
+        <span class="n">logvol</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;logvol&#39;</span><span class="p">]</span>
+        <span class="n">logl</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;logl&#39;</span><span class="p">]</span>
+        <span class="n">tmpstot2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tmpstot1</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Nestle  &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The code spent &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">tmpstot2</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; sec to run.&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">==</span> <span class="s1">&#39;pymultinest&#39;</span><span class="p">:</span>
+        <span class="kn">import</span> <span class="nn">pymultinest</span>
+        <span class="n">tmpstot1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
+        <span class="n">loglike_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">loglike</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">global_params</span><span class="p">,</span> <span class="n">main_file</span><span class="o">=</span><span class="n">main_file</span><span class="p">)</span>
+        <span class="n">prior_transform_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">prior_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">lim_param_grid</span><span class="p">,</span> <span class="n">global_params</span><span class="p">)</span>
+        <span class="n">result</span> <span class="o">=</span> <span class="n">pymultinest</span><span class="o">.</span><span class="n">solve</span><span class="p">(</span>
+                        <span class="n">LogLikelihood</span><span class="o">=</span><span class="n">loglike_gp</span><span class="p">,</span>
+                        <span class="n">Prior</span><span class="o">=</span><span class="n">prior_transform_gp</span><span class="p">,</span>
+                        <span class="n">n_dims</span><span class="o">=</span><span class="n">n_free_parameters</span><span class="p">,</span>
+                        <span class="n">n_live_points</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">npoint</span><span class="p">)),</span>
+                        <span class="n">outputfiles_basename</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;_RAW_&#39;</span><span class="p">,</span>
+                        <span class="n">verbose</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                        <span class="n">resume</span><span class="o">=</span><span class="kc">False</span>
+                        <span class="p">)</span>
+        <span class="c1"># Reformat the result file</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;_RAW_stats.dat&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">open_dat</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">open_dat</span><span class="p">):</span>
+                <span class="k">if</span> <span class="n">l</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                    <span class="n">logz_multi</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">5</span><span class="p">])</span>
+                    <span class="n">logzerr_multi</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">7</span><span class="p">])</span>
+        <span class="n">sample_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">logl_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">logvol_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;_RAW_ev.dat&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">open_dat</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">open_dat</span><span class="p">):</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                <span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">line</span><span class="p">[:</span><span class="o">-</span><span class="mi">3</span><span class="p">]:</span>
+                    <span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
+                <span class="n">sample_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+                <span class="n">logl_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="o">-</span><span class="mi">3</span><span class="p">]))</span>
+                <span class="n">logvol_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="o">-</span><span class="mi">2</span><span class="p">]))</span>
+        <span class="n">sample_multi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">sample_multi</span><span class="p">)</span>
+        <span class="n">logl_multi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">logl_multi</span><span class="p">)</span>
+        <span class="n">logvol_multi</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">logvol_multi</span><span class="p">)</span>
+        <span class="n">iter_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">weights_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">final_logl_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">final_logvol_multi</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;_RAW_.txt&#39;</span><span class="p">,</span>
+                  <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">open_dat</span><span class="p">:</span>
+            <span class="k">for</span> <span class="n">l</span><span class="p">,</span> <span class="n">line</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">open_dat</span><span class="p">):</span>
+                <span class="n">line</span> <span class="o">=</span> <span class="n">line</span><span class="o">.</span><span class="n">strip</span><span class="p">()</span><span class="o">.</span><span class="n">split</span><span class="p">()</span>
+                <span class="n">points</span> <span class="o">=</span> <span class="p">[]</span>
+                <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="n">line</span><span class="p">[</span><span class="mi">2</span><span class="p">:]:</span>
+                    <span class="n">points</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">p</span><span class="p">))</span>
+                <span class="k">if</span> <span class="n">points</span> <span class="ow">in</span> <span class="n">sample_multi</span><span class="p">:</span>
+                    <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">sample_multi</span> <span class="o">==</span> <span class="n">points</span><span class="p">)</span>
+                    <span class="n">iter_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">points</span><span class="p">)</span>
+                    <span class="n">weights_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">line</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
+                    <span class="n">final_logl_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">logl_multi</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+                    <span class="n">final_logvol_multi</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">logvol_multi</span><span class="p">[</span><span class="n">ind</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
+        <span class="n">logz</span> <span class="o">=</span> <span class="p">[</span><span class="n">logz_multi</span><span class="p">,</span> <span class="n">logzerr_multi</span><span class="p">]</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">iter_multi</span><span class="p">)</span>
+        <span class="n">weights</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">weights_multi</span><span class="p">)</span>
+        <span class="n">logvol</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">final_logvol_multi</span><span class="p">)</span>
+        <span class="n">logl</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">final_logl_multi</span><span class="p">)</span>
+        <span class="n">tmpstot2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tmpstot1</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; PyMultinest  &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The code spent &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">tmpstot2</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; sec to run.&#39;</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The evidence is: </span><span class="si">%(logZ).1f</span><span class="s1"> +- </span><span class="si">%(logZerr).1f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="n">result</span><span class="p">)</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;The parameter values are:&#39;</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">col</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">theta_index</span><span class="p">,</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">transpose</span><span class="p">()):</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="si">%15s</span><span class="s1"> : </span><span class="si">%.3f</span><span class="s1"> +- </span><span class="si">%.3f</span><span class="s1">&#39;</span> <span class="o">%</span> <span class="p">(</span><span class="n">name</span><span class="p">,</span> <span class="n">col</span><span class="o">.</span><span class="n">mean</span><span class="p">(),</span> <span class="n">col</span><span class="o">.</span><span class="n">std</span><span class="p">()))</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
+
+    <span class="c1"># if global_params.ns_algo == &#39;ultranest&#39;:</span>
+    <span class="c1">#     import ultranest, ultranest.stepsampler</span>
+
+    <span class="c1">#     tmpstot1 = time.time()</span>
+        
+    <span class="c1">#     loglike_gp = lambda theta: loglike(theta, theta_index, global_params)</span>
+
+    <span class="c1">#     prior_transform_gp = lambda theta: prior_transform(theta, theta_index, lim_param_grid, global_params)</span>
+
+    <span class="c1">#     sampler = ultranest.ReactiveNestedSampler(theta_index,loglike=loglike_gp, transform=prior_transform_gp,</span>
+    <span class="c1">#                                               wrapped_params=[False, False, False, False])#,</span>
+    <span class="c1">#                                             #log_dir=global_params.result_path, resume=True)</span>
+    <span class="c1">#     #result = sampler.run(min_num_live_points=100, max_ncalls=100000)</span>
+
+    <span class="c1">#     # have to choose the number of steps the slice sampler should take</span>
+    <span class="c1">#     # after first results, this should be increased and checked for consistency.</span>
+    <span class="c1">#     nsteps = 2 * len(theta_index)</span>
+    <span class="c1">#     # create step sampler:</span>
+    <span class="c1">#     sampler.stepsampler = ultranest.stepsampler.SliceSampler(nsteps=nsteps,</span>
+    <span class="c1">#                                                              generate_direction=ultranest.stepsampler.generate_mixture_random_direction,</span>
+    <span class="c1">#                                                              # adaptive_nsteps=False,</span>
+    <span class="c1">#                                                              # max_nsteps=40</span>
+    <span class="c1">#                                                              )</span>
+    <span class="c1">#     sampler.print_results()</span>
+    <span class="c1">#     #sampler.plot_corner()</span>
+
+    <span class="c1">#     tmpstot2 = time.time()-tmpstot1</span>
+    <span class="c1">#     print(&#39; &#39;)</span>
+    <span class="c1">#     print(&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;)</span>
+    <span class="c1">#     print(&#39;-&gt; Ultranest  &#39;)</span>
+    <span class="c1">#     print(&#39; &#39;)</span>
+    <span class="c1">#     print(&#39;The code spent &#39; + str(tmpstot2) + &#39; sec to run.&#39;)</span>
+    <span class="c1">#     print(result.summary())</span>
+    <span class="c1">#     print(&#39;\n&#39;)</span>
+    
+    <span class="c1"># if global_params.ns_algo == &#39;dynesty&#39;:</span>
+    <span class="c1">#     from dynesty import NestedSampler</span>
+
+    <span class="c1">#     initialize our nested sampler</span>
+    <span class="c1">#     sampler = NestedSampler(loglike, ptform, ndim)</span>
+
+    <span class="n">result_reformat</span> <span class="o">=</span> <span class="p">{</span><span class="s2">&quot;samples&quot;</span><span class="p">:</span> <span class="n">samples</span><span class="p">,</span>
+                       <span class="s2">&quot;weights&quot;</span><span class="p">:</span> <span class="n">weights</span><span class="p">,</span>
+                       <span class="s2">&quot;logl&quot;</span><span class="p">:</span> <span class="n">logl</span><span class="p">,</span>
+                       <span class="s2">&quot;logvol&quot;</span><span class="p">:</span> <span class="n">logvol</span><span class="p">,</span>
+                       <span class="s2">&quot;logz&quot;</span><span class="p">:</span> <span class="n">logz</span><span class="p">,}</span>
+
+    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s2">&quot;wb&quot;</span><span class="p">)</span> <span class="k">as</span> <span class="n">tf</span><span class="p">:</span>
+        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">result_reformat</span><span class="p">,</span> <span class="n">tf</span><span class="p">)</span>
+
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Voilà, on est prêt&#39;</span><span class="p">)</span>
+
+    <span class="k">return</span> </div>
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+
+
+<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
+    <span class="kn">from</span> <span class="nn">ForMoSA.main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
+
+    <span class="c1"># USER configuration path</span>
+    <span class="nb">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Configuration of environment&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Where is your configuration file?&#39;</span><span class="p">)</span>
+    <span class="n">config_file_path</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">()</span>
+
+    <span class="c1"># CONFIG_FILE reading and defining global parameters</span>
+    <span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  <span class="c1"># To access any param.: global_params.parameter_name</span>
+    <span class="nb">print</span><span class="p">()</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Nested sampling&#39;</span><span class="p">)</span>
+    <span class="nb">print</span><span class="p">()</span>
+    <span class="n">launch_nested_sampling</span><span class="p">(</span><span class="n">global_params</span><span class="p">)</span>
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
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+        </div>
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+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
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diff --git a/docs/_build/html/_modules/ForMoSA/plotting/plotting_class.html b/docs/_build/html/_modules/ForMoSA/plotting/plotting_class.html
new file mode 100644
index 0000000..82e8ad5
--- /dev/null
+++ b/docs/_build/html/_modules/ForMoSA/plotting/plotting_class.html
@@ -0,0 +1,1532 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../../../">
+<head>
+  <meta charset="utf-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>ForMoSA.plotting.plotting_class &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../../../_static/pygments.css?v=fa44fd50" />
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+<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../../../demo.html">Tutorials</a></li>
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+      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
+      <li class="breadcrumb-item active">ForMoSA.plotting.plotting_class</li>
+      <li class="wy-breadcrumbs-aside">
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <h1>Source code for ForMoSA.plotting.plotting_class</h1><div class="highlight"><pre>
+<span></span>
+<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
+<span class="kn">import</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">glob</span><span class="o">,</span> <span class="nn">sys</span>
+<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+<span class="kn">from</span> <span class="nn">matplotlib.backends.backend_pdf</span> <span class="kn">import</span> <span class="n">PdfPages</span>
+<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
+<span class="kn">import</span> <span class="nn">corner</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">pickle</span>
+<span class="kn">from</span> <span class="nn">tqdm</span> <span class="kn">import</span> <span class="n">tqdm</span>
+
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;../&#39;</span><span class="p">))</span>
+
+<span class="c1"># Import ForMoSA</span>
+<span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">modif_spec</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">doppler_fct</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">lsq_fct</span>
+<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">vsini_fct_accurate</span>
+<span class="kn">from</span> <span class="nn">adapt.extraction_functions</span> <span class="kn">import</span> <span class="n">resolution_decreasing</span><span class="p">,</span> <span class="n">adapt_model</span><span class="p">,</span> <span class="n">decoupe</span>
+<span class="kn">from</span> <span class="nn">adapt.extraction_functions</span> <span class="kn">import</span> <span class="n">adapt_observation_range</span>
+
+
+
+
+<div class="viewcode-block" id="bin_data">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.bin_data">[docs]</a>
+<span class="k">def</span> <span class="nf">bin_data</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">bin_size</span><span class="p">):</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Function to bin data given a bin size</span>
+
+<span class="sd">    Args:</span>
+<span class="sd">        wave         (array): wavelength of the data</span>
+<span class="sd">        data         (array): data</span>
+<span class="sd">        bin_size       (int): size of the bin to apply</span>
+
+<span class="sd">    Returns:</span>
+<span class="sd">        - wave_binned  (array): binned wavelength</span>
+<span class="sd">        - data_binned  (array): binned data</span>
+
+<span class="sd">    Author: Allan Denis</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+    <span class="c1"># First quick check that len of data is a multpiple of bin_size</span>
+    <span class="k">while</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">data</span><span class="p">)</span><span class="o">%</span><span class="n">bin_size</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">):</span>
+        <span class="n">wave</span><span class="p">,</span> <span class="n">data</span> <span class="o">=</span> <span class="n">wave</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span> <span class="n">data</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
+        
+    <span class="n">bins</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">wave</span><span class="p">),</span> <span class="n">bin_size</span><span class="p">)</span>
+    <span class="n">wave_binned</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">reduceat</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span> <span class="o">/</span> <span class="n">bin_size</span>
+    <span class="n">data_binned</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">add</span><span class="o">.</span><span class="n">reduceat</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="n">bins</span><span class="p">)</span> 
+    
+    <span class="k">return</span> <span class="n">wave_binned</span><span class="p">,</span> <span class="n">data_binned</span></div>
+
+    
+    
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<div class="viewcode-block" id="ComplexRadar">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar">[docs]</a>
+<span class="k">class</span> <span class="nc">ComplexRadar</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Class to create Radar plots with asymmetric error bars.</span>
+
+<span class="sd">    Author: Paulina Palma-Bifani</span>
+<span class="sd">            Adapted from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb</span>
+<span class="sd">   </span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">variables</span><span class="p">,</span> <span class="n">ranges</span><span class="p">,</span> <span class="n">n_ordinate_levels</span><span class="o">=</span><span class="mi">6</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize class.</span>
+<span class="sd">        </span>
+<span class="sd">        Args:</span>
+<span class="sd">            fig               (object): matplotlib figure object</span>
+<span class="sd">            variables           (list): list of parameters to plot</span>
+<span class="sd">            ranges       (list(tuple)): upper and lower limits for each parameters</span>
+<span class="sd">            n_ordinate_levels    (int): (default = 6) number of gridlines in the plot</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None   </span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">,</span> <span class="mf">360.</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">))</span>
+
+        <span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.9</span><span class="p">],</span> <span class="n">polar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;axes</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">))]</span>
+
+        <span class="n">l</span><span class="p">,</span> <span class="n">text</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_thetagrids</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">variables</span><span class="p">)</span>
+
+        <span class="p">[[</span><span class="n">txt</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;bold&#39;</span><span class="p">),</span>
+              <span class="n">txt</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="mi">12</span><span class="p">),</span>
+              <span class="n">txt</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mf">0.2</span><span class="p">))]</span> <span class="k">for</span> <span class="n">txt</span> <span class="ow">in</span> <span class="n">text</span><span class="p">]</span>
+
+        <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">&quot;off&quot;</span><span class="p">)</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">):</span>
+            <span class="n">grid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">*</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num</span><span class="o">=</span><span class="n">n_ordinate_levels</span><span class="p">)</span>
+            <span class="n">gridlabel</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">grid</span><span class="p">]</span>
+
+            <span class="n">gridlabel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> <span class="c1"># clean up origin</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_rgrids</span><span class="p">(</span><span class="n">grid</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">gridlabel</span><span class="p">,</span><span class="n">angle</span><span class="o">=</span><span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">*</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
+        
+        <span class="c1"># variables for plotting</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">angles</span><span class="p">,</span> <span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span> <span class="o">=</span> <span class="n">ranges</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+
+<div class="viewcode-block" id="ComplexRadar.plot">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.plot">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to display the plot.</span>
+<span class="sd">        </span>
+<span class="sd">        Args:</span>
+<span class="sd">            data       (list): best value for each parameter</span>
+<span class="sd">            *args           : Variable length argument list.</span>
+<span class="sd">            **kw            : Arbitrary keyword arguments.</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">sdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata</span><span class="p">,</span> <span class="n">sdata</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ComplexRadar.fill">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.fill">[docs]</a>
+    <span class="k">def</span> <span class="nf">fill</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Add symmetric error bars to the plot.</span>
+<span class="sd">        </span>
+<span class="sd">        Args:</span>
+<span class="sd">            data       (list): best value for each parameter</span>
+<span class="sd">            *args           : Variable length argument list.</span>
+<span class="sd">            **kw            : Arbitrary keyword arguments.</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">sdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata</span><span class="p">,</span> <span class="n">sdata</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+    
+<div class="viewcode-block" id="ComplexRadar.fill_between">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.fill_between">[docs]</a>
+    <span class="k">def</span> <span class="nf">fill_between</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">list_down</span><span class="p">,</span> <span class="n">list_up</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Add asymmetric error bars to the plot.</span>
+<span class="sd">        </span>
+<span class="sd">        Args:</span>
+<span class="sd">            list_down (list): list of lower error bars</span>
+<span class="sd">            list_up   (list): list of upper error bars</span>
+<span class="sd">            *args           : Variable length argument list.</span>
+<span class="sd">            **kw            : Arbitrary keyword arguments.</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="n">sdata_down</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">list_down</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
+        <span class="n">sdata_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">list_up</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata_down</span><span class="p">,</span><span class="n">sdata_down</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata_up</span><span class="p">,</span><span class="n">sdata_up</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span></div>
+
+
+<div class="viewcode-block" id="ComplexRadar.scale_data">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.scale_data">[docs]</a>
+    <span class="k">def</span> <span class="nf">scale_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ranges</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to check that lower and upper limits are correctly ordered. It scales data[1:] to ranges[0]</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            data              (list): best value for each parameter</span>
+<span class="sd">            ranges     (list(tuple)): upper and lower limits for each parameters</span>
+<span class="sd">            *args           : Variable length argument list.</span>
+<span class="sd">            **kw            : Arbitrary keyword arguments.</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
+            <span class="k">assert</span> <span class="p">(</span><span class="n">y1</span> <span class="o">&lt;=</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">y2</span> <span class="o">&lt;=</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="n">y1</span><span class="p">)</span>
+        <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">d</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
+        <span class="n">sdata</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">]</span>
+        <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
+            <span class="k">if</span> <span class="n">y1</span> <span class="o">&gt;</span> <span class="n">y2</span><span class="p">:</span>
+                <span class="n">d</span> <span class="o">=</span> <span class="n">_invert</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">))</span>
+                <span class="n">y1</span><span class="p">,</span> <span class="n">y2</span> <span class="o">=</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y1</span>
+            <span class="n">sdata</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">d</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">y2</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">)</span> <span class="o">+</span> <span class="n">x1</span><span class="p">)</span>
+        <span class="k">return</span> <span class="n">sdata</span></div>
+</div>
+
+
+
+
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
+<div class="viewcode-block" id="PlottingForMoSA">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA">[docs]</a>
+<span class="k">class</span> <span class="nc">PlottingForMoSA</span><span class="p">():</span>
+<span class="w">    </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">    Class containing all the plotting functionalities of ForMoSA.</span>
+
+<span class="sd">    Author: Paulina Palma-Bifani, Simon Petrus, Matthieu Ravet and Allan Denis</span>
+<span class="sd">    &#39;&#39;&#39;</span>
+
+    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config_file_path</span><span class="p">,</span> <span class="n">color_out</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Initialize class by inheriting the global parameter class of ForMoSA.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            config_file_path   (str): path to the config.ini file currently used</span>
+<span class="sd">            color_out          (str): color to use for the model</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        
+        <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  
+        <span class="bp">self</span><span class="o">.</span><span class="n">color_out</span>     <span class="o">=</span> <span class="n">color_out</span>
+
+
+    <span class="k">def</span> <span class="nf">_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to get the posteriors, including luminosity derivation and Bayesian evidence logz.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            None</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            None</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">open_pic</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">open_pic</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;weights&#39;</span><span class="p">]</span>
+
+        <span class="c1"># To test the quality of the fit</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">logl</span><span class="o">=</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logl&#39;</span><span class="p">]</span>
+        <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logl</span><span class="o">==</span><span class="nb">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">logl</span><span class="p">))</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">sample_logz</span>    <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logz&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">sample_logzerr</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logz&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span><span class="mi">1</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">outputs_string</span> <span class="o">=</span> <span class="s1">&#39;logz = &#39;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_logz</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39; ± &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_logzerr</span><span class="p">)</span>
+
+        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+        <span class="n">attrs</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">attrs</span>
+        <span class="n">extra_parameters</span> <span class="o">=</span> <span class="p">[[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(R$\mathrm{_</span><span class="si">{Jup}</span><span class="s1">}$)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;(pc)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="sa">r</span><span class="s1">&#39;$\alpha$&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;$\alpha$&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;rv&#39;</span><span class="p">,</span> <span class="s1">&#39;RV&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(km.s$\mathrm{^{-1}}$)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;av&#39;</span><span class="p">,</span> <span class="s1">&#39;Av&#39;</span><span class="p">,</span> <span class="s1">&#39;(mag)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;vsini&#39;</span><span class="p">,</span> <span class="s1">&#39;v.sin(i)&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(km.s$\mathrm{^{-1}}$)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;ld&#39;</span><span class="p">,</span> <span class="s1">&#39;limb darkening&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;bb_T&#39;</span><span class="p">,</span> <span class="s1">&#39;bb_T&#39;</span><span class="p">,</span> <span class="s1">&#39;(K)&#39;</span><span class="p">],</span>
+                            <span class="p">[</span><span class="s1">&#39;bb_R&#39;</span><span class="p">,</span> <span class="s1">&#39;bb_R&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(R$\mathrm{_</span><span class="si">{Jup}</span><span class="s1">}$)&#39;</span><span class="p">]</span>
+                            <span class="p">]</span>
+
+        <span class="n">tot_list_param_title</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par1&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par2&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par3&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par4&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par5&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Extra-grid parameters</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">)</span>
+
+        <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+                
+        <span class="c1"># Individual parameters / observation</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate alpha for each observations</span>
+            <span class="n">main_obs_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+            <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                    <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="sa">fr</span><span class="s1">&#39;$_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">$&#39;</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                    <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;alpha_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common alpha for all observations</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">alpha</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;alpha&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate rv for each observations</span>
+            <span class="n">main_obs_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+            <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                    <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="sa">fr</span><span class="s1">&#39;$_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">$&#39;</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                    <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;rv_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common rv for all observations</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;rv&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">4</span><span class="p">:</span> <span class="c1"># If you want separate vsini for each observations</span>
+            <span class="n">main_obs_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+            <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">4</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                    <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="sa">fr</span><span class="s1">&#39;$_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">$&#39;</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                    <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;vsini_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common vsini for all observations</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">3</span><span class="p">:</span> <span class="c1"># If you want separate ld for each observations</span>
+            <span class="n">main_obs_path</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span>
+            <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="n">main_obs_path</span><span class="p">))):</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="n">indobs</span><span class="o">*</span><span class="mi">3</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span> <span class="c1"># Check if the idobs is different from constant</span>
+                    <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">6</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="sa">fr</span><span class="s1">&#39;$_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">$&#39;</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">6</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                    <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;ld_</span><span class="si">{</span><span class="n">indobs</span><span class="si">}</span><span class="s1">&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span> <span class="c1"># If you want 1 common vsini for all observations</span>
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+                <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">6</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">6</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;ld&#39;</span><span class="p">)</span>
+
+        <span class="c1"># - - - - - - - - - - - - - - - - - - - - -</span>
+
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;av&#39;</span><span class="p">)</span>
+        <span class="c1">## cpd bb</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_T</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">7</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">7</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;bb_T&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">bb_R</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">8</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">8</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;bb_R&#39;</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">theta_index</span><span class="p">)</span>
+
+        <span class="n">posterior_to_plot</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">samples</span>
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
+            <span class="n">posterior_to_plot</span> <span class="o">=</span> <span class="p">[]</span>
+            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;log(L/L$\mathrm{_{\odot}}$)&#39;</span><span class="p">)</span>
+            
+            <span class="k">for</span> <span class="n">res</span><span class="p">,</span> <span class="n">results</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">samples</span><span class="p">):</span> 
+                <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+                <span class="n">r_picked</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
+                
+                <span class="n">lum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="n">r_picked</span> <span class="o">*</span> <span class="mf">69911000.</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">*</span> <span class="mf">5.670e-8</span> <span class="o">/</span> <span class="mf">3.83e26</span><span class="p">)</span>
+                <span class="c1">#print(lum)</span>
+                <span class="n">results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">results</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">lum</span><span class="p">)))</span>
+                <span class="c1">#print(results)</span>
+                <span class="n">posterior_to_plot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">posterior_to_plot</span><span class="p">)</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span> <span class="o">=</span> <span class="n">tot_list_param_title</span>
+
+
+<div class="viewcode-block" id="PlottingForMoSA.plot_corner">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_corner</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">levels_sig</span><span class="o">=</span><span class="p">[</span><span class="mf">0.997</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.68</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">(</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">),</span> <span class="n">burn_in</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to display the corner plot</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            levels_sig    (list): (default = [0.997, 0.95, 0.68]) 1, 2 and 3 sigma contour levels of the corner plot</span>
+<span class="sd">            bins           (int): (default = 100) number of bins for the posteriors</span>
+<span class="sd">            quantiles     (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values</span>
+<span class="sd">            burn_in        (int): (default = 0) number of steps to remove from the plot</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig         (object): matplotlib figure object</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Corner plot&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
+
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">corner</span><span class="o">.</span><span class="n">corner</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="n">burn_in</span><span class="p">:],</span>
+                            <span class="n">weights</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">burn_in</span><span class="p">:],</span>
+                            <span class="n">labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">,</span>
+                            <span class="nb">range</span><span class="o">=</span><span class="p">[</span><span class="mf">0.999999</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">],</span>
+                            <span class="n">levels</span><span class="o">=</span><span class="n">levels_sig</span><span class="p">,</span>
+                            <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
+                            <span class="n">smooth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
+                            <span class="n">quantiles</span><span class="o">=</span><span class="n">quantiles</span><span class="p">,</span>
+                            <span class="n">top_ticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">plot_datapoints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
+                            <span class="n">plot_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                            <span class="n">plot_contours</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                            <span class="n">fill_contours</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                            <span class="n">show_titles</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
+                            <span class="n">title_fmt</span><span class="o">=</span><span class="s1">&#39;.2f&#39;</span><span class="p">,</span>
+                            <span class="n">title_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">),</span>
+                            <span class="n">contour_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">colors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.7</span><span class="p">),</span>
+                            <span class="n">pcolor_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">),</span>
+                            <span class="n">label_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">14</span><span class="p">))</span>
+    
+
+        <span class="n">fig</span><span class="o">.</span><span class="n">supxlabel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">outputs_string</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fig</span></div>
+
+
+
+<div class="viewcode-block" id="PlottingForMoSA.plot_chains">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_chains</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">15</span><span class="p">)):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Plot to check the convergence of the posterior chains.</span>
+<span class="sd">        Multiple (sub-)axis plot.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            figsize     (tuple): (default = (7, 15)) size of the plot</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig  (object) : matplotlib figure object</span>
+<span class="sd">            - ax   (object) : matplotlib axes objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Posteriors chains for each parameter&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
+        
+        <span class="n">col</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">0</span><span class="p">][:])</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">0</span><span class="p">][:])</span><span class="o">%</span><span class="mi">2</span><span class="p">)</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+
+        <span class="n">n</span><span class="o">=</span><span class="mi">0</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
+            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
+                <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[:,</span><span class="n">n</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+                <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">[</span><span class="n">n</span><span class="p">]</span><span class="o">==</span><span class="s1">&#39;log(L/L$</span><span class="se">\\</span><span class="s1">mathrm{_{</span><span class="se">\\</span><span class="s1">odot}}$)&#39;</span><span class="p">:</span>
+                    <span class="k">pass</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">[</span><span class="n">n</span><span class="p">],</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span><span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
+                <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="k">break</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">n</span><span class="o">+=</span><span class="mi">1</span>
+        
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span></div>
+
+
+
+<div class="viewcode-block" id="PlottingForMoSA.plot_radar">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_radar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">ranges</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">]):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Radar plot to check the distribution of the parameters.</span>
+<span class="sd">        Useful to compare different models.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            ranges     (list(tuple)): upper and lower limits for each parameters</span>
+<span class="sd">            label              (str): (default = &#39;&#39;) label of the plot</span>
+<span class="sd">            quantiles         (list): (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig  (object) : matplotlib figure object</span>
+<span class="sd">            - radar.ax   (object) : matplotlib radar class axes object    </span>
+
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Radar plot&#39;</span><span class="p">)</span>
+
+        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
+
+        <span class="n">list_posteriors</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">list_uncert_down</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">list_uncert_up</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">1</span><span class="p">,:])):</span>
+            <span class="n">q16</span><span class="p">,</span> <span class="n">q50</span><span class="p">,</span> <span class="n">q84</span> <span class="o">=</span> <span class="n">corner</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[:,</span><span class="n">l</span><span class="p">],</span> <span class="n">quantiles</span><span class="p">)</span>
+            
+            <span class="n">list_posteriors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">)</span>
+            <span class="n">list_uncert_down</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">q16</span><span class="p">)</span>
+            <span class="n">list_uncert_up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">q84</span><span class="p">)</span>
+
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
+        <span class="n">radar</span> <span class="o">=</span> <span class="n">ComplexRadar</span><span class="p">(</span><span class="n">fig</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">,</span> <span class="n">ranges</span><span class="p">)</span>
+
+        <span class="n">radar</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">list_posteriors</span><span class="p">,</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
+        <span class="n">radar</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">list_uncert_down</span><span class="p">,</span><span class="n">list_uncert_up</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+
+        <span class="n">radar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.20</span><span class="p">),</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">radar</span><span class="o">.</span><span class="n">ax</span></div>
+
+
+
+    <span class="k">def</span> <span class="nf">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">theta</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to get the data and best model asociated.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            theta                   (list): best parameter values</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - modif_spec_chi2  list(n-array): list containing the spectroscopic wavelength, spectroscopic fluxes of the data, </span>
+<span class="sd">                                            spectroscopic errors of the data, spectroscopic fluxes of the model, </span>
+<span class="sd">                                            photometric wavelength, photometric fluxes of the data, photometric errors of the data, </span>
+<span class="sd">                                            spectroscopic fluxes of the model,</span>
+<span class="sd">                                            planet transmission, star fluxes, systematics</span>
+<span class="sd">            - ck                list(floats): list scaling factor(s)</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="c1"># Get the posteriors</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
+
+        <span class="c1"># Create a list for each spectra (obs and mod) for each observation + scaling factors</span>
+        <span class="n">modif_spec_MOSAIC</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="n">CK</span> <span class="o">=</span> <span class="p">[]</span>
+
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+
+            <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+            <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+
+            <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+            <span class="n">wav_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">flx_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">err_obs_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_spectro_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">transm_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">star_flx_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="n">system_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_opt_merge&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="k">if</span> <span class="s1">&#39;obs_photo&#39;</span> <span class="ow">in</span> <span class="n">spectrum_obs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
+                <span class="n">wav_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+                <span class="n">flx_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+                <span class="n">err_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">wav_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+                <span class="n">flx_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+                <span class="n">err_obs_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+
+            <span class="c1"># Recovery of the spectroscopy and photometry model</span>
+            <span class="n">path_grid_spectro</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_spectro_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">)</span>
+            <span class="n">path_grid_photo</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;adapted_grid_photo_</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span><span class="si">}</span><span class="s1">_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">_nonan.nc&#39;</span><span class="p">)</span>
+            <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_spectro</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+            <span class="n">grid_spectro</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+            <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+            <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_photo</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
+            <span class="n">grid_photo</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+            <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_spectro</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_spectro</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_photo</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">grid_photo</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
+                                                            <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
+                                                            <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">}))</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">flx_mod_photo</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([])</span>
+
+            <span class="c1"># Modification of the synthetic spectrum with the extra-grid parameters</span>
+            <span class="n">modif_spec_chi2</span> <span class="o">=</span> <span class="n">modif_spec</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span><span class="p">,</span>
+                                        <span class="n">wav_obs_spectro</span><span class="p">,</span> <span class="n">flx_obs_spectro</span><span class="p">,</span> <span class="n">err_obs_spectro</span><span class="p">,</span> <span class="n">flx_mod_spectro</span><span class="p">,</span>
+                                        <span class="n">wav_obs_photo</span><span class="p">,</span> <span class="n">flx_obs_photo</span><span class="p">,</span> <span class="n">err_obs_photo</span><span class="p">,</span> <span class="n">flx_mod_photo</span><span class="p">,</span>
+                                        <span class="n">transm_obs</span><span class="p">,</span> <span class="n">star_flx_obs</span><span class="p">,</span> <span class="n">system_obs</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+            <span class="n">ck</span> <span class="o">=</span> <span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">8</span><span class="p">]</span>
+
+            <span class="n">modif_spec_MOSAIC</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">modif_spec_chi2</span><span class="p">)</span>
+            <span class="n">CK</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ck</span><span class="p">)</span>
+
+        <span class="n">modif_spec_chi2</span> <span class="o">=</span> <span class="n">modif_spec_MOSAIC</span>
+        <span class="n">ck</span> <span class="o">=</span> <span class="n">CK</span>
+        
+        <span class="k">return</span> <span class="n">modif_spec_chi2</span><span class="p">,</span> <span class="n">ck</span>
+    
+
+<div class="viewcode-block" id="PlottingForMoSA.get_FULL_spectra">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.get_FULL_spectra">[docs]</a>
+    <span class="k">def</span> <span class="nf">get_FULL_spectra</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">grid_used</span> <span class="o">=</span> <span class="s1">&#39;original&#39;</span><span class="p">,</span> <span class="n">wavelengths</span><span class="o">=</span><span class="p">[],</span> <span class="n">N_points</span><span class="o">=</span><span class="mi">1000</span><span class="p">,</span> <span class="n">re_interp</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">int_method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Extract a model spectrum from another grid.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            theta:                       (list): best parameter values</span>
+<span class="sd">            grid_used:                    (str): (default = &#39;original&#39;) Path to the grid from where to extract the spectrum. If &#39;original&#39;, the current grid will be used.</span>
+<span class="sd">            wavelengths:                 (list): (default = []) Desired wavelength range. If [] max and min values of wav_for_adapt range will be use to create the wavelength range.</span>
+<span class="sd">            N_points:                     (int): (default = 1000) Number of points.</span>
+<span class="sd">            re_interp:                (boolean): (default = False). Option to reinterpolate or not the grid.</span>
+<span class="sd">            int_method:                   (str): (default = &quot;linear&quot;) Interpolation method for the grid (if reinterpolated).</span>
+<span class="sd">        Returns:   </span>
+<span class="sd">            - wav_final                   (array): Wavelength array of the full model</span>
+<span class="sd">            - flx_final                   (array): Flux array of the full model</span>
+<span class="sd">            - ck                          (float): Scaling factor of the full model</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
+
+        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">wavelengths</span><span class="p">)</span><span class="o">==</span><span class="mi">0</span><span class="p">:</span>
+            <span class="c1"># Define the wavelength grid for the full spectra as resolution and wavelength range function</span>
+            <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+                <span class="n">my_string_ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_adapt</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
+                <span class="n">wav_ind</span> <span class="o">=</span> <span class="p">[</span><span class="nb">float</span><span class="p">(</span><span class="n">x</span><span class="p">)</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">my_string_ind</span><span class="p">]</span>
+                <span class="k">if</span> <span class="n">indobs</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">wav</span> <span class="o">=</span> <span class="n">wav_ind</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">wav</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav</span><span class="p">,</span> <span class="n">wav_ind</span><span class="p">))</span>
+            <span class="n">wav</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sort</span><span class="p">(</span><span class="n">wav</span><span class="p">)</span>
+            <span class="n">wavelengths</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">wav</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">wav</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="n">N_points</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">wavelengths</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="n">wavelengths</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span><span class="n">wavelengths</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">],</span><span class="n">N_points</span><span class="p">)</span>
+
+        <span class="c1"># Recover the original grid</span>
+        <span class="k">if</span> <span class="n">grid_used</span> <span class="o">==</span> <span class="s1">&#39;original&#39;</span><span class="p">:</span>
+            <span class="n">path_grid</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span>
+        <span class="k">else</span><span class="p">:</span> 
+            <span class="n">path_grid</span> <span class="o">=</span> <span class="n">grid_used</span>
+
+        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">)</span>
+
+        <span class="c1"># Possibility of re-interpolating holes if the grid contains to much of them (WARNING: Very long process)</span>
+        <span class="k">if</span> <span class="n">re_interp</span> <span class="o">==</span> <span class="kc">True</span><span class="p">:</span>
+            <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; The possible holes in the grid are (re)interpolated: &#39;</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">key_ind</span><span class="p">,</span> <span class="n">key</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">]):</span>
+                <span class="nb">print</span><span class="p">(</span><span class="nb">str</span><span class="p">(</span><span class="n">key_ind</span><span class="o">+</span><span class="mi">1</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">])))</span>
+                <span class="n">ds</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span><span class="o">=</span><span class="n">key</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">,</span> <span class="n">limit</span><span class="o">=</span><span class="kc">None</span><span class="p">,</span>
+                                            <span class="n">max_gap</span><span class="o">=</span><span class="kc">None</span><span class="p">)</span>
+                
+        <span class="n">wav_mod_nativ</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+        <span class="n">grid</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+        <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        
+        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">flx_mod_nativ</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span><span class="n">method</span><span class="o">=</span><span class="n">int_method</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">flx_mod_nativ</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span><span class="n">method</span><span class="o">=</span><span class="n">int_method</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
+        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
+            <span class="n">flx_mod_nativ</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span><span class="n">method</span><span class="o">=</span><span class="n">int_method</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span> 
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">flx_mod_nativ</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span><span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span><span class="n">method</span><span class="o">=</span><span class="n">int_method</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
+        
+        <span class="c1"># Interpolate to desire wavelength range</span>
+        <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+        <span class="n">flx_mod_final</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">wavelengths</span><span class="p">)</span>
+        
+        <span class="n">spectra</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="n">theta</span><span class="p">)</span>
+        <span class="c1"># WARNING : In case of multiple spectra, it is possible to work with different scaling factors. Here we only take the scaling factor of the first spectrum</span>
+        <span class="c1">#in the MOSAIC (used for the plot_fit)</span>
+        <span class="n">ck</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+
+        <span class="n">wavelengths</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">wavelengths</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">flx_mod_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">flx_mod_final</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="n">flx_mod_final_calib</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">flx_mod_final</span><span class="o">*</span><span class="n">ck</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
+        <span class="c1">#print(flx_mod_final[100],ck)</span>
+        <span class="n">err_mod_final_calib</span> <span class="o">=</span> <span class="n">flx_mod_final_calib</span><span class="o">*</span><span class="mf">0.1</span>
+
+        <span class="n">wav_final</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">flx_final</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">modif_spec</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span><span class="p">,</span>
+                                                                                    <span class="n">wavelengths</span><span class="p">,</span> <span class="n">flx_mod_final_calib</span><span class="p">,</span> <span class="n">err_mod_final_calib</span><span class="p">,</span> <span class="n">flx_mod_final_calib</span><span class="o">/</span><span class="n">ck</span><span class="p">,</span>
+                                                                                    <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[])</span>
+    
+        <span class="k">return</span> <span class="n">wav_final</span><span class="p">,</span> <span class="n">flx_final</span><span class="p">,</span> <span class="n">ck</span></div>
+
+
+
+    
+<div class="viewcode-block" id="PlottingForMoSA.plot_fit">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">uncert</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">trans</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">logx</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Plot the best fit comparing with the data.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            figsize    (tuple): (default = (10, 5)) Size of the plot</span>
+<span class="sd">            uncert     (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot spectra with associated error bars</span>
+<span class="sd">            trans      (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot transmision curves for photometry</span>
+<span class="sd">            logx       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the wavelength in log scale</span>
+<span class="sd">            logy       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the flux in log scale</span>
+<span class="sd">            norm       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the normalized spectra</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig    (object) : matplotlib figure object</span>
+<span class="sd">            - ax     (object) : matplotlib axes objects, main spectra plot</span>
+<span class="sd">            - axr    (object) : matplotlib axes objects, residuals</span>
+<span class="sd">            - axr2   (object) : matplotlib axes objects, right side density histogram</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Best fit and residuals plot&#39;</span><span class="p">)</span>
+
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
+        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">5</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+        <span class="n">axr</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">2</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
+        <span class="n">axr2</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">2</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
+
+       
+        <span class="n">spectra</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">)</span>
+        <span class="n">iobs_spectro</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="n">iobs_photo</span> <span class="o">=</span> <span class="mi">0</span>
+        
+
+        <span class="c1"># Scale or not in absolute flux</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span> 
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">ck</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
+            <span class="k">else</span><span class="p">:</span>
+                <span class="n">ck</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
+
+
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+            
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+                <span class="c1"># If we used the lsq function, it means that our data is contaminated by the starlight difraction</span>
+                <span class="c1"># so the model is the sum of the planet model + the estimated stellar contribution</span>
+                <span class="n">spectra</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">)</span> <span class="c1"># Transform spectra to a list so that we can modify its values</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+                <span class="n">model</span><span class="p">,</span> <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">9</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">10</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">11</span><span class="p">]</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">planet_contribution</span> <span class="o">*</span> <span class="n">model</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+                <span class="n">systematics</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">12</span><span class="p">]</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">+=</span> <span class="n">systematics</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">iobs_spectro</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">iobs_photo</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="k">if</span> <span class="n">uncert</span><span class="o">==</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">yerr</span><span class="o">=</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+
+
+                <span class="n">residuals</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+                <span class="n">sigma_res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanstd</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span> <span class="c1"># Replace np.std by np.nanstd if nans are in the array to ignore them</span>
+                <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">residuals</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+                <span class="n">axr</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
+                <span class="n">axr2</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">residuals</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span> <span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">)</span>
+                <span class="n">axr2</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">handlelength</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">indobs</span> <span class="o">==</span> <span class="n">iobs_spectro</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># Add labels out of the loops</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Spectroscopic data&#39;</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Spectroscopic model&#39;</span><span class="p">)</span>
+                    <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Spectroscopic model-data&#39;</span><span class="p">)</span>
+                    <span class="n">axr2</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">residuals</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span> <span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;density&#39;</span><span class="p">)</span>
+                    
+                    <span class="n">iobs_spectro</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                    
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">iobs_photo</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="n">iobs_spectro</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="c1"># If you want to plot the transmission filters</span>
+                <span class="k">if</span> <span class="n">trans</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+                    <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+                    <span class="n">obs_photo_ins</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo_ins&#39;</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">pho_ind</span><span class="p">,</span> <span class="n">pho</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_photo_ins</span><span class="p">):</span>
+                        <span class="n">path_list</span> <span class="o">=</span> <span class="vm">__file__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="n">separator</span> <span class="o">=</span> <span class="s1">&#39;/&#39;</span>
+                        <span class="n">filter_pho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">separator</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_list</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/phototeque/&#39;</span> <span class="o">+</span> <span class="n">pho</span> <span class="o">+</span> <span class="s1">&#39;.npz&#39;</span><span class="p">)</span>
+                        <span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">],</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span><span class="o">*</span><span class="mf">0.8</span><span class="o">*</span><span class="nb">min</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">]),</span><span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+                        <span class="n">ax</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span><span class="o">*</span><span class="mf">0.4</span><span class="o">*</span><span class="nb">min</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">])),</span> <span class="n">pho</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span> <span class="o">/</span> <span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">7</span><span class="p">]</span> <span class="o">/</span> <span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">)</span>
+                
+
+                <span class="n">residuals_phot</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">7</span><span class="p">]</span><span class="o">-</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span>
+                <span class="n">sigma_res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">residuals_phot</span><span class="p">)</span>
+                <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">residuals_phot</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
+                <span class="n">axr</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
+
+                <span class="k">if</span> <span class="n">indobs</span> <span class="o">==</span> <span class="n">iobs_photo</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># Add labels out of the loops</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Photometry data&#39;</span><span class="p">)</span>
+                    <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Photometry model&#39;</span><span class="p">)</span>
+                    <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Photometry model-data&#39;</span><span class="p">)</span>
+                    
+                    <span class="n">iobs_photo</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+                    
+        <span class="c1"># Set xlog-scale</span>
+        <span class="k">if</span> <span class="n">logx</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+            <span class="n">axr</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="c1"># Set xlog-scale</span>
+        <span class="k">if</span> <span class="n">logy</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="c1"># Remove the xticks from the first ax</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
+        <span class="c1"># Labels</span>
+        <span class="n">axr</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Wavelength (µm)&#39;</span><span class="p">)</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span> 
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Flux (W m-2 µm-1)&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Normalised flux (W m-2 µm-1)&#39;</span><span class="p">)</span>
+        <span class="n">axr</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Residuals ($\sigma$)&#39;</span><span class="p">)</span>
+        
+        <span class="n">axr2</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+        <span class="n">axr</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+            
+        <span class="c1"># define the data as global</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spectra</span> <span class="o">=</span> <span class="n">spectra</span>
+
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">axr</span><span class="p">,</span> <span class="n">axr2</span></div>
+
+    
+    
+<div class="viewcode-block" id="PlottingForMoSA.plot_fit_HiRes">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit_HiRes">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_fit_HiRes</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">uncert</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">trans</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">logx</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">logy</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Same as plot_fit but with the stellar and planetary models for high-resolution spectroscopy. Does not include residuals in a sub-axis.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            figsize    (tuple): (default = (10, 5)) Size of the plot</span>
+<span class="sd">            uncert     (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot spectra with associated error bars</span>
+<span class="sd">            trans      (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot transmision curves for photometry</span>
+<span class="sd">            logx       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the wavelength in log scale</span>
+<span class="sd">            logy       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the flux in log scale</span>
+<span class="sd">            norm       (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the normalized spectra</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig  (object) : matplotlib figure object</span>
+<span class="sd">            - ax   (object) : matplotlib axes objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Best fit and residuals plot&#39;</span><span class="p">)</span>
+
+        <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+       
+        <span class="n">spectra</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">)</span>
+        <span class="n">iobs_spectro</span> <span class="o">=</span> <span class="mi">0</span>
+        
+
+        <span class="c1"># Scale or not in absolute flux</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span> 
+            <span class="n">ck</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
+
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+            
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+                <span class="c1"># If we used the lsq function, it means that our data is contaminated by the starlight difraction</span>
+                <span class="c1"># so the model is the sum of the planet model + the estimated stellar contribution</span>
+                <span class="n">spectra</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">)</span> <span class="c1"># Transform spectra to a list so that we can modify its values</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+                <span class="n">model</span><span class="p">,</span> <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">9</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">10</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">11</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">12</span><span class="p">]</span>
+                <span class="n">transm</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">13</span><span class="p">]</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">planet_contribution</span> <span class="o">*</span> <span class="n">model</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">+=</span> <span class="n">systematics</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="n">iobs_spectro</span> <span class="o">+=</span> <span class="mi">1</span>
+                <span class="k">if</span> <span class="n">uncert</span><span class="o">==</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">yerr</span><span class="o">=</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">alpha</span> <span class="o">=</span> <span class="mf">0.2</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;g&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">),</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;b&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">planet_contribution</span> <span class="o">*</span> <span class="n">model</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;purple&#39;</span><span class="p">)</span>
+
+ 
+                <span class="k">if</span> <span class="n">indobs</span> <span class="o">==</span> <span class="n">iobs_spectro</span> <span class="o">-</span> <span class="mi">1</span><span class="p">:</span>
+                    <span class="c1"># Add labels out of the loops</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;residuals&#39;</span><span class="p">)</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;data&#39;</span><span class="p">)</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;full model&#39;</span><span class="p">)</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;stellar model&#39;</span><span class="p">)</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;planetary model&#39;</span><span class="p">)</span>
+                    
+                    <span class="n">iobs_spectro</span> <span class="o">=</span> <span class="o">-</span><span class="mi">1</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+                <span class="c1"># If you want to plot the transmission filters</span>
+                <span class="k">if</span> <span class="n">trans</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">obs</span>
+                    <span class="n">obs_name</span> <span class="o">=</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">splitext</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">basename</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">))[</span><span class="mi">0</span><span class="p">]</span>
+                    <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">,</span> <span class="sa">f</span><span class="s1">&#39;spectrum_obs_</span><span class="si">{</span><span class="n">obs_name</span><span class="si">}</span><span class="s1">.npz&#39;</span><span class="p">),</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
+                    <span class="n">obs_photo_ins</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_photo_ins&#39;</span><span class="p">]</span>
+                    <span class="k">for</span> <span class="n">pho_ind</span><span class="p">,</span> <span class="n">pho</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_photo_ins</span><span class="p">):</span>
+                        <span class="n">path_list</span> <span class="o">=</span> <span class="vm">__file__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
+                        <span class="n">separator</span> <span class="o">=</span> <span class="s1">&#39;/&#39;</span>
+                        <span class="n">filter_pho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">separator</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_list</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39;/phototeque/&#39;</span> <span class="o">+</span> <span class="n">pho</span> <span class="o">+</span> <span class="s1">&#39;.npz&#39;</span><span class="p">)</span>
+                        <span class="n">ax1</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">],</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span><span class="o">*</span><span class="mf">0.8</span><span class="o">*</span><span class="nb">min</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">]),</span><span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.3</span><span class="p">)</span>
+                        <span class="n">ax1</span><span class="o">.</span><span class="n">text</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">]),</span> <span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span><span class="o">*</span><span class="mf">0.4</span><span class="o">*</span><span class="nb">min</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">])),</span> <span class="n">pho</span><span class="p">,</span> <span class="n">horizontalalignment</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;gray&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">7</span><span class="p">]</span><span class="o">/</span><span class="n">ck</span><span class="p">[</span><span class="n">indobs</span><span class="p">],</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">)</span>
+                
+
+                <span class="n">residuals_phot</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">7</span><span class="p">]</span><span class="o">-</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">5</span><span class="p">]</span>
+                <span class="n">sigma_res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">residuals_phot</span><span class="p">)</span>
+
+
+                <span class="k">if</span> <span class="n">indobs</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="c1"># Add labels out of the loops</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Photometry data&#39;</span><span class="p">)</span>
+                    <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">4</span><span class="p">]))</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Photometry model&#39;</span><span class="p">)</span>
+
+        <span class="c1"># Set xlog-scale</span>
+        <span class="k">if</span> <span class="n">logx</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+            <span class="n">ax1</span><span class="o">.</span><span class="n">set_xscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="c1"># Set xlog-scale</span>
+        <span class="k">if</span> <span class="n">logy</span> <span class="o">==</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span>
+            <span class="n">ax1</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="c1"># Remove the xticks from the first ax</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
+        <span class="c1"># Labels</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span> 
+            <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Flux (ADU)&#39;</span><span class="p">)</span>
+        <span class="k">else</span><span class="p">:</span>
+            <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Normalised flux (W m-2 µm-1)&#39;</span><span class="p">)</span>
+            
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;wavelength ($ \mu $m)&#39;</span><span class="p">)</span>
+        
+        <span class="n">fig1</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">fig1</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;full_data.pdf&#39;</span><span class="p">)</span>
+
+        <span class="c1"># define the data as global</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">spectra</span> <span class="o">=</span> <span class="n">spectra</span>
+
+        <span class="k">return</span> <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span></div>
+
+    
+    
+<div class="viewcode-block" id="PlottingForMoSA.plot_HiRes_comp_model">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_HiRes_comp_model">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_HiRes_comp_model</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">norm</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">,</span> <span class="n">data_resolution</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Specific function to plot the best fit comparing with the data for high-resolution spectroscopy.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            figsize             (tuple): (default = (10, 5)) Size of the plot</span>
+<span class="sd">            norm                  (str): (default = no) &#39;yes&#39; or &#39;no&#39; to plot the normalized spectra</span>
+<span class="sd">            data_resolution       (int): (default = 0) Custom resolution to broadened data</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig1  (object) : matplotlib figure object</span>
+<span class="sd">            - ax1   (object) : matplotlib axes objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Planet model and data&#39;</span><span class="p">)</span>
+       
+        <span class="n">spectra</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">)</span>
+        <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="n">figsize</span><span class="p">)</span>
+        <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="n">figsize</span><span class="p">)</span>
+
+        <span class="c1"># Scale or not in absolute flux</span>
+        <span class="k">if</span> <span class="n">norm</span> <span class="o">!=</span> <span class="s1">&#39;yes&#39;</span><span class="p">:</span> 
+            <span class="n">ck</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]),</span> <span class="mi">1</span><span class="p">)</span>
+            
+        <span class="n">pdf</span> <span class="o">=</span> <span class="n">PdfPages</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;PLanet_model_and_data_resolution_degraded.pdf&#39;</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">ioff</span><span class="p">()</span>
+        
+
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+            
+            
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+                <span class="c1"># If we used the lsq function, it means that our data is contaminated by the starlight difraction</span>
+                <span class="c1"># so the model is the sum of the planet model + the estimated stellar contribution</span>
+                <span class="n">spectra</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">)</span> <span class="c1"># Transform spectra to a list so that we can modify its values</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+                <span class="n">model</span><span class="p">,</span> <span class="n">planet_contribution</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">,</span> <span class="n">systematics</span><span class="p">,</span> <span class="n">transm</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">9</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">10</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">11</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">12</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">13</span><span class="p">]</span>
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">-</span> <span class="n">systematics</span>
+                <span class="k">elif</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>     <span class="c1"># if len(systematics) = 0 but len(star_flx) &gt; 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>  <span class="c1"># if len(star_flx) = 0 but len(systematics) &gt; 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">systematics</span>
+                <span class="k">else</span><span class="p">:</span>                         <span class="c1"># if len(star_flx) = 0 and len(systematics) = 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="n">wave</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
+                <span class="n">planet_model</span> <span class="o">=</span> <span class="n">planet_contribution</span> <span class="o">*</span> <span class="n">model</span> 
+                
+                <span class="c1"># Compute intrinsic resolution of the data because of the v.sini</span>
+                <span class="n">resolution</span> <span class="o">=</span> <span class="mf">3.0</span><span class="o">*</span><span class="mf">1e5</span> <span class="o">/</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">])</span>
+                <span class="n">resolution</span> <span class="o">=</span> <span class="n">resolution</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wave</span><span class="p">))</span>
+                
+                <span class="k">if</span> <span class="n">data_resolution</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">custom_reso</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">=</span> <span class="s1">&#39;NA&#39;</span>
+                    <span class="n">resolution_data</span> <span class="o">=</span> <span class="n">data_resolution</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">wave</span><span class="p">))</span>
+                    <span class="n">data_broadened</span> <span class="o">=</span> <span class="n">vsini_fct_accurate</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">])</span>
+                    <span class="n">data_broadened</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wave</span><span class="p">,</span> <span class="p">[],</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">wave</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">resolution_data</span><span class="p">,</span> <span class="s1">&#39;mod&#39;</span><span class="p">)</span>
+                    <span class="n">planet_model_broadened</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wave</span><span class="p">,</span> <span class="p">[],</span> <span class="n">resolution</span><span class="p">,</span> <span class="n">wave</span><span class="p">,</span> <span class="n">planet_model</span><span class="p">,</span> <span class="n">resolution_data</span><span class="p">,</span> <span class="s1">&#39;mod&#39;</span><span class="p">)</span>
+       
+                
+                <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="s1">&#39;comp_model&#39;</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+                <span class="n">fig</span><span class="o">.</span><span class="n">clf</span><span class="p">()</span>
+                <span class="n">ax</span> <span class="o">=</span> <span class="n">fig</span><span class="o">.</span><span class="n">add_subplot</span><span class="p">(</span><span class="mi">111</span><span class="p">)</span>
+                
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">data_broadened</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">planet_model_broadened</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
+                
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;wavelength ($\mu$m)&#39;</span><span class="p">)</span>
+                <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Flux (ADU)&#39;</span><span class="p">)</span>
+                
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">)</span>
+                <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">planet_model</span><span class="p">,</span> <span class="n">c</span> <span class="o">=</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
+                
+                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+                    <span class="n">legend_data</span> <span class="o">=</span> <span class="s1">&#39;data - star&#39;</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">legend_data</span> <span class="o">=</span> <span class="s1">&#39;data&#39;</span>
+                    
+                <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">legend_data</span><span class="p">,</span> <span class="s1">&#39;planet model&#39;</span><span class="p">])</span>
+                
+                <span class="n">pdf</span><span class="o">.</span><span class="n">savefig</span><span class="p">()</span>
+                
+        <span class="n">pdf</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
+        
+        <span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="n">legend_data</span><span class="p">,</span> <span class="s2">&quot;planet model&quot;</span><span class="p">],</span> <span class="n">fontsize</span> <span class="o">=</span> <span class="mi">18</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;wavelength ($ \mu $m)&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Flux (ADU)&#39;</span><span class="p">,</span> <span class="n">fontsize</span><span class="o">=</span><span class="mi">18</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">fig1</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;Planet_model_and_data.pdf&#39;</span><span class="p">)</span>
+                            
+        <span class="k">return</span> <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span></div>
+
+    
+    
+<div class="viewcode-block" id="PlottingForMoSA.plot_ccf">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_ccf">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_ccf</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span> <span class="o">=</span> <span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">rv_grid</span> <span class="o">=</span> <span class="p">[</span><span class="o">-</span><span class="mi">300</span><span class="p">,</span><span class="mi">300</span><span class="p">],</span> <span class="n">rv_step</span> <span class="o">=</span> <span class="mf">0.5</span><span class="p">,</span> <span class="n">window_normalisation</span> <span class="o">=</span> <span class="mi">100</span><span class="p">,</span> <span class="n">model_wavelength</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">model_spectra</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">model_resolution</span> <span class="o">=</span> <span class="p">[],</span> <span class="n">model_name</span> <span class="o">=</span> <span class="s1">&#39;Full&#39;</span><span class="p">,</span> <span class="n">rv_cor</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Plot the cross-correlation function. It is used for high resolution spectroscopy.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            figsize                   (tuple): (default = (10, 5)) Size of the plot</span>
+<span class="sd">            rv_grid                    (list): (default = [-300,300]) Maximum and minumum values of the radial velocity shift (in km/s)</span>
+<span class="sd">            rv_step                   (float): (default = 0.5) Radial velocity shift steps (in km/s)</span>
+<span class="sd">            window_normalisation        (int): (default = 100) ?</span>
+<span class="sd">            model_wavelength           (list): (default = []) ?</span>
+<span class="sd">            model_spectra =            (list): (default = []) ?</span>
+<span class="sd">            model_resolution           (list): (default = []) ?</span>
+<span class="sd">            model_name                  (str): (default = &#39;Full&#39;) ?</span>
+<span class="sd">            rv_cor                      (int): (default = 0) ?</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig1                    (object) : matplotlib figure object</span>
+<span class="sd">            - ax1                     (object) : matplotlib axes objects</span>
+<span class="sd">            - rv_grid                    (list): Radial velocity grid</span>
+<span class="sd">            - ccf                        (list): Cross-correlation function</span>
+<span class="sd">            - acf                        (list): Auto-correlation function  </span>
+
+<span class="sd">        Author: Allan Denis</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - CCF plot&#39;</span><span class="p">)</span>
+        
+        
+        <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span><span class="mi">1</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        
+        <span class="n">rv_grid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">rv_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">rv_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">rv_step</span><span class="p">)</span>
+        <span class="n">ccf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        <span class="n">acf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        
+        <span class="n">spectra</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">)</span>
+        
+        <span class="n">model_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        <span class="n">wave_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        <span class="n">err_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        <span class="n">transm_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        <span class="n">data_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([])</span>
+        
+        <span class="k">for</span> <span class="n">indobs</span><span class="p">,</span> <span class="n">obs</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="o">.</span><span class="n">glob</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">main_observation_path</span><span class="p">))):</span>
+            
+            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">use_lsqr</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;True&#39;</span><span class="p">:</span>
+                <span class="c1"># If we used the lsq function, it means that our data is contaminated by the starlight difraction</span>
+                <span class="c1"># so the model is the sum of the planet model + the estimated stellar contribution</span>
+                <span class="n">spectra</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">)</span> <span class="c1"># Transform spectra to a list so that we can modify its values</span>
+                <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">])</span>
+                <span class="n">wavelength</span><span class="p">,</span> <span class="n">err</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">,</span> <span class="n">systematics</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">10</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">11</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">12</span><span class="p">]</span>
+                <span class="n">transm</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">13</span><span class="p">]</span>
+     
+
+            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
+
+                <span class="k">if</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">)</span> <span class="ow">and</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span> <span class="o">-</span> <span class="n">systematics</span>
+                <span class="k">elif</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">star_flx</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>     <span class="c1"># if len(systematics) = 0 but len(star_flx) &gt; 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">dot</span><span class="p">(</span><span class="n">stellar_contribution</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">T</span><span class="p">)</span>
+                <span class="k">elif</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">systematics</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">):</span>  <span class="c1"># if len(star_flx) = 0 but len(systematics) &gt; 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">systematics</span>
+                <span class="k">else</span><span class="p">:</span>                         <span class="c1"># if len(star_flx) = 0 and len(systematics) = 0</span>
+                    <span class="n">data</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
+
+                <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">model</span> <span class="o">=</span> <span class="n">doppler_fct</span><span class="p">(</span><span class="n">wavelength</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">err</span><span class="p">,</span> <span class="n">model</span><span class="p">,</span> <span class="o">-</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;rv&#39;</span><span class="p">])</span>
+                    
+                <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">model_spectra</span><span class="p">)</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
+                    <span class="n">model_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model_array</span><span class="p">,</span> <span class="n">model</span><span class="p">)</span>
+                <span class="k">else</span><span class="p">:</span>
+                    <span class="n">obs_spectro</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">adapt_observation_range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                    <span class="n">res_obs</span> <span class="o">=</span> <span class="n">obs_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span>
+                    <span class="n">res_obs_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">obs_spectro</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">res_obs</span><span class="p">,</span> <span class="n">fill_value</span> <span class="o">=</span> <span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+                    <span class="n">res_obs</span> <span class="o">=</span> <span class="n">res_obs_interp</span><span class="p">(</span><span class="n">wavelength</span><span class="p">)</span>
+                    
+                    <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">model_wavelength</span> <span class="o">&gt;=</span> <span class="n">wavelength</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">model_wavelength</span> <span class="o">&lt;=</span> <span class="n">wavelength</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]))</span>
+                    <span class="n">model_wavelength_adapt</span><span class="p">,</span> <span class="n">model_resolution_adapt</span><span class="p">,</span> <span class="n">model_spectra_adapt</span> <span class="o">=</span> <span class="n">model_wavelength</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">model_resolution</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">model_spectra</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span>
+                    <span class="n">model_resolution_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">model_wavelength_adapt</span><span class="p">,</span> <span class="n">model_resolution_adapt</span><span class="p">,</span> <span class="n">fill_value</span> <span class="o">=</span> <span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+                    <span class="n">model_resolution_adapt</span> <span class="o">=</span> <span class="n">model_resolution_interp</span><span class="p">(</span><span class="n">wavelength</span><span class="p">)</span>
+                    <span class="n">model_adapted</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wavelength</span><span class="p">,</span> <span class="p">[],</span> <span class="n">res_obs</span><span class="p">,</span> <span class="n">model_wavelength_adapt</span><span class="p">,</span> <span class="n">model_spectra_adapt</span><span class="p">,</span> <span class="n">model_resolution_adapt</span><span class="p">,</span>
+                                                        <span class="s1">&#39;mod&#39;</span><span class="p">,</span> <span class="n">indobs</span><span class="o">=</span><span class="n">indobs</span><span class="p">)</span>
+                    
+                    
+                    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">transm</span><span class="p">)</span> <span class="o">&gt;</span> <span class="mi">0</span><span class="p">:</span>
+                        <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">model_adapted</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span> <span class="o">=</span> <span class="n">lsq_fct</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="n">indobs</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">err</span><span class="p">,</span> <span class="n">star_flx</span><span class="p">,</span> <span class="n">transm</span><span class="p">,</span> <span class="n">model_adapted</span><span class="p">,</span> <span class="n">systematics</span><span class="p">)</span>
+                        
+                    <span class="n">model_adapted</span> <span class="o">=</span> <span class="n">vsini_fct_accurate</span><span class="p">(</span><span class="n">wavelength</span><span class="p">,</span> <span class="n">model_adapted</span><span class="p">,</span> <span class="mf">0.6</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta_best</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span><span class="o">==</span><span class="s1">&#39;vsini&#39;</span><span class="p">])</span>
+                    
+                    <span class="n">model_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">model_array</span><span class="p">,</span> <span class="n">model_adapted</span><span class="p">)</span>
+                
+                <span class="n">wave_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">wave_array</span><span class="p">,</span> <span class="n">wavelength</span><span class="p">)</span>
+                <span class="n">data_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">data_array</span><span class="p">,</span> <span class="n">data</span><span class="p">)</span>
+                <span class="n">transm_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">transm_array</span><span class="p">,</span> <span class="n">transm</span><span class="p">)</span>
+                <span class="n">err_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">err_array</span><span class="p">,</span> <span class="n">err</span><span class="p">)</span>
+
+        <span class="n">max_ccf</span> <span class="o">=</span> <span class="mi">0</span>
+        <span class="k">for</span> <span class="n">rv</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">rv_grid</span><span class="p">):</span>
+            <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">model_doppler</span> <span class="o">=</span> <span class="n">doppler_fct</span><span class="p">(</span><span class="n">wave_array</span><span class="p">,</span> <span class="n">data_array</span><span class="p">,</span> <span class="n">err_array</span><span class="p">,</span> <span class="n">model_array</span><span class="p">,</span> <span class="n">rv</span><span class="o">+</span><span class="n">rv_cor</span><span class="p">)</span>
+            <span class="n">ccf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">ccf</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">model_doppler</span> <span class="o">*</span> <span class="n">data_array</span><span class="p">))</span>
+            <span class="n">acf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">acf</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">model_doppler</span> <span class="o">*</span> <span class="n">model_array</span><span class="p">))</span>
+            
+            <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">model_doppler</span> <span class="o">*</span> <span class="n">data_array</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">max_ccf</span><span class="p">:</span>
+                <span class="n">max_ccf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">model_doppler</span> <span class="o">*</span> <span class="n">data_array</span><span class="p">)</span>
+                <span class="n">model_max_ccf</span> <span class="o">=</span> <span class="n">model_doppler</span>
+            
+        <span class="c1"># Rescaling cross-correlation function to estimate a SNR</span>
+        <span class="n">acf_norm</span> <span class="o">=</span> <span class="n">acf</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">acf</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rv_grid</span><span class="p">)</span> <span class="o">&gt;</span> <span class="n">window_normalisation</span><span class="p">)])</span>
+        <span class="n">ccf_norm</span> <span class="o">=</span> <span class="n">ccf</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">ccf</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rv_grid</span><span class="o">-</span><span class="n">rv_grid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ccf</span><span class="p">)])</span> <span class="o">&gt;</span> <span class="n">window_normalisation</span><span class="p">)])</span>
+        <span class="n">ccf_noise</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">[(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">rv_grid</span><span class="o">-</span><span class="n">rv_grid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ccf</span><span class="p">)])</span> <span class="o">&gt;</span> <span class="n">window_normalisation</span><span class="p">)])</span>
+        <span class="n">ccf_norm</span> <span class="o">=</span> <span class="n">ccf_norm</span> <span class="o">/</span> <span class="n">ccf_noise</span>
+        
+        <span class="c1"># Rescaling autocorrelation function to make comparable with cross-correlation function</span>
+        <span class="n">acf_norm</span> <span class="o">=</span> <span class="n">acf_norm</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">acf_norm</span><span class="p">)</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">)</span>
+        
+        <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rv_grid</span><span class="p">,</span> <span class="n">ccf_norm</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;ccf&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">rv_grid</span> <span class="o">+</span> <span class="n">rv_grid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">)]</span> <span class="o">+</span> <span class="n">rv_cor</span><span class="p">,</span> <span class="n">acf_norm</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">axvline</span><span class="p">(</span><span class="n">x</span> <span class="o">=</span> <span class="n">rv_grid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">)],</span> <span class="n">linestyle</span> <span class="o">=</span> <span class="s1">&#39;--&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;C3&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;RV (km/s)&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;S/N&#39;</span><span class="p">)</span>
+        <span class="n">ax1</span><span class="o">.</span><span class="n">legend</span><span class="p">([</span><span class="s1">&#39;ccf&#39;</span><span class="p">,</span> <span class="s1">&#39;acf&#39;</span><span class="p">])</span>
+        <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">&#39;SNR = </span><span class="si">{</span><span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">)</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1">, RV = </span><span class="si">{</span><span class="n">rv_grid</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ccf_norm</span><span class="p">)]</span><span class="si">:</span><span class="s1">.1f</span><span class="si">}</span><span class="s1"> km/s&#39;</span><span class="p">)</span>
+        <span class="c1">#ax1.set_title(f&#39;SNR = {np.nanmax(ccf_norm):.1f}, RV = {rv_grid[np.argmax(ccf_norm)]:.1f} km/s&#39;)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">fig1</span><span class="p">)</span>
+        <span class="n">plt</span><span class="o">.</span><span class="n">savefig</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;ccf_&#39;</span> <span class="o">+</span> <span class="n">model_name</span> <span class="o">+</span> <span class="s1">&#39;.pdf&#39;</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fig1</span><span class="p">,</span> <span class="n">ax1</span><span class="p">,</span> <span class="n">rv_grid</span><span class="p">,</span> <span class="n">ccf</span><span class="p">,</span> <span class="n">acf</span></div>
+
+    
+
+
+    
+<div class="viewcode-block" id="PlottingForMoSA.plot_PT">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_PT</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">path_temp_profile</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">model</span> <span class="o">=</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39;</span>
+<span class="sd">        Function to plot the Pressure-Temperature profiles.</span>
+<span class="sd">        Adpated from Nathan Zimniak.</span>
+
+<span class="sd">        Args:</span>
+<span class="sd">            path_temp_profile    (str): Path to the temperature profile grid</span>
+<span class="sd">            figsize            (tuple): (default = (6, 5)) Size of the plot</span>
+<span class="sd">            model                (str): (default = &#39;ExoREM&#39;) Name of the model grid</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig  (object) : matplotlib figure object</span>
+<span class="sd">            - ax   (object) : matplotlib axes objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Pressure-Temperature profile&#39;</span><span class="p">)</span>
+
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
+            <span class="c1"># samples = result.samples</span>
+            <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+
+        <span class="c1"># put nans where data is not realistic</span>
+        <span class="n">out</span><span class="o">=</span><span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">400</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2000</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">3.00</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">5.00</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.32</span> <span class="ow">or</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">10.00</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.10</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.80</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span>
+            <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+        <span class="c1">#Crée une liste pour chaque paramètre</span>
+        <span class="n">Teffs</span><span class="p">,</span> <span class="n">loggs</span><span class="p">,</span> <span class="n">MHs</span><span class="p">,</span> <span class="n">COs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+        <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
+            <span class="n">gammas</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="n">Teffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">loggs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">:</span>
+                <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+                <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
+            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
+                <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
+                <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span>
+                <span class="n">gammas</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
+
+        <span class="c1">#Charge la grille de profils de température</span>
+        <span class="n">temperature_grid_xa</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataarray</span><span class="p">(</span><span class="n">path_temp_profile</span><span class="p">)</span>
+        <span class="c1">#Crée les profils de température associés aux points de la grille</span>
+        <span class="n">P</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">]</span>
+        <span class="n">temperature_profiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">:</span>
+                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
+            <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
+                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gammas</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
+        <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
+            <span class="c1">#Calcule le 2eme facteur de robustesse (pour ATMO)</span>
+            <span class="n">nbNans</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="mi">0</span><span class="p">,:])):</span>
+                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="mi">0</span><span class="p">])):</span>
+                    <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s2">&quot;nan&quot;</span><span class="p">:</span>
+                        <span class="n">nbNans</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">nbNans</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span>
+            <span class="n">FdR2</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">nbNans</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
+            <span class="n">FdR1</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;Facteur de robustesse 1&#39;</span><span class="p">]</span>
+            <span class="n">FdR</span> <span class="o">=</span> <span class="n">FdR1</span><span class="o">*</span><span class="n">FdR2</span>
+            <span class="c1">#Extrapole les températures</span>
+            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+                <span class="n">newT</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]),</span> <span class="p">[(</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">P</span><span class="p">)))])</span>
+                <span class="n">newT</span> <span class="o">=</span> <span class="n">newT</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span> <span class="o">=</span> <span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
+                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">newT</span><span class="p">)</span>
+        <span class="c1">#Calcule le profil le plus probable</span>
+        <span class="n">Tfit</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
+            <span class="n">Tfit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">50</span><span class="p">))</span>
+        <span class="c1">#Calcule les percentiles 68 et 96 du profil le plus probable</span>
+        <span class="n">Tinf68</span><span class="p">,</span> <span class="n">Tsup68</span><span class="p">,</span> <span class="n">Tinf95</span><span class="p">,</span> <span class="n">Tsup95</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
+            <span class="n">Tinf68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">16</span><span class="p">))</span>
+            <span class="n">Tsup68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">84</span><span class="p">))</span>
+            <span class="n">Tinf95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">Tsup95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">98</span><span class="p">))</span>
+        <span class="c1">#Plot le profil le plus probable et les percentiles associés</span>
+
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Tinf95</span><span class="p">,</span> <span class="n">Tsup95</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;2 $\sigma$&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Tinf68</span><span class="p">,</span> <span class="n">Tsup68</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;1 $\sigma$&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Tfit</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best fit&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">max</span><span class="p">(</span><span class="n">P</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">P</span><span class="p">)])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">minorticks_on</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (K)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (Pa)&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+
+       
+
+<div class="viewcode-block" id="PlottingForMoSA.plot_Clouds">
+<a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds">[docs]</a>
+    <span class="k">def</span> <span class="nf">plot_Clouds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cloud_prop</span><span class="p">,</span> <span class="n">path_cloud_profile</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">)):</span>
+<span class="w">        </span><span class="sd">&#39;&#39;&#39; </span>
+<span class="sd">        Function to plot cloud profiles.</span>
+<span class="sd">        Adapted from Nathan Zimniak</span>
+
+<span class="sd">        Args: </span>
+<span class="sd">            cloud_prop (str) : Choose the cloud species. The options are</span>
+<span class="sd">                                [&#39;eddy_diffusion_coefficient&#39;,</span>
+<span class="sd">                                &#39;vmr_CH4&#39;,</span>
+<span class="sd">                                &#39;vmr_CO&#39;,</span>
+<span class="sd">                                &#39;vmr_CO2&#39;,</span>
+<span class="sd">                                &#39;vmr_FeH&#39;,</span>
+<span class="sd">                                &#39;vmr_H2O&#39;,</span>
+<span class="sd">                                &#39;vmr_H2S&#39;,</span>
+<span class="sd">                                &#39;vmr_HCN&#39;,</span>
+<span class="sd">                                &#39;vmr_K&#39;,</span>
+<span class="sd">                                &#39;vmr_Na&#39;,</span>
+<span class="sd">                                &#39;vmr_NH3&#39;,</span>
+<span class="sd">                                &#39;vmr_PH3&#39;,</span>
+<span class="sd">                                &#39;vmr_TiO&#39;,</span>
+<span class="sd">                                &#39;vmr_VO&#39;,</span>
+<span class="sd">                                &#39;cloud_opacity_Fe&#39;,</span>
+<span class="sd">                                &#39;cloud_opacity_Mg2SiO4&#39;,</span>
+<span class="sd">                                &#39;cloud_particle_radius_Fe&#39;,</span>
+<span class="sd">                                &#39;cloud_particle_radius_Mg2SiO4&#39;,</span>
+<span class="sd">                                &#39;cloud_vmr_Fe&#39;,</span>
+<span class="sd">                                &#39;cloud_vmr_Mg2SiO4&#39;]</span>
+<span class="sd">        Returns:</span>
+<span class="sd">            - fig  (object) : matplotlib figure object</span>
+<span class="sd">            - ax   (object) : matplotlib axes objects</span>
+<span class="sd">        &#39;&#39;&#39;</span>
+        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Cloud profile&#39;</span><span class="p">)</span>
+        
+        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
+            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
+            <span class="c1"># samples = result.samples</span>
+            <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="p">[</span><span class="s1">&#39;samples&#39;</span><span class="p">]</span>
+
+        <span class="c1">#Supprime les points hors de la grille</span>
+        <span class="n">out</span><span class="o">=</span><span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="k">if</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">400</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2000</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">3.00</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">5.00</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.32</span> <span class="ow">or</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">10.00</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.10</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.80</span><span class="p">:</span>
+                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span>
+            <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
+        <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
+        <span class="c1">#Crée une liste pour chaque paramètre</span>
+        <span class="n">Teffs</span><span class="p">,</span> <span class="n">loggs</span><span class="p">,</span> <span class="n">MHs</span><span class="p">,</span> <span class="n">COs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="n">Teffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
+            <span class="n">loggs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
+            <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
+            <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
+        <span class="c1">#Charge la grille de profils d&#39;une propriété d&#39;un nuage</span>
+        <span class="n">cloud_props_grids_xa</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_cloud_profile</span><span class="p">)</span>
+        <span class="n">cloud_prop_grid_xa</span> <span class="o">=</span> <span class="n">cloud_props_grids_xa</span><span class="p">[</span><span class="s1">&#39;P_&#39;</span> <span class="o">+</span> <span class="n">cloud_prop</span><span class="p">]</span>
+        <span class="c1">#Crée les profils d&#39;une propriété d&#39;un nuage associés aux points de la grille</span>
+        <span class="n">P</span> <span class="o">=</span> <span class="n">cloud_prop_grid_xa</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">]</span>
+        <span class="n">cloud_prop_profiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
+            <span class="n">cloud_prop_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">cloud_prop_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
+        <span class="c1"># Calcule le profil le plus probable</span>
+        <span class="n">propfit</span> <span class="o">=</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
+            <span class="n">propfit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="mi">50</span><span class="p">))</span>
+        <span class="c1"># Calcule les percentiles 68 et 96 du profil le plus probable</span>
+        <span class="n">propinf68</span><span class="p">,</span> <span class="n">propsup68</span><span class="p">,</span> <span class="n">propinf95</span><span class="p">,</span> <span class="n">propsup95</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
+        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
+            <span class="n">propinf68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="mi">16</span><span class="p">))</span>
+            <span class="n">propsup68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="mi">84</span><span class="p">))</span>
+            <span class="n">propinf95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
+            <span class="n">propsup95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span> <span class="n">i</span><span class="p">],</span> <span class="mi">98</span><span class="p">))</span>
+
+        <span class="c1"># Plot le profil le plus probable et les percentiles associés</span>
+        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
+        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">propinf95</span><span class="p">,</span> <span class="n">propsup95</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;2 $\sigma$&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">propinf68</span><span class="p">,</span> <span class="n">propsup68</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="sa">r</span><span class="s1">&#39;1 $\sigma$&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">propfit</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best fit&#39;</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">max</span><span class="p">(</span><span class="n">P</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">P</span><span class="p">)])</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">minorticks_on</span><span class="p">()</span>
+        <span class="k">if</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;T&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (K)&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;eddy_diffusion_coefficient&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Eddy diffusion coefficient ($m^2.s^{-1}$)&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CH4&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$CH_4$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CO&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;CO volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CO2&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$CO_2$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_FeH&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;FeH volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_H2O&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$H_2O$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_H2S&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$H_2S$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_HCN&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;HCN volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_K&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;K volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_Na&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Na volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_NH3&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$NH_3$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_PH3&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$PH_3$ volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_TiO&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;TiO volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_VO&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;VO volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_opacity_Fe&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud opacity&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_opacity_Mg2SiO4&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud opacity&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_particle_radius_Fe&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud particle radius (m)&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_particle_radius_Mg2SiO4&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud particle radius (m)&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_vmr_Fe&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_vmr_Mg2SiO4&#39;</span><span class="p">:</span>
+            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud volume mixing ratio&#39;</span><span class="p">)</span>
+        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (Pa)&#39;</span><span class="p">)</span>
+
+        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div>
+</div>
+
+    
+
+
+</pre></div>
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/_modules/index.html b/docs/_build/html/_modules/index.html
similarity index 75%
rename from docs/build/html/_modules/index.html
rename to docs/_build/html/_modules/index.html
index ca5a3e3..8563045 100644
--- a/docs/build/html/_modules/index.html
+++ b/docs/_build/html/_modules/index.html
@@ -1,22 +1,24 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="../">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Overview: module code &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
+  <title>Overview: module code &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="../_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="../_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="../" id="documentation_options" src="../_static/documentation_options.js"></script>
-        <script src="../_static/jquery.js"></script>
-        <script src="../_static/underscore.js"></script>
-        <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../_static/doctools.js"></script>
-        <script src="../_static/sphinx_highlight.js"></script>
+        <script src="../_static/jquery.js?v=5d32c60e"></script>
+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="../_static/documentation_options.js?v=51b770b3"></script>
+        <script src="../_static/doctools.js?v=9a2dae69"></script>
+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="../_static/js/theme.js"></script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" /> 
@@ -41,9 +43,10 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="../demo.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="../api.html">ForMoSA API</a></li>
 </ul>
 
@@ -75,6 +78,7 @@ <h1>All modules for which code is available</h1>
 <li><a href="ForMoSA/adapt/adapt_obs_mod.html">ForMoSA.adapt.adapt_obs_mod</a></li>
 <li><a href="ForMoSA/adapt/extraction_functions.html">ForMoSA.adapt.extraction_functions</a></li>
 <li><a href="ForMoSA/main_utilities.html">ForMoSA.main_utilities</a></li>
+<li><a href="ForMoSA/nested_sampling/nested_logL_functions.html">ForMoSA.nested_sampling.nested_logL_functions</a></li>
 <li><a href="ForMoSA/nested_sampling/nested_modif_spec.html">ForMoSA.nested_sampling.nested_modif_spec</a></li>
 <li><a href="ForMoSA/nested_sampling/nested_prior_function.html">ForMoSA.nested_sampling.nested_prior_function</a></li>
 <li><a href="ForMoSA/nested_sampling/nested_sampling.html">ForMoSA.nested_sampling.nested_sampling</a></li>
@@ -88,7 +92,7 @@ <h1>All modules for which code is available</h1>
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/build/html/_sources/adapt.rst.txt b/docs/_build/html/_sources/adapt.rst.txt
similarity index 100%
rename from docs/build/html/_sources/adapt.rst.txt
rename to docs/_build/html/_sources/adapt.rst.txt
diff --git a/docs/_build/html/_sources/api.rst.txt b/docs/_build/html/_sources/api.rst.txt
new file mode 100644
index 0000000..92bc8c4
--- /dev/null
+++ b/docs/_build/html/_sources/api.rst.txt
@@ -0,0 +1,12 @@
+.. _api:
+
+ForMoSA API
+===========
+
+.. toctree::
+   :titlesonly:
+   
+   main_utilities.rst
+   adapt.rst
+   nested_sampling.rst
+   plotting.rst
\ No newline at end of file
diff --git a/docs/_build/html/_sources/atm_grids.rst.txt b/docs/_build/html/_sources/atm_grids.rst.txt
new file mode 100644
index 0000000..aef5ad4
--- /dev/null
+++ b/docs/_build/html/_sources/atm_grids.rst.txt
@@ -0,0 +1,9 @@
+.. _atm_grids:
+
+Atmospheric grids
+=================
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/exorem_info.ipynb
\ No newline at end of file
diff --git a/docs/_build/html/_sources/demo.rst.txt b/docs/_build/html/_sources/demo.rst.txt
new file mode 100644
index 0000000..05c79df
--- /dev/null
+++ b/docs/_build/html/_sources/demo.rst.txt
@@ -0,0 +1,64 @@
+.. _demo:
+
+Tutorials
+=========
+
+There are several ways of using ForMoSA.
+
+To get started, we recomend you to keep the following structure locally.
+
+.. code-block:: bash
+
+   ~/YOUR/PATH/formosa_desk/
+   ├── atm_grids/
+   ├── demo_ABPicb/      
+   │   ├── data.fits       
+   │   ├── config.ini      
+   │   ├── adapted_grid/ 
+   │   └── outputs/
+   ├── (ForMoSA/)
+   ├── (PyMultiNest/)
+   └── (MultiNest/)
+
+Depending on the way you installed ForMoSA, the ForMoSA, PyMultiNest, and MultiNest subfolders need to be cloned from GitHub. 
+Follow the :doc:`installation` 
+
+Atmospheric grids
++++++++++++++++++
+
+This is the list of the publically available atmospheric grids which we have formated for ForMoSA. 
+
+Download the grid you want to use by clicking over it's name. Ideally, save it inside the ``atm_grids/`` subdirectory.
+
+* `ATMO native <https://drive.google.com/file/d/1S1dcBD7UiuUCZIcNBNnJi6LMymrnkagM/view?usp=share_link>`_ from `M.W. Phillips et al. 2020 <https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract>`_
+
+
+* `BT-Settl native <https://drive.google.com/file/d/1wvf4A-DupdVnYIpK_HmHE-fobqnYtvEz/view?usp=share_link>`_ from `Allard et al. 2013 <https://ui.adsabs.harvard.edu/abs/2013MSAIS..24..128A/abstract>`_
+
+
+* `ExoREM native <https://drive.google.com/file/d/1k9SQjHLnMCwmGOHtraRnhCgiZ1-4J3Wk/view?usp=share_link>`_ from `B. Charnay et al. 2018 <https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract>`_
+
+
+Learn more about:
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/exorem_info.ipynb
+
+
+
+
+List of tutorials:
+++++++++++++++++++
+
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/inputs
+
+   tutorials/demoabpic
+..
+   demo_mosaic.rst
+   demo_pymultinest.rst
\ No newline at end of file
diff --git a/docs/_build/html/_sources/demo_basic.rst.txt b/docs/_build/html/_sources/demo_basic.rst.txt
new file mode 100644
index 0000000..5cbacf1
--- /dev/null
+++ b/docs/_build/html/_sources/demo_basic.rst.txt
@@ -0,0 +1,13 @@
+.. _demo_basic:
+
+demo AB Pic b
+=============
+
+This tutorial is intended as a quick start. 
+You will use medium resolution VLT/SINFONI K-band data, published in `P. Palma-Bifani et al (2023) <https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf>`_.
+
+
+.. toctree::
+   :maxdepth: 1
+
+   demoabpic
\ No newline at end of file
diff --git a/docs/_build/html/_sources/demo_classic.rst.txt b/docs/_build/html/_sources/demo_classic.rst.txt
new file mode 100644
index 0000000..f97bab9
--- /dev/null
+++ b/docs/_build/html/_sources/demo_classic.rst.txt
@@ -0,0 +1,4 @@
+.. _demo_classic:
+
+DEMO classic:
+=============
\ No newline at end of file
diff --git a/docs/_build/html/_sources/demoabpic.ipynb.txt b/docs/_build/html/_sources/demoabpic.ipynb.txt
new file mode 100644
index 0000000..72a10ab
--- /dev/null
+++ b/docs/_build/html/_sources/demoabpic.ipynb.txt
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB Pic demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ".. toctree::\n",
+    "   :maxdepth: 2\n",
+    "   :titlesonly:\n",
+    "\n",
+    "   demo_basic.rst\n",
+    "\n",
+    "   tutorials/demo_basic.ipynb\n",
+    "\n",
+    "   :doc:`tutorials/demo_basic.ipynb`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/_sources/index.rst.txt b/docs/_build/html/_sources/index.rst.txt
new file mode 100644
index 0000000..a0a1c16
--- /dev/null
+++ b/docs/_build/html/_sources/index.rst.txt
@@ -0,0 +1,59 @@
+.. ForMoSA documentation master file, created by
+   sphinx-quickstart on Sat Jul 27 09:35:15 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. |br| raw:: html
+
+   <br />
+
+.. image:: ForMoSA.png
+   :width: 150px
+   :height: 105px
+   :align: center
+
+Forward Modeling Tool for Spectral Analysis
+===========================================
+
+Welcome to the documentation of ForMoSA, an open-source Python package.
+Using a forward modeling approach, we designed this tool to model exoplanetary atmospheres.
+We encourage the community to exploit its capabilities!
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   installation.rst
+   demo.rst
+   api.rst
+
+
+
+Attribution
++++++++++++
+
+Please cite `Petrus et al (2023) <https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract>`_.
+
+
+
+Issues (?)
+++++++++++
+
+If you run into any other problem, please create an issue on `GitHub <https://github.com/exoAtmospheres/ForMoSA/issues>`_.
+
+
+
+Version Track
++++++++++++++
+
+- ``1.1.2`` Version adapted for including multiple instruments and high spectral resolution observations.
+
+- ``1.0.13`` First version distributed, presented at `Cloud Academy 3. <https://alienearths.space/cloud-academy-3/>`_.
+
+- ``1.0.5`` First operational release.
+
+
+Acknowledgments
++++++++++++++++
+Our sincere thanks to `Code/Astro <https://semaphorep.github.io/codeastro/>`_. 
diff --git a/docs/_build/html/_sources/installation.rst.txt b/docs/_build/html/_sources/installation.rst.txt
new file mode 100644
index 0000000..a3818ed
--- /dev/null
+++ b/docs/_build/html/_sources/installation.rst.txt
@@ -0,0 +1,136 @@
+.. _installation:
+
+Installation
+============
+
+There are two possibilities to install ``ForMoSA``. 
+Installation of the package through ``PyPI`` or through ``GitHub``. 
+
+
+Create a conda environment
+++++++++++++++++++++++++++
+
+We strongly recommend using a ``conda`` environment. You can `learn more about conda here <https://conda.io/docs/user-guide/tasks/manage-environments.html>`_ .
+
+(For all users)
+
+You can create your environment as:
+
+.. code-block:: console
+
+    $ conda create -n env_formosa python=3.11
+    $ conda activate env_formosa 
+
+(For macOS users) 
+
+To install and use our package in macOS with an M1 chip, a few extra steps are required. 
+To use PyMultiNest to paralelize the processes, make sure you are building your conda environment under an OSX-ARM64 architecture. You can `learn more here <https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple>`_ .
+
+.. code-block:: console
+
+    $ CONDA_SUBDIR=osx-arm64 conda create -n env_formosa python=3.11 numpy -c conda-forge
+    $ conda activate env_formosa 
+    $ conda config --env --set subdir osx-arm64
+
+
+
+A. Installation though PyPI
++++++++++++++++++++++++++++
+
+PyPI is the `pip package manager <https://packaging.python.org/tutorials/installing-packages/>`_, which will automatically install for you almost all the required dependencies in your environment.
+To install the latest version run: 
+
+.. code-block:: console
+
+    $ pip install ForMoSA
+
+To solve a standard error, run the following line in your environment:
+
+.. code-block:: console
+
+    $ conda install xarray dask netCDF4 bottleneck
+
+
+B. Installation though GitHub
++++++++++++++++++++++++++++++
+
+Install the following packages in your environment with pip install: 
+
+.. code-block:: console  
+
+    $ numpy
+    $ matplotlib
+    $ corner
+    $ astropy
+    $ scipy
+    $ configobj
+    $ extinction
+    $ nestle
+    $ PyAstronomy
+    $ spectres
+    $ pyyaml
+    $ importlib-metadata==4.13.0
+    $ xarray==2023.10.1
+
+To solve a standard error, run the following line in your environment:
+
+.. code-block:: console
+
+    $ conda install xarray dask netCDF4 bottleneck
+
+The last instalation step is to actually get ForMoSA. 
+ForMoSA can be cloned from the main branch from our GitHub repository. 
+You can do it by moving to the desired directory on your terminal and writing:
+
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/exoAtmospheres/ForMoSA.git
+
+
+PyMultiNest users
++++++++++++++++++
+
+If you want to use Pymultinest to run your inversion, follow the installation instructions from `PyMultinest <https://johannesbuchner.github.io/PyMultiNest/install.html>`_, detailed below. 
+
+First, you need to clone PyMultinest from GitHub and install it.
+
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/JohannesBuchner/PyMultiNest/
+    $ cd PyMultiNest
+    $ python setup.py install
+
+Second, you need to corroborate that your system has a C++ and a Fortran interpreter. 
+If you need to install brew, follow `these instructions <https://brew.sh/>`_.
+
+.. code-block:: console
+
+    $ brew install cmake
+    $ brew install gcc
+    $ brew install open-mpi
+
+Next, in your ForMoSA environment, install mpi4pi as:
+
+.. code-block:: console
+
+    $ pip install mpi4py
+
+Then, install MultiNest by cloning the GitHub repository and building it.
+Make sure you empty the build folder if you run this step more than once.
+    
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/JohannesBuchner/MultiNest
+    $ cd MultiNest/build
+    $ cmake ..
+    $ make
+
+Finally, copy the files that were generated by building MultiNest onto your conda environment by doing:
+
+.. code-block:: console
+
+    $ cp -v ~/YOUR_PATH/MultiNest/lib/* /YOUR_PATH/opt/anaconda3/envs/env_formosa/lib/
+	
diff --git a/docs/build/html/_sources/main_utilities.rst.txt b/docs/_build/html/_sources/main_utilities.rst.txt
similarity index 77%
rename from docs/build/html/_sources/main_utilities.rst.txt
rename to docs/_build/html/_sources/main_utilities.rst.txt
index 977df0f..c4f187e 100644
--- a/docs/build/html/_sources/main_utilities.rst.txt
+++ b/docs/_build/html/_sources/main_utilities.rst.txt
@@ -4,5 +4,7 @@ Main Utilities
 ==============
 .. module:: ForMoSA
 
+Global functions:
++++++++++++++++++
 .. automodule:: ForMoSA.main_utilities
 	:members:
\ No newline at end of file
diff --git a/docs/build/html/_sources/nested_sampling.rst.txt b/docs/_build/html/_sources/nested_sampling.rst.txt
similarity index 75%
rename from docs/build/html/_sources/nested_sampling.rst.txt
rename to docs/_build/html/_sources/nested_sampling.rst.txt
index 9253b5f..7261fb6 100644
--- a/docs/build/html/_sources/nested_sampling.rst.txt
+++ b/docs/_build/html/_sources/nested_sampling.rst.txt
@@ -16,4 +16,9 @@ Modify Spectra:
 Prior Functions:
 ++++++++++++++++
 .. automodule:: ForMoSA.nested_sampling.nested_prior_function
+	:members:
+
+Likelihood Functions:
++++++++++++++++++++++
+.. automodule:: ForMoSA.nested_sampling.nested_logL_functions
 	:members:
\ No newline at end of file
diff --git a/docs/build/html/_sources/plotting.rst.txt b/docs/_build/html/_sources/plotting.rst.txt
similarity index 61%
rename from docs/build/html/_sources/plotting.rst.txt
rename to docs/_build/html/_sources/plotting.rst.txt
index 6e0520f..bb5a1a2 100644
--- a/docs/build/html/_sources/plotting.rst.txt
+++ b/docs/_build/html/_sources/plotting.rst.txt
@@ -3,5 +3,7 @@
 Plotting Class
 ==============
 
+The plotting module of ForMoSA:
++++++++++++++++++++++++++++++++
 .. automodule:: ForMoSA.plotting.plotting_class
 	:members:
\ No newline at end of file
diff --git a/DEMO/inputs/atmospheric_grid_info.ipynb b/docs/_build/html/_sources/tutorials/atmospheric_grid_info.ipynb.txt
similarity index 100%
rename from DEMO/inputs/atmospheric_grid_info.ipynb
rename to docs/_build/html/_sources/tutorials/atmospheric_grid_info.ipynb.txt
diff --git a/docs/_build/html/_sources/tutorials/demoabpic.ipynb.txt b/docs/_build/html/_sources/tutorials/demoabpic.ipynb.txt
new file mode 100644
index 0000000..bfffa13
--- /dev/null
+++ b/docs/_build/html/_sources/tutorials/demoabpic.ipynb.txt
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# demo AB Pic b\n",
+    "\n",
+    "\n",
+    "This tutorial is intended as a quick start. \n",
+    "\n",
+    "\n",
+    "We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in [P. Palma-Bifani et al (2023)](https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf).\n",
+    "\n",
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic packages\n",
+    "import sys, time, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# ForMoSA modules\n",
+    "sys.path.insert(0, os.path.abspath('/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/'))\n",
+    "# For the interpolation & sampling\n",
+    "from ForMoSA.main_utilities import GlobFile\n",
+    "from ForMoSA.adapt.adapt_obs_mod import launch_adapt\n",
+    "from ForMoSA.nested_sampling.nested_sampling import launch_nested_sampling\n",
+    "# For the plots\n",
+    "from ForMoSA.plotting.plotting_class import PlottingForMoSA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0. Setup\n",
+    "\n",
+    "You need to create a config file with extension <.ini> and modify the parameters. Learn more about our config files in it's specific tutorial.\n",
+    "\n",
+    "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = 'data_abpic/'\n",
+    "\n",
+    "# CONFIG_FILE \n",
+    "# reading and defining global parameters\n",
+    "config_file_path = base_path + 'config_ABPicb.ini'\n",
+    "global_params = GlobFile(config_file_path)    \n",
+    "\n",
+    "# Optional: Add \"time_now\" and \"save_name\" to avoid overwriting results\n",
+    "time_now = time.strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_name = 'test'\n",
+    "\n",
+    "# Create directory to save the outputs \n",
+    "global_params.result_path = global_params.result_path+ save_name+'_t' + time_now+'/'\n",
+    "os.makedirs(global_params.result_path)\n",
+    "\n",
+    "# Overwrite some parameters\n",
+    "global_params.config.filename = global_params.result_path + 'config_used.ini'\n",
+    "global_params.config['config_path']['result_path']=global_params.result_path\n",
+    "global_params.config.write()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Interpolate the grid\n",
+    "\n",
+    "Once everything is setup, we start by adapting the models and observations. \n",
+    "\n",
+    "The grid of models is interpolated for this, but you don't need to repeat this step once you've adapted the grid for a specific dataset. \n",
+    "\n",
+    "(Answer 'no' only the first time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Have you already interpolated the grids for this data? \n",
+    "y_n_par = 'yes'\n",
+    "#y_n_par = 'no' # Only answer no the first time, then comment to save time\n",
+    "\n",
+    "launch_adapt(global_params, justobs=y_n_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Lunch Nested Sampling \n",
+    "\n",
+    "Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Likelihood functions check-ups\n",
+      "\n",
+      "ABPicb_SINFONI_K will be computed with chi2_classic\n",
+      "\n",
+      "Done !\n",
+      "\n",
+      "\u001b[Kit=  1330 logz=-1071.92517042 \n",
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Nestle  \n",
+      " \n",
+      "The code spent 234.33751487731934 sec to run.\n",
+      "niter: 1331\n",
+      "ncall: 13109\n",
+      "nsamples: 1431\n",
+      "logz: -1071.101 +/-  0.347\n",
+      "h: 12.010\n",
+      "\n",
+      "\n",
+      " \n",
+      "-> Voilà, on est prêt\n"
+     ]
+    }
+   ],
+   "source": [
+    "launch_nested_sampling(global_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting the outcomes\n",
+    "\n",
+    "ForMoSA has been designed with a plotting class. Bellow we show 4 main features: \n",
+    "\n",
+    "- Plotting corner-plots\n",
+    "- Plotting spectra and residuals\n",
+    "- Plotting chains \n",
+    "- Accessing the different parameters\n",
+    "\n",
+    "All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc...\n",
+    "\n",
+    "We need to start by initializing the plotting class as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to output file created in the first step\n",
+    "config_file_path_pl = 'data_abpic/outputs/test_t20240807_005025'\n",
+    "\n",
+    "# Initialize the plotting class and set the color\n",
+    "plotForMoSA = PlottingForMoSA(config_file_path_pl+'/config_used.ini', 'red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Corner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Corner plot\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 970x970 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plotForMoSA.plot_corner(levels_sig=[0.997, 0.95, 0.68], bins=20, quantiles=(0.16, 0.5, 0.84), burn_in=1100)\n",
+    "#plt.savefig('') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Spectrum and Residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Best fit and residuals plot\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/plotting/plotting_class.py:723: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  axr2.legend(frameon=False,handlelength=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax, axr, axr2 = plotForMoSA.plot_fit(figsize=(10, 5), uncert='no')\n",
+    "\n",
+    "# You can modify the different axes and includ further plotting features\n",
+    "axr.set_ylim(-5,5)\n",
+    "\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Chains of posteriors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Posteriors chains for each parameter\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plotForMoSA.plot_chains(figsize=(10,6))\n",
+    "#axs[i, j] #i=cols, j=0,1\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access information\n",
+    "\n",
+    "You can access different parametes since we are working with a class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "posteriors_chains = plotForMoSA.posterior_to_plot\n",
+    "posteriors_names = plotForMoSA.posteriors_names"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/_sources/tutorials/exorem_info.ipynb.txt b/docs/_build/html/_sources/tutorials/exorem_info.ipynb.txt
new file mode 100644
index 0000000..23fd7dd
--- /dev/null
+++ b/docs/_build/html/_sources/tutorials/exorem_info.ipynb.txt
@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exo-REM\n",
+    "\n",
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges.\n",
+    "\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Open the grid\n",
+    "\n",
+    "You can check the free parameters names, ranges and units together with the wavelength range and spectral resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_path = '/EXOREM_native.nc'\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a spectrum for specific values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fDW06deokAIjQ0NAU6yU2Y8YMte/ycePGCQBqn+Okrl+/LgCI8ePH67yezIRnoP7f6tWrUaFCBbUyc3Nztflq1aph9uzZmDp1Km7duoX379+r9T+5evUqgoODMWvWLLWO3h4eHqhUqRKOHDmC2bNn6xRPs2bN1OZLlSqFbdu2aT3lXapUKWzYsEGjPLnT4927d8e4ceNw+PBhtGrVCocPH9b5zJNK4lPfERER+Pz5Mxo3boz169fj/v370hk5YwkJCcGZM2cwZ84cREREqJ0xatmyJTw9PfH69WsUK1ZM77ZbtmypdknT1E6fPo2goCCtd/BER0drPROjej+p7jhS/Ztc3fDwcI3yoKAg5M+fH0WKFElT/NokJCTgxIkT6NixI8qUKSOVFylSBL1798aGDRsQHh4OW1tbHDt2DPXr14ezs7NUL3/+/OjTpw9WrlwplZ0+fRrx8fHSnVIqo0eP1riNvnDhwjofw8TvzQ8fPmDmzJmYMWMG7O3t9dji5CU+JgkJCfj48aN02Uzb3Y4DBw6EpaWlNK+6XP/06VNUq1ZN+l6ZM2eOWr0BAwZg0qRJqcajOut94MABDBw4UOPsJvClb0vhwoXRq1cvqSxHjhwYM2YMevXqJXUS1ibxd0FcXBzCw8NRrlw55M2bF9evX9c4y/Hdd99pfLemRe/evdGtWzcEBgbi9u3bCAwMTPHy3V9//YUqVapodCewtrbGoUOHtC7TvHnzNMWo7z4aMmQIFAqFNN+4cWMsW7YMz58/h5OTE06dOoXY2FiMHj1ard64ceN06tSu+n7QpwvCX3/9JV2+AyB9B6fUhuo1bd9HWQETqP9Xp04dnfqDTJo0CTt37sTly5cxf/58VKlSRXrt+fPnAICKFStqLFepUiVp+ICEhASN69n58+dX+3JUJXRhYWH4/fffce7cuWTvkMudO7dGwpUSe3t7NGvWDDt27MCnT5+QkJAgdVjW1Z07dzB9+nScOXNG48MRFhamV1u6ePz4MYQQmDFjBmbMmKG1TnBwsEEJVJEiRUySVCRn+/btMDc3R48ePTRey5kzp9TPKTHVJSvVF7Hq3+TqauvbsW3bNnzzzTdo3rw5zp8/j0KFCqVpOxJ79+4dPn36pPW9X7lyZSiVSrx8+RJVq1bF8+fPUb9+fY16Se94VX2ekpbnz59f41K2tbW1Xp8BlenTpyN//vwYPXq03ssmR6lUYvny5VizZg0CAgLU+gWqugQkVqJECbV51baFhoYC+G8/JL4DE/iS4CROVpPTo0cPbNy4EYMHD8aUKVPQtGlTdO7cGV27dpWSqefPn6N8+fIayVXlypXVYtAmOjoaCxYswKZNm/D69WsIIaTXtH0XaLvbOS3atGmDPHnyYNeuXbhx4wZq166NcuXKaR1SBPjS/6ldu3Ya5ebm5ga9h3Sh7z4y9D1hb2+vtZtHUra2tgC+JEG6dCsJDAzE9evX1e5aVCVHKXV/0CXJysyYQOnp6dOnePToEQDA39/foDZevnyp8SXi4+Oj1qk5cULXsWNHNGrUCL1798aDBw/S1I9IpXfv3vjuu+8QGBiI1q1b69U36+PHj3B1dYWtrS3mzJmDsmXLwtraGtevX8f//vc/KJXKNMeXlKrNiRMnomXLllrrGDrkRHR0tM5JX+HChQ1aR+J17du3D82aNdM6JEORIkXw9u1bjXJVmar/jCrhe/v2rUYfmLdv36JOnToabbi6umL37t3o3LkzWrZsCV9fX61DXWRG2n6UJEf1Y+XRo0f49ddf8csvv+DNmzfS6zExMYiLi8OzZ89ga2uL/Pnz6xXL/PnzMWPGDHz77beYO3cu8ufPDzMzM4wbN07rZyO5szGJ/8imRc6cOXHu3Dn4+PjgyJEjOHbsGHbt2oUmTZrgxIkTaT4bNHr0aGzatAnjxo1D/fr1pcFre/bsqXV7jd1x28rKCp07d8bmzZvx9OnTFAf5DAgIwP3799P9rjB995Gp3xOqO3T9/f2lM54pUd216O7uLpWpkutbt26pnU1O7NatWwCgdqIhK2ECpQelUokBAwbA1tZWOlXatWtX6Y4p1d1fDx480Lg99sGDB9Lr2i43pHTJy9zcHAsWLIC7uztWrVqFKVOmpHlbOnXqhKFDh+Kff/5R65ioC19fX3z48AF79+7F119/LZUHBARo1E18ejktVL+0c+TIYfRfibt27cLAgQN1qpvWL7CDBw8iIiJCo/O4irOzM/7++28olUq1swH//vsvcuXKJV1mVn1hXb16VS1ZevPmDV69eoUhQ4Zobb9du3b4/fff0b9/f7Rt2xYnTpwwyh80e3t75MqVCw8ePNB47f79+zAzM5MSvZIlS2q9yyxpmerz8vjxY7UfHB8+fJB+iato+1GSHNWPldevX0OpVGLMmDEYM2aMRr3SpUtj7Nixet+Z5+3tDXd3d/z2229q5R8/fjToJgTVfnj06JHa90pcXBwCAgJ0ulxuZmaGpk2bomnTpli6dCnmz5+PadOmwcfHB82aNUPJkiVx69Ytjffd/fv31WLQxtvbG/3798eSJUukspiYGLU740ytd+/e+P3332FmZoaePXsmW+/IkSOws7PTGAfP1Iy9jxK/JxKfhXz37p3GZ0Obdu3aYcGCBdi2bZtOCZS2uxZbt24Nc3NzbN26NdmO5Fu2bIGFhQVatWqV6joyIyZQeli6dCkuXryIgwcPwsPDA76+vhg+fDi+/vprFCxYELVq1UKhQoWwbt06fPvtt9Ilt6NHj+LevXuYOXMmAMMuN7i5uaFOnTr45ZdfMG7cOI3BNPVlY2ODtWvX4tmzZ1pPZ6dE9esocTIRGxuLNWvWaNTNnTu3US7pFSpUCG5ubli/fj1Gjx6tccnt3bt3BvdhSc8+UDt27ECuXLnQqVMnra937doV3t7e2Lt3r3RZ9f3799izZw/atWsnvaeqVq2KSpUq4ddff8XQoUOlY7J27VooFIoUL8n27dsXoaGhGDt2LLp06YIDBw6o3ZVmCHNzc7Ro0QIHDhzAs2fPpP4lQUFB0sCtqssGLVu2xOrVq3Hjxg0pEQwJCZHuTFRp2rQpLCwssHbtWrU+KKtWrdJYvyF9oKpVq4Z9+/ZpvD59+nRERERg+fLlKFu2rE5tJmZubq6RaO/ZswevX7826CxprVq1YG9vj3Xr1qn1l/Ly8tLpD3BISIjGWTTVflddAm7Tpg1OnDiBXbt2Sf2g4uPjsXLlStjY2EhDgGijbXtXrlypdunS1Nzd3TF37lwUKFAgxbPEf/31F1q0aKE29Ed6MPY+atasGXLkyIGVK1eiRYsW0g9VXZP9+vXro1WrVti4cSNat26Njh07qr0eGxuLH374AYsXL0ZcXBxOnjypMVhz8eLFMXDgQGzcuBFr167F8OHD1V5ft24dzpw5g6FDh6Z5uIqMignU/zt69Kj0ayuxBg0aoEyZMrh37x5mzJiBAQMGSAmHl5cXnJ2dMWLECOzevRs5cuTAwoULMXDgQLi6uqJXr17SMAalSpXC999/n6YYJ02ahG7dusHLywvDhg2TysPCwrBt2zaty6Q0CGX//v0NiqNBgwbIly8f+vfvjzFjxkChUGDr1q1az87UrFkTu3btwvjx41G7dm3Y2NjonbCprF69Go0aNUL16tXx3XffoUyZMggKCsKlS5fw6tUrjXF2dGVoH6iwsDCp07PqlvBVq1Yhb968yJs3r8bYKCEhITh69Ci6dOmS7GXYrl27ol69ehg4cCDu3r0rjUSekJCgcQPCokWL0L59e7Ro0QI9e/bE7du3sWrVKgwePFg6vZ6cMWPGICQkBLNnz0a/fv2wfft2mJmZwdfXF+7u7vD09NT7eWfz5s2TxhsaMWIELCwssH79enz+/Bk///yzVG/y5MnYtm0bmjdvjtGjR0vDGJQoUQIhISHSHwMHBweMHTsWS5YsQfv27dGqVSvcvHkTR48eRcGCBdXObhryo6RgwYIafziA//4IJX1t1qxZmD17tsbl9qTatm2LOXPmYODAgWjQoAH8/f2xfft2nforaZMjRw7MmzcPQ4cORZMmTdCjRw8EBARg06ZNOrU5Z84cnDt3Dh4eHihZsiSCg4OxZs0aODo6SmdihgwZgvXr12PAgAG4du0aSpUqBW9vb1y4cAG//PJLin1Y2rZti61bt8LOzg5VqlTBpUuXcOrUKa39vUzFzMwM06dPT7FOdHQ0fHx8sG7dunSK6j/G3kf29vaYOHEiFixYgLZt26JNmzbw8/OTPhu62LJlC1q0aIHOnTujXbt2aNq0KXLnzo1Hjx5h586dePv2LRYvXozz588jPDxcrQO5yrJly3D//n2MGDECx44dk840HT9+HAcOHICrq6vaWbcsR6a7/zKM1G5h37Rpk4iPjxe1a9cWjo6O0m2kKqpbjnft2iWV7dq1S3z11VfCyspK5M+fX/Tp00e8evVKr3iuXLmi8VpCQoIoW7asKFu2rHQbcErDGCQ+vCm1m5iuwxhcuHBB1KtXT+TMmVMULVpUTJ48WRw/flzjNvHIyEjRu3dvkTdvXulWel1oG8ZACCGePHki+vXrJwoXLixy5MghihUrJtq2bSu8vb2lOvoOY2ColG6d17ad69atEwDEwYMHU2w3JCREDBo0SBQoUEDkypVLuLq6Jnvc9u3bJ5ydnYWVlZVwdHQU06dPF7GxsWp1VMMYaLuNffTo0QKAGDZsmBBCiEOHDgkAYt26daluv7bjc/36ddGyZUthY2MjcuXKJdzd3cXFixc1lvXz8xONGzeW4l6wYIFYsWKFACACAwOlevHx8WLGjBmicOHCImfOnKJJkybi3r17okCBAlLMxpbcMAYTJkwQCoVC3Lt3L8XlY2JixIQJE0SRIkVEzpw5RcOGDcWlS5eEq6ur2pADyb0fVe+rTZs2qZWvWbNGlC5dWlhZWYlatWqJc+fOabSpzenTp0WHDh1E0aJFhaWlpShatKjo1auXePjwoVq9oKAgMXDgQFGwYEFhaWkpqlevrhGDEJrHPTQ0VFrOxsZGtGzZUty/f1+ULFlS7XZ6Xb+DktZPbRiD5CT9Hjh8+LBQKBQiKChI7/aQxmEM0rqPVOtJ/N2akJAgZs+eLb3P3NzcxO3btzXaTMmnT5/E4sWLRe3atYWNjY2wtLQU5cuXF6NHjxaPHz8WQggxceJEUaVKlWTb+Pz5s1i2bJmoWbOmyJ07t8iVK5dwcXERv/zyi8Z3UVaT7RMoIvrPpEmThKOjo9pYW+ll7NixwtraOtUxgkJDQwUAMW/evHSK7IvatWuLrl27pus6szNVMnH9+nXx7t07tfGODDF8+HBRu3Zto8QWHx8v3r17J/bv32/UH2UZUeXKlcWkSZPkDiND4iU8IpL4+PhgxowZRn+odFLR0dFqHVI/fPiArVu3olGjRmp3ICWtB/x3iS2tj+LRR3h4OG7evKnTo03IuFQjir979y5NTwJwdnY2uPtAUv7+/vjqq6+M0lZGFhsbix49emg8a5C+UAhhpPsiiYh05OzsDDc3N1SuXBlBQUH47bff8ObNG5w+fVrtzk4vLy94eXlJj+Q5f/48/vjjD7Ro0QLHjx+XcQvI1N6+fav2bDdXV9c03+xgLJGRkWrPeHRycjLquGqUOTCBIqJ098MPP8Db2xuvXr2CQqGAi4sLPD09NTqCX79+HZMnT8aNGzcQHh4OBwcHdOnSBfPmzTPKeGhERIZiAkVERESkJ82HIhERERFRiphAEREREelJlrvwlEol3rx5gzx58hjtUR9ERERE2gghEBERgaJFi2o8NNtQsiRQb9680XgAKhEREZEpvXz50miPlpElgVI9FuDly5fS87GIiIiITCE8PBzFixdP8bFE+pIlgVJdtrO1tWUCRUREROnCmN2G2ImciIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xAQqGzp+/DgUCgWOHj0qdyhERESZkkIIIdJ7peHh4bCzs0NYWBjHgZJB4nEwZDj8RERE6coUeQfPQBERERHpiQlUNrF//354enryjBMREZERyPIoF0p/nTp1AgDUr19f5kiIiIgyP56BymZat24tdwhERESZHhOobO7MmTNQKpVyh0FERJSpMIHK5po2bYpNmzbJHQYREVGmwgSK4O3tLXcIREREmQoTKCIiIiI9MYEiIiIi0hMTKMKLFy/kDoGIiChTYQJFuHv3rtwhEBERZSpMoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xASKiIiISE9MoIiIiIj0xAQqAzl//jzOnz8vdxhERESUCgu5A6AvPn36hMaNGwMAoqKikCtXLpkjIiIiouTwDFQGERERIf0/KipKxkiIiIgoNUygiIiIiPTEBIqIiIhIT0ygsqCAgAC0adMGPj4+codCRESUJbETeQakVCrTtPw333yDixcv4ujRoxBCGCkqIiIiUuEZqAyoTZs2Bi97+/ZtXLx4Ua0srQkZERERqWMClQFdv37d4GWrV6+uUbZgwYK0hENERERJMIHK4g4dOoTp06fLHQYREVGWwgQqi2vfvr3cIRAREWU5TKCIiIiI9MQEKoNQKBRyh0BEREQ6YgJFREREpCcmUERERER6YgJFREREpCcmUAQAuH//vtwhEBERZRpMoLIApVKJs2fPIjQ01OA2Dh48aMSIiIiIsjYmUFnA5s2b4ebmBhcXF7lDISIiyhaYQGUBu3btAgA8e/ZM3kCIiIiyCSZQGURERISs6+c4VERERLpjApVBPHr0yOBlhRBGjISIiIhSwwQqg5D7DNDDhw9lXT8REVFmwgQqCzDGGaiNGzcaIRIiIqLsgQlUFsBLeEREROmLCRQRERGRnphAZQE8A0VERJS+mEBlEGnpRM4EioiIKH0xgcoCmEARERGlLyZQGQSTICIiosyDCVQWEBUVJXcIRERE2QoTqCzg8uXLcodARESUrTCBygDi4uKwZMkSucMgIiIiHTGBygDWrl2LkydPyh0GERER6YgJVAZw6tQpg5d9+fKlESMhIiIiXTCBktm0adNw6NAhg5Z98OABSpQoYeSIiIiIKDVMoGQ2f/58g5c9cuSIESMhIiIiXTGByuASEhLg7+/PcaL+X3x8PGJjY+UOg4iIsjkmUBmUKmEaNGgQnJyc0nSmKiv4559/sGTJElSsWBGFChViEkVERLJSCBlObYSHh8POzg5hYWGwtbVN79VnKMk9A0+pVEKhUEivW1lZISYmRq3O0qVLMWHCBKPFkpHPciXdT7du3UL16tVlioaIiDITU+QdPAOVib19+1buEIiIiLIlJlCZ2OLFi+UOQTa7d++WOwQiIsrGmEBlUAkJCWrznz9/hr+/P96/fy9TRBnLvHnzEBoaKncYRESUTTGByqDKli2r0efJyckJ9vb2MkWU8YSHh8sdAhERZVNMoDKoFy9e4Pz583KHkaFl5E7vRESUtTGBogxv27ZtWstdXFzw5s2bdI6GiIiICVSGltxYR5cvX8bTp0/TORr59O3bV2t5aGgoJk2alM7REBERARZyB0DJ+/z5s9byunXrpnMkGVdYWJjcIRARUTbEM1CUqR05cgQPHz6UOwwiIspmmEBRplexYkW5QyAiomyGCRRlaFOnTpU7BCIiIg1MoDKwq1evyh2CrIQQ+Omnn+QOg4iISAMTqAzMx8dH7hBkldxdiERERHLjXXiUIYWEhPDsExERZVhMoChDKlCggNwhEBERJYuX8DIwhUIhdwiyuHjxotwhEBERpYgJVAb24cMHuUOQxdChQ+UOgYiIKEVMoGR08uTJFF9/8OBBOkWSsdy+fVvuEIiIiFLEBEpGLVq0kDsEIiIiMgATKCIiIiI9MYEiIiIi0hMTKMpQ4uPj5Q6BiIgoVUygKEPJkSOH3CEQERGligkUERERkZ6YQFGW8OrVK3h7eyMhIUHuUIiIKBtgAkUZRlRUlMHLFi9eHN26dcNvv/1mxIiIiIi0YwJFGUbt2rXT3EZqg5MSEREZAxMomURGRsodQoZz7949uUMgIiLSCRMomfj4+MgdAhERERnIoAQqJiYm2dfevn1rcDBEacVO5ERElB4MSqBcXFxw48YNjfI///wTTk5OaY0pW2jfvr3cIWRJ+/btkzsEIiLKBgxKoNzc3FCvXj0sXLgQwJe7pwYMGIC+ffvihx9+MGqARERERBmNhSELrVmzBh4eHhg8eDAOHz6Mt2/fwsbGBpcvX0a1atWMHSMRERFRhmJQAgUArVu3RufOnbF27VpYWFjg0KFDTJ50xOe9aXr16pXcIRAREenMoEt4T548Qf369XH48GEcP34ckydPRvv27TF58mTExcUZO8YsZ/369XKHkOGMHz/eaG1xOAQiIjI1gxIoZ2dnlC5dGjdv3kTz5s0xb948+Pj4YO/evahTp46xY8xybt++LXcIGcrbt2+xZ88eo7VXpUoVo7VFRESkjUEJ1Jo1a7Bz507kzZtXKmvQoAH8/Pzg4uJirNgomyhatKjcIRAREenFoASqb9++Wsvz5MnDZ5ERERFRlmdQJ/ItW7Yk+5pCoUg2wSJKL1euXDHKs/WIiIi0MSiBGjt2rNp8XFwcPn36BEtLS+TKlYsJVCqEEHKHoFVoaCjy5cuXrusMCAgwSbt16tTB5cuXmUQREZFJGHQJLzQ0VG2KjIzEgwcP0KhRI/zxxx/GjpHSycOHD9N9nWXKlDFZ23zeIBERmYrRHiZcvnx5/PTTTxpnp0iTQqGQO4RsISwsTO4QiIgoizJaAgUAFhYWePPmjTGbzJIy6iW8rGb+/Plyh0BERFmUQX2gDh48qDYvhMDbt2+xatUqNGzY0CiBUdZ36dIluUMgIiIyiEEJVMeOHdXmFQoF7O3t0aRJEyxZssQYcWVpGfXSUnpfWjx37ly6ro+IiMhYDEqglEqlsePIVnbu3Cl3CERERJQGRu0DRaSP69evyx0CERGRQXQ+A6XPw16XLl1qUDCUvezevVvuEIiIiAyicwLl5+enUz3eok9ERERZnc4JlI+PD54+fYpSpUrBzIxX/oiIiCj70isTKl++PN6/fy/N9+jRA0FBQUYPioiIiCgj0yuBSjoA5F9//YWoqCijBkRZ39mzZ9G7d2+5wyAiIjKYQcMYEKWFm5ub3CEQERGliV5noBQKhUYncXYazzp4LDMPIQRCQkIQHh4udyhERNmSXmeghBAYMGAArKysAAAxMTEYNmwYcufOrVZv7969xoswi8nIYx/t27cPtWvXljsMo6pQoQJOnz6N4sWLyx2KURw6dAjnzp1DYGAgtm3bBgDYvHkz+vXrB+DLZ2/JkiXYsWMHSpYsKWeoRERZmkLo8WTbgQMH6lRv06ZNKb4eHh4OOzs7hIWFwdbWVtfVZ3pXrlxBnTp15A4jRenxoGNDz3TNmjULs2bN0nu5nj174o8//jBonRlNcvvOy8sLHTp0QL58+QAALVu2xLFjx9IzNCKiDMsUeYdeCZSxZNcEKjNcIsuoCVTu3LkRGRlp0LKdO3fGn3/+qfdyGYW/vz+cnJz0WqZEiRJ4/vy5iSIiIspcTJF3cEAnyvAsLS3x8uVLucNId35+fqhWrZreyRMAvHjxwgQRERGRCu/Co3RlyGN+WrRoIV2aMkRmfPj1hw8f0K5dO7x+/VruUIiISAuegUonHz58kDuEDGHChAl6L9OoUaM0rfPcuXNpWj69zZ8/HwULFmTyRESUgTGBSie1atWSOwQ1efLkkTuEFLVu3Vr6f1ofHRQdHZ3WcNLVtGnT5A6BiIhSwQQqnTx79kzW9dvY2Ej/79ChAz5+/Ki13urVq02y/jVr1mDIkCE61//mm2+Mtm4Z7pNQc+vWLfTv3z/F90BsbCxmz56NixcvGm298fHxRmuLiIjUMYHKJu7fv682b2ZmhoULF2rUGzVqFMaMGQMASEhIwNKlS3HlypU0r3/kyJHYsGGDzvV79eolnSVr2bJlmtYtdwLl4uKCLVu2oFOnTsnWWb9+PWbNmoWGDRsabb05cuTA8uXLjdYeERH9hwlUNuDh4YFixYpplE+ePFlr/ZUrVwIAtmzZggkTJsgydpVCocDr16/x8OFDg+5CS0zuBCohIQEAcPv27WTrmGqA1XHjxsm+/UREWRETqGwg6R9QXf+g+vv7myKcFHXr1g3BwcEAvvTTKl++fJrbjI2NxdixYzNsIhEXFwcvLy+TtZ/WPmRERKSJ36xGduPGDSgUCixbtkzuUCRyJw763IG4e/du2NvbGz2GFStW4OzZs0Zv11AfPnxA9erVsXDhwnQZTHbhwoUoXbq0XpdRiYgoeUygjOyrr74CAIwfP14qS67DdnoxNIEyVuLVoUMHo7STVjt27EC/fv0QERGRbut89OiR9P/Eo6gvXLgQt2/fxpQpUxATE2PyOKZMmYJnz57p1ZGfiIiSxwQqHTRp0kTW9Rt6Cc9YLly4YLS2Xrx4gX379hm07IYNG7B161b8+OOPRosnNZUrV9Za/v79+3SLIansOKo7EZGxMYEyoW3btiEsLAx+fn6yxiHXGagXL14YPVkrXrw4OnbsmKY2Hj9+bJxgdKDqQJ5Uag/cNqUHDx7Itm4ioqyCCZQJ9e3bN8Vb1zO6tCQ/GzZsQMmSJTFo0CAjRmQc+pz9EUIgJCTEhNGkv/S4ZEhElNUxgTIxHx8fuUNIdwkJCVJfGznPtCQncV+k1IwZMwYFChTAoUOHTBhR+mrXrh0A+W8uICLKzJhAZQO5cuUCAAwYMAAAMHXqVJ2WM/QP7Pnz5w1a7sSJEwYtpy99butftWoVAN33GfBl2ISuXbtqnH2Li4vDoUOH0L59e53bMpW4uDjY2dnB3NyciRQRkQEs5A6ATC937twAgN9//x3Lly83+W3zhj5CpHnz5kaORDt9zkCp6JNkbNmyBX/++afW1zJC8gQAVapUke5GPHjwYIa5U5KIKLPgGahsRKFQ6JU8JU4a6tSpg6tXr+q8HlO7efOmwcsaEp9SqdS5rtzDVugicUf6jh074pdffpEvGCKiTIgJFGkVExOjlkBduXIF7u7uKS6TkJCAq1evpstDbNPyeBddE6hZs2ZJ/0/pDJQQItNfBvv+++/lDoGIKFNhAkVaqfr+JBYZGYnw8HC0atVK6hz+5MkTXL16Fffu3cOUKVNQu3ZtjBw5Uu/1pcdo3Crnz5/HkiVL0KRJE0RGRiZbb/bs2dL/hRD4999/0bRpU9y6dQuRkZFYvnw5nj9/Dnd3dzRo0EA6S5XZkykiIkod+0Clwfv37xEREYHSpUsDALZu3SpzRNoZ8gc9MDBQ63I///wzjh8/juPHj8PR0REtWrTQqGPIOEunT5/WexlDRUdHY+LEiQCA5cuXY9q0aakuI4RAvXr1AAA1atSQyseNGyf9//Xr17hy5UqyD2kmIqKsgwlUGqie2fb27VsULlwY/fr1kzki41myZInW8sT9ezZu3Gi09ZUpU8Zobenj6dOnGmWvX7/WGK1clz5QCoUCXbp0MVps6e3ChQto2LCh3GEQEWUKvIRnBP7+/nKHkG4S9x86evSo0drVp5O2Mf3+++8ayVKHDh2wdu1atbInT56k2lZ6dJ43pUaNGqF79+747bff5A6FiCjDYwJlBJm5z8vw4cP1qp84STDmQ3nlSqAAYPr06Wrz165dM6id9Hhkj5ubm0nb37NnDwYPHmzSdRARZQVMoEgvpjrLYm5ubpJ29XXnzh2Dl1WN8G0qgwYNgo+PD/r27WvS9RARUeqYQJFeTJVAFShQwCTt6iM2NhYuLi5yh4GmTZuibt26GuWq/lWLFi1CkyZNsGvXLgghUKRIkfQOkYgo22MClQXpkwToe/nRFAlUhQoVDFquf//+Ro1j8eLFiI2NNWqbhjh16pTafvbz88PmzZvRqlUrAICDgwNOnz6N7t27A0jboKLJiY+PR0JCgtHbJSLKKphAZTHjxo2Dg4ODWpkx+2jp8xw5XRmaCK1evdpoMXz8+BFHjhwxWnuGWrRoEQCgV69eAIBKlSrB2dkZ/fr1SzZ5tbe3R2BgoFHjyJEjBywsLPDs2TOjtktElFUwgTKCjNSJPOkDbFOjb+dtU5yBMnTcJNUz/owhX758uHjxotHa01eHDh0QGRkpjU81cuRInDhxQueYkibNxpL0DkUiIvqCCVQWU65cOY0kJ6Wk58CBA6YOKVUWFhyObP/+/WoJobm5OZo3b458+fLJGJVxx/oiIspKmEBlMdbW1hplKZ0hCwoK0qv9tNylRqZ15coVo56VU4mOjjZ6m0REmR0TKAPdunVL7hBkcezYMaO2xz/OxlOrVi107tzZ6O3mypUL+fLlw+fPn43eNhFRZsUEykDHjx+XO4RMb/jw4VrPmGU35cuXN1pbpuqP9/HjR1hbW2PhwoUmaZ+IKLNh5xOSjZOTk9whZAjGfBSQqW9omDJlCmrUqIHHjx/D0dERLi4uKFGihEnXSUSUETGB0sOaNWtQpkwZaTyejGb27NkATHdHVkY0YsQIrFmzRu4wDPbdd9/BysrKaO0VLFjQaG0lp3Xr1mrzGekuVCKi9MJLeDq6cuUKRo4cqfHHA0CGGHwRAFxdXQFAr8ssXbt2NVU4qTLGmFL58+c3QiTyUQ2GaSzZKXmmzCU+Pl7uEIiMigmUjl69eqU2v2nTJun/69evT+9wtMqbNy+ALwMr6sqY/W/0Vb9+fdnWnVXxbBBlRLt27ULu3Lmxf/9+AMDVq1dRoUIFaV4bIQQePXoEIQT8/Pxw4sQJvH37Fvfu3VOrExMTgylTpmTbG3tIPkygdJR0LKXEH+KXL1+mdzha1ahRQ+9lTPVsO11w/Cfj7//MmED5+PhwxPMM5t27d5g/fz7evHmj8zIRERHYv38/oqOjERgYqDZIb8+ePREbG4tOnToB+PJcx0ePHqFTp04ICQnRaCsqKgr9+vVDhQoVYGZmBhcXF7Rs2RJFixZFlSpV8Pr1a4SGhqJ06dLImTMnFi5cqPb9t2HDBvj4+KRhDxCljgmUEWT0P1oZPb7s5u7du9L/nZ2djdq2HMf6wIEDuHTpEoQQ8PX1xYMHDwAAmzdvhrOzM54/fy7VfffuHc6ePYvPnz/j9OnTWLt2LZo0aYLSpUune9zZzd27d/Hvv//qVLdDhw6YNm0aihUrhj/++EPj9W3btuH777/Hw4cPpbKePXuiU6dOqFSpEooUKYI+ffpobbtu3bp48eKFNF+gQAFMmzYNFhYWaNKkCaZNmwYbGxts27YtxW1Zv3692nsLAEaNGoU1a9ZgyJAhaNKkCRQKBRQKBZydnRETEwPgy2fkxIkTGlcViPQmZBAWFiYAiLCwMDlWb5BGjRoJAEK1y1T/ByCqVaumUSbHlFji8p49eya7XT/88INs8d67dy/Nx+XHH3+Ufb/rMzk5OQkhhAgPDxeBgYFp3v6k5syZI9u2de7cWfr/2bNnpf+3bdtWCCHEtGnTUn3/KpVK4efnJ2JjY42+b+T2+fNnoVQqRVRUlF7LRUVFiQsXLoiEhASD1qtap2o/BwYGih9++EF07txZJCQkiIiICKnurl27RMuWLTWOzadPn9TaTPxaq1atNMpUU/PmzcWFCxdk/9yppkmTJoljx45pvOcmTpwovLy8DNq/lDmYIu9gAqWjpF/0ieerVasmQkJCZP1iqFChQrLx9urVK9ntSu2PmiknYyRQ4eHhol69emLRokWyfznrMsXHx6d5m1Mye/ZsaV1lypSRfXtVk4eHh071evXqJf3/p59+SnFblUql1vJnz56JDRs2iM+fP5tiFxtk6dKlAoDIkSOHACBev36t87JNmjQRAMTSpUsNWnerVq3U9vGlS5ek/+fKlUv6v7m5ebLH5d27d8LOzk4AECdPntR4fcyYMbK/xwyZvLy8xOnTp6X5zp07i/Xr16vtv8ePH4uNGzeKuLg4g/Y/ZQxMoGSU+EOXdL5KlSri22+/lfWLwNAEavr06bLFbIwEKrltzohTUFCQUbdXm1mzZknr+/Dhg/T/+fPnCx8fH7V4fv/9d9n3SWqTEEJERkaKzZs3iw8fPojPnz+LrVu3ihs3boiSJUsKT09PjX2QM2dOAUDMmTNHCCFEbGys7H/8km7XokWLhBBCfP3116JUqVIa9a9evSoCAgLUli1btqyIj4/XODt34MAB0aZNG7F27VqhVCrFqVOnxFdffSX8/PzE1atXjXIcypYtK/t7wVRT48aNNcrevn0rKlSoIObOnSuV2draipCQEJO/V8g0mEDJKOmXeuL58uXLCxsbG1m/BMqXL59svBk1gbp//75Rj5Gx/liYYoqMjDTqtiYnKChI5M2bVwwePFgIIYS/v79YvHix+Pz5s4iOjhb29vaibt26Ijo6WgiR8ZPO+Ph40bt37xTrREREiBkzZggPDw9RrVo1qbxKlSoiPj5emk98GSokJCTZM1jJiYuLEwsXLhSXL1/WeC0iIkJ8/PhR7NixQ0RERAhfX18xffp0KdlJGvOAAQNEYGCgNL98+XIxd+5ccfToUXHnzh0BQNjY2IiIiAit2+zn5yeEEOLVq1dq5c7OzrIfs6wwjRgxQmt57969pWMeEBAgDh8+rNd7iOTDBEpGiT9ESeczwq+zzJhA6dsXRBePHj2S/Vhom9JTSpcJY2Nj1frSyL1fTD35+fmpza9bt05UqFBBABB9+vTRa7+uWbNG43iqkp3EU58+faT/L168WCQkJGiNLblkx9LSUqdtywjfO9ltKl26tMZn58CBA2LTpk3i+fPner2fKH0xgZJR4g+RUqnU+FDJ/cHObJfw3r17Z5LjFBQUJPux0DZlVKr47O3txcOHD2XfT+k9PXjwQCiVSjFr1iwxduxYsWfPHmnfHD9+XIwYMUJERUWJ2NhY0bp1a2m5+Ph48enTJ1GvXj3Zt4FT+k2JL7cmfc3GxkaOjzDpyBR5BwfiMUDiW3AzO7nGgUqPR45kFL/99pvcIaRq0aJFKF++PA4ePIj27dvLHU66qVixotr88uXLNepoe1QQxzDLnlIarywyMhIfP36EjY0NFAoFzM3N0y8wkgXHgTJA4gHitM1nJnIkUM2bNzdZ23IODJqUn58foqOj8e2338odSrJ27tyJoUOHSmP2tGvXDrt27ZI5KqL/eHp66lx32bJlJozkCz8/Pzx58kTray4uLihfvjycnJw4/l42wATKCDL6ByUjJRXNmzfHxo0bTdZ+jhw5TNa2rhYsWIB///0Xzs7OsLa2ljucFPXo0QPr1q1TO6Ni7OfzEenr5s2b2Lp1K3r16oWZM2dKn6dvvvkGlpaWAIAqVapI9WfOnAlvb2+MGjUKZcqUAQDpX+DLo2QSD/qZFi4uLihXrpzW1wICAvDs2TPcvXsXnz9/Nsr6KAMz2sVAPWT2PlBPnz5Vm3d0dJT92nxKfaAS3zmS1MyZM9MtxitXrpj6MAkhRKp3bZly8vX1TZdtNLXFixeLUaNGZdhO+Zwy93T16lWpE/zGjRs1Xk/Jp0+fxNu3b4UQXzrxr1u3Tu3GiRcvXohly5aJsLAwUa5cOVG0aFHpjsugoCC1mwFMOSUeoJTkx07kMkr64U88X6xYMdm/kAYMGJBsvCklUJ6enukW44sXL0x9mIQQQty9ezdd9/24ceNEuXLlMtTAjcYi9wCxnDLf9PPPP6sN0Lt582ZRr149ce3aNalMCCFCQ0OFj4+PUCqV4vbt2+K7774TdnZ2Yvjw4SZ/X9+5c0ccPnzYpO9v1ZhRSqVS3Lp1K0uOrp+ZMIGSUeIPhpOTk9p83rx5Zf3CmjNnjvj48WOy8Wa3BOrNmzfpuv9NMRxDRqFUKnUeRZyTaaZjx46JuLg4UaJEiWTrNGjQQK82y5cvr7WNqKgoUbVqVY3XEg/NAED873//k/4fHR0tihQpIoAvI84LIcSHDx9E5cqVxaxZs9TeT3fu3BFPnz5N8f2WnpLeUQ1ADBo0yCjHrWHDhsLa2lqt7NWrV9K6z5w5k25n5YkJlKwSfwhS+iKTY0ot3oySQKkGb0wPmzdvTlOsf//9t1i4cGGq9e7evZtu2ySn+/fvy/4+zyrT7NmzxfDhw3Wq26ZNG+kYREVFiUGDBonOnTuLPXv2SJ/dKlWqqI3tpUqOnJ2dxZEjRzTa/N///ieio6NF9erVpbIuXbqoJS/x8fHiw4cPonTp0sLV1VUI8d84WFu3bhVhYWGiT58+4tChQ0IIIZ4+fSpmzpxpsuFJTCkhIUHcuXNH+Pr6infv3onY2FiTHv/t27eLy5cvS/OUPphAyUjuL92UptTiTSmB2rFjh8njCw4OluWLNfGvZF0nb29v6X25du1arXXWrVsnzp8/nyUv2aUmaf+/tE6VKlWS/fNjykn1CJmPHz+Kjx8/qn3nvX//Xly9elUIIcS8efPE1KlTRUJCgnjz5o2YOXOmWLNmTYrHQnVpKOnAqQEBAWLq1KnS8/Z2794tChYsKBYvXizCw8OleiEhIeLQoUMpXlqKj49XS6zS+wyRXHbt2pVu75Hssk/lxgRKRnJ/Eac0pRZvSgnUX3/9ZdLYtm3bZqpDkqrkHoOR0nTq1Clp+XXr1knlN27cEMCXh41md4n31+bNm8X69ev12sdXrlwRo0ePFtu2bRNBQUFi06ZN0kN2M8tkZWUlgoODRadOnUSNGjWkP7j/+9//xNChQwUAMWHCBLkPFaVBer2XYmNj9Xq4NBmGCZSM5P7CTm4qUaJEqvGm9MiKo0ePmjQ+uWm7wyel6dy5c9KyERERoly5cmLkyJHSPH8tfum7UatWLXHt2jUhhFB73lxyU9++fcXDhw9FcHCw1jaTe9xJ4snS0lIsWrRIKJVKMWPGDFGgQIFk6zZt2lTExcUZ/L719/cXmzZtEmfOnBEAxJYtW6TXVqxYobXfm+q9ERsbKy5fvpziI3Uo49u5c6dwdHQUW7ZsEcWLFzfZd6Sq7aFDhwohvjyOKr36i2YnTKBklJ5JkT7T+PHjU403pQTKkLM0uk5bt2411eHQWVRUlHBychLjx48XgwcPVosv8Rkm1ZS4L4kQ2eeSRVoolUphYWGhsS9r1aolOnTooHM7Kb2XSpcuLV0OS26ZxB129+7dK4QQYtiwYQa9d7W5deuW2L9/v0H7iDK/xO+Pffv2mfy7vWbNmmLkyJGiePHiYuvWrfwuSiMmUDLR5Re2MSYXFxe9l0nuMkHiOildwkta15hTRpQ0vsTzfLK64T59+iRCQ0OlfXnv3j2923j69Km4efOm8PLyEsOGDRMLFy4UXbt2FdHR0cn+8VCtz8rKSsTExIgjR46I69evS69HR0eLffv2iYkTJ2q8P1evXi1Wr14tzTdo0EBMnTpVnD592uD9QFnXkCFDBAAxevRoIcSXHw5LlixJl78NqunYsWMy74XMiwmUTA4dOpQuHw59LzcBup2BYgL1n8qVKwsAolWrVkKI/7adp8yNY9u2beLXX39Nt/UtW7ZMp++S2NhYsXfvXvHu3TuxcOFCtb5uN27cEIMGDVK7xZwoqdjYWHHhwgWNTvdxcXFi8uTJAoD4/fffRZUqVaTvlVmzZhn9e9XFxUXkyZNHbNq0SZ4dkUkxgZLJ7t27M0wC9fz5c70TqJQu4SWta6ypdOnSpjgUafb69Wvx888/i/fv3wshvoxX8+TJE5mjIqLMLjIyUgghRGBgoNi8ebOIjo4WkZGRol27dmLJkiXi06dPRv+etba2FrNnzxZPnjwRnz59Eu/evRMHDx7Uerk7uzNF3sFHiusgIz0suESJEnKHkCpfX184OTnJHYZWRYsWxaRJk6T5/PnzI3/+/DJGRERZQe7cuQEADg4O6Nevn1R+8OBBjbqenp6oWLEievfunaZ1xsTEwNPTU+OBy5UrV8aYMWNga2uLZcuW4bfffsuw38mZGRMoHaRXAtWwYcN0WY8pLF68GBMnTgQAuLq6yhwNEVHG888//+DUqVP43//+B3Nzc0RERODAgQNQKpWYOnUqunTpgiJFisDf3z9N67l37x6GDx8uzdeoUQNmZmZo3bo1wsPD4eDggP79+8PZ2RmOjo5QKpVQKpWIj4/X+wHoCQkJuHTpEpydnWFjY5OmuDMbJlA6SK8EqlKlSrh58yYKFy4MBwcHnZYRQpg4qtQtWrQIEyZMQFxcHIoXLy53OEREGVLdunVRt25daX7IkCEYMmSINB8UFAQAGD16NOzt7dGpUydUqlQJSqUSRYoUQVhYmMHrViqVOHLkiDTv7e2dYn0PDw906NABw4YNU/sb6OzsDFtbW3To0AH16tXD2bNn8cMPPwAA5syZg5EjRyIqKgq5cuXC6dOn0bhxY2zduhXffPMNihYtanD8GZLRLgbqIbP1gUqPp3f/+eefautMrl7S18aNG6c15sR1TNkHqnfv3iImJsY4O5qIiFK0atUqUbt2bZP/TTLFpFAoxKRJk6SnRHh4eIjQ0FCxZs0aUaZMGTFlypRUt9/Q4RxMkXcohEj/Uxjh4eGws7NDWFgYbG1t03v1elMoFCZfR9LDkNw6hRBqr40bNw7Lli3TqJe4Tp8+fbBt27Zk152W7ZPh7UNElO19/vwZnz59wvTp0/Hq1Su8evUK169flzssk6levToKFCiAf/75BwUKFEC/fv3QpEkTuLi4wM7ODubm5rhz5w4KFSoEe3t7AMDjx48RHR2N6tWrmyTv4CU8IiKiTMbKygpWVlZYvXq1WrkQAn/99ReOHz+OlStXyhSd8SXuF/b69WssWLAACxYs0FrXxsYGkZGRamX58uUzekxmRm+R9LZixYpkX0vuDaKiyxmg1M4w9ezZM9U2tHn8+LFByxERkWkoFAp4eHhgxYoVEF+GKoJSqYSXlxe8vb3h4+ODpk2bqi0zcuRImaI1jaTJEwCEhoYafT1MoJIRHx+fLusZN24cRo8enezrdnZ2BrV7/vx5nes2aNDAoHWULVvWoOWIiCj9KBQK9O/fH126dIGbmxtOnTolJVdCCKxatQrR0dE4d+4coqOjce3aNaxbtw4AsHLlSvTp00dqa9euXahfvz6AL3cVRkZGYvHixTh69Kgs2yYnXsLT4sGDB3BycsL333+Pn376yaTrUl2rTU6ZMmUMateYQyKcPXsWDx8+xHfffQcAyJUrV7b8sBARZVXW1tZo3LgxAMDFxQUuLi7o378/rK2tMXLkSHTq1Alv3rxB9+7d0b17d7VlJ0yYAAAIDAxE3rx5cfz4cZQtWxZFihSBlZUVrl69ijdv3qB58+bIkycP7t69i1evXqF48eIQQuDTp08ICQnBnTt34O/vj3379iE6OlpqP0+ePIiIiEi/naEjdiL/f+/fv0fjxo3RvXt33LlzB3/++ScAzU7bxqa63TOps2fP4ubNmxg9ejTMzP47UZg0njFjxmD58uVa21bVW7NmjdqYIEm9ePECJUuWTPb1uLg4WFhYQKlUQggBc3PzVLeLiIjIEAkJCbCw+HJ+59OnT8iZMyfevHkDIQSio6ORN29edO/eHb169UKlSpVgZ2cHJycnvHr1CvPnz8fatWsBANOmTUN8fDwWLlyI0qVLIyAgwKh5BxOo/zd16lStZ5sSEhJMmjDosvvLlSuHJ0+eSPV1TaAePXqEc+fOYcCAAaluQ+I2ra2tERkZiRo1aiBPnjy4ePFiutyJSEREBADv3r1DfHw8ihQpotdyQgjcvn0blSpVQo4cOQAAYWFh+Pz5MxwcHHgXnikkd6lu8uTJJlvnyZMndarXp08fzJkzR+trKSVg5cuXR/ny5fWOS3Xq9NatW1AoFEyeiIgoXaXWvSU5CoUC1atXVyuzs7NDeHi4McJSw07kqViyZIle9StVqqRz3WbNmulULz0fjZJ4pFgzMzMmT0RERFowgTKyc+fOGb3NJk2a4NSpU3jx4oXR21ZRnSZt0aKFydZBRESUVfASnhH5+vrC3t4eV69eRa1atbTWsbW1RXh4OEaNGqVX20nH7VAxVhe2f//9F3v37sW3335rlPaIiIiyMiZQMN6YT19//TUAoGbNmnj16hUcHR016oSGhqrdVZdWBQsWNEo7xYsXx9ixY43SFhERUVbHS3gAYmNjjdJO4v5CxYoV0+gP9euvvxoledq7dy8sLCzQunVrTJw4Mc3tERERkX54BgqQBg8ztgYNGuD+/fvSvGogyrTq1KkT4uLijNIWERER6Y9noACDnmBdunRpeHt7p1hn/vz5hoZEREREGRgTKANduHABXbp0wevXrzF58mQEBgZq1HFwcICLi4sM0REREZEp8RKegVS3/RctWhQLFy5Mtl7+/PnTKyQiIiJKJ9n+DJSDg4PW8jlz5qBAgQJQKpUar+nzUMNVq1ahePHiWL16tcExEhERUcaSbRKosLAwreXBwcEaZYsXL8aMGTPw/v17KBQKODk5Sa8dPXoUNjY2Oq+3YsWKePHiBUaMGKF/0ERERJQhZfkEKiEhAQqFAnnz5tV4LElyjykpWbKk2nzNmjWl/7dq1cr4QRIREVGmkuUSKCEEDh8+jN27dwMALCzUu3mpHlUSExOTbBtJl1myZAlGjhyJf/75x8jREhERUWakEMZ6FogewsPDYWdnh7CwMNja2hq1bV0efpuQkABzc3O1Mn9/f+kJzgcPHkS7du2MGhcRERHJwxR5R5Y5AzVnzhydkicAqFOnjtp8fHy82rJJz0ARERERJZbpM4U1a9Zg5MiRqdYTQkhJ0rVr16Ty3r17w9zcHIUKFZLKknsQMBERERGQBRKoJUuWpFpHdZXy48ePyJs3r1ReunRpbN++HQBgb2+P/fv3QwgBe3t7k8RKREREWUOmv4SX9DEs169fx4ULF9C1a1ds2bIFibt42dnZqZ1pmjFjhtqyHTp0QMeOHU0aLxEREWV+Wa4TeWrOnz8vPTz4/fv3KFCgQLqun4iIiNKXKfKOTH8JT1+NGjXC06dPYWNjw+SJiIiIDJLtEijgS98nIiIiIkNl+j5QREREROmNCRQRERGRnphAEREREemJCRQRERGRnphAEREREemJCRQRERGRnphAEREREelJlnGgVIOfh4eHy7F6IiIiykZU+YYxH74iSwL14cMHAEDx4sXlWD0RERFlQx8+fICdnZ1R2pIlgcqfPz8A4MWLF0bbkIwsPDwcxYsXx8uXL9P92X9y4PZmfdltm7m9WVt2214g+21zWFgYSpQoIeUfxiBLAmVm9qXrlZ2dXbY4cCq2trbc3iwsu20vkP22mdubtWW37QWy3zar8g+jtGW0loiIiIiyCSZQRERERHqSJYGysrKCp6cnrKys5Fh9uuP2Zm3ZbXuB7LfN3N6sLbttL5D9ttkU26sQxrynj4iIiCgb4CU8IiIiIj0xgSIiIiLSExMoIiIiIj0xgSIiIiLSk0kSqNWrV6NUqVKwtrZG3bp1cfny5WTrenl5QaFQqE3W1tamCMskzp07h3bt2qFo0aJQKBTYv39/qsv4+vrCxcUFVlZWKFeuHLy8vEwepzHpu82+vr4ax1ihUCAwMDB9Ak6DBQsWoHbt2siTJw8KFSqEjh074sGDB6kut2fPHlSqVAnW1taoXr06/vrrr3SI1jgM2ebM/Dleu3YtnJycpAEF69evj6NHj6a4TGY+vvpub2Y+ttr89NNPUCgUGDduXIr1MvMxTkqXbc7Mx3nWrFkasVeqVCnFZYxxfI2eQO3atQvjx4+Hp6cnrl+/jho1aqBly5YIDg5OdhlbW1u8fftWmp4/f27ssEwmKioKNWrUwOrVq3WqHxAQAA8PD7i7u+PGjRsYN24cBg8ejOPHj5s4UuPRd5tVHjx4oHacCxUqZKIIjefs2bMYOXIk/vnnH5w8eRJxcXFo0aIFoqKikl3m4sWL6NWrFwYNGgQ/Pz907NgRHTt2xO3bt9MxcsMZss1A5v0cOzo64qeffsK1a9dw9epVNGnSBB06dMCdO3e01s/sx1ff7QUy77FN6sqVK1i/fj2cnJxSrJfZj3Fium4zkLmPc9WqVdViP3/+fLJ1jXZ8hZHVqVNHjBw5UppPSEgQRYsWFQsWLNBaf9OmTcLOzs7YYcgCgNi3b1+KdSZPniyqVq2qVtajRw/RsmVLE0ZmOrpss4+PjwAgQkND0yUmUwoODhYAxNmzZ5Ot0717d+Hh4aFWVrduXTF06FBTh2cSumxzVvocCyFEvnz5xMaNG7W+ltWOrxApb29WObYRERGifPny4uTJk8LV1VWMHTs22bpZ5Rjrs82Z+Th7enqKGjVq6FzfWMfXqGegYmNjce3aNTRr1kwqMzMzQ7NmzXDp0qVkl4uMjETJkiVRvHjxVH8JZXaXLl1S2z8A0LJlyxT3T1bh7OyMIkWKoHnz5rhw4YLc4RgkLCwMAFJ8IGVWO8a6bDOQNT7HCQkJ2LlzJ6KiolC/fn2tdbLS8dVle4GscWxHjhwJDw8PjWOnTVY5xvpsM5C5j/OjR49QtGhRlClTBn369MGLFy+SrWus42vUBOr9+/dISEiAg4ODWrmDg0Oy/V0qVqyI33//HQcOHMC2bdugVCrRoEEDvHr1ypihZRiBgYFa9094eDiio6Nlisq0ihQpgnXr1uHPP//En3/+ieLFi8PNzQ3Xr1+XOzS9KJVKjBs3Dg0bNkS1atWSrZfcMc4Mfb6S0nWbM/vn2N/fHzY2NrCyssKwYcOwb98+VKlSRWvdrHB89dnezH5sAWDnzp24fv06FixYoFP9rHCM9d3mzHyc69atCy8vLxw7dgxr165FQEAAGjdujIiICK31jXV8LQyO2Ejq16+v9sunQYMGqFy5MtavX4+5c+fKGBkZS8WKFVGxYkVpvkGDBnjy5AmWLVuGrVu3yhiZfkaOHInbt2+neG09q9F1mzP757hixYq4ceMGwsLC4O3tjf79++Ps2bPJJhWZnT7bm9mP7cuXLzF27FicPHky03SKTitDtjkzH+fWrVtL/3dyckLdunVRsmRJ7N69G4MGDTLZeo2aQBUsWBDm5uYICgpSKw8KCkLhwoV1aiNHjhz46quv8PjxY2OGlmEULlxY6/6xtbVFzpw5ZYoq/dWpUydTJSKjRo3C4cOHce7cOTg6OqZYN7ljrOtnIKPQZ5uTymyfY0tLS5QrVw4AULNmTVy5cgXLly/H+vXrNepmheOrz/YmldmO7bVr1xAcHAwXFxepLCEhAefOncOqVavw+fNnmJubqy2T2Y+xIducVGY7zonlzZsXFSpUSDZ2Yx1fo17Cs7S0RM2aNXH69GmpTKlU4vTp0yleX08sISEB/v7+KFKkiDFDyzDq16+vtn8A4OTJkzrvn6zixo0bmeIYCyEwatQo7Nu3D2fOnEHp0qVTXSazH2NDtjmpzP45ViqV+Pz5s9bXMvvx1Sal7U0qsx3bpk2bwt/fHzdu3JCmWrVqoU+fPrhx44bWRCKzH2NDtjmpzHacE4uMjMSTJ0+Sjd1ox1evLuc62Llzp7CyshJeXl7i7t27YsiQISJv3rwiMDBQCCFE3759xZQpU6T6s2fPFsePHxdPnjwR165dEz179hTW1tbizp07xg7NJCIiIoSfn5/w8/MTAMTSpUuFn5+feP78uRBCiClTpoi+fftK9Z8+fSpy5colJk2aJO7duydWr14tzM3NxbFjx+TaBL3pu83Lli0T+/fvF48ePRL+/v5i7NixwszMTJw6dUquTdDZ8OHDhZ2dnfD19RVv376Vpk+fPkl1kr6nL1y4ICwsLMTixYvFvXv3hKenp8iRI4fw9/eXYxP0Zsg2Z+bP8ZQpU8TZs2dFQECAuHXrlpgyZYpQKBTixIkTQoisd3z13d7MfGyTk/SOtKx2jLVJbZsz83GeMGGC8PX1FQEBAeLChQuiWbNmomDBgiI4OFgIYbrja/QESgghVq5cKUqUKCEsLS1FnTp1xD///CO95urqKvr37y/Njxs3Tqrr4OAg2rRpI65fv26KsExCdYt+0km1jf379xeurq4ayzg7OwtLS0tRpkwZsWnTpnSPOy303eaFCxeKsmXLCmtra5E/f37h5uYmzpw5I0/wetK2nQDUjlnS97QQQuzevVtUqFBBWFpaiqpVq4ojR46kb+BpYMg2Z+bP8bfffitKliwpLC0thb29vWjatKmUTAiR9Y6vvtubmY9tcpImE1ntGGuT2jZn5uPco0cPUaRIEWFpaSmKFSsmevToIR4/fiy9bqrjqxBCCP3OWRERERFlb3wWHhEREZGemEARERER6YkJFBEREZGemEARERER6YkJFBEREZGemEARERER6YkJFBEREZGemEARERER6YkJFBGZzKxZs+Ds7Cx3GBKFQoH9+/frvdyDBw9QuHBhREREGD+oRN6/f49ChQrh1atXJl0PEaUdEyiiTG7dunXIkycP4uPjpbLIyEjkyJEDbm5uanV9fX2hUCjw5MmTdI4yfRk7cZs6dSpGjx6NPHnyGK1NbQoWLIh+/frB09PTpOshorRjAkWUybm7uyMyMhJXr16Vyv7++28ULlwY//77L2JiYqRyHx8flChRAmXLlpUj1EzpxYsXOHz4MAYMGJAu6xs4cCC2b9+OkJCQdFkfERmGCRRRJlexYkUUKVIEvr6+Upmvry86dOiA0qVL459//lErd3d3BwBs3boVtWrVQp48eVC4cGH07t0bwcHBAAClUglHR0esXbtWbV1+fn4wMzPD8+fPAQAfP37E4MGDYW9vD1tbWzRp0gQ3b95MMd6NGzeicuXKsLa2RqVKlbBmzRrptWfPnkGhUGDv3r1wd3dHrly5UKNGDVy6dEmtjQ0bNqB48eLIlSsXOnXqhKVLlyJv3rwAAC8vL8yePRs3b96EQqGAQqGAl5eXtOz79+/RqVMn5MqVC+XLl8fBgwdTjHf37t2oUaMGihUrJpVpO8P1yy+/oFSpUtL8gAED0LFjR8yfPx8ODg7Imzcv5syZg/j4eEyaNAn58+eHo6MjNm3apNZO1apVUbRoUezbty/FuIhIXkygiLIAd3d3+Pj4SPM+Pj5wc3ODq6urVB4dHY1///1XSqDi4uIwd+5c3Lx5E/v378ezZ8+ksyxmZmbo1asXduzYobae7du3o2HDhihZsiQAoFu3bggODsbRo0dx7do1uLi4oGnTpsmePdm+fTtmzpyJH3/8Effu3cP8+fMxY8YMbN68Wa3etGnTMHHiRNy4cQMVKlRAr169pEuUFy5cwLBhwzB27FjcuHEDzZs3x48//igt26NHD0yYMAFVq1bF27dv8fbtW/To0UN6ffbs2ejevTtu3bqFNm3aoE+fPime7fn7779Rq1atFPd/cs6cOYM3b97g3LlzWLp0KTw9PdG2bVvky5cP//77L4YNG4ahQ4dq9HmqU6cO/v77b4PWSUTpRBBRprdhwwaRO3duERcXJ8LDw4WFhYUIDg4WO3bsEF9//bUQQojTp08LAOL58+da27hy5YoAICIiIoQQQvj5+QmFQiHVT0hIEMWKFRNr164VQgjx999/C1tbWxETE6PWTtmyZcX69euFEEJ4enqKGjVqqL22Y8cOtfpz584V9evXF0IIERAQIACIjRs3Sq/fuXNHABD37t0TQgjRo0cP4eHhodZGnz59hJ2dnTSfdL0qAMT06dOl+cjISAFAHD16VOs+EUKIGjVqiDlz5qiVaWt/2bJlomTJktJ8//79RcmSJUVCQoJUVrFiRdG4cWNpPj4+XuTOnVv88ccfam19//33ws3NLdmYiEh+PANFlAW4ubkhKioKV65cwd9//40KFSrA3t4erq6uUj8oX19flClTBiVKlAAAXLt2De3atUOJEiWQJ08euLq6AvjS5wcAnJ2dUblyZeks1NmzZxEcHIxu3boBAG7evInIyEgUKFAANjY20hQQEKC1k3pUVBSePHmCQYMGqdWfN2+eRn0nJyfp/0WKFAEA6fLigwcPUKdOHbX6SedTkrjt3Llzw9bWVmpbm+joaFhbW+vcfmJVq1aFmdl/X7MODg6oXr26NG9ubo4CBQporD9nzpz49OmTQeskovRhIXcARJR25cqVg6OjI3x8fBAaGiolQ0WLFkXx4sVx8eJF+Pj4oEmTJgC+JDMtW7ZEy5YtsX37dtjb2+PFixdo2bIlYmNjpXb79OmDHTt2YMqUKdixYwdatWqFAgUKAPhyp1/Svlcqqv5IiUVGRgL40n+pbt26aq+Zm5urzefIkUP6v0KhAPClX5YxJG5b1X5KbRcsWBChoaGptpuQkKDTunRZf0hICOzt7VNdJxHJhwkUURbh7u4OX19fhIaGYtKkSVL5119/jaNHj+Ly5csYPnw4AOD+/fv48OEDfvrpJxQvXhwA1O7iU+nduzemT5+Oa9euwdvbG+vWrZNec3FxQWBgICwsLNQ6TyfHwcEBRYsWxdOnT9GnTx+Dt7NixYq4cuWKWlnSeUtLS60JjSG++uor3L17V6M8KChIbf7p06dGWR8A3L59W2MICiLKWHgJjyiLcHd3x/nz53Hjxg3pDBQAuLq6Yv369YiNjZU6kJcoUQKWlpZYuXIlnj59ioMHD2Lu3LkabZYqVQoNGjTAoEGDkJCQgPbt20uvNWvWDPXr10fHjh1x4sQJPHv2DBcvXsS0adO0JmPAlw7cCxYswIoVK/Dw4UP4+/tj06ZNWLp0qc7bOXr0aPz1119YunQpHj16hPXr1+Po0aPSmSpV3AEBAbhx4wbev3+Pz58/69x+Ui1btsSlS5c0ErLAwEDMmTMHT58+xZ9//omtW7ciNDQU9+/fN3hdAPDp0ydcu3YNLVq0SFM7RGRaTKCIsgh3d3dER0ejXLlycHBwkMpdXV0REREhDXcAAPb29vDy8sKePXtQpUoV/PTTT1i8eLHWdvv06YObN2+iU6dOyJkzp1SuUCjw119/4euvv8bAgQNRoUIF9OzZE8+fP1dbf2KDBw/Gxo0bsWnTJlSvXh2urq7w8vJC6dKldd7Ohg0bYt26dVi6dClq1KiBY8eO4fvvv1frp9SlSxe0atUK7u7usLe3xx9//KFz+0m1bt0aFhYWOHXqlFp5tWrV8PDhQ1StWhUzZszAxo0bYWlpiYkTJxq8LgA4cOAASpQogcaNG6epHSIyLYUQQsgdBBFRWnz33Xe4f/++yW79X716NQ4ePIjjx48D+DIO1P79+3Hjxg2jr6tevXoYM2YMevfubfS2ich42AeKiDKdxYsXo3nz5sidOzeOHj2KzZs3qw3IaWxDhw7Fx48fERERYdLHubx//x6dO3dGr169TLYOIjIOnoEiokyne/fu8PX1RUREBMqUKYPRo0dj2LBh6bZ+U56BIqLMgQkUERERkZ7YiZyIiIhIT0ygiIiIiPTEBIqIiIhIT0ygiIiIiPTEBIqIiIhIT0ygiIiIiPTEBIqIiIhIT0ygiIiIiPT0f4COOS3d1MJhAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open the grid \n",
+    "grid = ds['grid']\n",
+    "\n",
+    "# get wavelength \n",
+    "wave = grid['wavelength'].values\n",
+    "\n",
+    "# for Exo-REM par1=Teff, par2=logg, par3=[M/H], par4=C/O\n",
+    "# get flux by selecting a value for each parameter\n",
+    "flux = grid.sel(par1=1700,par2=4.0, par3=0., par4=0.55, method=\"nearest\")\n",
+    "\n",
+    "# Plot the spectrum\n",
+    "plt.figure(figsize=(7,3))\n",
+    "plt.plot(wave, flux*wave, 'k')\n",
+    "plt.xlabel('Wavelength (µm)')\n",
+    "plt.ylabel('Flux')\n",
+    "plt.xlim(0.5,5) \n",
+    "plt.yticks([])\n",
+    "plt.title('Exo-REM at Teff=1700K, logg=4, and solar [M/H] and C/O')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/_sources/tutorials/inputs.ipynb.txt b/docs/_build/html/_sources/tutorials/inputs.ipynb.txt
new file mode 100644
index 0000000..6162c18
--- /dev/null
+++ b/docs/_build/html/_sources/tutorials/inputs.ipynb.txt
@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INPUTS\n",
+    "\n",
+    "## Observations (.fits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Config file (.ini)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000..8141580
--- /dev/null
+++ b/docs/_build/html/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
+
+/**
+ * small helper function to urldecode strings
+ *
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL
+ */
+jQuery.urldecode = function(x) {
+    if (!x) {
+        return x
+    }
+    return decodeURIComponent(x.replace(/\+/g, ' '));
+};
+
+/**
+ * small helper function to urlencode strings
+ */
+jQuery.urlencode = encodeURIComponent;
+
+/**
+ * This function returns the parsed url parameters of the
+ * current request. Multiple values per key are supported,
+ * it will always return arrays of strings for the value parts.
+ */
+jQuery.getQueryParameters = function(s) {
+    if (typeof s === 'undefined')
+        s = document.location.search;
+    var parts = s.substr(s.indexOf('?') + 1).split('&');
+    var result = {};
+    for (var i = 0; i < parts.length; i++) {
+        var tmp = parts[i].split('=', 2);
+        var key = jQuery.urldecode(tmp[0]);
+        var value = jQuery.urldecode(tmp[1]);
+        if (key in result)
+            result[key].push(value);
+        else
+            result[key] = [value];
+    }
+    return result;
+};
+
+/**
+ * highlight a given string on a jquery object by wrapping it in
+ * span elements with the given class name.
+ */
+jQuery.fn.highlightText = function(text, className) {
+    function highlight(node, addItems) {
+        if (node.nodeType === 3) {
+            var val = node.nodeValue;
+            var pos = val.toLowerCase().indexOf(text);
+            if (pos >= 0 &&
+                !jQuery(node.parentNode).hasClass(className) &&
+                !jQuery(node.parentNode).hasClass("nohighlight")) {
+                var span;
+                var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
+                if (isInSVG) {
+                    span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+                } else {
+                    span = document.createElement("span");
+                    span.className = className;
+                }
+                span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+                node.parentNode.insertBefore(span, node.parentNode.insertBefore(
+                    document.createTextNode(val.substr(pos + text.length)),
+                    node.nextSibling));
+                node.nodeValue = val.substr(0, pos);
+                if (isInSVG) {
+                    var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
+                    var bbox = node.parentElement.getBBox();
+                    rect.x.baseVal.value = bbox.x;
+                    rect.y.baseVal.value = bbox.y;
+                    rect.width.baseVal.value = bbox.width;
+                    rect.height.baseVal.value = bbox.height;
+                    rect.setAttribute('class', className);
+                    addItems.push({
+                        "parent": node.parentNode,
+                        "target": rect});
+                }
+            }
+        }
+        else if (!jQuery(node).is("button, select, textarea")) {
+            jQuery.each(node.childNodes, function() {
+                highlight(this, addItems);
+            });
+        }
+    }
+    var addItems = [];
+    var result = this.each(function() {
+        highlight(this, addItems);
+    });
+    for (var i = 0; i < addItems.length; ++i) {
+        jQuery(addItems[i].parent).before(addItems[i].target);
+    }
+    return result;
+};
+
+/*
+ * backward compatibility for jQuery.browser
+ * This will be supported until firefox bug is fixed.
+ */
+if (!jQuery.browser) {
+    jQuery.uaMatch = function(ua) {
+        ua = ua.toLowerCase();
+
+        var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
+            /(webkit)[ \/]([\w.]+)/.exec(ua) ||
+            /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
+            /(msie) ([\w.]+)/.exec(ua) ||
+            ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
+            [];
+
+        return {
+            browser: match[ 1 ] || "",
+            version: match[ 2 ] || "0"
+        };
+    };
+    jQuery.browser = {};
+    jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
+}
diff --git a/docs/_build/html/_static/background_b01.png b/docs/_build/html/_static/background_b01.png
new file mode 100644
index 0000000..353f26d
Binary files /dev/null and b/docs/_build/html/_static/background_b01.png differ
diff --git a/docs/build/html/_static/basic.css b/docs/_build/html/_static/basic.css
similarity index 97%
rename from docs/build/html/_static/basic.css
rename to docs/_build/html/_static/basic.css
index 7577acb..f316efc 100644
--- a/docs/build/html/_static/basic.css
+++ b/docs/_build/html/_static/basic.css
@@ -4,7 +4,7 @@
  *
  * Sphinx stylesheet -- basic theme.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -237,6 +237,10 @@ a.headerlink {
     visibility: hidden;
 }
 
+a:visited {
+    color: #551A8B;
+}
+
 h1:hover > a.headerlink,
 h2:hover > a.headerlink,
 h3:hover > a.headerlink,
@@ -670,6 +674,16 @@ dd {
     margin-left: 30px;
 }
 
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
 dl > dd:last-child,
 dl > dd:last-child > :last-child {
     margin-bottom: 0;
@@ -738,6 +752,14 @@ abbr, acronym {
     cursor: help;
 }
 
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
 /* -- code displays --------------------------------------------------------- */
 
 pre {
diff --git a/docs/_build/html/_static/basic.css.jinja b/docs/_build/html/_static/basic.css.jinja
new file mode 100644
index 0000000..297b9bf
--- /dev/null
+++ b/docs/_build/html/_static/basic.css.jinja
@@ -0,0 +1,925 @@
+/*
+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+/* -- main layout ----------------------------------------------------------- */
+
+div.clearer {
+    clear: both;
+}
+
+div.section::after {
+    display: block;
+    content: '';
+    clear: left;
+}
+
+/* -- relbar ---------------------------------------------------------------- */
+
+div.related {
+    width: 100%;
+    font-size: 90%;
+}
+
+div.related h3 {
+    display: none;
+}
+
+div.related ul {
+    margin: 0;
+    padding: 0 0 0 10px;
+    list-style: none;
+}
+
+div.related li {
+    display: inline;
+}
+
+div.related li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+/* -- sidebar --------------------------------------------------------------- */
+
+div.sphinxsidebarwrapper {
+    padding: 10px 5px 0 10px;
+}
+
+div.sphinxsidebar {
+    float: left;
+    width: {{ theme_sidebarwidth|todim }};
+    margin-left: -100%;
+    font-size: 90%;
+    word-wrap: break-word;
+    overflow-wrap : break-word;
+}
+
+div.sphinxsidebar ul {
+    list-style: none;
+}
+
+div.sphinxsidebar ul ul,
+div.sphinxsidebar ul.want-points {
+    margin-left: 20px;
+    list-style: square;
+}
+
+div.sphinxsidebar ul ul {
+    margin-top: 0;
+    margin-bottom: 0;
+}
+
+div.sphinxsidebar form {
+    margin-top: 10px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+div.sphinxsidebar #searchbox form.search {
+    overflow: hidden;
+}
+
+div.sphinxsidebar #searchbox input[type="text"] {
+    float: left;
+    width: 80%;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+div.sphinxsidebar #searchbox input[type="submit"] {
+    float: left;
+    width: 20%;
+    border-left: none;
+    padding: 0.25em;
+    box-sizing: border-box;
+}
+
+
+img {
+    border: 0;
+    max-width: 100%;
+}
+
+/* -- search page ----------------------------------------------------------- */
+
+ul.search {
+    margin: 10px 0 0 20px;
+    padding: 0;
+}
+
+ul.search li {
+    padding: 5px 0 5px 20px;
+    background-image: url(file.png);
+    background-repeat: no-repeat;
+    background-position: 0 7px;
+}
+
+ul.search li a {
+    font-weight: bold;
+}
+
+ul.search li p.context {
+    color: #888;
+    margin: 2px 0 0 30px;
+    text-align: left;
+}
+
+ul.keywordmatches li.goodmatch a {
+    font-weight: bold;
+}
+
+/* -- index page ------------------------------------------------------------ */
+
+table.contentstable {
+    width: 90%;
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.contentstable p.biglink {
+    line-height: 150%;
+}
+
+a.biglink {
+    font-size: 1.3em;
+}
+
+span.linkdescr {
+    font-style: italic;
+    padding-top: 5px;
+    font-size: 90%;
+}
+
+/* -- general index --------------------------------------------------------- */
+
+table.indextable {
+    width: 100%;
+}
+
+table.indextable td {
+    text-align: left;
+    vertical-align: top;
+}
+
+table.indextable ul {
+    margin-top: 0;
+    margin-bottom: 0;
+    list-style-type: none;
+}
+
+table.indextable > tbody > tr > td > ul {
+    padding-left: 0em;
+}
+
+table.indextable tr.pcap {
+    height: 10px;
+}
+
+table.indextable tr.cap {
+    margin-top: 10px;
+    background-color: #f2f2f2;
+}
+
+img.toggler {
+    margin-right: 3px;
+    margin-top: 3px;
+    cursor: pointer;
+}
+
+div.modindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+div.genindex-jumpbox {
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+    margin: 1em 0 1em 0;
+    padding: 0.4em;
+}
+
+/* -- domain module index --------------------------------------------------- */
+
+table.modindextable td {
+    padding: 2px;
+    border-collapse: collapse;
+}
+
+/* -- general body styles --------------------------------------------------- */
+
+div.body {
+    min-width: {{ theme_body_min_width|todim }};
+    max-width: {{ theme_body_max_width|todim }};
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    -moz-hyphens: auto;
+    -ms-hyphens: auto;
+    -webkit-hyphens: auto;
+    hyphens: auto;
+}
+
+a.headerlink {
+    visibility: hidden;
+}
+
+a:visited {
+    color: #551A8B;
+}
+
+h1:hover > a.headerlink,
+h2:hover > a.headerlink,
+h3:hover > a.headerlink,
+h4:hover > a.headerlink,
+h5:hover > a.headerlink,
+h6:hover > a.headerlink,
+dt:hover > a.headerlink,
+caption:hover > a.headerlink,
+p.caption:hover > a.headerlink,
+div.code-block-caption:hover > a.headerlink {
+    visibility: visible;
+}
+
+div.body p.caption {
+    text-align: inherit;
+}
+
+div.body td {
+    text-align: left;
+}
+
+.first {
+    margin-top: 0 !important;
+}
+
+p.rubric {
+    margin-top: 30px;
+    font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+    clear: left;
+    float: left;
+    margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+    clear: right;
+    float: right;
+    margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+  display: block;
+  margin-left: auto;
+  margin-right: auto;
+}
+
+.align-left {
+    text-align: left;
+}
+
+.align-center {
+    text-align: center;
+}
+
+.align-default {
+    text-align: center;
+}
+
+.align-right {
+    text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+    margin: 0 0 0.5em 1em;
+    border: 1px solid #ddb;
+    padding: 7px;
+    background-color: #ffe;
+    width: 40%;
+    float: right;
+    clear: right;
+    overflow-x: auto;
+}
+
+p.sidebar-title {
+    font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+    clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+    border: 1px solid #ccc;
+    padding: 7px;
+    margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+    font-size: 1.1em;
+    font-weight: bold;
+    margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    padding: 7px;
+}
+
+div.admonition dt {
+    font-weight: bold;
+}
+
+p.admonition-title {
+    margin: 0px 10px 5px 0px;
+    font-weight: bold;
+}
+
+div.body p.centered {
+    text-align: center;
+    margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+    margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+    display: block;
+    content: '';
+    clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+    margin-top: 10px;
+    margin-bottom: 10px;
+    border: 0;
+    border-collapse: collapse;
+}
+
+table.align-center {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table.align-default {
+    margin-left: auto;
+    margin-right: auto;
+}
+
+table caption span.caption-number {
+    font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 0;
+    border-bottom: 1px solid #aaa;
+}
+
+th {
+    text-align: left;
+    padding-right: 5px;
+}
+
+table.citation {
+    border-left: solid 1px gray;
+    margin-left: 1px;
+}
+
+table.citation td {
+    border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+    margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+    margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+    margin: 0.5em;
+    padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+    padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+    font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+}
+
+.field-list ul {
+    margin: 0;
+    padding-left: 1em;
+}
+
+.field-list p {
+    margin: 0;
+}
+
+.field-name {
+    -moz-hyphens: manual;
+    -ms-hyphens: manual;
+    -webkit-hyphens: manual;
+    hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+    margin: 1em 0;
+}
+
+table.hlist td {
+    vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+	font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+.sig-name {
+	font-size: 1.1em;
+}
+
+code.descname {
+    font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+    background-color: transparent;
+}
+
+.optional {
+    font-size: 1.3em;
+}
+
+.sig-paren {
+    font-size: larger;
+}
+
+.sig-param.n {
+	font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+	font-family: unset;
+}
+
+.sig.c   .k, .sig.c   .kt,
+.sig.cpp .k, .sig.cpp .kt {
+	color: #0033B3;
+}
+
+.sig.c   .m,
+.sig.cpp .m {
+	color: #1750EB;
+}
+
+.sig.c   .s, .sig.c   .sc,
+.sig.cpp .s, .sig.cpp .sc {
+	color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+    list-style: decimal;
+}
+
+ol.loweralpha {
+    list-style: lower-alpha;
+}
+
+ol.upperalpha {
+    list-style: upper-alpha;
+}
+
+ol.lowerroman {
+    list-style: lower-roman;
+}
+
+ol.upperroman {
+    list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+    margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+    margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+    margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+    margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+    margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+    float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+  padding-right: 0.5em;
+}
+aside.footnote > p {
+  margin-left: 2em;
+}
+div.citation > p {
+  margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+    margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+    content: "";
+    clear: both;
+}
+
+dl.field-list {
+    display: grid;
+    grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+    font-weight: bold;
+    word-break: break-word;
+    padding-left: 0.5em;
+    padding-right: 5px;
+}
+
+dl.field-list > dd {
+    padding-left: 0.5em;
+    margin-top: 0em;
+    margin-left: 0em;
+    margin-bottom: 0em;
+}
+
+dl {
+    margin-bottom: 15px;
+}
+
+dd > :first-child {
+    margin-top: 0px;
+}
+
+dd ul, dd table {
+    margin-bottom: 10px;
+}
+
+dd {
+    margin-top: 3px;
+    margin-bottom: 10px;
+    margin-left: 30px;
+}
+
+.sig dd {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+.sig dl {
+    margin-top: 0px;
+    margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+    margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+    background-color: #fbe54e;
+}
+
+rect.highlighted {
+    fill: #fbe54e;
+}
+
+dl.glossary dt {
+    font-weight: bold;
+    font-size: 1.1em;
+}
+
+.versionmodified {
+    font-style: italic;
+}
+
+.system-message {
+    background-color: #fda;
+    padding: 5px;
+    border: 3px solid red;
+}
+
+.footnote:target  {
+    background-color: #ffa;
+}
+
+.line-block {
+    display: block;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.line-block .line-block {
+    margin-top: 0;
+    margin-bottom: 0;
+    margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+    font-family: sans-serif;
+}
+
+.accelerator {
+    text-decoration: underline;
+}
+
+.classifier {
+    font-style: oblique;
+}
+
+.classifier:before {
+    font-style: normal;
+    margin: 0 0.5em;
+    content: ":";
+    display: inline-block;
+}
+
+abbr, acronym {
+    border-bottom: dotted 1px;
+    cursor: help;
+}
+
+.translated {
+    background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+    background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+    overflow: auto;
+    overflow-y: hidden;  /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+    clear: both;
+}
+
+span.pre {
+    -moz-hyphens: none;
+    -ms-hyphens: none;
+    -webkit-hyphens: none;
+    hyphens: none;
+    white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+    margin: 1em 0;
+}
+
+td.linenos pre {
+    border: 0;
+    background-color: transparent;
+    color: #aaa;
+}
+
+table.highlighttable {
+    display: block;
+}
+
+table.highlighttable tbody {
+    display: block;
+}
+
+table.highlighttable tr {
+    display: flex;
+}
+
+table.highlighttable td {
+    margin: 0;
+    padding: 0;
+}
+
+table.highlighttable td.linenos {
+    padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+    flex: 1;
+    overflow: hidden;
+}
+
+.highlight .hll {
+    display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+    margin: 0;
+}
+
+div.code-block-caption + div {
+    margin-top: 0;
+}
+
+div.code-block-caption {
+    margin-top: 1em;
+    padding: 2px 5px;
+    font-size: small;
+}
+
+div.code-block-caption code {
+    background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp {  /* gp: Generic.Prompt */
+  user-select: none;
+  -webkit-user-select: text; /* Safari fallback only */
+  -webkit-user-select: none; /* Chrome/Safari */
+  -moz-user-select: none; /* Firefox */
+  -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+    padding: 0.1em 0.3em;
+    font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+    margin: 1em 0;
+}
+
+code.xref, a code {
+    background-color: transparent;
+    font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+    background-color: transparent;
+}
+
+.viewcode-link {
+    float: right;
+}
+
+.viewcode-back {
+    float: right;
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    margin: -1px -10px;
+    padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+    vertical-align: middle;
+}
+
+div.body div.math p {
+    text-align: center;
+}
+
+span.eqno {
+    float: right;
+}
+
+span.eqno a.headerlink {
+    position: absolute;
+    z-index: 1;
+}
+
+div.math:hover a.headerlink {
+    visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+    div.document,
+    div.documentwrapper,
+    div.bodywrapper {
+        margin: 0 !important;
+        width: 100%;
+    }
+
+    div.sphinxsidebar,
+    div.related,
+    div.footer,
+    #top-link {
+        display: none;
+    }
+}
diff --git a/docs/_build/html/_static/bizstyle.css b/docs/_build/html/_static/bizstyle.css
new file mode 100644
index 0000000..7d42531
--- /dev/null
+++ b/docs/_build/html/_static/bizstyle.css
@@ -0,0 +1,512 @@
+/*
+ * bizstyle.css_t
+ * ~~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- business style theme.
+ *
+ * :copyright: Copyright 2007-2024 by Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+@import url("basic.css");
+
+/* -- page layout ----------------------------------------------------------- */
+
+body {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+    font-size: 14px;
+    letter-spacing: -0.01em;
+    line-height: 150%;
+    text-align: center;
+    background-color: white;
+    background-image: url(background_b01.png);
+    color: black;
+    padding: 0;
+    border-right: 1px solid #336699;
+    border-left: 1px solid #336699;
+
+    margin: 0px 40px 0px 40px;
+}
+
+div.document {
+    background-color: white;
+    text-align: left;
+    background-repeat: repeat-x;
+
+    -moz-box-shadow: 2px 2px 5px #000;
+    -webkit-box-shadow: 2px 2px 5px #000;
+}
+
+div.documentwrapper {
+    float: left;
+    width: 100%;
+}
+
+div.bodywrapper {
+    margin: 0 0 0 240px;
+    border-left: 1px solid #ccc;
+}
+
+div.body {
+    margin: 0;
+    padding: 0.5em 20px 20px 20px;
+}
+div.bodywrapper {
+    margin: 0 0 0 calc(210px + 30px);
+}
+
+div.related {
+    font-size: 1em;
+
+    -moz-box-shadow: 2px 2px 5px #000;
+    -webkit-box-shadow: 2px 2px 5px #000;
+}
+
+div.related ul {
+    background-color: #336699;
+    height: 100%;
+    overflow: hidden;
+    border-top: 1px solid #ddd;
+    border-bottom: 1px solid #ddd;
+}
+
+div.related ul li {
+    color: white;
+    margin: 0;
+    padding: 0;
+    height: 2em;
+    float: left;
+}
+
+div.related ul li.right {
+    float: right;
+    margin-right: 5px;
+}
+
+div.related ul li a {
+    margin: 0;
+    padding: 0 5px 0 5px;
+    line-height: 1.75em;
+    color: #fff;
+}
+
+div.related ul li a:hover {
+    color: #fff;
+    text-decoration: underline;
+}
+
+div.sphinxsidebarwrapper {
+    padding: 0;
+}
+
+div.sphinxsidebar {
+    padding: 0.5em 12px 12px 12px;
+    width: 210px;
+    font-size: 1em;
+    text-align: left;
+}
+
+div.sphinxsidebar h3, div.sphinxsidebar h4 {
+    margin: 1em 0 0.5em 0;
+    font-size: 1em;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border: 1px solid #336699;
+    background-color: #336699;
+}
+
+div.sphinxsidebar h3 a {
+    color: white;
+}
+
+div.sphinxsidebar ul {
+    padding-left: 1.5em;
+    margin-top: 7px;
+    padding: 0;
+    line-height: 130%;
+}
+
+div.sphinxsidebar ul ul {
+    margin-left: 20px;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #336699;
+}
+
+div.footer {
+    background-color: white;
+    color: #336699;
+    padding: 3px 8px 3px 0;
+    clear: both;
+    font-size: 0.8em;
+    text-align: right;
+    border-bottom: 1px solid #336699;
+
+    -moz-box-shadow: 2px 2px 5px #000;
+    -webkit-box-shadow: 2px 2px 5px #000;
+}
+
+div.footer a {
+    color: #336699;
+    text-decoration: underline;
+}
+
+/* -- body styles ----------------------------------------------------------- */
+
+p {
+    margin: 0.8em 0 0.5em 0;
+}
+
+a {
+    color: #336699;
+    text-decoration: none;
+}
+
+a:hover {
+    color: #336699;
+    text-decoration: underline;
+}
+
+a:visited {
+    color: #551a8b;
+}
+
+div.body a {
+    text-decoration: underline;
+}
+
+h1, h2, h3 {
+    color: #336699;
+}
+
+h1 {
+    margin: 0;
+    padding: 0.7em 0 0.3em 0;
+    font-size: 1.5em;
+}
+
+h2 {
+    margin: 1.3em 0 0.2em 0;
+    font-size: 1.35em;
+    padding-bottom: .5em;
+    border-bottom: 1px solid #336699;
+}
+
+h3 {
+    margin: 1em 0 -0.3em 0;
+    font-size: 1.2em;
+    padding-bottom: .3em;
+    border-bottom: 1px solid #CCCCCC;
+}
+
+div.body h1 a, div.body h2 a, div.body h3 a,
+div.body h4 a, div.body h5 a, div.body h6 a {
+    color: black!important;
+}
+
+h1 a.anchor, h2 a.anchor, h3 a.anchor,
+h4 a.anchor, h5 a.anchor, h6 a.anchor {
+    display: none;
+    margin: 0 0 0 0.3em;
+    padding: 0 0.2em 0 0.2em;
+    color: #aaa!important;
+}
+
+h1:hover a.anchor, h2:hover a.anchor, h3:hover a.anchor, h4:hover a.anchor,
+h5:hover a.anchor, h6:hover a.anchor {
+    display: inline;
+}
+
+h1 a.anchor:hover, h2 a.anchor:hover, h3 a.anchor:hover, h4 a.anchor:hover,
+h5 a.anchor:hover, h6 a.anchor:hover {
+    color: #777;
+    background-color: #eee;
+}
+
+a.headerlink {
+    color: #c60f0f!important;
+    font-size: 1em;
+    margin-left: 6px;
+    padding: 0 4px 0 4px;
+    text-decoration: none!important;
+}
+
+a.headerlink:hover {
+    background-color: #ccc;
+    color: white!important;
+}
+
+cite, code, tt {
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.01em;
+}
+
+code {
+    background-color: #F2F2F2;
+    border-bottom: 1px solid #ddd;
+    color: #333;
+}
+
+code.descname, code.descclassname, code.xref {
+    border: 0;
+}
+
+hr {
+    border: 1px solid #abc;
+    margin: 2em;
+}
+
+a code {
+    border: 0;
+    color: #CA7900;
+}
+
+a code:hover {
+    color: #2491CF;
+}
+
+pre {
+    background-color: transparent !important;
+    font-family: 'Consolas', 'Deja Vu Sans Mono',
+                 'Bitstream Vera Sans Mono', monospace;
+    font-size: 0.95em;
+    letter-spacing: 0.015em;
+    line-height: 120%;
+    padding: 0.5em;
+    border-right: 5px solid #ccc;
+    border-left: 5px solid #ccc;
+}
+
+pre a {
+    color: inherit;
+    text-decoration: underline;
+}
+
+td.linenos pre {
+    padding: 0.5em 0;
+}
+
+div.quotebar {
+    background-color: #f8f8f8;
+    max-width: 250px;
+    float: right;
+    padding: 2px 7px;
+    border: 1px solid #ccc;
+}
+
+nav.contents,
+aside.topic,
+div.topic {
+    background-color: #f8f8f8;
+}
+
+table {
+    border-collapse: collapse;
+    margin: 0 -0.5em 0 -0.5em;
+}
+
+table td, table th {
+    padding: 0.2em 0.5em 0.2em 0.5em;
+}
+
+div.admonition {
+    font-size: 0.9em;
+    margin: 1em 0 1em 0;
+    border: 3px solid #cccccc;
+    background-color: #f7f7f7;
+    padding: 0;
+}
+
+div.admonition p {
+    margin: 0.5em 1em 0.5em 1em;
+    padding: 0;
+}
+
+div.admonition li p {
+    margin-left: 0;
+}
+
+div.admonition pre, div.warning pre {
+    margin: 0;
+}
+
+div.highlight {
+    margin: 0.4em 1em;
+}
+
+div.admonition p.admonition-title {
+    margin: 0;
+    padding: 0.1em 0 0.1em 0.5em;
+    color: white;
+    border-bottom: 3px solid #cccccc;
+    font-weight: bold;
+    background-color: #165e83;
+}
+
+div.danger    { border: 3px solid #f0908d; background-color: #f0cfa0; }
+div.error     { border: 3px solid #f0908d; background-color: #ede4cd; }
+div.warning   { border: 3px solid #f8b862; background-color: #f0cfa0; }
+div.caution   { border: 3px solid #f8b862; background-color: #ede4cd; }
+div.attention { border: 3px solid #f8b862; background-color: #f3f3f3; }
+div.important { border: 3px solid #f0cfa0; background-color: #ede4cd; }
+div.note      { border: 3px solid #f0cfa0; background-color: #f3f3f3; }
+div.hint      { border: 3px solid #bed2c3; background-color: #f3f3f3; }
+div.tip       { border: 3px solid #bed2c3; background-color: #f3f3f3; }
+
+div.danger p.admonition-title, div.error p.admonition-title {
+    background-color: #b7282e;
+    border-bottom: 3px solid #f0908d;
+}
+
+div.caution p.admonition-title,
+div.warning p.admonition-title,
+div.attention p.admonition-title {
+    background-color: #f19072;
+    border-bottom: 3px solid #f8b862;
+}
+
+div.note p.admonition-title, div.important p.admonition-title {
+    background-color: #f8b862;
+    border-bottom: 3px solid #f0cfa0;
+}
+
+div.hint p.admonition-title, div.tip p.admonition-title {
+    background-color: #7ebea5;
+    border-bottom: 3px solid #bed2c3;
+}
+
+div.admonition ul, div.admonition ol,
+div.warning ul, div.warning ol {
+    margin: 0.1em 0.5em 0.5em 3em;
+    padding: 0;
+}
+
+div.versioninfo {
+    margin: 1em 0 0 0;
+    border: 1px solid #ccc;
+    background-color: #DDEAF0;
+    padding: 8px;
+    line-height: 1.3em;
+    font-size: 0.9em;
+}
+
+.viewcode-back {
+    font-family: 'Lucida Grande', 'Lucida Sans Unicode', 'Geneva',
+                 'Verdana', sans-serif;
+}
+
+div.viewcode-block:target {
+    background-color: #f4debf;
+    border-top: 1px solid #ac9;
+    border-bottom: 1px solid #ac9;
+}
+
+p.versionchanged span.versionmodified {
+    font-size: 0.9em;
+    margin-right: 0.2em;
+    padding: 0.1em;
+    background-color: #DCE6A0;
+}
+
+dl.field-list > dt {
+    color: white;
+    background-color: #82A0BE;
+}
+
+dl.field-list > dd {
+    background-color: #f7f7f7;
+}
+
+/* -- table styles ---------------------------------------------------------- */
+
+table.docutils {
+    margin: 1em 0;
+    padding: 0;
+    border: 1px solid white;
+    background-color: #f7f7f7;
+}
+
+table.docutils td, table.docutils th {
+    padding: 1px 8px 1px 5px;
+    border-top: 0;
+    border-left: 0;
+    border-right: 1px solid white;
+    border-bottom: 1px solid white;
+}
+
+table.docutils td p {
+    margin-top: 0;
+    margin-bottom: 0.3em;
+}
+
+table.field-list td, table.field-list th {
+    border: 0 !important;
+    word-break: break-word;
+}
+
+table.footnote td, table.footnote th {
+    border: 0 !important;
+}
+
+th {
+    color: white;
+    text-align: left;
+    padding-right: 5px;
+    background-color: #82A0BE;
+}
+
+div.literal-block-wrapper div.code-block-caption {
+    background-color: #EEE;
+    border-style: solid;
+    border-color: #CCC;
+    border-width: 1px 5px;
+}
+
+/* WIDE DESKTOP STYLE */
+@media only screen and (min-width: 1176px) {
+body {
+    margin: 0 40px 0 40px;
+}
+}
+
+/* TABLET STYLE */
+@media only screen and (min-width: 768px) and (max-width: 991px) {
+body {
+    margin: 0 40px 0 40px;
+}
+}
+
+/* MOBILE LAYOUT (PORTRAIT/320px) */
+@media only screen and (max-width: 767px) {
+body {
+    margin: 0;
+}
+div.bodywrapper {
+    margin: 0;
+    width: 100%;
+    border: none;
+}
+div.sphinxsidebar {
+    display: none;
+}
+}
+
+/* MOBILE LAYOUT (LANDSCAPE/480px) */
+@media only screen and (min-width: 480px) and (max-width: 767px) {
+body {
+    margin: 0 20px 0 20px;
+}
+}
+
+/* RETINA OVERRIDES */
+@media
+only screen and (-webkit-min-device-pixel-ratio: 2),
+only screen and (min-device-pixel-ratio: 2) {
+}
+
+/* -- end ------------------------------------------------------------------- */
\ No newline at end of file
diff --git a/docs/_build/html/_static/bizstyle.js b/docs/_build/html/_static/bizstyle.js
new file mode 100644
index 0000000..a594bbd
--- /dev/null
+++ b/docs/_build/html/_static/bizstyle.js
@@ -0,0 +1,30 @@
+//
+// bizstyle.js
+// ~~~~~~~~~~~
+//
+// Sphinx javascript -- for bizstyle theme.
+//
+// This theme was created by referring to 'sphinxdoc'
+//
+// :copyright: Copyright 2007-2024 by Sphinx team, see AUTHORS.
+// :license: BSD, see LICENSE for details.
+//
+const initialiseBizStyle = () => {
+    if (navigator.userAgent.indexOf("iPhone") > 0 || navigator.userAgent.indexOf("Android") > 0) {
+        document.querySelector("li.nav-item-0 a").innerText = "Top"
+    }
+    const truncator = item => {if (item.textContent.length > 20) {
+        item.title = item.innerText
+        item.innerText = item.innerText.substr(0, 17) + "..."
+      }
+    }
+    document.querySelectorAll("div.related:first ul li:not(.right) a").slice(1).forEach(truncator);
+    document.querySelectorAll("div.related:last ul li:not(.right) a").slice(1).forEach(truncator);
+}
+
+window.addEventListener("resize",
+  () => (document.querySelector("li.nav-item-0 a").innerText = (window.innerWidth <= 776) ? "Top" : "ForMoSA 2.0.0 documentation")
+)
+
+if (document.readyState !== "loading") initialiseBizStyle()
+else document.addEventListener("DOMContentLoaded", initialiseBizStyle)
\ No newline at end of file
diff --git a/docs/_build/html/_static/classic.css b/docs/_build/html/_static/classic.css
new file mode 100644
index 0000000..5530147
--- /dev/null
+++ b/docs/_build/html/_static/classic.css
@@ -0,0 +1,269 @@
+/*
+ * classic.css_t
+ * ~~~~~~~~~~~~~
+ *
+ * Sphinx stylesheet -- classic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+@import url("basic.css");
+
+/* -- page layout ----------------------------------------------------------- */
+
+html {
+    /* CSS hack for macOS's scrollbar (see #1125) */
+    background-color: #FFFFFF;
+}
+
+body {
+    font-family: sans-serif;
+    font-size: 100%;
+    background-color: #11303d;
+    color: #000;
+    margin: 0;
+    padding: 0;
+}
+
+div.document {
+    display: flex;
+    background-color: #1c4e63;
+}
+
+div.documentwrapper {
+    float: left;
+    width: 100%;
+}
+
+div.bodywrapper {
+    margin: 0 0 0 230px;
+}
+
+div.body {
+    background-color: #ffffff;
+    color: #000000;
+    padding: 0 20px 30px 20px;
+}
+
+div.footer {
+    color: #ffffff;
+    width: 100%;
+    padding: 9px 0 9px 0;
+    text-align: center;
+    font-size: 75%;
+}
+
+div.footer a {
+    color: #ffffff;
+    text-decoration: underline;
+}
+
+div.related {
+    background-color: #133f52;
+    line-height: 30px;
+    color: #ffffff;
+}
+
+div.related a {
+    color: #ffffff;
+}
+
+div.sphinxsidebar {
+}
+
+div.sphinxsidebar h3 {
+    font-family: 'Trebuchet MS', sans-serif;
+    color: #ffffff;
+    font-size: 1.4em;
+    font-weight: normal;
+    margin: 0;
+    padding: 0;
+}
+
+div.sphinxsidebar h3 a {
+    color: #ffffff;
+}
+
+div.sphinxsidebar h4 {
+    font-family: 'Trebuchet MS', sans-serif;
+    color: #ffffff;
+    font-size: 1.3em;
+    font-weight: normal;
+    margin: 5px 0 0 0;
+    padding: 0;
+}
+
+div.sphinxsidebar p {
+    color: #ffffff;
+}
+
+div.sphinxsidebar p.topless {
+    margin: 5px 10px 10px 10px;
+}
+
+div.sphinxsidebar ul {
+    margin: 10px;
+    padding: 0;
+    color: #ffffff;
+}
+
+div.sphinxsidebar a {
+    color: #98dbcc;
+}
+
+div.sphinxsidebar input {
+    border: 1px solid #98dbcc;
+    font-family: sans-serif;
+    font-size: 1em;
+}
+
+
+
+/* -- hyperlink styles ------------------------------------------------------ */
+
+a {
+    color: #355f7c;
+    text-decoration: none;
+}
+
+a:visited {
+    color: #551a8b;
+    text-decoration: none;
+}
+
+a:hover {
+    text-decoration: underline;
+}
+
+
+
+/* -- body styles ----------------------------------------------------------- */
+
+div.body h1,
+div.body h2,
+div.body h3,
+div.body h4,
+div.body h5,
+div.body h6 {
+    font-family: 'Trebuchet MS', sans-serif;
+    background-color: #f2f2f2;
+    font-weight: normal;
+    color: #20435c;
+    border-bottom: 1px solid #ccc;
+    margin: 20px -20px 10px -20px;
+    padding: 3px 0 3px 10px;
+}
+
+div.body h1 { margin-top: 0; font-size: 200%; }
+div.body h2 { font-size: 160%; }
+div.body h3 { font-size: 140%; }
+div.body h4 { font-size: 120%; }
+div.body h5 { font-size: 110%; }
+div.body h6 { font-size: 100%; }
+
+a.headerlink {
+    color: #c60f0f;
+    font-size: 0.8em;
+    padding: 0 4px 0 4px;
+    text-decoration: none;
+}
+
+a.headerlink:hover {
+    background-color: #c60f0f;
+    color: white;
+}
+
+div.body p, div.body dd, div.body li, div.body blockquote {
+    text-align: justify;
+    line-height: 130%;
+}
+
+div.admonition p.admonition-title + p {
+    display: inline;
+}
+
+div.admonition p {
+    margin-bottom: 5px;
+}
+
+div.admonition pre {
+    margin-bottom: 5px;
+}
+
+div.admonition ul, div.admonition ol {
+    margin-bottom: 5px;
+}
+
+div.note {
+    background-color: #eee;
+    border: 1px solid #ccc;
+}
+
+div.seealso {
+    background-color: #ffc;
+    border: 1px solid #ff6;
+}
+
+nav.contents,
+aside.topic,
+div.topic {
+    background-color: #eee;
+}
+
+div.warning {
+    background-color: #ffe4e4;
+    border: 1px solid #f66;
+}
+
+p.admonition-title {
+    display: inline;
+}
+
+p.admonition-title:after {
+    content: ":";
+}
+
+pre {
+    padding: 5px;
+    background-color: unset;
+    color: unset;
+    line-height: 120%;
+    border: 1px solid #ac9;
+    border-left: none;
+    border-right: none;
+}
+
+code {
+    background-color: #ecf0f3;
+    padding: 0 1px 0 1px;
+    font-size: 0.95em;
+}
+
+th, dl.field-list > dt {
+    background-color: #ede;
+}
+
+.warning code {
+    background: #efc2c2;
+}
+
+.note code {
+    background: #d6d6d6;
+}
+
+.viewcode-back {
+    font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+    background-color: #f4debf;
+    border-top: 1px solid #ac9;
+    border-bottom: 1px solid #ac9;
+}
+
+div.code-block-caption {
+    color: #efefef;
+    background-color: #1c4e63;
+}
\ No newline at end of file
diff --git a/docs/build/html/_static/css/badge_only.css b/docs/_build/html/_static/css/badge_only.css
similarity index 100%
rename from docs/build/html/_static/css/badge_only.css
rename to docs/_build/html/_static/css/badge_only.css
diff --git a/docs/build/html/_static/css/fonts/Roboto-Slab-Bold.woff b/docs/_build/html/_static/css/fonts/Roboto-Slab-Bold.woff
similarity index 100%
rename from docs/build/html/_static/css/fonts/Roboto-Slab-Bold.woff
rename to docs/_build/html/_static/css/fonts/Roboto-Slab-Bold.woff
diff --git a/docs/build/html/_static/css/fonts/Roboto-Slab-Bold.woff2 b/docs/_build/html/_static/css/fonts/Roboto-Slab-Bold.woff2
similarity index 100%
rename from docs/build/html/_static/css/fonts/Roboto-Slab-Bold.woff2
rename to docs/_build/html/_static/css/fonts/Roboto-Slab-Bold.woff2
diff --git a/docs/build/html/_static/css/fonts/Roboto-Slab-Regular.woff b/docs/_build/html/_static/css/fonts/Roboto-Slab-Regular.woff
similarity index 100%
rename from docs/build/html/_static/css/fonts/Roboto-Slab-Regular.woff
rename to docs/_build/html/_static/css/fonts/Roboto-Slab-Regular.woff
diff --git a/docs/build/html/_static/css/fonts/Roboto-Slab-Regular.woff2 b/docs/_build/html/_static/css/fonts/Roboto-Slab-Regular.woff2
similarity index 100%
rename from docs/build/html/_static/css/fonts/Roboto-Slab-Regular.woff2
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diff --git a/docs/_build/html/_static/css3-mediaqueries.js b/docs/_build/html/_static/css3-mediaqueries.js
new file mode 100644
index 0000000..59735f5
--- /dev/null
+++ b/docs/_build/html/_static/css3-mediaqueries.js
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n=0;n<e.length;n++){f(e[n],t)}};var c=function(e){var t=e.getAttrMediaQueries();var n=false;var i={};for(var s=0;s<t.length;s++){if(a(t[s])){i[t[s].getMediaType()]=t[s].getExpressions().length>0}}var o=[],u=[];for(var f in i){if(i.hasOwnProperty(f)){o[o.length]=f;if(i[f]){u[u.length]=f}if(f==="all"){n=true}}}if(u.length>0){r[r.length]=cssHelper.addStyle(e.getCssText(),u,false)}var c=e.getMediaQueryLists();if(n){l(c)}else{l(c,o)}};var h=function(e){for(var t=0;t<e.length;t++){c(e[t])}if(ua.ie){document.documentElement.style.display="block";setTimeout(function(){document.documentElement.style.display=""},0);setTimeout(function(){cssHelper.broadcast("cssMediaQueriesTested")},100)}else{cssHelper.broadcast("cssMediaQueriesTested")}};var p=function(){for(var e=0;e<r.length;e++){cssHelper.removeStyle(r[e])}r=[];cssHelper.stylesheets(h)};var d=0;var v=function(){var e=cssHelper.getViewportWidth();var t=cssHelper.getViewportHeight();if(ua.ie){var n=document.createElement("div");n.style.position="absolute";n.style.top="-9999em";n.style.overflow="scroll";document.body.appendChild(n);d=n.offsetWidth-n.clientWidth;document.body.removeChild(n)}var r;var s=function(){var n=cssHelper.getViewportWidth();var s=cssHelper.getViewportHeight();if(Math.abs(n-e)>d||Math.abs(s-t)>d){e=n;t=s;clearTimeout(r);r=setTimeout(function(){if(!i()){p()}else{cssHelper.broadcast("cssMediaQueriesTested")}},500)}};window.onresize=function(){var e=window.onresize||function(){};return function(){e();s()}}()};var m=document.documentElement;m.style.marginLeft="-32767px";setTimeout(function(){m.style.marginLeft=""},5e3);return function(){if(!i()){cssHelper.addListener("newStyleParsed",function(e){c(e.cssHelperParsed.stylesheet)});cssHelper.addListener("cssMediaQueriesTested",function(){if(ua.ie){m.style.width="1px"}setTimeout(function(){m.style.width="";m.style.marginLeft=""},0);cssHelper.removeListener("cssMediaQueriesTested",arguments.callee)});s();p()}else{m.style.marginLeft=""}v()}}());try{document.execCommand("BackgroundImageCache",false,true)}catch(e){}
diff --git a/docs/_build/html/_static/css3-mediaqueries_src.js b/docs/_build/html/_static/css3-mediaqueries_src.js
new file mode 100644
index 0000000..8aedaab
--- /dev/null
+++ b/docs/_build/html/_static/css3-mediaqueries_src.js
@@ -0,0 +1,1104 @@
+/*
+css3-mediaqueries.js - CSS Helper and CSS3 Media Queries Enabler
+
+author: Wouter van der Graaf <wouter at dynora nl>
+version: 1.0 (20110330)
+license: MIT
+website: https://code.google.com/p/css3-mediaqueries-js/
+
+W3C spec: https://www.w3.org/TR/css3-mediaqueries/
+
+Note: use of embedded <style> is not recommended when using media queries, because IE  has no way of returning the raw literal css text from a <style> element.
+*/
+
+
+// true prototypal inheritance (https://javascript.crockford.com/prototypal.html)
+if (typeof Object.create !== 'function') {
+	Object.create = function (o) {
+		function F() {}
+		F.prototype = o;
+		return new F();
+	};
+}
+
+
+// user agent sniffing shortcuts
+var ua = {
+	toString: function () {
+		return navigator.userAgent;
+	},
+	test: function (s) {
+		return this.toString().toLowerCase().indexOf(s.toLowerCase()) > -1;
+	}
+};
+ua.version = (ua.toString().toLowerCase().match(/[\s\S]+(?:rv|it|ra|ie)[\/: ]([\d.]+)/) || [])[1];
+ua.webkit = ua.test('webkit');
+ua.gecko = ua.test('gecko') && !ua.webkit;
+ua.opera = ua.test('opera');
+ua.ie = ua.test('msie') && !ua.opera;
+ua.ie6 = ua.ie && document.compatMode && typeof document.documentElement.style.maxHeight === 'undefined';
+ua.ie7 = ua.ie && document.documentElement && typeof document.documentElement.style.maxHeight !== 'undefined' && typeof XDomainRequest === 'undefined';
+ua.ie8 = ua.ie && typeof XDomainRequest !== 'undefined';
+
+
+
+// initialize when DOM content is loaded
+var domReady = function () {
+	var fns = [];
+	var init = function () {
+		if (!arguments.callee.done) { // run init functions once
+			arguments.callee.done = true;
+			for (var i = 0; i < fns.length; i++) {
+				fns[i]();
+			}
+		}
+	};
+
+	// listeners for different browsers
+	if (document.addEventListener) {
+		document.addEventListener('DOMContentLoaded', init, false);
+	}
+	if (ua.ie) {
+		(function () {
+			try {
+				// throws errors until after ondocumentready
+				document.documentElement.doScroll('left');
+			}
+			catch (e) {
+				setTimeout(arguments.callee, 50);
+				return;
+			}
+			// no errors, fire
+			init();
+		})();
+		// trying to always fire before onload
+		document.onreadystatechange = function () {
+			if (document.readyState === 'complete') {
+				document.onreadystatechange = null;
+				init();
+			}
+		};
+	}
+	if (ua.webkit && document.readyState) {
+		(function () {
+			if (document.readyState !== 'loading') {
+				init();
+			}
+			else {
+				setTimeout(arguments.callee, 10);
+			}
+		})();
+	}
+	window.onload = init; // fallback
+
+	return function (fn) { // add fn to init functions
+		if (typeof fn === 'function') {
+			fns[fns.length] = fn;
+		}
+		return fn;
+	};
+}();
+
+
+
+// helper library for parsing css to objects
+var cssHelper = function () {
+
+	var regExp = {
+		BLOCKS: /[^\s{;][^{;]*\{(?:[^{}]*\{[^{}]*\}[^{}]*|[^{}]*)*\}/g,
+		BLOCKS_INSIDE: /[^\s{][^{]*\{[^{}]*\}/g,
+		DECLARATIONS: /[a-zA-Z\-]+[^;]*:[^;]+;/g,
+		RELATIVE_URLS: /url\(['"]?([^\/\)'"][^:\)'"]+)['"]?\)/g,
+		// strip whitespace and comments, @import is evil
+		REDUNDANT_COMPONENTS: /(?:\/\*([^*\\\\]|\*(?!\/))+\*\/|@import[^;]+;)/g,
+		REDUNDANT_WHITESPACE: /\s*(,|:|;|\{|\})\s*/g,
+        WHITESPACE_IN_PARENTHESES: /\(\s*(\S*)\s*\)/g,
+		MORE_WHITESPACE: /\s{2,}/g,
+		FINAL_SEMICOLONS: /;\}/g,
+		NOT_WHITESPACE: /\S+/g
+	};
+
+	var parsed, parsing = false;
+
+	var waiting = [];
+	var wait = function (fn) {
+		if (typeof fn === 'function') {
+			waiting[waiting.length] = fn;
+		}
+	};
+	var ready = function () {
+		for (var i = 0; i < waiting.length; i++) {
+			waiting[i](parsed);
+		}
+	};
+	var events = {};
+	var broadcast = function (n, v) {
+		if (events[n]) {
+			var listeners = events[n].listeners;
+			if (listeners) {
+				for (var i = 0; i < listeners.length; i++) {
+					listeners[i](v);
+				}
+			}
+		}
+	};
+
+	var requestText = function (url, fnSuccess, fnFailure) {
+		if (ua.ie && !window.XMLHttpRequest) {
+			window.XMLHttpRequest = function () {
+				return new ActiveXObject('Microsoft.XMLHTTP');
+			};
+		}
+		if (!XMLHttpRequest) {
+			return '';
+		}
+		var r = new XMLHttpRequest();
+		try {
+			r.open('get', url, true);
+			r.setRequestHeader('X_REQUESTED_WITH', 'XMLHttpRequest');
+		}
+		catch (e) {
+			fnFailure();
+			return;
+		}
+		var done = false;
+		setTimeout(function () {
+			done = true;
+		}, 5000);
+		document.documentElement.style.cursor = 'progress';
+		r.onreadystatechange = function () {
+			if (r.readyState === 4 && !done) {
+				if (!r.status && location.protocol === 'file:' ||
+						(r.status >= 200 && r.status < 300) ||
+						r.status === 304 ||
+						navigator.userAgent.indexOf('Safari') > -1 && typeof r.status === 'undefined') {
+					fnSuccess(r.responseText);
+				}
+				else {
+					fnFailure();
+				}
+				document.documentElement.style.cursor = '';
+				r = null; // avoid memory leaks
+			}
+		};
+		r.send('');
+	};
+
+	var sanitize = function (text) {
+		text = text.replace(regExp.REDUNDANT_COMPONENTS, '');
+		text = text.replace(regExp.REDUNDANT_WHITESPACE, '$1');
+        text = text.replace(regExp.WHITESPACE_IN_PARENTHESES, '($1)');
+		text = text.replace(regExp.MORE_WHITESPACE, ' ');
+		text = text.replace(regExp.FINAL_SEMICOLONS, '}'); // optional final semicolons
+		return text;
+	};
+
+	var objects = {
+	    stylesheet: function (el) {
+	        var o = {};
+	        var amqs = [], mqls = [], rs = [], rsw = [];
+	        var s = el.cssHelperText;
+
+	        // add attribute media queries
+	        var attr = el.getAttribute('media');
+	        if (attr) {
+	            var qts = attr.toLowerCase().split(',')
+	        }
+	        else {
+	            var qts = ['all'] // imply 'all'
+            }
+	        for (var i = 0; i < qts.length; i++) {
+	            amqs[amqs.length] = objects.mediaQuery(qts[i], o);
+	        }
+
+	        // add media query lists and rules (top down order)
+		    var blocks = s.match(regExp.BLOCKS); // @charset is not a block
+		    if (blocks !== null) {
+			    for (var i = 0; i < blocks.length; i++) {
+				    if (blocks[i].substring(0, 7) === '@media ') { // media query (list)
+					    var mql = objects.mediaQueryList(blocks[i], o);
+					    rs = rs.concat(mql.getRules());
+					    mqls[mqls.length] = mql;
+				    }
+				    else { // regular rule set, page context (@page) or font description (@font-face)
+					    rs[rs.length] = rsw[rsw.length] = objects.rule(blocks[i], o, null);
+				    }
+			    }
+		    }
+
+	        o.element = el;
+	        o.getCssText = function () {
+	            return s;
+	        };
+	        o.getAttrMediaQueries = function () {
+	            return amqs;
+	        };
+	        o.getMediaQueryLists = function () {
+	            return mqls;
+	        };
+	        o.getRules = function () {
+	            return rs;
+	        };
+	        o.getRulesWithoutMQ = function () {
+	            return rsw;
+	        };
+	        return o;
+	    },
+
+		mediaQueryList: function (s, stsh) {
+			var o = {};
+			var idx = s.indexOf('{');
+			var lt = s.substring(0, idx);
+			s = s.substring(idx + 1, s.length - 1);
+			var mqs = [], rs = [];
+
+			// add media queries
+			var qts = lt.toLowerCase().substring(7).split(',');
+			for (var i = 0; i < qts.length; i++) { // parse each media query
+				mqs[mqs.length] = objects.mediaQuery(qts[i], o);
+			}
+
+			// add rule sets
+			var rts = s.match(regExp.BLOCKS_INSIDE);
+			if (rts !== null) {
+				for (i = 0; i < rts.length; i++) {
+					rs[rs.length] = objects.rule(rts[i], stsh, o);
+				}
+			}
+
+			o.type = 'mediaQueryList';
+			o.getMediaQueries = function () {
+				return mqs;
+			};
+			o.getRules = function () {
+				return rs;
+			};
+			o.getListText = function () {
+				return lt;
+			};
+			o.getCssText = function () {
+				return s;
+			};
+			return o;
+		},
+
+		mediaQuery: function (s, listOrSheet) {
+			s = s || '';
+			var mql, stsh;
+			if (listOrSheet.type === 'mediaQueryList') {
+			    mql = listOrSheet;
+		    }
+		    else {
+		        stsh = listOrSheet;
+		    }
+			var not = false, type;
+			var expr = [];
+			var valid = true;
+			var tokens = s.match(regExp.NOT_WHITESPACE);
+
+
+
+			for (var i = 0; i < tokens.length; i++) {
+				var token = tokens[i];
+				if (!type && (token === 'not' || token === 'only')) { // 'not' and 'only' keywords
+					// keyword 'only' does nothing, as if it was not present
+					if (token === 'not') {
+						not = true;
+					}
+				}
+				else if (!type) { // media type
+					type = token;
+				}
+				else if (token.charAt(0) === '(') { // media feature expression
+					var pair = token.substring(1, token.length - 1).split(':');
+					expr[expr.length] = {
+						mediaFeature: pair[0],
+						value: pair[1] || null
+					};
+				}
+			}
+
+			return {
+			    getQueryText: function () {
+			        return s;
+			    },
+			    getAttrStyleSheet: function () {
+			        return stsh || null;
+			    },
+				getList: function () {
+					return mql || null;
+				},
+				getValid: function () {
+					return valid;
+				},
+				getNot: function () {
+					return not;
+				},
+				getMediaType: function () {
+					return type;
+				},
+				getExpressions: function () {
+					return expr;
+				}
+			};
+		},
+
+		rule: function (s, stsh, mql) {
+			var o = {};
+			var idx = s.indexOf('{');
+			var st = s.substring(0, idx);
+			var ss = st.split(',');
+			var ds = [];
+			var dts = s.substring(idx + 1, s.length - 1).split(';');
+			for (var i = 0; i < dts.length; i++) {
+				ds[ds.length] = objects.declaration(dts[i], o);
+			}
+
+			o.getStylesheet = function () {
+			    return stsh || null;
+			};
+			o.getMediaQueryList = function () {
+				return mql || null;
+			};
+			o.getSelectors = function () {
+				return ss;
+			};
+			o.getSelectorText = function () {
+				return st;
+			};
+			o.getDeclarations = function () {
+				return ds;
+			};
+			o.getPropertyValue = function (n) {
+				for (var i = 0; i < ds.length; i++) {
+					if (ds[i].getProperty() === n) {
+						return ds[i].getValue();
+					}
+				}
+				return null;
+			};
+			return o;
+		},
+
+		declaration: function (s, r) {
+			var idx = s.indexOf(':');
+			var p = s.substring(0, idx);
+			var v = s.substring(idx + 1);
+			return {
+				getRule: function () {
+					return r || null;
+				},
+				getProperty: function () {
+					return p;
+				},
+				getValue: function () {
+					return v;
+				}
+			};
+		}
+	};
+
+	var parseText = function (el) {
+		if (typeof el.cssHelperText !== 'string') {
+			return;
+		}
+		var o = {
+		    stylesheet: null,
+			mediaQueryLists: [],
+			rules: [],
+			selectors: {},
+			declarations: [],
+			properties: {}
+		};
+
+		// build stylesheet object
+		var stsh = o.stylesheet = objects.stylesheet(el);
+
+		// collect media query lists
+		var mqls = o.mediaQueryLists = stsh.getMediaQueryLists();
+
+		// collect all rules
+		var ors = o.rules = stsh.getRules();
+
+		// collect all selectors
+		var oss = o.selectors;
+		var collectSelectors = function (r) {
+			var ss = r.getSelectors();
+			for (var i = 0; i < ss.length; i++) {
+				var n = ss[i];
+				if (!oss[n]) {
+					oss[n] = [];
+				}
+				oss[n][oss[n].length] = r;
+			}
+		};
+		for (var i = 0; i < ors.length; i++) {
+			collectSelectors(ors[i]);
+		}
+
+		// collect all declarations
+		var ods = o.declarations;
+		for (i = 0; i < ors.length; i++) {
+			ods = o.declarations = ods.concat(ors[i].getDeclarations());
+		}
+
+		// collect all properties
+		var ops = o.properties;
+		for (i = 0; i < ods.length; i++) {
+			var n = ods[i].getProperty();
+			if (!ops[n]) {
+				ops[n] = [];
+			}
+			ops[n][ops[n].length] = ods[i];
+		}
+
+		el.cssHelperParsed = o;
+		parsed[parsed.length] = el;
+		return o;
+	};
+
+	var parseEmbedded = function (el, s) {
+	    return;
+	    // This function doesn't work because of a bug in IE, where innerHTML gives us parsed css instead of raw literal.
+		el.cssHelperText = sanitize(s || el.innerHTML);
+		return parseText(el);
+	};
+
+	var parse = function () {
+		parsing = true;
+		parsed = [];
+		var linked = [];
+		var finish = function () {
+			for (var i = 0; i < linked.length; i++) {
+				parseText(linked[i]);
+			}
+			var styles = document.getElementsByTagName('style');
+			for (i = 0; i < styles.length; i++) {
+				parseEmbedded(styles[i]);
+			}
+			parsing = false;
+			ready();
+		};
+		var links = document.getElementsByTagName('link');
+		for (var i = 0; i < links.length; i++) {
+			var link = links[i];
+			if (link.getAttribute('rel').indexOf('style') > -1 && link.href && link.href.length !== 0 && !link.disabled) {
+				linked[linked.length] = link;
+			}
+		}
+		if (linked.length > 0) {
+			var c = 0;
+			var checkForFinish = function () {
+				c++;
+				if (c === linked.length) { // parse in right order, so after last link is read
+					finish();
+				}
+			};
+			var processLink = function (link) {
+				var href = link.href;
+				requestText(href, function (text) {
+					// fix url's
+					text = sanitize(text).replace(regExp.RELATIVE_URLS, 'url(' + href.substring(0, href.lastIndexOf('/')) + '/$1)');
+					link.cssHelperText = text;
+					checkForFinish();
+				}, checkForFinish);
+			};
+			for (i = 0; i < linked.length; i++) {
+				processLink(linked[i]);
+			}
+		}
+		else {
+			finish();
+		}
+	};
+
+	var types = {
+	    stylesheets: 'array',
+		mediaQueryLists: 'array',
+		rules: 'array',
+		selectors: 'object',
+		declarations: 'array',
+		properties: 'object'
+	};
+
+	var collections = {
+	    stylesheets: null,
+		mediaQueryLists: null,
+		rules: null,
+		selectors: null,
+		declarations: null,
+		properties: null
+	};
+
+	var addToCollection = function (name, v) {
+		if (collections[name] !== null) {
+			if (types[name] === 'array') {
+				return (collections[name] = collections[name].concat(v));
+			}
+			else {
+				var c = collections[name];
+				for (var n in v) {
+					if (v.hasOwnProperty(n)) {
+						if (!c[n]) {
+							c[n] = v[n];
+						}
+						else {
+							c[n] = c[n].concat(v[n]);
+						}
+					}
+				}
+				return c;
+			}
+		}
+	};
+
+	var collect = function (name) {
+		collections[name] = (types[name] === 'array') ? [] : {};
+		for (var i = 0; i < parsed.length; i++) {
+		    var pname = name === 'stylesheets' ? 'stylesheet' : name; // the exception
+			addToCollection(name, parsed[i].cssHelperParsed[pname]);
+		}
+		return collections[name];
+	};
+
+	// viewport size
+	var getViewportSize = function (d) {
+		if (typeof window.innerWidth != 'undefined') {
+			return window['inner' + d];
+		}
+		else if (typeof document.documentElement !== 'undefined'
+				&& typeof document.documentElement.clientWidth !== 'undefined'
+				&& document.documentElement.clientWidth != 0) {
+			return document.documentElement['client' + d];
+		}
+	};
+
+	// public static functions
+	return {
+		addStyle: function (s, mediaTypes, process) {
+			var el = document.createElement('style');
+			el.setAttribute('type', 'text/css');
+			if (mediaTypes && mediaTypes.length > 0) {
+			    el.setAttribute('media', mediaTypes.join(','));
+			}
+			document.getElementsByTagName('head')[0].appendChild(el);
+			if (el.styleSheet) { // IE
+				el.styleSheet.cssText = s;
+			}
+			else {
+				el.appendChild(document.createTextNode(s));
+			}
+			el.addedWithCssHelper = true;
+			if (typeof process === 'undefined' || process === true) {
+				cssHelper.parsed(function (parsed) {
+					var o = parseEmbedded(el, s);
+					for (var n in o) {
+						if (o.hasOwnProperty(n)) {
+							addToCollection(n, o[n]);
+						}
+					}
+					broadcast('newStyleParsed', el);
+				});
+			}
+			else {
+				el.parsingDisallowed = true;
+			}
+			return el;
+		},
+
+		removeStyle: function (el) {
+			return el.parentNode.removeChild(el);
+		},
+
+		parsed: function (fn) {
+			if (parsing) {
+				wait(fn);
+			}
+			else {
+				if (typeof parsed !== 'undefined') {
+					if (typeof fn === 'function') {
+						fn(parsed);
+					}
+				}
+				else {
+					wait(fn);
+					parse();
+				}
+			}
+		},
+
+		stylesheets: function (fn) {
+		    cssHelper.parsed(function (parsed) {
+		        fn(collections.stylesheets || collect('stylesheets'));
+		    });
+		},
+
+		mediaQueryLists: function (fn) {
+			cssHelper.parsed(function (parsed) {
+				fn(collections.mediaQueryLists || collect('mediaQueryLists'));
+			});
+		},
+
+		rules: function (fn) {
+			cssHelper.parsed(function (parsed) {
+				fn(collections.rules || collect('rules'));
+			});
+		},
+
+		selectors: function (fn) {
+			cssHelper.parsed(function (parsed) {
+				fn(collections.selectors || collect('selectors'));
+			});
+		},
+
+		declarations: function (fn) {
+			cssHelper.parsed(function (parsed) {
+				fn(collections.declarations || collect('declarations'));
+			});
+		},
+
+		properties: function (fn) {
+			cssHelper.parsed(function (parsed) {
+				fn(collections.properties || collect('properties'));
+			});
+		},
+
+		broadcast: broadcast,
+
+		addListener: function (n, fn) { // in case n is 'styleadd': added function is called everytime style is added and parsed
+			if (typeof fn === 'function') {
+				if (!events[n]) {
+					events[n] = {
+						listeners: []
+					};
+				}
+				events[n].listeners[events[n].listeners.length] = fn;
+			}
+		},
+
+		removeListener: function (n, fn) {
+			if (typeof fn === 'function' && events[n]) {
+				var ls = events[n].listeners;
+				for (var i = 0; i < ls.length; i++) {
+					if (ls[i] === fn) {
+						ls.splice(i, 1);
+						i -= 1;
+					}
+				}
+			}
+		},
+
+		getViewportWidth: function () {
+			return getViewportSize('Width');
+		},
+
+		getViewportHeight: function () {
+			return getViewportSize('Height');
+		}
+	};
+}();
+
+
+
+// function to test and apply parsed media queries against browser capabilities
+domReady(function enableCssMediaQueries() {
+	var meter;
+
+	var regExp = {
+		LENGTH_UNIT: /[0-9]+(em|ex|px|in|cm|mm|pt|pc)$/,
+		RESOLUTION_UNIT: /[0-9]+(dpi|dpcm)$/,
+		ASPECT_RATIO: /^[0-9]+\/[0-9]+$/,
+		ABSOLUTE_VALUE: /^[0-9]*(\.[0-9]+)*$/
+	};
+
+	var styles = [];
+
+	var nativeSupport = function () {
+		// check support for media queries
+		var id = 'css3-mediaqueries-test';
+		var el = document.createElement('div');
+		el.id = id;
+		var style = cssHelper.addStyle('@media all and (width) { #' + id +
+			' { width: 1px !important; } }', [], false); // false means don't parse this temp style
+		document.body.appendChild(el);
+		var ret = el.offsetWidth === 1;
+		style.parentNode.removeChild(style);
+		el.parentNode.removeChild(el);
+		nativeSupport = function () {
+			return ret;
+		};
+		return ret;
+	};
+
+	var createMeter = function () { // create measuring element
+		meter = document.createElement('div');
+		meter.style.cssText = 'position:absolute;top:-9999em;left:-9999em;' +
+			'margin:0;border:none;padding:0;width:1em;font-size:1em;'; // cssText is needed for IE, works for the others
+		document.body.appendChild(meter);
+		// meter must have browser default font size of 16px
+		if (meter.offsetWidth !== 16) {
+			meter.style.fontSize = 16 / meter.offsetWidth + 'em';
+		}
+		meter.style.width = '';
+	};
+
+	var measure = function (value) {
+		meter.style.width = value;
+		var amount = meter.offsetWidth;
+		meter.style.width = '';
+		return amount;
+	};
+
+	var testMediaFeature = function (feature, value) {
+		// non-testable features: monochrome|min-monochrome|max-monochrome|scan|grid
+		var l = feature.length;
+		var min = (feature.substring(0, 4) === 'min-');
+		var max = (!min && feature.substring(0, 4) === 'max-');
+
+		if (value !== null) { // determine value type and parse to usable amount
+			var valueType;
+			var amount;
+			if (regExp.LENGTH_UNIT.exec(value)) {
+				valueType = 'length';
+				amount = measure(value);
+			}
+			else if (regExp.RESOLUTION_UNIT.exec(value)) {
+				valueType = 'resolution';
+				amount = parseInt(value, 10);
+				var unit = value.substring((amount + '').length);
+			}
+			else if (regExp.ASPECT_RATIO.exec(value)) {
+				valueType = 'aspect-ratio';
+				amount = value.split('/');
+			}
+			else if (regExp.ABSOLUTE_VALUE) {
+				valueType = 'absolute';
+				amount = value;
+			}
+			else {
+				valueType = 'unknown';
+			}
+		}
+
+		var width, height;
+		if ('device-width' === feature.substring(l - 12, l)) { // screen width
+			width = screen.width;
+			if (value !== null) {
+				if (valueType === 'length') {
+					return ((min && width >= amount) || (max && width < amount) || (!min && !max && width === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test width without value
+				return width > 0;
+			}
+		}
+		else if ('device-height' === feature.substring(l - 13, l)) { // screen height
+			height = screen.height;
+			if (value !== null) {
+				if (valueType === 'length') {
+					return ((min && height >= amount) || (max && height < amount) || (!min && !max && height === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test height without value
+				return height > 0;
+			}
+		}
+		else if ('width' === feature.substring(l - 5, l)) { // viewport width
+			width = document.documentElement.clientWidth || document.body.clientWidth; // the latter for IE quirks mode
+			if (value !== null) {
+				if (valueType === 'length') {
+					return ((min && width >= amount) || (max && width < amount) || (!min && !max && width === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test width without value
+				return width > 0;
+			}
+		}
+		else if ('height' === feature.substring(l - 6, l)) { // viewport height
+			height = document.documentElement.clientHeight || document.body.clientHeight; // the latter for IE quirks mode
+			if (value !== null) {
+				if (valueType === 'length') {
+					return ((min && height >= amount) || (max && height < amount) || (!min && !max && height === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test height without value
+				return height > 0;
+			}
+		}
+		else if ('device-aspect-ratio' === feature.substring(l - 19, l)) { // screen aspect ratio
+			return valueType === 'aspect-ratio' && screen.width * amount[1] === screen.height * amount[0];
+		}
+		else if ('color-index' === feature.substring(l - 11, l)) { // number of colors
+			var colors = Math.pow(2, screen.colorDepth);
+			if (value !== null) {
+				if (valueType === 'absolute') {
+					return ((min && colors >= amount) || (max && colors < amount) || (!min && !max && colors === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test height without value
+				return colors > 0;
+			}
+		}
+		else if ('color' === feature.substring(l - 5, l)) { // bits per color component
+			var color = screen.colorDepth;
+			if (value !== null) {
+				if (valueType === 'absolute') {
+					return ((min && color >= amount) || (max && color < amount) || (!min && !max && color === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test height without value
+				return color > 0;
+			}
+		}
+		else if ('resolution' === feature.substring(l - 10, l)) {
+			var res;
+			if (unit === 'dpcm') {
+				res = measure('1cm');
+			}
+			else {
+				res = measure('1in');
+			}
+			if (value !== null) {
+				if (valueType === 'resolution') {
+					return ((min && res >= amount) || (max && res < amount) || (!min && !max && res === amount));
+				}
+				else {
+					return false;
+				}
+			}
+			else { // test height without value
+				return res > 0;
+			}
+		}
+		else {
+			return false;
+		}
+	};
+
+	var testMediaQuery = function (mq) {
+		var test = mq.getValid();
+		var expressions = mq.getExpressions();
+		var l = expressions.length;
+		if (l > 0) {
+			for (var i = 0; i < l && test; i++) {
+				test = testMediaFeature(expressions[i].mediaFeature, expressions[i].value);
+			}
+			var not = mq.getNot();
+			return (test && !not || not && !test);
+		}
+		return test;
+	};
+
+	var testMediaQueryList = function (mql, ts) {
+	    // ts is null or an array with any media type but 'all'.
+		var mqs = mql.getMediaQueries();
+		var t = {};
+		for (var i = 0; i < mqs.length; i++) {
+		    var type = mqs[i].getMediaType();
+		    if (mqs[i].getExpressions().length === 0) {
+		        continue;
+		        // TODO: Browser check! Assuming old browsers do apply the bare media types, even in a list with media queries.
+		    }
+		    var typeAllowed = true;
+		    if (type !== 'all' && ts && ts.length > 0) {
+		        typeAllowed = false;
+		        for (var j = 0; j < ts.length; j++) {
+		            if (ts[j] === type) {
+		                typeAllowed = true;
+                    }
+		        }
+		    }
+			if (typeAllowed && testMediaQuery(mqs[i])) {
+				t[type] = true;
+			}
+		}
+		var s = [], c = 0;
+		for (var n in t) {
+			if (t.hasOwnProperty(n)) {
+				if (c > 0) {
+					s[c++] = ',';
+				}
+				s[c++] = n;
+			}
+		}
+		if (s.length > 0) {
+			styles[styles.length] = cssHelper.addStyle('@media ' + s.join('') + '{' + mql.getCssText() + '}', ts, false);
+		}
+	};
+
+	var testMediaQueryLists = function (mqls, ts) {
+		for (var i = 0; i < mqls.length; i++) {
+			testMediaQueryList(mqls[i], ts);
+		}
+	};
+
+	var testStylesheet = function (stsh) {
+	    var amqs = stsh.getAttrMediaQueries();
+	    var allPassed = false;
+	    var t = {};
+		for (var i = 0; i < amqs.length; i++) {
+			if (testMediaQuery(amqs[i])) {
+				t[amqs[i].getMediaType()] = amqs[i].getExpressions().length > 0;
+			}
+		}
+		var ts = [], tswe = [];
+		for (var n in t) {
+			if (t.hasOwnProperty(n)) {
+				ts[ts.length] = n;
+				if (t[n]) {
+				    tswe[tswe.length] = n
+				}
+			    if (n === 'all') {
+			        allPassed = true;
+                }
+			}
+		}
+		if (tswe.length > 0) { // types with query expressions that passed the test
+		    styles[styles.length] = cssHelper.addStyle(stsh.getCssText(), tswe, false);
+		}
+		var mqls = stsh.getMediaQueryLists();
+		if (allPassed) {
+		    // If 'all' in media attribute passed the test, then test all @media types in linked CSS and create style with those types.
+		    testMediaQueryLists(mqls);
+		}
+		else {
+		    // Or else, test only media attribute types that passed the test and also 'all'.
+		    // For positive '@media all', create style with attribute types that passed their test.
+		    testMediaQueryLists(mqls, ts);
+	    }
+    };
+
+	var testStylesheets = function (stshs) {
+	    for (var i = 0; i < stshs.length; i++) {
+	        testStylesheet(stshs[i]);
+	    }
+	    if (ua.ie) {
+			// force repaint in IE
+			document.documentElement.style.display = 'block';
+			setTimeout(function () {
+				document.documentElement.style.display = '';
+			}, 0);
+			// delay broadcast somewhat for IE
+			setTimeout(function () {
+				cssHelper.broadcast('cssMediaQueriesTested');
+			}, 100);
+		}
+		else {
+			cssHelper.broadcast('cssMediaQueriesTested');
+		}
+	};
+
+	var test = function () {
+		for (var i = 0; i < styles.length; i++) {
+			cssHelper.removeStyle(styles[i]);
+		}
+		styles = [];
+		cssHelper.stylesheets(testStylesheets);
+	};
+
+	var scrollbarWidth = 0;
+	var checkForResize = function () {
+		var cvpw = cssHelper.getViewportWidth();
+		var cvph = cssHelper.getViewportHeight();
+
+		// determine scrollbar width in IE, see resizeHandler
+		if (ua.ie) {
+			var el = document.createElement('div');
+			el.style.position = 'absolute';
+			el.style.top = '-9999em';
+			el.style.overflow = 'scroll';
+			document.body.appendChild(el);
+			scrollbarWidth = el.offsetWidth - el.clientWidth;
+			document.body.removeChild(el);
+		}
+
+		var timer;
+		var resizeHandler = function () {
+			var vpw = cssHelper.getViewportWidth();
+			var vph = cssHelper.getViewportHeight();
+			// check whether vp size has really changed, because IE also triggers resize event when body size changes
+			// 20px allowance to accommodate short appearance of scrollbars in IE in some cases
+			if (Math.abs(vpw - cvpw) > scrollbarWidth || Math.abs(vph - cvph) > scrollbarWidth) {
+				cvpw = vpw;
+				cvph = vph;
+				clearTimeout(timer);
+				timer = setTimeout(function () {
+					if (!nativeSupport()) {
+						test();
+					}
+					else {
+						cssHelper.broadcast('cssMediaQueriesTested');
+					}
+				}, 500);
+			}
+		};
+
+		window.onresize = function () {
+			var x = window.onresize || function () {}; // save original
+			return function () {
+				x();
+				resizeHandler();
+			};
+		}();
+	};
+
+	// prevent jumping of layout by hiding everything before painting <body>
+    var docEl = document.documentElement;
+	docEl.style.marginLeft = '-32767px';
+
+	// make sure it comes back after a while
+	setTimeout(function () {
+		docEl.style.marginLeft = '';
+	}, 5000);
+
+	return function () {
+		if (!nativeSupport()) { // if browser doesn't support media queries
+			cssHelper.addListener('newStyleParsed', function (el) {
+				testStylesheet(el.cssHelperParsed.stylesheet);
+			});
+			// return visibility after media queries are tested
+			cssHelper.addListener('cssMediaQueriesTested', function () {
+				// force repaint in IE by changing width
+				if (ua.ie) {
+					docEl.style.width = '1px';
+				}
+				setTimeout(function () {
+					docEl.style.width = ''; // undo width
+					docEl.style.marginLeft = ''; // undo hide
+				}, 0);
+				// remove this listener to prevent following execution
+				cssHelper.removeListener('cssMediaQueriesTested', arguments.callee);
+			});
+			createMeter();
+			test();
+		}
+		else {
+			docEl.style.marginLeft = ''; // undo visibility hidden
+		}
+		checkForResize();
+	};
+}());
+
+
+// bonus: hotfix for IE6 SP1 (bug KB823727)
+try {
+	document.execCommand('BackgroundImageCache', false, true);
+} catch (e) {}
diff --git a/docs/build/html/_static/doctools.js b/docs/_build/html/_static/doctools.js
similarity index 98%
rename from docs/build/html/_static/doctools.js
rename to docs/_build/html/_static/doctools.js
index d06a71d..4d67807 100644
--- a/docs/build/html/_static/doctools.js
+++ b/docs/_build/html/_static/doctools.js
@@ -4,7 +4,7 @@
  *
  * Base JavaScript utilities for all Sphinx HTML documentation.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
diff --git a/docs/build/html/_static/documentation_options.js b/docs/_build/html/_static/documentation_options.js
similarity index 60%
rename from docs/build/html/_static/documentation_options.js
rename to docs/_build/html/_static/documentation_options.js
index e7e52c8..7f51004 100644
--- a/docs/build/html/_static/documentation_options.js
+++ b/docs/_build/html/_static/documentation_options.js
@@ -1,7 +1,6 @@
-var DOCUMENTATION_OPTIONS = {
-    URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
-    VERSION: '',
-    LANGUAGE: 'English',
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '2.0.0',
+    LANGUAGE: 'en',
     COLLAPSE_INDEX: false,
     BUILDER: 'html',
     FILE_SUFFIX: '.html',
diff --git a/docs/_build/html/_static/documentation_options.js.jinja b/docs/_build/html/_static/documentation_options.js.jinja
new file mode 100644
index 0000000..b66690a
--- /dev/null
+++ b/docs/_build/html/_static/documentation_options.js.jinja
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+    VERSION: '{{ release|e }}',
+    LANGUAGE: '{{ language }}',
+    COLLAPSE_INDEX: false,
+    BUILDER: '{{ builder }}',
+    FILE_SUFFIX: '{{ file_suffix }}',
+    LINK_SUFFIX: '{{ link_suffix }}',
+    HAS_SOURCE: {{ has_source|lower }},
+    SOURCELINK_SUFFIX: '{{ sourcelink_suffix }}',
+    NAVIGATION_WITH_KEYS: {{ 'true' if theme_navigation_with_keys|tobool else 'false'}},
+    SHOW_SEARCH_SUMMARY: {{ 'true' if show_search_summary else 'false' }},
+    ENABLE_SEARCH_SHORTCUTS: {{ 'true' if theme_enable_search_shortcuts|tobool else 'false'}},
+};
diff --git a/docs/build/html/_static/favicon.ico b/docs/_build/html/_static/favicon.ico
similarity index 100%
rename from docs/build/html/_static/favicon.ico
rename to docs/_build/html/_static/favicon.ico
diff --git a/docs/build/html/_static/file.png b/docs/_build/html/_static/file.png
similarity index 100%
rename from docs/build/html/_static/file.png
rename to docs/_build/html/_static/file.png
diff --git a/docs/_build/html/_static/jquery-3.6.0.js b/docs/_build/html/_static/jquery-3.6.0.js
new file mode 100644
index 0000000..fc6c299
--- /dev/null
+++ b/docs/_build/html/_static/jquery-3.6.0.js
@@ -0,0 +1,10881 @@
+/*!
+ * jQuery JavaScript Library v3.6.0
+ * https://jquery.com/
+ *
+ * Includes Sizzle.js
+ * https://sizzlejs.com/
+ *
+ * Copyright OpenJS Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2021-03-02T17:08Z
+ */
+( function( global, factory ) {
+
+	"use strict";
+
+	if ( typeof module === "object" && typeof module.exports === "object" ) {
+
+		// For CommonJS and CommonJS-like environments where a proper `window`
+		// is present, execute the factory and get jQuery.
+		// For environments that do not have a `window` with a `document`
+		// (such as Node.js), expose a factory as module.exports.
+		// This accentuates the need for the creation of a real `window`.
+		// e.g. var jQuery = require("jquery")(window);
+		// See ticket #14549 for more info.
+		module.exports = global.document ?
+			factory( global, true ) :
+			function( w ) {
+				if ( !w.document ) {
+					throw new Error( "jQuery requires a window with a document" );
+				}
+				return factory( w );
+			};
+	} else {
+		factory( global );
+	}
+
+// Pass this if window is not defined yet
+} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
+
+// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
+// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
+// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
+// enough that all such attempts are guarded in a try block.
+"use strict";
+
+var arr = [];
+
+var getProto = Object.getPrototypeOf;
+
+var slice = arr.slice;
+
+var flat = arr.flat ? function( array ) {
+	return arr.flat.call( array );
+} : function( array ) {
+	return arr.concat.apply( [], array );
+};
+
+
+var push = arr.push;
+
+var indexOf = arr.indexOf;
+
+var class2type = {};
+
+var toString = class2type.toString;
+
+var hasOwn = class2type.hasOwnProperty;
+
+var fnToString = hasOwn.toString;
+
+var ObjectFunctionString = fnToString.call( Object );
+
+var support = {};
+
+var isFunction = function isFunction( obj ) {
+
+		// Support: Chrome <=57, Firefox <=52
+		// In some browsers, typeof returns "function" for HTML <object> elements
+		// (i.e., `typeof document.createElement( "object" ) === "function"`).
+		// We don't want to classify *any* DOM node as a function.
+		// Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5
+		// Plus for old WebKit, typeof returns "function" for HTML collections
+		// (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756)
+		return typeof obj === "function" && typeof obj.nodeType !== "number" &&
+			typeof obj.item !== "function";
+	};
+
+
+var isWindow = function isWindow( obj ) {
+		return obj != null && obj === obj.window;
+	};
+
+
+var document = window.document;
+
+
+
+	var preservedScriptAttributes = {
+		type: true,
+		src: true,
+		nonce: true,
+		noModule: true
+	};
+
+	function DOMEval( code, node, doc ) {
+		doc = doc || document;
+
+		var i, val,
+			script = doc.createElement( "script" );
+
+		script.text = code;
+		if ( node ) {
+			for ( i in preservedScriptAttributes ) {
+
+				// Support: Firefox 64+, Edge 18+
+				// Some browsers don't support the "nonce" property on scripts.
+				// On the other hand, just using `getAttribute` is not enough as
+				// the `nonce` attribute is reset to an empty string whenever it
+				// becomes browsing-context connected.
+				// See https://github.com/whatwg/html/issues/2369
+				// See https://html.spec.whatwg.org/#nonce-attributes
+				// The `node.getAttribute` check was added for the sake of
+				// `jQuery.globalEval` so that it can fake a nonce-containing node
+				// via an object.
+				val = node[ i ] || node.getAttribute && node.getAttribute( i );
+				if ( val ) {
+					script.setAttribute( i, val );
+				}
+			}
+		}
+		doc.head.appendChild( script ).parentNode.removeChild( script );
+	}
+
+
+function toType( obj ) {
+	if ( obj == null ) {
+		return obj + "";
+	}
+
+	// Support: Android <=2.3 only (functionish RegExp)
+	return typeof obj === "object" || typeof obj === "function" ?
+		class2type[ toString.call( obj ) ] || "object" :
+		typeof obj;
+}
+/* global Symbol */
+// Defining this global in .eslintrc.json would create a danger of using the global
+// unguarded in another place, it seems safer to define global only for this module
+
+
+
+var
+	version = "3.6.0",
+
+	// Define a local copy of jQuery
+	jQuery = function( selector, context ) {
+
+		// The jQuery object is actually just the init constructor 'enhanced'
+		// Need init if jQuery is called (just allow error to be thrown if not included)
+		return new jQuery.fn.init( selector, context );
+	};
+
+jQuery.fn = jQuery.prototype = {
+
+	// The current version of jQuery being used
+	jquery: version,
+
+	constructor: jQuery,
+
+	// The default length of a jQuery object is 0
+	length: 0,
+
+	toArray: function() {
+		return slice.call( this );
+	},
+
+	// Get the Nth element in the matched element set OR
+	// Get the whole matched element set as a clean array
+	get: function( num ) {
+
+		// Return all the elements in a clean array
+		if ( num == null ) {
+			return slice.call( this );
+		}
+
+		// Return just the one element from the set
+		return num < 0 ? this[ num + this.length ] : this[ num ];
+	},
+
+	// Take an array of elements and push it onto the stack
+	// (returning the new matched element set)
+	pushStack: function( elems ) {
+
+		// Build a new jQuery matched element set
+		var ret = jQuery.merge( this.constructor(), elems );
+
+		// Add the old object onto the stack (as a reference)
+		ret.prevObject = this;
+
+		// Return the newly-formed element set
+		return ret;
+	},
+
+	// Execute a callback for every element in the matched set.
+	each: function( callback ) {
+		return jQuery.each( this, callback );
+	},
+
+	map: function( callback ) {
+		return this.pushStack( jQuery.map( this, function( elem, i ) {
+			return callback.call( elem, i, elem );
+		} ) );
+	},
+
+	slice: function() {
+		return this.pushStack( slice.apply( this, arguments ) );
+	},
+
+	first: function() {
+		return this.eq( 0 );
+	},
+
+	last: function() {
+		return this.eq( -1 );
+	},
+
+	even: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return ( i + 1 ) % 2;
+		} ) );
+	},
+
+	odd: function() {
+		return this.pushStack( jQuery.grep( this, function( _elem, i ) {
+			return i % 2;
+		} ) );
+	},
+
+	eq: function( i ) {
+		var len = this.length,
+			j = +i + ( i < 0 ? len : 0 );
+		return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
+	},
+
+	end: function() {
+		return this.prevObject || this.constructor();
+	},
+
+	// For internal use only.
+	// Behaves like an Array's method, not like a jQuery method.
+	push: push,
+	sort: arr.sort,
+	splice: arr.splice
+};
+
+jQuery.extend = jQuery.fn.extend = function() {
+	var options, name, src, copy, copyIsArray, clone,
+		target = arguments[ 0 ] || {},
+		i = 1,
+		length = arguments.length,
+		deep = false;
+
+	// Handle a deep copy situation
+	if ( typeof target === "boolean" ) {
+		deep = target;
+
+		// Skip the boolean and the target
+		target = arguments[ i ] || {};
+		i++;
+	}
+
+	// Handle case when target is a string or something (possible in deep copy)
+	if ( typeof target !== "object" && !isFunction( target ) ) {
+		target = {};
+	}
+
+	// Extend jQuery itself if only one argument is passed
+	if ( i === length ) {
+		target = this;
+		i--;
+	}
+
+	for ( ; i < length; i++ ) {
+
+		// Only deal with non-null/undefined values
+		if ( ( options = arguments[ i ] ) != null ) {
+
+			// Extend the base object
+			for ( name in options ) {
+				copy = options[ name ];
+
+				// Prevent Object.prototype pollution
+				// Prevent never-ending loop
+				if ( name === "__proto__" || target === copy ) {
+					continue;
+				}
+
+				// Recurse if we're merging plain objects or arrays
+				if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
+					( copyIsArray = Array.isArray( copy ) ) ) ) {
+					src = target[ name ];
+
+					// Ensure proper type for the source value
+					if ( copyIsArray && !Array.isArray( src ) ) {
+						clone = [];
+					} else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
+						clone = {};
+					} else {
+						clone = src;
+					}
+					copyIsArray = false;
+
+					// Never move original objects, clone them
+					target[ name ] = jQuery.extend( deep, clone, copy );
+
+				// Don't bring in undefined values
+				} else if ( copy !== undefined ) {
+					target[ name ] = copy;
+				}
+			}
+		}
+	}
+
+	// Return the modified object
+	return target;
+};
+
+jQuery.extend( {
+
+	// Unique for each copy of jQuery on the page
+	expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
+
+	// Assume jQuery is ready without the ready module
+	isReady: true,
+
+	error: function( msg ) {
+		throw new Error( msg );
+	},
+
+	noop: function() {},
+
+	isPlainObject: function( obj ) {
+		var proto, Ctor;
+
+		// Detect obvious negatives
+		// Use toString instead of jQuery.type to catch host objects
+		if ( !obj || toString.call( obj ) !== "[object Object]" ) {
+			return false;
+		}
+
+		proto = getProto( obj );
+
+		// Objects with no prototype (e.g., `Object.create( null )`) are plain
+		if ( !proto ) {
+			return true;
+		}
+
+		// Objects with prototype are plain iff they were constructed by a global Object function
+		Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
+		return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
+	},
+
+	isEmptyObject: function( obj ) {
+		var name;
+
+		for ( name in obj ) {
+			return false;
+		}
+		return true;
+	},
+
+	// Evaluates a script in a provided context; falls back to the global one
+	// if not specified.
+	globalEval: function( code, options, doc ) {
+		DOMEval( code, { nonce: options && options.nonce }, doc );
+	},
+
+	each: function( obj, callback ) {
+		var length, i = 0;
+
+		if ( isArrayLike( obj ) ) {
+			length = obj.length;
+			for ( ; i < length; i++ ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		} else {
+			for ( i in obj ) {
+				if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
+					break;
+				}
+			}
+		}
+
+		return obj;
+	},
+
+	// results is for internal usage only
+	makeArray: function( arr, results ) {
+		var ret = results || [];
+
+		if ( arr != null ) {
+			if ( isArrayLike( Object( arr ) ) ) {
+				jQuery.merge( ret,
+					typeof arr === "string" ?
+						[ arr ] : arr
+				);
+			} else {
+				push.call( ret, arr );
+			}
+		}
+
+		return ret;
+	},
+
+	inArray: function( elem, arr, i ) {
+		return arr == null ? -1 : indexOf.call( arr, elem, i );
+	},
+
+	// Support: Android <=4.0 only, PhantomJS 1 only
+	// push.apply(_, arraylike) throws on ancient WebKit
+	merge: function( first, second ) {
+		var len = +second.length,
+			j = 0,
+			i = first.length;
+
+		for ( ; j < len; j++ ) {
+			first[ i++ ] = second[ j ];
+		}
+
+		first.length = i;
+
+		return first;
+	},
+
+	grep: function( elems, callback, invert ) {
+		var callbackInverse,
+			matches = [],
+			i = 0,
+			length = elems.length,
+			callbackExpect = !invert;
+
+		// Go through the array, only saving the items
+		// that pass the validator function
+		for ( ; i < length; i++ ) {
+			callbackInverse = !callback( elems[ i ], i );
+			if ( callbackInverse !== callbackExpect ) {
+				matches.push( elems[ i ] );
+			}
+		}
+
+		return matches;
+	},
+
+	// arg is for internal usage only
+	map: function( elems, callback, arg ) {
+		var length, value,
+			i = 0,
+			ret = [];
+
+		// Go through the array, translating each of the items to their new values
+		if ( isArrayLike( elems ) ) {
+			length = elems.length;
+			for ( ; i < length; i++ ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+
+		// Go through every key on the object,
+		} else {
+			for ( i in elems ) {
+				value = callback( elems[ i ], i, arg );
+
+				if ( value != null ) {
+					ret.push( value );
+				}
+			}
+		}
+
+		// Flatten any nested arrays
+		return flat( ret );
+	},
+
+	// A global GUID counter for objects
+	guid: 1,
+
+	// jQuery.support is not used in Core but other projects attach their
+	// properties to it so it needs to exist.
+	support: support
+} );
+
+if ( typeof Symbol === "function" ) {
+	jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
+}
+
+// Populate the class2type map
+jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
+	function( _i, name ) {
+		class2type[ "[object " + name + "]" ] = name.toLowerCase();
+	} );
+
+function isArrayLike( obj ) {
+
+	// Support: real iOS 8.2 only (not reproducible in simulator)
+	// `in` check used to prevent JIT error (gh-2145)
+	// hasOwn isn't used here due to false negatives
+	// regarding Nodelist length in IE
+	var length = !!obj && "length" in obj && obj.length,
+		type = toType( obj );
+
+	if ( isFunction( obj ) || isWindow( obj ) ) {
+		return false;
+	}
+
+	return type === "array" || length === 0 ||
+		typeof length === "number" && length > 0 && ( length - 1 ) in obj;
+}
+var Sizzle =
+/*!
+ * Sizzle CSS Selector Engine v2.3.6
+ * https://sizzlejs.com/
+ *
+ * Copyright JS Foundation and other contributors
+ * Released under the MIT license
+ * https://js.foundation/
+ *
+ * Date: 2021-02-16
+ */
+( function( window ) {
+var i,
+	support,
+	Expr,
+	getText,
+	isXML,
+	tokenize,
+	compile,
+	select,
+	outermostContext,
+	sortInput,
+	hasDuplicate,
+
+	// Local document vars
+	setDocument,
+	document,
+	docElem,
+	documentIsHTML,
+	rbuggyQSA,
+	rbuggyMatches,
+	matches,
+	contains,
+
+	// Instance-specific data
+	expando = "sizzle" + 1 * new Date(),
+	preferredDoc = window.document,
+	dirruns = 0,
+	done = 0,
+	classCache = createCache(),
+	tokenCache = createCache(),
+	compilerCache = createCache(),
+	nonnativeSelectorCache = createCache(),
+	sortOrder = function( a, b ) {
+		if ( a === b ) {
+			hasDuplicate = true;
+		}
+		return 0;
+	},
+
+	// Instance methods
+	hasOwn = ( {} ).hasOwnProperty,
+	arr = [],
+	pop = arr.pop,
+	pushNative = arr.push,
+	push = arr.push,
+	slice = arr.slice,
+
+	// Use a stripped-down indexOf as it's faster than native
+	// https://jsperf.com/thor-indexof-vs-for/5
+	indexOf = function( list, elem ) {
+		var i = 0,
+			len = list.length;
+		for ( ; i < len; i++ ) {
+			if ( list[ i ] === elem ) {
+				return i;
+			}
+		}
+		return -1;
+	},
+
+	booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
+		"ismap|loop|multiple|open|readonly|required|scoped",
+
+	// Regular expressions
+
+	// http://www.w3.org/TR/css3-selectors/#whitespace
+	whitespace = "[\\x20\\t\\r\\n\\f]",
+
+	// https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
+	identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
+		"?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
+
+	// Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
+	attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
+
+		// Operator (capture 2)
+		"*([*^$|!~]?=)" + whitespace +
+
+		// "Attribute values must be CSS identifiers [capture 5]
+		// or strings [capture 3 or capture 4]"
+		"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
+		whitespace + "*\\]",
+
+	pseudos = ":(" + identifier + ")(?:\\((" +
+
+		// To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
+		// 1. quoted (capture 3; capture 4 or capture 5)
+		"('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
+
+		// 2. simple (capture 6)
+		"((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
+
+		// 3. anything else (capture 2)
+		".*" +
+		")\\)|)",
+
+	// Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
+	rwhitespace = new RegExp( whitespace + "+", "g" ),
+	rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
+		whitespace + "+$", "g" ),
+
+	rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
+	rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
+		"*" ),
+	rdescend = new RegExp( whitespace + "|>" ),
+
+	rpseudo = new RegExp( pseudos ),
+	ridentifier = new RegExp( "^" + identifier + "$" ),
+
+	matchExpr = {
+		"ID": new RegExp( "^#(" + identifier + ")" ),
+		"CLASS": new RegExp( "^\\.(" + identifier + ")" ),
+		"TAG": new RegExp( "^(" + identifier + "|[*])" ),
+		"ATTR": new RegExp( "^" + attributes ),
+		"PSEUDO": new RegExp( "^" + pseudos ),
+		"CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
+			whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
+			whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
+		"bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
+
+		// For use in libraries implementing .is()
+		// We use this for POS matching in `select`
+		"needsContext": new RegExp( "^" + whitespace +
+			"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
+			"*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
+	},
+
+	rhtml = /HTML$/i,
+	rinputs = /^(?:input|select|textarea|button)$/i,
+	rheader = /^h\d$/i,
+
+	rnative = /^[^{]+\{\s*\[native \w/,
+
+	// Easily-parseable/retrievable ID or TAG or CLASS selectors
+	rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
+
+	rsibling = /[+~]/,
+
+	// CSS escapes
+	// http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
+	runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
+	funescape = function( escape, nonHex ) {
+		var high = "0x" + escape.slice( 1 ) - 0x10000;
+
+		return nonHex ?
+
+			// Strip the backslash prefix from a non-hex escape sequence
+			nonHex :
+
+			// Replace a hexadecimal escape sequence with the encoded Unicode code point
+			// Support: IE <=11+
+			// For values outside the Basic Multilingual Plane (BMP), manually construct a
+			// surrogate pair
+			high < 0 ?
+				String.fromCharCode( high + 0x10000 ) :
+				String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
+	},
+
+	// CSS string/identifier serialization
+	// https://drafts.csswg.org/cssom/#common-serializing-idioms
+	rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
+	fcssescape = function( ch, asCodePoint ) {
+		if ( asCodePoint ) {
+
+			// U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
+			if ( ch === "\0" ) {
+				return "\uFFFD";
+			}
+
+			// Control characters and (dependent upon position) numbers get escaped as code points
+			return ch.slice( 0, -1 ) + "\\" +
+				ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
+		}
+
+		// Other potentially-special ASCII characters get backslash-escaped
+		return "\\" + ch;
+	},
+
+	// Used for iframes
+	// See setDocument()
+	// Removing the function wrapper causes a "Permission Denied"
+	// error in IE
+	unloadHandler = function() {
+		setDocument();
+	},
+
+	inDisabledFieldset = addCombinator(
+		function( elem ) {
+			return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
+		},
+		{ dir: "parentNode", next: "legend" }
+	);
+
+// Optimize for push.apply( _, NodeList )
+try {
+	push.apply(
+		( arr = slice.call( preferredDoc.childNodes ) ),
+		preferredDoc.childNodes
+	);
+
+	// Support: Android<4.0
+	// Detect silently failing push.apply
+	// eslint-disable-next-line no-unused-expressions
+	arr[ preferredDoc.childNodes.length ].nodeType;
+} catch ( e ) {
+	push = { apply: arr.length ?
+
+		// Leverage slice if possible
+		function( target, els ) {
+			pushNative.apply( target, slice.call( els ) );
+		} :
+
+		// Support: IE<9
+		// Otherwise append directly
+		function( target, els ) {
+			var j = target.length,
+				i = 0;
+
+			// Can't trust NodeList.length
+			while ( ( target[ j++ ] = els[ i++ ] ) ) {}
+			target.length = j - 1;
+		}
+	};
+}
+
+function Sizzle( selector, context, results, seed ) {
+	var m, i, elem, nid, match, groups, newSelector,
+		newContext = context && context.ownerDocument,
+
+		// nodeType defaults to 9, since context defaults to document
+		nodeType = context ? context.nodeType : 9;
+
+	results = results || [];
+
+	// Return early from calls with invalid selector or context
+	if ( typeof selector !== "string" || !selector ||
+		nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
+
+		return results;
+	}
+
+	// Try to shortcut find operations (as opposed to filters) in HTML documents
+	if ( !seed ) {
+		setDocument( context );
+		context = context || document;
+
+		if ( documentIsHTML ) {
+
+			// If the selector is sufficiently simple, try using a "get*By*" DOM method
+			// (excepting DocumentFragment context, where the methods don't exist)
+			if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
+
+				// ID selector
+				if ( ( m = match[ 1 ] ) ) {
+
+					// Document context
+					if ( nodeType === 9 ) {
+						if ( ( elem = context.getElementById( m ) ) ) {
+
+							// Support: IE, Opera, Webkit
+							// TODO: identify versions
+							// getElementById can match elements by name instead of ID
+							if ( elem.id === m ) {
+								results.push( elem );
+								return results;
+							}
+						} else {
+							return results;
+						}
+
+					// Element context
+					} else {
+
+						// Support: IE, Opera, Webkit
+						// TODO: identify versions
+						// getElementById can match elements by name instead of ID
+						if ( newContext && ( elem = newContext.getElementById( m ) ) &&
+							contains( context, elem ) &&
+							elem.id === m ) {
+
+							results.push( elem );
+							return results;
+						}
+					}
+
+				// Type selector
+				} else if ( match[ 2 ] ) {
+					push.apply( results, context.getElementsByTagName( selector ) );
+					return results;
+
+				// Class selector
+				} else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
+					context.getElementsByClassName ) {
+
+					push.apply( results, context.getElementsByClassName( m ) );
+					return results;
+				}
+			}
+
+			// Take advantage of querySelectorAll
+			if ( support.qsa &&
+				!nonnativeSelectorCache[ selector + " " ] &&
+				( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
+
+				// Support: IE 8 only
+				// Exclude object elements
+				( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
+
+				newSelector = selector;
+				newContext = context;
+
+				// qSA considers elements outside a scoping root when evaluating child or
+				// descendant combinators, which is not what we want.
+				// In such cases, we work around the behavior by prefixing every selector in the
+				// list with an ID selector referencing the scope context.
+				// The technique has to be used as well when a leading combinator is used
+				// as such selectors are not recognized by querySelectorAll.
+				// Thanks to Andrew Dupont for this technique.
+				if ( nodeType === 1 &&
+					( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
+
+					// Expand context for sibling selectors
+					newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
+						context;
+
+					// We can use :scope instead of the ID hack if the browser
+					// supports it & if we're not changing the context.
+					if ( newContext !== context || !support.scope ) {
+
+						// Capture the context ID, setting it first if necessary
+						if ( ( nid = context.getAttribute( "id" ) ) ) {
+							nid = nid.replace( rcssescape, fcssescape );
+						} else {
+							context.setAttribute( "id", ( nid = expando ) );
+						}
+					}
+
+					// Prefix every selector in the list
+					groups = tokenize( selector );
+					i = groups.length;
+					while ( i-- ) {
+						groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
+							toSelector( groups[ i ] );
+					}
+					newSelector = groups.join( "," );
+				}
+
+				try {
+					push.apply( results,
+						newContext.querySelectorAll( newSelector )
+					);
+					return results;
+				} catch ( qsaError ) {
+					nonnativeSelectorCache( selector, true );
+				} finally {
+					if ( nid === expando ) {
+						context.removeAttribute( "id" );
+					}
+				}
+			}
+		}
+	}
+
+	// All others
+	return select( selector.replace( rtrim, "$1" ), context, results, seed );
+}
+
+/**
+ * Create key-value caches of limited size
+ * @returns {function(string, object)} Returns the Object data after storing it on itself with
+ *	property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
+ *	deleting the oldest entry
+ */
+function createCache() {
+	var keys = [];
+
+	function cache( key, value ) {
+
+		// Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
+		if ( keys.push( key + " " ) > Expr.cacheLength ) {
+
+			// Only keep the most recent entries
+			delete cache[ keys.shift() ];
+		}
+		return ( cache[ key + " " ] = value );
+	}
+	return cache;
+}
+
+/**
+ * Mark a function for special use by Sizzle
+ * @param {Function} fn The function to mark
+ */
+function markFunction( fn ) {
+	fn[ expando ] = true;
+	return fn;
+}
+
+/**
+ * Support testing using an element
+ * @param {Function} fn Passed the created element and returns a boolean result
+ */
+function assert( fn ) {
+	var el = document.createElement( "fieldset" );
+
+	try {
+		return !!fn( el );
+	} catch ( e ) {
+		return false;
+	} finally {
+
+		// Remove from its parent by default
+		if ( el.parentNode ) {
+			el.parentNode.removeChild( el );
+		}
+
+		// release memory in IE
+		el = null;
+	}
+}
+
+/**
+ * Adds the same handler for all of the specified attrs
+ * @param {String} attrs Pipe-separated list of attributes
+ * @param {Function} handler The method that will be applied
+ */
+function addHandle( attrs, handler ) {
+	var arr = attrs.split( "|" ),
+		i = arr.length;
+
+	while ( i-- ) {
+		Expr.attrHandle[ arr[ i ] ] = handler;
+	}
+}
+
+/**
+ * Checks document order of two siblings
+ * @param {Element} a
+ * @param {Element} b
+ * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
+ */
+function siblingCheck( a, b ) {
+	var cur = b && a,
+		diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
+			a.sourceIndex - b.sourceIndex;
+
+	// Use IE sourceIndex if available on both nodes
+	if ( diff ) {
+		return diff;
+	}
+
+	// Check if b follows a
+	if ( cur ) {
+		while ( ( cur = cur.nextSibling ) ) {
+			if ( cur === b ) {
+				return -1;
+			}
+		}
+	}
+
+	return a ? 1 : -1;
+}
+
+/**
+ * Returns a function to use in pseudos for input types
+ * @param {String} type
+ */
+function createInputPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return name === "input" && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for buttons
+ * @param {String} type
+ */
+function createButtonPseudo( type ) {
+	return function( elem ) {
+		var name = elem.nodeName.toLowerCase();
+		return ( name === "input" || name === "button" ) && elem.type === type;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for :enabled/:disabled
+ * @param {Boolean} disabled true for :disabled; false for :enabled
+ */
+function createDisabledPseudo( disabled ) {
+
+	// Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
+	return function( elem ) {
+
+		// Only certain elements can match :enabled or :disabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
+		// https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
+		if ( "form" in elem ) {
+
+			// Check for inherited disabledness on relevant non-disabled elements:
+			// * listed form-associated elements in a disabled fieldset
+			//   https://html.spec.whatwg.org/multipage/forms.html#category-listed
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
+			// * option elements in a disabled optgroup
+			//   https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
+			// All such elements have a "form" property.
+			if ( elem.parentNode && elem.disabled === false ) {
+
+				// Option elements defer to a parent optgroup if present
+				if ( "label" in elem ) {
+					if ( "label" in elem.parentNode ) {
+						return elem.parentNode.disabled === disabled;
+					} else {
+						return elem.disabled === disabled;
+					}
+				}
+
+				// Support: IE 6 - 11
+				// Use the isDisabled shortcut property to check for disabled fieldset ancestors
+				return elem.isDisabled === disabled ||
+
+					// Where there is no isDisabled, check manually
+					/* jshint -W018 */
+					elem.isDisabled !== !disabled &&
+					inDisabledFieldset( elem ) === disabled;
+			}
+
+			return elem.disabled === disabled;
+
+		// Try to winnow out elements that can't be disabled before trusting the disabled property.
+		// Some victims get caught in our net (label, legend, menu, track), but it shouldn't
+		// even exist on them, let alone have a boolean value.
+		} else if ( "label" in elem ) {
+			return elem.disabled === disabled;
+		}
+
+		// Remaining elements are neither :enabled nor :disabled
+		return false;
+	};
+}
+
+/**
+ * Returns a function to use in pseudos for positionals
+ * @param {Function} fn
+ */
+function createPositionalPseudo( fn ) {
+	return markFunction( function( argument ) {
+		argument = +argument;
+		return markFunction( function( seed, matches ) {
+			var j,
+				matchIndexes = fn( [], seed.length, argument ),
+				i = matchIndexes.length;
+
+			// Match elements found at the specified indexes
+			while ( i-- ) {
+				if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
+					seed[ j ] = !( matches[ j ] = seed[ j ] );
+				}
+			}
+		} );
+	} );
+}
+
+/**
+ * Checks a node for validity as a Sizzle context
+ * @param {Element|Object=} context
+ * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
+ */
+function testContext( context ) {
+	return context && typeof context.getElementsByTagName !== "undefined" && context;
+}
+
+// Expose support vars for convenience
+support = Sizzle.support = {};
+
+/**
+ * Detects XML nodes
+ * @param {Element|Object} elem An element or a document
+ * @returns {Boolean} True iff elem is a non-HTML XML node
+ */
+isXML = Sizzle.isXML = function( elem ) {
+	var namespace = elem && elem.namespaceURI,
+		docElem = elem && ( elem.ownerDocument || elem ).documentElement;
+
+	// Support: IE <=8
+	// Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
+	// https://bugs.jquery.com/ticket/4833
+	return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
+};
+
+/**
+ * Sets document-related variables once based on the current document
+ * @param {Element|Object} [doc] An element or document object to use to set the document
+ * @returns {Object} Returns the current document
+ */
+setDocument = Sizzle.setDocument = function( node ) {
+	var hasCompare, subWindow,
+		doc = node ? node.ownerDocument || node : preferredDoc;
+
+	// Return early if doc is invalid or already selected
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
+		return document;
+	}
+
+	// Update global variables
+	document = doc;
+	docElem = document.documentElement;
+	documentIsHTML = !isXML( document );
+
+	// Support: IE 9 - 11+, Edge 12 - 18+
+	// Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( preferredDoc != document &&
+		( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
+
+		// Support: IE 11, Edge
+		if ( subWindow.addEventListener ) {
+			subWindow.addEventListener( "unload", unloadHandler, false );
+
+		// Support: IE 9 - 10 only
+		} else if ( subWindow.attachEvent ) {
+			subWindow.attachEvent( "onunload", unloadHandler );
+		}
+	}
+
+	// Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
+	// Safari 4 - 5 only, Opera <=11.6 - 12.x only
+	// IE/Edge & older browsers don't support the :scope pseudo-class.
+	// Support: Safari 6.0 only
+	// Safari 6.0 supports :scope but it's an alias of :root there.
+	support.scope = assert( function( el ) {
+		docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
+		return typeof el.querySelectorAll !== "undefined" &&
+			!el.querySelectorAll( ":scope fieldset div" ).length;
+	} );
+
+	/* Attributes
+	---------------------------------------------------------------------- */
+
+	// Support: IE<8
+	// Verify that getAttribute really returns attributes and not properties
+	// (excepting IE8 booleans)
+	support.attributes = assert( function( el ) {
+		el.className = "i";
+		return !el.getAttribute( "className" );
+	} );
+
+	/* getElement(s)By*
+	---------------------------------------------------------------------- */
+
+	// Check if getElementsByTagName("*") returns only elements
+	support.getElementsByTagName = assert( function( el ) {
+		el.appendChild( document.createComment( "" ) );
+		return !el.getElementsByTagName( "*" ).length;
+	} );
+
+	// Support: IE<9
+	support.getElementsByClassName = rnative.test( document.getElementsByClassName );
+
+	// Support: IE<10
+	// Check if getElementById returns elements by name
+	// The broken getElementById methods don't pick up programmatically-set names,
+	// so use a roundabout getElementsByName test
+	support.getById = assert( function( el ) {
+		docElem.appendChild( el ).id = expando;
+		return !document.getElementsByName || !document.getElementsByName( expando ).length;
+	} );
+
+	// ID filter and find
+	if ( support.getById ) {
+		Expr.filter[ "ID" ] = function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				return elem.getAttribute( "id" ) === attrId;
+			};
+		};
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var elem = context.getElementById( id );
+				return elem ? [ elem ] : [];
+			}
+		};
+	} else {
+		Expr.filter[ "ID" ] =  function( id ) {
+			var attrId = id.replace( runescape, funescape );
+			return function( elem ) {
+				var node = typeof elem.getAttributeNode !== "undefined" &&
+					elem.getAttributeNode( "id" );
+				return node && node.value === attrId;
+			};
+		};
+
+		// Support: IE 6 - 7 only
+		// getElementById is not reliable as a find shortcut
+		Expr.find[ "ID" ] = function( id, context ) {
+			if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
+				var node, i, elems,
+					elem = context.getElementById( id );
+
+				if ( elem ) {
+
+					// Verify the id attribute
+					node = elem.getAttributeNode( "id" );
+					if ( node && node.value === id ) {
+						return [ elem ];
+					}
+
+					// Fall back on getElementsByName
+					elems = context.getElementsByName( id );
+					i = 0;
+					while ( ( elem = elems[ i++ ] ) ) {
+						node = elem.getAttributeNode( "id" );
+						if ( node && node.value === id ) {
+							return [ elem ];
+						}
+					}
+				}
+
+				return [];
+			}
+		};
+	}
+
+	// Tag
+	Expr.find[ "TAG" ] = support.getElementsByTagName ?
+		function( tag, context ) {
+			if ( typeof context.getElementsByTagName !== "undefined" ) {
+				return context.getElementsByTagName( tag );
+
+			// DocumentFragment nodes don't have gEBTN
+			} else if ( support.qsa ) {
+				return context.querySelectorAll( tag );
+			}
+		} :
+
+		function( tag, context ) {
+			var elem,
+				tmp = [],
+				i = 0,
+
+				// By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
+				results = context.getElementsByTagName( tag );
+
+			// Filter out possible comments
+			if ( tag === "*" ) {
+				while ( ( elem = results[ i++ ] ) ) {
+					if ( elem.nodeType === 1 ) {
+						tmp.push( elem );
+					}
+				}
+
+				return tmp;
+			}
+			return results;
+		};
+
+	// Class
+	Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
+		if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
+			return context.getElementsByClassName( className );
+		}
+	};
+
+	/* QSA/matchesSelector
+	---------------------------------------------------------------------- */
+
+	// QSA and matchesSelector support
+
+	// matchesSelector(:active) reports false when true (IE9/Opera 11.5)
+	rbuggyMatches = [];
+
+	// qSa(:focus) reports false when true (Chrome 21)
+	// We allow this because of a bug in IE8/9 that throws an error
+	// whenever `document.activeElement` is accessed on an iframe
+	// So, we allow :focus to pass through QSA all the time to avoid the IE error
+	// See https://bugs.jquery.com/ticket/13378
+	rbuggyQSA = [];
+
+	if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
+
+		// Build QSA regex
+		// Regex strategy adopted from Diego Perini
+		assert( function( el ) {
+
+			var input;
+
+			// Select is set to empty string on purpose
+			// This is to test IE's treatment of not explicitly
+			// setting a boolean content attribute,
+			// since its presence should be enough
+			// https://bugs.jquery.com/ticket/12359
+			docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
+				"<select id='" + expando + "-\r\\' msallowcapture=''>" +
+				"<option selected=''></option></select>";
+
+			// Support: IE8, Opera 11-12.16
+			// Nothing should be selected when empty strings follow ^= or $= or *=
+			// The test attribute must be unknown in Opera but "safe" for WinRT
+			// https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
+			if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
+				rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
+			}
+
+			// Support: IE8
+			// Boolean attributes and "value" are not treated correctly
+			if ( !el.querySelectorAll( "[selected]" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
+			}
+
+			// Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
+			if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
+				rbuggyQSA.push( "~=" );
+			}
+
+			// Support: IE 11+, Edge 15 - 18+
+			// IE 11/Edge don't find elements on a `[name='']` query in some cases.
+			// Adding a temporary attribute to the document before the selection works
+			// around the issue.
+			// Interestingly, IE 10 & older don't seem to have the issue.
+			input = document.createElement( "input" );
+			input.setAttribute( "name", "" );
+			el.appendChild( input );
+			if ( !el.querySelectorAll( "[name='']" ).length ) {
+				rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
+					whitespace + "*(?:''|\"\")" );
+			}
+
+			// Webkit/Opera - :checked should return selected option elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			// IE8 throws error here and will not see later tests
+			if ( !el.querySelectorAll( ":checked" ).length ) {
+				rbuggyQSA.push( ":checked" );
+			}
+
+			// Support: Safari 8+, iOS 8+
+			// https://bugs.webkit.org/show_bug.cgi?id=136851
+			// In-page `selector#id sibling-combinator selector` fails
+			if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
+				rbuggyQSA.push( ".#.+[+~]" );
+			}
+
+			// Support: Firefox <=3.6 - 5 only
+			// Old Firefox doesn't throw on a badly-escaped identifier.
+			el.querySelectorAll( "\\\f" );
+			rbuggyQSA.push( "[\\r\\n\\f]" );
+		} );
+
+		assert( function( el ) {
+			el.innerHTML = "<a href='' disabled='disabled'></a>" +
+				"<select disabled='disabled'><option/></select>";
+
+			// Support: Windows 8 Native Apps
+			// The type and name attributes are restricted during .innerHTML assignment
+			var input = document.createElement( "input" );
+			input.setAttribute( "type", "hidden" );
+			el.appendChild( input ).setAttribute( "name", "D" );
+
+			// Support: IE8
+			// Enforce case-sensitivity of name attribute
+			if ( el.querySelectorAll( "[name=d]" ).length ) {
+				rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
+			}
+
+			// FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
+			// IE8 throws error here and will not see later tests
+			if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: IE9-11+
+			// IE's :disabled selector does not pick up the children of disabled fieldsets
+			docElem.appendChild( el ).disabled = true;
+			if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
+				rbuggyQSA.push( ":enabled", ":disabled" );
+			}
+
+			// Support: Opera 10 - 11 only
+			// Opera 10-11 does not throw on post-comma invalid pseudos
+			el.querySelectorAll( "*,:x" );
+			rbuggyQSA.push( ",.*:" );
+		} );
+	}
+
+	if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
+		docElem.webkitMatchesSelector ||
+		docElem.mozMatchesSelector ||
+		docElem.oMatchesSelector ||
+		docElem.msMatchesSelector ) ) ) ) {
+
+		assert( function( el ) {
+
+			// Check to see if it's possible to do matchesSelector
+			// on a disconnected node (IE 9)
+			support.disconnectedMatch = matches.call( el, "*" );
+
+			// This should fail with an exception
+			// Gecko does not error, returns false instead
+			matches.call( el, "[s!='']:x" );
+			rbuggyMatches.push( "!=", pseudos );
+		} );
+	}
+
+	rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
+	rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
+
+	/* Contains
+	---------------------------------------------------------------------- */
+	hasCompare = rnative.test( docElem.compareDocumentPosition );
+
+	// Element contains another
+	// Purposefully self-exclusive
+	// As in, an element does not contain itself
+	contains = hasCompare || rnative.test( docElem.contains ) ?
+		function( a, b ) {
+			var adown = a.nodeType === 9 ? a.documentElement : a,
+				bup = b && b.parentNode;
+			return a === bup || !!( bup && bup.nodeType === 1 && (
+				adown.contains ?
+					adown.contains( bup ) :
+					a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
+			) );
+		} :
+		function( a, b ) {
+			if ( b ) {
+				while ( ( b = b.parentNode ) ) {
+					if ( b === a ) {
+						return true;
+					}
+				}
+			}
+			return false;
+		};
+
+	/* Sorting
+	---------------------------------------------------------------------- */
+
+	// Document order sorting
+	sortOrder = hasCompare ?
+	function( a, b ) {
+
+		// Flag for duplicate removal
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		// Sort on method existence if only one input has compareDocumentPosition
+		var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
+		if ( compare ) {
+			return compare;
+		}
+
+		// Calculate position if both inputs belong to the same document
+		// Support: IE 11+, Edge 17 - 18+
+		// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+		// two documents; shallow comparisons work.
+		// eslint-disable-next-line eqeqeq
+		compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
+			a.compareDocumentPosition( b ) :
+
+			// Otherwise we know they are disconnected
+			1;
+
+		// Disconnected nodes
+		if ( compare & 1 ||
+			( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
+
+			// Choose the first element that is related to our preferred document
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( a == document || a.ownerDocument == preferredDoc &&
+				contains( preferredDoc, a ) ) {
+				return -1;
+			}
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			// eslint-disable-next-line eqeqeq
+			if ( b == document || b.ownerDocument == preferredDoc &&
+				contains( preferredDoc, b ) ) {
+				return 1;
+			}
+
+			// Maintain original order
+			return sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+		}
+
+		return compare & 4 ? -1 : 1;
+	} :
+	function( a, b ) {
+
+		// Exit early if the nodes are identical
+		if ( a === b ) {
+			hasDuplicate = true;
+			return 0;
+		}
+
+		var cur,
+			i = 0,
+			aup = a.parentNode,
+			bup = b.parentNode,
+			ap = [ a ],
+			bp = [ b ];
+
+		// Parentless nodes are either documents or disconnected
+		if ( !aup || !bup ) {
+
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			return a == document ? -1 :
+				b == document ? 1 :
+				/* eslint-enable eqeqeq */
+				aup ? -1 :
+				bup ? 1 :
+				sortInput ?
+				( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
+				0;
+
+		// If the nodes are siblings, we can do a quick check
+		} else if ( aup === bup ) {
+			return siblingCheck( a, b );
+		}
+
+		// Otherwise we need full lists of their ancestors for comparison
+		cur = a;
+		while ( ( cur = cur.parentNode ) ) {
+			ap.unshift( cur );
+		}
+		cur = b;
+		while ( ( cur = cur.parentNode ) ) {
+			bp.unshift( cur );
+		}
+
+		// Walk down the tree looking for a discrepancy
+		while ( ap[ i ] === bp[ i ] ) {
+			i++;
+		}
+
+		return i ?
+
+			// Do a sibling check if the nodes have a common ancestor
+			siblingCheck( ap[ i ], bp[ i ] ) :
+
+			// Otherwise nodes in our document sort first
+			// Support: IE 11+, Edge 17 - 18+
+			// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+			// two documents; shallow comparisons work.
+			/* eslint-disable eqeqeq */
+			ap[ i ] == preferredDoc ? -1 :
+			bp[ i ] == preferredDoc ? 1 :
+			/* eslint-enable eqeqeq */
+			0;
+	};
+
+	return document;
+};
+
+Sizzle.matches = function( expr, elements ) {
+	return Sizzle( expr, null, null, elements );
+};
+
+Sizzle.matchesSelector = function( elem, expr ) {
+	setDocument( elem );
+
+	if ( support.matchesSelector && documentIsHTML &&
+		!nonnativeSelectorCache[ expr + " " ] &&
+		( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
+		( !rbuggyQSA     || !rbuggyQSA.test( expr ) ) ) {
+
+		try {
+			var ret = matches.call( elem, expr );
+
+			// IE 9's matchesSelector returns false on disconnected nodes
+			if ( ret || support.disconnectedMatch ||
+
+				// As well, disconnected nodes are said to be in a document
+				// fragment in IE 9
+				elem.document && elem.document.nodeType !== 11 ) {
+				return ret;
+			}
+		} catch ( e ) {
+			nonnativeSelectorCache( expr, true );
+		}
+	}
+
+	return Sizzle( expr, document, null, [ elem ] ).length > 0;
+};
+
+Sizzle.contains = function( context, elem ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( context.ownerDocument || context ) != document ) {
+		setDocument( context );
+	}
+	return contains( context, elem );
+};
+
+Sizzle.attr = function( elem, name ) {
+
+	// Set document vars if needed
+	// Support: IE 11+, Edge 17 - 18+
+	// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+	// two documents; shallow comparisons work.
+	// eslint-disable-next-line eqeqeq
+	if ( ( elem.ownerDocument || elem ) != document ) {
+		setDocument( elem );
+	}
+
+	var fn = Expr.attrHandle[ name.toLowerCase() ],
+
+		// Don't get fooled by Object.prototype properties (jQuery #13807)
+		val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
+			fn( elem, name, !documentIsHTML ) :
+			undefined;
+
+	return val !== undefined ?
+		val :
+		support.attributes || !documentIsHTML ?
+			elem.getAttribute( name ) :
+			( val = elem.getAttributeNode( name ) ) && val.specified ?
+				val.value :
+				null;
+};
+
+Sizzle.escape = function( sel ) {
+	return ( sel + "" ).replace( rcssescape, fcssescape );
+};
+
+Sizzle.error = function( msg ) {
+	throw new Error( "Syntax error, unrecognized expression: " + msg );
+};
+
+/**
+ * Document sorting and removing duplicates
+ * @param {ArrayLike} results
+ */
+Sizzle.uniqueSort = function( results ) {
+	var elem,
+		duplicates = [],
+		j = 0,
+		i = 0;
+
+	// Unless we *know* we can detect duplicates, assume their presence
+	hasDuplicate = !support.detectDuplicates;
+	sortInput = !support.sortStable && results.slice( 0 );
+	results.sort( sortOrder );
+
+	if ( hasDuplicate ) {
+		while ( ( elem = results[ i++ ] ) ) {
+			if ( elem === results[ i ] ) {
+				j = duplicates.push( i );
+			}
+		}
+		while ( j-- ) {
+			results.splice( duplicates[ j ], 1 );
+		}
+	}
+
+	// Clear input after sorting to release objects
+	// See https://github.com/jquery/sizzle/pull/225
+	sortInput = null;
+
+	return results;
+};
+
+/**
+ * Utility function for retrieving the text value of an array of DOM nodes
+ * @param {Array|Element} elem
+ */
+getText = Sizzle.getText = function( elem ) {
+	var node,
+		ret = "",
+		i = 0,
+		nodeType = elem.nodeType;
+
+	if ( !nodeType ) {
+
+		// If no nodeType, this is expected to be an array
+		while ( ( node = elem[ i++ ] ) ) {
+
+			// Do not traverse comment nodes
+			ret += getText( node );
+		}
+	} else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
+
+		// Use textContent for elements
+		// innerText usage removed for consistency of new lines (jQuery #11153)
+		if ( typeof elem.textContent === "string" ) {
+			return elem.textContent;
+		} else {
+
+			// Traverse its children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				ret += getText( elem );
+			}
+		}
+	} else if ( nodeType === 3 || nodeType === 4 ) {
+		return elem.nodeValue;
+	}
+
+	// Do not include comment or processing instruction nodes
+
+	return ret;
+};
+
+Expr = Sizzle.selectors = {
+
+	// Can be adjusted by the user
+	cacheLength: 50,
+
+	createPseudo: markFunction,
+
+	match: matchExpr,
+
+	attrHandle: {},
+
+	find: {},
+
+	relative: {
+		">": { dir: "parentNode", first: true },
+		" ": { dir: "parentNode" },
+		"+": { dir: "previousSibling", first: true },
+		"~": { dir: "previousSibling" }
+	},
+
+	preFilter: {
+		"ATTR": function( match ) {
+			match[ 1 ] = match[ 1 ].replace( runescape, funescape );
+
+			// Move the given value to match[3] whether quoted or unquoted
+			match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
+				match[ 5 ] || "" ).replace( runescape, funescape );
+
+			if ( match[ 2 ] === "~=" ) {
+				match[ 3 ] = " " + match[ 3 ] + " ";
+			}
+
+			return match.slice( 0, 4 );
+		},
+
+		"CHILD": function( match ) {
+
+			/* matches from matchExpr["CHILD"]
+				1 type (only|nth|...)
+				2 what (child|of-type)
+				3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
+				4 xn-component of xn+y argument ([+-]?\d*n|)
+				5 sign of xn-component
+				6 x of xn-component
+				7 sign of y-component
+				8 y of y-component
+			*/
+			match[ 1 ] = match[ 1 ].toLowerCase();
+
+			if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
+
+				// nth-* requires argument
+				if ( !match[ 3 ] ) {
+					Sizzle.error( match[ 0 ] );
+				}
+
+				// numeric x and y parameters for Expr.filter.CHILD
+				// remember that false/true cast respectively to 0/1
+				match[ 4 ] = +( match[ 4 ] ?
+					match[ 5 ] + ( match[ 6 ] || 1 ) :
+					2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
+				match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
+
+				// other types prohibit arguments
+			} else if ( match[ 3 ] ) {
+				Sizzle.error( match[ 0 ] );
+			}
+
+			return match;
+		},
+
+		"PSEUDO": function( match ) {
+			var excess,
+				unquoted = !match[ 6 ] && match[ 2 ];
+
+			if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
+				return null;
+			}
+
+			// Accept quoted arguments as-is
+			if ( match[ 3 ] ) {
+				match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
+
+			// Strip excess characters from unquoted arguments
+			} else if ( unquoted && rpseudo.test( unquoted ) &&
+
+				// Get excess from tokenize (recursively)
+				( excess = tokenize( unquoted, true ) ) &&
+
+				// advance to the next closing parenthesis
+				( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
+
+				// excess is a negative index
+				match[ 0 ] = match[ 0 ].slice( 0, excess );
+				match[ 2 ] = unquoted.slice( 0, excess );
+			}
+
+			// Return only captures needed by the pseudo filter method (type and argument)
+			return match.slice( 0, 3 );
+		}
+	},
+
+	filter: {
+
+		"TAG": function( nodeNameSelector ) {
+			var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
+			return nodeNameSelector === "*" ?
+				function() {
+					return true;
+				} :
+				function( elem ) {
+					return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
+				};
+		},
+
+		"CLASS": function( className ) {
+			var pattern = classCache[ className + " " ];
+
+			return pattern ||
+				( pattern = new RegExp( "(^|" + whitespace +
+					")" + className + "(" + whitespace + "|$)" ) ) && classCache(
+						className, function( elem ) {
+							return pattern.test(
+								typeof elem.className === "string" && elem.className ||
+								typeof elem.getAttribute !== "undefined" &&
+									elem.getAttribute( "class" ) ||
+								""
+							);
+				} );
+		},
+
+		"ATTR": function( name, operator, check ) {
+			return function( elem ) {
+				var result = Sizzle.attr( elem, name );
+
+				if ( result == null ) {
+					return operator === "!=";
+				}
+				if ( !operator ) {
+					return true;
+				}
+
+				result += "";
+
+				/* eslint-disable max-len */
+
+				return operator === "=" ? result === check :
+					operator === "!=" ? result !== check :
+					operator === "^=" ? check && result.indexOf( check ) === 0 :
+					operator === "*=" ? check && result.indexOf( check ) > -1 :
+					operator === "$=" ? check && result.slice( -check.length ) === check :
+					operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
+					operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
+					false;
+				/* eslint-enable max-len */
+
+			};
+		},
+
+		"CHILD": function( type, what, _argument, first, last ) {
+			var simple = type.slice( 0, 3 ) !== "nth",
+				forward = type.slice( -4 ) !== "last",
+				ofType = what === "of-type";
+
+			return first === 1 && last === 0 ?
+
+				// Shortcut for :nth-*(n)
+				function( elem ) {
+					return !!elem.parentNode;
+				} :
+
+				function( elem, _context, xml ) {
+					var cache, uniqueCache, outerCache, node, nodeIndex, start,
+						dir = simple !== forward ? "nextSibling" : "previousSibling",
+						parent = elem.parentNode,
+						name = ofType && elem.nodeName.toLowerCase(),
+						useCache = !xml && !ofType,
+						diff = false;
+
+					if ( parent ) {
+
+						// :(first|last|only)-(child|of-type)
+						if ( simple ) {
+							while ( dir ) {
+								node = elem;
+								while ( ( node = node[ dir ] ) ) {
+									if ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) {
+
+										return false;
+									}
+								}
+
+								// Reverse direction for :only-* (if we haven't yet done so)
+								start = dir = type === "only" && !start && "nextSibling";
+							}
+							return true;
+						}
+
+						start = [ forward ? parent.firstChild : parent.lastChild ];
+
+						// non-xml :nth-child(...) stores cache data on `parent`
+						if ( forward && useCache ) {
+
+							// Seek `elem` from a previously-cached index
+
+							// ...in a gzip-friendly way
+							node = parent;
+							outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+							// Support: IE <9 only
+							// Defend against cloned attroperties (jQuery gh-1709)
+							uniqueCache = outerCache[ node.uniqueID ] ||
+								( outerCache[ node.uniqueID ] = {} );
+
+							cache = uniqueCache[ type ] || [];
+							nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+							diff = nodeIndex && cache[ 2 ];
+							node = nodeIndex && parent.childNodes[ nodeIndex ];
+
+							while ( ( node = ++nodeIndex && node && node[ dir ] ||
+
+								// Fallback to seeking `elem` from the start
+								( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+								// When found, cache indexes on `parent` and break
+								if ( node.nodeType === 1 && ++diff && node === elem ) {
+									uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
+									break;
+								}
+							}
+
+						} else {
+
+							// Use previously-cached element index if available
+							if ( useCache ) {
+
+								// ...in a gzip-friendly way
+								node = elem;
+								outerCache = node[ expando ] || ( node[ expando ] = {} );
+
+								// Support: IE <9 only
+								// Defend against cloned attroperties (jQuery gh-1709)
+								uniqueCache = outerCache[ node.uniqueID ] ||
+									( outerCache[ node.uniqueID ] = {} );
+
+								cache = uniqueCache[ type ] || [];
+								nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
+								diff = nodeIndex;
+							}
+
+							// xml :nth-child(...)
+							// or :nth-last-child(...) or :nth(-last)?-of-type(...)
+							if ( diff === false ) {
+
+								// Use the same loop as above to seek `elem` from the start
+								while ( ( node = ++nodeIndex && node && node[ dir ] ||
+									( diff = nodeIndex = 0 ) || start.pop() ) ) {
+
+									if ( ( ofType ?
+										node.nodeName.toLowerCase() === name :
+										node.nodeType === 1 ) &&
+										++diff ) {
+
+										// Cache the index of each encountered element
+										if ( useCache ) {
+											outerCache = node[ expando ] ||
+												( node[ expando ] = {} );
+
+											// Support: IE <9 only
+											// Defend against cloned attroperties (jQuery gh-1709)
+											uniqueCache = outerCache[ node.uniqueID ] ||
+												( outerCache[ node.uniqueID ] = {} );
+
+											uniqueCache[ type ] = [ dirruns, diff ];
+										}
+
+										if ( node === elem ) {
+											break;
+										}
+									}
+								}
+							}
+						}
+
+						// Incorporate the offset, then check against cycle size
+						diff -= last;
+						return diff === first || ( diff % first === 0 && diff / first >= 0 );
+					}
+				};
+		},
+
+		"PSEUDO": function( pseudo, argument ) {
+
+			// pseudo-class names are case-insensitive
+			// http://www.w3.org/TR/selectors/#pseudo-classes
+			// Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
+			// Remember that setFilters inherits from pseudos
+			var args,
+				fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
+					Sizzle.error( "unsupported pseudo: " + pseudo );
+
+			// The user may use createPseudo to indicate that
+			// arguments are needed to create the filter function
+			// just as Sizzle does
+			if ( fn[ expando ] ) {
+				return fn( argument );
+			}
+
+			// But maintain support for old signatures
+			if ( fn.length > 1 ) {
+				args = [ pseudo, pseudo, "", argument ];
+				return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
+					markFunction( function( seed, matches ) {
+						var idx,
+							matched = fn( seed, argument ),
+							i = matched.length;
+						while ( i-- ) {
+							idx = indexOf( seed, matched[ i ] );
+							seed[ idx ] = !( matches[ idx ] = matched[ i ] );
+						}
+					} ) :
+					function( elem ) {
+						return fn( elem, 0, args );
+					};
+			}
+
+			return fn;
+		}
+	},
+
+	pseudos: {
+
+		// Potentially complex pseudos
+		"not": markFunction( function( selector ) {
+
+			// Trim the selector passed to compile
+			// to avoid treating leading and trailing
+			// spaces as combinators
+			var input = [],
+				results = [],
+				matcher = compile( selector.replace( rtrim, "$1" ) );
+
+			return matcher[ expando ] ?
+				markFunction( function( seed, matches, _context, xml ) {
+					var elem,
+						unmatched = matcher( seed, null, xml, [] ),
+						i = seed.length;
+
+					// Match elements unmatched by `matcher`
+					while ( i-- ) {
+						if ( ( elem = unmatched[ i ] ) ) {
+							seed[ i ] = !( matches[ i ] = elem );
+						}
+					}
+				} ) :
+				function( elem, _context, xml ) {
+					input[ 0 ] = elem;
+					matcher( input, null, xml, results );
+
+					// Don't keep the element (issue #299)
+					input[ 0 ] = null;
+					return !results.pop();
+				};
+		} ),
+
+		"has": markFunction( function( selector ) {
+			return function( elem ) {
+				return Sizzle( selector, elem ).length > 0;
+			};
+		} ),
+
+		"contains": markFunction( function( text ) {
+			text = text.replace( runescape, funescape );
+			return function( elem ) {
+				return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
+			};
+		} ),
+
+		// "Whether an element is represented by a :lang() selector
+		// is based solely on the element's language value
+		// being equal to the identifier C,
+		// or beginning with the identifier C immediately followed by "-".
+		// The matching of C against the element's language value is performed case-insensitively.
+		// The identifier C does not have to be a valid language name."
+		// http://www.w3.org/TR/selectors/#lang-pseudo
+		"lang": markFunction( function( lang ) {
+
+			// lang value must be a valid identifier
+			if ( !ridentifier.test( lang || "" ) ) {
+				Sizzle.error( "unsupported lang: " + lang );
+			}
+			lang = lang.replace( runescape, funescape ).toLowerCase();
+			return function( elem ) {
+				var elemLang;
+				do {
+					if ( ( elemLang = documentIsHTML ?
+						elem.lang :
+						elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
+
+						elemLang = elemLang.toLowerCase();
+						return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
+					}
+				} while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
+				return false;
+			};
+		} ),
+
+		// Miscellaneous
+		"target": function( elem ) {
+			var hash = window.location && window.location.hash;
+			return hash && hash.slice( 1 ) === elem.id;
+		},
+
+		"root": function( elem ) {
+			return elem === docElem;
+		},
+
+		"focus": function( elem ) {
+			return elem === document.activeElement &&
+				( !document.hasFocus || document.hasFocus() ) &&
+				!!( elem.type || elem.href || ~elem.tabIndex );
+		},
+
+		// Boolean properties
+		"enabled": createDisabledPseudo( false ),
+		"disabled": createDisabledPseudo( true ),
+
+		"checked": function( elem ) {
+
+			// In CSS3, :checked should return both checked and selected elements
+			// http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
+			var nodeName = elem.nodeName.toLowerCase();
+			return ( nodeName === "input" && !!elem.checked ) ||
+				( nodeName === "option" && !!elem.selected );
+		},
+
+		"selected": function( elem ) {
+
+			// Accessing this property makes selected-by-default
+			// options in Safari work properly
+			if ( elem.parentNode ) {
+				// eslint-disable-next-line no-unused-expressions
+				elem.parentNode.selectedIndex;
+			}
+
+			return elem.selected === true;
+		},
+
+		// Contents
+		"empty": function( elem ) {
+
+			// http://www.w3.org/TR/selectors/#empty-pseudo
+			// :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
+			//   but not by others (comment: 8; processing instruction: 7; etc.)
+			// nodeType < 6 works because attributes (2) do not appear as children
+			for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
+				if ( elem.nodeType < 6 ) {
+					return false;
+				}
+			}
+			return true;
+		},
+
+		"parent": function( elem ) {
+			return !Expr.pseudos[ "empty" ]( elem );
+		},
+
+		// Element/input types
+		"header": function( elem ) {
+			return rheader.test( elem.nodeName );
+		},
+
+		"input": function( elem ) {
+			return rinputs.test( elem.nodeName );
+		},
+
+		"button": function( elem ) {
+			var name = elem.nodeName.toLowerCase();
+			return name === "input" && elem.type === "button" || name === "button";
+		},
+
+		"text": function( elem ) {
+			var attr;
+			return elem.nodeName.toLowerCase() === "input" &&
+				elem.type === "text" &&
+
+				// Support: IE<8
+				// New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
+				( ( attr = elem.getAttribute( "type" ) ) == null ||
+					attr.toLowerCase() === "text" );
+		},
+
+		// Position-in-collection
+		"first": createPositionalPseudo( function() {
+			return [ 0 ];
+		} ),
+
+		"last": createPositionalPseudo( function( _matchIndexes, length ) {
+			return [ length - 1 ];
+		} ),
+
+		"eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
+			return [ argument < 0 ? argument + length : argument ];
+		} ),
+
+		"even": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 0;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"odd": createPositionalPseudo( function( matchIndexes, length ) {
+			var i = 1;
+			for ( ; i < length; i += 2 ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ?
+				argument + length :
+				argument > length ?
+					length :
+					argument;
+			for ( ; --i >= 0; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} ),
+
+		"gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
+			var i = argument < 0 ? argument + length : argument;
+			for ( ; ++i < length; ) {
+				matchIndexes.push( i );
+			}
+			return matchIndexes;
+		} )
+	}
+};
+
+Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
+
+// Add button/input type pseudos
+for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
+	Expr.pseudos[ i ] = createInputPseudo( i );
+}
+for ( i in { submit: true, reset: true } ) {
+	Expr.pseudos[ i ] = createButtonPseudo( i );
+}
+
+// Easy API for creating new setFilters
+function setFilters() {}
+setFilters.prototype = Expr.filters = Expr.pseudos;
+Expr.setFilters = new setFilters();
+
+tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
+	var matched, match, tokens, type,
+		soFar, groups, preFilters,
+		cached = tokenCache[ selector + " " ];
+
+	if ( cached ) {
+		return parseOnly ? 0 : cached.slice( 0 );
+	}
+
+	soFar = selector;
+	groups = [];
+	preFilters = Expr.preFilter;
+
+	while ( soFar ) {
+
+		// Comma and first run
+		if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
+			if ( match ) {
+
+				// Don't consume trailing commas as valid
+				soFar = soFar.slice( match[ 0 ].length ) || soFar;
+			}
+			groups.push( ( tokens = [] ) );
+		}
+
+		matched = false;
+
+		// Combinators
+		if ( ( match = rcombinators.exec( soFar ) ) ) {
+			matched = match.shift();
+			tokens.push( {
+				value: matched,
+
+				// Cast descendant combinators to space
+				type: match[ 0 ].replace( rtrim, " " )
+			} );
+			soFar = soFar.slice( matched.length );
+		}
+
+		// Filters
+		for ( type in Expr.filter ) {
+			if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
+				( match = preFilters[ type ]( match ) ) ) ) {
+				matched = match.shift();
+				tokens.push( {
+					value: matched,
+					type: type,
+					matches: match
+				} );
+				soFar = soFar.slice( matched.length );
+			}
+		}
+
+		if ( !matched ) {
+			break;
+		}
+	}
+
+	// Return the length of the invalid excess
+	// if we're just parsing
+	// Otherwise, throw an error or return tokens
+	return parseOnly ?
+		soFar.length :
+		soFar ?
+			Sizzle.error( selector ) :
+
+			// Cache the tokens
+			tokenCache( selector, groups ).slice( 0 );
+};
+
+function toSelector( tokens ) {
+	var i = 0,
+		len = tokens.length,
+		selector = "";
+	for ( ; i < len; i++ ) {
+		selector += tokens[ i ].value;
+	}
+	return selector;
+}
+
+function addCombinator( matcher, combinator, base ) {
+	var dir = combinator.dir,
+		skip = combinator.next,
+		key = skip || dir,
+		checkNonElements = base && key === "parentNode",
+		doneName = done++;
+
+	return combinator.first ?
+
+		// Check against closest ancestor/preceding element
+		function( elem, context, xml ) {
+			while ( ( elem = elem[ dir ] ) ) {
+				if ( elem.nodeType === 1 || checkNonElements ) {
+					return matcher( elem, context, xml );
+				}
+			}
+			return false;
+		} :
+
+		// Check against all ancestor/preceding elements
+		function( elem, context, xml ) {
+			var oldCache, uniqueCache, outerCache,
+				newCache = [ dirruns, doneName ];
+
+			// We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
+			if ( xml ) {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						if ( matcher( elem, context, xml ) ) {
+							return true;
+						}
+					}
+				}
+			} else {
+				while ( ( elem = elem[ dir ] ) ) {
+					if ( elem.nodeType === 1 || checkNonElements ) {
+						outerCache = elem[ expando ] || ( elem[ expando ] = {} );
+
+						// Support: IE <9 only
+						// Defend against cloned attroperties (jQuery gh-1709)
+						uniqueCache = outerCache[ elem.uniqueID ] ||
+							( outerCache[ elem.uniqueID ] = {} );
+
+						if ( skip && skip === elem.nodeName.toLowerCase() ) {
+							elem = elem[ dir ] || elem;
+						} else if ( ( oldCache = uniqueCache[ key ] ) &&
+							oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
+
+							// Assign to newCache so results back-propagate to previous elements
+							return ( newCache[ 2 ] = oldCache[ 2 ] );
+						} else {
+
+							// Reuse newcache so results back-propagate to previous elements
+							uniqueCache[ key ] = newCache;
+
+							// A match means we're done; a fail means we have to keep checking
+							if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
+								return true;
+							}
+						}
+					}
+				}
+			}
+			return false;
+		};
+}
+
+function elementMatcher( matchers ) {
+	return matchers.length > 1 ?
+		function( elem, context, xml ) {
+			var i = matchers.length;
+			while ( i-- ) {
+				if ( !matchers[ i ]( elem, context, xml ) ) {
+					return false;
+				}
+			}
+			return true;
+		} :
+		matchers[ 0 ];
+}
+
+function multipleContexts( selector, contexts, results ) {
+	var i = 0,
+		len = contexts.length;
+	for ( ; i < len; i++ ) {
+		Sizzle( selector, contexts[ i ], results );
+	}
+	return results;
+}
+
+function condense( unmatched, map, filter, context, xml ) {
+	var elem,
+		newUnmatched = [],
+		i = 0,
+		len = unmatched.length,
+		mapped = map != null;
+
+	for ( ; i < len; i++ ) {
+		if ( ( elem = unmatched[ i ] ) ) {
+			if ( !filter || filter( elem, context, xml ) ) {
+				newUnmatched.push( elem );
+				if ( mapped ) {
+					map.push( i );
+				}
+			}
+		}
+	}
+
+	return newUnmatched;
+}
+
+function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
+	if ( postFilter && !postFilter[ expando ] ) {
+		postFilter = setMatcher( postFilter );
+	}
+	if ( postFinder && !postFinder[ expando ] ) {
+		postFinder = setMatcher( postFinder, postSelector );
+	}
+	return markFunction( function( seed, results, context, xml ) {
+		var temp, i, elem,
+			preMap = [],
+			postMap = [],
+			preexisting = results.length,
+
+			// Get initial elements from seed or context
+			elems = seed || multipleContexts(
+				selector || "*",
+				context.nodeType ? [ context ] : context,
+				[]
+			),
+
+			// Prefilter to get matcher input, preserving a map for seed-results synchronization
+			matcherIn = preFilter && ( seed || !selector ) ?
+				condense( elems, preMap, preFilter, context, xml ) :
+				elems,
+
+			matcherOut = matcher ?
+
+				// If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
+				postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
+
+					// ...intermediate processing is necessary
+					[] :
+
+					// ...otherwise use results directly
+					results :
+				matcherIn;
+
+		// Find primary matches
+		if ( matcher ) {
+			matcher( matcherIn, matcherOut, context, xml );
+		}
+
+		// Apply postFilter
+		if ( postFilter ) {
+			temp = condense( matcherOut, postMap );
+			postFilter( temp, [], context, xml );
+
+			// Un-match failing elements by moving them back to matcherIn
+			i = temp.length;
+			while ( i-- ) {
+				if ( ( elem = temp[ i ] ) ) {
+					matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
+				}
+			}
+		}
+
+		if ( seed ) {
+			if ( postFinder || preFilter ) {
+				if ( postFinder ) {
+
+					// Get the final matcherOut by condensing this intermediate into postFinder contexts
+					temp = [];
+					i = matcherOut.length;
+					while ( i-- ) {
+						if ( ( elem = matcherOut[ i ] ) ) {
+
+							// Restore matcherIn since elem is not yet a final match
+							temp.push( ( matcherIn[ i ] = elem ) );
+						}
+					}
+					postFinder( null, ( matcherOut = [] ), temp, xml );
+				}
+
+				// Move matched elements from seed to results to keep them synchronized
+				i = matcherOut.length;
+				while ( i-- ) {
+					if ( ( elem = matcherOut[ i ] ) &&
+						( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
+
+						seed[ temp ] = !( results[ temp ] = elem );
+					}
+				}
+			}
+
+		// Add elements to results, through postFinder if defined
+		} else {
+			matcherOut = condense(
+				matcherOut === results ?
+					matcherOut.splice( preexisting, matcherOut.length ) :
+					matcherOut
+			);
+			if ( postFinder ) {
+				postFinder( null, results, matcherOut, xml );
+			} else {
+				push.apply( results, matcherOut );
+			}
+		}
+	} );
+}
+
+function matcherFromTokens( tokens ) {
+	var checkContext, matcher, j,
+		len = tokens.length,
+		leadingRelative = Expr.relative[ tokens[ 0 ].type ],
+		implicitRelative = leadingRelative || Expr.relative[ " " ],
+		i = leadingRelative ? 1 : 0,
+
+		// The foundational matcher ensures that elements are reachable from top-level context(s)
+		matchContext = addCombinator( function( elem ) {
+			return elem === checkContext;
+		}, implicitRelative, true ),
+		matchAnyContext = addCombinator( function( elem ) {
+			return indexOf( checkContext, elem ) > -1;
+		}, implicitRelative, true ),
+		matchers = [ function( elem, context, xml ) {
+			var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
+				( checkContext = context ).nodeType ?
+					matchContext( elem, context, xml ) :
+					matchAnyContext( elem, context, xml ) );
+
+			// Avoid hanging onto element (issue #299)
+			checkContext = null;
+			return ret;
+		} ];
+
+	for ( ; i < len; i++ ) {
+		if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
+			matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
+		} else {
+			matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
+
+			// Return special upon seeing a positional matcher
+			if ( matcher[ expando ] ) {
+
+				// Find the next relative operator (if any) for proper handling
+				j = ++i;
+				for ( ; j < len; j++ ) {
+					if ( Expr.relative[ tokens[ j ].type ] ) {
+						break;
+					}
+				}
+				return setMatcher(
+					i > 1 && elementMatcher( matchers ),
+					i > 1 && toSelector(
+
+					// If the preceding token was a descendant combinator, insert an implicit any-element `*`
+					tokens
+						.slice( 0, i - 1 )
+						.concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
+					).replace( rtrim, "$1" ),
+					matcher,
+					i < j && matcherFromTokens( tokens.slice( i, j ) ),
+					j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
+					j < len && toSelector( tokens )
+				);
+			}
+			matchers.push( matcher );
+		}
+	}
+
+	return elementMatcher( matchers );
+}
+
+function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
+	var bySet = setMatchers.length > 0,
+		byElement = elementMatchers.length > 0,
+		superMatcher = function( seed, context, xml, results, outermost ) {
+			var elem, j, matcher,
+				matchedCount = 0,
+				i = "0",
+				unmatched = seed && [],
+				setMatched = [],
+				contextBackup = outermostContext,
+
+				// We must always have either seed elements or outermost context
+				elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
+
+				// Use integer dirruns iff this is the outermost matcher
+				dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
+				len = elems.length;
+
+			if ( outermost ) {
+
+				// Support: IE 11+, Edge 17 - 18+
+				// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+				// two documents; shallow comparisons work.
+				// eslint-disable-next-line eqeqeq
+				outermostContext = context == document || context || outermost;
+			}
+
+			// Add elements passing elementMatchers directly to results
+			// Support: IE<9, Safari
+			// Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
+			for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
+				if ( byElement && elem ) {
+					j = 0;
+
+					// Support: IE 11+, Edge 17 - 18+
+					// IE/Edge sometimes throw a "Permission denied" error when strict-comparing
+					// two documents; shallow comparisons work.
+					// eslint-disable-next-line eqeqeq
+					if ( !context && elem.ownerDocument != document ) {
+						setDocument( elem );
+						xml = !documentIsHTML;
+					}
+					while ( ( matcher = elementMatchers[ j++ ] ) ) {
+						if ( matcher( elem, context || document, xml ) ) {
+							results.push( elem );
+							break;
+						}
+					}
+					if ( outermost ) {
+						dirruns = dirrunsUnique;
+					}
+				}
+
+				// Track unmatched elements for set filters
+				if ( bySet ) {
+
+					// They will have gone through all possible matchers
+					if ( ( elem = !matcher && elem ) ) {
+						matchedCount--;
+					}
+
+					// Lengthen the array for every element, matched or not
+					if ( seed ) {
+						unmatched.push( elem );
+					}
+				}
+			}
+
+			// `i` is now the count of elements visited above, and adding it to `matchedCount`
+			// makes the latter nonnegative.
+			matchedCount += i;
+
+			// Apply set filters to unmatched elements
+			// NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
+			// equals `i`), unless we didn't visit _any_ elements in the above loop because we have
+			// no element matchers and no seed.
+			// Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
+			// case, which will result in a "00" `matchedCount` that differs from `i` but is also
+			// numerically zero.
+			if ( bySet && i !== matchedCount ) {
+				j = 0;
+				while ( ( matcher = setMatchers[ j++ ] ) ) {
+					matcher( unmatched, setMatched, context, xml );
+				}
+
+				if ( seed ) {
+
+					// Reintegrate element matches to eliminate the need for sorting
+					if ( matchedCount > 0 ) {
+						while ( i-- ) {
+							if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
+								setMatched[ i ] = pop.call( results );
+							}
+						}
+					}
+
+					// Discard index placeholder values to get only actual matches
+					setMatched = condense( setMatched );
+				}
+
+				// Add matches to results
+				push.apply( results, setMatched );
+
+				// Seedless set matches succeeding multiple successful matchers stipulate sorting
+				if ( outermost && !seed && setMatched.length > 0 &&
+					( matchedCount + setMatchers.length ) > 1 ) {
+
+					Sizzle.uniqueSort( results );
+				}
+			}
+
+			// Override manipulation of globals by nested matchers
+			if ( outermost ) {
+				dirruns = dirrunsUnique;
+				outermostContext = contextBackup;
+			}
+
+			return unmatched;
+		};
+
+	return bySet ?
+		markFunction( superMatcher ) :
+		superMatcher;
+}
+
+compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
+	var i,
+		setMatchers = [],
+		elementMatchers = [],
+		cached = compilerCache[ selector + " " ];
+
+	if ( !cached ) {
+
+		// Generate a function of recursive functions that can be used to check each element
+		if ( !match ) {
+			match = tokenize( selector );
+		}
+		i = match.length;
+		while ( i-- ) {
+			cached = matcherFromTokens( match[ i ] );
+			if ( cached[ expando ] ) {
+				setMatchers.push( cached );
+			} else {
+				elementMatchers.push( cached );
+			}
+		}
+
+		// Cache the compiled function
+		cached = compilerCache(
+			selector,
+			matcherFromGroupMatchers( elementMatchers, setMatchers )
+		);
+
+		// Save selector and tokenization
+		cached.selector = selector;
+	}
+	return cached;
+};
+
+/**
+ * A low-level selection function that works with Sizzle's compiled
+ *  selector functions
+ * @param {String|Function} selector A selector or a pre-compiled
+ *  selector function built with Sizzle.compile
+ * @param {Element} context
+ * @param {Array} [results]
+ * @param {Array} [seed] A set of elements to match against
+ */
+select = Sizzle.select = function( selector, context, results, seed ) {
+	var i, tokens, token, type, find,
+		compiled = typeof selector === "function" && selector,
+		match = !seed && tokenize( ( selector = compiled.selector || selector ) );
+
+	results = results || [];
+
+	// Try to minimize operations if there is only one selector in the list and no seed
+	// (the latter of which guarantees us context)
+	if ( match.length === 1 ) {
+
+		// Reduce context if the leading compound selector is an ID
+		tokens = match[ 0 ] = match[ 0 ].slice( 0 );
+		if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
+			context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
+
+			context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
+				.replace( runescape, funescape ), context ) || [] )[ 0 ];
+			if ( !context ) {
+				return results;
+
+			// Precompiled matchers will still verify ancestry, so step up a level
+			} else if ( compiled ) {
+				context = context.parentNode;
+			}
+
+			selector = selector.slice( tokens.shift().value.length );
+		}
+
+		// Fetch a seed set for right-to-left matching
+		i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
+		while ( i-- ) {
+			token = tokens[ i ];
+
+			// Abort if we hit a combinator
+			if ( Expr.relative[ ( type = token.type ) ] ) {
+				break;
+			}
+			if ( ( find = Expr.find[ type ] ) ) {
+
+				// Search, expanding context for leading sibling combinators
+				if ( ( seed = find(
+					token.matches[ 0 ].replace( runescape, funescape ),
+					rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
+						context
+				) ) ) {
+
+					// If seed is empty or no tokens remain, we can return early
+					tokens.splice( i, 1 );
+					selector = seed.length && toSelector( tokens );
+					if ( !selector ) {
+						push.apply( results, seed );
+						return results;
+					}
+
+					break;
+				}
+			}
+		}
+	}
+
+	// Compile and execute a filtering function if one is not provided
+	// Provide `match` to avoid retokenization if we modified the selector above
+	( compiled || compile( selector, match ) )(
+		seed,
+		context,
+		!documentIsHTML,
+		results,
+		!context || rsibling.test( selector ) && testContext( context.parentNode ) || context
+	);
+	return results;
+};
+
+// One-time assignments
+
+// Sort stability
+support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
+
+// Support: Chrome 14-35+
+// Always assume duplicates if they aren't passed to the comparison function
+support.detectDuplicates = !!hasDuplicate;
+
+// Initialize against the default document
+setDocument();
+
+// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
+// Detached nodes confoundingly follow *each other*
+support.sortDetached = assert( function( el ) {
+
+	// Should return 1, but returns 4 (following)
+	return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
+} );
+
+// Support: IE<8
+// Prevent attribute/property "interpolation"
+// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
+if ( !assert( function( el ) {
+	el.innerHTML = "<a href='#'></a>";
+	return el.firstChild.getAttribute( "href" ) === "#";
+} ) ) {
+	addHandle( "type|href|height|width", function( elem, name, isXML ) {
+		if ( !isXML ) {
+			return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
+		}
+	} );
+}
+
+// Support: IE<9
+// Use defaultValue in place of getAttribute("value")
+if ( !support.attributes || !assert( function( el ) {
+	el.innerHTML = "<input/>";
+	el.firstChild.setAttribute( "value", "" );
+	return el.firstChild.getAttribute( "value" ) === "";
+} ) ) {
+	addHandle( "value", function( elem, _name, isXML ) {
+		if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
+			return elem.defaultValue;
+		}
+	} );
+}
+
+// Support: IE<9
+// Use getAttributeNode to fetch booleans when getAttribute lies
+if ( !assert( function( el ) {
+	return el.getAttribute( "disabled" ) == null;
+} ) ) {
+	addHandle( booleans, function( elem, name, isXML ) {
+		var val;
+		if ( !isXML ) {
+			return elem[ name ] === true ? name.toLowerCase() :
+				( val = elem.getAttributeNode( name ) ) && val.specified ?
+					val.value :
+					null;
+		}
+	} );
+}
+
+return Sizzle;
+
+} )( window );
+
+
+
+jQuery.find = Sizzle;
+jQuery.expr = Sizzle.selectors;
+
+// Deprecated
+jQuery.expr[ ":" ] = jQuery.expr.pseudos;
+jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
+jQuery.text = Sizzle.getText;
+jQuery.isXMLDoc = Sizzle.isXML;
+jQuery.contains = Sizzle.contains;
+jQuery.escapeSelector = Sizzle.escape;
+
+
+
+
+var dir = function( elem, dir, until ) {
+	var matched = [],
+		truncate = until !== undefined;
+
+	while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
+		if ( elem.nodeType === 1 ) {
+			if ( truncate && jQuery( elem ).is( until ) ) {
+				break;
+			}
+			matched.push( elem );
+		}
+	}
+	return matched;
+};
+
+
+var siblings = function( n, elem ) {
+	var matched = [];
+
+	for ( ; n; n = n.nextSibling ) {
+		if ( n.nodeType === 1 && n !== elem ) {
+			matched.push( n );
+		}
+	}
+
+	return matched;
+};
+
+
+var rneedsContext = jQuery.expr.match.needsContext;
+
+
+
+function nodeName( elem, name ) {
+
+	return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
+
+}
+var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
+
+
+
+// Implement the identical functionality for filter and not
+function winnow( elements, qualifier, not ) {
+	if ( isFunction( qualifier ) ) {
+		return jQuery.grep( elements, function( elem, i ) {
+			return !!qualifier.call( elem, i, elem ) !== not;
+		} );
+	}
+
+	// Single element
+	if ( qualifier.nodeType ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( elem === qualifier ) !== not;
+		} );
+	}
+
+	// Arraylike of elements (jQuery, arguments, Array)
+	if ( typeof qualifier !== "string" ) {
+		return jQuery.grep( elements, function( elem ) {
+			return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
+		} );
+	}
+
+	// Filtered directly for both simple and complex selectors
+	return jQuery.filter( qualifier, elements, not );
+}
+
+jQuery.filter = function( expr, elems, not ) {
+	var elem = elems[ 0 ];
+
+	if ( not ) {
+		expr = ":not(" + expr + ")";
+	}
+
+	if ( elems.length === 1 && elem.nodeType === 1 ) {
+		return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
+	}
+
+	return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
+		return elem.nodeType === 1;
+	} ) );
+};
+
+jQuery.fn.extend( {
+	find: function( selector ) {
+		var i, ret,
+			len = this.length,
+			self = this;
+
+		if ( typeof selector !== "string" ) {
+			return this.pushStack( jQuery( selector ).filter( function() {
+				for ( i = 0; i < len; i++ ) {
+					if ( jQuery.contains( self[ i ], this ) ) {
+						return true;
+					}
+				}
+			} ) );
+		}
+
+		ret = this.pushStack( [] );
+
+		for ( i = 0; i < len; i++ ) {
+			jQuery.find( selector, self[ i ], ret );
+		}
+
+		return len > 1 ? jQuery.uniqueSort( ret ) : ret;
+	},
+	filter: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], false ) );
+	},
+	not: function( selector ) {
+		return this.pushStack( winnow( this, selector || [], true ) );
+	},
+	is: function( selector ) {
+		return !!winnow(
+			this,
+
+			// If this is a positional/relative selector, check membership in the returned set
+			// so $("p:first").is("p:last") won't return true for a doc with two "p".
+			typeof selector === "string" && rneedsContext.test( selector ) ?
+				jQuery( selector ) :
+				selector || [],
+			false
+		).length;
+	}
+} );
+
+
+// Initialize a jQuery object
+
+
+// A central reference to the root jQuery(document)
+var rootjQuery,
+
+	// A simple way to check for HTML strings
+	// Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
+	// Strict HTML recognition (#11290: must start with <)
+	// Shortcut simple #id case for speed
+	rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
+
+	init = jQuery.fn.init = function( selector, context, root ) {
+		var match, elem;
+
+		// HANDLE: $(""), $(null), $(undefined), $(false)
+		if ( !selector ) {
+			return this;
+		}
+
+		// Method init() accepts an alternate rootjQuery
+		// so migrate can support jQuery.sub (gh-2101)
+		root = root || rootjQuery;
+
+		// Handle HTML strings
+		if ( typeof selector === "string" ) {
+			if ( selector[ 0 ] === "<" &&
+				selector[ selector.length - 1 ] === ">" &&
+				selector.length >= 3 ) {
+
+				// Assume that strings that start and end with <> are HTML and skip the regex check
+				match = [ null, selector, null ];
+
+			} else {
+				match = rquickExpr.exec( selector );
+			}
+
+			// Match html or make sure no context is specified for #id
+			if ( match && ( match[ 1 ] || !context ) ) {
+
+				// HANDLE: $(html) -> $(array)
+				if ( match[ 1 ] ) {
+					context = context instanceof jQuery ? context[ 0 ] : context;
+
+					// Option to run scripts is true for back-compat
+					// Intentionally let the error be thrown if parseHTML is not present
+					jQuery.merge( this, jQuery.parseHTML(
+						match[ 1 ],
+						context && context.nodeType ? context.ownerDocument || context : document,
+						true
+					) );
+
+					// HANDLE: $(html, props)
+					if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
+						for ( match in context ) {
+
+							// Properties of context are called as methods if possible
+							if ( isFunction( this[ match ] ) ) {
+								this[ match ]( context[ match ] );
+
+							// ...and otherwise set as attributes
+							} else {
+								this.attr( match, context[ match ] );
+							}
+						}
+					}
+
+					return this;
+
+				// HANDLE: $(#id)
+				} else {
+					elem = document.getElementById( match[ 2 ] );
+
+					if ( elem ) {
+
+						// Inject the element directly into the jQuery object
+						this[ 0 ] = elem;
+						this.length = 1;
+					}
+					return this;
+				}
+
+			// HANDLE: $(expr, $(...))
+			} else if ( !context || context.jquery ) {
+				return ( context || root ).find( selector );
+
+			// HANDLE: $(expr, context)
+			// (which is just equivalent to: $(context).find(expr)
+			} else {
+				return this.constructor( context ).find( selector );
+			}
+
+		// HANDLE: $(DOMElement)
+		} else if ( selector.nodeType ) {
+			this[ 0 ] = selector;
+			this.length = 1;
+			return this;
+
+		// HANDLE: $(function)
+		// Shortcut for document ready
+		} else if ( isFunction( selector ) ) {
+			return root.ready !== undefined ?
+				root.ready( selector ) :
+
+				// Execute immediately if ready is not present
+				selector( jQuery );
+		}
+
+		return jQuery.makeArray( selector, this );
+	};
+
+// Give the init function the jQuery prototype for later instantiation
+init.prototype = jQuery.fn;
+
+// Initialize central reference
+rootjQuery = jQuery( document );
+
+
+var rparentsprev = /^(?:parents|prev(?:Until|All))/,
+
+	// Methods guaranteed to produce a unique set when starting from a unique set
+	guaranteedUnique = {
+		children: true,
+		contents: true,
+		next: true,
+		prev: true
+	};
+
+jQuery.fn.extend( {
+	has: function( target ) {
+		var targets = jQuery( target, this ),
+			l = targets.length;
+
+		return this.filter( function() {
+			var i = 0;
+			for ( ; i < l; i++ ) {
+				if ( jQuery.contains( this, targets[ i ] ) ) {
+					return true;
+				}
+			}
+		} );
+	},
+
+	closest: function( selectors, context ) {
+		var cur,
+			i = 0,
+			l = this.length,
+			matched = [],
+			targets = typeof selectors !== "string" && jQuery( selectors );
+
+		// Positional selectors never match, since there's no _selection_ context
+		if ( !rneedsContext.test( selectors ) ) {
+			for ( ; i < l; i++ ) {
+				for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
+
+					// Always skip document fragments
+					if ( cur.nodeType < 11 && ( targets ?
+						targets.index( cur ) > -1 :
+
+						// Don't pass non-elements to Sizzle
+						cur.nodeType === 1 &&
+							jQuery.find.matchesSelector( cur, selectors ) ) ) {
+
+						matched.push( cur );
+						break;
+					}
+				}
+			}
+		}
+
+		return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
+	},
+
+	// Determine the position of an element within the set
+	index: function( elem ) {
+
+		// No argument, return index in parent
+		if ( !elem ) {
+			return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
+		}
+
+		// Index in selector
+		if ( typeof elem === "string" ) {
+			return indexOf.call( jQuery( elem ), this[ 0 ] );
+		}
+
+		// Locate the position of the desired element
+		return indexOf.call( this,
+
+			// If it receives a jQuery object, the first element is used
+			elem.jquery ? elem[ 0 ] : elem
+		);
+	},
+
+	add: function( selector, context ) {
+		return this.pushStack(
+			jQuery.uniqueSort(
+				jQuery.merge( this.get(), jQuery( selector, context ) )
+			)
+		);
+	},
+
+	addBack: function( selector ) {
+		return this.add( selector == null ?
+			this.prevObject : this.prevObject.filter( selector )
+		);
+	}
+} );
+
+function sibling( cur, dir ) {
+	while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
+	return cur;
+}
+
+jQuery.each( {
+	parent: function( elem ) {
+		var parent = elem.parentNode;
+		return parent && parent.nodeType !== 11 ? parent : null;
+	},
+	parents: function( elem ) {
+		return dir( elem, "parentNode" );
+	},
+	parentsUntil: function( elem, _i, until ) {
+		return dir( elem, "parentNode", until );
+	},
+	next: function( elem ) {
+		return sibling( elem, "nextSibling" );
+	},
+	prev: function( elem ) {
+		return sibling( elem, "previousSibling" );
+	},
+	nextAll: function( elem ) {
+		return dir( elem, "nextSibling" );
+	},
+	prevAll: function( elem ) {
+		return dir( elem, "previousSibling" );
+	},
+	nextUntil: function( elem, _i, until ) {
+		return dir( elem, "nextSibling", until );
+	},
+	prevUntil: function( elem, _i, until ) {
+		return dir( elem, "previousSibling", until );
+	},
+	siblings: function( elem ) {
+		return siblings( ( elem.parentNode || {} ).firstChild, elem );
+	},
+	children: function( elem ) {
+		return siblings( elem.firstChild );
+	},
+	contents: function( elem ) {
+		if ( elem.contentDocument != null &&
+
+			// Support: IE 11+
+			// <object> elements with no `data` attribute has an object
+			// `contentDocument` with a `null` prototype.
+			getProto( elem.contentDocument ) ) {
+
+			return elem.contentDocument;
+		}
+
+		// Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
+		// Treat the template element as a regular one in browsers that
+		// don't support it.
+		if ( nodeName( elem, "template" ) ) {
+			elem = elem.content || elem;
+		}
+
+		return jQuery.merge( [], elem.childNodes );
+	}
+}, function( name, fn ) {
+	jQuery.fn[ name ] = function( until, selector ) {
+		var matched = jQuery.map( this, fn, until );
+
+		if ( name.slice( -5 ) !== "Until" ) {
+			selector = until;
+		}
+
+		if ( selector && typeof selector === "string" ) {
+			matched = jQuery.filter( selector, matched );
+		}
+
+		if ( this.length > 1 ) {
+
+			// Remove duplicates
+			if ( !guaranteedUnique[ name ] ) {
+				jQuery.uniqueSort( matched );
+			}
+
+			// Reverse order for parents* and prev-derivatives
+			if ( rparentsprev.test( name ) ) {
+				matched.reverse();
+			}
+		}
+
+		return this.pushStack( matched );
+	};
+} );
+var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
+
+
+
+// Convert String-formatted options into Object-formatted ones
+function createOptions( options ) {
+	var object = {};
+	jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
+		object[ flag ] = true;
+	} );
+	return object;
+}
+
+/*
+ * Create a callback list using the following parameters:
+ *
+ *	options: an optional list of space-separated options that will change how
+ *			the callback list behaves or a more traditional option object
+ *
+ * By default a callback list will act like an event callback list and can be
+ * "fired" multiple times.
+ *
+ * Possible options:
+ *
+ *	once:			will ensure the callback list can only be fired once (like a Deferred)
+ *
+ *	memory:			will keep track of previous values and will call any callback added
+ *					after the list has been fired right away with the latest "memorized"
+ *					values (like a Deferred)
+ *
+ *	unique:			will ensure a callback can only be added once (no duplicate in the list)
+ *
+ *	stopOnFalse:	interrupt callings when a callback returns false
+ *
+ */
+jQuery.Callbacks = function( options ) {
+
+	// Convert options from String-formatted to Object-formatted if needed
+	// (we check in cache first)
+	options = typeof options === "string" ?
+		createOptions( options ) :
+		jQuery.extend( {}, options );
+
+	var // Flag to know if list is currently firing
+		firing,
+
+		// Last fire value for non-forgettable lists
+		memory,
+
+		// Flag to know if list was already fired
+		fired,
+
+		// Flag to prevent firing
+		locked,
+
+		// Actual callback list
+		list = [],
+
+		// Queue of execution data for repeatable lists
+		queue = [],
+
+		// Index of currently firing callback (modified by add/remove as needed)
+		firingIndex = -1,
+
+		// Fire callbacks
+		fire = function() {
+
+			// Enforce single-firing
+			locked = locked || options.once;
+
+			// Execute callbacks for all pending executions,
+			// respecting firingIndex overrides and runtime changes
+			fired = firing = true;
+			for ( ; queue.length; firingIndex = -1 ) {
+				memory = queue.shift();
+				while ( ++firingIndex < list.length ) {
+
+					// Run callback and check for early termination
+					if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
+						options.stopOnFalse ) {
+
+						// Jump to end and forget the data so .add doesn't re-fire
+						firingIndex = list.length;
+						memory = false;
+					}
+				}
+			}
+
+			// Forget the data if we're done with it
+			if ( !options.memory ) {
+				memory = false;
+			}
+
+			firing = false;
+
+			// Clean up if we're done firing for good
+			if ( locked ) {
+
+				// Keep an empty list if we have data for future add calls
+				if ( memory ) {
+					list = [];
+
+				// Otherwise, this object is spent
+				} else {
+					list = "";
+				}
+			}
+		},
+
+		// Actual Callbacks object
+		self = {
+
+			// Add a callback or a collection of callbacks to the list
+			add: function() {
+				if ( list ) {
+
+					// If we have memory from a past run, we should fire after adding
+					if ( memory && !firing ) {
+						firingIndex = list.length - 1;
+						queue.push( memory );
+					}
+
+					( function add( args ) {
+						jQuery.each( args, function( _, arg ) {
+							if ( isFunction( arg ) ) {
+								if ( !options.unique || !self.has( arg ) ) {
+									list.push( arg );
+								}
+							} else if ( arg && arg.length && toType( arg ) !== "string" ) {
+
+								// Inspect recursively
+								add( arg );
+							}
+						} );
+					} )( arguments );
+
+					if ( memory && !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Remove a callback from the list
+			remove: function() {
+				jQuery.each( arguments, function( _, arg ) {
+					var index;
+					while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
+						list.splice( index, 1 );
+
+						// Handle firing indexes
+						if ( index <= firingIndex ) {
+							firingIndex--;
+						}
+					}
+				} );
+				return this;
+			},
+
+			// Check if a given callback is in the list.
+			// If no argument is given, return whether or not list has callbacks attached.
+			has: function( fn ) {
+				return fn ?
+					jQuery.inArray( fn, list ) > -1 :
+					list.length > 0;
+			},
+
+			// Remove all callbacks from the list
+			empty: function() {
+				if ( list ) {
+					list = [];
+				}
+				return this;
+			},
+
+			// Disable .fire and .add
+			// Abort any current/pending executions
+			// Clear all callbacks and values
+			disable: function() {
+				locked = queue = [];
+				list = memory = "";
+				return this;
+			},
+			disabled: function() {
+				return !list;
+			},
+
+			// Disable .fire
+			// Also disable .add unless we have memory (since it would have no effect)
+			// Abort any pending executions
+			lock: function() {
+				locked = queue = [];
+				if ( !memory && !firing ) {
+					list = memory = "";
+				}
+				return this;
+			},
+			locked: function() {
+				return !!locked;
+			},
+
+			// Call all callbacks with the given context and arguments
+			fireWith: function( context, args ) {
+				if ( !locked ) {
+					args = args || [];
+					args = [ context, args.slice ? args.slice() : args ];
+					queue.push( args );
+					if ( !firing ) {
+						fire();
+					}
+				}
+				return this;
+			},
+
+			// Call all the callbacks with the given arguments
+			fire: function() {
+				self.fireWith( this, arguments );
+				return this;
+			},
+
+			// To know if the callbacks have already been called at least once
+			fired: function() {
+				return !!fired;
+			}
+		};
+
+	return self;
+};
+
+
+function Identity( v ) {
+	return v;
+}
+function Thrower( ex ) {
+	throw ex;
+}
+
+function adoptValue( value, resolve, reject, noValue ) {
+	var method;
+
+	try {
+
+		// Check for promise aspect first to privilege synchronous behavior
+		if ( value && isFunction( ( method = value.promise ) ) ) {
+			method.call( value ).done( resolve ).fail( reject );
+
+		// Other thenables
+		} else if ( value && isFunction( ( method = value.then ) ) ) {
+			method.call( value, resolve, reject );
+
+		// Other non-thenables
+		} else {
+
+			// Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
+			// * false: [ value ].slice( 0 ) => resolve( value )
+			// * true: [ value ].slice( 1 ) => resolve()
+			resolve.apply( undefined, [ value ].slice( noValue ) );
+		}
+
+	// For Promises/A+, convert exceptions into rejections
+	// Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
+	// Deferred#then to conditionally suppress rejection.
+	} catch ( value ) {
+
+		// Support: Android 4.0 only
+		// Strict mode functions invoked without .call/.apply get global-object context
+		reject.apply( undefined, [ value ] );
+	}
+}
+
+jQuery.extend( {
+
+	Deferred: function( func ) {
+		var tuples = [
+
+				// action, add listener, callbacks,
+				// ... .then handlers, argument index, [final state]
+				[ "notify", "progress", jQuery.Callbacks( "memory" ),
+					jQuery.Callbacks( "memory" ), 2 ],
+				[ "resolve", "done", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 0, "resolved" ],
+				[ "reject", "fail", jQuery.Callbacks( "once memory" ),
+					jQuery.Callbacks( "once memory" ), 1, "rejected" ]
+			],
+			state = "pending",
+			promise = {
+				state: function() {
+					return state;
+				},
+				always: function() {
+					deferred.done( arguments ).fail( arguments );
+					return this;
+				},
+				"catch": function( fn ) {
+					return promise.then( null, fn );
+				},
+
+				// Keep pipe for back-compat
+				pipe: function( /* fnDone, fnFail, fnProgress */ ) {
+					var fns = arguments;
+
+					return jQuery.Deferred( function( newDefer ) {
+						jQuery.each( tuples, function( _i, tuple ) {
+
+							// Map tuples (progress, done, fail) to arguments (done, fail, progress)
+							var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
+
+							// deferred.progress(function() { bind to newDefer or newDefer.notify })
+							// deferred.done(function() { bind to newDefer or newDefer.resolve })
+							// deferred.fail(function() { bind to newDefer or newDefer.reject })
+							deferred[ tuple[ 1 ] ]( function() {
+								var returned = fn && fn.apply( this, arguments );
+								if ( returned && isFunction( returned.promise ) ) {
+									returned.promise()
+										.progress( newDefer.notify )
+										.done( newDefer.resolve )
+										.fail( newDefer.reject );
+								} else {
+									newDefer[ tuple[ 0 ] + "With" ](
+										this,
+										fn ? [ returned ] : arguments
+									);
+								}
+							} );
+						} );
+						fns = null;
+					} ).promise();
+				},
+				then: function( onFulfilled, onRejected, onProgress ) {
+					var maxDepth = 0;
+					function resolve( depth, deferred, handler, special ) {
+						return function() {
+							var that = this,
+								args = arguments,
+								mightThrow = function() {
+									var returned, then;
+
+									// Support: Promises/A+ section 2.3.3.3.3
+									// https://promisesaplus.com/#point-59
+									// Ignore double-resolution attempts
+									if ( depth < maxDepth ) {
+										return;
+									}
+
+									returned = handler.apply( that, args );
+
+									// Support: Promises/A+ section 2.3.1
+									// https://promisesaplus.com/#point-48
+									if ( returned === deferred.promise() ) {
+										throw new TypeError( "Thenable self-resolution" );
+									}
+
+									// Support: Promises/A+ sections 2.3.3.1, 3.5
+									// https://promisesaplus.com/#point-54
+									// https://promisesaplus.com/#point-75
+									// Retrieve `then` only once
+									then = returned &&
+
+										// Support: Promises/A+ section 2.3.4
+										// https://promisesaplus.com/#point-64
+										// Only check objects and functions for thenability
+										( typeof returned === "object" ||
+											typeof returned === "function" ) &&
+										returned.then;
+
+									// Handle a returned thenable
+									if ( isFunction( then ) ) {
+
+										// Special processors (notify) just wait for resolution
+										if ( special ) {
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special )
+											);
+
+										// Normal processors (resolve) also hook into progress
+										} else {
+
+											// ...and disregard older resolution values
+											maxDepth++;
+
+											then.call(
+												returned,
+												resolve( maxDepth, deferred, Identity, special ),
+												resolve( maxDepth, deferred, Thrower, special ),
+												resolve( maxDepth, deferred, Identity,
+													deferred.notifyWith )
+											);
+										}
+
+									// Handle all other returned values
+									} else {
+
+										// Only substitute handlers pass on context
+										// and multiple values (non-spec behavior)
+										if ( handler !== Identity ) {
+											that = undefined;
+											args = [ returned ];
+										}
+
+										// Process the value(s)
+										// Default process is resolve
+										( special || deferred.resolveWith )( that, args );
+									}
+								},
+
+								// Only normal processors (resolve) catch and reject exceptions
+								process = special ?
+									mightThrow :
+									function() {
+										try {
+											mightThrow();
+										} catch ( e ) {
+
+											if ( jQuery.Deferred.exceptionHook ) {
+												jQuery.Deferred.exceptionHook( e,
+													process.stackTrace );
+											}
+
+											// Support: Promises/A+ section 2.3.3.3.4.1
+											// https://promisesaplus.com/#point-61
+											// Ignore post-resolution exceptions
+											if ( depth + 1 >= maxDepth ) {
+
+												// Only substitute handlers pass on context
+												// and multiple values (non-spec behavior)
+												if ( handler !== Thrower ) {
+													that = undefined;
+													args = [ e ];
+												}
+
+												deferred.rejectWith( that, args );
+											}
+										}
+									};
+
+							// Support: Promises/A+ section 2.3.3.3.1
+							// https://promisesaplus.com/#point-57
+							// Re-resolve promises immediately to dodge false rejection from
+							// subsequent errors
+							if ( depth ) {
+								process();
+							} else {
+
+								// Call an optional hook to record the stack, in case of exception
+								// since it's otherwise lost when execution goes async
+								if ( jQuery.Deferred.getStackHook ) {
+									process.stackTrace = jQuery.Deferred.getStackHook();
+								}
+								window.setTimeout( process );
+							}
+						};
+					}
+
+					return jQuery.Deferred( function( newDefer ) {
+
+						// progress_handlers.add( ... )
+						tuples[ 0 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onProgress ) ?
+									onProgress :
+									Identity,
+								newDefer.notifyWith
+							)
+						);
+
+						// fulfilled_handlers.add( ... )
+						tuples[ 1 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onFulfilled ) ?
+									onFulfilled :
+									Identity
+							)
+						);
+
+						// rejected_handlers.add( ... )
+						tuples[ 2 ][ 3 ].add(
+							resolve(
+								0,
+								newDefer,
+								isFunction( onRejected ) ?
+									onRejected :
+									Thrower
+							)
+						);
+					} ).promise();
+				},
+
+				// Get a promise for this deferred
+				// If obj is provided, the promise aspect is added to the object
+				promise: function( obj ) {
+					return obj != null ? jQuery.extend( obj, promise ) : promise;
+				}
+			},
+			deferred = {};
+
+		// Add list-specific methods
+		jQuery.each( tuples, function( i, tuple ) {
+			var list = tuple[ 2 ],
+				stateString = tuple[ 5 ];
+
+			// promise.progress = list.add
+			// promise.done = list.add
+			// promise.fail = list.add
+			promise[ tuple[ 1 ] ] = list.add;
+
+			// Handle state
+			if ( stateString ) {
+				list.add(
+					function() {
+
+						// state = "resolved" (i.e., fulfilled)
+						// state = "rejected"
+						state = stateString;
+					},
+
+					// rejected_callbacks.disable
+					// fulfilled_callbacks.disable
+					tuples[ 3 - i ][ 2 ].disable,
+
+					// rejected_handlers.disable
+					// fulfilled_handlers.disable
+					tuples[ 3 - i ][ 3 ].disable,
+
+					// progress_callbacks.lock
+					tuples[ 0 ][ 2 ].lock,
+
+					// progress_handlers.lock
+					tuples[ 0 ][ 3 ].lock
+				);
+			}
+
+			// progress_handlers.fire
+			// fulfilled_handlers.fire
+			// rejected_handlers.fire
+			list.add( tuple[ 3 ].fire );
+
+			// deferred.notify = function() { deferred.notifyWith(...) }
+			// deferred.resolve = function() { deferred.resolveWith(...) }
+			// deferred.reject = function() { deferred.rejectWith(...) }
+			deferred[ tuple[ 0 ] ] = function() {
+				deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
+				return this;
+			};
+
+			// deferred.notifyWith = list.fireWith
+			// deferred.resolveWith = list.fireWith
+			// deferred.rejectWith = list.fireWith
+			deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
+		} );
+
+		// Make the deferred a promise
+		promise.promise( deferred );
+
+		// Call given func if any
+		if ( func ) {
+			func.call( deferred, deferred );
+		}
+
+		// All done!
+		return deferred;
+	},
+
+	// Deferred helper
+	when: function( singleValue ) {
+		var
+
+			// count of uncompleted subordinates
+			remaining = arguments.length,
+
+			// count of unprocessed arguments
+			i = remaining,
+
+			// subordinate fulfillment data
+			resolveContexts = Array( i ),
+			resolveValues = slice.call( arguments ),
+
+			// the primary Deferred
+			primary = jQuery.Deferred(),
+
+			// subordinate callback factory
+			updateFunc = function( i ) {
+				return function( value ) {
+					resolveContexts[ i ] = this;
+					resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
+					if ( !( --remaining ) ) {
+						primary.resolveWith( resolveContexts, resolveValues );
+					}
+				};
+			};
+
+		// Single- and empty arguments are adopted like Promise.resolve
+		if ( remaining <= 1 ) {
+			adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject,
+				!remaining );
+
+			// Use .then() to unwrap secondary thenables (cf. gh-3000)
+			if ( primary.state() === "pending" ||
+				isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
+
+				return primary.then();
+			}
+		}
+
+		// Multiple arguments are aggregated like Promise.all array elements
+		while ( i-- ) {
+			adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject );
+		}
+
+		return primary.promise();
+	}
+} );
+
+
+// These usually indicate a programmer mistake during development,
+// warn about them ASAP rather than swallowing them by default.
+var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
+
+jQuery.Deferred.exceptionHook = function( error, stack ) {
+
+	// Support: IE 8 - 9 only
+	// Console exists when dev tools are open, which can happen at any time
+	if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
+		window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
+	}
+};
+
+
+
+
+jQuery.readyException = function( error ) {
+	window.setTimeout( function() {
+		throw error;
+	} );
+};
+
+
+
+
+// The deferred used on DOM ready
+var readyList = jQuery.Deferred();
+
+jQuery.fn.ready = function( fn ) {
+
+	readyList
+		.then( fn )
+
+		// Wrap jQuery.readyException in a function so that the lookup
+		// happens at the time of error handling instead of callback
+		// registration.
+		.catch( function( error ) {
+			jQuery.readyException( error );
+		} );
+
+	return this;
+};
+
+jQuery.extend( {
+
+	// Is the DOM ready to be used? Set to true once it occurs.
+	isReady: false,
+
+	// A counter to track how many items to wait for before
+	// the ready event fires. See #6781
+	readyWait: 1,
+
+	// Handle when the DOM is ready
+	ready: function( wait ) {
+
+		// Abort if there are pending holds or we're already ready
+		if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
+			return;
+		}
+
+		// Remember that the DOM is ready
+		jQuery.isReady = true;
+
+		// If a normal DOM Ready event fired, decrement, and wait if need be
+		if ( wait !== true && --jQuery.readyWait > 0 ) {
+			return;
+		}
+
+		// If there are functions bound, to execute
+		readyList.resolveWith( document, [ jQuery ] );
+	}
+} );
+
+jQuery.ready.then = readyList.then;
+
+// The ready event handler and self cleanup method
+function completed() {
+	document.removeEventListener( "DOMContentLoaded", completed );
+	window.removeEventListener( "load", completed );
+	jQuery.ready();
+}
+
+// Catch cases where $(document).ready() is called
+// after the browser event has already occurred.
+// Support: IE <=9 - 10 only
+// Older IE sometimes signals "interactive" too soon
+if ( document.readyState === "complete" ||
+	( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
+
+	// Handle it asynchronously to allow scripts the opportunity to delay ready
+	window.setTimeout( jQuery.ready );
+
+} else {
+
+	// Use the handy event callback
+	document.addEventListener( "DOMContentLoaded", completed );
+
+	// A fallback to window.onload, that will always work
+	window.addEventListener( "load", completed );
+}
+
+
+
+
+// Multifunctional method to get and set values of a collection
+// The value/s can optionally be executed if it's a function
+var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
+	var i = 0,
+		len = elems.length,
+		bulk = key == null;
+
+	// Sets many values
+	if ( toType( key ) === "object" ) {
+		chainable = true;
+		for ( i in key ) {
+			access( elems, fn, i, key[ i ], true, emptyGet, raw );
+		}
+
+	// Sets one value
+	} else if ( value !== undefined ) {
+		chainable = true;
+
+		if ( !isFunction( value ) ) {
+			raw = true;
+		}
+
+		if ( bulk ) {
+
+			// Bulk operations run against the entire set
+			if ( raw ) {
+				fn.call( elems, value );
+				fn = null;
+
+			// ...except when executing function values
+			} else {
+				bulk = fn;
+				fn = function( elem, _key, value ) {
+					return bulk.call( jQuery( elem ), value );
+				};
+			}
+		}
+
+		if ( fn ) {
+			for ( ; i < len; i++ ) {
+				fn(
+					elems[ i ], key, raw ?
+						value :
+						value.call( elems[ i ], i, fn( elems[ i ], key ) )
+				);
+			}
+		}
+	}
+
+	if ( chainable ) {
+		return elems;
+	}
+
+	// Gets
+	if ( bulk ) {
+		return fn.call( elems );
+	}
+
+	return len ? fn( elems[ 0 ], key ) : emptyGet;
+};
+
+
+// Matches dashed string for camelizing
+var rmsPrefix = /^-ms-/,
+	rdashAlpha = /-([a-z])/g;
+
+// Used by camelCase as callback to replace()
+function fcamelCase( _all, letter ) {
+	return letter.toUpperCase();
+}
+
+// Convert dashed to camelCase; used by the css and data modules
+// Support: IE <=9 - 11, Edge 12 - 15
+// Microsoft forgot to hump their vendor prefix (#9572)
+function camelCase( string ) {
+	return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
+}
+var acceptData = function( owner ) {
+
+	// Accepts only:
+	//  - Node
+	//    - Node.ELEMENT_NODE
+	//    - Node.DOCUMENT_NODE
+	//  - Object
+	//    - Any
+	return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
+};
+
+
+
+
+function Data() {
+	this.expando = jQuery.expando + Data.uid++;
+}
+
+Data.uid = 1;
+
+Data.prototype = {
+
+	cache: function( owner ) {
+
+		// Check if the owner object already has a cache
+		var value = owner[ this.expando ];
+
+		// If not, create one
+		if ( !value ) {
+			value = {};
+
+			// We can accept data for non-element nodes in modern browsers,
+			// but we should not, see #8335.
+			// Always return an empty object.
+			if ( acceptData( owner ) ) {
+
+				// If it is a node unlikely to be stringify-ed or looped over
+				// use plain assignment
+				if ( owner.nodeType ) {
+					owner[ this.expando ] = value;
+
+				// Otherwise secure it in a non-enumerable property
+				// configurable must be true to allow the property to be
+				// deleted when data is removed
+				} else {
+					Object.defineProperty( owner, this.expando, {
+						value: value,
+						configurable: true
+					} );
+				}
+			}
+		}
+
+		return value;
+	},
+	set: function( owner, data, value ) {
+		var prop,
+			cache = this.cache( owner );
+
+		// Handle: [ owner, key, value ] args
+		// Always use camelCase key (gh-2257)
+		if ( typeof data === "string" ) {
+			cache[ camelCase( data ) ] = value;
+
+		// Handle: [ owner, { properties } ] args
+		} else {
+
+			// Copy the properties one-by-one to the cache object
+			for ( prop in data ) {
+				cache[ camelCase( prop ) ] = data[ prop ];
+			}
+		}
+		return cache;
+	},
+	get: function( owner, key ) {
+		return key === undefined ?
+			this.cache( owner ) :
+
+			// Always use camelCase key (gh-2257)
+			owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
+	},
+	access: function( owner, key, value ) {
+
+		// In cases where either:
+		//
+		//   1. No key was specified
+		//   2. A string key was specified, but no value provided
+		//
+		// Take the "read" path and allow the get method to determine
+		// which value to return, respectively either:
+		//
+		//   1. The entire cache object
+		//   2. The data stored at the key
+		//
+		if ( key === undefined ||
+				( ( key && typeof key === "string" ) && value === undefined ) ) {
+
+			return this.get( owner, key );
+		}
+
+		// When the key is not a string, or both a key and value
+		// are specified, set or extend (existing objects) with either:
+		//
+		//   1. An object of properties
+		//   2. A key and value
+		//
+		this.set( owner, key, value );
+
+		// Since the "set" path can have two possible entry points
+		// return the expected data based on which path was taken[*]
+		return value !== undefined ? value : key;
+	},
+	remove: function( owner, key ) {
+		var i,
+			cache = owner[ this.expando ];
+
+		if ( cache === undefined ) {
+			return;
+		}
+
+		if ( key !== undefined ) {
+
+			// Support array or space separated string of keys
+			if ( Array.isArray( key ) ) {
+
+				// If key is an array of keys...
+				// We always set camelCase keys, so remove that.
+				key = key.map( camelCase );
+			} else {
+				key = camelCase( key );
+
+				// If a key with the spaces exists, use it.
+				// Otherwise, create an array by matching non-whitespace
+				key = key in cache ?
+					[ key ] :
+					( key.match( rnothtmlwhite ) || [] );
+			}
+
+			i = key.length;
+
+			while ( i-- ) {
+				delete cache[ key[ i ] ];
+			}
+		}
+
+		// Remove the expando if there's no more data
+		if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
+
+			// Support: Chrome <=35 - 45
+			// Webkit & Blink performance suffers when deleting properties
+			// from DOM nodes, so set to undefined instead
+			// https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
+			if ( owner.nodeType ) {
+				owner[ this.expando ] = undefined;
+			} else {
+				delete owner[ this.expando ];
+			}
+		}
+	},
+	hasData: function( owner ) {
+		var cache = owner[ this.expando ];
+		return cache !== undefined && !jQuery.isEmptyObject( cache );
+	}
+};
+var dataPriv = new Data();
+
+var dataUser = new Data();
+
+
+
+//	Implementation Summary
+//
+//	1. Enforce API surface and semantic compatibility with 1.9.x branch
+//	2. Improve the module's maintainability by reducing the storage
+//		paths to a single mechanism.
+//	3. Use the same single mechanism to support "private" and "user" data.
+//	4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
+//	5. Avoid exposing implementation details on user objects (eg. expando properties)
+//	6. Provide a clear path for implementation upgrade to WeakMap in 2014
+
+var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
+	rmultiDash = /[A-Z]/g;
+
+function getData( data ) {
+	if ( data === "true" ) {
+		return true;
+	}
+
+	if ( data === "false" ) {
+		return false;
+	}
+
+	if ( data === "null" ) {
+		return null;
+	}
+
+	// Only convert to a number if it doesn't change the string
+	if ( data === +data + "" ) {
+		return +data;
+	}
+
+	if ( rbrace.test( data ) ) {
+		return JSON.parse( data );
+	}
+
+	return data;
+}
+
+function dataAttr( elem, key, data ) {
+	var name;
+
+	// If nothing was found internally, try to fetch any
+	// data from the HTML5 data-* attribute
+	if ( data === undefined && elem.nodeType === 1 ) {
+		name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
+		data = elem.getAttribute( name );
+
+		if ( typeof data === "string" ) {
+			try {
+				data = getData( data );
+			} catch ( e ) {}
+
+			// Make sure we set the data so it isn't changed later
+			dataUser.set( elem, key, data );
+		} else {
+			data = undefined;
+		}
+	}
+	return data;
+}
+
+jQuery.extend( {
+	hasData: function( elem ) {
+		return dataUser.hasData( elem ) || dataPriv.hasData( elem );
+	},
+
+	data: function( elem, name, data ) {
+		return dataUser.access( elem, name, data );
+	},
+
+	removeData: function( elem, name ) {
+		dataUser.remove( elem, name );
+	},
+
+	// TODO: Now that all calls to _data and _removeData have been replaced
+	// with direct calls to dataPriv methods, these can be deprecated.
+	_data: function( elem, name, data ) {
+		return dataPriv.access( elem, name, data );
+	},
+
+	_removeData: function( elem, name ) {
+		dataPriv.remove( elem, name );
+	}
+} );
+
+jQuery.fn.extend( {
+	data: function( key, value ) {
+		var i, name, data,
+			elem = this[ 0 ],
+			attrs = elem && elem.attributes;
+
+		// Gets all values
+		if ( key === undefined ) {
+			if ( this.length ) {
+				data = dataUser.get( elem );
+
+				if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
+					i = attrs.length;
+					while ( i-- ) {
+
+						// Support: IE 11 only
+						// The attrs elements can be null (#14894)
+						if ( attrs[ i ] ) {
+							name = attrs[ i ].name;
+							if ( name.indexOf( "data-" ) === 0 ) {
+								name = camelCase( name.slice( 5 ) );
+								dataAttr( elem, name, data[ name ] );
+							}
+						}
+					}
+					dataPriv.set( elem, "hasDataAttrs", true );
+				}
+			}
+
+			return data;
+		}
+
+		// Sets multiple values
+		if ( typeof key === "object" ) {
+			return this.each( function() {
+				dataUser.set( this, key );
+			} );
+		}
+
+		return access( this, function( value ) {
+			var data;
+
+			// The calling jQuery object (element matches) is not empty
+			// (and therefore has an element appears at this[ 0 ]) and the
+			// `value` parameter was not undefined. An empty jQuery object
+			// will result in `undefined` for elem = this[ 0 ] which will
+			// throw an exception if an attempt to read a data cache is made.
+			if ( elem && value === undefined ) {
+
+				// Attempt to get data from the cache
+				// The key will always be camelCased in Data
+				data = dataUser.get( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// Attempt to "discover" the data in
+				// HTML5 custom data-* attrs
+				data = dataAttr( elem, key );
+				if ( data !== undefined ) {
+					return data;
+				}
+
+				// We tried really hard, but the data doesn't exist.
+				return;
+			}
+
+			// Set the data...
+			this.each( function() {
+
+				// We always store the camelCased key
+				dataUser.set( this, key, value );
+			} );
+		}, null, value, arguments.length > 1, null, true );
+	},
+
+	removeData: function( key ) {
+		return this.each( function() {
+			dataUser.remove( this, key );
+		} );
+	}
+} );
+
+
+jQuery.extend( {
+	queue: function( elem, type, data ) {
+		var queue;
+
+		if ( elem ) {
+			type = ( type || "fx" ) + "queue";
+			queue = dataPriv.get( elem, type );
+
+			// Speed up dequeue by getting out quickly if this is just a lookup
+			if ( data ) {
+				if ( !queue || Array.isArray( data ) ) {
+					queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
+				} else {
+					queue.push( data );
+				}
+			}
+			return queue || [];
+		}
+	},
+
+	dequeue: function( elem, type ) {
+		type = type || "fx";
+
+		var queue = jQuery.queue( elem, type ),
+			startLength = queue.length,
+			fn = queue.shift(),
+			hooks = jQuery._queueHooks( elem, type ),
+			next = function() {
+				jQuery.dequeue( elem, type );
+			};
+
+		// If the fx queue is dequeued, always remove the progress sentinel
+		if ( fn === "inprogress" ) {
+			fn = queue.shift();
+			startLength--;
+		}
+
+		if ( fn ) {
+
+			// Add a progress sentinel to prevent the fx queue from being
+			// automatically dequeued
+			if ( type === "fx" ) {
+				queue.unshift( "inprogress" );
+			}
+
+			// Clear up the last queue stop function
+			delete hooks.stop;
+			fn.call( elem, next, hooks );
+		}
+
+		if ( !startLength && hooks ) {
+			hooks.empty.fire();
+		}
+	},
+
+	// Not public - generate a queueHooks object, or return the current one
+	_queueHooks: function( elem, type ) {
+		var key = type + "queueHooks";
+		return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
+			empty: jQuery.Callbacks( "once memory" ).add( function() {
+				dataPriv.remove( elem, [ type + "queue", key ] );
+			} )
+		} );
+	}
+} );
+
+jQuery.fn.extend( {
+	queue: function( type, data ) {
+		var setter = 2;
+
+		if ( typeof type !== "string" ) {
+			data = type;
+			type = "fx";
+			setter--;
+		}
+
+		if ( arguments.length < setter ) {
+			return jQuery.queue( this[ 0 ], type );
+		}
+
+		return data === undefined ?
+			this :
+			this.each( function() {
+				var queue = jQuery.queue( this, type, data );
+
+				// Ensure a hooks for this queue
+				jQuery._queueHooks( this, type );
+
+				if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
+					jQuery.dequeue( this, type );
+				}
+			} );
+	},
+	dequeue: function( type ) {
+		return this.each( function() {
+			jQuery.dequeue( this, type );
+		} );
+	},
+	clearQueue: function( type ) {
+		return this.queue( type || "fx", [] );
+	},
+
+	// Get a promise resolved when queues of a certain type
+	// are emptied (fx is the type by default)
+	promise: function( type, obj ) {
+		var tmp,
+			count = 1,
+			defer = jQuery.Deferred(),
+			elements = this,
+			i = this.length,
+			resolve = function() {
+				if ( !( --count ) ) {
+					defer.resolveWith( elements, [ elements ] );
+				}
+			};
+
+		if ( typeof type !== "string" ) {
+			obj = type;
+			type = undefined;
+		}
+		type = type || "fx";
+
+		while ( i-- ) {
+			tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
+			if ( tmp && tmp.empty ) {
+				count++;
+				tmp.empty.add( resolve );
+			}
+		}
+		resolve();
+		return defer.promise( obj );
+	}
+} );
+var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
+
+var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
+
+
+var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
+
+var documentElement = document.documentElement;
+
+
+
+	var isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem );
+		},
+		composed = { composed: true };
+
+	// Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
+	// Check attachment across shadow DOM boundaries when possible (gh-3504)
+	// Support: iOS 10.0-10.2 only
+	// Early iOS 10 versions support `attachShadow` but not `getRootNode`,
+	// leading to errors. We need to check for `getRootNode`.
+	if ( documentElement.getRootNode ) {
+		isAttached = function( elem ) {
+			return jQuery.contains( elem.ownerDocument, elem ) ||
+				elem.getRootNode( composed ) === elem.ownerDocument;
+		};
+	}
+var isHiddenWithinTree = function( elem, el ) {
+
+		// isHiddenWithinTree might be called from jQuery#filter function;
+		// in that case, element will be second argument
+		elem = el || elem;
+
+		// Inline style trumps all
+		return elem.style.display === "none" ||
+			elem.style.display === "" &&
+
+			// Otherwise, check computed style
+			// Support: Firefox <=43 - 45
+			// Disconnected elements can have computed display: none, so first confirm that elem is
+			// in the document.
+			isAttached( elem ) &&
+
+			jQuery.css( elem, "display" ) === "none";
+	};
+
+
+
+function adjustCSS( elem, prop, valueParts, tween ) {
+	var adjusted, scale,
+		maxIterations = 20,
+		currentValue = tween ?
+			function() {
+				return tween.cur();
+			} :
+			function() {
+				return jQuery.css( elem, prop, "" );
+			},
+		initial = currentValue(),
+		unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
+
+		// Starting value computation is required for potential unit mismatches
+		initialInUnit = elem.nodeType &&
+			( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
+			rcssNum.exec( jQuery.css( elem, prop ) );
+
+	if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
+
+		// Support: Firefox <=54
+		// Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
+		initial = initial / 2;
+
+		// Trust units reported by jQuery.css
+		unit = unit || initialInUnit[ 3 ];
+
+		// Iteratively approximate from a nonzero starting point
+		initialInUnit = +initial || 1;
+
+		while ( maxIterations-- ) {
+
+			// Evaluate and update our best guess (doubling guesses that zero out).
+			// Finish if the scale equals or crosses 1 (making the old*new product non-positive).
+			jQuery.style( elem, prop, initialInUnit + unit );
+			if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
+				maxIterations = 0;
+			}
+			initialInUnit = initialInUnit / scale;
+
+		}
+
+		initialInUnit = initialInUnit * 2;
+		jQuery.style( elem, prop, initialInUnit + unit );
+
+		// Make sure we update the tween properties later on
+		valueParts = valueParts || [];
+	}
+
+	if ( valueParts ) {
+		initialInUnit = +initialInUnit || +initial || 0;
+
+		// Apply relative offset (+=/-=) if specified
+		adjusted = valueParts[ 1 ] ?
+			initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
+			+valueParts[ 2 ];
+		if ( tween ) {
+			tween.unit = unit;
+			tween.start = initialInUnit;
+			tween.end = adjusted;
+		}
+	}
+	return adjusted;
+}
+
+
+var defaultDisplayMap = {};
+
+function getDefaultDisplay( elem ) {
+	var temp,
+		doc = elem.ownerDocument,
+		nodeName = elem.nodeName,
+		display = defaultDisplayMap[ nodeName ];
+
+	if ( display ) {
+		return display;
+	}
+
+	temp = doc.body.appendChild( doc.createElement( nodeName ) );
+	display = jQuery.css( temp, "display" );
+
+	temp.parentNode.removeChild( temp );
+
+	if ( display === "none" ) {
+		display = "block";
+	}
+	defaultDisplayMap[ nodeName ] = display;
+
+	return display;
+}
+
+function showHide( elements, show ) {
+	var display, elem,
+		values = [],
+		index = 0,
+		length = elements.length;
+
+	// Determine new display value for elements that need to change
+	for ( ; index < length; index++ ) {
+		elem = elements[ index ];
+		if ( !elem.style ) {
+			continue;
+		}
+
+		display = elem.style.display;
+		if ( show ) {
+
+			// Since we force visibility upon cascade-hidden elements, an immediate (and slow)
+			// check is required in this first loop unless we have a nonempty display value (either
+			// inline or about-to-be-restored)
+			if ( display === "none" ) {
+				values[ index ] = dataPriv.get( elem, "display" ) || null;
+				if ( !values[ index ] ) {
+					elem.style.display = "";
+				}
+			}
+			if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
+				values[ index ] = getDefaultDisplay( elem );
+			}
+		} else {
+			if ( display !== "none" ) {
+				values[ index ] = "none";
+
+				// Remember what we're overwriting
+				dataPriv.set( elem, "display", display );
+			}
+		}
+	}
+
+	// Set the display of the elements in a second loop to avoid constant reflow
+	for ( index = 0; index < length; index++ ) {
+		if ( values[ index ] != null ) {
+			elements[ index ].style.display = values[ index ];
+		}
+	}
+
+	return elements;
+}
+
+jQuery.fn.extend( {
+	show: function() {
+		return showHide( this, true );
+	},
+	hide: function() {
+		return showHide( this );
+	},
+	toggle: function( state ) {
+		if ( typeof state === "boolean" ) {
+			return state ? this.show() : this.hide();
+		}
+
+		return this.each( function() {
+			if ( isHiddenWithinTree( this ) ) {
+				jQuery( this ).show();
+			} else {
+				jQuery( this ).hide();
+			}
+		} );
+	}
+} );
+var rcheckableType = ( /^(?:checkbox|radio)$/i );
+
+var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
+
+var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
+
+
+
+( function() {
+	var fragment = document.createDocumentFragment(),
+		div = fragment.appendChild( document.createElement( "div" ) ),
+		input = document.createElement( "input" );
+
+	// Support: Android 4.0 - 4.3 only
+	// Check state lost if the name is set (#11217)
+	// Support: Windows Web Apps (WWA)
+	// `name` and `type` must use .setAttribute for WWA (#14901)
+	input.setAttribute( "type", "radio" );
+	input.setAttribute( "checked", "checked" );
+	input.setAttribute( "name", "t" );
+
+	div.appendChild( input );
+
+	// Support: Android <=4.1 only
+	// Older WebKit doesn't clone checked state correctly in fragments
+	support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
+
+	// Support: IE <=11 only
+	// Make sure textarea (and checkbox) defaultValue is properly cloned
+	div.innerHTML = "<textarea>x</textarea>";
+	support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
+
+	// Support: IE <=9 only
+	// IE <=9 replaces <option> tags with their contents when inserted outside of
+	// the select element.
+	div.innerHTML = "<option></option>";
+	support.option = !!div.lastChild;
+} )();
+
+
+// We have to close these tags to support XHTML (#13200)
+var wrapMap = {
+
+	// XHTML parsers do not magically insert elements in the
+	// same way that tag soup parsers do. So we cannot shorten
+	// this by omitting <tbody> or other required elements.
+	thead: [ 1, "<table>", "</table>" ],
+	col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
+	tr: [ 2, "<table><tbody>", "</tbody></table>" ],
+	td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
+
+	_default: [ 0, "", "" ]
+};
+
+wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
+wrapMap.th = wrapMap.td;
+
+// Support: IE <=9 only
+if ( !support.option ) {
+	wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
+}
+
+
+function getAll( context, tag ) {
+
+	// Support: IE <=9 - 11 only
+	// Use typeof to avoid zero-argument method invocation on host objects (#15151)
+	var ret;
+
+	if ( typeof context.getElementsByTagName !== "undefined" ) {
+		ret = context.getElementsByTagName( tag || "*" );
+
+	} else if ( typeof context.querySelectorAll !== "undefined" ) {
+		ret = context.querySelectorAll( tag || "*" );
+
+	} else {
+		ret = [];
+	}
+
+	if ( tag === undefined || tag && nodeName( context, tag ) ) {
+		return jQuery.merge( [ context ], ret );
+	}
+
+	return ret;
+}
+
+
+// Mark scripts as having already been evaluated
+function setGlobalEval( elems, refElements ) {
+	var i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		dataPriv.set(
+			elems[ i ],
+			"globalEval",
+			!refElements || dataPriv.get( refElements[ i ], "globalEval" )
+		);
+	}
+}
+
+
+var rhtml = /<|&#?\w+;/;
+
+function buildFragment( elems, context, scripts, selection, ignored ) {
+	var elem, tmp, tag, wrap, attached, j,
+		fragment = context.createDocumentFragment(),
+		nodes = [],
+		i = 0,
+		l = elems.length;
+
+	for ( ; i < l; i++ ) {
+		elem = elems[ i ];
+
+		if ( elem || elem === 0 ) {
+
+			// Add nodes directly
+			if ( toType( elem ) === "object" ) {
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
+
+			// Convert non-html into a text node
+			} else if ( !rhtml.test( elem ) ) {
+				nodes.push( context.createTextNode( elem ) );
+
+			// Convert html into DOM nodes
+			} else {
+				tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
+
+				// Deserialize a standard representation
+				tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
+				wrap = wrapMap[ tag ] || wrapMap._default;
+				tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
+
+				// Descend through wrappers to the right content
+				j = wrap[ 0 ];
+				while ( j-- ) {
+					tmp = tmp.lastChild;
+				}
+
+				// Support: Android <=4.0 only, PhantomJS 1 only
+				// push.apply(_, arraylike) throws on ancient WebKit
+				jQuery.merge( nodes, tmp.childNodes );
+
+				// Remember the top-level container
+				tmp = fragment.firstChild;
+
+				// Ensure the created nodes are orphaned (#12392)
+				tmp.textContent = "";
+			}
+		}
+	}
+
+	// Remove wrapper from fragment
+	fragment.textContent = "";
+
+	i = 0;
+	while ( ( elem = nodes[ i++ ] ) ) {
+
+		// Skip elements already in the context collection (trac-4087)
+		if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
+			if ( ignored ) {
+				ignored.push( elem );
+			}
+			continue;
+		}
+
+		attached = isAttached( elem );
+
+		// Append to fragment
+		tmp = getAll( fragment.appendChild( elem ), "script" );
+
+		// Preserve script evaluation history
+		if ( attached ) {
+			setGlobalEval( tmp );
+		}
+
+		// Capture executables
+		if ( scripts ) {
+			j = 0;
+			while ( ( elem = tmp[ j++ ] ) ) {
+				if ( rscriptType.test( elem.type || "" ) ) {
+					scripts.push( elem );
+				}
+			}
+		}
+	}
+
+	return fragment;
+}
+
+
+var rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
+
+function returnTrue() {
+	return true;
+}
+
+function returnFalse() {
+	return false;
+}
+
+// Support: IE <=9 - 11+
+// focus() and blur() are asynchronous, except when they are no-op.
+// So expect focus to be synchronous when the element is already active,
+// and blur to be synchronous when the element is not already active.
+// (focus and blur are always synchronous in other supported browsers,
+// this just defines when we can count on it).
+function expectSync( elem, type ) {
+	return ( elem === safeActiveElement() ) === ( type === "focus" );
+}
+
+// Support: IE <=9 only
+// Accessing document.activeElement can throw unexpectedly
+// https://bugs.jquery.com/ticket/13393
+function safeActiveElement() {
+	try {
+		return document.activeElement;
+	} catch ( err ) { }
+}
+
+function on( elem, types, selector, data, fn, one ) {
+	var origFn, type;
+
+	// Types can be a map of types/handlers
+	if ( typeof types === "object" ) {
+
+		// ( types-Object, selector, data )
+		if ( typeof selector !== "string" ) {
+
+			// ( types-Object, data )
+			data = data || selector;
+			selector = undefined;
+		}
+		for ( type in types ) {
+			on( elem, type, selector, data, types[ type ], one );
+		}
+		return elem;
+	}
+
+	if ( data == null && fn == null ) {
+
+		// ( types, fn )
+		fn = selector;
+		data = selector = undefined;
+	} else if ( fn == null ) {
+		if ( typeof selector === "string" ) {
+
+			// ( types, selector, fn )
+			fn = data;
+			data = undefined;
+		} else {
+
+			// ( types, data, fn )
+			fn = data;
+			data = selector;
+			selector = undefined;
+		}
+	}
+	if ( fn === false ) {
+		fn = returnFalse;
+	} else if ( !fn ) {
+		return elem;
+	}
+
+	if ( one === 1 ) {
+		origFn = fn;
+		fn = function( event ) {
+
+			// Can use an empty set, since event contains the info
+			jQuery().off( event );
+			return origFn.apply( this, arguments );
+		};
+
+		// Use same guid so caller can remove using origFn
+		fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
+	}
+	return elem.each( function() {
+		jQuery.event.add( this, types, fn, data, selector );
+	} );
+}
+
+/*
+ * Helper functions for managing events -- not part of the public interface.
+ * Props to Dean Edwards' addEvent library for many of the ideas.
+ */
+jQuery.event = {
+
+	global: {},
+
+	add: function( elem, types, handler, data, selector ) {
+
+		var handleObjIn, eventHandle, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.get( elem );
+
+		// Only attach events to objects that accept data
+		if ( !acceptData( elem ) ) {
+			return;
+		}
+
+		// Caller can pass in an object of custom data in lieu of the handler
+		if ( handler.handler ) {
+			handleObjIn = handler;
+			handler = handleObjIn.handler;
+			selector = handleObjIn.selector;
+		}
+
+		// Ensure that invalid selectors throw exceptions at attach time
+		// Evaluate against documentElement in case elem is a non-element node (e.g., document)
+		if ( selector ) {
+			jQuery.find.matchesSelector( documentElement, selector );
+		}
+
+		// Make sure that the handler has a unique ID, used to find/remove it later
+		if ( !handler.guid ) {
+			handler.guid = jQuery.guid++;
+		}
+
+		// Init the element's event structure and main handler, if this is the first
+		if ( !( events = elemData.events ) ) {
+			events = elemData.events = Object.create( null );
+		}
+		if ( !( eventHandle = elemData.handle ) ) {
+			eventHandle = elemData.handle = function( e ) {
+
+				// Discard the second event of a jQuery.event.trigger() and
+				// when an event is called after a page has unloaded
+				return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
+					jQuery.event.dispatch.apply( elem, arguments ) : undefined;
+			};
+		}
+
+		// Handle multiple events separated by a space
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// There *must* be a type, no attaching namespace-only handlers
+			if ( !type ) {
+				continue;
+			}
+
+			// If event changes its type, use the special event handlers for the changed type
+			special = jQuery.event.special[ type ] || {};
+
+			// If selector defined, determine special event api type, otherwise given type
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+
+			// Update special based on newly reset type
+			special = jQuery.event.special[ type ] || {};
+
+			// handleObj is passed to all event handlers
+			handleObj = jQuery.extend( {
+				type: type,
+				origType: origType,
+				data: data,
+				handler: handler,
+				guid: handler.guid,
+				selector: selector,
+				needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
+				namespace: namespaces.join( "." )
+			}, handleObjIn );
+
+			// Init the event handler queue if we're the first
+			if ( !( handlers = events[ type ] ) ) {
+				handlers = events[ type ] = [];
+				handlers.delegateCount = 0;
+
+				// Only use addEventListener if the special events handler returns false
+				if ( !special.setup ||
+					special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
+
+					if ( elem.addEventListener ) {
+						elem.addEventListener( type, eventHandle );
+					}
+				}
+			}
+
+			if ( special.add ) {
+				special.add.call( elem, handleObj );
+
+				if ( !handleObj.handler.guid ) {
+					handleObj.handler.guid = handler.guid;
+				}
+			}
+
+			// Add to the element's handler list, delegates in front
+			if ( selector ) {
+				handlers.splice( handlers.delegateCount++, 0, handleObj );
+			} else {
+				handlers.push( handleObj );
+			}
+
+			// Keep track of which events have ever been used, for event optimization
+			jQuery.event.global[ type ] = true;
+		}
+
+	},
+
+	// Detach an event or set of events from an element
+	remove: function( elem, types, handler, selector, mappedTypes ) {
+
+		var j, origCount, tmp,
+			events, t, handleObj,
+			special, handlers, type, namespaces, origType,
+			elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
+
+		if ( !elemData || !( events = elemData.events ) ) {
+			return;
+		}
+
+		// Once for each type.namespace in types; type may be omitted
+		types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
+		t = types.length;
+		while ( t-- ) {
+			tmp = rtypenamespace.exec( types[ t ] ) || [];
+			type = origType = tmp[ 1 ];
+			namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
+
+			// Unbind all events (on this namespace, if provided) for the element
+			if ( !type ) {
+				for ( type in events ) {
+					jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
+				}
+				continue;
+			}
+
+			special = jQuery.event.special[ type ] || {};
+			type = ( selector ? special.delegateType : special.bindType ) || type;
+			handlers = events[ type ] || [];
+			tmp = tmp[ 2 ] &&
+				new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
+
+			// Remove matching events
+			origCount = j = handlers.length;
+			while ( j-- ) {
+				handleObj = handlers[ j ];
+
+				if ( ( mappedTypes || origType === handleObj.origType ) &&
+					( !handler || handler.guid === handleObj.guid ) &&
+					( !tmp || tmp.test( handleObj.namespace ) ) &&
+					( !selector || selector === handleObj.selector ||
+						selector === "**" && handleObj.selector ) ) {
+					handlers.splice( j, 1 );
+
+					if ( handleObj.selector ) {
+						handlers.delegateCount--;
+					}
+					if ( special.remove ) {
+						special.remove.call( elem, handleObj );
+					}
+				}
+			}
+
+			// Remove generic event handler if we removed something and no more handlers exist
+			// (avoids potential for endless recursion during removal of special event handlers)
+			if ( origCount && !handlers.length ) {
+				if ( !special.teardown ||
+					special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
+
+					jQuery.removeEvent( elem, type, elemData.handle );
+				}
+
+				delete events[ type ];
+			}
+		}
+
+		// Remove data and the expando if it's no longer used
+		if ( jQuery.isEmptyObject( events ) ) {
+			dataPriv.remove( elem, "handle events" );
+		}
+	},
+
+	dispatch: function( nativeEvent ) {
+
+		var i, j, ret, matched, handleObj, handlerQueue,
+			args = new Array( arguments.length ),
+
+			// Make a writable jQuery.Event from the native event object
+			event = jQuery.event.fix( nativeEvent ),
+
+			handlers = (
+				dataPriv.get( this, "events" ) || Object.create( null )
+			)[ event.type ] || [],
+			special = jQuery.event.special[ event.type ] || {};
+
+		// Use the fix-ed jQuery.Event rather than the (read-only) native event
+		args[ 0 ] = event;
+
+		for ( i = 1; i < arguments.length; i++ ) {
+			args[ i ] = arguments[ i ];
+		}
+
+		event.delegateTarget = this;
+
+		// Call the preDispatch hook for the mapped type, and let it bail if desired
+		if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
+			return;
+		}
+
+		// Determine handlers
+		handlerQueue = jQuery.event.handlers.call( this, event, handlers );
+
+		// Run delegates first; they may want to stop propagation beneath us
+		i = 0;
+		while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
+			event.currentTarget = matched.elem;
+
+			j = 0;
+			while ( ( handleObj = matched.handlers[ j++ ] ) &&
+				!event.isImmediatePropagationStopped() ) {
+
+				// If the event is namespaced, then each handler is only invoked if it is
+				// specially universal or its namespaces are a superset of the event's.
+				if ( !event.rnamespace || handleObj.namespace === false ||
+					event.rnamespace.test( handleObj.namespace ) ) {
+
+					event.handleObj = handleObj;
+					event.data = handleObj.data;
+
+					ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
+						handleObj.handler ).apply( matched.elem, args );
+
+					if ( ret !== undefined ) {
+						if ( ( event.result = ret ) === false ) {
+							event.preventDefault();
+							event.stopPropagation();
+						}
+					}
+				}
+			}
+		}
+
+		// Call the postDispatch hook for the mapped type
+		if ( special.postDispatch ) {
+			special.postDispatch.call( this, event );
+		}
+
+		return event.result;
+	},
+
+	handlers: function( event, handlers ) {
+		var i, handleObj, sel, matchedHandlers, matchedSelectors,
+			handlerQueue = [],
+			delegateCount = handlers.delegateCount,
+			cur = event.target;
+
+		// Find delegate handlers
+		if ( delegateCount &&
+
+			// Support: IE <=9
+			// Black-hole SVG <use> instance trees (trac-13180)
+			cur.nodeType &&
+
+			// Support: Firefox <=42
+			// Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
+			// https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
+			// Support: IE 11 only
+			// ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
+			!( event.type === "click" && event.button >= 1 ) ) {
+
+			for ( ; cur !== this; cur = cur.parentNode || this ) {
+
+				// Don't check non-elements (#13208)
+				// Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
+				if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
+					matchedHandlers = [];
+					matchedSelectors = {};
+					for ( i = 0; i < delegateCount; i++ ) {
+						handleObj = handlers[ i ];
+
+						// Don't conflict with Object.prototype properties (#13203)
+						sel = handleObj.selector + " ";
+
+						if ( matchedSelectors[ sel ] === undefined ) {
+							matchedSelectors[ sel ] = handleObj.needsContext ?
+								jQuery( sel, this ).index( cur ) > -1 :
+								jQuery.find( sel, this, null, [ cur ] ).length;
+						}
+						if ( matchedSelectors[ sel ] ) {
+							matchedHandlers.push( handleObj );
+						}
+					}
+					if ( matchedHandlers.length ) {
+						handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
+					}
+				}
+			}
+		}
+
+		// Add the remaining (directly-bound) handlers
+		cur = this;
+		if ( delegateCount < handlers.length ) {
+			handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
+		}
+
+		return handlerQueue;
+	},
+
+	addProp: function( name, hook ) {
+		Object.defineProperty( jQuery.Event.prototype, name, {
+			enumerable: true,
+			configurable: true,
+
+			get: isFunction( hook ) ?
+				function() {
+					if ( this.originalEvent ) {
+						return hook( this.originalEvent );
+					}
+				} :
+				function() {
+					if ( this.originalEvent ) {
+						return this.originalEvent[ name ];
+					}
+				},
+
+			set: function( value ) {
+				Object.defineProperty( this, name, {
+					enumerable: true,
+					configurable: true,
+					writable: true,
+					value: value
+				} );
+			}
+		} );
+	},
+
+	fix: function( originalEvent ) {
+		return originalEvent[ jQuery.expando ] ?
+			originalEvent :
+			new jQuery.Event( originalEvent );
+	},
+
+	special: {
+		load: {
+
+			// Prevent triggered image.load events from bubbling to window.load
+			noBubble: true
+		},
+		click: {
+
+			// Utilize native event to ensure correct state for checkable inputs
+			setup: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Claim the first handler
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					// dataPriv.set( el, "click", ... )
+					leverageNative( el, "click", returnTrue );
+				}
+
+				// Return false to allow normal processing in the caller
+				return false;
+			},
+			trigger: function( data ) {
+
+				// For mutual compressibility with _default, replace `this` access with a local var.
+				// `|| data` is dead code meant only to preserve the variable through minification.
+				var el = this || data;
+
+				// Force setup before triggering a click
+				if ( rcheckableType.test( el.type ) &&
+					el.click && nodeName( el, "input" ) ) {
+
+					leverageNative( el, "click" );
+				}
+
+				// Return non-false to allow normal event-path propagation
+				return true;
+			},
+
+			// For cross-browser consistency, suppress native .click() on links
+			// Also prevent it if we're currently inside a leveraged native-event stack
+			_default: function( event ) {
+				var target = event.target;
+				return rcheckableType.test( target.type ) &&
+					target.click && nodeName( target, "input" ) &&
+					dataPriv.get( target, "click" ) ||
+					nodeName( target, "a" );
+			}
+		},
+
+		beforeunload: {
+			postDispatch: function( event ) {
+
+				// Support: Firefox 20+
+				// Firefox doesn't alert if the returnValue field is not set.
+				if ( event.result !== undefined && event.originalEvent ) {
+					event.originalEvent.returnValue = event.result;
+				}
+			}
+		}
+	}
+};
+
+// Ensure the presence of an event listener that handles manually-triggered
+// synthetic events by interrupting progress until reinvoked in response to
+// *native* events that it fires directly, ensuring that state changes have
+// already occurred before other listeners are invoked.
+function leverageNative( el, type, expectSync ) {
+
+	// Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
+	if ( !expectSync ) {
+		if ( dataPriv.get( el, type ) === undefined ) {
+			jQuery.event.add( el, type, returnTrue );
+		}
+		return;
+	}
+
+	// Register the controller as a special universal handler for all event namespaces
+	dataPriv.set( el, type, false );
+	jQuery.event.add( el, type, {
+		namespace: false,
+		handler: function( event ) {
+			var notAsync, result,
+				saved = dataPriv.get( this, type );
+
+			if ( ( event.isTrigger & 1 ) && this[ type ] ) {
+
+				// Interrupt processing of the outer synthetic .trigger()ed event
+				// Saved data should be false in such cases, but might be a leftover capture object
+				// from an async native handler (gh-4350)
+				if ( !saved.length ) {
+
+					// Store arguments for use when handling the inner native event
+					// There will always be at least one argument (an event object), so this array
+					// will not be confused with a leftover capture object.
+					saved = slice.call( arguments );
+					dataPriv.set( this, type, saved );
+
+					// Trigger the native event and capture its result
+					// Support: IE <=9 - 11+
+					// focus() and blur() are asynchronous
+					notAsync = expectSync( this, type );
+					this[ type ]();
+					result = dataPriv.get( this, type );
+					if ( saved !== result || notAsync ) {
+						dataPriv.set( this, type, false );
+					} else {
+						result = {};
+					}
+					if ( saved !== result ) {
+
+						// Cancel the outer synthetic event
+						event.stopImmediatePropagation();
+						event.preventDefault();
+
+						// Support: Chrome 86+
+						// In Chrome, if an element having a focusout handler is blurred by
+						// clicking outside of it, it invokes the handler synchronously. If
+						// that handler calls `.remove()` on the element, the data is cleared,
+						// leaving `result` undefined. We need to guard against this.
+						return result && result.value;
+					}
+
+				// If this is an inner synthetic event for an event with a bubbling surrogate
+				// (focus or blur), assume that the surrogate already propagated from triggering the
+				// native event and prevent that from happening again here.
+				// This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
+				// bubbling surrogate propagates *after* the non-bubbling base), but that seems
+				// less bad than duplication.
+				} else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
+					event.stopPropagation();
+				}
+
+			// If this is a native event triggered above, everything is now in order
+			// Fire an inner synthetic event with the original arguments
+			} else if ( saved.length ) {
+
+				// ...and capture the result
+				dataPriv.set( this, type, {
+					value: jQuery.event.trigger(
+
+						// Support: IE <=9 - 11+
+						// Extend with the prototype to reset the above stopImmediatePropagation()
+						jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
+						saved.slice( 1 ),
+						this
+					)
+				} );
+
+				// Abort handling of the native event
+				event.stopImmediatePropagation();
+			}
+		}
+	} );
+}
+
+jQuery.removeEvent = function( elem, type, handle ) {
+
+	// This "if" is needed for plain objects
+	if ( elem.removeEventListener ) {
+		elem.removeEventListener( type, handle );
+	}
+};
+
+jQuery.Event = function( src, props ) {
+
+	// Allow instantiation without the 'new' keyword
+	if ( !( this instanceof jQuery.Event ) ) {
+		return new jQuery.Event( src, props );
+	}
+
+	// Event object
+	if ( src && src.type ) {
+		this.originalEvent = src;
+		this.type = src.type;
+
+		// Events bubbling up the document may have been marked as prevented
+		// by a handler lower down the tree; reflect the correct value.
+		this.isDefaultPrevented = src.defaultPrevented ||
+				src.defaultPrevented === undefined &&
+
+				// Support: Android <=2.3 only
+				src.returnValue === false ?
+			returnTrue :
+			returnFalse;
+
+		// Create target properties
+		// Support: Safari <=6 - 7 only
+		// Target should not be a text node (#504, #13143)
+		this.target = ( src.target && src.target.nodeType === 3 ) ?
+			src.target.parentNode :
+			src.target;
+
+		this.currentTarget = src.currentTarget;
+		this.relatedTarget = src.relatedTarget;
+
+	// Event type
+	} else {
+		this.type = src;
+	}
+
+	// Put explicitly provided properties onto the event object
+	if ( props ) {
+		jQuery.extend( this, props );
+	}
+
+	// Create a timestamp if incoming event doesn't have one
+	this.timeStamp = src && src.timeStamp || Date.now();
+
+	// Mark it as fixed
+	this[ jQuery.expando ] = true;
+};
+
+// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
+// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
+jQuery.Event.prototype = {
+	constructor: jQuery.Event,
+	isDefaultPrevented: returnFalse,
+	isPropagationStopped: returnFalse,
+	isImmediatePropagationStopped: returnFalse,
+	isSimulated: false,
+
+	preventDefault: function() {
+		var e = this.originalEvent;
+
+		this.isDefaultPrevented = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.preventDefault();
+		}
+	},
+	stopPropagation: function() {
+		var e = this.originalEvent;
+
+		this.isPropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopPropagation();
+		}
+	},
+	stopImmediatePropagation: function() {
+		var e = this.originalEvent;
+
+		this.isImmediatePropagationStopped = returnTrue;
+
+		if ( e && !this.isSimulated ) {
+			e.stopImmediatePropagation();
+		}
+
+		this.stopPropagation();
+	}
+};
+
+// Includes all common event props including KeyEvent and MouseEvent specific props
+jQuery.each( {
+	altKey: true,
+	bubbles: true,
+	cancelable: true,
+	changedTouches: true,
+	ctrlKey: true,
+	detail: true,
+	eventPhase: true,
+	metaKey: true,
+	pageX: true,
+	pageY: true,
+	shiftKey: true,
+	view: true,
+	"char": true,
+	code: true,
+	charCode: true,
+	key: true,
+	keyCode: true,
+	button: true,
+	buttons: true,
+	clientX: true,
+	clientY: true,
+	offsetX: true,
+	offsetY: true,
+	pointerId: true,
+	pointerType: true,
+	screenX: true,
+	screenY: true,
+	targetTouches: true,
+	toElement: true,
+	touches: true,
+	which: true
+}, jQuery.event.addProp );
+
+jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
+	jQuery.event.special[ type ] = {
+
+		// Utilize native event if possible so blur/focus sequence is correct
+		setup: function() {
+
+			// Claim the first handler
+			// dataPriv.set( this, "focus", ... )
+			// dataPriv.set( this, "blur", ... )
+			leverageNative( this, type, expectSync );
+
+			// Return false to allow normal processing in the caller
+			return false;
+		},
+		trigger: function() {
+
+			// Force setup before trigger
+			leverageNative( this, type );
+
+			// Return non-false to allow normal event-path propagation
+			return true;
+		},
+
+		// Suppress native focus or blur as it's already being fired
+		// in leverageNative.
+		_default: function() {
+			return true;
+		},
+
+		delegateType: delegateType
+	};
+} );
+
+// Create mouseenter/leave events using mouseover/out and event-time checks
+// so that event delegation works in jQuery.
+// Do the same for pointerenter/pointerleave and pointerover/pointerout
+//
+// Support: Safari 7 only
+// Safari sends mouseenter too often; see:
+// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
+// for the description of the bug (it existed in older Chrome versions as well).
+jQuery.each( {
+	mouseenter: "mouseover",
+	mouseleave: "mouseout",
+	pointerenter: "pointerover",
+	pointerleave: "pointerout"
+}, function( orig, fix ) {
+	jQuery.event.special[ orig ] = {
+		delegateType: fix,
+		bindType: fix,
+
+		handle: function( event ) {
+			var ret,
+				target = this,
+				related = event.relatedTarget,
+				handleObj = event.handleObj;
+
+			// For mouseenter/leave call the handler if related is outside the target.
+			// NB: No relatedTarget if the mouse left/entered the browser window
+			if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
+				event.type = handleObj.origType;
+				ret = handleObj.handler.apply( this, arguments );
+				event.type = fix;
+			}
+			return ret;
+		}
+	};
+} );
+
+jQuery.fn.extend( {
+
+	on: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn );
+	},
+	one: function( types, selector, data, fn ) {
+		return on( this, types, selector, data, fn, 1 );
+	},
+	off: function( types, selector, fn ) {
+		var handleObj, type;
+		if ( types && types.preventDefault && types.handleObj ) {
+
+			// ( event )  dispatched jQuery.Event
+			handleObj = types.handleObj;
+			jQuery( types.delegateTarget ).off(
+				handleObj.namespace ?
+					handleObj.origType + "." + handleObj.namespace :
+					handleObj.origType,
+				handleObj.selector,
+				handleObj.handler
+			);
+			return this;
+		}
+		if ( typeof types === "object" ) {
+
+			// ( types-object [, selector] )
+			for ( type in types ) {
+				this.off( type, selector, types[ type ] );
+			}
+			return this;
+		}
+		if ( selector === false || typeof selector === "function" ) {
+
+			// ( types [, fn] )
+			fn = selector;
+			selector = undefined;
+		}
+		if ( fn === false ) {
+			fn = returnFalse;
+		}
+		return this.each( function() {
+			jQuery.event.remove( this, types, fn, selector );
+		} );
+	}
+} );
+
+
+var
+
+	// Support: IE <=10 - 11, Edge 12 - 13 only
+	// In IE/Edge using regex groups here causes severe slowdowns.
+	// See https://connect.microsoft.com/IE/feedback/details/1736512/
+	rnoInnerhtml = /<script|<style|<link/i,
+
+	// checked="checked" or checked
+	rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
+	rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
+
+// Prefer a tbody over its parent table for containing new rows
+function manipulationTarget( elem, content ) {
+	if ( nodeName( elem, "table" ) &&
+		nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
+
+		return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
+	}
+
+	return elem;
+}
+
+// Replace/restore the type attribute of script elements for safe DOM manipulation
+function disableScript( elem ) {
+	elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
+	return elem;
+}
+function restoreScript( elem ) {
+	if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
+		elem.type = elem.type.slice( 5 );
+	} else {
+		elem.removeAttribute( "type" );
+	}
+
+	return elem;
+}
+
+function cloneCopyEvent( src, dest ) {
+	var i, l, type, pdataOld, udataOld, udataCur, events;
+
+	if ( dest.nodeType !== 1 ) {
+		return;
+	}
+
+	// 1. Copy private data: events, handlers, etc.
+	if ( dataPriv.hasData( src ) ) {
+		pdataOld = dataPriv.get( src );
+		events = pdataOld.events;
+
+		if ( events ) {
+			dataPriv.remove( dest, "handle events" );
+
+			for ( type in events ) {
+				for ( i = 0, l = events[ type ].length; i < l; i++ ) {
+					jQuery.event.add( dest, type, events[ type ][ i ] );
+				}
+			}
+		}
+	}
+
+	// 2. Copy user data
+	if ( dataUser.hasData( src ) ) {
+		udataOld = dataUser.access( src );
+		udataCur = jQuery.extend( {}, udataOld );
+
+		dataUser.set( dest, udataCur );
+	}
+}
+
+// Fix IE bugs, see support tests
+function fixInput( src, dest ) {
+	var nodeName = dest.nodeName.toLowerCase();
+
+	// Fails to persist the checked state of a cloned checkbox or radio button.
+	if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
+		dest.checked = src.checked;
+
+	// Fails to return the selected option to the default selected state when cloning options
+	} else if ( nodeName === "input" || nodeName === "textarea" ) {
+		dest.defaultValue = src.defaultValue;
+	}
+}
+
+function domManip( collection, args, callback, ignored ) {
+
+	// Flatten any nested arrays
+	args = flat( args );
+
+	var fragment, first, scripts, hasScripts, node, doc,
+		i = 0,
+		l = collection.length,
+		iNoClone = l - 1,
+		value = args[ 0 ],
+		valueIsFunction = isFunction( value );
+
+	// We can't cloneNode fragments that contain checked, in WebKit
+	if ( valueIsFunction ||
+			( l > 1 && typeof value === "string" &&
+				!support.checkClone && rchecked.test( value ) ) ) {
+		return collection.each( function( index ) {
+			var self = collection.eq( index );
+			if ( valueIsFunction ) {
+				args[ 0 ] = value.call( this, index, self.html() );
+			}
+			domManip( self, args, callback, ignored );
+		} );
+	}
+
+	if ( l ) {
+		fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
+		first = fragment.firstChild;
+
+		if ( fragment.childNodes.length === 1 ) {
+			fragment = first;
+		}
+
+		// Require either new content or an interest in ignored elements to invoke the callback
+		if ( first || ignored ) {
+			scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
+			hasScripts = scripts.length;
+
+			// Use the original fragment for the last item
+			// instead of the first because it can end up
+			// being emptied incorrectly in certain situations (#8070).
+			for ( ; i < l; i++ ) {
+				node = fragment;
+
+				if ( i !== iNoClone ) {
+					node = jQuery.clone( node, true, true );
+
+					// Keep references to cloned scripts for later restoration
+					if ( hasScripts ) {
+
+						// Support: Android <=4.0 only, PhantomJS 1 only
+						// push.apply(_, arraylike) throws on ancient WebKit
+						jQuery.merge( scripts, getAll( node, "script" ) );
+					}
+				}
+
+				callback.call( collection[ i ], node, i );
+			}
+
+			if ( hasScripts ) {
+				doc = scripts[ scripts.length - 1 ].ownerDocument;
+
+				// Reenable scripts
+				jQuery.map( scripts, restoreScript );
+
+				// Evaluate executable scripts on first document insertion
+				for ( i = 0; i < hasScripts; i++ ) {
+					node = scripts[ i ];
+					if ( rscriptType.test( node.type || "" ) &&
+						!dataPriv.access( node, "globalEval" ) &&
+						jQuery.contains( doc, node ) ) {
+
+						if ( node.src && ( node.type || "" ).toLowerCase()  !== "module" ) {
+
+							// Optional AJAX dependency, but won't run scripts if not present
+							if ( jQuery._evalUrl && !node.noModule ) {
+								jQuery._evalUrl( node.src, {
+									nonce: node.nonce || node.getAttribute( "nonce" )
+								}, doc );
+							}
+						} else {
+							DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return collection;
+}
+
+function remove( elem, selector, keepData ) {
+	var node,
+		nodes = selector ? jQuery.filter( selector, elem ) : elem,
+		i = 0;
+
+	for ( ; ( node = nodes[ i ] ) != null; i++ ) {
+		if ( !keepData && node.nodeType === 1 ) {
+			jQuery.cleanData( getAll( node ) );
+		}
+
+		if ( node.parentNode ) {
+			if ( keepData && isAttached( node ) ) {
+				setGlobalEval( getAll( node, "script" ) );
+			}
+			node.parentNode.removeChild( node );
+		}
+	}
+
+	return elem;
+}
+
+jQuery.extend( {
+	htmlPrefilter: function( html ) {
+		return html;
+	},
+
+	clone: function( elem, dataAndEvents, deepDataAndEvents ) {
+		var i, l, srcElements, destElements,
+			clone = elem.cloneNode( true ),
+			inPage = isAttached( elem );
+
+		// Fix IE cloning issues
+		if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
+				!jQuery.isXMLDoc( elem ) ) {
+
+			// We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
+			destElements = getAll( clone );
+			srcElements = getAll( elem );
+
+			for ( i = 0, l = srcElements.length; i < l; i++ ) {
+				fixInput( srcElements[ i ], destElements[ i ] );
+			}
+		}
+
+		// Copy the events from the original to the clone
+		if ( dataAndEvents ) {
+			if ( deepDataAndEvents ) {
+				srcElements = srcElements || getAll( elem );
+				destElements = destElements || getAll( clone );
+
+				for ( i = 0, l = srcElements.length; i < l; i++ ) {
+					cloneCopyEvent( srcElements[ i ], destElements[ i ] );
+				}
+			} else {
+				cloneCopyEvent( elem, clone );
+			}
+		}
+
+		// Preserve script evaluation history
+		destElements = getAll( clone, "script" );
+		if ( destElements.length > 0 ) {
+			setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
+		}
+
+		// Return the cloned set
+		return clone;
+	},
+
+	cleanData: function( elems ) {
+		var data, elem, type,
+			special = jQuery.event.special,
+			i = 0;
+
+		for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
+			if ( acceptData( elem ) ) {
+				if ( ( data = elem[ dataPriv.expando ] ) ) {
+					if ( data.events ) {
+						for ( type in data.events ) {
+							if ( special[ type ] ) {
+								jQuery.event.remove( elem, type );
+
+							// This is a shortcut to avoid jQuery.event.remove's overhead
+							} else {
+								jQuery.removeEvent( elem, type, data.handle );
+							}
+						}
+					}
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataPriv.expando ] = undefined;
+				}
+				if ( elem[ dataUser.expando ] ) {
+
+					// Support: Chrome <=35 - 45+
+					// Assign undefined instead of using delete, see Data#remove
+					elem[ dataUser.expando ] = undefined;
+				}
+			}
+		}
+	}
+} );
+
+jQuery.fn.extend( {
+	detach: function( selector ) {
+		return remove( this, selector, true );
+	},
+
+	remove: function( selector ) {
+		return remove( this, selector );
+	},
+
+	text: function( value ) {
+		return access( this, function( value ) {
+			return value === undefined ?
+				jQuery.text( this ) :
+				this.empty().each( function() {
+					if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+						this.textContent = value;
+					}
+				} );
+		}, null, value, arguments.length );
+	},
+
+	append: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.appendChild( elem );
+			}
+		} );
+	},
+
+	prepend: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
+				var target = manipulationTarget( this, elem );
+				target.insertBefore( elem, target.firstChild );
+			}
+		} );
+	},
+
+	before: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this );
+			}
+		} );
+	},
+
+	after: function() {
+		return domManip( this, arguments, function( elem ) {
+			if ( this.parentNode ) {
+				this.parentNode.insertBefore( elem, this.nextSibling );
+			}
+		} );
+	},
+
+	empty: function() {
+		var elem,
+			i = 0;
+
+		for ( ; ( elem = this[ i ] ) != null; i++ ) {
+			if ( elem.nodeType === 1 ) {
+
+				// Prevent memory leaks
+				jQuery.cleanData( getAll( elem, false ) );
+
+				// Remove any remaining nodes
+				elem.textContent = "";
+			}
+		}
+
+		return this;
+	},
+
+	clone: function( dataAndEvents, deepDataAndEvents ) {
+		dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
+		deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
+
+		return this.map( function() {
+			return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
+		} );
+	},
+
+	html: function( value ) {
+		return access( this, function( value ) {
+			var elem = this[ 0 ] || {},
+				i = 0,
+				l = this.length;
+
+			if ( value === undefined && elem.nodeType === 1 ) {
+				return elem.innerHTML;
+			}
+
+			// See if we can take a shortcut and just use innerHTML
+			if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
+				!wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
+
+				value = jQuery.htmlPrefilter( value );
+
+				try {
+					for ( ; i < l; i++ ) {
+						elem = this[ i ] || {};
+
+						// Remove element nodes and prevent memory leaks
+						if ( elem.nodeType === 1 ) {
+							jQuery.cleanData( getAll( elem, false ) );
+							elem.innerHTML = value;
+						}
+					}
+
+					elem = 0;
+
+				// If using innerHTML throws an exception, use the fallback method
+				} catch ( e ) {}
+			}
+
+			if ( elem ) {
+				this.empty().append( value );
+			}
+		}, null, value, arguments.length );
+	},
+
+	replaceWith: function() {
+		var ignored = [];
+
+		// Make the changes, replacing each non-ignored context element with the new content
+		return domManip( this, arguments, function( elem ) {
+			var parent = this.parentNode;
+
+			if ( jQuery.inArray( this, ignored ) < 0 ) {
+				jQuery.cleanData( getAll( this ) );
+				if ( parent ) {
+					parent.replaceChild( elem, this );
+				}
+			}
+
+		// Force callback invocation
+		}, ignored );
+	}
+} );
+
+jQuery.each( {
+	appendTo: "append",
+	prependTo: "prepend",
+	insertBefore: "before",
+	insertAfter: "after",
+	replaceAll: "replaceWith"
+}, function( name, original ) {
+	jQuery.fn[ name ] = function( selector ) {
+		var elems,
+			ret = [],
+			insert = jQuery( selector ),
+			last = insert.length - 1,
+			i = 0;
+
+		for ( ; i <= last; i++ ) {
+			elems = i === last ? this : this.clone( true );
+			jQuery( insert[ i ] )[ original ]( elems );
+
+			// Support: Android <=4.0 only, PhantomJS 1 only
+			// .get() because push.apply(_, arraylike) throws on ancient WebKit
+			push.apply( ret, elems.get() );
+		}
+
+		return this.pushStack( ret );
+	};
+} );
+var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
+
+var getStyles = function( elem ) {
+
+		// Support: IE <=11 only, Firefox <=30 (#15098, #14150)
+		// IE throws on elements created in popups
+		// FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
+		var view = elem.ownerDocument.defaultView;
+
+		if ( !view || !view.opener ) {
+			view = window;
+		}
+
+		return view.getComputedStyle( elem );
+	};
+
+var swap = function( elem, options, callback ) {
+	var ret, name,
+		old = {};
+
+	// Remember the old values, and insert the new ones
+	for ( name in options ) {
+		old[ name ] = elem.style[ name ];
+		elem.style[ name ] = options[ name ];
+	}
+
+	ret = callback.call( elem );
+
+	// Revert the old values
+	for ( name in options ) {
+		elem.style[ name ] = old[ name ];
+	}
+
+	return ret;
+};
+
+
+var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
+
+
+
+( function() {
+
+	// Executing both pixelPosition & boxSizingReliable tests require only one layout
+	// so they're executed at the same time to save the second computation.
+	function computeStyleTests() {
+
+		// This is a singleton, we need to execute it only once
+		if ( !div ) {
+			return;
+		}
+
+		container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
+			"margin-top:1px;padding:0;border:0";
+		div.style.cssText =
+			"position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
+			"margin:auto;border:1px;padding:1px;" +
+			"width:60%;top:1%";
+		documentElement.appendChild( container ).appendChild( div );
+
+		var divStyle = window.getComputedStyle( div );
+		pixelPositionVal = divStyle.top !== "1%";
+
+		// Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
+		reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
+
+		// Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
+		// Some styles come back with percentage values, even though they shouldn't
+		div.style.right = "60%";
+		pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
+
+		// Support: IE 9 - 11 only
+		// Detect misreporting of content dimensions for box-sizing:border-box elements
+		boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
+
+		// Support: IE 9 only
+		// Detect overflow:scroll screwiness (gh-3699)
+		// Support: Chrome <=64
+		// Don't get tricked when zoom affects offsetWidth (gh-4029)
+		div.style.position = "absolute";
+		scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
+
+		documentElement.removeChild( container );
+
+		// Nullify the div so it wouldn't be stored in the memory and
+		// it will also be a sign that checks already performed
+		div = null;
+	}
+
+	function roundPixelMeasures( measure ) {
+		return Math.round( parseFloat( measure ) );
+	}
+
+	var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
+		reliableTrDimensionsVal, reliableMarginLeftVal,
+		container = document.createElement( "div" ),
+		div = document.createElement( "div" );
+
+	// Finish early in limited (non-browser) environments
+	if ( !div.style ) {
+		return;
+	}
+
+	// Support: IE <=9 - 11 only
+	// Style of cloned element affects source element cloned (#8908)
+	div.style.backgroundClip = "content-box";
+	div.cloneNode( true ).style.backgroundClip = "";
+	support.clearCloneStyle = div.style.backgroundClip === "content-box";
+
+	jQuery.extend( support, {
+		boxSizingReliable: function() {
+			computeStyleTests();
+			return boxSizingReliableVal;
+		},
+		pixelBoxStyles: function() {
+			computeStyleTests();
+			return pixelBoxStylesVal;
+		},
+		pixelPosition: function() {
+			computeStyleTests();
+			return pixelPositionVal;
+		},
+		reliableMarginLeft: function() {
+			computeStyleTests();
+			return reliableMarginLeftVal;
+		},
+		scrollboxSize: function() {
+			computeStyleTests();
+			return scrollboxSizeVal;
+		},
+
+		// Support: IE 9 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Behavior in IE 9 is more subtle than in newer versions & it passes
+		// some versions of this test; make sure not to make it pass there!
+		//
+		// Support: Firefox 70+
+		// Only Firefox includes border widths
+		// in computed dimensions. (gh-4529)
+		reliableTrDimensions: function() {
+			var table, tr, trChild, trStyle;
+			if ( reliableTrDimensionsVal == null ) {
+				table = document.createElement( "table" );
+				tr = document.createElement( "tr" );
+				trChild = document.createElement( "div" );
+
+				table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate";
+				tr.style.cssText = "border:1px solid";
+
+				// Support: Chrome 86+
+				// Height set through cssText does not get applied.
+				// Computed height then comes back as 0.
+				tr.style.height = "1px";
+				trChild.style.height = "9px";
+
+				// Support: Android 8 Chrome 86+
+				// In our bodyBackground.html iframe,
+				// display for all div elements is set to "inline",
+				// which causes a problem only in Android 8 Chrome 86.
+				// Ensuring the div is display: block
+				// gets around this issue.
+				trChild.style.display = "block";
+
+				documentElement
+					.appendChild( table )
+					.appendChild( tr )
+					.appendChild( trChild );
+
+				trStyle = window.getComputedStyle( tr );
+				reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) +
+					parseInt( trStyle.borderTopWidth, 10 ) +
+					parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight;
+
+				documentElement.removeChild( table );
+			}
+			return reliableTrDimensionsVal;
+		}
+	} );
+} )();
+
+
+function curCSS( elem, name, computed ) {
+	var width, minWidth, maxWidth, ret,
+
+		// Support: Firefox 51+
+		// Retrieving style before computed somehow
+		// fixes an issue with getting wrong values
+		// on detached elements
+		style = elem.style;
+
+	computed = computed || getStyles( elem );
+
+	// getPropertyValue is needed for:
+	//   .css('filter') (IE 9 only, #12537)
+	//   .css('--customProperty) (#3144)
+	if ( computed ) {
+		ret = computed.getPropertyValue( name ) || computed[ name ];
+
+		if ( ret === "" && !isAttached( elem ) ) {
+			ret = jQuery.style( elem, name );
+		}
+
+		// A tribute to the "awesome hack by Dean Edwards"
+		// Android Browser returns percentage for some values,
+		// but width seems to be reliably pixels.
+		// This is against the CSSOM draft spec:
+		// https://drafts.csswg.org/cssom/#resolved-values
+		if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
+
+			// Remember the original values
+			width = style.width;
+			minWidth = style.minWidth;
+			maxWidth = style.maxWidth;
+
+			// Put in the new values to get a computed value out
+			style.minWidth = style.maxWidth = style.width = ret;
+			ret = computed.width;
+
+			// Revert the changed values
+			style.width = width;
+			style.minWidth = minWidth;
+			style.maxWidth = maxWidth;
+		}
+	}
+
+	return ret !== undefined ?
+
+		// Support: IE <=9 - 11 only
+		// IE returns zIndex value as an integer.
+		ret + "" :
+		ret;
+}
+
+
+function addGetHookIf( conditionFn, hookFn ) {
+
+	// Define the hook, we'll check on the first run if it's really needed.
+	return {
+		get: function() {
+			if ( conditionFn() ) {
+
+				// Hook not needed (or it's not possible to use it due
+				// to missing dependency), remove it.
+				delete this.get;
+				return;
+			}
+
+			// Hook needed; redefine it so that the support test is not executed again.
+			return ( this.get = hookFn ).apply( this, arguments );
+		}
+	};
+}
+
+
+var cssPrefixes = [ "Webkit", "Moz", "ms" ],
+	emptyStyle = document.createElement( "div" ).style,
+	vendorProps = {};
+
+// Return a vendor-prefixed property or undefined
+function vendorPropName( name ) {
+
+	// Check for vendor prefixed names
+	var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
+		i = cssPrefixes.length;
+
+	while ( i-- ) {
+		name = cssPrefixes[ i ] + capName;
+		if ( name in emptyStyle ) {
+			return name;
+		}
+	}
+}
+
+// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
+function finalPropName( name ) {
+	var final = jQuery.cssProps[ name ] || vendorProps[ name ];
+
+	if ( final ) {
+		return final;
+	}
+	if ( name in emptyStyle ) {
+		return name;
+	}
+	return vendorProps[ name ] = vendorPropName( name ) || name;
+}
+
+
+var
+
+	// Swappable if display is none or starts with table
+	// except "table", "table-cell", or "table-caption"
+	// See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
+	rdisplayswap = /^(none|table(?!-c[ea]).+)/,
+	rcustomProp = /^--/,
+	cssShow = { position: "absolute", visibility: "hidden", display: "block" },
+	cssNormalTransform = {
+		letterSpacing: "0",
+		fontWeight: "400"
+	};
+
+function setPositiveNumber( _elem, value, subtract ) {
+
+	// Any relative (+/-) values have already been
+	// normalized at this point
+	var matches = rcssNum.exec( value );
+	return matches ?
+
+		// Guard against undefined "subtract", e.g., when used as in cssHooks
+		Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
+		value;
+}
+
+function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
+	var i = dimension === "width" ? 1 : 0,
+		extra = 0,
+		delta = 0;
+
+	// Adjustment may not be necessary
+	if ( box === ( isBorderBox ? "border" : "content" ) ) {
+		return 0;
+	}
+
+	for ( ; i < 4; i += 2 ) {
+
+		// Both box models exclude margin
+		if ( box === "margin" ) {
+			delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
+		}
+
+		// If we get here with a content-box, we're seeking "padding" or "border" or "margin"
+		if ( !isBorderBox ) {
+
+			// Add padding
+			delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+
+			// For "border" or "margin", add border
+			if ( box !== "padding" ) {
+				delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+
+			// But still keep track of it otherwise
+			} else {
+				extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+
+		// If we get here with a border-box (content + padding + border), we're seeking "content" or
+		// "padding" or "margin"
+		} else {
+
+			// For "content", subtract padding
+			if ( box === "content" ) {
+				delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
+			}
+
+			// For "content" or "padding", subtract border
+			if ( box !== "margin" ) {
+				delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
+			}
+		}
+	}
+
+	// Account for positive content-box scroll gutter when requested by providing computedVal
+	if ( !isBorderBox && computedVal >= 0 ) {
+
+		// offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
+		// Assuming integer scroll gutter, subtract the rest and round down
+		delta += Math.max( 0, Math.ceil(
+			elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+			computedVal -
+			delta -
+			extra -
+			0.5
+
+		// If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
+		// Use an explicit zero to avoid NaN (gh-3964)
+		) ) || 0;
+	}
+
+	return delta;
+}
+
+function getWidthOrHeight( elem, dimension, extra ) {
+
+	// Start with computed style
+	var styles = getStyles( elem ),
+
+		// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
+		// Fake content-box until we know it's needed to know the true value.
+		boxSizingNeeded = !support.boxSizingReliable() || extra,
+		isBorderBox = boxSizingNeeded &&
+			jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+		valueIsBorderBox = isBorderBox,
+
+		val = curCSS( elem, dimension, styles ),
+		offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
+
+	// Support: Firefox <=54
+	// Return a confounding non-pixel value or feign ignorance, as appropriate.
+	if ( rnumnonpx.test( val ) ) {
+		if ( !extra ) {
+			return val;
+		}
+		val = "auto";
+	}
+
+
+	// Support: IE 9 - 11 only
+	// Use offsetWidth/offsetHeight for when box sizing is unreliable.
+	// In those cases, the computed value can be trusted to be border-box.
+	if ( ( !support.boxSizingReliable() && isBorderBox ||
+
+		// Support: IE 10 - 11+, Edge 15 - 18+
+		// IE/Edge misreport `getComputedStyle` of table rows with width/height
+		// set in CSS while `offset*` properties report correct values.
+		// Interestingly, in some cases IE 9 doesn't suffer from this issue.
+		!support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
+
+		// Fall back to offsetWidth/offsetHeight when value is "auto"
+		// This happens for inline elements with no explicit setting (gh-3571)
+		val === "auto" ||
+
+		// Support: Android <=4.1 - 4.3 only
+		// Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
+		!parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
+
+		// Make sure the element is visible & connected
+		elem.getClientRects().length ) {
+
+		isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
+
+		// Where available, offsetWidth/offsetHeight approximate border box dimensions.
+		// Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
+		// retrieved value as a content box dimension.
+		valueIsBorderBox = offsetProp in elem;
+		if ( valueIsBorderBox ) {
+			val = elem[ offsetProp ];
+		}
+	}
+
+	// Normalize "" and auto
+	val = parseFloat( val ) || 0;
+
+	// Adjust for the element's box model
+	return ( val +
+		boxModelAdjustment(
+			elem,
+			dimension,
+			extra || ( isBorderBox ? "border" : "content" ),
+			valueIsBorderBox,
+			styles,
+
+			// Provide the current computed size to request scroll gutter calculation (gh-3589)
+			val
+		)
+	) + "px";
+}
+
+jQuery.extend( {
+
+	// Add in style property hooks for overriding the default
+	// behavior of getting and setting a style property
+	cssHooks: {
+		opacity: {
+			get: function( elem, computed ) {
+				if ( computed ) {
+
+					// We should always get a number back from opacity
+					var ret = curCSS( elem, "opacity" );
+					return ret === "" ? "1" : ret;
+				}
+			}
+		}
+	},
+
+	// Don't automatically add "px" to these possibly-unitless properties
+	cssNumber: {
+		"animationIterationCount": true,
+		"columnCount": true,
+		"fillOpacity": true,
+		"flexGrow": true,
+		"flexShrink": true,
+		"fontWeight": true,
+		"gridArea": true,
+		"gridColumn": true,
+		"gridColumnEnd": true,
+		"gridColumnStart": true,
+		"gridRow": true,
+		"gridRowEnd": true,
+		"gridRowStart": true,
+		"lineHeight": true,
+		"opacity": true,
+		"order": true,
+		"orphans": true,
+		"widows": true,
+		"zIndex": true,
+		"zoom": true
+	},
+
+	// Add in properties whose names you wish to fix before
+	// setting or getting the value
+	cssProps: {},
+
+	// Get and set the style property on a DOM Node
+	style: function( elem, name, value, extra ) {
+
+		// Don't set styles on text and comment nodes
+		if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
+			return;
+		}
+
+		// Make sure that we're working with the right name
+		var ret, type, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name ),
+			style = elem.style;
+
+		// Make sure that we're working with the right name. We don't
+		// want to query the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Gets hook for the prefixed version, then unprefixed version
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// Check if we're setting a value
+		if ( value !== undefined ) {
+			type = typeof value;
+
+			// Convert "+=" or "-=" to relative numbers (#7345)
+			if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
+				value = adjustCSS( elem, name, ret );
+
+				// Fixes bug #9237
+				type = "number";
+			}
+
+			// Make sure that null and NaN values aren't set (#7116)
+			if ( value == null || value !== value ) {
+				return;
+			}
+
+			// If a number was passed in, add the unit (except for certain CSS properties)
+			// The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
+			// "px" to a few hardcoded values.
+			if ( type === "number" && !isCustomProp ) {
+				value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
+			}
+
+			// background-* props affect original clone's values
+			if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
+				style[ name ] = "inherit";
+			}
+
+			// If a hook was provided, use that value, otherwise just set the specified value
+			if ( !hooks || !( "set" in hooks ) ||
+				( value = hooks.set( elem, value, extra ) ) !== undefined ) {
+
+				if ( isCustomProp ) {
+					style.setProperty( name, value );
+				} else {
+					style[ name ] = value;
+				}
+			}
+
+		} else {
+
+			// If a hook was provided get the non-computed value from there
+			if ( hooks && "get" in hooks &&
+				( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
+
+				return ret;
+			}
+
+			// Otherwise just get the value from the style object
+			return style[ name ];
+		}
+	},
+
+	css: function( elem, name, extra, styles ) {
+		var val, num, hooks,
+			origName = camelCase( name ),
+			isCustomProp = rcustomProp.test( name );
+
+		// Make sure that we're working with the right name. We don't
+		// want to modify the value if it is a CSS custom property
+		// since they are user-defined.
+		if ( !isCustomProp ) {
+			name = finalPropName( origName );
+		}
+
+		// Try prefixed name followed by the unprefixed name
+		hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
+
+		// If a hook was provided get the computed value from there
+		if ( hooks && "get" in hooks ) {
+			val = hooks.get( elem, true, extra );
+		}
+
+		// Otherwise, if a way to get the computed value exists, use that
+		if ( val === undefined ) {
+			val = curCSS( elem, name, styles );
+		}
+
+		// Convert "normal" to computed value
+		if ( val === "normal" && name in cssNormalTransform ) {
+			val = cssNormalTransform[ name ];
+		}
+
+		// Make numeric if forced or a qualifier was provided and val looks numeric
+		if ( extra === "" || extra ) {
+			num = parseFloat( val );
+			return extra === true || isFinite( num ) ? num || 0 : val;
+		}
+
+		return val;
+	}
+} );
+
+jQuery.each( [ "height", "width" ], function( _i, dimension ) {
+	jQuery.cssHooks[ dimension ] = {
+		get: function( elem, computed, extra ) {
+			if ( computed ) {
+
+				// Certain elements can have dimension info if we invisibly show them
+				// but it must have a current display style that would benefit
+				return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
+
+					// Support: Safari 8+
+					// Table columns in Safari have non-zero offsetWidth & zero
+					// getBoundingClientRect().width unless display is changed.
+					// Support: IE <=11 only
+					// Running getBoundingClientRect on a disconnected node
+					// in IE throws an error.
+					( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
+					swap( elem, cssShow, function() {
+						return getWidthOrHeight( elem, dimension, extra );
+					} ) :
+					getWidthOrHeight( elem, dimension, extra );
+			}
+		},
+
+		set: function( elem, value, extra ) {
+			var matches,
+				styles = getStyles( elem ),
+
+				// Only read styles.position if the test has a chance to fail
+				// to avoid forcing a reflow.
+				scrollboxSizeBuggy = !support.scrollboxSize() &&
+					styles.position === "absolute",
+
+				// To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
+				boxSizingNeeded = scrollboxSizeBuggy || extra,
+				isBorderBox = boxSizingNeeded &&
+					jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
+				subtract = extra ?
+					boxModelAdjustment(
+						elem,
+						dimension,
+						extra,
+						isBorderBox,
+						styles
+					) :
+					0;
+
+			// Account for unreliable border-box dimensions by comparing offset* to computed and
+			// faking a content-box to get border and padding (gh-3699)
+			if ( isBorderBox && scrollboxSizeBuggy ) {
+				subtract -= Math.ceil(
+					elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
+					parseFloat( styles[ dimension ] ) -
+					boxModelAdjustment( elem, dimension, "border", false, styles ) -
+					0.5
+				);
+			}
+
+			// Convert to pixels if value adjustment is needed
+			if ( subtract && ( matches = rcssNum.exec( value ) ) &&
+				( matches[ 3 ] || "px" ) !== "px" ) {
+
+				elem.style[ dimension ] = value;
+				value = jQuery.css( elem, dimension );
+			}
+
+			return setPositiveNumber( elem, value, subtract );
+		}
+	};
+} );
+
+jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
+	function( elem, computed ) {
+		if ( computed ) {
+			return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
+				elem.getBoundingClientRect().left -
+					swap( elem, { marginLeft: 0 }, function() {
+						return elem.getBoundingClientRect().left;
+					} )
+			) + "px";
+		}
+	}
+);
+
+// These hooks are used by animate to expand properties
+jQuery.each( {
+	margin: "",
+	padding: "",
+	border: "Width"
+}, function( prefix, suffix ) {
+	jQuery.cssHooks[ prefix + suffix ] = {
+		expand: function( value ) {
+			var i = 0,
+				expanded = {},
+
+				// Assumes a single number if not a string
+				parts = typeof value === "string" ? value.split( " " ) : [ value ];
+
+			for ( ; i < 4; i++ ) {
+				expanded[ prefix + cssExpand[ i ] + suffix ] =
+					parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
+			}
+
+			return expanded;
+		}
+	};
+
+	if ( prefix !== "margin" ) {
+		jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
+	}
+} );
+
+jQuery.fn.extend( {
+	css: function( name, value ) {
+		return access( this, function( elem, name, value ) {
+			var styles, len,
+				map = {},
+				i = 0;
+
+			if ( Array.isArray( name ) ) {
+				styles = getStyles( elem );
+				len = name.length;
+
+				for ( ; i < len; i++ ) {
+					map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
+				}
+
+				return map;
+			}
+
+			return value !== undefined ?
+				jQuery.style( elem, name, value ) :
+				jQuery.css( elem, name );
+		}, name, value, arguments.length > 1 );
+	}
+} );
+
+
+function Tween( elem, options, prop, end, easing ) {
+	return new Tween.prototype.init( elem, options, prop, end, easing );
+}
+jQuery.Tween = Tween;
+
+Tween.prototype = {
+	constructor: Tween,
+	init: function( elem, options, prop, end, easing, unit ) {
+		this.elem = elem;
+		this.prop = prop;
+		this.easing = easing || jQuery.easing._default;
+		this.options = options;
+		this.start = this.now = this.cur();
+		this.end = end;
+		this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
+	},
+	cur: function() {
+		var hooks = Tween.propHooks[ this.prop ];
+
+		return hooks && hooks.get ?
+			hooks.get( this ) :
+			Tween.propHooks._default.get( this );
+	},
+	run: function( percent ) {
+		var eased,
+			hooks = Tween.propHooks[ this.prop ];
+
+		if ( this.options.duration ) {
+			this.pos = eased = jQuery.easing[ this.easing ](
+				percent, this.options.duration * percent, 0, 1, this.options.duration
+			);
+		} else {
+			this.pos = eased = percent;
+		}
+		this.now = ( this.end - this.start ) * eased + this.start;
+
+		if ( this.options.step ) {
+			this.options.step.call( this.elem, this.now, this );
+		}
+
+		if ( hooks && hooks.set ) {
+			hooks.set( this );
+		} else {
+			Tween.propHooks._default.set( this );
+		}
+		return this;
+	}
+};
+
+Tween.prototype.init.prototype = Tween.prototype;
+
+Tween.propHooks = {
+	_default: {
+		get: function( tween ) {
+			var result;
+
+			// Use a property on the element directly when it is not a DOM element,
+			// or when there is no matching style property that exists.
+			if ( tween.elem.nodeType !== 1 ||
+				tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
+				return tween.elem[ tween.prop ];
+			}
+
+			// Passing an empty string as a 3rd parameter to .css will automatically
+			// attempt a parseFloat and fallback to a string if the parse fails.
+			// Simple values such as "10px" are parsed to Float;
+			// complex values such as "rotate(1rad)" are returned as-is.
+			result = jQuery.css( tween.elem, tween.prop, "" );
+
+			// Empty strings, null, undefined and "auto" are converted to 0.
+			return !result || result === "auto" ? 0 : result;
+		},
+		set: function( tween ) {
+
+			// Use step hook for back compat.
+			// Use cssHook if its there.
+			// Use .style if available and use plain properties where available.
+			if ( jQuery.fx.step[ tween.prop ] ) {
+				jQuery.fx.step[ tween.prop ]( tween );
+			} else if ( tween.elem.nodeType === 1 && (
+				jQuery.cssHooks[ tween.prop ] ||
+					tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
+				jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
+			} else {
+				tween.elem[ tween.prop ] = tween.now;
+			}
+		}
+	}
+};
+
+// Support: IE <=9 only
+// Panic based approach to setting things on disconnected nodes
+Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
+	set: function( tween ) {
+		if ( tween.elem.nodeType && tween.elem.parentNode ) {
+			tween.elem[ tween.prop ] = tween.now;
+		}
+	}
+};
+
+jQuery.easing = {
+	linear: function( p ) {
+		return p;
+	},
+	swing: function( p ) {
+		return 0.5 - Math.cos( p * Math.PI ) / 2;
+	},
+	_default: "swing"
+};
+
+jQuery.fx = Tween.prototype.init;
+
+// Back compat <1.8 extension point
+jQuery.fx.step = {};
+
+
+
+
+var
+	fxNow, inProgress,
+	rfxtypes = /^(?:toggle|show|hide)$/,
+	rrun = /queueHooks$/;
+
+function schedule() {
+	if ( inProgress ) {
+		if ( document.hidden === false && window.requestAnimationFrame ) {
+			window.requestAnimationFrame( schedule );
+		} else {
+			window.setTimeout( schedule, jQuery.fx.interval );
+		}
+
+		jQuery.fx.tick();
+	}
+}
+
+// Animations created synchronously will run synchronously
+function createFxNow() {
+	window.setTimeout( function() {
+		fxNow = undefined;
+	} );
+	return ( fxNow = Date.now() );
+}
+
+// Generate parameters to create a standard animation
+function genFx( type, includeWidth ) {
+	var which,
+		i = 0,
+		attrs = { height: type };
+
+	// If we include width, step value is 1 to do all cssExpand values,
+	// otherwise step value is 2 to skip over Left and Right
+	includeWidth = includeWidth ? 1 : 0;
+	for ( ; i < 4; i += 2 - includeWidth ) {
+		which = cssExpand[ i ];
+		attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
+	}
+
+	if ( includeWidth ) {
+		attrs.opacity = attrs.width = type;
+	}
+
+	return attrs;
+}
+
+function createTween( value, prop, animation ) {
+	var tween,
+		collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
+		index = 0,
+		length = collection.length;
+	for ( ; index < length; index++ ) {
+		if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
+
+			// We're done with this property
+			return tween;
+		}
+	}
+}
+
+function defaultPrefilter( elem, props, opts ) {
+	var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
+		isBox = "width" in props || "height" in props,
+		anim = this,
+		orig = {},
+		style = elem.style,
+		hidden = elem.nodeType && isHiddenWithinTree( elem ),
+		dataShow = dataPriv.get( elem, "fxshow" );
+
+	// Queue-skipping animations hijack the fx hooks
+	if ( !opts.queue ) {
+		hooks = jQuery._queueHooks( elem, "fx" );
+		if ( hooks.unqueued == null ) {
+			hooks.unqueued = 0;
+			oldfire = hooks.empty.fire;
+			hooks.empty.fire = function() {
+				if ( !hooks.unqueued ) {
+					oldfire();
+				}
+			};
+		}
+		hooks.unqueued++;
+
+		anim.always( function() {
+
+			// Ensure the complete handler is called before this completes
+			anim.always( function() {
+				hooks.unqueued--;
+				if ( !jQuery.queue( elem, "fx" ).length ) {
+					hooks.empty.fire();
+				}
+			} );
+		} );
+	}
+
+	// Detect show/hide animations
+	for ( prop in props ) {
+		value = props[ prop ];
+		if ( rfxtypes.test( value ) ) {
+			delete props[ prop ];
+			toggle = toggle || value === "toggle";
+			if ( value === ( hidden ? "hide" : "show" ) ) {
+
+				// Pretend to be hidden if this is a "show" and
+				// there is still data from a stopped show/hide
+				if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
+					hidden = true;
+
+				// Ignore all other no-op show/hide data
+				} else {
+					continue;
+				}
+			}
+			orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
+		}
+	}
+
+	// Bail out if this is a no-op like .hide().hide()
+	propTween = !jQuery.isEmptyObject( props );
+	if ( !propTween && jQuery.isEmptyObject( orig ) ) {
+		return;
+	}
+
+	// Restrict "overflow" and "display" styles during box animations
+	if ( isBox && elem.nodeType === 1 ) {
+
+		// Support: IE <=9 - 11, Edge 12 - 15
+		// Record all 3 overflow attributes because IE does not infer the shorthand
+		// from identically-valued overflowX and overflowY and Edge just mirrors
+		// the overflowX value there.
+		opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
+
+		// Identify a display type, preferring old show/hide data over the CSS cascade
+		restoreDisplay = dataShow && dataShow.display;
+		if ( restoreDisplay == null ) {
+			restoreDisplay = dataPriv.get( elem, "display" );
+		}
+		display = jQuery.css( elem, "display" );
+		if ( display === "none" ) {
+			if ( restoreDisplay ) {
+				display = restoreDisplay;
+			} else {
+
+				// Get nonempty value(s) by temporarily forcing visibility
+				showHide( [ elem ], true );
+				restoreDisplay = elem.style.display || restoreDisplay;
+				display = jQuery.css( elem, "display" );
+				showHide( [ elem ] );
+			}
+		}
+
+		// Animate inline elements as inline-block
+		if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
+			if ( jQuery.css( elem, "float" ) === "none" ) {
+
+				// Restore the original display value at the end of pure show/hide animations
+				if ( !propTween ) {
+					anim.done( function() {
+						style.display = restoreDisplay;
+					} );
+					if ( restoreDisplay == null ) {
+						display = style.display;
+						restoreDisplay = display === "none" ? "" : display;
+					}
+				}
+				style.display = "inline-block";
+			}
+		}
+	}
+
+	if ( opts.overflow ) {
+		style.overflow = "hidden";
+		anim.always( function() {
+			style.overflow = opts.overflow[ 0 ];
+			style.overflowX = opts.overflow[ 1 ];
+			style.overflowY = opts.overflow[ 2 ];
+		} );
+	}
+
+	// Implement show/hide animations
+	propTween = false;
+	for ( prop in orig ) {
+
+		// General show/hide setup for this element animation
+		if ( !propTween ) {
+			if ( dataShow ) {
+				if ( "hidden" in dataShow ) {
+					hidden = dataShow.hidden;
+				}
+			} else {
+				dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
+			}
+
+			// Store hidden/visible for toggle so `.stop().toggle()` "reverses"
+			if ( toggle ) {
+				dataShow.hidden = !hidden;
+			}
+
+			// Show elements before animating them
+			if ( hidden ) {
+				showHide( [ elem ], true );
+			}
+
+			/* eslint-disable no-loop-func */
+
+			anim.done( function() {
+
+				/* eslint-enable no-loop-func */
+
+				// The final step of a "hide" animation is actually hiding the element
+				if ( !hidden ) {
+					showHide( [ elem ] );
+				}
+				dataPriv.remove( elem, "fxshow" );
+				for ( prop in orig ) {
+					jQuery.style( elem, prop, orig[ prop ] );
+				}
+			} );
+		}
+
+		// Per-property setup
+		propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
+		if ( !( prop in dataShow ) ) {
+			dataShow[ prop ] = propTween.start;
+			if ( hidden ) {
+				propTween.end = propTween.start;
+				propTween.start = 0;
+			}
+		}
+	}
+}
+
+function propFilter( props, specialEasing ) {
+	var index, name, easing, value, hooks;
+
+	// camelCase, specialEasing and expand cssHook pass
+	for ( index in props ) {
+		name = camelCase( index );
+		easing = specialEasing[ name ];
+		value = props[ index ];
+		if ( Array.isArray( value ) ) {
+			easing = value[ 1 ];
+			value = props[ index ] = value[ 0 ];
+		}
+
+		if ( index !== name ) {
+			props[ name ] = value;
+			delete props[ index ];
+		}
+
+		hooks = jQuery.cssHooks[ name ];
+		if ( hooks && "expand" in hooks ) {
+			value = hooks.expand( value );
+			delete props[ name ];
+
+			// Not quite $.extend, this won't overwrite existing keys.
+			// Reusing 'index' because we have the correct "name"
+			for ( index in value ) {
+				if ( !( index in props ) ) {
+					props[ index ] = value[ index ];
+					specialEasing[ index ] = easing;
+				}
+			}
+		} else {
+			specialEasing[ name ] = easing;
+		}
+	}
+}
+
+function Animation( elem, properties, options ) {
+	var result,
+		stopped,
+		index = 0,
+		length = Animation.prefilters.length,
+		deferred = jQuery.Deferred().always( function() {
+
+			// Don't match elem in the :animated selector
+			delete tick.elem;
+		} ),
+		tick = function() {
+			if ( stopped ) {
+				return false;
+			}
+			var currentTime = fxNow || createFxNow(),
+				remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
+
+				// Support: Android 2.3 only
+				// Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
+				temp = remaining / animation.duration || 0,
+				percent = 1 - temp,
+				index = 0,
+				length = animation.tweens.length;
+
+			for ( ; index < length; index++ ) {
+				animation.tweens[ index ].run( percent );
+			}
+
+			deferred.notifyWith( elem, [ animation, percent, remaining ] );
+
+			// If there's more to do, yield
+			if ( percent < 1 && length ) {
+				return remaining;
+			}
+
+			// If this was an empty animation, synthesize a final progress notification
+			if ( !length ) {
+				deferred.notifyWith( elem, [ animation, 1, 0 ] );
+			}
+
+			// Resolve the animation and report its conclusion
+			deferred.resolveWith( elem, [ animation ] );
+			return false;
+		},
+		animation = deferred.promise( {
+			elem: elem,
+			props: jQuery.extend( {}, properties ),
+			opts: jQuery.extend( true, {
+				specialEasing: {},
+				easing: jQuery.easing._default
+			}, options ),
+			originalProperties: properties,
+			originalOptions: options,
+			startTime: fxNow || createFxNow(),
+			duration: options.duration,
+			tweens: [],
+			createTween: function( prop, end ) {
+				var tween = jQuery.Tween( elem, animation.opts, prop, end,
+					animation.opts.specialEasing[ prop ] || animation.opts.easing );
+				animation.tweens.push( tween );
+				return tween;
+			},
+			stop: function( gotoEnd ) {
+				var index = 0,
+
+					// If we are going to the end, we want to run all the tweens
+					// otherwise we skip this part
+					length = gotoEnd ? animation.tweens.length : 0;
+				if ( stopped ) {
+					return this;
+				}
+				stopped = true;
+				for ( ; index < length; index++ ) {
+					animation.tweens[ index ].run( 1 );
+				}
+
+				// Resolve when we played the last frame; otherwise, reject
+				if ( gotoEnd ) {
+					deferred.notifyWith( elem, [ animation, 1, 0 ] );
+					deferred.resolveWith( elem, [ animation, gotoEnd ] );
+				} else {
+					deferred.rejectWith( elem, [ animation, gotoEnd ] );
+				}
+				return this;
+			}
+		} ),
+		props = animation.props;
+
+	propFilter( props, animation.opts.specialEasing );
+
+	for ( ; index < length; index++ ) {
+		result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
+		if ( result ) {
+			if ( isFunction( result.stop ) ) {
+				jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
+					result.stop.bind( result );
+			}
+			return result;
+		}
+	}
+
+	jQuery.map( props, createTween, animation );
+
+	if ( isFunction( animation.opts.start ) ) {
+		animation.opts.start.call( elem, animation );
+	}
+
+	// Attach callbacks from options
+	animation
+		.progress( animation.opts.progress )
+		.done( animation.opts.done, animation.opts.complete )
+		.fail( animation.opts.fail )
+		.always( animation.opts.always );
+
+	jQuery.fx.timer(
+		jQuery.extend( tick, {
+			elem: elem,
+			anim: animation,
+			queue: animation.opts.queue
+		} )
+	);
+
+	return animation;
+}
+
+jQuery.Animation = jQuery.extend( Animation, {
+
+	tweeners: {
+		"*": [ function( prop, value ) {
+			var tween = this.createTween( prop, value );
+			adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
+			return tween;
+		} ]
+	},
+
+	tweener: function( props, callback ) {
+		if ( isFunction( props ) ) {
+			callback = props;
+			props = [ "*" ];
+		} else {
+			props = props.match( rnothtmlwhite );
+		}
+
+		var prop,
+			index = 0,
+			length = props.length;
+
+		for ( ; index < length; index++ ) {
+			prop = props[ index ];
+			Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
+			Animation.tweeners[ prop ].unshift( callback );
+		}
+	},
+
+	prefilters: [ defaultPrefilter ],
+
+	prefilter: function( callback, prepend ) {
+		if ( prepend ) {
+			Animation.prefilters.unshift( callback );
+		} else {
+			Animation.prefilters.push( callback );
+		}
+	}
+} );
+
+jQuery.speed = function( speed, easing, fn ) {
+	var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
+		complete: fn || !fn && easing ||
+			isFunction( speed ) && speed,
+		duration: speed,
+		easing: fn && easing || easing && !isFunction( easing ) && easing
+	};
+
+	// Go to the end state if fx are off
+	if ( jQuery.fx.off ) {
+		opt.duration = 0;
+
+	} else {
+		if ( typeof opt.duration !== "number" ) {
+			if ( opt.duration in jQuery.fx.speeds ) {
+				opt.duration = jQuery.fx.speeds[ opt.duration ];
+
+			} else {
+				opt.duration = jQuery.fx.speeds._default;
+			}
+		}
+	}
+
+	// Normalize opt.queue - true/undefined/null -> "fx"
+	if ( opt.queue == null || opt.queue === true ) {
+		opt.queue = "fx";
+	}
+
+	// Queueing
+	opt.old = opt.complete;
+
+	opt.complete = function() {
+		if ( isFunction( opt.old ) ) {
+			opt.old.call( this );
+		}
+
+		if ( opt.queue ) {
+			jQuery.dequeue( this, opt.queue );
+		}
+	};
+
+	return opt;
+};
+
+jQuery.fn.extend( {
+	fadeTo: function( speed, to, easing, callback ) {
+
+		// Show any hidden elements after setting opacity to 0
+		return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
+
+			// Animate to the value specified
+			.end().animate( { opacity: to }, speed, easing, callback );
+	},
+	animate: function( prop, speed, easing, callback ) {
+		var empty = jQuery.isEmptyObject( prop ),
+			optall = jQuery.speed( speed, easing, callback ),
+			doAnimation = function() {
+
+				// Operate on a copy of prop so per-property easing won't be lost
+				var anim = Animation( this, jQuery.extend( {}, prop ), optall );
+
+				// Empty animations, or finishing resolves immediately
+				if ( empty || dataPriv.get( this, "finish" ) ) {
+					anim.stop( true );
+				}
+			};
+
+		doAnimation.finish = doAnimation;
+
+		return empty || optall.queue === false ?
+			this.each( doAnimation ) :
+			this.queue( optall.queue, doAnimation );
+	},
+	stop: function( type, clearQueue, gotoEnd ) {
+		var stopQueue = function( hooks ) {
+			var stop = hooks.stop;
+			delete hooks.stop;
+			stop( gotoEnd );
+		};
+
+		if ( typeof type !== "string" ) {
+			gotoEnd = clearQueue;
+			clearQueue = type;
+			type = undefined;
+		}
+		if ( clearQueue ) {
+			this.queue( type || "fx", [] );
+		}
+
+		return this.each( function() {
+			var dequeue = true,
+				index = type != null && type + "queueHooks",
+				timers = jQuery.timers,
+				data = dataPriv.get( this );
+
+			if ( index ) {
+				if ( data[ index ] && data[ index ].stop ) {
+					stopQueue( data[ index ] );
+				}
+			} else {
+				for ( index in data ) {
+					if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
+						stopQueue( data[ index ] );
+					}
+				}
+			}
+
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this &&
+					( type == null || timers[ index ].queue === type ) ) {
+
+					timers[ index ].anim.stop( gotoEnd );
+					dequeue = false;
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Start the next in the queue if the last step wasn't forced.
+			// Timers currently will call their complete callbacks, which
+			// will dequeue but only if they were gotoEnd.
+			if ( dequeue || !gotoEnd ) {
+				jQuery.dequeue( this, type );
+			}
+		} );
+	},
+	finish: function( type ) {
+		if ( type !== false ) {
+			type = type || "fx";
+		}
+		return this.each( function() {
+			var index,
+				data = dataPriv.get( this ),
+				queue = data[ type + "queue" ],
+				hooks = data[ type + "queueHooks" ],
+				timers = jQuery.timers,
+				length = queue ? queue.length : 0;
+
+			// Enable finishing flag on private data
+			data.finish = true;
+
+			// Empty the queue first
+			jQuery.queue( this, type, [] );
+
+			if ( hooks && hooks.stop ) {
+				hooks.stop.call( this, true );
+			}
+
+			// Look for any active animations, and finish them
+			for ( index = timers.length; index--; ) {
+				if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
+					timers[ index ].anim.stop( true );
+					timers.splice( index, 1 );
+				}
+			}
+
+			// Look for any animations in the old queue and finish them
+			for ( index = 0; index < length; index++ ) {
+				if ( queue[ index ] && queue[ index ].finish ) {
+					queue[ index ].finish.call( this );
+				}
+			}
+
+			// Turn off finishing flag
+			delete data.finish;
+		} );
+	}
+} );
+
+jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
+	var cssFn = jQuery.fn[ name ];
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return speed == null || typeof speed === "boolean" ?
+			cssFn.apply( this, arguments ) :
+			this.animate( genFx( name, true ), speed, easing, callback );
+	};
+} );
+
+// Generate shortcuts for custom animations
+jQuery.each( {
+	slideDown: genFx( "show" ),
+	slideUp: genFx( "hide" ),
+	slideToggle: genFx( "toggle" ),
+	fadeIn: { opacity: "show" },
+	fadeOut: { opacity: "hide" },
+	fadeToggle: { opacity: "toggle" }
+}, function( name, props ) {
+	jQuery.fn[ name ] = function( speed, easing, callback ) {
+		return this.animate( props, speed, easing, callback );
+	};
+} );
+
+jQuery.timers = [];
+jQuery.fx.tick = function() {
+	var timer,
+		i = 0,
+		timers = jQuery.timers;
+
+	fxNow = Date.now();
+
+	for ( ; i < timers.length; i++ ) {
+		timer = timers[ i ];
+
+		// Run the timer and safely remove it when done (allowing for external removal)
+		if ( !timer() && timers[ i ] === timer ) {
+			timers.splice( i--, 1 );
+		}
+	}
+
+	if ( !timers.length ) {
+		jQuery.fx.stop();
+	}
+	fxNow = undefined;
+};
+
+jQuery.fx.timer = function( timer ) {
+	jQuery.timers.push( timer );
+	jQuery.fx.start();
+};
+
+jQuery.fx.interval = 13;
+jQuery.fx.start = function() {
+	if ( inProgress ) {
+		return;
+	}
+
+	inProgress = true;
+	schedule();
+};
+
+jQuery.fx.stop = function() {
+	inProgress = null;
+};
+
+jQuery.fx.speeds = {
+	slow: 600,
+	fast: 200,
+
+	// Default speed
+	_default: 400
+};
+
+
+// Based off of the plugin by Clint Helfers, with permission.
+// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
+jQuery.fn.delay = function( time, type ) {
+	time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
+	type = type || "fx";
+
+	return this.queue( type, function( next, hooks ) {
+		var timeout = window.setTimeout( next, time );
+		hooks.stop = function() {
+			window.clearTimeout( timeout );
+		};
+	} );
+};
+
+
+( function() {
+	var input = document.createElement( "input" ),
+		select = document.createElement( "select" ),
+		opt = select.appendChild( document.createElement( "option" ) );
+
+	input.type = "checkbox";
+
+	// Support: Android <=4.3 only
+	// Default value for a checkbox should be "on"
+	support.checkOn = input.value !== "";
+
+	// Support: IE <=11 only
+	// Must access selectedIndex to make default options select
+	support.optSelected = opt.selected;
+
+	// Support: IE <=11 only
+	// An input loses its value after becoming a radio
+	input = document.createElement( "input" );
+	input.value = "t";
+	input.type = "radio";
+	support.radioValue = input.value === "t";
+} )();
+
+
+var boolHook,
+	attrHandle = jQuery.expr.attrHandle;
+
+jQuery.fn.extend( {
+	attr: function( name, value ) {
+		return access( this, jQuery.attr, name, value, arguments.length > 1 );
+	},
+
+	removeAttr: function( name ) {
+		return this.each( function() {
+			jQuery.removeAttr( this, name );
+		} );
+	}
+} );
+
+jQuery.extend( {
+	attr: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set attributes on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		// Fallback to prop when attributes are not supported
+		if ( typeof elem.getAttribute === "undefined" ) {
+			return jQuery.prop( elem, name, value );
+		}
+
+		// Attribute hooks are determined by the lowercase version
+		// Grab necessary hook if one is defined
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+			hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
+				( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
+		}
+
+		if ( value !== undefined ) {
+			if ( value === null ) {
+				jQuery.removeAttr( elem, name );
+				return;
+			}
+
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			elem.setAttribute( name, value + "" );
+			return value;
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		ret = jQuery.find.attr( elem, name );
+
+		// Non-existent attributes return null, we normalize to undefined
+		return ret == null ? undefined : ret;
+	},
+
+	attrHooks: {
+		type: {
+			set: function( elem, value ) {
+				if ( !support.radioValue && value === "radio" &&
+					nodeName( elem, "input" ) ) {
+					var val = elem.value;
+					elem.setAttribute( "type", value );
+					if ( val ) {
+						elem.value = val;
+					}
+					return value;
+				}
+			}
+		}
+	},
+
+	removeAttr: function( elem, value ) {
+		var name,
+			i = 0,
+
+			// Attribute names can contain non-HTML whitespace characters
+			// https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
+			attrNames = value && value.match( rnothtmlwhite );
+
+		if ( attrNames && elem.nodeType === 1 ) {
+			while ( ( name = attrNames[ i++ ] ) ) {
+				elem.removeAttribute( name );
+			}
+		}
+	}
+} );
+
+// Hooks for boolean attributes
+boolHook = {
+	set: function( elem, value, name ) {
+		if ( value === false ) {
+
+			// Remove boolean attributes when set to false
+			jQuery.removeAttr( elem, name );
+		} else {
+			elem.setAttribute( name, name );
+		}
+		return name;
+	}
+};
+
+jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
+	var getter = attrHandle[ name ] || jQuery.find.attr;
+
+	attrHandle[ name ] = function( elem, name, isXML ) {
+		var ret, handle,
+			lowercaseName = name.toLowerCase();
+
+		if ( !isXML ) {
+
+			// Avoid an infinite loop by temporarily removing this function from the getter
+			handle = attrHandle[ lowercaseName ];
+			attrHandle[ lowercaseName ] = ret;
+			ret = getter( elem, name, isXML ) != null ?
+				lowercaseName :
+				null;
+			attrHandle[ lowercaseName ] = handle;
+		}
+		return ret;
+	};
+} );
+
+
+
+
+var rfocusable = /^(?:input|select|textarea|button)$/i,
+	rclickable = /^(?:a|area)$/i;
+
+jQuery.fn.extend( {
+	prop: function( name, value ) {
+		return access( this, jQuery.prop, name, value, arguments.length > 1 );
+	},
+
+	removeProp: function( name ) {
+		return this.each( function() {
+			delete this[ jQuery.propFix[ name ] || name ];
+		} );
+	}
+} );
+
+jQuery.extend( {
+	prop: function( elem, name, value ) {
+		var ret, hooks,
+			nType = elem.nodeType;
+
+		// Don't get/set properties on text, comment and attribute nodes
+		if ( nType === 3 || nType === 8 || nType === 2 ) {
+			return;
+		}
+
+		if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
+
+			// Fix name and attach hooks
+			name = jQuery.propFix[ name ] || name;
+			hooks = jQuery.propHooks[ name ];
+		}
+
+		if ( value !== undefined ) {
+			if ( hooks && "set" in hooks &&
+				( ret = hooks.set( elem, value, name ) ) !== undefined ) {
+				return ret;
+			}
+
+			return ( elem[ name ] = value );
+		}
+
+		if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
+			return ret;
+		}
+
+		return elem[ name ];
+	},
+
+	propHooks: {
+		tabIndex: {
+			get: function( elem ) {
+
+				// Support: IE <=9 - 11 only
+				// elem.tabIndex doesn't always return the
+				// correct value when it hasn't been explicitly set
+				// https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
+				// Use proper attribute retrieval(#12072)
+				var tabindex = jQuery.find.attr( elem, "tabindex" );
+
+				if ( tabindex ) {
+					return parseInt( tabindex, 10 );
+				}
+
+				if (
+					rfocusable.test( elem.nodeName ) ||
+					rclickable.test( elem.nodeName ) &&
+					elem.href
+				) {
+					return 0;
+				}
+
+				return -1;
+			}
+		}
+	},
+
+	propFix: {
+		"for": "htmlFor",
+		"class": "className"
+	}
+} );
+
+// Support: IE <=11 only
+// Accessing the selectedIndex property
+// forces the browser to respect setting selected
+// on the option
+// The getter ensures a default option is selected
+// when in an optgroup
+// eslint rule "no-unused-expressions" is disabled for this code
+// since it considers such accessions noop
+if ( !support.optSelected ) {
+	jQuery.propHooks.selected = {
+		get: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent && parent.parentNode ) {
+				parent.parentNode.selectedIndex;
+			}
+			return null;
+		},
+		set: function( elem ) {
+
+			/* eslint no-unused-expressions: "off" */
+
+			var parent = elem.parentNode;
+			if ( parent ) {
+				parent.selectedIndex;
+
+				if ( parent.parentNode ) {
+					parent.parentNode.selectedIndex;
+				}
+			}
+		}
+	};
+}
+
+jQuery.each( [
+	"tabIndex",
+	"readOnly",
+	"maxLength",
+	"cellSpacing",
+	"cellPadding",
+	"rowSpan",
+	"colSpan",
+	"useMap",
+	"frameBorder",
+	"contentEditable"
+], function() {
+	jQuery.propFix[ this.toLowerCase() ] = this;
+} );
+
+
+
+
+	// Strip and collapse whitespace according to HTML spec
+	// https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
+	function stripAndCollapse( value ) {
+		var tokens = value.match( rnothtmlwhite ) || [];
+		return tokens.join( " " );
+	}
+
+
+function getClass( elem ) {
+	return elem.getAttribute && elem.getAttribute( "class" ) || "";
+}
+
+function classesToArray( value ) {
+	if ( Array.isArray( value ) ) {
+		return value;
+	}
+	if ( typeof value === "string" ) {
+		return value.match( rnothtmlwhite ) || [];
+	}
+	return [];
+}
+
+jQuery.fn.extend( {
+	addClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+						if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
+							cur += clazz + " ";
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	removeClass: function( value ) {
+		var classes, elem, cur, curValue, clazz, j, finalValue,
+			i = 0;
+
+		if ( isFunction( value ) ) {
+			return this.each( function( j ) {
+				jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
+			} );
+		}
+
+		if ( !arguments.length ) {
+			return this.attr( "class", "" );
+		}
+
+		classes = classesToArray( value );
+
+		if ( classes.length ) {
+			while ( ( elem = this[ i++ ] ) ) {
+				curValue = getClass( elem );
+
+				// This expression is here for better compressibility (see addClass)
+				cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
+
+				if ( cur ) {
+					j = 0;
+					while ( ( clazz = classes[ j++ ] ) ) {
+
+						// Remove *all* instances
+						while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
+							cur = cur.replace( " " + clazz + " ", " " );
+						}
+					}
+
+					// Only assign if different to avoid unneeded rendering.
+					finalValue = stripAndCollapse( cur );
+					if ( curValue !== finalValue ) {
+						elem.setAttribute( "class", finalValue );
+					}
+				}
+			}
+		}
+
+		return this;
+	},
+
+	toggleClass: function( value, stateVal ) {
+		var type = typeof value,
+			isValidValue = type === "string" || Array.isArray( value );
+
+		if ( typeof stateVal === "boolean" && isValidValue ) {
+			return stateVal ? this.addClass( value ) : this.removeClass( value );
+		}
+
+		if ( isFunction( value ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).toggleClass(
+					value.call( this, i, getClass( this ), stateVal ),
+					stateVal
+				);
+			} );
+		}
+
+		return this.each( function() {
+			var className, i, self, classNames;
+
+			if ( isValidValue ) {
+
+				// Toggle individual class names
+				i = 0;
+				self = jQuery( this );
+				classNames = classesToArray( value );
+
+				while ( ( className = classNames[ i++ ] ) ) {
+
+					// Check each className given, space separated list
+					if ( self.hasClass( className ) ) {
+						self.removeClass( className );
+					} else {
+						self.addClass( className );
+					}
+				}
+
+			// Toggle whole class name
+			} else if ( value === undefined || type === "boolean" ) {
+				className = getClass( this );
+				if ( className ) {
+
+					// Store className if set
+					dataPriv.set( this, "__className__", className );
+				}
+
+				// If the element has a class name or if we're passed `false`,
+				// then remove the whole classname (if there was one, the above saved it).
+				// Otherwise bring back whatever was previously saved (if anything),
+				// falling back to the empty string if nothing was stored.
+				if ( this.setAttribute ) {
+					this.setAttribute( "class",
+						className || value === false ?
+							"" :
+							dataPriv.get( this, "__className__" ) || ""
+					);
+				}
+			}
+		} );
+	},
+
+	hasClass: function( selector ) {
+		var className, elem,
+			i = 0;
+
+		className = " " + selector + " ";
+		while ( ( elem = this[ i++ ] ) ) {
+			if ( elem.nodeType === 1 &&
+				( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
+				return true;
+			}
+		}
+
+		return false;
+	}
+} );
+
+
+
+
+var rreturn = /\r/g;
+
+jQuery.fn.extend( {
+	val: function( value ) {
+		var hooks, ret, valueIsFunction,
+			elem = this[ 0 ];
+
+		if ( !arguments.length ) {
+			if ( elem ) {
+				hooks = jQuery.valHooks[ elem.type ] ||
+					jQuery.valHooks[ elem.nodeName.toLowerCase() ];
+
+				if ( hooks &&
+					"get" in hooks &&
+					( ret = hooks.get( elem, "value" ) ) !== undefined
+				) {
+					return ret;
+				}
+
+				ret = elem.value;
+
+				// Handle most common string cases
+				if ( typeof ret === "string" ) {
+					return ret.replace( rreturn, "" );
+				}
+
+				// Handle cases where value is null/undef or number
+				return ret == null ? "" : ret;
+			}
+
+			return;
+		}
+
+		valueIsFunction = isFunction( value );
+
+		return this.each( function( i ) {
+			var val;
+
+			if ( this.nodeType !== 1 ) {
+				return;
+			}
+
+			if ( valueIsFunction ) {
+				val = value.call( this, i, jQuery( this ).val() );
+			} else {
+				val = value;
+			}
+
+			// Treat null/undefined as ""; convert numbers to string
+			if ( val == null ) {
+				val = "";
+
+			} else if ( typeof val === "number" ) {
+				val += "";
+
+			} else if ( Array.isArray( val ) ) {
+				val = jQuery.map( val, function( value ) {
+					return value == null ? "" : value + "";
+				} );
+			}
+
+			hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
+
+			// If set returns undefined, fall back to normal setting
+			if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
+				this.value = val;
+			}
+		} );
+	}
+} );
+
+jQuery.extend( {
+	valHooks: {
+		option: {
+			get: function( elem ) {
+
+				var val = jQuery.find.attr( elem, "value" );
+				return val != null ?
+					val :
+
+					// Support: IE <=10 - 11 only
+					// option.text throws exceptions (#14686, #14858)
+					// Strip and collapse whitespace
+					// https://html.spec.whatwg.org/#strip-and-collapse-whitespace
+					stripAndCollapse( jQuery.text( elem ) );
+			}
+		},
+		select: {
+			get: function( elem ) {
+				var value, option, i,
+					options = elem.options,
+					index = elem.selectedIndex,
+					one = elem.type === "select-one",
+					values = one ? null : [],
+					max = one ? index + 1 : options.length;
+
+				if ( index < 0 ) {
+					i = max;
+
+				} else {
+					i = one ? index : 0;
+				}
+
+				// Loop through all the selected options
+				for ( ; i < max; i++ ) {
+					option = options[ i ];
+
+					// Support: IE <=9 only
+					// IE8-9 doesn't update selected after form reset (#2551)
+					if ( ( option.selected || i === index ) &&
+
+							// Don't return options that are disabled or in a disabled optgroup
+							!option.disabled &&
+							( !option.parentNode.disabled ||
+								!nodeName( option.parentNode, "optgroup" ) ) ) {
+
+						// Get the specific value for the option
+						value = jQuery( option ).val();
+
+						// We don't need an array for one selects
+						if ( one ) {
+							return value;
+						}
+
+						// Multi-Selects return an array
+						values.push( value );
+					}
+				}
+
+				return values;
+			},
+
+			set: function( elem, value ) {
+				var optionSet, option,
+					options = elem.options,
+					values = jQuery.makeArray( value ),
+					i = options.length;
+
+				while ( i-- ) {
+					option = options[ i ];
+
+					/* eslint-disable no-cond-assign */
+
+					if ( option.selected =
+						jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
+					) {
+						optionSet = true;
+					}
+
+					/* eslint-enable no-cond-assign */
+				}
+
+				// Force browsers to behave consistently when non-matching value is set
+				if ( !optionSet ) {
+					elem.selectedIndex = -1;
+				}
+				return values;
+			}
+		}
+	}
+} );
+
+// Radios and checkboxes getter/setter
+jQuery.each( [ "radio", "checkbox" ], function() {
+	jQuery.valHooks[ this ] = {
+		set: function( elem, value ) {
+			if ( Array.isArray( value ) ) {
+				return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
+			}
+		}
+	};
+	if ( !support.checkOn ) {
+		jQuery.valHooks[ this ].get = function( elem ) {
+			return elem.getAttribute( "value" ) === null ? "on" : elem.value;
+		};
+	}
+} );
+
+
+
+
+// Return jQuery for attributes-only inclusion
+
+
+support.focusin = "onfocusin" in window;
+
+
+var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
+	stopPropagationCallback = function( e ) {
+		e.stopPropagation();
+	};
+
+jQuery.extend( jQuery.event, {
+
+	trigger: function( event, data, elem, onlyHandlers ) {
+
+		var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
+			eventPath = [ elem || document ],
+			type = hasOwn.call( event, "type" ) ? event.type : event,
+			namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
+
+		cur = lastElement = tmp = elem = elem || document;
+
+		// Don't do events on text and comment nodes
+		if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
+			return;
+		}
+
+		// focus/blur morphs to focusin/out; ensure we're not firing them right now
+		if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
+			return;
+		}
+
+		if ( type.indexOf( "." ) > -1 ) {
+
+			// Namespaced trigger; create a regexp to match event type in handle()
+			namespaces = type.split( "." );
+			type = namespaces.shift();
+			namespaces.sort();
+		}
+		ontype = type.indexOf( ":" ) < 0 && "on" + type;
+
+		// Caller can pass in a jQuery.Event object, Object, or just an event type string
+		event = event[ jQuery.expando ] ?
+			event :
+			new jQuery.Event( type, typeof event === "object" && event );
+
+		// Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
+		event.isTrigger = onlyHandlers ? 2 : 3;
+		event.namespace = namespaces.join( "." );
+		event.rnamespace = event.namespace ?
+			new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
+			null;
+
+		// Clean up the event in case it is being reused
+		event.result = undefined;
+		if ( !event.target ) {
+			event.target = elem;
+		}
+
+		// Clone any incoming data and prepend the event, creating the handler arg list
+		data = data == null ?
+			[ event ] :
+			jQuery.makeArray( data, [ event ] );
+
+		// Allow special events to draw outside the lines
+		special = jQuery.event.special[ type ] || {};
+		if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
+			return;
+		}
+
+		// Determine event propagation path in advance, per W3C events spec (#9951)
+		// Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
+		if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
+
+			bubbleType = special.delegateType || type;
+			if ( !rfocusMorph.test( bubbleType + type ) ) {
+				cur = cur.parentNode;
+			}
+			for ( ; cur; cur = cur.parentNode ) {
+				eventPath.push( cur );
+				tmp = cur;
+			}
+
+			// Only add window if we got to document (e.g., not plain obj or detached DOM)
+			if ( tmp === ( elem.ownerDocument || document ) ) {
+				eventPath.push( tmp.defaultView || tmp.parentWindow || window );
+			}
+		}
+
+		// Fire handlers on the event path
+		i = 0;
+		while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
+			lastElement = cur;
+			event.type = i > 1 ?
+				bubbleType :
+				special.bindType || type;
+
+			// jQuery handler
+			handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] &&
+				dataPriv.get( cur, "handle" );
+			if ( handle ) {
+				handle.apply( cur, data );
+			}
+
+			// Native handler
+			handle = ontype && cur[ ontype ];
+			if ( handle && handle.apply && acceptData( cur ) ) {
+				event.result = handle.apply( cur, data );
+				if ( event.result === false ) {
+					event.preventDefault();
+				}
+			}
+		}
+		event.type = type;
+
+		// If nobody prevented the default action, do it now
+		if ( !onlyHandlers && !event.isDefaultPrevented() ) {
+
+			if ( ( !special._default ||
+				special._default.apply( eventPath.pop(), data ) === false ) &&
+				acceptData( elem ) ) {
+
+				// Call a native DOM method on the target with the same name as the event.
+				// Don't do default actions on window, that's where global variables be (#6170)
+				if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
+
+					// Don't re-trigger an onFOO event when we call its FOO() method
+					tmp = elem[ ontype ];
+
+					if ( tmp ) {
+						elem[ ontype ] = null;
+					}
+
+					// Prevent re-triggering of the same event, since we already bubbled it above
+					jQuery.event.triggered = type;
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.addEventListener( type, stopPropagationCallback );
+					}
+
+					elem[ type ]();
+
+					if ( event.isPropagationStopped() ) {
+						lastElement.removeEventListener( type, stopPropagationCallback );
+					}
+
+					jQuery.event.triggered = undefined;
+
+					if ( tmp ) {
+						elem[ ontype ] = tmp;
+					}
+				}
+			}
+		}
+
+		return event.result;
+	},
+
+	// Piggyback on a donor event to simulate a different one
+	// Used only for `focus(in | out)` events
+	simulate: function( type, elem, event ) {
+		var e = jQuery.extend(
+			new jQuery.Event(),
+			event,
+			{
+				type: type,
+				isSimulated: true
+			}
+		);
+
+		jQuery.event.trigger( e, null, elem );
+	}
+
+} );
+
+jQuery.fn.extend( {
+
+	trigger: function( type, data ) {
+		return this.each( function() {
+			jQuery.event.trigger( type, data, this );
+		} );
+	},
+	triggerHandler: function( type, data ) {
+		var elem = this[ 0 ];
+		if ( elem ) {
+			return jQuery.event.trigger( type, data, elem, true );
+		}
+	}
+} );
+
+
+// Support: Firefox <=44
+// Firefox doesn't have focus(in | out) events
+// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
+//
+// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
+// focus(in | out) events fire after focus & blur events,
+// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
+// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
+if ( !support.focusin ) {
+	jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
+
+		// Attach a single capturing handler on the document while someone wants focusin/focusout
+		var handler = function( event ) {
+			jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
+		};
+
+		jQuery.event.special[ fix ] = {
+			setup: function() {
+
+				// Handle: regular nodes (via `this.ownerDocument`), window
+				// (via `this.document`) & document (via `this`).
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix );
+
+				if ( !attaches ) {
+					doc.addEventListener( orig, handler, true );
+				}
+				dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
+			},
+			teardown: function() {
+				var doc = this.ownerDocument || this.document || this,
+					attaches = dataPriv.access( doc, fix ) - 1;
+
+				if ( !attaches ) {
+					doc.removeEventListener( orig, handler, true );
+					dataPriv.remove( doc, fix );
+
+				} else {
+					dataPriv.access( doc, fix, attaches );
+				}
+			}
+		};
+	} );
+}
+var location = window.location;
+
+var nonce = { guid: Date.now() };
+
+var rquery = ( /\?/ );
+
+
+
+// Cross-browser xml parsing
+jQuery.parseXML = function( data ) {
+	var xml, parserErrorElem;
+	if ( !data || typeof data !== "string" ) {
+		return null;
+	}
+
+	// Support: IE 9 - 11 only
+	// IE throws on parseFromString with invalid input.
+	try {
+		xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
+	} catch ( e ) {}
+
+	parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ];
+	if ( !xml || parserErrorElem ) {
+		jQuery.error( "Invalid XML: " + (
+			parserErrorElem ?
+				jQuery.map( parserErrorElem.childNodes, function( el ) {
+					return el.textContent;
+				} ).join( "\n" ) :
+				data
+		) );
+	}
+	return xml;
+};
+
+
+var
+	rbracket = /\[\]$/,
+	rCRLF = /\r?\n/g,
+	rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
+	rsubmittable = /^(?:input|select|textarea|keygen)/i;
+
+function buildParams( prefix, obj, traditional, add ) {
+	var name;
+
+	if ( Array.isArray( obj ) ) {
+
+		// Serialize array item.
+		jQuery.each( obj, function( i, v ) {
+			if ( traditional || rbracket.test( prefix ) ) {
+
+				// Treat each array item as a scalar.
+				add( prefix, v );
+
+			} else {
+
+				// Item is non-scalar (array or object), encode its numeric index.
+				buildParams(
+					prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
+					v,
+					traditional,
+					add
+				);
+			}
+		} );
+
+	} else if ( !traditional && toType( obj ) === "object" ) {
+
+		// Serialize object item.
+		for ( name in obj ) {
+			buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
+		}
+
+	} else {
+
+		// Serialize scalar item.
+		add( prefix, obj );
+	}
+}
+
+// Serialize an array of form elements or a set of
+// key/values into a query string
+jQuery.param = function( a, traditional ) {
+	var prefix,
+		s = [],
+		add = function( key, valueOrFunction ) {
+
+			// If value is a function, invoke it and use its return value
+			var value = isFunction( valueOrFunction ) ?
+				valueOrFunction() :
+				valueOrFunction;
+
+			s[ s.length ] = encodeURIComponent( key ) + "=" +
+				encodeURIComponent( value == null ? "" : value );
+		};
+
+	if ( a == null ) {
+		return "";
+	}
+
+	// If an array was passed in, assume that it is an array of form elements.
+	if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
+
+		// Serialize the form elements
+		jQuery.each( a, function() {
+			add( this.name, this.value );
+		} );
+
+	} else {
+
+		// If traditional, encode the "old" way (the way 1.3.2 or older
+		// did it), otherwise encode params recursively.
+		for ( prefix in a ) {
+			buildParams( prefix, a[ prefix ], traditional, add );
+		}
+	}
+
+	// Return the resulting serialization
+	return s.join( "&" );
+};
+
+jQuery.fn.extend( {
+	serialize: function() {
+		return jQuery.param( this.serializeArray() );
+	},
+	serializeArray: function() {
+		return this.map( function() {
+
+			// Can add propHook for "elements" to filter or add form elements
+			var elements = jQuery.prop( this, "elements" );
+			return elements ? jQuery.makeArray( elements ) : this;
+		} ).filter( function() {
+			var type = this.type;
+
+			// Use .is( ":disabled" ) so that fieldset[disabled] works
+			return this.name && !jQuery( this ).is( ":disabled" ) &&
+				rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
+				( this.checked || !rcheckableType.test( type ) );
+		} ).map( function( _i, elem ) {
+			var val = jQuery( this ).val();
+
+			if ( val == null ) {
+				return null;
+			}
+
+			if ( Array.isArray( val ) ) {
+				return jQuery.map( val, function( val ) {
+					return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+				} );
+			}
+
+			return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
+		} ).get();
+	}
+} );
+
+
+var
+	r20 = /%20/g,
+	rhash = /#.*$/,
+	rantiCache = /([?&])_=[^&]*/,
+	rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
+
+	// #7653, #8125, #8152: local protocol detection
+	rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
+	rnoContent = /^(?:GET|HEAD)$/,
+	rprotocol = /^\/\//,
+
+	/* Prefilters
+	 * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
+	 * 2) These are called:
+	 *    - BEFORE asking for a transport
+	 *    - AFTER param serialization (s.data is a string if s.processData is true)
+	 * 3) key is the dataType
+	 * 4) the catchall symbol "*" can be used
+	 * 5) execution will start with transport dataType and THEN continue down to "*" if needed
+	 */
+	prefilters = {},
+
+	/* Transports bindings
+	 * 1) key is the dataType
+	 * 2) the catchall symbol "*" can be used
+	 * 3) selection will start with transport dataType and THEN go to "*" if needed
+	 */
+	transports = {},
+
+	// Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
+	allTypes = "*/".concat( "*" ),
+
+	// Anchor tag for parsing the document origin
+	originAnchor = document.createElement( "a" );
+
+originAnchor.href = location.href;
+
+// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
+function addToPrefiltersOrTransports( structure ) {
+
+	// dataTypeExpression is optional and defaults to "*"
+	return function( dataTypeExpression, func ) {
+
+		if ( typeof dataTypeExpression !== "string" ) {
+			func = dataTypeExpression;
+			dataTypeExpression = "*";
+		}
+
+		var dataType,
+			i = 0,
+			dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
+
+		if ( isFunction( func ) ) {
+
+			// For each dataType in the dataTypeExpression
+			while ( ( dataType = dataTypes[ i++ ] ) ) {
+
+				// Prepend if requested
+				if ( dataType[ 0 ] === "+" ) {
+					dataType = dataType.slice( 1 ) || "*";
+					( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
+
+				// Otherwise append
+				} else {
+					( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
+				}
+			}
+		}
+	};
+}
+
+// Base inspection function for prefilters and transports
+function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
+
+	var inspected = {},
+		seekingTransport = ( structure === transports );
+
+	function inspect( dataType ) {
+		var selected;
+		inspected[ dataType ] = true;
+		jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
+			var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
+			if ( typeof dataTypeOrTransport === "string" &&
+				!seekingTransport && !inspected[ dataTypeOrTransport ] ) {
+
+				options.dataTypes.unshift( dataTypeOrTransport );
+				inspect( dataTypeOrTransport );
+				return false;
+			} else if ( seekingTransport ) {
+				return !( selected = dataTypeOrTransport );
+			}
+		} );
+		return selected;
+	}
+
+	return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
+}
+
+// A special extend for ajax options
+// that takes "flat" options (not to be deep extended)
+// Fixes #9887
+function ajaxExtend( target, src ) {
+	var key, deep,
+		flatOptions = jQuery.ajaxSettings.flatOptions || {};
+
+	for ( key in src ) {
+		if ( src[ key ] !== undefined ) {
+			( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
+		}
+	}
+	if ( deep ) {
+		jQuery.extend( true, target, deep );
+	}
+
+	return target;
+}
+
+/* Handles responses to an ajax request:
+ * - finds the right dataType (mediates between content-type and expected dataType)
+ * - returns the corresponding response
+ */
+function ajaxHandleResponses( s, jqXHR, responses ) {
+
+	var ct, type, finalDataType, firstDataType,
+		contents = s.contents,
+		dataTypes = s.dataTypes;
+
+	// Remove auto dataType and get content-type in the process
+	while ( dataTypes[ 0 ] === "*" ) {
+		dataTypes.shift();
+		if ( ct === undefined ) {
+			ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
+		}
+	}
+
+	// Check if we're dealing with a known content-type
+	if ( ct ) {
+		for ( type in contents ) {
+			if ( contents[ type ] && contents[ type ].test( ct ) ) {
+				dataTypes.unshift( type );
+				break;
+			}
+		}
+	}
+
+	// Check to see if we have a response for the expected dataType
+	if ( dataTypes[ 0 ] in responses ) {
+		finalDataType = dataTypes[ 0 ];
+	} else {
+
+		// Try convertible dataTypes
+		for ( type in responses ) {
+			if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
+				finalDataType = type;
+				break;
+			}
+			if ( !firstDataType ) {
+				firstDataType = type;
+			}
+		}
+
+		// Or just use first one
+		finalDataType = finalDataType || firstDataType;
+	}
+
+	// If we found a dataType
+	// We add the dataType to the list if needed
+	// and return the corresponding response
+	if ( finalDataType ) {
+		if ( finalDataType !== dataTypes[ 0 ] ) {
+			dataTypes.unshift( finalDataType );
+		}
+		return responses[ finalDataType ];
+	}
+}
+
+/* Chain conversions given the request and the original response
+ * Also sets the responseXXX fields on the jqXHR instance
+ */
+function ajaxConvert( s, response, jqXHR, isSuccess ) {
+	var conv2, current, conv, tmp, prev,
+		converters = {},
+
+		// Work with a copy of dataTypes in case we need to modify it for conversion
+		dataTypes = s.dataTypes.slice();
+
+	// Create converters map with lowercased keys
+	if ( dataTypes[ 1 ] ) {
+		for ( conv in s.converters ) {
+			converters[ conv.toLowerCase() ] = s.converters[ conv ];
+		}
+	}
+
+	current = dataTypes.shift();
+
+	// Convert to each sequential dataType
+	while ( current ) {
+
+		if ( s.responseFields[ current ] ) {
+			jqXHR[ s.responseFields[ current ] ] = response;
+		}
+
+		// Apply the dataFilter if provided
+		if ( !prev && isSuccess && s.dataFilter ) {
+			response = s.dataFilter( response, s.dataType );
+		}
+
+		prev = current;
+		current = dataTypes.shift();
+
+		if ( current ) {
+
+			// There's only work to do if current dataType is non-auto
+			if ( current === "*" ) {
+
+				current = prev;
+
+			// Convert response if prev dataType is non-auto and differs from current
+			} else if ( prev !== "*" && prev !== current ) {
+
+				// Seek a direct converter
+				conv = converters[ prev + " " + current ] || converters[ "* " + current ];
+
+				// If none found, seek a pair
+				if ( !conv ) {
+					for ( conv2 in converters ) {
+
+						// If conv2 outputs current
+						tmp = conv2.split( " " );
+						if ( tmp[ 1 ] === current ) {
+
+							// If prev can be converted to accepted input
+							conv = converters[ prev + " " + tmp[ 0 ] ] ||
+								converters[ "* " + tmp[ 0 ] ];
+							if ( conv ) {
+
+								// Condense equivalence converters
+								if ( conv === true ) {
+									conv = converters[ conv2 ];
+
+								// Otherwise, insert the intermediate dataType
+								} else if ( converters[ conv2 ] !== true ) {
+									current = tmp[ 0 ];
+									dataTypes.unshift( tmp[ 1 ] );
+								}
+								break;
+							}
+						}
+					}
+				}
+
+				// Apply converter (if not an equivalence)
+				if ( conv !== true ) {
+
+					// Unless errors are allowed to bubble, catch and return them
+					if ( conv && s.throws ) {
+						response = conv( response );
+					} else {
+						try {
+							response = conv( response );
+						} catch ( e ) {
+							return {
+								state: "parsererror",
+								error: conv ? e : "No conversion from " + prev + " to " + current
+							};
+						}
+					}
+				}
+			}
+		}
+	}
+
+	return { state: "success", data: response };
+}
+
+jQuery.extend( {
+
+	// Counter for holding the number of active queries
+	active: 0,
+
+	// Last-Modified header cache for next request
+	lastModified: {},
+	etag: {},
+
+	ajaxSettings: {
+		url: location.href,
+		type: "GET",
+		isLocal: rlocalProtocol.test( location.protocol ),
+		global: true,
+		processData: true,
+		async: true,
+		contentType: "application/x-www-form-urlencoded; charset=UTF-8",
+
+		/*
+		timeout: 0,
+		data: null,
+		dataType: null,
+		username: null,
+		password: null,
+		cache: null,
+		throws: false,
+		traditional: false,
+		headers: {},
+		*/
+
+		accepts: {
+			"*": allTypes,
+			text: "text/plain",
+			html: "text/html",
+			xml: "application/xml, text/xml",
+			json: "application/json, text/javascript"
+		},
+
+		contents: {
+			xml: /\bxml\b/,
+			html: /\bhtml/,
+			json: /\bjson\b/
+		},
+
+		responseFields: {
+			xml: "responseXML",
+			text: "responseText",
+			json: "responseJSON"
+		},
+
+		// Data converters
+		// Keys separate source (or catchall "*") and destination types with a single space
+		converters: {
+
+			// Convert anything to text
+			"* text": String,
+
+			// Text to html (true = no transformation)
+			"text html": true,
+
+			// Evaluate text as a json expression
+			"text json": JSON.parse,
+
+			// Parse text as xml
+			"text xml": jQuery.parseXML
+		},
+
+		// For options that shouldn't be deep extended:
+		// you can add your own custom options here if
+		// and when you create one that shouldn't be
+		// deep extended (see ajaxExtend)
+		flatOptions: {
+			url: true,
+			context: true
+		}
+	},
+
+	// Creates a full fledged settings object into target
+	// with both ajaxSettings and settings fields.
+	// If target is omitted, writes into ajaxSettings.
+	ajaxSetup: function( target, settings ) {
+		return settings ?
+
+			// Building a settings object
+			ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
+
+			// Extending ajaxSettings
+			ajaxExtend( jQuery.ajaxSettings, target );
+	},
+
+	ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
+	ajaxTransport: addToPrefiltersOrTransports( transports ),
+
+	// Main method
+	ajax: function( url, options ) {
+
+		// If url is an object, simulate pre-1.5 signature
+		if ( typeof url === "object" ) {
+			options = url;
+			url = undefined;
+		}
+
+		// Force options to be an object
+		options = options || {};
+
+		var transport,
+
+			// URL without anti-cache param
+			cacheURL,
+
+			// Response headers
+			responseHeadersString,
+			responseHeaders,
+
+			// timeout handle
+			timeoutTimer,
+
+			// Url cleanup var
+			urlAnchor,
+
+			// Request state (becomes false upon send and true upon completion)
+			completed,
+
+			// To know if global events are to be dispatched
+			fireGlobals,
+
+			// Loop variable
+			i,
+
+			// uncached part of the url
+			uncached,
+
+			// Create the final options object
+			s = jQuery.ajaxSetup( {}, options ),
+
+			// Callbacks context
+			callbackContext = s.context || s,
+
+			// Context for global events is callbackContext if it is a DOM node or jQuery collection
+			globalEventContext = s.context &&
+				( callbackContext.nodeType || callbackContext.jquery ) ?
+				jQuery( callbackContext ) :
+				jQuery.event,
+
+			// Deferreds
+			deferred = jQuery.Deferred(),
+			completeDeferred = jQuery.Callbacks( "once memory" ),
+
+			// Status-dependent callbacks
+			statusCode = s.statusCode || {},
+
+			// Headers (they are sent all at once)
+			requestHeaders = {},
+			requestHeadersNames = {},
+
+			// Default abort message
+			strAbort = "canceled",
+
+			// Fake xhr
+			jqXHR = {
+				readyState: 0,
+
+				// Builds headers hashtable if needed
+				getResponseHeader: function( key ) {
+					var match;
+					if ( completed ) {
+						if ( !responseHeaders ) {
+							responseHeaders = {};
+							while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
+								responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
+									( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
+										.concat( match[ 2 ] );
+							}
+						}
+						match = responseHeaders[ key.toLowerCase() + " " ];
+					}
+					return match == null ? null : match.join( ", " );
+				},
+
+				// Raw string
+				getAllResponseHeaders: function() {
+					return completed ? responseHeadersString : null;
+				},
+
+				// Caches the header
+				setRequestHeader: function( name, value ) {
+					if ( completed == null ) {
+						name = requestHeadersNames[ name.toLowerCase() ] =
+							requestHeadersNames[ name.toLowerCase() ] || name;
+						requestHeaders[ name ] = value;
+					}
+					return this;
+				},
+
+				// Overrides response content-type header
+				overrideMimeType: function( type ) {
+					if ( completed == null ) {
+						s.mimeType = type;
+					}
+					return this;
+				},
+
+				// Status-dependent callbacks
+				statusCode: function( map ) {
+					var code;
+					if ( map ) {
+						if ( completed ) {
+
+							// Execute the appropriate callbacks
+							jqXHR.always( map[ jqXHR.status ] );
+						} else {
+
+							// Lazy-add the new callbacks in a way that preserves old ones
+							for ( code in map ) {
+								statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
+							}
+						}
+					}
+					return this;
+				},
+
+				// Cancel the request
+				abort: function( statusText ) {
+					var finalText = statusText || strAbort;
+					if ( transport ) {
+						transport.abort( finalText );
+					}
+					done( 0, finalText );
+					return this;
+				}
+			};
+
+		// Attach deferreds
+		deferred.promise( jqXHR );
+
+		// Add protocol if not provided (prefilters might expect it)
+		// Handle falsy url in the settings object (#10093: consistency with old signature)
+		// We also use the url parameter if available
+		s.url = ( ( url || s.url || location.href ) + "" )
+			.replace( rprotocol, location.protocol + "//" );
+
+		// Alias method option to type as per ticket #12004
+		s.type = options.method || options.type || s.method || s.type;
+
+		// Extract dataTypes list
+		s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
+
+		// A cross-domain request is in order when the origin doesn't match the current origin.
+		if ( s.crossDomain == null ) {
+			urlAnchor = document.createElement( "a" );
+
+			// Support: IE <=8 - 11, Edge 12 - 15
+			// IE throws exception on accessing the href property if url is malformed,
+			// e.g. http://example.com:80x/
+			try {
+				urlAnchor.href = s.url;
+
+				// Support: IE <=8 - 11 only
+				// Anchor's host property isn't correctly set when s.url is relative
+				urlAnchor.href = urlAnchor.href;
+				s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
+					urlAnchor.protocol + "//" + urlAnchor.host;
+			} catch ( e ) {
+
+				// If there is an error parsing the URL, assume it is crossDomain,
+				// it can be rejected by the transport if it is invalid
+				s.crossDomain = true;
+			}
+		}
+
+		// Convert data if not already a string
+		if ( s.data && s.processData && typeof s.data !== "string" ) {
+			s.data = jQuery.param( s.data, s.traditional );
+		}
+
+		// Apply prefilters
+		inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
+
+		// If request was aborted inside a prefilter, stop there
+		if ( completed ) {
+			return jqXHR;
+		}
+
+		// We can fire global events as of now if asked to
+		// Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
+		fireGlobals = jQuery.event && s.global;
+
+		// Watch for a new set of requests
+		if ( fireGlobals && jQuery.active++ === 0 ) {
+			jQuery.event.trigger( "ajaxStart" );
+		}
+
+		// Uppercase the type
+		s.type = s.type.toUpperCase();
+
+		// Determine if request has content
+		s.hasContent = !rnoContent.test( s.type );
+
+		// Save the URL in case we're toying with the If-Modified-Since
+		// and/or If-None-Match header later on
+		// Remove hash to simplify url manipulation
+		cacheURL = s.url.replace( rhash, "" );
+
+		// More options handling for requests with no content
+		if ( !s.hasContent ) {
+
+			// Remember the hash so we can put it back
+			uncached = s.url.slice( cacheURL.length );
+
+			// If data is available and should be processed, append data to url
+			if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
+				cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
+
+				// #9682: remove data so that it's not used in an eventual retry
+				delete s.data;
+			}
+
+			// Add or update anti-cache param if needed
+			if ( s.cache === false ) {
+				cacheURL = cacheURL.replace( rantiCache, "$1" );
+				uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
+					uncached;
+			}
+
+			// Put hash and anti-cache on the URL that will be requested (gh-1732)
+			s.url = cacheURL + uncached;
+
+		// Change '%20' to '+' if this is encoded form body content (gh-2658)
+		} else if ( s.data && s.processData &&
+			( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
+			s.data = s.data.replace( r20, "+" );
+		}
+
+		// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+		if ( s.ifModified ) {
+			if ( jQuery.lastModified[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
+			}
+			if ( jQuery.etag[ cacheURL ] ) {
+				jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
+			}
+		}
+
+		// Set the correct header, if data is being sent
+		if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
+			jqXHR.setRequestHeader( "Content-Type", s.contentType );
+		}
+
+		// Set the Accepts header for the server, depending on the dataType
+		jqXHR.setRequestHeader(
+			"Accept",
+			s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
+				s.accepts[ s.dataTypes[ 0 ] ] +
+					( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
+				s.accepts[ "*" ]
+		);
+
+		// Check for headers option
+		for ( i in s.headers ) {
+			jqXHR.setRequestHeader( i, s.headers[ i ] );
+		}
+
+		// Allow custom headers/mimetypes and early abort
+		if ( s.beforeSend &&
+			( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
+
+			// Abort if not done already and return
+			return jqXHR.abort();
+		}
+
+		// Aborting is no longer a cancellation
+		strAbort = "abort";
+
+		// Install callbacks on deferreds
+		completeDeferred.add( s.complete );
+		jqXHR.done( s.success );
+		jqXHR.fail( s.error );
+
+		// Get transport
+		transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
+
+		// If no transport, we auto-abort
+		if ( !transport ) {
+			done( -1, "No Transport" );
+		} else {
+			jqXHR.readyState = 1;
+
+			// Send global event
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
+			}
+
+			// If request was aborted inside ajaxSend, stop there
+			if ( completed ) {
+				return jqXHR;
+			}
+
+			// Timeout
+			if ( s.async && s.timeout > 0 ) {
+				timeoutTimer = window.setTimeout( function() {
+					jqXHR.abort( "timeout" );
+				}, s.timeout );
+			}
+
+			try {
+				completed = false;
+				transport.send( requestHeaders, done );
+			} catch ( e ) {
+
+				// Rethrow post-completion exceptions
+				if ( completed ) {
+					throw e;
+				}
+
+				// Propagate others as results
+				done( -1, e );
+			}
+		}
+
+		// Callback for when everything is done
+		function done( status, nativeStatusText, responses, headers ) {
+			var isSuccess, success, error, response, modified,
+				statusText = nativeStatusText;
+
+			// Ignore repeat invocations
+			if ( completed ) {
+				return;
+			}
+
+			completed = true;
+
+			// Clear timeout if it exists
+			if ( timeoutTimer ) {
+				window.clearTimeout( timeoutTimer );
+			}
+
+			// Dereference transport for early garbage collection
+			// (no matter how long the jqXHR object will be used)
+			transport = undefined;
+
+			// Cache response headers
+			responseHeadersString = headers || "";
+
+			// Set readyState
+			jqXHR.readyState = status > 0 ? 4 : 0;
+
+			// Determine if successful
+			isSuccess = status >= 200 && status < 300 || status === 304;
+
+			// Get response data
+			if ( responses ) {
+				response = ajaxHandleResponses( s, jqXHR, responses );
+			}
+
+			// Use a noop converter for missing script but not if jsonp
+			if ( !isSuccess &&
+				jQuery.inArray( "script", s.dataTypes ) > -1 &&
+				jQuery.inArray( "json", s.dataTypes ) < 0 ) {
+				s.converters[ "text script" ] = function() {};
+			}
+
+			// Convert no matter what (that way responseXXX fields are always set)
+			response = ajaxConvert( s, response, jqXHR, isSuccess );
+
+			// If successful, handle type chaining
+			if ( isSuccess ) {
+
+				// Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
+				if ( s.ifModified ) {
+					modified = jqXHR.getResponseHeader( "Last-Modified" );
+					if ( modified ) {
+						jQuery.lastModified[ cacheURL ] = modified;
+					}
+					modified = jqXHR.getResponseHeader( "etag" );
+					if ( modified ) {
+						jQuery.etag[ cacheURL ] = modified;
+					}
+				}
+
+				// if no content
+				if ( status === 204 || s.type === "HEAD" ) {
+					statusText = "nocontent";
+
+				// if not modified
+				} else if ( status === 304 ) {
+					statusText = "notmodified";
+
+				// If we have data, let's convert it
+				} else {
+					statusText = response.state;
+					success = response.data;
+					error = response.error;
+					isSuccess = !error;
+				}
+			} else {
+
+				// Extract error from statusText and normalize for non-aborts
+				error = statusText;
+				if ( status || !statusText ) {
+					statusText = "error";
+					if ( status < 0 ) {
+						status = 0;
+					}
+				}
+			}
+
+			// Set data for the fake xhr object
+			jqXHR.status = status;
+			jqXHR.statusText = ( nativeStatusText || statusText ) + "";
+
+			// Success/Error
+			if ( isSuccess ) {
+				deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
+			} else {
+				deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
+			}
+
+			// Status-dependent callbacks
+			jqXHR.statusCode( statusCode );
+			statusCode = undefined;
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
+					[ jqXHR, s, isSuccess ? success : error ] );
+			}
+
+			// Complete
+			completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
+
+			if ( fireGlobals ) {
+				globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
+
+				// Handle the global AJAX counter
+				if ( !( --jQuery.active ) ) {
+					jQuery.event.trigger( "ajaxStop" );
+				}
+			}
+		}
+
+		return jqXHR;
+	},
+
+	getJSON: function( url, data, callback ) {
+		return jQuery.get( url, data, callback, "json" );
+	},
+
+	getScript: function( url, callback ) {
+		return jQuery.get( url, undefined, callback, "script" );
+	}
+} );
+
+jQuery.each( [ "get", "post" ], function( _i, method ) {
+	jQuery[ method ] = function( url, data, callback, type ) {
+
+		// Shift arguments if data argument was omitted
+		if ( isFunction( data ) ) {
+			type = type || callback;
+			callback = data;
+			data = undefined;
+		}
+
+		// The url can be an options object (which then must have .url)
+		return jQuery.ajax( jQuery.extend( {
+			url: url,
+			type: method,
+			dataType: type,
+			data: data,
+			success: callback
+		}, jQuery.isPlainObject( url ) && url ) );
+	};
+} );
+
+jQuery.ajaxPrefilter( function( s ) {
+	var i;
+	for ( i in s.headers ) {
+		if ( i.toLowerCase() === "content-type" ) {
+			s.contentType = s.headers[ i ] || "";
+		}
+	}
+} );
+
+
+jQuery._evalUrl = function( url, options, doc ) {
+	return jQuery.ajax( {
+		url: url,
+
+		// Make this explicit, since user can override this through ajaxSetup (#11264)
+		type: "GET",
+		dataType: "script",
+		cache: true,
+		async: false,
+		global: false,
+
+		// Only evaluate the response if it is successful (gh-4126)
+		// dataFilter is not invoked for failure responses, so using it instead
+		// of the default converter is kludgy but it works.
+		converters: {
+			"text script": function() {}
+		},
+		dataFilter: function( response ) {
+			jQuery.globalEval( response, options, doc );
+		}
+	} );
+};
+
+
+jQuery.fn.extend( {
+	wrapAll: function( html ) {
+		var wrap;
+
+		if ( this[ 0 ] ) {
+			if ( isFunction( html ) ) {
+				html = html.call( this[ 0 ] );
+			}
+
+			// The elements to wrap the target around
+			wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
+
+			if ( this[ 0 ].parentNode ) {
+				wrap.insertBefore( this[ 0 ] );
+			}
+
+			wrap.map( function() {
+				var elem = this;
+
+				while ( elem.firstElementChild ) {
+					elem = elem.firstElementChild;
+				}
+
+				return elem;
+			} ).append( this );
+		}
+
+		return this;
+	},
+
+	wrapInner: function( html ) {
+		if ( isFunction( html ) ) {
+			return this.each( function( i ) {
+				jQuery( this ).wrapInner( html.call( this, i ) );
+			} );
+		}
+
+		return this.each( function() {
+			var self = jQuery( this ),
+				contents = self.contents();
+
+			if ( contents.length ) {
+				contents.wrapAll( html );
+
+			} else {
+				self.append( html );
+			}
+		} );
+	},
+
+	wrap: function( html ) {
+		var htmlIsFunction = isFunction( html );
+
+		return this.each( function( i ) {
+			jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
+		} );
+	},
+
+	unwrap: function( selector ) {
+		this.parent( selector ).not( "body" ).each( function() {
+			jQuery( this ).replaceWith( this.childNodes );
+		} );
+		return this;
+	}
+} );
+
+
+jQuery.expr.pseudos.hidden = function( elem ) {
+	return !jQuery.expr.pseudos.visible( elem );
+};
+jQuery.expr.pseudos.visible = function( elem ) {
+	return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
+};
+
+
+
+
+jQuery.ajaxSettings.xhr = function() {
+	try {
+		return new window.XMLHttpRequest();
+	} catch ( e ) {}
+};
+
+var xhrSuccessStatus = {
+
+		// File protocol always yields status code 0, assume 200
+		0: 200,
+
+		// Support: IE <=9 only
+		// #1450: sometimes IE returns 1223 when it should be 204
+		1223: 204
+	},
+	xhrSupported = jQuery.ajaxSettings.xhr();
+
+support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
+support.ajax = xhrSupported = !!xhrSupported;
+
+jQuery.ajaxTransport( function( options ) {
+	var callback, errorCallback;
+
+	// Cross domain only allowed if supported through XMLHttpRequest
+	if ( support.cors || xhrSupported && !options.crossDomain ) {
+		return {
+			send: function( headers, complete ) {
+				var i,
+					xhr = options.xhr();
+
+				xhr.open(
+					options.type,
+					options.url,
+					options.async,
+					options.username,
+					options.password
+				);
+
+				// Apply custom fields if provided
+				if ( options.xhrFields ) {
+					for ( i in options.xhrFields ) {
+						xhr[ i ] = options.xhrFields[ i ];
+					}
+				}
+
+				// Override mime type if needed
+				if ( options.mimeType && xhr.overrideMimeType ) {
+					xhr.overrideMimeType( options.mimeType );
+				}
+
+				// X-Requested-With header
+				// For cross-domain requests, seeing as conditions for a preflight are
+				// akin to a jigsaw puzzle, we simply never set it to be sure.
+				// (it can always be set on a per-request basis or even using ajaxSetup)
+				// For same-domain requests, won't change header if already provided.
+				if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
+					headers[ "X-Requested-With" ] = "XMLHttpRequest";
+				}
+
+				// Set headers
+				for ( i in headers ) {
+					xhr.setRequestHeader( i, headers[ i ] );
+				}
+
+				// Callback
+				callback = function( type ) {
+					return function() {
+						if ( callback ) {
+							callback = errorCallback = xhr.onload =
+								xhr.onerror = xhr.onabort = xhr.ontimeout =
+									xhr.onreadystatechange = null;
+
+							if ( type === "abort" ) {
+								xhr.abort();
+							} else if ( type === "error" ) {
+
+								// Support: IE <=9 only
+								// On a manual native abort, IE9 throws
+								// errors on any property access that is not readyState
+								if ( typeof xhr.status !== "number" ) {
+									complete( 0, "error" );
+								} else {
+									complete(
+
+										// File: protocol always yields status 0; see #8605, #14207
+										xhr.status,
+										xhr.statusText
+									);
+								}
+							} else {
+								complete(
+									xhrSuccessStatus[ xhr.status ] || xhr.status,
+									xhr.statusText,
+
+									// Support: IE <=9 only
+									// IE9 has no XHR2 but throws on binary (trac-11426)
+									// For XHR2 non-text, let the caller handle it (gh-2498)
+									( xhr.responseType || "text" ) !== "text"  ||
+									typeof xhr.responseText !== "string" ?
+										{ binary: xhr.response } :
+										{ text: xhr.responseText },
+									xhr.getAllResponseHeaders()
+								);
+							}
+						}
+					};
+				};
+
+				// Listen to events
+				xhr.onload = callback();
+				errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
+
+				// Support: IE 9 only
+				// Use onreadystatechange to replace onabort
+				// to handle uncaught aborts
+				if ( xhr.onabort !== undefined ) {
+					xhr.onabort = errorCallback;
+				} else {
+					xhr.onreadystatechange = function() {
+
+						// Check readyState before timeout as it changes
+						if ( xhr.readyState === 4 ) {
+
+							// Allow onerror to be called first,
+							// but that will not handle a native abort
+							// Also, save errorCallback to a variable
+							// as xhr.onerror cannot be accessed
+							window.setTimeout( function() {
+								if ( callback ) {
+									errorCallback();
+								}
+							} );
+						}
+					};
+				}
+
+				// Create the abort callback
+				callback = callback( "abort" );
+
+				try {
+
+					// Do send the request (this may raise an exception)
+					xhr.send( options.hasContent && options.data || null );
+				} catch ( e ) {
+
+					// #14683: Only rethrow if this hasn't been notified as an error yet
+					if ( callback ) {
+						throw e;
+					}
+				}
+			},
+
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432)
+jQuery.ajaxPrefilter( function( s ) {
+	if ( s.crossDomain ) {
+		s.contents.script = false;
+	}
+} );
+
+// Install script dataType
+jQuery.ajaxSetup( {
+	accepts: {
+		script: "text/javascript, application/javascript, " +
+			"application/ecmascript, application/x-ecmascript"
+	},
+	contents: {
+		script: /\b(?:java|ecma)script\b/
+	},
+	converters: {
+		"text script": function( text ) {
+			jQuery.globalEval( text );
+			return text;
+		}
+	}
+} );
+
+// Handle cache's special case and crossDomain
+jQuery.ajaxPrefilter( "script", function( s ) {
+	if ( s.cache === undefined ) {
+		s.cache = false;
+	}
+	if ( s.crossDomain ) {
+		s.type = "GET";
+	}
+} );
+
+// Bind script tag hack transport
+jQuery.ajaxTransport( "script", function( s ) {
+
+	// This transport only deals with cross domain or forced-by-attrs requests
+	if ( s.crossDomain || s.scriptAttrs ) {
+		var script, callback;
+		return {
+			send: function( _, complete ) {
+				script = jQuery( "<script>" )
+					.attr( s.scriptAttrs || {} )
+					.prop( { charset: s.scriptCharset, src: s.url } )
+					.on( "load error", callback = function( evt ) {
+						script.remove();
+						callback = null;
+						if ( evt ) {
+							complete( evt.type === "error" ? 404 : 200, evt.type );
+						}
+					} );
+
+				// Use native DOM manipulation to avoid our domManip AJAX trickery
+				document.head.appendChild( script[ 0 ] );
+			},
+			abort: function() {
+				if ( callback ) {
+					callback();
+				}
+			}
+		};
+	}
+} );
+
+
+
+
+var oldCallbacks = [],
+	rjsonp = /(=)\?(?=&|$)|\?\?/;
+
+// Default jsonp settings
+jQuery.ajaxSetup( {
+	jsonp: "callback",
+	jsonpCallback: function() {
+		var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
+		this[ callback ] = true;
+		return callback;
+	}
+} );
+
+// Detect, normalize options and install callbacks for jsonp requests
+jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
+
+	var callbackName, overwritten, responseContainer,
+		jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
+			"url" :
+			typeof s.data === "string" &&
+				( s.contentType || "" )
+					.indexOf( "application/x-www-form-urlencoded" ) === 0 &&
+				rjsonp.test( s.data ) && "data"
+		);
+
+	// Handle iff the expected data type is "jsonp" or we have a parameter to set
+	if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
+
+		// Get callback name, remembering preexisting value associated with it
+		callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
+			s.jsonpCallback() :
+			s.jsonpCallback;
+
+		// Insert callback into url or form data
+		if ( jsonProp ) {
+			s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
+		} else if ( s.jsonp !== false ) {
+			s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
+		}
+
+		// Use data converter to retrieve json after script execution
+		s.converters[ "script json" ] = function() {
+			if ( !responseContainer ) {
+				jQuery.error( callbackName + " was not called" );
+			}
+			return responseContainer[ 0 ];
+		};
+
+		// Force json dataType
+		s.dataTypes[ 0 ] = "json";
+
+		// Install callback
+		overwritten = window[ callbackName ];
+		window[ callbackName ] = function() {
+			responseContainer = arguments;
+		};
+
+		// Clean-up function (fires after converters)
+		jqXHR.always( function() {
+
+			// If previous value didn't exist - remove it
+			if ( overwritten === undefined ) {
+				jQuery( window ).removeProp( callbackName );
+
+			// Otherwise restore preexisting value
+			} else {
+				window[ callbackName ] = overwritten;
+			}
+
+			// Save back as free
+			if ( s[ callbackName ] ) {
+
+				// Make sure that re-using the options doesn't screw things around
+				s.jsonpCallback = originalSettings.jsonpCallback;
+
+				// Save the callback name for future use
+				oldCallbacks.push( callbackName );
+			}
+
+			// Call if it was a function and we have a response
+			if ( responseContainer && isFunction( overwritten ) ) {
+				overwritten( responseContainer[ 0 ] );
+			}
+
+			responseContainer = overwritten = undefined;
+		} );
+
+		// Delegate to script
+		return "script";
+	}
+} );
+
+
+
+
+// Support: Safari 8 only
+// In Safari 8 documents created via document.implementation.createHTMLDocument
+// collapse sibling forms: the second one becomes a child of the first one.
+// Because of that, this security measure has to be disabled in Safari 8.
+// https://bugs.webkit.org/show_bug.cgi?id=137337
+support.createHTMLDocument = ( function() {
+	var body = document.implementation.createHTMLDocument( "" ).body;
+	body.innerHTML = "<form></form><form></form>";
+	return body.childNodes.length === 2;
+} )();
+
+
+// Argument "data" should be string of html
+// context (optional): If specified, the fragment will be created in this context,
+// defaults to document
+// keepScripts (optional): If true, will include scripts passed in the html string
+jQuery.parseHTML = function( data, context, keepScripts ) {
+	if ( typeof data !== "string" ) {
+		return [];
+	}
+	if ( typeof context === "boolean" ) {
+		keepScripts = context;
+		context = false;
+	}
+
+	var base, parsed, scripts;
+
+	if ( !context ) {
+
+		// Stop scripts or inline event handlers from being executed immediately
+		// by using document.implementation
+		if ( support.createHTMLDocument ) {
+			context = document.implementation.createHTMLDocument( "" );
+
+			// Set the base href for the created document
+			// so any parsed elements with URLs
+			// are based on the document's URL (gh-2965)
+			base = context.createElement( "base" );
+			base.href = document.location.href;
+			context.head.appendChild( base );
+		} else {
+			context = document;
+		}
+	}
+
+	parsed = rsingleTag.exec( data );
+	scripts = !keepScripts && [];
+
+	// Single tag
+	if ( parsed ) {
+		return [ context.createElement( parsed[ 1 ] ) ];
+	}
+
+	parsed = buildFragment( [ data ], context, scripts );
+
+	if ( scripts && scripts.length ) {
+		jQuery( scripts ).remove();
+	}
+
+	return jQuery.merge( [], parsed.childNodes );
+};
+
+
+/**
+ * Load a url into a page
+ */
+jQuery.fn.load = function( url, params, callback ) {
+	var selector, type, response,
+		self = this,
+		off = url.indexOf( " " );
+
+	if ( off > -1 ) {
+		selector = stripAndCollapse( url.slice( off ) );
+		url = url.slice( 0, off );
+	}
+
+	// If it's a function
+	if ( isFunction( params ) ) {
+
+		// We assume that it's the callback
+		callback = params;
+		params = undefined;
+
+	// Otherwise, build a param string
+	} else if ( params && typeof params === "object" ) {
+		type = "POST";
+	}
+
+	// If we have elements to modify, make the request
+	if ( self.length > 0 ) {
+		jQuery.ajax( {
+			url: url,
+
+			// If "type" variable is undefined, then "GET" method will be used.
+			// Make value of this field explicit since
+			// user can override it through ajaxSetup method
+			type: type || "GET",
+			dataType: "html",
+			data: params
+		} ).done( function( responseText ) {
+
+			// Save response for use in complete callback
+			response = arguments;
+
+			self.html( selector ?
+
+				// If a selector was specified, locate the right elements in a dummy div
+				// Exclude scripts to avoid IE 'Permission Denied' errors
+				jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
+
+				// Otherwise use the full result
+				responseText );
+
+		// If the request succeeds, this function gets "data", "status", "jqXHR"
+		// but they are ignored because response was set above.
+		// If it fails, this function gets "jqXHR", "status", "error"
+		} ).always( callback && function( jqXHR, status ) {
+			self.each( function() {
+				callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
+			} );
+		} );
+	}
+
+	return this;
+};
+
+
+
+
+jQuery.expr.pseudos.animated = function( elem ) {
+	return jQuery.grep( jQuery.timers, function( fn ) {
+		return elem === fn.elem;
+	} ).length;
+};
+
+
+
+
+jQuery.offset = {
+	setOffset: function( elem, options, i ) {
+		var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
+			position = jQuery.css( elem, "position" ),
+			curElem = jQuery( elem ),
+			props = {};
+
+		// Set position first, in-case top/left are set even on static elem
+		if ( position === "static" ) {
+			elem.style.position = "relative";
+		}
+
+		curOffset = curElem.offset();
+		curCSSTop = jQuery.css( elem, "top" );
+		curCSSLeft = jQuery.css( elem, "left" );
+		calculatePosition = ( position === "absolute" || position === "fixed" ) &&
+			( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
+
+		// Need to be able to calculate position if either
+		// top or left is auto and position is either absolute or fixed
+		if ( calculatePosition ) {
+			curPosition = curElem.position();
+			curTop = curPosition.top;
+			curLeft = curPosition.left;
+
+		} else {
+			curTop = parseFloat( curCSSTop ) || 0;
+			curLeft = parseFloat( curCSSLeft ) || 0;
+		}
+
+		if ( isFunction( options ) ) {
+
+			// Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
+			options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
+		}
+
+		if ( options.top != null ) {
+			props.top = ( options.top - curOffset.top ) + curTop;
+		}
+		if ( options.left != null ) {
+			props.left = ( options.left - curOffset.left ) + curLeft;
+		}
+
+		if ( "using" in options ) {
+			options.using.call( elem, props );
+
+		} else {
+			curElem.css( props );
+		}
+	}
+};
+
+jQuery.fn.extend( {
+
+	// offset() relates an element's border box to the document origin
+	offset: function( options ) {
+
+		// Preserve chaining for setter
+		if ( arguments.length ) {
+			return options === undefined ?
+				this :
+				this.each( function( i ) {
+					jQuery.offset.setOffset( this, options, i );
+				} );
+		}
+
+		var rect, win,
+			elem = this[ 0 ];
+
+		if ( !elem ) {
+			return;
+		}
+
+		// Return zeros for disconnected and hidden (display: none) elements (gh-2310)
+		// Support: IE <=11 only
+		// Running getBoundingClientRect on a
+		// disconnected node in IE throws an error
+		if ( !elem.getClientRects().length ) {
+			return { top: 0, left: 0 };
+		}
+
+		// Get document-relative position by adding viewport scroll to viewport-relative gBCR
+		rect = elem.getBoundingClientRect();
+		win = elem.ownerDocument.defaultView;
+		return {
+			top: rect.top + win.pageYOffset,
+			left: rect.left + win.pageXOffset
+		};
+	},
+
+	// position() relates an element's margin box to its offset parent's padding box
+	// This corresponds to the behavior of CSS absolute positioning
+	position: function() {
+		if ( !this[ 0 ] ) {
+			return;
+		}
+
+		var offsetParent, offset, doc,
+			elem = this[ 0 ],
+			parentOffset = { top: 0, left: 0 };
+
+		// position:fixed elements are offset from the viewport, which itself always has zero offset
+		if ( jQuery.css( elem, "position" ) === "fixed" ) {
+
+			// Assume position:fixed implies availability of getBoundingClientRect
+			offset = elem.getBoundingClientRect();
+
+		} else {
+			offset = this.offset();
+
+			// Account for the *real* offset parent, which can be the document or its root element
+			// when a statically positioned element is identified
+			doc = elem.ownerDocument;
+			offsetParent = elem.offsetParent || doc.documentElement;
+			while ( offsetParent &&
+				( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
+				jQuery.css( offsetParent, "position" ) === "static" ) {
+
+				offsetParent = offsetParent.parentNode;
+			}
+			if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
+
+				// Incorporate borders into its offset, since they are outside its content origin
+				parentOffset = jQuery( offsetParent ).offset();
+				parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
+				parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
+			}
+		}
+
+		// Subtract parent offsets and element margins
+		return {
+			top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
+			left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
+		};
+	},
+
+	// This method will return documentElement in the following cases:
+	// 1) For the element inside the iframe without offsetParent, this method will return
+	//    documentElement of the parent window
+	// 2) For the hidden or detached element
+	// 3) For body or html element, i.e. in case of the html node - it will return itself
+	//
+	// but those exceptions were never presented as a real life use-cases
+	// and might be considered as more preferable results.
+	//
+	// This logic, however, is not guaranteed and can change at any point in the future
+	offsetParent: function() {
+		return this.map( function() {
+			var offsetParent = this.offsetParent;
+
+			while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
+				offsetParent = offsetParent.offsetParent;
+			}
+
+			return offsetParent || documentElement;
+		} );
+	}
+} );
+
+// Create scrollLeft and scrollTop methods
+jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
+	var top = "pageYOffset" === prop;
+
+	jQuery.fn[ method ] = function( val ) {
+		return access( this, function( elem, method, val ) {
+
+			// Coalesce documents and windows
+			var win;
+			if ( isWindow( elem ) ) {
+				win = elem;
+			} else if ( elem.nodeType === 9 ) {
+				win = elem.defaultView;
+			}
+
+			if ( val === undefined ) {
+				return win ? win[ prop ] : elem[ method ];
+			}
+
+			if ( win ) {
+				win.scrollTo(
+					!top ? val : win.pageXOffset,
+					top ? val : win.pageYOffset
+				);
+
+			} else {
+				elem[ method ] = val;
+			}
+		}, method, val, arguments.length );
+	};
+} );
+
+// Support: Safari <=7 - 9.1, Chrome <=37 - 49
+// Add the top/left cssHooks using jQuery.fn.position
+// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
+// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
+// getComputedStyle returns percent when specified for top/left/bottom/right;
+// rather than make the css module depend on the offset module, just check for it here
+jQuery.each( [ "top", "left" ], function( _i, prop ) {
+	jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
+		function( elem, computed ) {
+			if ( computed ) {
+				computed = curCSS( elem, prop );
+
+				// If curCSS returns percentage, fallback to offset
+				return rnumnonpx.test( computed ) ?
+					jQuery( elem ).position()[ prop ] + "px" :
+					computed;
+			}
+		}
+	);
+} );
+
+
+// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
+jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
+	jQuery.each( {
+		padding: "inner" + name,
+		content: type,
+		"": "outer" + name
+	}, function( defaultExtra, funcName ) {
+
+		// Margin is only for outerHeight, outerWidth
+		jQuery.fn[ funcName ] = function( margin, value ) {
+			var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
+				extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
+
+			return access( this, function( elem, type, value ) {
+				var doc;
+
+				if ( isWindow( elem ) ) {
+
+					// $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
+					return funcName.indexOf( "outer" ) === 0 ?
+						elem[ "inner" + name ] :
+						elem.document.documentElement[ "client" + name ];
+				}
+
+				// Get document width or height
+				if ( elem.nodeType === 9 ) {
+					doc = elem.documentElement;
+
+					// Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
+					// whichever is greatest
+					return Math.max(
+						elem.body[ "scroll" + name ], doc[ "scroll" + name ],
+						elem.body[ "offset" + name ], doc[ "offset" + name ],
+						doc[ "client" + name ]
+					);
+				}
+
+				return value === undefined ?
+
+					// Get width or height on the element, requesting but not forcing parseFloat
+					jQuery.css( elem, type, extra ) :
+
+					// Set width or height on the element
+					jQuery.style( elem, type, value, extra );
+			}, type, chainable ? margin : undefined, chainable );
+		};
+	} );
+} );
+
+
+jQuery.each( [
+	"ajaxStart",
+	"ajaxStop",
+	"ajaxComplete",
+	"ajaxError",
+	"ajaxSuccess",
+	"ajaxSend"
+], function( _i, type ) {
+	jQuery.fn[ type ] = function( fn ) {
+		return this.on( type, fn );
+	};
+} );
+
+
+
+
+jQuery.fn.extend( {
+
+	bind: function( types, data, fn ) {
+		return this.on( types, null, data, fn );
+	},
+	unbind: function( types, fn ) {
+		return this.off( types, null, fn );
+	},
+
+	delegate: function( selector, types, data, fn ) {
+		return this.on( types, selector, data, fn );
+	},
+	undelegate: function( selector, types, fn ) {
+
+		// ( namespace ) or ( selector, types [, fn] )
+		return arguments.length === 1 ?
+			this.off( selector, "**" ) :
+			this.off( types, selector || "**", fn );
+	},
+
+	hover: function( fnOver, fnOut ) {
+		return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
+	}
+} );
+
+jQuery.each(
+	( "blur focus focusin focusout resize scroll click dblclick " +
+	"mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
+	"change select submit keydown keypress keyup contextmenu" ).split( " " ),
+	function( _i, name ) {
+
+		// Handle event binding
+		jQuery.fn[ name ] = function( data, fn ) {
+			return arguments.length > 0 ?
+				this.on( name, null, data, fn ) :
+				this.trigger( name );
+		};
+	}
+);
+
+
+
+
+// Support: Android <=4.0 only
+// Make sure we trim BOM and NBSP
+var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
+
+// Bind a function to a context, optionally partially applying any
+// arguments.
+// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
+// However, it is not slated for removal any time soon
+jQuery.proxy = function( fn, context ) {
+	var tmp, args, proxy;
+
+	if ( typeof context === "string" ) {
+		tmp = fn[ context ];
+		context = fn;
+		fn = tmp;
+	}
+
+	// Quick check to determine if target is callable, in the spec
+	// this throws a TypeError, but we will just return undefined.
+	if ( !isFunction( fn ) ) {
+		return undefined;
+	}
+
+	// Simulated bind
+	args = slice.call( arguments, 2 );
+	proxy = function() {
+		return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
+	};
+
+	// Set the guid of unique handler to the same of original handler, so it can be removed
+	proxy.guid = fn.guid = fn.guid || jQuery.guid++;
+
+	return proxy;
+};
+
+jQuery.holdReady = function( hold ) {
+	if ( hold ) {
+		jQuery.readyWait++;
+	} else {
+		jQuery.ready( true );
+	}
+};
+jQuery.isArray = Array.isArray;
+jQuery.parseJSON = JSON.parse;
+jQuery.nodeName = nodeName;
+jQuery.isFunction = isFunction;
+jQuery.isWindow = isWindow;
+jQuery.camelCase = camelCase;
+jQuery.type = toType;
+
+jQuery.now = Date.now;
+
+jQuery.isNumeric = function( obj ) {
+
+	// As of jQuery 3.0, isNumeric is limited to
+	// strings and numbers (primitives or objects)
+	// that can be coerced to finite numbers (gh-2662)
+	var type = jQuery.type( obj );
+	return ( type === "number" || type === "string" ) &&
+
+		// parseFloat NaNs numeric-cast false positives ("")
+		// ...but misinterprets leading-number strings, particularly hex literals ("0x...")
+		// subtraction forces infinities to NaN
+		!isNaN( obj - parseFloat( obj ) );
+};
+
+jQuery.trim = function( text ) {
+	return text == null ?
+		"" :
+		( text + "" ).replace( rtrim, "" );
+};
+
+
+
+// Register as a named AMD module, since jQuery can be concatenated with other
+// files that may use define, but not via a proper concatenation script that
+// understands anonymous AMD modules. A named AMD is safest and most robust
+// way to register. Lowercase jquery is used because AMD module names are
+// derived from file names, and jQuery is normally delivered in a lowercase
+// file name. Do this after creating the global so that if an AMD module wants
+// to call noConflict to hide this version of jQuery, it will work.
+
+// Note that for maximum portability, libraries that are not jQuery should
+// declare themselves as anonymous modules, and avoid setting a global if an
+// AMD loader is present. jQuery is a special case. For more information, see
+// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
+
+if ( typeof define === "function" && define.amd ) {
+	define( "jquery", [], function() {
+		return jQuery;
+	} );
+}
+
+
+
+
+var
+
+	// Map over jQuery in case of overwrite
+	_jQuery = window.jQuery,
+
+	// Map over the $ in case of overwrite
+	_$ = window.$;
+
+jQuery.noConflict = function( deep ) {
+	if ( window.$ === jQuery ) {
+		window.$ = _$;
+	}
+
+	if ( deep && window.jQuery === jQuery ) {
+		window.jQuery = _jQuery;
+	}
+
+	return jQuery;
+};
+
+// Expose jQuery and $ identifiers, even in AMD
+// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
+// and CommonJS for browser emulators (#13566)
+if ( typeof noGlobal === "undefined" ) {
+	window.jQuery = window.$ = jQuery;
+}
+
+
+
+
+return jQuery;
+} );
diff --git a/docs/_build/html/_static/jquery.js b/docs/_build/html/_static/jquery.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/docs/_build/html/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
+!function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(C,e){"use strict";var t=[],r=Object.getPrototypeOf,s=t.slice,g=t.flat?function(e){return t.flat.call(e)}:function(e){return t.concat.apply([],e)},u=t.push,i=t.indexOf,n={},o=n.toString,v=n.hasOwnProperty,a=v.toString,l=a.call(Object),y={},m=function(e){return"function"==typeof e&&"number"!=typeof e.nodeType&&"function"!=typeof e.item},x=function(e){return null!=e&&e===e.window},E=C.document,c={type:!0,src:!0,nonce:!0,noModule:!0};function b(e,t,n){var r,i,o=(n=n||E).createElement("script");if(o.text=e,t)for(r in c)(i=t[r]||t.getAttribute&&t.getAttribute(r))&&o.setAttribute(r,i);n.head.appendChild(o).parentNode.removeChild(o)}function w(e){return null==e?e+"":"object"==typeof e||"function"==typeof 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diff --git a/docs/build/html/_static/js/badge_only.js b/docs/_build/html/_static/js/badge_only.js
similarity index 100%
rename from docs/build/html/_static/js/badge_only.js
rename to docs/_build/html/_static/js/badge_only.js
diff --git a/docs/build/html/_static/js/html5shiv-printshiv.min.js b/docs/_build/html/_static/js/html5shiv-printshiv.min.js
similarity index 100%
rename from docs/build/html/_static/js/html5shiv-printshiv.min.js
rename to docs/_build/html/_static/js/html5shiv-printshiv.min.js
diff --git a/docs/build/html/_static/js/html5shiv.min.js b/docs/_build/html/_static/js/html5shiv.min.js
similarity index 100%
rename from docs/build/html/_static/js/html5shiv.min.js
rename to docs/_build/html/_static/js/html5shiv.min.js
diff --git a/docs/build/html/_static/js/theme.js b/docs/_build/html/_static/js/theme.js
similarity index 100%
rename from docs/build/html/_static/js/theme.js
rename to docs/_build/html/_static/js/theme.js
diff --git a/docs/build/html/_static/language_data.js b/docs/_build/html/_static/language_data.js
similarity index 97%
rename from docs/build/html/_static/language_data.js
rename to docs/_build/html/_static/language_data.js
index 250f566..367b8ed 100644
--- a/docs/build/html/_static/language_data.js
+++ b/docs/_build/html/_static/language_data.js
@@ -5,7 +5,7 @@
  * This script contains the language-specific data used by searchtools.js,
  * namely the list of stopwords, stemmer, scorer and splitter.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -13,7 +13,7 @@
 var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"];
 
 
-/* Non-minified version is copied as a separate JS file, is available */
+/* Non-minified version is copied as a separate JS file, if available */
 
 /**
  * Porter Stemmer
diff --git a/docs/_build/html/_static/language_data.js.jinja b/docs/_build/html/_static/language_data.js.jinja
new file mode 100644
index 0000000..3666e5e
--- /dev/null
+++ b/docs/_build/html/_static/language_data.js.jinja
@@ -0,0 +1,26 @@
+/*
+ * language_data.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * This script contains the language-specific data used by searchtools.js,
+ * namely the list of stopwords, stemmer, scorer and splitter.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+var stopwords = {{ search_language_stop_words }};
+
+{% if search_language_stemming_code %}
+/* Non-minified version is copied as a separate JS file, if available */
+{{ search_language_stemming_code|safe }}
+{% endif -%}
+
+{% if search_scorer_tool %}
+{{ search_scorer_tool|safe }}
+{% endif -%}
+
+{% if search_word_splitter_code %}
+{{ search_word_splitter_code }}
+{% endif -%}
diff --git a/docs/build/html/_static/minus.png b/docs/_build/html/_static/minus.png
similarity index 100%
rename from docs/build/html/_static/minus.png
rename to docs/_build/html/_static/minus.png
diff --git a/docs/_build/html/_static/nbsphinx-broken-thumbnail.svg b/docs/_build/html/_static/nbsphinx-broken-thumbnail.svg
new file mode 100644
index 0000000..4919ca8
--- /dev/null
+++ b/docs/_build/html/_static/nbsphinx-broken-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: lightcoral; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: crimson; }
+    }
+  </style>
+  <path d="m 80.856887,38.34474 h 2.903414 l 7.066397,13.332221 V 38.34474 h 2.092166 V 54.281494 H 90.01545 L 82.949053,40.949273 v 13.332221 h -2.092166 z m -13.812565,0 h 10.076555 v 1.814633 h -7.920343 v 4.718048 h 7.589439 v 1.814634 h -7.589439 v 5.774805 h 8.11248 v 1.814634 H 67.044322 Z m -14.335606,0 h 2.156212 v 6.735493 L 62.01672,38.34474 h 2.775323 l -7.909669,7.429324 8.475407,8.50743 h -2.839368 l -7.653485,-7.674833 v 7.674833 h -2.156212 z m -10.738365,1.462381 q -2.348349,0 -3.73601,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010158 1.376987,4.760746 1.387661,1.750587 3.73601,1.750587 2.34835,0 3.714663,-1.750587 1.376986,-1.750588 1.376986,-4.760746 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714663,-1.750588 z m 0,-1.750588 q 3.351736,0 5.358507,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358507,2.241606 -3.362409,0 -5.379855,-2.241606 -2.006772,-2.241607 -2.006772,-6.020315 0,-3.778708 2.006772,-6.020314 2.017446,-2.252281 5.379855,-2.252281 z m -14.100771,8.75294 q 0.693831,0.234835 1.344964,1.003385 0.661808,0.768551 1.323615,2.113515 l 2.188235,4.355121 H 30.410068 L 28.371273,50.193231 Q 27.581374,48.592083 26.834172,48.069042 26.097644,47.546 24.816726,47.546 h -2.34835 v 6.735494 H 20.312164 V 38.34474 h 4.867489 q 2.732625,0 4.077588,1.142152 1.344964,1.142151 1.344964,3.447804 0,1.505078 -0.704505,2.49779 -0.69383,0.992711 -2.02812,1.376987 z m -5.401204,-6.692797 v 5.657388 h 2.711277 q 1.55845,0 2.348349,-0.715179 0.800574,-0.725854 0.800574,-2.124189 0,-1.398336 -0.800574,-2.102841 -0.789899,-0.715179 -2.348349,-0.715179 z M 7.4709616,46.670707 v 5.838851 H 10.92944 q 1.739914,0 2.57251,-0.71518 0.843271,-0.725853 0.843271,-2.209583 0,-1.494404 -0.843271,-2.198909 -0.832596,-0.715179 -2.57251,-0.715179 z m 0,-6.554031 v 4.803443 h 3.1916204 q 1.579799,0 2.34835,-0.587088 0.779225,-0.597761 0.779225,-1.814634 0,-1.206197 -0.779225,-1.803959 -0.768551,-0.597762 -2.34835,-0.597762 z M 5.3147497,38.34474 h 5.5079473 q 2.465767,0 3.800057,1.024734 1.334289,1.024734 1.334289,2.914088 0,1.462382 -0.683156,2.327001 -0.683156,0.86462 -2.006772,1.078106 1.590474,0.341579 2.465768,1.430359 0.885968,1.078106 0.885968,2.700602 0,2.134863 -1.451707,3.298364 -1.451707,1.1635 -4.130961,1.1635 H 5.3147497 Z M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 h -5.657391 z" />
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diff --git a/docs/_build/html/_static/nbsphinx-code-cells.css b/docs/_build/html/_static/nbsphinx-code-cells.css
new file mode 100644
index 0000000..a3fb27c
--- /dev/null
+++ b/docs/_build/html/_static/nbsphinx-code-cells.css
@@ -0,0 +1,259 @@
+/* remove conflicting styling from Sphinx themes */
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt *,
+div.nbinput.container div.input_area pre,
+div.nboutput.container div.output_area pre,
+div.nbinput.container div.input_area .highlight,
+div.nboutput.container div.output_area .highlight {
+    border: none;
+    padding: 0;
+    margin: 0;
+    box-shadow: none;
+}
+
+div.nbinput.container > div[class*=highlight],
+div.nboutput.container > div[class*=highlight] {
+    margin: 0;
+}
+
+div.nbinput.container div.prompt *,
+div.nboutput.container div.prompt * {
+    background: none;
+}
+
+div.nboutput.container div.output_area .highlight,
+div.nboutput.container div.output_area pre {
+    background: unset;
+}
+
+div.nboutput.container div.output_area div.highlight {
+    color: unset;  /* override Pygments text color */
+}
+
+/* avoid gaps between output lines */
+div.nboutput.container div[class*=highlight] pre {
+    line-height: normal;
+}
+
+/* input/output containers */
+div.nbinput.container,
+div.nboutput.container {
+    display: -webkit-flex;
+    display: flex;
+    align-items: flex-start;
+    margin: 0;
+    width: 100%;
+}
+@media (max-width: 540px) {
+    div.nbinput.container,
+    div.nboutput.container {
+        flex-direction: column;
+    }
+}
+
+/* input container */
+div.nbinput.container {
+    padding-top: 5px;
+}
+
+/* last container */
+div.nblast.container {
+    padding-bottom: 5px;
+}
+
+/* input prompt */
+div.nbinput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nbinput.container div.prompt pre > code {
+    color: #307FC1;
+}
+
+/* output prompt */
+div.nboutput.container div.prompt pre,
+/* for sphinx_immaterial theme: */
+div.nboutput.container div.prompt pre > code {
+    color: #BF5B3D;
+}
+
+/* all prompts */
+div.nbinput.container div.prompt,
+div.nboutput.container div.prompt {
+    width: 4.5ex;
+    padding-top: 5px;
+    position: relative;
+    user-select: none;
+}
+
+div.nbinput.container div.prompt > div,
+div.nboutput.container div.prompt > div {
+    position: absolute;
+    right: 0;
+    margin-right: 0.3ex;
+}
+
+@media (max-width: 540px) {
+    div.nbinput.container div.prompt,
+    div.nboutput.container div.prompt {
+        width: unset;
+        text-align: left;
+        padding: 0.4em;
+    }
+    div.nboutput.container div.prompt.empty {
+        padding: 0;
+    }
+
+    div.nbinput.container div.prompt > div,
+    div.nboutput.container div.prompt > div {
+        position: unset;
+    }
+}
+
+/* disable scrollbars and line breaks on prompts */
+div.nbinput.container div.prompt pre,
+div.nboutput.container div.prompt pre {
+    overflow: hidden;
+    white-space: pre;
+}
+
+/* input/output area */
+div.nbinput.container div.input_area,
+div.nboutput.container div.output_area {
+    -webkit-flex: 1;
+    flex: 1;
+    overflow: auto;
+}
+@media (max-width: 540px) {
+    div.nbinput.container div.input_area,
+    div.nboutput.container div.output_area {
+        width: 100%;
+    }
+}
+
+/* input area */
+div.nbinput.container div.input_area {
+    border: 1px solid #e0e0e0;
+    border-radius: 2px;
+    /*background: #f5f5f5;*/
+}
+
+/* override MathJax center alignment in output cells */
+div.nboutput.container div[class*=MathJax] {
+    text-align: left !important;
+}
+
+/* override sphinx.ext.imgmath center alignment in output cells */
+div.nboutput.container div.math p {
+    text-align: left;
+}
+
+/* standard error */
+div.nboutput.container div.output_area.stderr {
+    background: #fdd;
+}
+
+/* ANSI colors */
+.ansi-black-fg { color: #3E424D; }
+.ansi-black-bg { background-color: #3E424D; }
+.ansi-black-intense-fg { color: #282C36; }
+.ansi-black-intense-bg { background-color: #282C36; }
+.ansi-red-fg { color: #E75C58; }
+.ansi-red-bg { background-color: #E75C58; }
+.ansi-red-intense-fg { color: #B22B31; }
+.ansi-red-intense-bg { background-color: #B22B31; }
+.ansi-green-fg { color: #00A250; }
+.ansi-green-bg { background-color: #00A250; }
+.ansi-green-intense-fg { color: #007427; }
+.ansi-green-intense-bg { background-color: #007427; }
+.ansi-yellow-fg { color: #DDB62B; }
+.ansi-yellow-bg { background-color: #DDB62B; }
+.ansi-yellow-intense-fg { color: #B27D12; }
+.ansi-yellow-intense-bg { background-color: #B27D12; }
+.ansi-blue-fg { color: #208FFB; }
+.ansi-blue-bg { background-color: #208FFB; }
+.ansi-blue-intense-fg { color: #0065CA; }
+.ansi-blue-intense-bg { background-color: #0065CA; }
+.ansi-magenta-fg { color: #D160C4; }
+.ansi-magenta-bg { background-color: #D160C4; }
+.ansi-magenta-intense-fg { color: #A03196; }
+.ansi-magenta-intense-bg { background-color: #A03196; }
+.ansi-cyan-fg { color: #60C6C8; }
+.ansi-cyan-bg { background-color: #60C6C8; }
+.ansi-cyan-intense-fg { color: #258F8F; }
+.ansi-cyan-intense-bg { background-color: #258F8F; }
+.ansi-white-fg { color: #C5C1B4; }
+.ansi-white-bg { background-color: #C5C1B4; }
+.ansi-white-intense-fg { color: #A1A6B2; }
+.ansi-white-intense-bg { background-color: #A1A6B2; }
+
+.ansi-default-inverse-fg { color: #FFFFFF; }
+.ansi-default-inverse-bg { background-color: #000000; }
+
+.ansi-bold { font-weight: bold; }
+.ansi-underline { text-decoration: underline; }
+
+
+div.nbinput.container div.input_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight] > pre,
+div.nboutput.container div.output_area div[class*=highlight].math,
+div.nboutput.container div.output_area.rendered_html,
+div.nboutput.container div.output_area > div.output_javascript,
+div.nboutput.container div.output_area:not(.rendered_html) > img{
+    padding: 5px;
+    margin: 0;
+}
+
+/* fix copybtn overflow problem in chromium (needed for 'sphinx_copybutton') */
+div.nbinput.container div.input_area > div[class^='highlight'],
+div.nboutput.container div.output_area > div[class^='highlight']{
+    overflow-y: hidden;
+}
+
+/* hide copy button on prompts for 'sphinx_copybutton' extension ... */
+.prompt .copybtn,
+/* ... and 'sphinx_immaterial' theme */
+.prompt .md-clipboard.md-icon {
+    display: none;
+}
+
+/* Some additional styling taken form the Jupyter notebook CSS */
+.jp-RenderedHTMLCommon table,
+div.rendered_html table {
+  border: none;
+  border-collapse: collapse;
+  border-spacing: 0;
+  color: black;
+  font-size: 12px;
+  table-layout: fixed;
+}
+.jp-RenderedHTMLCommon thead,
+div.rendered_html thead {
+  border-bottom: 1px solid black;
+  vertical-align: bottom;
+}
+.jp-RenderedHTMLCommon tr,
+.jp-RenderedHTMLCommon th,
+.jp-RenderedHTMLCommon td,
+div.rendered_html tr,
+div.rendered_html th,
+div.rendered_html td {
+  text-align: right;
+  vertical-align: middle;
+  padding: 0.5em 0.5em;
+  line-height: normal;
+  white-space: normal;
+  max-width: none;
+  border: none;
+}
+.jp-RenderedHTMLCommon th,
+div.rendered_html th {
+  font-weight: bold;
+}
+.jp-RenderedHTMLCommon tbody tr:nth-child(odd),
+div.rendered_html tbody tr:nth-child(odd) {
+  background: #f5f5f5;
+}
+.jp-RenderedHTMLCommon tbody tr:hover,
+div.rendered_html tbody tr:hover {
+  background: rgba(66, 165, 245, 0.2);
+}
+
diff --git a/docs/_build/html/_static/nbsphinx-gallery.css b/docs/_build/html/_static/nbsphinx-gallery.css
new file mode 100644
index 0000000..365c27a
--- /dev/null
+++ b/docs/_build/html/_static/nbsphinx-gallery.css
@@ -0,0 +1,31 @@
+.nbsphinx-gallery {
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 5px;
+    margin-top: 1em;
+    margin-bottom: 1em;
+}
+
+.nbsphinx-gallery > a {
+    padding: 5px;
+    border: 1px dotted currentColor;
+    border-radius: 2px;
+    text-align: center;
+}
+
+.nbsphinx-gallery > a:hover {
+    border-style: solid;
+}
+
+.nbsphinx-gallery img {
+    max-width: 100%;
+    max-height: 100%;
+}
+
+.nbsphinx-gallery > a > div:first-child {
+    display: flex;
+    align-items: start;
+    justify-content: center;
+    height: 120px;
+    margin-bottom: 5px;
+}
diff --git a/docs/_build/html/_static/nbsphinx-no-thumbnail.svg b/docs/_build/html/_static/nbsphinx-no-thumbnail.svg
new file mode 100644
index 0000000..9dca758
--- /dev/null
+++ b/docs/_build/html/_static/nbsphinx-no-thumbnail.svg
@@ -0,0 +1,9 @@
+<svg xmlns="http://www.w3.org/2000/svg" width="100" height="100">
+  <style>
+    svg { fill: #ccc; }
+    @media (prefers-color-scheme: dark) {
+      svg { fill: #999; }
+    }
+  </style>
+  <path d="M 65.365328,74.550032 H 67.52154 V 88.67217 h 7.760238 v 1.814634 h -9.91645 z m -6.447298,0 h 2.156214 V 90.486804 H 58.91803 Z m -9.955303,2.124192 -2.924765,7.931025 h 5.860208 z m -1.216872,-2.124192 h 2.444423 l 6.073692,15.936772 h -2.241606 l -1.451709,-4.088268 h -7.183824 l -1.451707,4.088268 H 41.66149 Z m -21.264514,0 h 2.903417 L 36.45116,87.882271 V 74.550032 h 2.092171 V 90.486804 H 35.639914 L 28.573506,77.154569 v 13.332235 h -2.092165 z m 53.454813,-9.781663 v 5.838858 h 3.458479 q 1.739919,0 2.572513,-0.71518 0.843272,-0.725854 0.843272,-2.209586 0,-1.494406 -0.843272,-2.198912 -0.832594,-0.71518 -2.572513,-0.71518 z m 0,-6.554037 v 4.803447 h 3.191622 q 1.579803,0 2.348355,-0.587087 0.779225,-0.597763 0.779225,-1.814636 0,-1.206199 -0.779225,-1.803962 -0.768552,-0.597762 -2.348355,-0.597762 z m -2.156212,-1.771939 h 5.507949 q 2.465772,0 3.800065,1.024736 1.334288,1.024735 1.334288,2.914092 0,1.462383 -0.683157,2.327003 -0.683157,0.864621 -2.006771,1.078107 1.590471,0.341579 2.46577,1.43036 0.885967,1.078107 0.885967,2.700605 0,2.134866 -1.451708,3.298368 -1.451709,1.163502 -4.130963,1.163502 h -5.72144 z m -18.86154,0 h 3.212969 l 4.066921,10.845118 4.088269,-10.845118 H 73.49953 V 72.379166 H 71.396692 V 58.385121 L 67.287075,69.315634 H 65.120183 L 61.010567,58.385121 v 13.994045 h -2.092165 z m -16.246333,0 h 2.166892 v 9.681616 q 0,2.561839 0.928668,3.693318 0.928667,1.120804 3.010159,1.120804 2.070819,0 2.999486,-1.120804 0.928667,-1.131479 0.928667,-3.693318 v -9.681616 h 2.166886 v 9.948474 q 0,3.116904 -1.547776,4.70738 -1.537104,1.590474 -4.547263,1.590474 -3.020833,0 -4.568615,-1.590474 -1.537104,-1.590476 -1.537104,-4.70738 z m -16.192953,0 h 2.156213 v 6.532689 h 7.83496 v -6.532689 h 2.156213 v 15.936773 h -2.156213 v -7.589448 h -7.83496 v 7.589448 h -2.156213 z m -15.563168,0 h 13.481671 v 1.814636 H 18.740226 V 72.379166 H 16.573339 V 58.257029 H 10.915948 Z M 65.497127,39.809967 q -2.34835,0 -3.736011,1.750588 -1.376987,1.750588 -1.376987,4.771419 0,3.010157 1.376987,4.760745 1.387661,1.750588 3.736011,1.750588 2.348349,0 3.714662,-1.750588 1.376986,-1.750588 1.376986,-4.760745 0,-3.020831 -1.376986,-4.771419 -1.366313,-1.750588 -3.714662,-1.750588 z m 0,-1.750588 q 3.351735,0 5.358506,2.252281 2.006772,2.241606 2.006772,6.020314 0,3.768034 -2.006772,6.020315 -2.006771,2.241606 -5.358506,2.241606 -3.36241,0 -5.379856,-2.241606 -2.006771,-2.241607 -2.006771,-6.020315 0,-3.778708 2.006771,-6.020314 2.017446,-2.252281 5.379856,-2.252281 z m -22.821688,0.288206 h 2.903414 L 52.64525,51.679807 V 38.347585 h 2.092166 V 54.28434 H 51.834001 L 44.767605,40.952119 V 54.28434 h -2.092166 z" />
+</svg>
diff --git a/docs/build/html/_static/plus.png b/docs/_build/html/_static/plus.png
similarity index 100%
rename from docs/build/html/_static/plus.png
rename to docs/_build/html/_static/plus.png
diff --git a/docs/build/html/_static/pygments.css b/docs/_build/html/_static/pygments.css
similarity index 98%
rename from docs/build/html/_static/pygments.css
rename to docs/_build/html/_static/pygments.css
index 691aeb8..0d49244 100644
--- a/docs/build/html/_static/pygments.css
+++ b/docs/_build/html/_static/pygments.css
@@ -17,6 +17,7 @@ span.linenos.special { color: #000000; background-color: #ffffc0; padding-left:
 .highlight .cs { color: #408090; background-color: #fff0f0 } /* Comment.Special */
 .highlight .gd { color: #A00000 } /* Generic.Deleted */
 .highlight .ge { font-style: italic } /* Generic.Emph */
+.highlight .ges { font-weight: bold; font-style: italic } /* Generic.EmphStrong */
 .highlight .gr { color: #FF0000 } /* Generic.Error */
 .highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
 .highlight .gi { color: #00A000 } /* Generic.Inserted */
diff --git a/docs/build/html/_static/searchtools.js b/docs/_build/html/_static/searchtools.js
similarity index 74%
rename from docs/build/html/_static/searchtools.js
rename to docs/_build/html/_static/searchtools.js
index 97d56a7..92da3f8 100644
--- a/docs/build/html/_static/searchtools.js
+++ b/docs/_build/html/_static/searchtools.js
@@ -4,7 +4,7 @@
  *
  * Sphinx JavaScript utilities for the full-text search.
  *
- * :copyright: Copyright 2007-2023 by the Sphinx team, see AUTHORS.
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
  * :license: BSD, see LICENSE for details.
  *
  */
@@ -57,12 +57,12 @@ const _removeChildren = (element) => {
 const _escapeRegExp = (string) =>
   string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
 
-const _displayItem = (item, searchTerms) => {
+const _displayItem = (item, searchTerms, highlightTerms) => {
   const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
-  const docUrlRoot = DOCUMENTATION_OPTIONS.URL_ROOT;
   const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
   const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
   const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+  const contentRoot = document.documentElement.dataset.content_root;
 
   const [docName, title, anchor, descr, score, _filename] = item;
 
@@ -75,28 +75,35 @@ const _displayItem = (item, searchTerms) => {
     if (dirname.match(/\/index\/$/))
       dirname = dirname.substring(0, dirname.length - 6);
     else if (dirname === "index/") dirname = "";
-    requestUrl = docUrlRoot + dirname;
+    requestUrl = contentRoot + dirname;
     linkUrl = requestUrl;
   } else {
     // normal html builders
-    requestUrl = docUrlRoot + docName + docFileSuffix;
+    requestUrl = contentRoot + docName + docFileSuffix;
     linkUrl = docName + docLinkSuffix;
   }
   let linkEl = listItem.appendChild(document.createElement("a"));
   linkEl.href = linkUrl + anchor;
   linkEl.dataset.score = score;
   linkEl.innerHTML = title;
-  if (descr)
+  if (descr) {
     listItem.appendChild(document.createElement("span")).innerHTML =
       " (" + descr + ")";
+    // highlight search terms in the description
+    if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+      highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+  }
   else if (showSearchSummary)
     fetch(requestUrl)
       .then((responseData) => responseData.text())
       .then((data) => {
         if (data)
           listItem.appendChild(
-            Search.makeSearchSummary(data, searchTerms)
+            Search.makeSearchSummary(data, searchTerms, anchor)
           );
+        // highlight search terms in the summary
+        if (SPHINX_HIGHLIGHT_ENABLED)  // set in sphinx_highlight.js
+          highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
       });
   Search.output.appendChild(listItem);
 };
@@ -109,26 +116,43 @@ const _finishSearch = (resultCount) => {
     );
   else
     Search.status.innerText = _(
-      `Search finished, found ${resultCount} page(s) matching the search query.`
-    );
+      "Search finished, found ${resultCount} page(s) matching the search query."
+    ).replace('${resultCount}', resultCount);
 };
 const _displayNextItem = (
   results,
   resultCount,
-  searchTerms
+  searchTerms,
+  highlightTerms,
 ) => {
   // results left, load the summary and display it
   // this is intended to be dynamic (don't sub resultsCount)
   if (results.length) {
-    _displayItem(results.pop(), searchTerms);
+    _displayItem(results.pop(), searchTerms, highlightTerms);
     setTimeout(
-      () => _displayNextItem(results, resultCount, searchTerms),
+      () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
       5
     );
   }
   // search finished, update title and status message
   else _finishSearch(resultCount);
 };
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+  const leftScore = a[4];
+  const rightScore = b[4];
+  if (leftScore === rightScore) {
+    // same score: sort alphabetically
+    const leftTitle = a[1].toLowerCase();
+    const rightTitle = b[1].toLowerCase();
+    if (leftTitle === rightTitle) return 0;
+    return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+  }
+  return leftScore > rightScore ? 1 : -1;
+};
 
 /**
  * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
@@ -152,13 +176,26 @@ const Search = {
   _queued_query: null,
   _pulse_status: -1,
 
-  htmlToText: (htmlString) => {
+  htmlToText: (htmlString, anchor) => {
     const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
-    htmlElement.querySelectorAll(".headerlink").forEach((el) => { el.remove() });
+    for (const removalQuery of [".headerlinks", "script", "style"]) {
+      htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+    }
+    if (anchor) {
+      const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+      if (anchorContent) return anchorContent.textContent;
+
+      console.warn(
+        `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+      );
+    }
+
+    // if anchor not specified or not found, fall back to main content
     const docContent = htmlElement.querySelector('[role="main"]');
-    if (docContent !== undefined) return docContent.textContent;
+    if (docContent) return docContent.textContent;
+
     console.warn(
-      "Content block not found. Sphinx search tries to obtain it via '[role=main]'. Could you check your theme or template."
+      "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
     );
     return "";
   },
@@ -231,16 +268,7 @@ const Search = {
     else Search.deferQuery(query);
   },
 
-  /**
-   * execute search (requires search index to be loaded)
-   */
-  query: (query) => {
-    const filenames = Search._index.filenames;
-    const docNames = Search._index.docnames;
-    const titles = Search._index.titles;
-    const allTitles = Search._index.alltitles;
-    const indexEntries = Search._index.indexentries;
-
+  _parseQuery: (query) => {
     // stem the search terms and add them to the correct list
     const stemmer = new Stemmer();
     const searchTerms = new Set();
@@ -276,16 +304,32 @@ const Search = {
     // console.info("required: ", [...searchTerms]);
     // console.info("excluded: ", [...excludedTerms]);
 
-    // array of [docname, title, anchor, descr, score, filename]
-    let results = [];
+    return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+  },
+
+  /**
+   * execute search (requires search index to be loaded)
+   */
+  _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+    const filenames = Search._index.filenames;
+    const docNames = Search._index.docnames;
+    const titles = Search._index.titles;
+    const allTitles = Search._index.alltitles;
+    const indexEntries = Search._index.indexentries;
+
+    // Collect multiple result groups to be sorted separately and then ordered.
+    // Each is an array of [docname, title, anchor, descr, score, filename].
+    const normalResults = [];
+    const nonMainIndexResults = [];
+
     _removeChildren(document.getElementById("search-progress"));
 
-    const queryLower = query.toLowerCase();
+    const queryLower = query.toLowerCase().trim();
     for (const [title, foundTitles] of Object.entries(allTitles)) {
-      if (title.toLowerCase().includes(queryLower) && (queryLower.length >= title.length/2)) {
+      if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
         for (const [file, id] of foundTitles) {
           let score = Math.round(100 * queryLower.length / title.length)
-          results.push([
+          normalResults.push([
             docNames[file],
             titles[file] !== title ? `${titles[file]} > ${title}` : title,
             id !== null ? "#" + id : "",
@@ -300,46 +344,47 @@ const Search = {
     // search for explicit entries in index directives
     for (const [entry, foundEntries] of Object.entries(indexEntries)) {
       if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
-        for (const [file, id] of foundEntries) {
-          let score = Math.round(100 * queryLower.length / entry.length)
-          results.push([
+        for (const [file, id, isMain] of foundEntries) {
+          const score = Math.round(100 * queryLower.length / entry.length);
+          const result = [
             docNames[file],
             titles[file],
             id ? "#" + id : "",
             null,
             score,
             filenames[file],
-          ]);
+          ];
+          if (isMain) {
+            normalResults.push(result);
+          } else {
+            nonMainIndexResults.push(result);
+          }
         }
       }
     }
 
     // lookup as object
     objectTerms.forEach((term) =>
-      results.push(...Search.performObjectSearch(term, objectTerms))
+      normalResults.push(...Search.performObjectSearch(term, objectTerms))
     );
 
     // lookup as search terms in fulltext
-    results.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+    normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
 
     // let the scorer override scores with a custom scoring function
-    if (Scorer.score) results.forEach((item) => (item[4] = Scorer.score(item)));
-
-    // now sort the results by score (in opposite order of appearance, since the
-    // display function below uses pop() to retrieve items) and then
-    // alphabetically
-    results.sort((a, b) => {
-      const leftScore = a[4];
-      const rightScore = b[4];
-      if (leftScore === rightScore) {
-        // same score: sort alphabetically
-        const leftTitle = a[1].toLowerCase();
-        const rightTitle = b[1].toLowerCase();
-        if (leftTitle === rightTitle) return 0;
-        return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
-      }
-      return leftScore > rightScore ? 1 : -1;
-    });
+    if (Scorer.score) {
+      normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+      nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+    }
+
+    // Sort each group of results by score and then alphabetically by name.
+    normalResults.sort(_orderResultsByScoreThenName);
+    nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+    // Combine the result groups in (reverse) order.
+    // Non-main index entries are typically arbitrary cross-references,
+    // so display them after other results.
+    let results = [...nonMainIndexResults, ...normalResults];
 
     // remove duplicate search results
     // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
@@ -353,14 +398,19 @@ const Search = {
       return acc;
     }, []);
 
-    results = results.reverse();
+    return results.reverse();
+  },
+
+  query: (query) => {
+    const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+    const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
 
     // for debugging
     //Search.lastresults = results.slice();  // a copy
     // console.info("search results:", Search.lastresults);
 
     // print the results
-    _displayNextItem(results, results.length, searchTerms);
+    _displayNextItem(results, results.length, searchTerms, highlightTerms);
   },
 
   /**
@@ -458,14 +508,18 @@ const Search = {
       // add support for partial matches
       if (word.length > 2) {
         const escapedWord = _escapeRegExp(word);
-        Object.keys(terms).forEach((term) => {
-          if (term.match(escapedWord) && !terms[word])
-            arr.push({ files: terms[term], score: Scorer.partialTerm });
-        });
-        Object.keys(titleTerms).forEach((term) => {
-          if (term.match(escapedWord) && !titleTerms[word])
-            arr.push({ files: titleTerms[word], score: Scorer.partialTitle });
-        });
+        if (!terms.hasOwnProperty(word)) {
+          Object.keys(terms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: terms[term], score: Scorer.partialTerm });
+          });
+        }
+        if (!titleTerms.hasOwnProperty(word)) {
+          Object.keys(titleTerms).forEach((term) => {
+            if (term.match(escapedWord))
+              arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+          });
+        }
       }
 
       // no match but word was a required one
@@ -488,9 +542,8 @@ const Search = {
 
       // create the mapping
       files.forEach((file) => {
-        if (fileMap.has(file) && fileMap.get(file).indexOf(word) === -1)
-          fileMap.get(file).push(word);
-        else fileMap.set(file, [word]);
+        if (!fileMap.has(file)) fileMap.set(file, [word]);
+        else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
       });
     });
 
@@ -541,8 +594,8 @@ const Search = {
    * search summary for a given text. keywords is a list
    * of stemmed words.
    */
-  makeSearchSummary: (htmlText, keywords) => {
-    const text = Search.htmlToText(htmlText);
+  makeSearchSummary: (htmlText, keywords, anchor) => {
+    const text = Search.htmlToText(htmlText, anchor);
     if (text === "") return null;
 
     const textLower = text.toLowerCase();
diff --git a/docs/_build/html/_static/sidebar.js b/docs/_build/html/_static/sidebar.js
new file mode 100644
index 0000000..f28c206
--- /dev/null
+++ b/docs/_build/html/_static/sidebar.js
@@ -0,0 +1,70 @@
+/*
+ * sidebar.js
+ * ~~~~~~~~~~
+ *
+ * This script makes the Sphinx sidebar collapsible.
+ *
+ * .sphinxsidebar contains .sphinxsidebarwrapper.  This script adds
+ * in .sphixsidebar, after .sphinxsidebarwrapper, the #sidebarbutton
+ * used to collapse and expand the sidebar.
+ *
+ * When the sidebar is collapsed the .sphinxsidebarwrapper is hidden
+ * and the width of the sidebar and the margin-left of the document
+ * are decreased. When the sidebar is expanded the opposite happens.
+ * This script saves a per-browser/per-session cookie used to
+ * remember the position of the sidebar among the pages.
+ * Once the browser is closed the cookie is deleted and the position
+ * reset to the default (expanded).
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+
+const initialiseSidebar = () => {
+  
+    
+  
+
+  // global elements used by the functions.
+  const bodyWrapper = document.getElementsByClassName("bodywrapper")[0]
+  const sidebar = document.getElementsByClassName("sphinxsidebar")[0]
+  const sidebarWrapper = document.getElementsByClassName('sphinxsidebarwrapper')[0]
+  const sidebarButton = document.getElementById("sidebarbutton")
+  const sidebarArrow = sidebarButton.querySelector('span')
+
+  // for some reason, the document has no sidebar; do not run into errors
+  if (typeof sidebar === "undefined") return;
+
+  const flipArrow = element => element.innerText = (element.innerText === "»") ? "«" : "»"
+
+  const collapse_sidebar = () => {
+    bodyWrapper.style.marginLeft = ".8em";
+    sidebar.style.width = ".8em"
+    sidebarWrapper.style.display = "none"
+    flipArrow(sidebarArrow)
+    sidebarButton.title = _('Expand sidebar')
+    window.localStorage.setItem("sidebar", "collapsed")
+  }
+
+  const expand_sidebar = () => {
+    bodyWrapper.style.marginLeft = ""
+    sidebar.style.removeProperty("width")
+    sidebarWrapper.style.display = ""
+    flipArrow(sidebarArrow)
+    sidebarButton.title = _('Collapse sidebar')
+    window.localStorage.setItem("sidebar", "expanded")
+  }
+
+  sidebarButton.addEventListener("click", () => {
+    (sidebarWrapper.style.display === "none") ? expand_sidebar() : collapse_sidebar()
+  })
+
+  if (!window.localStorage.getItem("sidebar")) return
+  const value = window.localStorage.getItem("sidebar")
+  if (value === "collapsed") collapse_sidebar();
+  else if (value === "expanded") expand_sidebar();
+}
+
+if (document.readyState !== "loading") initialiseSidebar()
+else document.addEventListener("DOMContentLoaded", initialiseSidebar)
\ No newline at end of file
diff --git a/docs/build/html/_static/sphinx_highlight.js b/docs/_build/html/_static/sphinx_highlight.js
similarity index 88%
rename from docs/build/html/_static/sphinx_highlight.js
rename to docs/_build/html/_static/sphinx_highlight.js
index aae669d..8a96c69 100644
--- a/docs/build/html/_static/sphinx_highlight.js
+++ b/docs/_build/html/_static/sphinx_highlight.js
@@ -29,14 +29,19 @@ const _highlight = (node, addItems, text, className) => {
       }
 
       span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+      const rest = document.createTextNode(val.substr(pos + text.length));
       parent.insertBefore(
         span,
         parent.insertBefore(
-          document.createTextNode(val.substr(pos + text.length)),
+          rest,
           node.nextSibling
         )
       );
       node.nodeValue = val.substr(0, pos);
+      /* There may be more occurrences of search term in this node. So call this
+       * function recursively on the remaining fragment.
+       */
+      _highlight(rest, addItems, text, className);
 
       if (isInSVG) {
         const rect = document.createElementNS(
@@ -140,5 +145,10 @@ const SphinxHighlight = {
   },
 };
 
-_ready(SphinxHighlight.highlightSearchWords);
-_ready(SphinxHighlight.initEscapeListener);
+_ready(() => {
+  /* Do not call highlightSearchWords() when we are on the search page.
+   * It will highlight words from the *previous* search query.
+   */
+  if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords();
+  SphinxHighlight.initEscapeListener();
+});
diff --git a/docs/_build/html/_static/underscore-1.13.1.js b/docs/_build/html/_static/underscore-1.13.1.js
new file mode 100644
index 0000000..ffd77af
--- /dev/null
+++ b/docs/_build/html/_static/underscore-1.13.1.js
@@ -0,0 +1,2042 @@
+(function (global, factory) {
+  typeof exports === 'object' && typeof module !== 'undefined' ? module.exports = factory() :
+  typeof define === 'function' && define.amd ? define('underscore', factory) :
+  (global = typeof globalThis !== 'undefined' ? globalThis : global || self, (function () {
+    var current = global._;
+    var exports = global._ = factory();
+    exports.noConflict = function () { global._ = current; return exports; };
+  }()));
+}(this, (function () {
+  //     Underscore.js 1.13.1
+  //     https://underscorejs.org
+  //     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+  //     Underscore may be freely distributed under the MIT license.
+
+  // Current version.
+  var VERSION = '1.13.1';
+
+  // Establish the root object, `window` (`self`) in the browser, `global`
+  // on the server, or `this` in some virtual machines. We use `self`
+  // instead of `window` for `WebWorker` support.
+  var root = typeof self == 'object' && self.self === self && self ||
+            typeof global == 'object' && global.global === global && global ||
+            Function('return this')() ||
+            {};
+
+  // Save bytes in the minified (but not gzipped) version:
+  var ArrayProto = Array.prototype, ObjProto = Object.prototype;
+  var SymbolProto = typeof Symbol !== 'undefined' ? Symbol.prototype : null;
+
+  // Create quick reference variables for speed access to core prototypes.
+  var push = ArrayProto.push,
+      slice = ArrayProto.slice,
+      toString = ObjProto.toString,
+      hasOwnProperty = ObjProto.hasOwnProperty;
+
+  // Modern feature detection.
+  var supportsArrayBuffer = typeof ArrayBuffer !== 'undefined',
+      supportsDataView = typeof DataView !== 'undefined';
+
+  // All **ECMAScript 5+** native function implementations that we hope to use
+  // are declared here.
+  var nativeIsArray = Array.isArray,
+      nativeKeys = Object.keys,
+      nativeCreate = Object.create,
+      nativeIsView = supportsArrayBuffer && ArrayBuffer.isView;
+
+  // Create references to these builtin functions because we override them.
+  var _isNaN = isNaN,
+      _isFinite = isFinite;
+
+  // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed.
+  var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString');
+  var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString',
+    'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString'];
+
+  // The largest integer that can be represented exactly.
+  var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1;
+
+  // Some functions take a variable number of arguments, or a few expected
+  // arguments at the beginning and then a variable number of values to operate
+  // on. This helper accumulates all remaining arguments past the function’s
+  // argument length (or an explicit `startIndex`), into an array that becomes
+  // the last argument. Similar to ES6’s "rest parameter".
+  function restArguments(func, startIndex) {
+    startIndex = startIndex == null ? func.length - 1 : +startIndex;
+    return function() {
+      var length = Math.max(arguments.length - startIndex, 0),
+          rest = Array(length),
+          index = 0;
+      for (; index < length; index++) {
+        rest[index] = arguments[index + startIndex];
+      }
+      switch (startIndex) {
+        case 0: return func.call(this, rest);
+        case 1: return func.call(this, arguments[0], rest);
+        case 2: return func.call(this, arguments[0], arguments[1], rest);
+      }
+      var args = Array(startIndex + 1);
+      for (index = 0; index < startIndex; index++) {
+        args[index] = arguments[index];
+      }
+      args[startIndex] = rest;
+      return func.apply(this, args);
+    };
+  }
+
+  // Is a given variable an object?
+  function isObject(obj) {
+    var type = typeof obj;
+    return type === 'function' || type === 'object' && !!obj;
+  }
+
+  // Is a given value equal to null?
+  function isNull(obj) {
+    return obj === null;
+  }
+
+  // Is a given variable undefined?
+  function isUndefined(obj) {
+    return obj === void 0;
+  }
+
+  // Is a given value a boolean?
+  function isBoolean(obj) {
+    return obj === true || obj === false || toString.call(obj) === '[object Boolean]';
+  }
+
+  // Is a given value a DOM element?
+  function isElement(obj) {
+    return !!(obj && obj.nodeType === 1);
+  }
+
+  // Internal function for creating a `toString`-based type tester.
+  function tagTester(name) {
+    var tag = '[object ' + name + ']';
+    return function(obj) {
+      return toString.call(obj) === tag;
+    };
+  }
+
+  var isString = tagTester('String');
+
+  var isNumber = tagTester('Number');
+
+  var isDate = tagTester('Date');
+
+  var isRegExp = tagTester('RegExp');
+
+  var isError = tagTester('Error');
+
+  var isSymbol = tagTester('Symbol');
+
+  var isArrayBuffer = tagTester('ArrayBuffer');
+
+  var isFunction = tagTester('Function');
+
+  // Optimize `isFunction` if appropriate. Work around some `typeof` bugs in old
+  // v8, IE 11 (#1621), Safari 8 (#1929), and PhantomJS (#2236).
+  var nodelist = root.document && root.document.childNodes;
+  if (typeof /./ != 'function' && typeof Int8Array != 'object' && typeof nodelist != 'function') {
+    isFunction = function(obj) {
+      return typeof obj == 'function' || false;
+    };
+  }
+
+  var isFunction$1 = isFunction;
+
+  var hasObjectTag = tagTester('Object');
+
+  // In IE 10 - Edge 13, `DataView` has string tag `'[object Object]'`.
+  // In IE 11, the most common among them, this problem also applies to
+  // `Map`, `WeakMap` and `Set`.
+  var hasStringTagBug = (
+        supportsDataView && hasObjectTag(new DataView(new ArrayBuffer(8)))
+      ),
+      isIE11 = (typeof Map !== 'undefined' && hasObjectTag(new Map));
+
+  var isDataView = tagTester('DataView');
+
+  // In IE 10 - Edge 13, we need a different heuristic
+  // to determine whether an object is a `DataView`.
+  function ie10IsDataView(obj) {
+    return obj != null && isFunction$1(obj.getInt8) && isArrayBuffer(obj.buffer);
+  }
+
+  var isDataView$1 = (hasStringTagBug ? ie10IsDataView : isDataView);
+
+  // Is a given value an array?
+  // Delegates to ECMA5's native `Array.isArray`.
+  var isArray = nativeIsArray || tagTester('Array');
+
+  // Internal function to check whether `key` is an own property name of `obj`.
+  function has$1(obj, key) {
+    return obj != null && hasOwnProperty.call(obj, key);
+  }
+
+  var isArguments = tagTester('Arguments');
+
+  // Define a fallback version of the method in browsers (ahem, IE < 9), where
+  // there isn't any inspectable "Arguments" type.
+  (function() {
+    if (!isArguments(arguments)) {
+      isArguments = function(obj) {
+        return has$1(obj, 'callee');
+      };
+    }
+  }());
+
+  var isArguments$1 = isArguments;
+
+  // Is a given object a finite number?
+  function isFinite$1(obj) {
+    return !isSymbol(obj) && _isFinite(obj) && !isNaN(parseFloat(obj));
+  }
+
+  // Is the given value `NaN`?
+  function isNaN$1(obj) {
+    return isNumber(obj) && _isNaN(obj);
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function constant(value) {
+    return function() {
+      return value;
+    };
+  }
+
+  // Common internal logic for `isArrayLike` and `isBufferLike`.
+  function createSizePropertyCheck(getSizeProperty) {
+    return function(collection) {
+      var sizeProperty = getSizeProperty(collection);
+      return typeof sizeProperty == 'number' && sizeProperty >= 0 && sizeProperty <= MAX_ARRAY_INDEX;
+    }
+  }
+
+  // Internal helper to generate a function to obtain property `key` from `obj`.
+  function shallowProperty(key) {
+    return function(obj) {
+      return obj == null ? void 0 : obj[key];
+    };
+  }
+
+  // Internal helper to obtain the `byteLength` property of an object.
+  var getByteLength = shallowProperty('byteLength');
+
+  // Internal helper to determine whether we should spend extensive checks against
+  // `ArrayBuffer` et al.
+  var isBufferLike = createSizePropertyCheck(getByteLength);
+
+  // Is a given value a typed array?
+  var typedArrayPattern = /\[object ((I|Ui)nt(8|16|32)|Float(32|64)|Uint8Clamped|Big(I|Ui)nt64)Array\]/;
+  function isTypedArray(obj) {
+    // `ArrayBuffer.isView` is the most future-proof, so use it when available.
+    // Otherwise, fall back on the above regular expression.
+    return nativeIsView ? (nativeIsView(obj) && !isDataView$1(obj)) :
+                  isBufferLike(obj) && typedArrayPattern.test(toString.call(obj));
+  }
+
+  var isTypedArray$1 = supportsArrayBuffer ? isTypedArray : constant(false);
+
+  // Internal helper to obtain the `length` property of an object.
+  var getLength = shallowProperty('length');
+
+  // Internal helper to create a simple lookup structure.
+  // `collectNonEnumProps` used to depend on `_.contains`, but this led to
+  // circular imports. `emulatedSet` is a one-off solution that only works for
+  // arrays of strings.
+  function emulatedSet(keys) {
+    var hash = {};
+    for (var l = keys.length, i = 0; i < l; ++i) hash[keys[i]] = true;
+    return {
+      contains: function(key) { return hash[key]; },
+      push: function(key) {
+        hash[key] = true;
+        return keys.push(key);
+      }
+    };
+  }
+
+  // Internal helper. Checks `keys` for the presence of keys in IE < 9 that won't
+  // be iterated by `for key in ...` and thus missed. Extends `keys` in place if
+  // needed.
+  function collectNonEnumProps(obj, keys) {
+    keys = emulatedSet(keys);
+    var nonEnumIdx = nonEnumerableProps.length;
+    var constructor = obj.constructor;
+    var proto = isFunction$1(constructor) && constructor.prototype || ObjProto;
+
+    // Constructor is a special case.
+    var prop = 'constructor';
+    if (has$1(obj, prop) && !keys.contains(prop)) keys.push(prop);
+
+    while (nonEnumIdx--) {
+      prop = nonEnumerableProps[nonEnumIdx];
+      if (prop in obj && obj[prop] !== proto[prop] && !keys.contains(prop)) {
+        keys.push(prop);
+      }
+    }
+  }
+
+  // Retrieve the names of an object's own properties.
+  // Delegates to **ECMAScript 5**'s native `Object.keys`.
+  function keys(obj) {
+    if (!isObject(obj)) return [];
+    if (nativeKeys) return nativeKeys(obj);
+    var keys = [];
+    for (var key in obj) if (has$1(obj, key)) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Is a given array, string, or object empty?
+  // An "empty" object has no enumerable own-properties.
+  function isEmpty(obj) {
+    if (obj == null) return true;
+    // Skip the more expensive `toString`-based type checks if `obj` has no
+    // `.length`.
+    var length = getLength(obj);
+    if (typeof length == 'number' && (
+      isArray(obj) || isString(obj) || isArguments$1(obj)
+    )) return length === 0;
+    return getLength(keys(obj)) === 0;
+  }
+
+  // Returns whether an object has a given set of `key:value` pairs.
+  function isMatch(object, attrs) {
+    var _keys = keys(attrs), length = _keys.length;
+    if (object == null) return !length;
+    var obj = Object(object);
+    for (var i = 0; i < length; i++) {
+      var key = _keys[i];
+      if (attrs[key] !== obj[key] || !(key in obj)) return false;
+    }
+    return true;
+  }
+
+  // If Underscore is called as a function, it returns a wrapped object that can
+  // be used OO-style. This wrapper holds altered versions of all functions added
+  // through `_.mixin`. Wrapped objects may be chained.
+  function _$1(obj) {
+    if (obj instanceof _$1) return obj;
+    if (!(this instanceof _$1)) return new _$1(obj);
+    this._wrapped = obj;
+  }
+
+  _$1.VERSION = VERSION;
+
+  // Extracts the result from a wrapped and chained object.
+  _$1.prototype.value = function() {
+    return this._wrapped;
+  };
+
+  // Provide unwrapping proxies for some methods used in engine operations
+  // such as arithmetic and JSON stringification.
+  _$1.prototype.valueOf = _$1.prototype.toJSON = _$1.prototype.value;
+
+  _$1.prototype.toString = function() {
+    return String(this._wrapped);
+  };
+
+  // Internal function to wrap or shallow-copy an ArrayBuffer,
+  // typed array or DataView to a new view, reusing the buffer.
+  function toBufferView(bufferSource) {
+    return new Uint8Array(
+      bufferSource.buffer || bufferSource,
+      bufferSource.byteOffset || 0,
+      getByteLength(bufferSource)
+    );
+  }
+
+  // We use this string twice, so give it a name for minification.
+  var tagDataView = '[object DataView]';
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function eq(a, b, aStack, bStack) {
+    // Identical objects are equal. `0 === -0`, but they aren't identical.
+    // See the [Harmony `egal` proposal](https://wiki.ecmascript.org/doku.php?id=harmony:egal).
+    if (a === b) return a !== 0 || 1 / a === 1 / b;
+    // `null` or `undefined` only equal to itself (strict comparison).
+    if (a == null || b == null) return false;
+    // `NaN`s are equivalent, but non-reflexive.
+    if (a !== a) return b !== b;
+    // Exhaust primitive checks
+    var type = typeof a;
+    if (type !== 'function' && type !== 'object' && typeof b != 'object') return false;
+    return deepEq(a, b, aStack, bStack);
+  }
+
+  // Internal recursive comparison function for `_.isEqual`.
+  function deepEq(a, b, aStack, bStack) {
+    // Unwrap any wrapped objects.
+    if (a instanceof _$1) a = a._wrapped;
+    if (b instanceof _$1) b = b._wrapped;
+    // Compare `[[Class]]` names.
+    var className = toString.call(a);
+    if (className !== toString.call(b)) return false;
+    // Work around a bug in IE 10 - Edge 13.
+    if (hasStringTagBug && className == '[object Object]' && isDataView$1(a)) {
+      if (!isDataView$1(b)) return false;
+      className = tagDataView;
+    }
+    switch (className) {
+      // These types are compared by value.
+      case '[object RegExp]':
+        // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i')
+      case '[object String]':
+        // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
+        // equivalent to `new String("5")`.
+        return '' + a === '' + b;
+      case '[object Number]':
+        // `NaN`s are equivalent, but non-reflexive.
+        // Object(NaN) is equivalent to NaN.
+        if (+a !== +a) return +b !== +b;
+        // An `egal` comparison is performed for other numeric values.
+        return +a === 0 ? 1 / +a === 1 / b : +a === +b;
+      case '[object Date]':
+      case '[object Boolean]':
+        // Coerce dates and booleans to numeric primitive values. Dates are compared by their
+        // millisecond representations. Note that invalid dates with millisecond representations
+        // of `NaN` are not equivalent.
+        return +a === +b;
+      case '[object Symbol]':
+        return SymbolProto.valueOf.call(a) === SymbolProto.valueOf.call(b);
+      case '[object ArrayBuffer]':
+      case tagDataView:
+        // Coerce to typed array so we can fall through.
+        return deepEq(toBufferView(a), toBufferView(b), aStack, bStack);
+    }
+
+    var areArrays = className === '[object Array]';
+    if (!areArrays && isTypedArray$1(a)) {
+        var byteLength = getByteLength(a);
+        if (byteLength !== getByteLength(b)) return false;
+        if (a.buffer === b.buffer && a.byteOffset === b.byteOffset) return true;
+        areArrays = true;
+    }
+    if (!areArrays) {
+      if (typeof a != 'object' || typeof b != 'object') return false;
+
+      // Objects with different constructors are not equivalent, but `Object`s or `Array`s
+      // from different frames are.
+      var aCtor = a.constructor, bCtor = b.constructor;
+      if (aCtor !== bCtor && !(isFunction$1(aCtor) && aCtor instanceof aCtor &&
+                               isFunction$1(bCtor) && bCtor instanceof bCtor)
+                          && ('constructor' in a && 'constructor' in b)) {
+        return false;
+      }
+    }
+    // Assume equality for cyclic structures. The algorithm for detecting cyclic
+    // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
+
+    // Initializing stack of traversed objects.
+    // It's done here since we only need them for objects and arrays comparison.
+    aStack = aStack || [];
+    bStack = bStack || [];
+    var length = aStack.length;
+    while (length--) {
+      // Linear search. Performance is inversely proportional to the number of
+      // unique nested structures.
+      if (aStack[length] === a) return bStack[length] === b;
+    }
+
+    // Add the first object to the stack of traversed objects.
+    aStack.push(a);
+    bStack.push(b);
+
+    // Recursively compare objects and arrays.
+    if (areArrays) {
+      // Compare array lengths to determine if a deep comparison is necessary.
+      length = a.length;
+      if (length !== b.length) return false;
+      // Deep compare the contents, ignoring non-numeric properties.
+      while (length--) {
+        if (!eq(a[length], b[length], aStack, bStack)) return false;
+      }
+    } else {
+      // Deep compare objects.
+      var _keys = keys(a), key;
+      length = _keys.length;
+      // Ensure that both objects contain the same number of properties before comparing deep equality.
+      if (keys(b).length !== length) return false;
+      while (length--) {
+        // Deep compare each member
+        key = _keys[length];
+        if (!(has$1(b, key) && eq(a[key], b[key], aStack, bStack))) return false;
+      }
+    }
+    // Remove the first object from the stack of traversed objects.
+    aStack.pop();
+    bStack.pop();
+    return true;
+  }
+
+  // Perform a deep comparison to check if two objects are equal.
+  function isEqual(a, b) {
+    return eq(a, b);
+  }
+
+  // Retrieve all the enumerable property names of an object.
+  function allKeys(obj) {
+    if (!isObject(obj)) return [];
+    var keys = [];
+    for (var key in obj) keys.push(key);
+    // Ahem, IE < 9.
+    if (hasEnumBug) collectNonEnumProps(obj, keys);
+    return keys;
+  }
+
+  // Since the regular `Object.prototype.toString` type tests don't work for
+  // some types in IE 11, we use a fingerprinting heuristic instead, based
+  // on the methods. It's not great, but it's the best we got.
+  // The fingerprint method lists are defined below.
+  function ie11fingerprint(methods) {
+    var length = getLength(methods);
+    return function(obj) {
+      if (obj == null) return false;
+      // `Map`, `WeakMap` and `Set` have no enumerable keys.
+      var keys = allKeys(obj);
+      if (getLength(keys)) return false;
+      for (var i = 0; i < length; i++) {
+        if (!isFunction$1(obj[methods[i]])) return false;
+      }
+      // If we are testing against `WeakMap`, we need to ensure that
+      // `obj` doesn't have a `forEach` method in order to distinguish
+      // it from a regular `Map`.
+      return methods !== weakMapMethods || !isFunction$1(obj[forEachName]);
+    };
+  }
+
+  // In the interest of compact minification, we write
+  // each string in the fingerprints only once.
+  var forEachName = 'forEach',
+      hasName = 'has',
+      commonInit = ['clear', 'delete'],
+      mapTail = ['get', hasName, 'set'];
+
+  // `Map`, `WeakMap` and `Set` each have slightly different
+  // combinations of the above sublists.
+  var mapMethods = commonInit.concat(forEachName, mapTail),
+      weakMapMethods = commonInit.concat(mapTail),
+      setMethods = ['add'].concat(commonInit, forEachName, hasName);
+
+  var isMap = isIE11 ? ie11fingerprint(mapMethods) : tagTester('Map');
+
+  var isWeakMap = isIE11 ? ie11fingerprint(weakMapMethods) : tagTester('WeakMap');
+
+  var isSet = isIE11 ? ie11fingerprint(setMethods) : tagTester('Set');
+
+  var isWeakSet = tagTester('WeakSet');
+
+  // Retrieve the values of an object's properties.
+  function values(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var values = Array(length);
+    for (var i = 0; i < length; i++) {
+      values[i] = obj[_keys[i]];
+    }
+    return values;
+  }
+
+  // Convert an object into a list of `[key, value]` pairs.
+  // The opposite of `_.object` with one argument.
+  function pairs(obj) {
+    var _keys = keys(obj);
+    var length = _keys.length;
+    var pairs = Array(length);
+    for (var i = 0; i < length; i++) {
+      pairs[i] = [_keys[i], obj[_keys[i]]];
+    }
+    return pairs;
+  }
+
+  // Invert the keys and values of an object. The values must be serializable.
+  function invert(obj) {
+    var result = {};
+    var _keys = keys(obj);
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      result[obj[_keys[i]]] = _keys[i];
+    }
+    return result;
+  }
+
+  // Return a sorted list of the function names available on the object.
+  function functions(obj) {
+    var names = [];
+    for (var key in obj) {
+      if (isFunction$1(obj[key])) names.push(key);
+    }
+    return names.sort();
+  }
+
+  // An internal function for creating assigner functions.
+  function createAssigner(keysFunc, defaults) {
+    return function(obj) {
+      var length = arguments.length;
+      if (defaults) obj = Object(obj);
+      if (length < 2 || obj == null) return obj;
+      for (var index = 1; index < length; index++) {
+        var source = arguments[index],
+            keys = keysFunc(source),
+            l = keys.length;
+        for (var i = 0; i < l; i++) {
+          var key = keys[i];
+          if (!defaults || obj[key] === void 0) obj[key] = source[key];
+        }
+      }
+      return obj;
+    };
+  }
+
+  // Extend a given object with all the properties in passed-in object(s).
+  var extend = createAssigner(allKeys);
+
+  // Assigns a given object with all the own properties in the passed-in
+  // object(s).
+  // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign)
+  var extendOwn = createAssigner(keys);
+
+  // Fill in a given object with default properties.
+  var defaults = createAssigner(allKeys, true);
+
+  // Create a naked function reference for surrogate-prototype-swapping.
+  function ctor() {
+    return function(){};
+  }
+
+  // An internal function for creating a new object that inherits from another.
+  function baseCreate(prototype) {
+    if (!isObject(prototype)) return {};
+    if (nativeCreate) return nativeCreate(prototype);
+    var Ctor = ctor();
+    Ctor.prototype = prototype;
+    var result = new Ctor;
+    Ctor.prototype = null;
+    return result;
+  }
+
+  // Creates an object that inherits from the given prototype object.
+  // If additional properties are provided then they will be added to the
+  // created object.
+  function create(prototype, props) {
+    var result = baseCreate(prototype);
+    if (props) extendOwn(result, props);
+    return result;
+  }
+
+  // Create a (shallow-cloned) duplicate of an object.
+  function clone(obj) {
+    if (!isObject(obj)) return obj;
+    return isArray(obj) ? obj.slice() : extend({}, obj);
+  }
+
+  // Invokes `interceptor` with the `obj` and then returns `obj`.
+  // The primary purpose of this method is to "tap into" a method chain, in
+  // order to perform operations on intermediate results within the chain.
+  function tap(obj, interceptor) {
+    interceptor(obj);
+    return obj;
+  }
+
+  // Normalize a (deep) property `path` to array.
+  // Like `_.iteratee`, this function can be customized.
+  function toPath$1(path) {
+    return isArray(path) ? path : [path];
+  }
+  _$1.toPath = toPath$1;
+
+  // Internal wrapper for `_.toPath` to enable minification.
+  // Similar to `cb` for `_.iteratee`.
+  function toPath(path) {
+    return _$1.toPath(path);
+  }
+
+  // Internal function to obtain a nested property in `obj` along `path`.
+  function deepGet(obj, path) {
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      if (obj == null) return void 0;
+      obj = obj[path[i]];
+    }
+    return length ? obj : void 0;
+  }
+
+  // Get the value of the (deep) property on `path` from `object`.
+  // If any property in `path` does not exist or if the value is
+  // `undefined`, return `defaultValue` instead.
+  // The `path` is normalized through `_.toPath`.
+  function get(object, path, defaultValue) {
+    var value = deepGet(object, toPath(path));
+    return isUndefined(value) ? defaultValue : value;
+  }
+
+  // Shortcut function for checking if an object has a given property directly on
+  // itself (in other words, not on a prototype). Unlike the internal `has`
+  // function, this public version can also traverse nested properties.
+  function has(obj, path) {
+    path = toPath(path);
+    var length = path.length;
+    for (var i = 0; i < length; i++) {
+      var key = path[i];
+      if (!has$1(obj, key)) return false;
+      obj = obj[key];
+    }
+    return !!length;
+  }
+
+  // Keep the identity function around for default iteratees.
+  function identity(value) {
+    return value;
+  }
+
+  // Returns a predicate for checking whether an object has a given set of
+  // `key:value` pairs.
+  function matcher(attrs) {
+    attrs = extendOwn({}, attrs);
+    return function(obj) {
+      return isMatch(obj, attrs);
+    };
+  }
+
+  // Creates a function that, when passed an object, will traverse that object’s
+  // properties down the given `path`, specified as an array of keys or indices.
+  function property(path) {
+    path = toPath(path);
+    return function(obj) {
+      return deepGet(obj, path);
+    };
+  }
+
+  // Internal function that returns an efficient (for current engines) version
+  // of the passed-in callback, to be repeatedly applied in other Underscore
+  // functions.
+  function optimizeCb(func, context, argCount) {
+    if (context === void 0) return func;
+    switch (argCount == null ? 3 : argCount) {
+      case 1: return function(value) {
+        return func.call(context, value);
+      };
+      // The 2-argument case is omitted because we’re not using it.
+      case 3: return function(value, index, collection) {
+        return func.call(context, value, index, collection);
+      };
+      case 4: return function(accumulator, value, index, collection) {
+        return func.call(context, accumulator, value, index, collection);
+      };
+    }
+    return function() {
+      return func.apply(context, arguments);
+    };
+  }
+
+  // An internal function to generate callbacks that can be applied to each
+  // element in a collection, returning the desired result — either `_.identity`,
+  // an arbitrary callback, a property matcher, or a property accessor.
+  function baseIteratee(value, context, argCount) {
+    if (value == null) return identity;
+    if (isFunction$1(value)) return optimizeCb(value, context, argCount);
+    if (isObject(value) && !isArray(value)) return matcher(value);
+    return property(value);
+  }
+
+  // External wrapper for our callback generator. Users may customize
+  // `_.iteratee` if they want additional predicate/iteratee shorthand styles.
+  // This abstraction hides the internal-only `argCount` argument.
+  function iteratee(value, context) {
+    return baseIteratee(value, context, Infinity);
+  }
+  _$1.iteratee = iteratee;
+
+  // The function we call internally to generate a callback. It invokes
+  // `_.iteratee` if overridden, otherwise `baseIteratee`.
+  function cb(value, context, argCount) {
+    if (_$1.iteratee !== iteratee) return _$1.iteratee(value, context);
+    return baseIteratee(value, context, argCount);
+  }
+
+  // Returns the results of applying the `iteratee` to each element of `obj`.
+  // In contrast to `_.map` it returns an object.
+  function mapObject(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = keys(obj),
+        length = _keys.length,
+        results = {};
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys[index];
+      results[currentKey] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Predicate-generating function. Often useful outside of Underscore.
+  function noop(){}
+
+  // Generates a function for a given object that returns a given property.
+  function propertyOf(obj) {
+    if (obj == null) return noop;
+    return function(path) {
+      return get(obj, path);
+    };
+  }
+
+  // Run a function **n** times.
+  function times(n, iteratee, context) {
+    var accum = Array(Math.max(0, n));
+    iteratee = optimizeCb(iteratee, context, 1);
+    for (var i = 0; i < n; i++) accum[i] = iteratee(i);
+    return accum;
+  }
+
+  // Return a random integer between `min` and `max` (inclusive).
+  function random(min, max) {
+    if (max == null) {
+      max = min;
+      min = 0;
+    }
+    return min + Math.floor(Math.random() * (max - min + 1));
+  }
+
+  // A (possibly faster) way to get the current timestamp as an integer.
+  var now = Date.now || function() {
+    return new Date().getTime();
+  };
+
+  // Internal helper to generate functions for escaping and unescaping strings
+  // to/from HTML interpolation.
+  function createEscaper(map) {
+    var escaper = function(match) {
+      return map[match];
+    };
+    // Regexes for identifying a key that needs to be escaped.
+    var source = '(?:' + keys(map).join('|') + ')';
+    var testRegexp = RegExp(source);
+    var replaceRegexp = RegExp(source, 'g');
+    return function(string) {
+      string = string == null ? '' : '' + string;
+      return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string;
+    };
+  }
+
+  // Internal list of HTML entities for escaping.
+  var escapeMap = {
+    '&': '&amp;',
+    '<': '&lt;',
+    '>': '&gt;',
+    '"': '&quot;',
+    "'": '&#x27;',
+    '`': '&#x60;'
+  };
+
+  // Function for escaping strings to HTML interpolation.
+  var _escape = createEscaper(escapeMap);
+
+  // Internal list of HTML entities for unescaping.
+  var unescapeMap = invert(escapeMap);
+
+  // Function for unescaping strings from HTML interpolation.
+  var _unescape = createEscaper(unescapeMap);
+
+  // By default, Underscore uses ERB-style template delimiters. Change the
+  // following template settings to use alternative delimiters.
+  var templateSettings = _$1.templateSettings = {
+    evaluate: /<%([\s\S]+?)%>/g,
+    interpolate: /<%=([\s\S]+?)%>/g,
+    escape: /<%-([\s\S]+?)%>/g
+  };
+
+  // When customizing `_.templateSettings`, if you don't want to define an
+  // interpolation, evaluation or escaping regex, we need one that is
+  // guaranteed not to match.
+  var noMatch = /(.)^/;
+
+  // Certain characters need to be escaped so that they can be put into a
+  // string literal.
+  var escapes = {
+    "'": "'",
+    '\\': '\\',
+    '\r': 'r',
+    '\n': 'n',
+    '\u2028': 'u2028',
+    '\u2029': 'u2029'
+  };
+
+  var escapeRegExp = /\\|'|\r|\n|\u2028|\u2029/g;
+
+  function escapeChar(match) {
+    return '\\' + escapes[match];
+  }
+
+  // In order to prevent third-party code injection through
+  // `_.templateSettings.variable`, we test it against the following regular
+  // expression. It is intentionally a bit more liberal than just matching valid
+  // identifiers, but still prevents possible loopholes through defaults or
+  // destructuring assignment.
+  var bareIdentifier = /^\s*(\w|\$)+\s*$/;
+
+  // JavaScript micro-templating, similar to John Resig's implementation.
+  // Underscore templating handles arbitrary delimiters, preserves whitespace,
+  // and correctly escapes quotes within interpolated code.
+  // NB: `oldSettings` only exists for backwards compatibility.
+  function template(text, settings, oldSettings) {
+    if (!settings && oldSettings) settings = oldSettings;
+    settings = defaults({}, settings, _$1.templateSettings);
+
+    // Combine delimiters into one regular expression via alternation.
+    var matcher = RegExp([
+      (settings.escape || noMatch).source,
+      (settings.interpolate || noMatch).source,
+      (settings.evaluate || noMatch).source
+    ].join('|') + '|$', 'g');
+
+    // Compile the template source, escaping string literals appropriately.
+    var index = 0;
+    var source = "__p+='";
+    text.replace(matcher, function(match, escape, interpolate, evaluate, offset) {
+      source += text.slice(index, offset).replace(escapeRegExp, escapeChar);
+      index = offset + match.length;
+
+      if (escape) {
+        source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'";
+      } else if (interpolate) {
+        source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'";
+      } else if (evaluate) {
+        source += "';\n" + evaluate + "\n__p+='";
+      }
+
+      // Adobe VMs need the match returned to produce the correct offset.
+      return match;
+    });
+    source += "';\n";
+
+    var argument = settings.variable;
+    if (argument) {
+      // Insure against third-party code injection. (CVE-2021-23358)
+      if (!bareIdentifier.test(argument)) throw new Error(
+        'variable is not a bare identifier: ' + argument
+      );
+    } else {
+      // If a variable is not specified, place data values in local scope.
+      source = 'with(obj||{}){\n' + source + '}\n';
+      argument = 'obj';
+    }
+
+    source = "var __t,__p='',__j=Array.prototype.join," +
+      "print=function(){__p+=__j.call(arguments,'');};\n" +
+      source + 'return __p;\n';
+
+    var render;
+    try {
+      render = new Function(argument, '_', source);
+    } catch (e) {
+      e.source = source;
+      throw e;
+    }
+
+    var template = function(data) {
+      return render.call(this, data, _$1);
+    };
+
+    // Provide the compiled source as a convenience for precompilation.
+    template.source = 'function(' + argument + '){\n' + source + '}';
+
+    return template;
+  }
+
+  // Traverses the children of `obj` along `path`. If a child is a function, it
+  // is invoked with its parent as context. Returns the value of the final
+  // child, or `fallback` if any child is undefined.
+  function result(obj, path, fallback) {
+    path = toPath(path);
+    var length = path.length;
+    if (!length) {
+      return isFunction$1(fallback) ? fallback.call(obj) : fallback;
+    }
+    for (var i = 0; i < length; i++) {
+      var prop = obj == null ? void 0 : obj[path[i]];
+      if (prop === void 0) {
+        prop = fallback;
+        i = length; // Ensure we don't continue iterating.
+      }
+      obj = isFunction$1(prop) ? prop.call(obj) : prop;
+    }
+    return obj;
+  }
+
+  // Generate a unique integer id (unique within the entire client session).
+  // Useful for temporary DOM ids.
+  var idCounter = 0;
+  function uniqueId(prefix) {
+    var id = ++idCounter + '';
+    return prefix ? prefix + id : id;
+  }
+
+  // Start chaining a wrapped Underscore object.
+  function chain(obj) {
+    var instance = _$1(obj);
+    instance._chain = true;
+    return instance;
+  }
+
+  // Internal function to execute `sourceFunc` bound to `context` with optional
+  // `args`. Determines whether to execute a function as a constructor or as a
+  // normal function.
+  function executeBound(sourceFunc, boundFunc, context, callingContext, args) {
+    if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args);
+    var self = baseCreate(sourceFunc.prototype);
+    var result = sourceFunc.apply(self, args);
+    if (isObject(result)) return result;
+    return self;
+  }
+
+  // Partially apply a function by creating a version that has had some of its
+  // arguments pre-filled, without changing its dynamic `this` context. `_` acts
+  // as a placeholder by default, allowing any combination of arguments to be
+  // pre-filled. Set `_.partial.placeholder` for a custom placeholder argument.
+  var partial = restArguments(function(func, boundArgs) {
+    var placeholder = partial.placeholder;
+    var bound = function() {
+      var position = 0, length = boundArgs.length;
+      var args = Array(length);
+      for (var i = 0; i < length; i++) {
+        args[i] = boundArgs[i] === placeholder ? arguments[position++] : boundArgs[i];
+      }
+      while (position < arguments.length) args.push(arguments[position++]);
+      return executeBound(func, bound, this, this, args);
+    };
+    return bound;
+  });
+
+  partial.placeholder = _$1;
+
+  // Create a function bound to a given object (assigning `this`, and arguments,
+  // optionally).
+  var bind = restArguments(function(func, context, args) {
+    if (!isFunction$1(func)) throw new TypeError('Bind must be called on a function');
+    var bound = restArguments(function(callArgs) {
+      return executeBound(func, bound, context, this, args.concat(callArgs));
+    });
+    return bound;
+  });
+
+  // Internal helper for collection methods to determine whether a collection
+  // should be iterated as an array or as an object.
+  // Related: https://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength
+  // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094
+  var isArrayLike = createSizePropertyCheck(getLength);
+
+  // Internal implementation of a recursive `flatten` function.
+  function flatten$1(input, depth, strict, output) {
+    output = output || [];
+    if (!depth && depth !== 0) {
+      depth = Infinity;
+    } else if (depth <= 0) {
+      return output.concat(input);
+    }
+    var idx = output.length;
+    for (var i = 0, length = getLength(input); i < length; i++) {
+      var value = input[i];
+      if (isArrayLike(value) && (isArray(value) || isArguments$1(value))) {
+        // Flatten current level of array or arguments object.
+        if (depth > 1) {
+          flatten$1(value, depth - 1, strict, output);
+          idx = output.length;
+        } else {
+          var j = 0, len = value.length;
+          while (j < len) output[idx++] = value[j++];
+        }
+      } else if (!strict) {
+        output[idx++] = value;
+      }
+    }
+    return output;
+  }
+
+  // Bind a number of an object's methods to that object. Remaining arguments
+  // are the method names to be bound. Useful for ensuring that all callbacks
+  // defined on an object belong to it.
+  var bindAll = restArguments(function(obj, keys) {
+    keys = flatten$1(keys, false, false);
+    var index = keys.length;
+    if (index < 1) throw new Error('bindAll must be passed function names');
+    while (index--) {
+      var key = keys[index];
+      obj[key] = bind(obj[key], obj);
+    }
+    return obj;
+  });
+
+  // Memoize an expensive function by storing its results.
+  function memoize(func, hasher) {
+    var memoize = function(key) {
+      var cache = memoize.cache;
+      var address = '' + (hasher ? hasher.apply(this, arguments) : key);
+      if (!has$1(cache, address)) cache[address] = func.apply(this, arguments);
+      return cache[address];
+    };
+    memoize.cache = {};
+    return memoize;
+  }
+
+  // Delays a function for the given number of milliseconds, and then calls
+  // it with the arguments supplied.
+  var delay = restArguments(function(func, wait, args) {
+    return setTimeout(function() {
+      return func.apply(null, args);
+    }, wait);
+  });
+
+  // Defers a function, scheduling it to run after the current call stack has
+  // cleared.
+  var defer = partial(delay, _$1, 1);
+
+  // Returns a function, that, when invoked, will only be triggered at most once
+  // during a given window of time. Normally, the throttled function will run
+  // as much as it can, without ever going more than once per `wait` duration;
+  // but if you'd like to disable the execution on the leading edge, pass
+  // `{leading: false}`. To disable execution on the trailing edge, ditto.
+  function throttle(func, wait, options) {
+    var timeout, context, args, result;
+    var previous = 0;
+    if (!options) options = {};
+
+    var later = function() {
+      previous = options.leading === false ? 0 : now();
+      timeout = null;
+      result = func.apply(context, args);
+      if (!timeout) context = args = null;
+    };
+
+    var throttled = function() {
+      var _now = now();
+      if (!previous && options.leading === false) previous = _now;
+      var remaining = wait - (_now - previous);
+      context = this;
+      args = arguments;
+      if (remaining <= 0 || remaining > wait) {
+        if (timeout) {
+          clearTimeout(timeout);
+          timeout = null;
+        }
+        previous = _now;
+        result = func.apply(context, args);
+        if (!timeout) context = args = null;
+      } else if (!timeout && options.trailing !== false) {
+        timeout = setTimeout(later, remaining);
+      }
+      return result;
+    };
+
+    throttled.cancel = function() {
+      clearTimeout(timeout);
+      previous = 0;
+      timeout = context = args = null;
+    };
+
+    return throttled;
+  }
+
+  // When a sequence of calls of the returned function ends, the argument
+  // function is triggered. The end of a sequence is defined by the `wait`
+  // parameter. If `immediate` is passed, the argument function will be
+  // triggered at the beginning of the sequence instead of at the end.
+  function debounce(func, wait, immediate) {
+    var timeout, previous, args, result, context;
+
+    var later = function() {
+      var passed = now() - previous;
+      if (wait > passed) {
+        timeout = setTimeout(later, wait - passed);
+      } else {
+        timeout = null;
+        if (!immediate) result = func.apply(context, args);
+        // This check is needed because `func` can recursively invoke `debounced`.
+        if (!timeout) args = context = null;
+      }
+    };
+
+    var debounced = restArguments(function(_args) {
+      context = this;
+      args = _args;
+      previous = now();
+      if (!timeout) {
+        timeout = setTimeout(later, wait);
+        if (immediate) result = func.apply(context, args);
+      }
+      return result;
+    });
+
+    debounced.cancel = function() {
+      clearTimeout(timeout);
+      timeout = args = context = null;
+    };
+
+    return debounced;
+  }
+
+  // Returns the first function passed as an argument to the second,
+  // allowing you to adjust arguments, run code before and after, and
+  // conditionally execute the original function.
+  function wrap(func, wrapper) {
+    return partial(wrapper, func);
+  }
+
+  // Returns a negated version of the passed-in predicate.
+  function negate(predicate) {
+    return function() {
+      return !predicate.apply(this, arguments);
+    };
+  }
+
+  // Returns a function that is the composition of a list of functions, each
+  // consuming the return value of the function that follows.
+  function compose() {
+    var args = arguments;
+    var start = args.length - 1;
+    return function() {
+      var i = start;
+      var result = args[start].apply(this, arguments);
+      while (i--) result = args[i].call(this, result);
+      return result;
+    };
+  }
+
+  // Returns a function that will only be executed on and after the Nth call.
+  function after(times, func) {
+    return function() {
+      if (--times < 1) {
+        return func.apply(this, arguments);
+      }
+    };
+  }
+
+  // Returns a function that will only be executed up to (but not including) the
+  // Nth call.
+  function before(times, func) {
+    var memo;
+    return function() {
+      if (--times > 0) {
+        memo = func.apply(this, arguments);
+      }
+      if (times <= 1) func = null;
+      return memo;
+    };
+  }
+
+  // Returns a function that will be executed at most one time, no matter how
+  // often you call it. Useful for lazy initialization.
+  var once = partial(before, 2);
+
+  // Returns the first key on an object that passes a truth test.
+  function findKey(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = keys(obj), key;
+    for (var i = 0, length = _keys.length; i < length; i++) {
+      key = _keys[i];
+      if (predicate(obj[key], key, obj)) return key;
+    }
+  }
+
+  // Internal function to generate `_.findIndex` and `_.findLastIndex`.
+  function createPredicateIndexFinder(dir) {
+    return function(array, predicate, context) {
+      predicate = cb(predicate, context);
+      var length = getLength(array);
+      var index = dir > 0 ? 0 : length - 1;
+      for (; index >= 0 && index < length; index += dir) {
+        if (predicate(array[index], index, array)) return index;
+      }
+      return -1;
+    };
+  }
+
+  // Returns the first index on an array-like that passes a truth test.
+  var findIndex = createPredicateIndexFinder(1);
+
+  // Returns the last index on an array-like that passes a truth test.
+  var findLastIndex = createPredicateIndexFinder(-1);
+
+  // Use a comparator function to figure out the smallest index at which
+  // an object should be inserted so as to maintain order. Uses binary search.
+  function sortedIndex(array, obj, iteratee, context) {
+    iteratee = cb(iteratee, context, 1);
+    var value = iteratee(obj);
+    var low = 0, high = getLength(array);
+    while (low < high) {
+      var mid = Math.floor((low + high) / 2);
+      if (iteratee(array[mid]) < value) low = mid + 1; else high = mid;
+    }
+    return low;
+  }
+
+  // Internal function to generate the `_.indexOf` and `_.lastIndexOf` functions.
+  function createIndexFinder(dir, predicateFind, sortedIndex) {
+    return function(array, item, idx) {
+      var i = 0, length = getLength(array);
+      if (typeof idx == 'number') {
+        if (dir > 0) {
+          i = idx >= 0 ? idx : Math.max(idx + length, i);
+        } else {
+          length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1;
+        }
+      } else if (sortedIndex && idx && length) {
+        idx = sortedIndex(array, item);
+        return array[idx] === item ? idx : -1;
+      }
+      if (item !== item) {
+        idx = predicateFind(slice.call(array, i, length), isNaN$1);
+        return idx >= 0 ? idx + i : -1;
+      }
+      for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) {
+        if (array[idx] === item) return idx;
+      }
+      return -1;
+    };
+  }
+
+  // Return the position of the first occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  // If the array is large and already in sort order, pass `true`
+  // for **isSorted** to use binary search.
+  var indexOf = createIndexFinder(1, findIndex, sortedIndex);
+
+  // Return the position of the last occurrence of an item in an array,
+  // or -1 if the item is not included in the array.
+  var lastIndexOf = createIndexFinder(-1, findLastIndex);
+
+  // Return the first value which passes a truth test.
+  function find(obj, predicate, context) {
+    var keyFinder = isArrayLike(obj) ? findIndex : findKey;
+    var key = keyFinder(obj, predicate, context);
+    if (key !== void 0 && key !== -1) return obj[key];
+  }
+
+  // Convenience version of a common use case of `_.find`: getting the first
+  // object containing specific `key:value` pairs.
+  function findWhere(obj, attrs) {
+    return find(obj, matcher(attrs));
+  }
+
+  // The cornerstone for collection functions, an `each`
+  // implementation, aka `forEach`.
+  // Handles raw objects in addition to array-likes. Treats all
+  // sparse array-likes as if they were dense.
+  function each(obj, iteratee, context) {
+    iteratee = optimizeCb(iteratee, context);
+    var i, length;
+    if (isArrayLike(obj)) {
+      for (i = 0, length = obj.length; i < length; i++) {
+        iteratee(obj[i], i, obj);
+      }
+    } else {
+      var _keys = keys(obj);
+      for (i = 0, length = _keys.length; i < length; i++) {
+        iteratee(obj[_keys[i]], _keys[i], obj);
+      }
+    }
+    return obj;
+  }
+
+  // Return the results of applying the iteratee to each element.
+  function map(obj, iteratee, context) {
+    iteratee = cb(iteratee, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length,
+        results = Array(length);
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      results[index] = iteratee(obj[currentKey], currentKey, obj);
+    }
+    return results;
+  }
+
+  // Internal helper to create a reducing function, iterating left or right.
+  function createReduce(dir) {
+    // Wrap code that reassigns argument variables in a separate function than
+    // the one that accesses `arguments.length` to avoid a perf hit. (#1991)
+    var reducer = function(obj, iteratee, memo, initial) {
+      var _keys = !isArrayLike(obj) && keys(obj),
+          length = (_keys || obj).length,
+          index = dir > 0 ? 0 : length - 1;
+      if (!initial) {
+        memo = obj[_keys ? _keys[index] : index];
+        index += dir;
+      }
+      for (; index >= 0 && index < length; index += dir) {
+        var currentKey = _keys ? _keys[index] : index;
+        memo = iteratee(memo, obj[currentKey], currentKey, obj);
+      }
+      return memo;
+    };
+
+    return function(obj, iteratee, memo, context) {
+      var initial = arguments.length >= 3;
+      return reducer(obj, optimizeCb(iteratee, context, 4), memo, initial);
+    };
+  }
+
+  // **Reduce** builds up a single result from a list of values, aka `inject`,
+  // or `foldl`.
+  var reduce = createReduce(1);
+
+  // The right-associative version of reduce, also known as `foldr`.
+  var reduceRight = createReduce(-1);
+
+  // Return all the elements that pass a truth test.
+  function filter(obj, predicate, context) {
+    var results = [];
+    predicate = cb(predicate, context);
+    each(obj, function(value, index, list) {
+      if (predicate(value, index, list)) results.push(value);
+    });
+    return results;
+  }
+
+  // Return all the elements for which a truth test fails.
+  function reject(obj, predicate, context) {
+    return filter(obj, negate(cb(predicate)), context);
+  }
+
+  // Determine whether all of the elements pass a truth test.
+  function every(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (!predicate(obj[currentKey], currentKey, obj)) return false;
+    }
+    return true;
+  }
+
+  // Determine if at least one element in the object passes a truth test.
+  function some(obj, predicate, context) {
+    predicate = cb(predicate, context);
+    var _keys = !isArrayLike(obj) && keys(obj),
+        length = (_keys || obj).length;
+    for (var index = 0; index < length; index++) {
+      var currentKey = _keys ? _keys[index] : index;
+      if (predicate(obj[currentKey], currentKey, obj)) return true;
+    }
+    return false;
+  }
+
+  // Determine if the array or object contains a given item (using `===`).
+  function contains(obj, item, fromIndex, guard) {
+    if (!isArrayLike(obj)) obj = values(obj);
+    if (typeof fromIndex != 'number' || guard) fromIndex = 0;
+    return indexOf(obj, item, fromIndex) >= 0;
+  }
+
+  // Invoke a method (with arguments) on every item in a collection.
+  var invoke = restArguments(function(obj, path, args) {
+    var contextPath, func;
+    if (isFunction$1(path)) {
+      func = path;
+    } else {
+      path = toPath(path);
+      contextPath = path.slice(0, -1);
+      path = path[path.length - 1];
+    }
+    return map(obj, function(context) {
+      var method = func;
+      if (!method) {
+        if (contextPath && contextPath.length) {
+          context = deepGet(context, contextPath);
+        }
+        if (context == null) return void 0;
+        method = context[path];
+      }
+      return method == null ? method : method.apply(context, args);
+    });
+  });
+
+  // Convenience version of a common use case of `_.map`: fetching a property.
+  function pluck(obj, key) {
+    return map(obj, property(key));
+  }
+
+  // Convenience version of a common use case of `_.filter`: selecting only
+  // objects containing specific `key:value` pairs.
+  function where(obj, attrs) {
+    return filter(obj, matcher(attrs));
+  }
+
+  // Return the maximum element (or element-based computation).
+  function max(obj, iteratee, context) {
+    var result = -Infinity, lastComputed = -Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value > result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed > lastComputed || computed === -Infinity && result === -Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Return the minimum element (or element-based computation).
+  function min(obj, iteratee, context) {
+    var result = Infinity, lastComputed = Infinity,
+        value, computed;
+    if (iteratee == null || typeof iteratee == 'number' && typeof obj[0] != 'object' && obj != null) {
+      obj = isArrayLike(obj) ? obj : values(obj);
+      for (var i = 0, length = obj.length; i < length; i++) {
+        value = obj[i];
+        if (value != null && value < result) {
+          result = value;
+        }
+      }
+    } else {
+      iteratee = cb(iteratee, context);
+      each(obj, function(v, index, list) {
+        computed = iteratee(v, index, list);
+        if (computed < lastComputed || computed === Infinity && result === Infinity) {
+          result = v;
+          lastComputed = computed;
+        }
+      });
+    }
+    return result;
+  }
+
+  // Sample **n** random values from a collection using the modern version of the
+  // [Fisher-Yates shuffle](https://en.wikipedia.org/wiki/Fisher–Yates_shuffle).
+  // If **n** is not specified, returns a single random element.
+  // The internal `guard` argument allows it to work with `_.map`.
+  function sample(obj, n, guard) {
+    if (n == null || guard) {
+      if (!isArrayLike(obj)) obj = values(obj);
+      return obj[random(obj.length - 1)];
+    }
+    var sample = isArrayLike(obj) ? clone(obj) : values(obj);
+    var length = getLength(sample);
+    n = Math.max(Math.min(n, length), 0);
+    var last = length - 1;
+    for (var index = 0; index < n; index++) {
+      var rand = random(index, last);
+      var temp = sample[index];
+      sample[index] = sample[rand];
+      sample[rand] = temp;
+    }
+    return sample.slice(0, n);
+  }
+
+  // Shuffle a collection.
+  function shuffle(obj) {
+    return sample(obj, Infinity);
+  }
+
+  // Sort the object's values by a criterion produced by an iteratee.
+  function sortBy(obj, iteratee, context) {
+    var index = 0;
+    iteratee = cb(iteratee, context);
+    return pluck(map(obj, function(value, key, list) {
+      return {
+        value: value,
+        index: index++,
+        criteria: iteratee(value, key, list)
+      };
+    }).sort(function(left, right) {
+      var a = left.criteria;
+      var b = right.criteria;
+      if (a !== b) {
+        if (a > b || a === void 0) return 1;
+        if (a < b || b === void 0) return -1;
+      }
+      return left.index - right.index;
+    }), 'value');
+  }
+
+  // An internal function used for aggregate "group by" operations.
+  function group(behavior, partition) {
+    return function(obj, iteratee, context) {
+      var result = partition ? [[], []] : {};
+      iteratee = cb(iteratee, context);
+      each(obj, function(value, index) {
+        var key = iteratee(value, index, obj);
+        behavior(result, value, key);
+      });
+      return result;
+    };
+  }
+
+  // Groups the object's values by a criterion. Pass either a string attribute
+  // to group by, or a function that returns the criterion.
+  var groupBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key].push(value); else result[key] = [value];
+  });
+
+  // Indexes the object's values by a criterion, similar to `_.groupBy`, but for
+  // when you know that your index values will be unique.
+  var indexBy = group(function(result, value, key) {
+    result[key] = value;
+  });
+
+  // Counts instances of an object that group by a certain criterion. Pass
+  // either a string attribute to count by, or a function that returns the
+  // criterion.
+  var countBy = group(function(result, value, key) {
+    if (has$1(result, key)) result[key]++; else result[key] = 1;
+  });
+
+  // Split a collection into two arrays: one whose elements all pass the given
+  // truth test, and one whose elements all do not pass the truth test.
+  var partition = group(function(result, value, pass) {
+    result[pass ? 0 : 1].push(value);
+  }, true);
+
+  // Safely create a real, live array from anything iterable.
+  var reStrSymbol = /[^\ud800-\udfff]|[\ud800-\udbff][\udc00-\udfff]|[\ud800-\udfff]/g;
+  function toArray(obj) {
+    if (!obj) return [];
+    if (isArray(obj)) return slice.call(obj);
+    if (isString(obj)) {
+      // Keep surrogate pair characters together.
+      return obj.match(reStrSymbol);
+    }
+    if (isArrayLike(obj)) return map(obj, identity);
+    return values(obj);
+  }
+
+  // Return the number of elements in a collection.
+  function size(obj) {
+    if (obj == null) return 0;
+    return isArrayLike(obj) ? obj.length : keys(obj).length;
+  }
+
+  // Internal `_.pick` helper function to determine whether `key` is an enumerable
+  // property name of `obj`.
+  function keyInObj(value, key, obj) {
+    return key in obj;
+  }
+
+  // Return a copy of the object only containing the allowed properties.
+  var pick = restArguments(function(obj, keys) {
+    var result = {}, iteratee = keys[0];
+    if (obj == null) return result;
+    if (isFunction$1(iteratee)) {
+      if (keys.length > 1) iteratee = optimizeCb(iteratee, keys[1]);
+      keys = allKeys(obj);
+    } else {
+      iteratee = keyInObj;
+      keys = flatten$1(keys, false, false);
+      obj = Object(obj);
+    }
+    for (var i = 0, length = keys.length; i < length; i++) {
+      var key = keys[i];
+      var value = obj[key];
+      if (iteratee(value, key, obj)) result[key] = value;
+    }
+    return result;
+  });
+
+  // Return a copy of the object without the disallowed properties.
+  var omit = restArguments(function(obj, keys) {
+    var iteratee = keys[0], context;
+    if (isFunction$1(iteratee)) {
+      iteratee = negate(iteratee);
+      if (keys.length > 1) context = keys[1];
+    } else {
+      keys = map(flatten$1(keys, false, false), String);
+      iteratee = function(value, key) {
+        return !contains(keys, key);
+      };
+    }
+    return pick(obj, iteratee, context);
+  });
+
+  // Returns everything but the last entry of the array. Especially useful on
+  // the arguments object. Passing **n** will return all the values in
+  // the array, excluding the last N.
+  function initial(array, n, guard) {
+    return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n)));
+  }
+
+  // Get the first element of an array. Passing **n** will return the first N
+  // values in the array. The **guard** check allows it to work with `_.map`.
+  function first(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[0];
+    return initial(array, array.length - n);
+  }
+
+  // Returns everything but the first entry of the `array`. Especially useful on
+  // the `arguments` object. Passing an **n** will return the rest N values in the
+  // `array`.
+  function rest(array, n, guard) {
+    return slice.call(array, n == null || guard ? 1 : n);
+  }
+
+  // Get the last element of an array. Passing **n** will return the last N
+  // values in the array.
+  function last(array, n, guard) {
+    if (array == null || array.length < 1) return n == null || guard ? void 0 : [];
+    if (n == null || guard) return array[array.length - 1];
+    return rest(array, Math.max(0, array.length - n));
+  }
+
+  // Trim out all falsy values from an array.
+  function compact(array) {
+    return filter(array, Boolean);
+  }
+
+  // Flatten out an array, either recursively (by default), or up to `depth`.
+  // Passing `true` or `false` as `depth` means `1` or `Infinity`, respectively.
+  function flatten(array, depth) {
+    return flatten$1(array, depth, false);
+  }
+
+  // Take the difference between one array and a number of other arrays.
+  // Only the elements present in just the first array will remain.
+  var difference = restArguments(function(array, rest) {
+    rest = flatten$1(rest, true, true);
+    return filter(array, function(value){
+      return !contains(rest, value);
+    });
+  });
+
+  // Return a version of the array that does not contain the specified value(s).
+  var without = restArguments(function(array, otherArrays) {
+    return difference(array, otherArrays);
+  });
+
+  // Produce a duplicate-free version of the array. If the array has already
+  // been sorted, you have the option of using a faster algorithm.
+  // The faster algorithm will not work with an iteratee if the iteratee
+  // is not a one-to-one function, so providing an iteratee will disable
+  // the faster algorithm.
+  function uniq(array, isSorted, iteratee, context) {
+    if (!isBoolean(isSorted)) {
+      context = iteratee;
+      iteratee = isSorted;
+      isSorted = false;
+    }
+    if (iteratee != null) iteratee = cb(iteratee, context);
+    var result = [];
+    var seen = [];
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var value = array[i],
+          computed = iteratee ? iteratee(value, i, array) : value;
+      if (isSorted && !iteratee) {
+        if (!i || seen !== computed) result.push(value);
+        seen = computed;
+      } else if (iteratee) {
+        if (!contains(seen, computed)) {
+          seen.push(computed);
+          result.push(value);
+        }
+      } else if (!contains(result, value)) {
+        result.push(value);
+      }
+    }
+    return result;
+  }
+
+  // Produce an array that contains the union: each distinct element from all of
+  // the passed-in arrays.
+  var union = restArguments(function(arrays) {
+    return uniq(flatten$1(arrays, true, true));
+  });
+
+  // Produce an array that contains every item shared between all the
+  // passed-in arrays.
+  function intersection(array) {
+    var result = [];
+    var argsLength = arguments.length;
+    for (var i = 0, length = getLength(array); i < length; i++) {
+      var item = array[i];
+      if (contains(result, item)) continue;
+      var j;
+      for (j = 1; j < argsLength; j++) {
+        if (!contains(arguments[j], item)) break;
+      }
+      if (j === argsLength) result.push(item);
+    }
+    return result;
+  }
+
+  // Complement of zip. Unzip accepts an array of arrays and groups
+  // each array's elements on shared indices.
+  function unzip(array) {
+    var length = array && max(array, getLength).length || 0;
+    var result = Array(length);
+
+    for (var index = 0; index < length; index++) {
+      result[index] = pluck(array, index);
+    }
+    return result;
+  }
+
+  // Zip together multiple lists into a single array -- elements that share
+  // an index go together.
+  var zip = restArguments(unzip);
+
+  // Converts lists into objects. Pass either a single array of `[key, value]`
+  // pairs, or two parallel arrays of the same length -- one of keys, and one of
+  // the corresponding values. Passing by pairs is the reverse of `_.pairs`.
+  function object(list, values) {
+    var result = {};
+    for (var i = 0, length = getLength(list); i < length; i++) {
+      if (values) {
+        result[list[i]] = values[i];
+      } else {
+        result[list[i][0]] = list[i][1];
+      }
+    }
+    return result;
+  }
+
+  // Generate an integer Array containing an arithmetic progression. A port of
+  // the native Python `range()` function. See
+  // [the Python documentation](https://docs.python.org/library/functions.html#range).
+  function range(start, stop, step) {
+    if (stop == null) {
+      stop = start || 0;
+      start = 0;
+    }
+    if (!step) {
+      step = stop < start ? -1 : 1;
+    }
+
+    var length = Math.max(Math.ceil((stop - start) / step), 0);
+    var range = Array(length);
+
+    for (var idx = 0; idx < length; idx++, start += step) {
+      range[idx] = start;
+    }
+
+    return range;
+  }
+
+  // Chunk a single array into multiple arrays, each containing `count` or fewer
+  // items.
+  function chunk(array, count) {
+    if (count == null || count < 1) return [];
+    var result = [];
+    var i = 0, length = array.length;
+    while (i < length) {
+      result.push(slice.call(array, i, i += count));
+    }
+    return result;
+  }
+
+  // Helper function to continue chaining intermediate results.
+  function chainResult(instance, obj) {
+    return instance._chain ? _$1(obj).chain() : obj;
+  }
+
+  // Add your own custom functions to the Underscore object.
+  function mixin(obj) {
+    each(functions(obj), function(name) {
+      var func = _$1[name] = obj[name];
+      _$1.prototype[name] = function() {
+        var args = [this._wrapped];
+        push.apply(args, arguments);
+        return chainResult(this, func.apply(_$1, args));
+      };
+    });
+    return _$1;
+  }
+
+  // Add all mutator `Array` functions to the wrapper.
+  each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) {
+        method.apply(obj, arguments);
+        if ((name === 'shift' || name === 'splice') && obj.length === 0) {
+          delete obj[0];
+        }
+      }
+      return chainResult(this, obj);
+    };
+  });
+
+  // Add all accessor `Array` functions to the wrapper.
+  each(['concat', 'join', 'slice'], function(name) {
+    var method = ArrayProto[name];
+    _$1.prototype[name] = function() {
+      var obj = this._wrapped;
+      if (obj != null) obj = method.apply(obj, arguments);
+      return chainResult(this, obj);
+    };
+  });
+
+  // Named Exports
+
+  var allExports = {
+    __proto__: null,
+    VERSION: VERSION,
+    restArguments: restArguments,
+    isObject: isObject,
+    isNull: isNull,
+    isUndefined: isUndefined,
+    isBoolean: isBoolean,
+    isElement: isElement,
+    isString: isString,
+    isNumber: isNumber,
+    isDate: isDate,
+    isRegExp: isRegExp,
+    isError: isError,
+    isSymbol: isSymbol,
+    isArrayBuffer: isArrayBuffer,
+    isDataView: isDataView$1,
+    isArray: isArray,
+    isFunction: isFunction$1,
+    isArguments: isArguments$1,
+    isFinite: isFinite$1,
+    isNaN: isNaN$1,
+    isTypedArray: isTypedArray$1,
+    isEmpty: isEmpty,
+    isMatch: isMatch,
+    isEqual: isEqual,
+    isMap: isMap,
+    isWeakMap: isWeakMap,
+    isSet: isSet,
+    isWeakSet: isWeakSet,
+    keys: keys,
+    allKeys: allKeys,
+    values: values,
+    pairs: pairs,
+    invert: invert,
+    functions: functions,
+    methods: functions,
+    extend: extend,
+    extendOwn: extendOwn,
+    assign: extendOwn,
+    defaults: defaults,
+    create: create,
+    clone: clone,
+    tap: tap,
+    get: get,
+    has: has,
+    mapObject: mapObject,
+    identity: identity,
+    constant: constant,
+    noop: noop,
+    toPath: toPath$1,
+    property: property,
+    propertyOf: propertyOf,
+    matcher: matcher,
+    matches: matcher,
+    times: times,
+    random: random,
+    now: now,
+    escape: _escape,
+    unescape: _unescape,
+    templateSettings: templateSettings,
+    template: template,
+    result: result,
+    uniqueId: uniqueId,
+    chain: chain,
+    iteratee: iteratee,
+    partial: partial,
+    bind: bind,
+    bindAll: bindAll,
+    memoize: memoize,
+    delay: delay,
+    defer: defer,
+    throttle: throttle,
+    debounce: debounce,
+    wrap: wrap,
+    negate: negate,
+    compose: compose,
+    after: after,
+    before: before,
+    once: once,
+    findKey: findKey,
+    findIndex: findIndex,
+    findLastIndex: findLastIndex,
+    sortedIndex: sortedIndex,
+    indexOf: indexOf,
+    lastIndexOf: lastIndexOf,
+    find: find,
+    detect: find,
+    findWhere: findWhere,
+    each: each,
+    forEach: each,
+    map: map,
+    collect: map,
+    reduce: reduce,
+    foldl: reduce,
+    inject: reduce,
+    reduceRight: reduceRight,
+    foldr: reduceRight,
+    filter: filter,
+    select: filter,
+    reject: reject,
+    every: every,
+    all: every,
+    some: some,
+    any: some,
+    contains: contains,
+    includes: contains,
+    include: contains,
+    invoke: invoke,
+    pluck: pluck,
+    where: where,
+    max: max,
+    min: min,
+    shuffle: shuffle,
+    sample: sample,
+    sortBy: sortBy,
+    groupBy: groupBy,
+    indexBy: indexBy,
+    countBy: countBy,
+    partition: partition,
+    toArray: toArray,
+    size: size,
+    pick: pick,
+    omit: omit,
+    first: first,
+    head: first,
+    take: first,
+    initial: initial,
+    last: last,
+    rest: rest,
+    tail: rest,
+    drop: rest,
+    compact: compact,
+    flatten: flatten,
+    without: without,
+    uniq: uniq,
+    unique: uniq,
+    union: union,
+    intersection: intersection,
+    difference: difference,
+    unzip: unzip,
+    transpose: unzip,
+    zip: zip,
+    object: object,
+    range: range,
+    chunk: chunk,
+    mixin: mixin,
+    'default': _$1
+  };
+
+  // Default Export
+
+  // Add all of the Underscore functions to the wrapper object.
+  var _ = mixin(allExports);
+  // Legacy Node.js API.
+  _._ = _;
+
+  return _;
+
+})));
+//# sourceMappingURL=underscore-umd.js.map
diff --git a/docs/_build/html/_static/underscore.js b/docs/_build/html/_static/underscore.js
new file mode 100644
index 0000000..cf177d4
--- /dev/null
+++ b/docs/_build/html/_static/underscore.js
@@ -0,0 +1,6 @@
+!function(n,r){"object"==typeof exports&&"undefined"!=typeof module?module.exports=r():"function"==typeof define&&define.amd?define("underscore",r):(n="undefined"!=typeof globalThis?globalThis:n||self,function(){var t=n._,e=n._=r();e.noConflict=function(){return n._=t,e}}())}(this,(function(){
+//     Underscore.js 1.13.1
+//     https://underscorejs.org
+//     (c) 2009-2021 Jeremy Ashkenas, Julian Gonggrijp, and DocumentCloud and Investigative Reporters & Editors
+//     Underscore may be freely distributed under the MIT license.
+var n="1.13.1",r="object"==typeof self&&self.self===self&&self||"object"==typeof global&&global.global===global&&global||Function("return this")()||{},t=Array.prototype,e=Object.prototype,u="undefined"!=typeof Symbol?Symbol.prototype:null,o=t.push,i=t.slice,a=e.toString,f=e.hasOwnProperty,c="undefined"!=typeof ArrayBuffer,l="undefined"!=typeof DataView,s=Array.isArray,p=Object.keys,v=Object.create,h=c&&ArrayBuffer.isView,y=isNaN,d=isFinite,g=!{toString:null}.propertyIsEnumerable("toString"),b=["valueOf","isPrototypeOf","toString","propertyIsEnumerable","hasOwnProperty","toLocaleString"],m=Math.pow(2,53)-1;function j(n,r){return r=null==r?n.length-1:+r,function(){for(var t=Math.max(arguments.length-r,0),e=Array(t),u=0;u<t;u++)e[u]=arguments[u+r];switch(r){case 0:return n.call(this,e);case 1:return n.call(this,arguments[0],e);case 2:return n.call(this,arguments[0],arguments[1],e)}var o=Array(r+1);for(u=0;u<r;u++)o[u]=arguments[u];return o[r]=e,n.apply(this,o)}}function _(n){var r=typeof 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diff --git a/docs/_build/html/adapt.html b/docs/_build/html/adapt.html
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+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
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+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="api.html">ForMoSA API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Adapt the data</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.adapt_grid">Adapt Grid:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.adapt_grid.adapt_grid"><code class="docutils literal notranslate"><span class="pre">adapt_grid()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.adapt_obs_mod">Adapt Observations and Models:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.adapt_obs_mod.launch_adapt"><code class="docutils literal notranslate"><span class="pre">launch_adapt()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.extraction_functions">Extraction Functions:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.adapt_model"><code class="docutils literal notranslate"><span class="pre">adapt_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.adapt_observation_range"><code class="docutils literal notranslate"><span class="pre">adapt_observation_range()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.continuum_estimate"><code class="docutils literal notranslate"><span class="pre">continuum_estimate()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.convolve_and_sample"><code class="docutils literal notranslate"><span class="pre">convolve_and_sample()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.decoupe"><code class="docutils literal notranslate"><span class="pre">decoupe()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.extract_model"><code class="docutils literal notranslate"><span class="pre">extract_model()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.extract_observation"><code class="docutils literal notranslate"><span class="pre">extract_observation()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.find_nearest"><code class="docutils literal notranslate"><span class="pre">find_nearest()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.resolution_decreasing"><code class="docutils literal notranslate"><span class="pre">resolution_decreasing()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="nested_sampling.html">Nested Sampling</a></li>
+<li class="toctree-l2"><a class="reference internal" href="plotting.html">Plotting Class</a></li>
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+            <a href="_sources/adapt.rst.txt" rel="nofollow"> View page source</a>
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+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="adapt-the-data">
+<span id="adapt"></span><h1>Adapt the data<a class="headerlink" href="#adapt-the-data" title="Link to this heading"></a></h1>
+<section id="module-ForMoSA.adapt.adapt_grid">
+<span id="adapt-grid"></span><h2>Adapt Grid:<a class="headerlink" href="#module-ForMoSA.adapt.adapt_grid" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.adapt_grid.adapt_grid">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.adapt_grid.</span></span><span class="sig-name descname"><span class="pre">adapt_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/adapt_grid.html#adapt_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.adapt_grid.adapt_grid" title="Link to this definition"></a></dt>
+<dd><p>Adapt the synthetic spectra of a grid to make them comparable with the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter</p></li>
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Merged wavelength grid of the data</p></li>
+<li><p><strong>wav_obs_photo</strong> (<em>array</em>) – Wavelengths of the photometry points</p></li>
+<li><p><strong>obs_name</strong> (<em>str</em>) – Name of the current observation looping</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet and Paulina Palma-Bifani</p>
+</dd></dl>
+
+</section>
+<section id="module-ForMoSA.adapt.adapt_obs_mod">
+<span id="adapt-observations-and-models"></span><h2>Adapt Observations and Models:<a class="headerlink" href="#module-ForMoSA.adapt.adapt_obs_mod" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.adapt_obs_mod.launch_adapt">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.adapt_obs_mod.</span></span><span class="sig-name descname"><span class="pre">launch_adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">justobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/adapt_obs_mod.html#launch_adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.adapt_obs_mod.launch_adapt" title="Link to this definition"></a></dt>
+<dd><p>Adapt the synthetic spectra of a grid to make them comparable with the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter</p></li>
+<li><p><strong>justobs</strong> (<em>'yes'/'no'</em>) – ‘no’ by default to also adapt the grid</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet, Paulina Palma-Bifani and Allan Denis</p>
+</dd></dl>
+
+</section>
+<section id="module-ForMoSA.adapt.extraction_functions">
+<span id="extraction-functions"></span><h2>Extraction Functions:<a class="headerlink" href="#module-ForMoSA.adapt.extraction_functions" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.adapt_model">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">adapt_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#adapt_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.adapt_model" title="Link to this definition"></a></dt>
+<dd><p>Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
+calculated too. Then each sub-spectrum are merged.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter used in ForMoSA</p></li>
+<li><p><strong>wav_mod_nativ</strong> (<em>array</em>) – Wavelength grid of the model</p></li>
+<li><p><strong>wave_reso_tab</strong> (<em>array</em>) – Wavelength grid of the model at specified resolution</p></li>
+<li><p><strong>flx_mod_nativ</strong> (<em>array</em>) – Flux of the model</p></li>
+<li><p><strong>res_mod_nativ</strong> (<em>array</em>) – Spectral resolution of the model as a function of the wavelength grid</p></li>
+<li><p><strong>obs_name</strong> (<em>str</em>) – Name of the current observation looping</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid
+- mod_photo (array)       : List containing the photometry (‘0’ replace the spectral resolution here).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.adapt_observation_range">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">adapt_observation_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#adapt_observation_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.adapt_observation_range" title="Link to this definition"></a></dt>
+<dd><p>Extract the information from the observation file, including the wavelengths (um - vacuum), flux (W.m-2.um.1), errors (W.m-2.um.1), covariance (W.m-2.um.1)**2, spectral resolution,
+instrument/filter name, transmission (Atmo+inst) and star flux (W.m-2.um.1). The wavelength range is define by the parameter “wav_for_adapt”.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter</p></li>
+<li><p><strong>obs_name</strong> (<em>str</em>) – Name of the current observation looping</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>List containing the sub-spectra defined by the parameter “wav_for_adapt” with decreased resolution  [[wav_1, flx_1, err_1, reso_1], …, [wav_n, flx_n, err_n, reso_n]]
+- obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here).  [wav_phot, flx_phot, err_phot, 0]
+- obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], …, [instru_range_n]]
+- obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, …, filter_phot_n]
+- obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter “wav_for_adapt”. [[cov_1, tran_1, star_1], …, [cov_n, tran_n, star_n]]</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>obs_spectro (n-array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet and Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.continuum_estimate">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">continuum_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#continuum_estimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.continuum_estimate" title="Link to this definition"></a></dt>
+<dd><p>Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
+wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease
+the resolution of the spectrum to this custom FWHM for each wavelength using a gaussian filter and resample it on
+the wavelength grid of the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter used in ForMoSA</p></li>
+<li><p><strong>wav</strong> (<em>array</em>) – Wavelength grid of the spectrum for which you want to estimate the continuum</p></li>
+<li><p><strong>flx</strong> (<em>array</em>) – Flux of the spectrum for which you want to estimate the continuum</p></li>
+<li><p><strong>res</strong> (<em>int</em>) – Spectral resolution of the spectrum for which you want to estimate the continuum</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Estimated continuum of the spectrum re-sampled on the data wavelength grid</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>continuum (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.convolve_and_sample">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">convolve_and_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wv_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas_wvs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_wvs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_fluxes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#convolve_and_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.convolve_and_sample" title="Link to this definition"></a></dt>
+<dd><p>Simulate the observations of a model. Convolves the model with a variable Gaussian LSF, sampled at each desired
+spectral channel.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wv_channels</strong> (<em>list</em><em>(</em><em>floats</em><em>)</em>) – the wavelengths values desired</p></li>
+<li><p><strong>sigmas_wvs</strong> (<em>list</em><em>(</em><em>floats</em><em>)</em>) – the LSF gaussian standard deviation of each wv_channels [IN UNITS OF model_wvs]</p></li>
+<li><p><strong>model_wvs</strong> (<em>array</em>) – the wavelengths of the model</p></li>
+<li><p><strong>model_fluxes</strong> (<em>array</em>) – the fluxes of the model</p></li>
+<li><p><strong>num_sigma</strong> (<em>float</em>) – number of +/- sigmas to evaluate the LSF to.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the fluxes in each of the wavelength channels</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>output_model (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Jason Wang</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.decoupe">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">decoupe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">second</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#decoupe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.decoupe" title="Link to this definition"></a></dt>
+<dd><p>Re-arranged a number of seconds in the hours-minutes-seconds format.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>second</strong> (<em>float</em>) – number of second</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>hours
+- float     : minutes
+- float     : seconds</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>float</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.extract_model">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">extract_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cont</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#extract_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.extract_model" title="Link to this definition"></a></dt>
+<dd><p>Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
+calculated too.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter used in ForMoSA</p></li>
+<li><p><strong>wav_mod_nativ</strong> (<em>array</em>) – Wavelength grid of the model</p></li>
+<li><p><strong>flx_mod_nativ</strong> (<em>array</em>) – Flux of the model</p></li>
+<li><p><strong>res_mod_nativ</strong> (<em>array</em>) – Spectral resolution of the model as a function of the wavelength grid</p></li>
+<li><p><strong>cont</strong> (<em>str</em>) – Boolean string. If the function is used to estimate the continuum cont=’yes’</p></li>
+<li><p><strong>obs_name</strong> (<em>str</em>) – Name of the current observation looping</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>List containing the sub-spectra defined by the parameter “wav_for_adapt”.
+- mod (array)         : List containing the photometry (‘0’ replace the spectral resolution here).</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.extract_observation">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">extract_observation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cont</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#extract_observation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.extract_observation" title="Link to this definition"></a></dt>
+<dd><p>Take back the extracted data spectrum from the function ‘adapt_observation_range’ and decrease its spectral
+resolution.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter</p></li>
+<li><p><strong>wav_mod_nativ</strong> (<em>array</em>) – Wavelength grid of the model</p></li>
+<li><p><strong>res_mod_nativ</strong> (<em>array</em><em>(</em><em>int</em><em>)</em>) – Spectral resolution of the model</p></li>
+<li><p><strong>cont</strong> (<em>str</em>) – Boolean string. If the function is used to estimate the continuum cont=’yes’</p></li>
+<li><p><strong>obs_name</strong> (<em>str</em>) – Name of the current observation looping</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>List containing the sub-spectra defined by the parameter “wav_for_adapt” with decreased resolution [[wav_1, flx_1, err_1, reso_1], …, [wav_n, flx_n, err_n, reso_n]]
+- obs_photo (array)       : List containing the photometry (0 replace the spectral resolution here). [wav_phot, flx_phot, err_phot, 0]
+- obs_spectro_ins(array)  : List containing different instruments used for the data (1 per wavelength). [[instru_range_1], …, [instru_range_n]]
+- obs_photo_ins (array)   : List containing different filters used for the data (1 per photometric point). [filter_phot_1, filter_phot_2, …, filter_phot_n]
+- obs_opt (n-array)       : List containing the optional sub-arrays defined by the parameter “wav_for_adapt”. [[cov_1, tran_1, star_1], …, [cov_n, tran_n, star_n]]</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>obs_spectro (n-array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.find_nearest">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">find_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#find_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.find_nearest" title="Link to this definition"></a></dt>
+<dd><p>Return the indice of the closest values from a desire value in an array.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>array</strong> (<em>array</em>) – Array to explore</p></li>
+<li><p><strong>value</strong> (<em>float</em>) – Desire value</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Indice of the closest values from the desire value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>idx (int)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.resolution_decreasing">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">resolution_decreasing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_or_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#resolution_decreasing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.resolution_decreasing" title="Link to this definition"></a></dt>
+<dd><p>Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
+wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one
+for each wavelength (the lowest spectral resolution). It then calculates a sigma to decrease the resolution of the
+spectrum to this lowest FWHM for each wavelength and resample it on the wavelength grid of the data using the
+function ‘convolve_and_sample’.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter used in ForMoSA</p></li>
+<li><p><strong>wav_obs</strong> (<em>array</em>) – Wavelength grid of the data</p></li>
+<li><p><strong>flx_obs</strong> (<em>array</em>) – Flux of the data</p></li>
+<li><p><strong>res_obs</strong> (<em>array</em>) – Spectral resolution of the data</p></li>
+<li><p><strong>wav_mod_nativ</strong> (<em>array</em>) – Wavelength grid of the model</p></li>
+<li><p><strong>flx_mod_nativ</strong> (<em>array</em>) – Flux of the model</p></li>
+<li><p><strong>res_mod_obs</strong> (<em>array</em>) – Spectral resolution of the model as a function of the wavelength grid of the data</p></li>
+<li><p><strong>obs_or_mod</strong> (<em>str</em>) – Parameter to identify if you want to manage a data or a model spectrum. ‘obs’ or ‘mod’</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_obs_final (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet</p>
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="main_utilities.html" class="btn btn-neutral float-left" title="Main Utilities" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="nested_sampling.html" class="btn btn-neutral float-right" title="Nested Sampling" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
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+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
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diff --git a/docs/build/html/api.html b/docs/_build/html/api.html
similarity index 73%
rename from docs/build/html/api.html
rename to docs/_build/html/api.html
index 2fd6522..40be3cb 100644
--- a/docs/build/html/api.html
+++ b/docs/_build/html/api.html
@@ -1,27 +1,30 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
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+
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-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
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-    <link rel="prev" title="DEMO" href="demo.html" /> 
+    <link rel="prev" title="demo AB Pic b" href="tutorials/demoabpic.html" /> 
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@@ -43,9 +46,10 @@
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         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul class="current">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="current reference internal" href="#">ForMoSA API</a><ul>
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@@ -79,8 +83,8 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="formosa-api">
-<span id="api"></span><h1>ForMoSA API<a class="headerlink" href="#formosa-api" title="Permalink to this heading"></a></h1>
+  <section id="formosa-api">
+<span id="api"></span><h1>ForMoSA API<a class="headerlink" href="#formosa-api" title="Link to this heading"></a></h1>
 <div class="toctree-wrapper compound">
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
@@ -89,20 +93,20 @@
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-</div>
+</section>
 
 
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+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
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diff --git a/docs/_build/html/atm_grids.html b/docs/_build/html/atm_grids.html
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+             
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+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/exorem_info.html">Exo-REM</a></li>
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+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#atmospheric-grids">Atmospheric grids</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/exorem_info.html">Exo-REM</a></li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="#list-of-tutorials">List of tutorials:</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/inputs.html">INPUTS</a></li>
+<li class="toctree-l3"><a class="reference internal" href="tutorials/demoabpic.html">demo AB Pic b</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Tutorials</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/demo.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="tutorials">
+<span id="demo"></span><h1>Tutorials<a class="headerlink" href="#tutorials" title="Link to this heading"></a></h1>
+<p>There are several ways of using ForMoSA.</p>
+<p>To get started, we recomend you to keep the following structure locally.</p>
+<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>~/YOUR/PATH/formosa_desk/
+├──<span class="w"> </span>atm_grids/
+├──<span class="w"> </span>demo_ABPicb/
+│<span class="w">   </span>├──<span class="w"> </span>data.fits
+│<span class="w">   </span>├──<span class="w"> </span>config.ini
+│<span class="w">   </span>├──<span class="w"> </span>adapted_grid/
+│<span class="w">   </span>└──<span class="w"> </span>outputs/
+├──<span class="w"> </span><span class="o">(</span>ForMoSA/<span class="o">)</span>
+├──<span class="w"> </span><span class="o">(</span>PyMultiNest/<span class="o">)</span>
+└──<span class="w"> </span><span class="o">(</span>MultiNest/<span class="o">)</span>
+</pre></div>
+</div>
+<p>Depending on the way you installed ForMoSA, the ForMoSA, PyMultiNest, and MultiNest subfolders need to be cloned from GitHub.
+Follow the <a class="reference internal" href="installation.html"><span class="doc">Installation</span></a></p>
+<section id="atmospheric-grids">
+<h2>Atmospheric grids<a class="headerlink" href="#atmospheric-grids" title="Link to this heading"></a></h2>
+<p>This is the list of the publically available atmospheric grids which we have formated for ForMoSA.</p>
+<p>Download the grid you want to use by clicking over it’s name. Ideally, save it inside the <code class="docutils literal notranslate"><span class="pre">atm_grids/</span></code> subdirectory.</p>
+<ul class="simple">
+<li><p><a class="reference external" href="https://drive.google.com/file/d/1S1dcBD7UiuUCZIcNBNnJi6LMymrnkagM/view?usp=share_link">ATMO native</a> from <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract">M.W. Phillips et al. 2020</a></p></li>
+<li><p><a class="reference external" href="https://drive.google.com/file/d/1wvf4A-DupdVnYIpK_HmHE-fobqnYtvEz/view?usp=share_link">BT-Settl native</a> from <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2013MSAIS..24..128A/abstract">Allard et al. 2013</a></p></li>
+<li><p><a class="reference external" href="https://drive.google.com/file/d/1k9SQjHLnMCwmGOHtraRnhCgiZ1-4J3Wk/view?usp=share_link">ExoREM native</a> from <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract">B. Charnay et al. 2018</a></p></li>
+</ul>
+<p>Learn more about:</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/exorem_info.html">Exo-REM</a></li>
+</ul>
+</div>
+</section>
+<section id="list-of-tutorials">
+<h2>List of tutorials:<a class="headerlink" href="#list-of-tutorials" title="Link to this heading"></a></h2>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/inputs.html">INPUTS</a></li>
+<li class="toctree-l1"><a class="reference internal" href="tutorials/demoabpic.html">demo AB Pic b</a></li>
+</ul>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="installation.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="tutorials/exorem_info.html" class="btn btn-neutral float-right" title="Exo-REM" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/_build/html/demo_basic.html b/docs/_build/html/demo_basic.html
new file mode 100644
index 0000000..afd57a5
--- /dev/null
+++ b/docs/_build/html/demo_basic.html
@@ -0,0 +1,135 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>demo AB Pic b &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
+    <link rel="shortcut icon" href="_static/favicon.ico"/>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="AB Pic demo" href="demoabpic.html" />
+    <link rel="prev" title="Tutorials" href="demo.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            ForMoSA
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="demo.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="demo.html#atmospheric-grids">Atmospheric grids</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="demo.html#list-of-tutorials">List of tutorials:</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">demo AB Pic b</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="demoabpic.html">AB Pic demo</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="demo.html">Tutorials</a></li>
+      <li class="breadcrumb-item active">demo AB Pic b</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/demo_basic.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="demo-ab-pic-b">
+<span id="demo-basic"></span><h1>demo AB Pic b<a class="headerlink" href="#demo-ab-pic-b" title="Link to this heading"></a></h1>
+<p>This tutorial is intended as a quick start.
+You will use medium resolution VLT/SINFONI K-band data, published in <a class="reference external" href="https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf">P. Palma-Bifani et al (2023)</a>.</p>
+<div class="toctree-wrapper compound">
+<ul>
+<li class="toctree-l1"><a class="reference internal" href="demoabpic.html">AB Pic demo</a></li>
+</ul>
+</div>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="demo.html" class="btn btn-neutral float-left" title="Tutorials" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="demoabpic.html" class="btn btn-neutral float-right" title="AB Pic demo" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/demo.html b/docs/_build/html/demo_classic.html
similarity index 62%
rename from docs/build/html/demo.html
rename to docs/_build/html/demo_classic.html
index f42c7d4..71a1fb2 100644
--- a/docs/build/html/demo.html
+++ b/docs/_build/html/demo_classic.html
@@ -1,27 +1,29 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>DEMO &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>DEMO classic: &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
     <link rel="next" title="ForMoSA API" href="api.html" />
-    <link rel="prev" title="Installation" href="installation.html" /> 
+    <link rel="prev" title="DEMO" href="demo.html" /> 
 </head>
 
 <body class="wy-body-for-nav"> 
@@ -43,9 +45,13 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul class="current">
+              <p class="caption" role="heading"><span class="caption-text">Contents:</span></p>
+<ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1 current"><a class="current reference internal" href="#">DEMO</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="demo.html">DEMO</a><ul class="current">
+<li class="toctree-l2 current"><a class="current reference internal" href="#">DEMO classic:</a></li>
+</ul>
+</li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
 </ul>
 
@@ -63,9 +69,10 @@
           <div role="navigation" aria-label="Page navigation">
   <ul class="wy-breadcrumbs">
       <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
-      <li class="breadcrumb-item active">DEMO</li>
+          <li class="breadcrumb-item"><a href="demo.html">DEMO</a></li>
+      <li class="breadcrumb-item active">DEMO classic:</li>
       <li class="wy-breadcrumbs-aside">
-            <a href="_sources/demo.rst.txt" rel="nofollow"> View page source</a>
+            <a href="_sources/demo_classic.rst.txt" rel="nofollow"> View page source</a>
       </li>
   </ul>
   <hr/>
@@ -73,23 +80,22 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="demo">
-<span id="id1"></span><h1>DEMO<a class="headerlink" href="#demo" title="Permalink to this heading"></a></h1>
-<p>There are several ways of using ForMoSA.</p>
-</div>
+  <section id="demo-classic">
+<span id="id1"></span><h1>DEMO classic:<a class="headerlink" href="#demo-classic" title="Link to this heading"></a></h1>
+</section>
 
 
            </div>
           </div>
           <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="installation.html" class="btn btn-neutral float-left" title="Installation" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="demo.html" class="btn btn-neutral float-left" title="DEMO" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
         <a href="api.html" class="btn btn-neutral float-right" title="ForMoSA API" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
     </div>
 
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/demoabpic.html b/docs/_build/html/demoabpic.html
new file mode 100644
index 0000000..318723b
--- /dev/null
+++ b/docs/_build/html/demoabpic.html
@@ -0,0 +1,141 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>AB Pic demo &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="_static/nbsphinx-code-cells.css?v=2aa19091" />
+
+  
+    <link rel="shortcut icon" href="_static/favicon.ico"/>
+  <!--[if lt IE 9]>
+    <script src="_static/js/html5shiv.min.js"></script>
+  <![endif]-->
+  
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="ForMoSA API" href="api.html" />
+    <link rel="prev" title="demo AB Pic b" href="demo_basic.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            ForMoSA
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="demo.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="demo.html#atmospheric-grids">Atmospheric grids</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="demo.html#list-of-tutorials">List of tutorials:</a><ul class="current">
+<li class="toctree-l3 current"><a class="reference internal" href="demo_basic.html">demo AB Pic b</a><ul class="current">
+<li class="toctree-l4 current"><a class="current reference internal" href="#">AB Pic demo</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="demo.html">Tutorials</a></li>
+          <li class="breadcrumb-item"><a href="demo_basic.html">demo AB Pic b</a></li>
+      <li class="breadcrumb-item active">AB Pic demo</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/demoabpic.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="AB-Pic-demo">
+<h1>AB Pic demo<a class="headerlink" href="#AB-Pic-demo" title="Link to this heading"></a></h1>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<br/></pre></div>
+</div>
+</div>
+<p>demo_basic.rst</p>
+<p>tutorials/demo_basic.ipynb</p>
+<p>:doc:<code class="docutils literal notranslate"><span class="pre">tutorials/demo_basic.ipynb</span></code></p>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="demo_basic.html" class="btn btn-neutral float-left" title="demo AB Pic b" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="api.html" class="btn btn-neutral float-right" title="ForMoSA API" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/_build/html/demoabpic.ipynb b/docs/_build/html/demoabpic.ipynb
new file mode 100644
index 0000000..72a10ab
--- /dev/null
+++ b/docs/_build/html/demoabpic.ipynb
@@ -0,0 +1,56 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AB Pic demo"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    ".. toctree::\n",
+    "   :maxdepth: 2\n",
+    "   :titlesonly:\n",
+    "\n",
+    "   demo_basic.rst\n",
+    "\n",
+    "   tutorials/demo_basic.ipynb\n",
+    "\n",
+    "   :doc:`tutorials/demo_basic.ipynb`"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/build/html/genindex.html b/docs/_build/html/genindex.html
similarity index 69%
rename from docs/build/html/genindex.html
rename to docs/_build/html/genindex.html
index fbf5e5b..c6b064b 100644
--- a/docs/build/html/genindex.html
+++ b/docs/_build/html/genindex.html
@@ -1,19 +1,24 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Index &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>Index &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="#" />
     <link rel="search" title="Search" href="search.html" /> 
@@ -38,9 +43,10 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
 </ul>
 
@@ -72,11 +78,13 @@ <h1 id="index">Index</h1>
 
 <div class="genindex-jumpbox">
  <a href="#A"><strong>A</strong></a>
+ | <a href="#B"><strong>B</strong></a>
  | <a href="#C"><strong>C</strong></a>
  | <a href="#D"><strong>D</strong></a>
  | <a href="#E"><strong>E</strong></a>
  | <a href="#F"><strong>F</strong></a>
  | <a href="#G"><strong>G</strong></a>
+ | <a href="#I"><strong>I</strong></a>
  | <a href="#L"><strong>L</strong></a>
  | <a href="#M"><strong>M</strong></a>
  | <a href="#P"><strong>P</strong></a>
@@ -101,6 +109,18 @@ <h2 id="A">A</h2>
   </ul></td>
 </tr></table>
 
+<h2 id="B">B</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.bb_cpd_fct">bb_cpd_fct() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.bin_data">bin_data() (in module ForMoSA.plotting.plotting_class)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="C">C</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
@@ -124,6 +144,8 @@ <h2 id="D">D</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="main_utilities.html#ForMoSA.main_utilities.diag_mat">diag_mat() (in module ForMoSA.main_utilities)</a>
+</li>
       <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct">doppler_fct() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
 </li>
   </ul></td>
@@ -144,6 +166,10 @@ <h2 id="E">E</h2>
 <h2 id="F">F</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.fill">fill() (ForMoSA.plotting.plotting_class.ComplexRadar method)</a>
+</li>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.fill_between">fill_between() (ForMoSA.plotting.plotting_class.ComplexRadar method)</a>
+</li>
       <li><a href="adapt.html#ForMoSA.adapt.extraction_functions.find_nearest">find_nearest() (in module ForMoSA.adapt.extraction_functions)</a>
 </li>
       <li>
@@ -181,6 +207,13 @@ <h2 id="F">F</h2>
 
       <ul>
         <li><a href="main_utilities.html#module-ForMoSA.main_utilities">module</a>
+</li>
+      </ul></li>
+      <li>
+    ForMoSA.nested_sampling.nested_logL_functions
+
+      <ul>
+        <li><a href="nested_sampling.html#module-ForMoSA.nested_sampling.nested_logL_functions">module</a>
 </li>
       </ul></li>
       <li>
@@ -221,21 +254,49 @@ <h2 id="G">G</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.get_FULL_spectra">get_FULL_spectra() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
+</li>
       <li><a href="main_utilities.html#ForMoSA.main_utilities.GlobFile">GlobFile (class in ForMoSA.main_utilities)</a>
 </li>
   </ul></td>
 </tr></table>
 
+<h2 id="I">I</h2>
+<table style="width: 100%" class="indextable genindextable"><tr>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.import_obsmod">import_obsmod() (in module ForMoSA.nested_sampling.nested_sampling)</a>
+</li>
+  </ul></td>
+</tr></table>
+
 <h2 id="L">L</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
       <li><a href="adapt.html#ForMoSA.adapt.adapt_obs_mod.launch_adapt">launch_adapt() (in module ForMoSA.adapt.adapt_obs_mod)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">launch_nested_sampling() (in module ForMoSA.nested_sampling.nested_sampling)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Brogi">logL_CCF_Brogi() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_custom">logL_CCF_custom() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Zucker">logL_CCF_Zucker() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_classic">logL_chi2_classic() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">launch_nested_sampling() (in module ForMoSA.nested_sampling.nested_sampling)</a>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_covariance">logL_chi2_covariance() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended">logL_chi2_extended() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended_covariance">logL_chi2_extended_covariance() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_logL_functions.logL_full_covariance">logL_full_covariance() (in module ForMoSA.nested_sampling.nested_logL_functions)</a>
 </li>
       <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.loglike">loglike() (in module ForMoSA.nested_sampling.nested_sampling)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.lsq_fct">lsq_fct() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -258,6 +319,8 @@ <h2 id="M">M</h2>
         <li><a href="adapt.html#module-ForMoSA.adapt.extraction_functions">ForMoSA.adapt.extraction_functions</a>
 </li>
         <li><a href="main_utilities.html#module-ForMoSA.main_utilities">ForMoSA.main_utilities</a>
+</li>
+        <li><a href="nested_sampling.html#module-ForMoSA.nested_sampling.nested_logL_functions">ForMoSA.nested_sampling.nested_logL_functions</a>
 </li>
         <li><a href="nested_sampling.html#module-ForMoSA.nested_sampling.nested_modif_spec">ForMoSA.nested_sampling.nested_modif_spec</a>
 </li>
@@ -274,6 +337,10 @@ <h2 id="M">M</h2>
 <h2 id="P">P</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.plot">plot() (ForMoSA.plotting.plotting_class.ComplexRadar method)</a>
+</li>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_ccf">plot_ccf() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
+</li>
       <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains">plot_chains() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
 </li>
       <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds">plot_Clouds() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
@@ -284,6 +351,10 @@ <h2 id="P">P</h2>
 </li>
   </ul></td>
   <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit_HiRes">plot_fit_HiRes() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
+</li>
+      <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_HiRes_comp_model">plot_HiRes_comp_model() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
+</li>
       <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT">plot_PT() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
 </li>
       <li><a href="plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar">plot_radar() (ForMoSA.plotting.plotting_class.PlottingForMoSA method)</a>
@@ -326,7 +397,13 @@ <h2 id="U">U</h2>
 <h2 id="V">V</h2>
 <table style="width: 100%" class="indextable genindextable"><tr>
   <td style="width: 33%; vertical-align: top;"><ul>
-      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct">vsini_fct() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_accurate">vsini_fct_accurate() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
+</li>
+  </ul></td>
+  <td style="width: 33%; vertical-align: top;"><ul>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_fast_rot_broad">vsini_fct_fast_rot_broad() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
+</li>
+      <li><a href="nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_rot_broad">vsini_fct_rot_broad() (in module ForMoSA.nested_sampling.nested_modif_spec)</a>
 </li>
   </ul></td>
 </tr></table>
@@ -348,7 +425,7 @@ <h2 id="Y">Y</h2>
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/build/html/index.html b/docs/_build/html/index.html
similarity index 53%
rename from docs/build/html/index.html
rename to docs/_build/html/index.html
index 716fbb5..ef01975 100644
--- a/docs/build/html/index.html
+++ b/docs/_build/html/index.html
@@ -1,22 +1,27 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Forward Modeling Tool for Spectral Analysis &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>Forward Modeling Tool for Spectral Analysis &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
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@@ -42,9 +47,10 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
 </ul>
 
@@ -72,26 +78,28 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <a class="reference internal image-reference" href="_images/ForMoSA.png"><img alt="_images/ForMoSA.png" class="align-center" src="_images/ForMoSA.png" style="width: 150px; height: 105px;" /></a>
-<div class="section" id="forward-modeling-tool-for-spectral-analysis">
-<h1>Forward Modeling Tool for Spectral Analysis<a class="headerlink" href="#forward-modeling-tool-for-spectral-analysis" title="Permalink to this heading"></a></h1>
-<p>Welcome to ForMoSA, an open-source Python package.
+  <a class="reference internal image-reference" href="_images/ForMoSA.png"><img alt="_images/ForMoSA.png" class="align-center" src="_images/ForMoSA.png" style="width: 150px; height: 105px;" />
+</a>
+<section id="forward-modeling-tool-for-spectral-analysis">
+<h1>Forward Modeling Tool for Spectral Analysis<a class="headerlink" href="#forward-modeling-tool-for-spectral-analysis" title="Link to this heading"></a></h1>
+<p>Welcome to the documentation of ForMoSA, an open-source Python package.
 Using a forward modeling approach, we designed this tool to model exoplanetary atmospheres.
 We encourage the community to exploit its capabilities!</p>
-<div class="section" id="attribution">
-<h2>Attribution:<a class="headerlink" href="#attribution" title="Permalink to this heading"></a></h2>
-<p>When using ForMoSA please cite <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract">Petrus et al (2023)</a>.</p>
-</div>
-<div class="section" id="user-guide">
-<h2>User Guide:<a class="headerlink" href="#user-guide" title="Permalink to this heading"></a></h2>
 <div class="toctree-wrapper compound">
+<p class="caption" role="heading"><span class="caption-text">Contents</span></p>
 <ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a><ul>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#for-all-users">For all Users</a></li>
-<li class="toctree-l2"><a class="reference internal" href="installation.html#issues">Issues?</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#create-a-conda-environment">Create a conda environment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#a-installation-though-pypi">A. Installation though PyPI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#b-installation-though-github">B. Installation though GitHub</a></li>
+<li class="toctree-l2"><a class="reference internal" href="installation.html#pymultinest-users">PyMultiNest users</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="demo.html#atmospheric-grids">Atmospheric grids</a></li>
+<li class="toctree-l2"><a class="reference internal" href="demo.html#list-of-tutorials">List of tutorials:</a></li>
 </ul>
 </li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a><ul>
 <li class="toctree-l2"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
 <li class="toctree-l2"><a class="reference internal" href="adapt.html">Adapt the data</a></li>
@@ -101,13 +109,27 @@ <h2>User Guide:<a class="headerlink" href="#user-guide" title="Permalink to this
 </li>
 </ul>
 </div>
-</div>
-<div class="section" id="version-track">
-<h2>Version Track:<a class="headerlink" href="#version-track" title="Permalink to this heading"></a></h2>
-<p><a href="#id1"><span class="problematic" id="id2">**</span></a>1.0.0</p>
-<p>This is the first official release.</p>
-</div>
-</div>
+<section id="attribution">
+<h2>Attribution<a class="headerlink" href="#attribution" title="Link to this heading"></a></h2>
+<p>Please cite <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract">Petrus et al (2023)</a>.</p>
+</section>
+<section id="issues">
+<h2>Issues (?)<a class="headerlink" href="#issues" title="Link to this heading"></a></h2>
+<p>If you run into any other problem, please create an issue on <a class="reference external" href="https://github.com/exoAtmospheres/ForMoSA/issues">GitHub</a>.</p>
+</section>
+<section id="version-track">
+<h2>Version Track<a class="headerlink" href="#version-track" title="Link to this heading"></a></h2>
+<ul class="simple">
+<li><p><code class="docutils literal notranslate"><span class="pre">1.1.2</span></code> Version adapted for including multiple instruments and high spectral resolution observations.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">1.0.13</span></code> First version distributed, presented at <a class="reference external" href="https://alienearths.space/cloud-academy-3/">Cloud Academy 3.</a>.</p></li>
+<li><p><code class="docutils literal notranslate"><span class="pre">1.0.5</span></code> First operational release.</p></li>
+</ul>
+</section>
+<section id="acknowledgments">
+<h2>Acknowledgments<a class="headerlink" href="#acknowledgments" title="Link to this heading"></a></h2>
+<p>Our sincere thanks to <a class="reference external" href="https://semaphorep.github.io/codeastro/">Code/Astro</a>.</p>
+</section>
+</section>
 
 
            </div>
@@ -119,7 +141,7 @@ <h2>Version Track:<a class="headerlink" href="#version-track" title="Permalink t
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/installation.html b/docs/_build/html/installation.html
new file mode 100644
index 0000000..5124fe6
--- /dev/null
+++ b/docs/_build/html/installation.html
@@ -0,0 +1,218 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Installation &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
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+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+    <script src="_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="genindex.html" />
+    <link rel="search" title="Search" href="search.html" />
+    <link rel="next" title="Tutorials" href="demo.html" />
+    <link rel="prev" title="Forward Modeling Tool for Spectral Analysis" href="index.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="index.html" class="icon icon-home">
+            ForMoSA
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1 current"><a class="current reference internal" href="#">Installation</a><ul>
+<li class="toctree-l2"><a class="reference internal" href="#create-a-conda-environment">Create a conda environment</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#a-installation-though-pypi">A. Installation though PyPI</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#b-installation-though-github">B. Installation though GitHub</a></li>
+<li class="toctree-l2"><a class="reference internal" href="#pymultinest-users">PyMultiNest users</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
+<li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+      <li class="breadcrumb-item active">Installation</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/installation.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="installation">
+<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Link to this heading"></a></h1>
+<p>There are two possibilities to install <code class="docutils literal notranslate"><span class="pre">ForMoSA</span></code>.
+Installation of the package through <code class="docutils literal notranslate"><span class="pre">PyPI</span></code> or through <code class="docutils literal notranslate"><span class="pre">GitHub</span></code>.</p>
+<section id="create-a-conda-environment">
+<h2>Create a conda environment<a class="headerlink" href="#create-a-conda-environment" title="Link to this heading"></a></h2>
+<p>We strongly recommend using a <code class="docutils literal notranslate"><span class="pre">conda</span></code> environment. You can <a class="reference external" href="https://conda.io/docs/user-guide/tasks/manage-environments.html">learn more about conda here</a> .</p>
+<p>(For all users)</p>
+<p>You can create your environment as:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>env_formosa<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.11
+<span class="gp">$ </span>conda<span class="w"> </span>activate<span class="w"> </span>env_formosa
+</pre></div>
+</div>
+<p>(For macOS users)</p>
+<p>To install and use our package in macOS with an M1 chip, a few extra steps are required.
+To use PyMultiNest to paralelize the processes, make sure you are building your conda environment under an OSX-ARM64 architecture. You can <a class="reference external" href="https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple">learn more here</a> .</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span><span class="nv">CONDA_SUBDIR</span><span class="o">=</span>osx-arm64<span class="w"> </span>conda<span class="w"> </span>create<span class="w"> </span>-n<span class="w"> </span>env_formosa<span class="w"> </span><span class="nv">python</span><span class="o">=</span><span class="m">3</span>.11<span class="w"> </span>numpy<span class="w"> </span>-c<span class="w"> </span>conda-forge
+<span class="gp">$ </span>conda<span class="w"> </span>activate<span class="w"> </span>env_formosa
+<span class="gp">$ </span>conda<span class="w"> </span>config<span class="w"> </span>--env<span class="w"> </span>--set<span class="w"> </span>subdir<span class="w"> </span>osx-arm64
+</pre></div>
+</div>
+</section>
+<section id="a-installation-though-pypi">
+<h2>A. Installation though PyPI<a class="headerlink" href="#a-installation-though-pypi" title="Link to this heading"></a></h2>
+<p>PyPI is the <a class="reference external" href="https://packaging.python.org/tutorials/installing-packages/">pip package manager</a>, which will automatically install for you almost all the required dependencies in your environment.
+To install the latest version run:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>pip<span class="w"> </span>install<span class="w"> </span>ForMoSA
+</pre></div>
+</div>
+<p>To solve a standard error, run the following line in your environment:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>conda<span class="w"> </span>install<span class="w"> </span>xarray<span class="w"> </span>dask<span class="w"> </span>netCDF4<span class="w"> </span>bottleneck
+</pre></div>
+</div>
+</section>
+<section id="b-installation-though-github">
+<h2>B. Installation though GitHub<a class="headerlink" href="#b-installation-though-github" title="Link to this heading"></a></h2>
+<p>Install the following packages in your environment with pip install:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>numpy
+<span class="gp">$ </span>matplotlib
+<span class="gp">$ </span>corner
+<span class="gp">$ </span>astropy
+<span class="gp">$ </span>scipy
+<span class="gp">$ </span>configobj
+<span class="gp">$ </span>extinction
+<span class="gp">$ </span>nestle
+<span class="gp">$ </span>PyAstronomy
+<span class="gp">$ </span>spectres
+<span class="gp">$ </span>pyyaml
+<span class="gp">$ </span>importlib-metadata<span class="o">==</span><span class="m">4</span>.13.0
+<span class="gp">$ </span><span class="nv">xarray</span><span class="o">==</span><span class="m">2023</span>.10.1
+</pre></div>
+</div>
+<p>To solve a standard error, run the following line in your environment:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>conda<span class="w"> </span>install<span class="w"> </span>xarray<span class="w"> </span>dask<span class="w"> </span>netCDF4<span class="w"> </span>bottleneck
+</pre></div>
+</div>
+<p>The last instalation step is to actually get ForMoSA.
+ForMoSA can be cloned from the main branch from our GitHub repository.
+You can do it by moving to the desired directory on your terminal and writing:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span><span class="nb">cd</span><span class="w"> </span>~/YOUR_PATH/formosa_desk/
+<span class="gp">$ </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/exoAtmospheres/ForMoSA.git
+</pre></div>
+</div>
+</section>
+<section id="pymultinest-users">
+<h2>PyMultiNest users<a class="headerlink" href="#pymultinest-users" title="Link to this heading"></a></h2>
+<p>If you want to use Pymultinest to run your inversion, follow the installation instructions from <a class="reference external" href="https://johannesbuchner.github.io/PyMultiNest/install.html">PyMultinest</a>, detailed below.</p>
+<p>First, you need to clone PyMultinest from GitHub and install it.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span><span class="nb">cd</span><span class="w"> </span>~/YOUR_PATH/formosa_desk/
+<span class="gp">$ </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/JohannesBuchner/PyMultiNest/
+<span class="gp">$ </span><span class="nb">cd</span><span class="w"> </span>PyMultiNest
+<span class="gp">$ </span>python<span class="w"> </span>setup.py<span class="w"> </span>install
+</pre></div>
+</div>
+<p>Second, you need to corroborate that your system has a C++ and a Fortran interpreter.
+If you need to install brew, follow <a class="reference external" href="https://brew.sh/">these instructions</a>.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>brew<span class="w"> </span>install<span class="w"> </span>cmake
+<span class="gp">$ </span>brew<span class="w"> </span>install<span class="w"> </span>gcc
+<span class="gp">$ </span>brew<span class="w"> </span>install<span class="w"> </span>open-mpi
+</pre></div>
+</div>
+<p>Next, in your ForMoSA environment, install mpi4pi as:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>pip<span class="w"> </span>install<span class="w"> </span>mpi4py
+</pre></div>
+</div>
+<p>Then, install MultiNest by cloning the GitHub repository and building it.
+Make sure you empty the build folder if you run this step more than once.</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span><span class="nb">cd</span><span class="w"> </span>~/YOUR_PATH/formosa_desk/
+<span class="gp">$ </span>git<span class="w"> </span>clone<span class="w"> </span>https://github.com/JohannesBuchner/MultiNest
+<span class="gp">$ </span><span class="nb">cd</span><span class="w"> </span>MultiNest/build
+<span class="gp">$ </span>cmake<span class="w"> </span>..
+<span class="gp">$ </span>make
+</pre></div>
+</div>
+<p>Finally, copy the files that were generated by building MultiNest onto your conda environment by doing:</p>
+<div class="highlight-console notranslate"><div class="highlight"><pre><span></span><span class="gp">$ </span>cp<span class="w"> </span>-v<span class="w"> </span>~/YOUR_PATH/MultiNest/lib/*<span class="w"> </span>/YOUR_PATH/opt/anaconda3/envs/env_formosa/lib/
+</pre></div>
+</div>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="index.html" class="btn btn-neutral float-left" title="Forward Modeling Tool for Spectral Analysis" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+        <a href="demo.html" class="btn btn-neutral float-right" title="Tutorials" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right" aria-hidden="true"></span></a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
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diff --git a/docs/build/html/main_utilities.html b/docs/_build/html/main_utilities.html
similarity index 52%
rename from docs/build/html/main_utilities.html
rename to docs/_build/html/main_utilities.html
index 84f3395..c2837a3 100644
--- a/docs/build/html/main_utilities.html
+++ b/docs/_build/html/main_utilities.html
@@ -1,22 +1,25 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
-  <meta charset="utf-8" />
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Main Utilities &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>Main Utilities &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
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@@ -43,13 +46,18 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul class="current">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1 current"><a class="reference internal" href="api.html">ForMoSA API</a><ul class="current">
 <li class="toctree-l2 current"><a class="current reference internal" href="#">Main Utilities</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#ForMoSA.main_utilities.GlobFile"><code class="docutils literal notranslate"><span class="pre">GlobFile</span></code></a></li>
-<li class="toctree-l3"><a class="reference internal" href="#ForMoSA.main_utilities.yesno"><code class="docutils literal notranslate"><span class="pre">yesno()</span></code></a></li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.main_utilities">Global functions:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.main_utilities.GlobFile"><code class="docutils literal notranslate"><span class="pre">GlobFile</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.main_utilities.diag_mat"><code class="docutils literal notranslate"><span class="pre">diag_mat()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.main_utilities.yesno"><code class="docutils literal notranslate"><span class="pre">yesno()</span></code></a></li>
+</ul>
+</li>
 </ul>
 </li>
 <li class="toctree-l2"><a class="reference internal" href="adapt.html">Adapt the data</a></li>
@@ -84,27 +92,59 @@
           <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
            <div itemprop="articleBody">
              
-  <div class="section" id="module-ForMoSA">
-<span id="id1"></span><span id="main-utilities"></span><h1>Main Utilities<a class="headerlink" href="#module-ForMoSA" title="Permalink to this heading"></a></h1>
-<span class="target" id="module-ForMoSA.main_utilities"></span><dl class="py class">
+  <section id="module-ForMoSA">
+<span id="id1"></span><span id="main-utilities"></span><h1>Main Utilities<a class="headerlink" href="#module-ForMoSA" title="Link to this heading"></a></h1>
+<section id="module-ForMoSA.main_utilities">
+<span id="global-functions"></span><h2>Global functions:<a class="headerlink" href="#module-ForMoSA.main_utilities" title="Link to this heading"></a></h2>
+<dl class="py class">
 <dt class="sig sig-object py" id="ForMoSA.main_utilities.GlobFile">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.main_utilities.</span></span><span class="sig-name descname"><span class="pre">GlobFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config_file_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/main_utilities.html#GlobFile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.main_utilities.GlobFile" title="Permalink to this definition"></a></dt>
-<dd><p>Import config variables and make them GLOBAL FORMOSA VARIABLES</p>
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.main_utilities.</span></span><span class="sig-name descname"><span class="pre">GlobFile</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config_file_path</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/main_utilities.html#GlobFile"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.main_utilities.GlobFile" title="Link to this definition"></a></dt>
+<dd><p>Class that import all the parameters from the config file and make them GLOBAL FORMOSA VARIABLES.</p>
 <p>Author: Paulina Palma-Bifani</p>
 </dd></dl>
 
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.main_utilities.diag_mat">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.main_utilities.</span></span><span class="sig-name descname"><span class="pre">diag_mat</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">rem</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">result</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">array([],</span> <span class="pre">shape=(0,</span> <span class="pre">0),</span> <span class="pre">dtype=float64)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/main_utilities.html#diag_mat"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.main_utilities.diag_mat" title="Link to this definition"></a></dt>
+<dd><p>Function to concatenate and align iterativly block matrices (usefull during the extraction and the inversion).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>rem</strong> (<em>list</em>) – matrices to be add iterativly (use diag([mat1, mat2]))</p></li>
+<li><p><strong>result</strong> (<em>array</em>) – final array with each sub-matrices aligned allong the diagonal</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Generated diagonal matrix
+(If rem input is empty, it wull return an empy array)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>diag_mat (matrix)</p>
+</dd>
+</dl>
+<p>Author : Ishigoya, Stack-overflow : <a class="reference external" href="https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array">https://stackoverflow.com/questions/42154606/python-numpy-how-to-construct-a-big-diagonal-arraymatrix-from-two-small-array</a></p>
+</dd></dl>
+
 <dl class="py function">
 <dt class="sig sig-object py" id="ForMoSA.main_utilities.yesno">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.main_utilities.</span></span><span class="sig-name descname"><span class="pre">yesno</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/main_utilities.html#yesno"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.main_utilities.yesno" title="Permalink to this definition"></a></dt>
-<dd><p>Function to interact with the terminal and decide for different options when running ForMoSA
-Parameters:
-- text (str)= (y/n) answer in the terminall in interactive mode
-Returns:
-- repeat yesno, if no match for y or n
-- asw (str) = answer y or n</p>
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.main_utilities.</span></span><span class="sig-name descname"><span class="pre">yesno</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">text</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/main_utilities.html#yesno"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.main_utilities.yesno" title="Link to this definition"></a></dt>
+<dd><p>Function to interact with the terminal and decide for different options when running ForMoSA (Loop to repeat question if answer is different to ‘y’ or ‘n).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>text</strong> (<em>str</em>) – (y/n) answer in the terminall in interactive mode</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>answer y or n</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p>asw     (str)</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
 </dd></dl>
 
-</div>
+</section>
+</section>
 
 
            </div>
@@ -117,7 +157,7 @@
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/nested_sampling.html b/docs/_build/html/nested_sampling.html
new file mode 100644
index 0000000..9856078
--- /dev/null
+++ b/docs/_build/html/nested_sampling.html
@@ -0,0 +1,812 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="./">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
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+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Nested Sampling &mdash; ForMoSA 2.0.0 documentation</title>
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+    <script src="_static/js/theme.js"></script>
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+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="api.html">ForMoSA API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
+<li class="toctree-l2"><a class="reference internal" href="adapt.html">Adapt the data</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Nested Sampling</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_sampling">Nested Main:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.import_obsmod"><code class="docutils literal notranslate"><span class="pre">import_obsmod()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling"><code class="docutils literal notranslate"><span class="pre">launch_nested_sampling()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.loglike"><code class="docutils literal notranslate"><span class="pre">loglike()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.prior_transform"><code class="docutils literal notranslate"><span class="pre">prior_transform()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_modif_spec">Modify Spectra:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.bb_cpd_fct"><code class="docutils literal notranslate"><span class="pre">bb_cpd_fct()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.calc_ck"><code class="docutils literal notranslate"><span class="pre">calc_ck()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct"><code class="docutils literal notranslate"><span class="pre">doppler_fct()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.lsq_fct"><code class="docutils literal notranslate"><span class="pre">lsq_fct()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.modif_spec"><code class="docutils literal notranslate"><span class="pre">modif_spec()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct"><code class="docutils literal notranslate"><span class="pre">reddening_fct()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_accurate"><code class="docutils literal notranslate"><span class="pre">vsini_fct_accurate()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_fast_rot_broad"><code class="docutils literal notranslate"><span class="pre">vsini_fct_fast_rot_broad()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_rot_broad"><code class="docutils literal notranslate"><span class="pre">vsini_fct_rot_broad()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_prior_function">Prior Functions:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior"><code class="docutils literal notranslate"><span class="pre">gaussian_prior()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_prior_function.uniform_prior"><code class="docutils literal notranslate"><span class="pre">uniform_prior()</span></code></a></li>
+</ul>
+</li>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_logL_functions">Likelihood Functions:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Brogi"><code class="docutils literal notranslate"><span class="pre">logL_CCF_Brogi()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Zucker"><code class="docutils literal notranslate"><span class="pre">logL_CCF_Zucker()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_custom"><code class="docutils literal notranslate"><span class="pre">logL_CCF_custom()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_classic"><code class="docutils literal notranslate"><span class="pre">logL_chi2_classic()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_covariance"><code class="docutils literal notranslate"><span class="pre">logL_chi2_covariance()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended"><code class="docutils literal notranslate"><span class="pre">logL_chi2_extended()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended_covariance"><code class="docutils literal notranslate"><span class="pre">logL_chi2_extended_covariance()</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_full_covariance"><code class="docutils literal notranslate"><span class="pre">logL_full_covariance()</span></code></a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="plotting.html">Plotting Class</a></li>
+</ul>
+</li>
+</ul>
+
+        </div>
+      </div>
+    </nav>
+
+    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
+          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
+          <a href="index.html">ForMoSA</a>
+      </nav>
+
+      <div class="wy-nav-content">
+        <div class="rst-content">
+          <div role="navigation" aria-label="Page navigation">
+  <ul class="wy-breadcrumbs">
+      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
+          <li class="breadcrumb-item"><a href="api.html">ForMoSA API</a></li>
+      <li class="breadcrumb-item active">Nested Sampling</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="_sources/nested_sampling.rst.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="nested-sampling">
+<span id="id1"></span><h1>Nested Sampling<a class="headerlink" href="#nested-sampling" title="Link to this heading"></a></h1>
+<section id="module-ForMoSA.nested_sampling.nested_sampling">
+<span id="nested-main"></span><h2>Nested Main:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_sampling" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.import_obsmod">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">import_obsmod</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#import_obsmod"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.import_obsmod" title="Link to this definition"></a></dt>
+<dd><p>Function to import spectra (model and data) before the inversion</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>global_params</strong> (<em>object</em>) – Class containing every input from the .ini file.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p><dl class="simple">
+<dt>Return a list of lists with the wavelengths, flux, errors, covariance matrix,</dt><dd><p>transmission, star flux, systematics and the grids for both spectroscopic and photometric data.</p>
+</dd>
+</dl>
+</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>main_file (list(array))</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Authors: Simon Petrus, Matthieu Ravet and Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">launch_nested_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#launch_nested_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling" title="Link to this definition"></a></dt>
+<dd><p>Function to launch the nested sampling.
+We first perform LogL function check-ups.
+Then the free parameters are counted and the data imported.
+Finally, depending on the nested sampling methode chosen in the config file, we perform the inversion.
+(Methods succesfully implemented are Nestle and PyMultinest)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>global_params</strong> (<em>object</em>) – Class containing every input from the .ini file.</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus and Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.loglike">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">loglike</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">main_file</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#loglike"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.loglike" title="Link to this definition"></a></dt>
+<dd><p>Function that calculates the logarithm of the likelihood.
+The evaluation depends on the choice of likelihood.
+(If this function is used on the plotting module, it returns the outputs of the modif_spec function)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> (<em>list</em>) – Parameter values randomly picked by the nested sampling</p></li>
+<li><p><strong>theta_index</strong> (<em>list</em>) – Index for the parameter values randomly picked</p></li>
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing every input from the .ini file.</p></li>
+<li><p><strong>main_file</strong> (<em>list</em><em>(</em><em>list</em><em>)</em>) – List containing the wavelengths, flux, errors, covariance, and grid information</p></li>
+<li><p><strong>for_plot</strong> (<em>str</em>) – Default is ‘no’. When this function is called from the plotting functions module, we use ‘yes’</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Final evaluated loglikelihood for both spectra and photometry.</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>FINAL_logL     (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Authors: Simon Petrus, Matthieu Ravet and Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.prior_transform">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">prior_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lim_param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#prior_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.prior_transform" title="Link to this definition"></a></dt>
+<dd><p>Function that define the priors to be used for the inversion.
+We check that the boundaries are consistent with the grid extension.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> (<em>list</em>) – Parameter values randomly picked by the nested sampling</p></li>
+<li><p><strong>theta_index</strong> (<em>list</em>) – Index for the parameter values randomly picked</p></li>
+<li><p><strong>lim_param_grid</strong> (<em>list</em>) – Boundaries for the parameters explored</p></li>
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing every input from the .ini file.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>List containing all the prior information</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>prior           (list)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Matthieu Ravet, Allan Denis</p>
+</dd></dl>
+
+</section>
+<section id="module-ForMoSA.nested_sampling.nested_modif_spec">
+<span id="modify-spectra"></span><h2>Modify Spectra:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_modif_spec" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.bb_cpd_fct">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">bb_cpd_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">distance</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bb_T_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bb_R_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#bb_cpd_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.bb_cpd_fct" title="Link to this definition"></a></dt>
+<dd><p>Function to add the effect of a cpd (circum planetary disc) to the models.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data (spectroscopy)</p></li>
+<li><p><strong>wav_obs_photo</strong> (<em>array</em>) – Wavelength of the data (photometry)</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
+<li><p><strong>flx_mod_photo</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (photometry)</p></li>
+<li><p><strong>bb_temp</strong> (<em>float</em>) – Temperature value randomly picked by the nested sampling in K units</p></li>
+<li><p><strong>bb_rad</strong> (<em>float</em>) – Radius randomly picked by the nested sampling in units of planetary radius</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New flux of the interpolated synthetic spectrum (spectroscopy)
+- flx_mod_photo (array)     : New flux of the interpolated synthetic spectrum (photometry)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Paulina Palma-Bifani</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.calc_ck">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">calc_ck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">alpha</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analytic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#calc_ck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.calc_ck" title="Link to this definition"></a></dt>
+<dd><p>Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum (from the radius
+and distance or analytically).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flx_obs_spectro</strong> (<em>array</em>) – Flux of the data (spectroscopy)</p></li>
+<li><p><strong>err_obs_spectro</strong> (<em>array</em>) – Error of the data (spectroscopy)</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
+<li><p><strong>flx_obs_photo</strong> (<em>array</em>) – Flux of the data (photometry)</p></li>
+<li><p><strong>err_obs_photo</strong> (<em>array</em>) – Error of the data (photometry)</p></li>
+<li><p><strong>flx_mod_photo</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (photometry)</p></li>
+<li><p><strong>r_picked</strong> (<em>float</em>) – Radius randomly picked by the nested sampling (in RJup)</p></li>
+<li><p><strong>d_picked</strong> (<em>float</em>) – Distance randomly picked by the nested sampling (in pc)</p></li>
+<li><p><strong>alpha</strong> (<em>float</em>) – Manual scaling factor (set to 1 by default) such that ck = alpha * (r/d)²</p></li>
+<li><p><strong>analytic</strong> (<em>str</em>) – = ‘yes’ if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Re-normalysed model spectrum
+- flx_mod_photo (array)     : Re-normalysed model photometry
+- ck (float)                : Ck calculated</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.doppler_fct">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">doppler_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rv_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#doppler_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct" title="Link to this definition"></a></dt>
+<dd><p>Application of a Doppler shifting to the interpolated synthetic spectrum using the function pyasl.dopplerShift.
+Note: Observation can change due to side effects of the shifting.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data</p></li>
+<li><p><strong>flx_obs_spectro</strong> (<em>array</em>) – Flux of the data</p></li>
+<li><p><strong>err_obs_spectro</strong> (<em>array</em>) – Error of the data</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum</p></li>
+<li><p><strong>rv_picked</strong> (<em>float</em>) – Radial velocity randomly picked by the nested sampling (in km.s-1)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New wavelength grid of the data
+- flx_obs_spectro (array)       : New flux of the data
+- err_obs_spectro (array)       : New error of the data
+- flx_post_doppler (array)      : New flux of the interpolated synthetic spectrum</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>wav_obs_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.lsq_fct">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">lsq_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">star_flx_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transm_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ccf_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'continuum_unfiltered'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#lsq_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.lsq_fct" title="Link to this definition"></a></dt>
+<dd><p>Estimation of the contribution of the planet and of the star to a spectrum (Used for HiRISE data)</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flx_obs_spectro</strong> (<em>array</em>) – Flux of the data (spectroscopy)</p></li>
+<li><p><strong>err_obs_spectro</strong> (<em>array</em>) – Error of the data (spectroscopy)</p></li>
+<li><p><strong>star_flx_obs</strong> (<em>n-array</em>) – Flux of star observation data (spectroscopy)</p></li>
+<li><p><strong>transm_obs</strong> (<em>array</em>) – Transmission (Atmospheric + Instrumental)</p></li>
+<li><p><strong>system_obs</strong> (<em>n-array</em>) – Systematics of the data (spectroscopy)</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of interpolated synthetic spectrum (spectroscopy)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Planetary contribution to the data (Spectroscopy)
+- cs (array)                : Stellar contribution to the data (Spectroscopy)
+- flx_mod_spectro (array)   : New model of the companion
+- flx_obs_spectro (array)   : New flux of the data
+- star_flx_obs (n-array)    : New star flux of the data
+- systematics (array)       : The systematics</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>cp (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author : Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.modif_spec">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">modif_spec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">transm_obs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">star_flx_obs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">system_obs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">indobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#modif_spec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.modif_spec" title="Link to this definition"></a></dt>
+<dd><p>Modification of the interpolated synthetic spectra with the different extra-grid parameters.
+It can perform : Re-calibration on the data, Doppler shifting, Application of a substellar extinction, Application of a rotational velocity,
+Application of a circumplanetary disk (CPD).</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>global_params</strong> (<em>object</em>) – Class containing each parameter</p></li>
+<li><p><strong>theta</strong> (<em>list</em>) – Parameter values randomly picked by the nested sampling</p></li>
+<li><p><strong>theta_index</strong> (<em>list</em>) – Parameter index identificator</p></li>
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data (spectroscopy)</p></li>
+<li><p><strong>flx_obs_spectro</strong> (<em>array</em>) – Flux of the data (spectroscopy)</p></li>
+<li><p><strong>err_obs_spectro</strong> (<em>array</em>) – Error of the data (spectroscopy)</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
+<li><p><strong>wav_obs_photo</strong> (<em>array</em>) – Wavelength grid of the data (photometry)</p></li>
+<li><p><strong>flx_obs_photo</strong> (<em>array</em>) – Flux of the data (photometry)</p></li>
+<li><p><strong>err_obs_photo</strong> (<em>array</em>) – Error of the data (photometry)</p></li>
+<li><p><strong>flx_mod_photo</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (photometry)</p></li>
+<li><p><strong>transm_obs</strong> (<em>array</em>) – Transmission (Atmospheric + Instrumental)</p></li>
+<li><p><strong>star_flx_obs</strong> (<em>n-array</em>) – Flux of star observation data (spectroscopy)</p></li>
+<li><p><strong>system_obs</strong> (<em>n-array</em>) – Systematics of the data (spectroscopy)</p></li>
+<li><p><strong>indobs</strong> (<em>int</em>) – Index of the current observation looping</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New wavelength grid of the data (may change with the Doppler shift)
+- flx_obs_spectro (array)  : New flux of the data (may change with the Doppler shift)
+- err_obs_spectro (array)  : New error of the data (may change with the Doppler shift)
+- flx_mod_spectro (array)  : New flux of the interpolated synthetic spectrum (spectroscopy)
+- wav_obs_photo (array)    : Wavelength grid of the data (photometry)
+- flx_obs_photo (array)    : Flux of the data (photometry)
+- err_obs_photo (array)    : Error of the data (photometry)
+- flx_mod_photo (array)    : New flux of the interpolated synthetic spectrum (photometry)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>wav_obs_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus, Paulina Palma-Bifani, Allan Denis and Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.reddening_fct">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">reddening_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_photo</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">av_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#reddening_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct" title="Link to this definition"></a></dt>
+<dd><p>Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function
+extinction.fm07.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data (spectroscopy)</p></li>
+<li><p><strong>wav_obs_photo</strong> (<em>array</em>) – Wavelength of the data (photometry)</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
+<li><p><strong>flx_mod_photo</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum (photometry)</p></li>
+<li><p><strong>av_picked</strong> (<em>float</em>) – Extinction randomly picked by the nested sampling (in mag)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New flux of the interpolated synthetic spectrum (spectroscopy)
+- flx_mod_photo (array)   : New flux of the interpolated synthetic spectrum (photometry)</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_accurate">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">vsini_fct_accurate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wave_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ld_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vsini_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">nr</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">50</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ntheta</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">dif</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#vsini_fct_accurate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_accurate" title="Link to this definition"></a></dt>
+<dd><p>A routine to quickly rotationally broaden a spectrum in linear time.
+Adapted from Carvalho &amp; Johns-Krull 2023 <a class="reference external" href="https://ui.adsabs.harvard.edu/abs/2023RNAAS...7...91C/abstract">https://ui.adsabs.harvard.edu/abs/2023RNAAS…7…91C/abstract</a></p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum</p></li>
+<li><p><strong>ld_picked</strong> (<em>float</em>) – Limd darkening randomly picked by the nested sampling</p></li>
+<li><p><strong>vsini_picked</strong> (<em>float</em>) – v.sin(i) randomly picked by the nested sampling (in km.s-1)</p></li>
+<li><p><strong>nr</strong> (<em>int</em>) – (default = 10) The number of radial bins on the projected disk</p></li>
+<li><p><strong>ntheta</strong> (<em>int</em>) – (default = 100) The number of azimuthal bins in the largest radial annulus
+note: the number of bins at each r is int(r*ntheta) where r &lt; 1</p></li>
+<li><p><strong>dif</strong> (<em>float</em>) – (default = 0) The differential rotation coefficient, applied according to the law Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)).
+Dif = .675 nicely reproduces the law proposed by Smith, 1994, A&amp;A, Vol. 287, p. 523-534, to unify WTTS and CTTS.
+Dif = .23 is similar to observed solar differential rotation. Note: the th in the above expression is the stellar co-latitude, not the same as the integration variable used below.
+This is a disk integration routine.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New flux of the interpolated synthetic spectrum</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_fast_rot_broad">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">vsini_fct_fast_rot_broad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ld_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vsini_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#vsini_fct_fast_rot_broad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_fast_rot_broad" title="Link to this definition"></a></dt>
+<dd><p>Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function
+extinction.fm07.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum</p></li>
+<li><p><strong>ld_picked</strong> (<em>float</em>) – Limd darkening randomly picked by the nested sampling</p></li>
+<li><p><strong>vsini_picked</strong> (<em>float</em>) – v.sin(i) randomly picked by the nested sampling (in km.s-1)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New flux of the interpolated synthetic spectrum</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_rot_broad">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">vsini_fct_rot_broad</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_spectro</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ld_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vsini_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#vsini_fct_rot_broad"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct_rot_broad" title="Link to this definition"></a></dt>
+<dd><p>Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function
+extinction.fm07.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wav_obs_spectro</strong> (<em>array</em>) – Wavelength grid of the data</p></li>
+<li><p><strong>flx_mod_spectro</strong> (<em>array</em>) – Flux of the interpolated synthetic spectrum</p></li>
+<li><p><strong>ld_picked</strong> (<em>float</em>) – Limd darkening randomly picked by the nested sampling</p></li>
+<li><p><strong>vsini_picked</strong> (<em>float</em>) – v.sin(i) randomly picked by the nested sampling (in km.s-1)</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>New flux of the interpolated synthetic spectrum</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>flx_mod_spectro (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+</section>
+<section id="module-ForMoSA.nested_sampling.nested_prior_function">
+<span id="prior-functions"></span><h2>Prior Functions:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_prior_function" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_prior_function.gaussian_prior">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_prior_function.</span></span><span class="sig-name descname"><span class="pre">gaussian_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_fct_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_prior_function.html#gaussian_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior" title="Link to this definition"></a></dt>
+<dd><p>Gaussian prior for nested sampling.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>prior_fct_arg</strong> (<em>list</em>) – Two-values list with uniform prior boundaries.</p></li>
+<li><p><strong>theta</strong> (<em>list</em>) – Parameter values randomly picked by the nested sampling</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Evaluated prior</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>Evaluated      (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_prior_function.uniform_prior">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_prior_function.</span></span><span class="sig-name descname"><span class="pre">uniform_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_fct_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_prior_function.html#uniform_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_prior_function.uniform_prior" title="Link to this definition"></a></dt>
+<dd><p>Uniform prior for nested sampling.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>prior_fct_arg</strong> (<em>list</em>) – Two-values list with uniform prior boundaries.</p></li>
+<li><p><strong>theta</strong> (<em>list</em>) – Parameter values randomly picked by the nested sampling</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Evaluated prior</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>Evaluated      (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Simon Petrus</p>
+</dd></dl>
+
+</section>
+<section id="module-ForMoSA.nested_sampling.nested_logL_functions">
+<span id="likelihood-functions"></span><h2>Likelihood Functions:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_logL_functions" title="Link to this heading"></a></h2>
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Brogi">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_CCF_Brogi</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_CCF_Brogi"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Brogi" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the CCF mapping from Brogi et al. 2019
+under the assumption of gaussian and spectrally constant noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flx_obs</strong> (<em>array</em>) – flux of the observation as a function of wavelength</p></li>
+<li><p><strong>flx_mod</strong> (<em>array</em>) – flux of the model as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Zucker">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_CCF_Zucker</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_CCF_Zucker"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_Zucker" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the CCF mapping from Zucker 2003
+under the assumption of gaussian and spectrally constant noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flx_obs</strong> (<em>array</em>) – flux of the observation as a function of wavelength</p></li>
+<li><p><strong>flx_mod</strong> (<em>array</em>) – flux of the model as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_custom">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_CCF_custom</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_CCF_custom"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_CCF_custom" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the custom CCF mapping from Me
+under the assumption of gaussian and spectrally constant noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>flx_obs</strong> (<em>array</em>) – flux of the observation as a function of wavelength</p></li>
+<li><p><strong>flx_mod</strong> (<em>array</em>) – flux of the model as a function of wavelength</p></li>
+<li><p><strong>err_obs</strong> (<em>array</em>) – errors of the observation as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_classic">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_chi2_classic</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta_flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_chi2_classic"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_classic" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the classical chi2
+under the assumption of gaussian and spectrally uncorrelated noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta_flx</strong> (<em>array</em>) – residual data-model as a function of wavelength</p></li>
+<li><p><strong>err</strong> (<em>array</em>) – error (=standard deviation) of the observed spectrum as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_covariance">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_chi2_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta_flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_cov</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_chi2_covariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_covariance" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the generalized chi2
+under the assumption of gaussian and spectrally correlated noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta_flx</strong> (<em>array</em>) – residual data-model as a function of wavelength</p></li>
+<li><p><strong>inv_cov</strong> (<em>n-array</em>) – inverse of the covariance matrix of the observed spectrum as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_chi2_extended</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta_flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_chi2_extended"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the extended chi2
+under the assumption of gaussian and spectrally uncorrelated noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta_flx</strong> (<em>array</em>) – residual data-model as a function of wavelength</p></li>
+<li><p><strong>err</strong> (<em>array</em>) – error (=standard deviation) of the observed spectrum as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended_covariance">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_chi2_extended_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta_flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_cov</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_chi2_extended_covariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_chi2_extended_covariance" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL based on the extended chi2
+under the assumption of gaussian and spectrally uncorrelated noise.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta_flx</strong> (<em>array</em>) – residual data-model as a function of wavelength</p></li>
+<li><p><strong>err</strong> (<em>array</em>) – error (=standard deviation) of the observed spectrum as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Allan Denis</p>
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_logL_functions.logL_full_covariance">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_logL_functions.</span></span><span class="sig-name descname"><span class="pre">logL_full_covariance</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">delta_flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">inv_cov</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_logL_functions.html#logL_full_covariance"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_logL_functions.logL_full_covariance" title="Link to this definition"></a></dt>
+<dd><p>Function to compute logL under the assumption of gaussian and spectrally correlated noise.
+This function is a generalized version of the logL_chi2_covariance and is to be used when dealing
+with GP extimation of the covariance matrix.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>delta_flx</strong> (<em>array</em>) – residual data-model as a function of wavelength</p></li>
+<li><p><strong>inv_cov</strong> (<em>n-array</em>) – inverse of the covariance matrix of the observed spectrum as a function of wavelength</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>the loglikelihood value</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>logL (float)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Matthieu Ravet</p>
+</dd></dl>
+
+</section>
+</section>
+
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+<li class="toctree-l1 current"><a class="reference internal" href="api.html">ForMoSA API</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
+<li class="toctree-l2"><a class="reference internal" href="adapt.html">Adapt the data</a></li>
+<li class="toctree-l2"><a class="reference internal" href="nested_sampling.html">Nested Sampling</a></li>
+<li class="toctree-l2 current"><a class="current reference internal" href="#">Plotting Class</a><ul>
+<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.plotting.plotting_class">The plotting module of ForMoSA:</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.plotting.plotting_class.ComplexRadar"><code class="docutils literal notranslate"><span class="pre">ComplexRadar</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA"><code class="docutils literal notranslate"><span class="pre">PlottingForMoSA</span></code></a></li>
+<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.plotting.plotting_class.bin_data"><code class="docutils literal notranslate"><span class="pre">bin_data()</span></code></a></li>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="plotting-class">
+<span id="plotting"></span><h1>Plotting Class<a class="headerlink" href="#plotting-class" title="Link to this heading"></a></h1>
+<section id="module-ForMoSA.plotting.plotting_class">
+<span id="the-plotting-module-of-formosa"></span><h2>The plotting module of ForMoSA:<a class="headerlink" href="#module-ForMoSA.plotting.plotting_class" title="Link to this heading"></a></h2>
+<dl class="py class">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.plotting.plotting_class.</span></span><span class="sig-name descname"><span class="pre">ComplexRadar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_ordinate_levels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar" title="Link to this definition"></a></dt>
+<dd><p>Class to create Radar plots with asymmetric error bars.</p>
+<dl class="simple">
+<dt>Author: Paulina Palma-Bifani</dt><dd><p>Adapted from Damian Cummins: <a class="reference external" href="https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb">https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb</a></p>
+</dd>
+</dl>
+<p>Initialize class.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>fig</strong> (<em>object</em>) – matplotlib figure object</p></li>
+<li><p><strong>variables</strong> (<em>list</em>) – list of parameters to plot</p></li>
+<li><p><strong>ranges</strong> (<em>list</em><em>(</em><em>tuple</em><em>)</em>) – upper and lower limits for each parameters</p></li>
+<li><p><strong>n_ordinate_levels</strong> (<em>int</em>) – (default = 6) number of gridlines in the plot</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar.fill">
+<span class="sig-name descname"><span class="pre">fill</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar.fill"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar.fill" title="Link to this definition"></a></dt>
+<dd><p>Add symmetric error bars to the plot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>list</em>) – best value for each parameter</p></li>
+<li><p><strong>*args</strong> – Variable length argument list.</p></li>
+<li><p><strong>**kw</strong> – Arbitrary keyword arguments.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar.fill_between">
+<span class="sig-name descname"><span class="pre">fill_between</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">list_down</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">list_up</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar.fill_between"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar.fill_between" title="Link to this definition"></a></dt>
+<dd><p>Add asymmetric error bars to the plot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>list_down</strong> (<em>list</em>) – list of lower error bars</p></li>
+<li><p><strong>list_up</strong> (<em>list</em>) – list of upper error bars</p></li>
+<li><p><strong>*args</strong> – Variable length argument list.</p></li>
+<li><p><strong>**kw</strong> – Arbitrary keyword arguments.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar.plot">
+<span class="sig-name descname"><span class="pre">plot</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">args</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">**</span></span><span class="n"><span class="pre">kw</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar.plot"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar.plot" title="Link to this definition"></a></dt>
+<dd><p>Function to display the plot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>list</em>) – best value for each parameter</p></li>
+<li><p><strong>*args</strong> – Variable length argument list.</p></li>
+<li><p><strong>**kw</strong> – Arbitrary keyword arguments.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar.scale_data">
+<span class="sig-name descname"><span class="pre">scale_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar.scale_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar.scale_data" title="Link to this definition"></a></dt>
+<dd><p>Function to check that lower and upper limits are correctly ordered. It scales data[1:] to ranges[0]</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>data</strong> (<em>list</em>) – best value for each parameter</p></li>
+<li><p><strong>ranges</strong> (<em>list</em><em>(</em><em>tuple</em><em>)</em>) – upper and lower limits for each parameters</p></li>
+<li><p><strong>*args</strong> – Variable length argument list.</p></li>
+<li><p><strong>**kw</strong> – Arbitrary keyword arguments.</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py class">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA">
+<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.plotting.plotting_class.</span></span><span class="sig-name descname"><span class="pre">PlottingForMoSA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config_file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA" title="Link to this definition"></a></dt>
+<dd><p>Class containing all the plotting functionalities of ForMoSA.</p>
+<p>Author: Paulina Palma-Bifani, Simon Petrus, Matthieu Ravet and Allan Denis</p>
+<p>Initialize class by inheriting the global parameter class of ForMoSA.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>config_file_path</strong> (<em>str</em>) – path to the config.ini file currently used</p></li>
+<li><p><strong>color_out</strong> (<em>str</em>) – color to use for the model</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>None</p>
+</dd>
+</dl>
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.get_FULL_spectra">
+<span class="sig-name descname"><span class="pre">get_FULL_spectra</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">grid_used</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'original'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wavelengths</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">N_points</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">1000</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">re_interp</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">False</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">int_method</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'linear'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.get_FULL_spectra"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.get_FULL_spectra" title="Link to this definition"></a></dt>
+<dd><p>Extract a model spectrum from another grid.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>theta</strong> – (list): best parameter values</p></li>
+<li><p><strong>grid_used</strong> – (str): (default = ‘original’) Path to the grid from where to extract the spectrum. If ‘original’, the current grid will be used.</p></li>
+<li><p><strong>wavelengths</strong> – (list): (default = []) Desired wavelength range. If [] max and min values of wav_for_adapt range will be use to create the wavelength range.</p></li>
+<li><p><strong>N_points</strong> – (int): (default = 1000) Number of points.</p></li>
+<li><p><strong>re_interp</strong> – (boolean): (default = False). Option to reinterpolate or not the grid.</p></li>
+<li><p><strong>int_method</strong> – (str): (default = “linear”) Interpolation method for the grid (if reinterpolated).</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>Wavelength array of the full model
+- flx_final                   (array): Flux array of the full model
+- ck                          (float): Scaling factor of the full model</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>wav_final                   (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds">
+<span class="sig-name descname"><span class="pre">plot_Clouds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cloud_prop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_cloud_profile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(6,</span> <span class="pre">5)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_Clouds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds" title="Link to this definition"></a></dt>
+<dd><p>Function to plot cloud profiles.
+Adapted from Nathan Zimniak</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>cloud_prop</strong> (<em>str</em>) – Choose the cloud species. The options are
+[‘eddy_diffusion_coefficient’,
+‘vmr_CH4’,
+‘vmr_CO’,
+‘vmr_CO2’,
+‘vmr_FeH’,
+‘vmr_H2O’,
+‘vmr_H2S’,
+‘vmr_HCN’,
+‘vmr_K’,
+‘vmr_Na’,
+‘vmr_NH3’,
+‘vmr_PH3’,
+‘vmr_TiO’,
+‘vmr_VO’,
+‘cloud_opacity_Fe’,
+‘cloud_opacity_Mg2SiO4’,
+‘cloud_particle_radius_Fe’,
+‘cloud_particle_radius_Mg2SiO4’,
+‘cloud_vmr_Fe’,
+‘cloud_vmr_Mg2SiO4’]</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax   (object) : matplotlib axes objects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_HiRes_comp_model">
+<span class="sig-name descname"><span class="pre">plot_HiRes_comp_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_HiRes_comp_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_HiRes_comp_model" title="Link to this definition"></a></dt>
+<dd><p>Specific function to plot the best fit comparing with the data for high-resolution spectroscopy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>figsize</strong> (<em>tuple</em>) – (default = (10, 5)) Size of the plot</p></li>
+<li><p><strong>norm</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the normalized spectra</p></li>
+<li><p><strong>data_resolution</strong> (<em>int</em>) – (default = 0) Custom resolution to broadened data</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax1   (object) : matplotlib axes objects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig1  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT">
+<span class="sig-name descname"><span class="pre">plot_PT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_temp_profile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(6,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ExoREM'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_PT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT" title="Link to this definition"></a></dt>
+<dd><p>Function to plot the Pressure-Temperature profiles.
+Adpated from Nathan Zimniak.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>path_temp_profile</strong> (<em>str</em>) – Path to the temperature profile grid</p></li>
+<li><p><strong>figsize</strong> (<em>tuple</em>) – (default = (6, 5)) Size of the plot</p></li>
+<li><p><strong>model</strong> (<em>str</em>) – (default = ‘ExoREM’) Name of the model grid</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax   (object) : matplotlib axes objects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_ccf">
+<span class="sig-name descname"><span class="pre">plot_ccf</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rv_grid</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[-300,</span> <span class="pre">300]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rv_step</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0.5</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">window_normalisation</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_wavelength</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_spectra</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_resolution</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_name</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'Full'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rv_cor</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_ccf"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_ccf" title="Link to this definition"></a></dt>
+<dd><p>Plot the cross-correlation function. It is used for high resolution spectroscopy.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>figsize</strong> (<em>tuple</em>) – (default = (10, 5)) Size of the plot</p></li>
+<li><p><strong>rv_grid</strong> (<em>list</em>) – (default = [-300,300]) Maximum and minumum values of the radial velocity shift (in km/s)</p></li>
+<li><p><strong>rv_step</strong> (<em>float</em>) – (default = 0.5) Radial velocity shift steps (in km/s)</p></li>
+<li><p><strong>window_normalisation</strong> (<em>int</em>) – (default = 100) ?</p></li>
+<li><p><strong>model_wavelength</strong> (<em>list</em>) – (default = []) ?</p></li>
+<li><p><strong>=</strong> (<em>model_spectra</em>) – (default = []) ?</p></li>
+<li><p><strong>model_resolution</strong> (<em>list</em>) – (default = []) ?</p></li>
+<li><p><strong>model_name</strong> (<em>str</em>) – (default = ‘Full’) ?</p></li>
+<li><p><strong>rv_cor</strong> (<em>int</em>) – (default = 0) ?</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax1                     (object) : matplotlib axes objects
+- rv_grid                    (list): Radial velocity grid
+- ccf                        (list): Cross-correlation function
+- acf                        (list): Auto-correlation function</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig1                    (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Allan Denis</p>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains">
+<span class="sig-name descname"><span class="pre">plot_chains</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(7,</span> <span class="pre">15)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_chains"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains" title="Link to this definition"></a></dt>
+<dd><p>Plot to check the convergence of the posterior chains.
+Multiple (sub-)axis plot.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><p><strong>figsize</strong> (<em>tuple</em>) – (default = (7, 15)) size of the plot</p>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax   (object) : matplotlib axes objects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner">
+<span class="sig-name descname"><span class="pre">plot_corner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">levels_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.997,</span> <span class="pre">0.95,</span> <span class="pre">0.68]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.16,</span> <span class="pre">0.5,</span> <span class="pre">0.84)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">burn_in</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_corner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner" title="Link to this definition"></a></dt>
+<dd><p>Function to display the corner plot</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>levels_sig</strong> (<em>list</em>) – (default = [0.997, 0.95, 0.68]) 1, 2 and 3 sigma contour levels of the corner plot</p></li>
+<li><p><strong>bins</strong> (<em>int</em>) – (default = 100) number of bins for the posteriors</p></li>
+<li><p><strong>quantiles</strong> (<em>list</em>) – (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values</p></li>
+<li><p><strong>burn_in</strong> (<em>int</em>) – (default = 0) number of steps to remove from the plot</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig         (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit">
+<span class="sig-name descname"><span class="pre">plot_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uncert</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit" title="Link to this definition"></a></dt>
+<dd><p>Plot the best fit comparing with the data.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>figsize</strong> (<em>tuple</em>) – (default = (10, 5)) Size of the plot</p></li>
+<li><p><strong>uncert</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot spectra with associated error bars</p></li>
+<li><p><strong>trans</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot transmision curves for photometry</p></li>
+<li><p><strong>logx</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the wavelength in log scale</p></li>
+<li><p><strong>logy</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the flux in log scale</p></li>
+<li><p><strong>norm</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the normalized spectra</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax     (object) : matplotlib axes objects, main spectra plot
+- axr    (object) : matplotlib axes objects, residuals
+- axr2   (object) : matplotlib axes objects, right side density histogram</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig    (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit_HiRes">
+<span class="sig-name descname"><span class="pre">plot_fit_HiRes</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uncert</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">trans</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logx</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">logy</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">norm</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_fit_HiRes"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit_HiRes" title="Link to this definition"></a></dt>
+<dd><p>Same as plot_fit but with the stellar and planetary models for high-resolution spectroscopy. Does not include residuals in a sub-axis.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>figsize</strong> (<em>tuple</em>) – (default = (10, 5)) Size of the plot</p></li>
+<li><p><strong>uncert</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot spectra with associated error bars</p></li>
+<li><p><strong>trans</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot transmision curves for photometry</p></li>
+<li><p><strong>logx</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the wavelength in log scale</p></li>
+<li><p><strong>logy</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the flux in log scale</p></li>
+<li><p><strong>norm</strong> (<em>str</em>) – (default = no) ‘yes’ or ‘no’ to plot the normalized spectra</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- ax   (object) : matplotlib axes objects</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+<dl class="py method">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar">
+<span class="sig-name descname"><span class="pre">plot_radar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.16,</span> <span class="pre">0.5,</span> <span class="pre">0.84]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_radar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar" title="Link to this definition"></a></dt>
+<dd><p>Radar plot to check the distribution of the parameters.
+Useful to compare different models.</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>ranges</strong> (<em>list</em><em>(</em><em>tuple</em><em>)</em>) – upper and lower limits for each parameters</p></li>
+<li><p><strong>label</strong> (<em>str</em>) – (default = ‘’) label of the plot</p></li>
+<li><p><strong>quantiles</strong> (<em>list</em>) – (default = (0.16, 0.5, 0.84)) mean +- sigma to report the posterior values</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>matplotlib figure object
+- radar.ax   (object) : matplotlib radar class axes object</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>fig  (object)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+</dd></dl>
+
+</dd></dl>
+
+<dl class="py function">
+<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.bin_data">
+<span class="sig-prename descclassname"><span class="pre">ForMoSA.plotting.plotting_class.</span></span><span class="sig-name descname"><span class="pre">bin_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wave</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bin_size</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#bin_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.bin_data" title="Link to this definition"></a></dt>
+<dd><p>Function to bin data given a bin size</p>
+<dl class="field-list simple">
+<dt class="field-odd">Parameters<span class="colon">:</span></dt>
+<dd class="field-odd"><ul class="simple">
+<li><p><strong>wave</strong> (<em>array</em>) – wavelength of the data</p></li>
+<li><p><strong>data</strong> (<em>array</em>) – data</p></li>
+<li><p><strong>bin_size</strong> (<em>int</em>) – size of the bin to apply</p></li>
+</ul>
+</dd>
+<dt class="field-even">Returns<span class="colon">:</span></dt>
+<dd class="field-even"><p>binned wavelength
+- data_binned  (array): binned data</p>
+</dd>
+<dt class="field-odd">Return type<span class="colon">:</span></dt>
+<dd class="field-odd"><p><ul class="simple">
+<li><p>wave_binned  (array)</p></li>
+</ul>
+</p>
+</dd>
+</dl>
+<p>Author: Allan Denis</p>
+</dd></dl>
+
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
+        <a href="nested_sampling.html" class="btn btn-neutral float-left" title="Nested Sampling" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
+    </div>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/build/html/py-modindex.html b/docs/_build/html/py-modindex.html
similarity index 80%
rename from docs/build/html/py-modindex.html
rename to docs/_build/html/py-modindex.html
index 1099efc..0be430e 100644
--- a/docs/build/html/py-modindex.html
+++ b/docs/_build/html/py-modindex.html
@@ -1,22 +1,24 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Python Module Index &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>Python Module Index &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="_static/favicon.ico"/>
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
     <script src="_static/js/theme.js"></script>
     <link rel="index" title="Index" href="genindex.html" />
     <link rel="search" title="Search" href="search.html" />
@@ -44,9 +46,10 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
 </ul>
 
@@ -110,6 +113,11 @@ <h1>Python Module Index</h1>
        <td>&#160;&#160;&#160;
        <a href="main_utilities.html#module-ForMoSA.main_utilities"><code class="xref">ForMoSA.main_utilities</code></a></td><td>
        <em></em></td></tr>
+     <tr class="cg-1">
+       <td></td>
+       <td>&#160;&#160;&#160;
+       <a href="nested_sampling.html#module-ForMoSA.nested_sampling.nested_logL_functions"><code class="xref">ForMoSA.nested_sampling.nested_logL_functions</code></a></td><td>
+       <em></em></td></tr>
      <tr class="cg-1">
        <td></td>
        <td>&#160;&#160;&#160;
@@ -140,7 +148,7 @@ <h1>Python Module Index</h1>
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/build/html/search.html b/docs/_build/html/search.html
similarity index 74%
rename from docs/build/html/search.html
rename to docs/_build/html/search.html
index 9d2ae1a..e78c449 100644
--- a/docs/build/html/search.html
+++ b/docs/_build/html/search.html
@@ -1,20 +1,25 @@
 <!DOCTYPE html>
-<html class="writer-html5" lang="English" >
+<html class="writer-html5" lang="en" data-content_root="./">
 <head>
   <meta charset="utf-8" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Search &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
+  <title>Search &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="_static/css/theme.css?v=19f00094" />
+
+  
     <link rel="shortcut icon" href="_static/favicon.ico"/>
     
   <!--[if lt IE 9]>
     <script src="_static/js/html5shiv.min.js"></script>
   <![endif]-->
   
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
+        <script src="_static/jquery.js?v=5d32c60e"></script>
+        <script src="_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
+        <script src="_static/documentation_options.js?v=51b770b3"></script>
+        <script src="_static/doctools.js?v=9a2dae69"></script>
+        <script src="_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
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@@ -41,9 +46,10 @@
   </form>
 </div>
         </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul>
 <li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
+<li class="toctree-l1"><a class="reference internal" href="demo.html">Tutorials</a></li>
 <li class="toctree-l1"><a class="reference internal" href="api.html">ForMoSA API</a></li>
 </ul>
 
@@ -90,7 +96,7 @@
   <hr/>
 
   <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
   </div>
 
   Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
diff --git a/docs/_build/html/searchindex.js b/docs/_build/html/searchindex.js
new file mode 100644
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\ No newline at end of file
diff --git a/docs/_build/html/tutorials/atmospheric_grid_info.html b/docs/_build/html/tutorials/atmospheric_grid_info.html
new file mode 100644
index 0000000..78e7e0c
--- /dev/null
+++ b/docs/_build/html/tutorials/atmospheric_grid_info.html
@@ -0,0 +1,164 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>&lt;no title&gt; &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
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+</head>
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+<body class="wy-body-for-nav"> 
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <p>Example on how to access to the atmospheric grid info to check the parameters and their ranges.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[1]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
+<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+</pre></div>
+</div>
+</div>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Here Exo-REM grid is used as example</span>
+<span class="n">grid_path</span> <span class="o">=</span> <span class="s1">&#39;/Users/ppalmabifani/Desktop/exoAtm/c0_ForMoSA/INPUTS/atm_grids/atm_grids_native/EXOREM_native.nc&#39;</span>
+
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">grid_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+
+<span class="nb">print</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.Dataset&gt;
+Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)
+Coordinates:
+  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6
+  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03
+  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0
+  * par3        (par3) float64 -0.5 0.0 0.5 1.0
+  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8
+Data variables:
+    grid        (wavelength, par1, par2, par3, par4) float64 ...
+Attributes:
+    key:      [&#39;par1&#39;, &#39;par2&#39;, &#39;par3&#39;, &#39;par4&#39;]
+    par:      [&#39;teff&#39;, &#39;logg&#39;, &#39;mh&#39;, &#39;co&#39;]
+    title:    [&#39;Teff&#39;, &#39;log(g)&#39;, &#39;[M/H]&#39;, &#39;C/O&#39;]
+    unit:     [&#39;(K)&#39;, &#39;(dex)&#39;, &#39;&#39;, &#39;&#39;]
+    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\n    ...
+</pre></div></div>
+</div>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
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diff --git a/docs/_build/html/tutorials/atmospheric_grid_info.ipynb b/docs/_build/html/tutorials/atmospheric_grid_info.ipynb
new file mode 100644
index 0000000..5e240b0
--- /dev/null
+++ b/docs/_build/html/tutorials/atmospheric_grid_info.ipynb
@@ -0,0 +1,92 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Here Exo-REM grid is used as example\n",
+    "grid_path = '/Users/ppalmabifani/Desktop/exoAtm/c0_ForMoSA/INPUTS/atm_grids/atm_grids_native/EXOREM_native.nc'\n",
+    "\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "    \n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.13"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/tutorials/demoabpic.html b/docs/_build/html/tutorials/demoabpic.html
new file mode 100644
index 0000000..cf62214
--- /dev/null
+++ b/docs/_build/html/tutorials/demoabpic.html
@@ -0,0 +1,381 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>demo AB Pic b &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
+      <link rel="stylesheet" type="text/css" href="../_static/css/theme.css?v=19f00094" />
+      <link rel="stylesheet" type="text/css" href="../_static/nbsphinx-code-cells.css?v=2aa19091" />
+
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+    <link rel="shortcut icon" href="../_static/favicon.ico"/>
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+        <script src="../_static/_sphinx_javascript_frameworks_compat.js?v=2cd50e6c"></script>
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+        <script src="../_static/sphinx_highlight.js?v=dc90522c"></script>
+        <script crossorigin="anonymous" integrity="sha256-Ae2Vz/4ePdIu6ZyI/5ZGsYnb+m0JlOmKPjt6XZ9JJkA=" src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.3.4/require.min.js"></script>
+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
+        <script defer="defer" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
+    <script src="../_static/js/theme.js"></script>
+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
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+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
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+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../demo.html">Tutorials</a><ul class="current">
+<li class="toctree-l2"><a class="reference internal" href="../demo.html#atmospheric-grids">Atmospheric grids</a></li>
+<li class="toctree-l2 current"><a class="reference internal" href="../demo.html#list-of-tutorials">List of tutorials:</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">demo AB Pic b</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Imports">Imports</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#0.-Setup">0. Setup</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#1.-Interpolate-the-grid">1. Interpolate the grid</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#2.-Lunch-Nested-Sampling">2. Lunch Nested Sampling</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#3.-Plotting-the-outcomes">3. Plotting the outcomes</a></li>
+</ul>
+</li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api.html">ForMoSA API</a></li>
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+      <li class="breadcrumb-item active">demo AB Pic b</li>
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+            <a href="../_sources/tutorials/demoabpic.ipynb.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="demo-AB-Pic-b">
+<h1>demo AB Pic b<a class="headerlink" href="#demo-AB-Pic-b" title="Link to this heading"></a></h1>
+<p>This tutorial is intended as a quick start.</p>
+<p>We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in <a class="reference external" href="https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf">P. Palma-Bifani et al (2023)</a>.</p>
+<section id="Imports">
+<h2>Imports<a class="headerlink" href="#Imports" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[33]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Generic packages</span>
+<span class="kn">import</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">time</span><span class="o">,</span> <span class="nn">os</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+
+<span class="c1"># ForMoSA modules</span>
+<span class="n">sys</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">insert</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">abspath</span><span class="p">(</span><span class="s1">&#39;/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/&#39;</span><span class="p">))</span>
+<span class="c1"># For the interpolation &amp; sampling</span>
+<span class="kn">from</span> <span class="nn">ForMoSA.main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
+<span class="kn">from</span> <span class="nn">ForMoSA.adapt.adapt_obs_mod</span> <span class="kn">import</span> <span class="n">launch_adapt</span>
+<span class="kn">from</span> <span class="nn">ForMoSA.nested_sampling.nested_sampling</span> <span class="kn">import</span> <span class="n">launch_nested_sampling</span>
+<span class="c1"># For the plots</span>
+<span class="kn">from</span> <span class="nn">ForMoSA.plotting.plotting_class</span> <span class="kn">import</span> <span class="n">PlottingForMoSA</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="0.-Setup">
+<h2>0. Setup<a class="headerlink" href="#0.-Setup" title="Link to this heading"></a></h2>
+<p>You need to create a config file with extension &lt;.ini&gt; and modify the parameters. Learn more about our config files in it’s specific tutorial.</p>
+<p>To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[34]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">base_path</span> <span class="o">=</span> <span class="s1">&#39;data_abpic/&#39;</span>
+
+<span class="c1"># CONFIG_FILE</span>
+<span class="c1"># reading and defining global parameters</span>
+<span class="n">config_file_path</span> <span class="o">=</span> <span class="n">base_path</span> <span class="o">+</span> <span class="s1">&#39;config_ABPicb.ini&#39;</span>
+<span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>
+
+<span class="c1"># Optional: Add &quot;time_now&quot; and &quot;save_name&quot; to avoid overwriting results</span>
+<span class="n">time_now</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">strftime</span><span class="p">(</span><span class="s2">&quot;%Y%m</span><span class="si">%d</span><span class="s2">_%H%M%S&quot;</span><span class="p">)</span>
+<span class="n">save_name</span> <span class="o">=</span> <span class="s1">&#39;test&#39;</span>
+
+<span class="c1"># Create directory to save the outputs</span>
+<span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="o">+</span> <span class="n">save_name</span><span class="o">+</span><span class="s1">&#39;_t&#39;</span> <span class="o">+</span> <span class="n">time_now</span><span class="o">+</span><span class="s1">&#39;/&#39;</span>
+<span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span><span class="p">)</span>
+
+<span class="c1"># Overwrite some parameters</span>
+<span class="n">global_params</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">filename</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;config_used.ini&#39;</span>
+<span class="n">global_params</span><span class="o">.</span><span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;result_path&#39;</span><span class="p">]</span><span class="o">=</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span>
+<span class="n">global_params</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">write</span><span class="p">()</span>
+<br/></pre></div>
+</div>
+</div>
+</section>
+<section id="1.-Interpolate-the-grid">
+<h2>1. Interpolate the grid<a class="headerlink" href="#1.-Interpolate-the-grid" title="Link to this heading"></a></h2>
+<p>Once everything is setup, we start by adapting the models and observations.</p>
+<p>The grid of models is interpolated for this, but you don’t need to repeat this step once you’ve adapted the grid for a specific dataset.</p>
+<p>(Answer ‘no’ only the first time)</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[7]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Have you already interpolated the grids for this data?</span>
+<span class="n">y_n_par</span> <span class="o">=</span> <span class="s1">&#39;yes&#39;</span>
+<span class="c1">#y_n_par = &#39;no&#39; # Only answer no the first time, then comment to save time</span>
+
+<span class="n">launch_adapt</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">justobs</span><span class="o">=</span><span class="n">y_n_par</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="2.-Lunch-Nested-Sampling">
+<h2>2. Lunch Nested Sampling<a class="headerlink" href="#2.-Lunch-Nested-Sampling" title="Link to this heading"></a></h2>
+<p>Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[8]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">launch_nested_sampling</span><span class="p">(</span><span class="n">global_params</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+-&gt; Likelihood functions check-ups
+
+ABPicb_SINFONI_K will be computed with chi2_classic
+
+Done !
+
+it=  1330 logz=-1071.92517042
+- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
+-&gt; Nestle
+
+The code spent 234.33751487731934 sec to run.
+niter: 1331
+ncall: 13109
+nsamples: 1431
+logz: -1071.101 +/-  0.347
+h: 12.010
+
+
+
+-&gt; Voilà, on est prêt
+</pre></div></div>
+</div>
+</section>
+<section id="3.-Plotting-the-outcomes">
+<h2>3. Plotting the outcomes<a class="headerlink" href="#3.-Plotting-the-outcomes" title="Link to this heading"></a></h2>
+<p>ForMoSA has been designed with a plotting class. Bellow we show 4 main features:</p>
+<ul class="simple">
+<li><p>Plotting corner-plots</p></li>
+<li><p>Plotting spectra and residuals</p></li>
+<li><p>Plotting chains</p></li>
+<li><p>Accessing the different parameters</p></li>
+</ul>
+<p>All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc…</p>
+<p>We need to start by initializing the plotting class as follows.</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[26]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Path to output file created in the first step</span>
+<span class="n">config_file_path_pl</span> <span class="o">=</span> <span class="s1">&#39;data_abpic/outputs/test_t20240807_005025&#39;</span>
+
+<span class="c1"># Initialize the plotting class and set the color</span>
+<span class="n">plotForMoSA</span> <span class="o">=</span> <span class="n">PlottingForMoSA</span><span class="p">(</span><span class="n">config_file_path_pl</span><span class="o">+</span><span class="s1">&#39;/config_used.ini&#39;</span><span class="p">,</span> <span class="s1">&#39;red&#39;</span><span class="p">)</span>
+<br/></pre></div>
+</div>
+</div>
+<section id="PLOT-Corner">
+<h3>PLOT Corner<a class="headerlink" href="#PLOT-Corner" title="Link to this heading"></a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[27]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span> <span class="o">=</span> <span class="n">plotForMoSA</span><span class="o">.</span><span class="n">plot_corner</span><span class="p">(</span><span class="n">levels_sig</span><span class="o">=</span><span class="p">[</span><span class="mf">0.997</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.68</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">(</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">),</span> <span class="n">burn_in</span><span class="o">=</span><span class="mi">1100</span><span class="p">)</span>
+<span class="c1">#plt.savefig(&#39;&#39;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ForMoSA - Corner plot
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_demoabpic_11_1.png" src="../_images/tutorials_demoabpic_11_1.png" />
+</div>
+</div>
+</section>
+<section id="PLOT-Spectrum-and-Residuals">
+<h3>PLOT Spectrum and Residuals<a class="headerlink" href="#PLOT-Spectrum-and-Residuals" title="Link to this heading"></a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[32]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">axr</span><span class="p">,</span> <span class="n">axr2</span> <span class="o">=</span> <span class="n">plotForMoSA</span><span class="o">.</span><span class="n">plot_fit</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">uncert</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">)</span>
+
+<span class="c1"># You can modify the different axes and includ further plotting features</span>
+<span class="n">axr</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">-</span><span class="mi">5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
+
+<span class="c1">#plt.savefig(&#39;&#39;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ForMoSA - Best fit and residuals plot
+</pre></div></div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area stderr docutils container">
+<div class="highlight"><pre>
+/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/plotting/plotting_class.py:723: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.
+  axr2.legend(frameon=False,handlelength=0)
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_demoabpic_13_2.png" src="../_images/tutorials_demoabpic_13_2.png" />
+</div>
+</div>
+</section>
+<section id="PLOT-Chains-of-posteriors">
+<h3>PLOT Chains of posteriors<a class="headerlink" href="#PLOT-Chains-of-posteriors" title="Link to this heading"></a></h3>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[29]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plotForMoSA</span><span class="o">.</span><span class="n">plot_chains</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span><span class="mi">6</span><span class="p">))</span>
+<span class="c1">#axs[i, j] #i=cols, j=0,1</span>
+<span class="c1">#plt.savefig(&#39;&#39;)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+ForMoSA - Posteriors chains for each parameter
+</pre></div></div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_demoabpic_15_1.png" src="../_images/tutorials_demoabpic_15_1.png" />
+</div>
+</div>
+</section>
+<section id="Access-information">
+<h3>Access information<a class="headerlink" href="#Access-information" title="Link to this heading"></a></h3>
+<p>You can access different parametes since we are working with a class</p>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[30]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">posteriors_chains</span> <span class="o">=</span> <span class="n">plotForMoSA</span><span class="o">.</span><span class="n">posterior_to_plot</span>
+<span class="n">posteriors_names</span> <span class="o">=</span> <span class="n">plotForMoSA</span><span class="o">.</span><span class="n">posteriors_names</span>
+</pre></div>
+</div>
+</div>
+</section>
+</section>
+</section>
+
+
+           </div>
+          </div>
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+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# demo AB Pic b\n",
+    "\n",
+    "\n",
+    "This tutorial is intended as a quick start. \n",
+    "\n",
+    "\n",
+    "We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in [P. Palma-Bifani et al (2023)](https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf).\n",
+    "\n",
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic packages\n",
+    "import sys, time, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# ForMoSA modules\n",
+    "sys.path.insert(0, os.path.abspath('/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/'))\n",
+    "# For the interpolation & sampling\n",
+    "from ForMoSA.main_utilities import GlobFile\n",
+    "from ForMoSA.adapt.adapt_obs_mod import launch_adapt\n",
+    "from ForMoSA.nested_sampling.nested_sampling import launch_nested_sampling\n",
+    "# For the plots\n",
+    "from ForMoSA.plotting.plotting_class import PlottingForMoSA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0. Setup\n",
+    "\n",
+    "You need to create a config file with extension <.ini> and modify the parameters. Learn more about our config files in it's specific tutorial.\n",
+    "\n",
+    "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = 'data_abpic/'\n",
+    "\n",
+    "# CONFIG_FILE \n",
+    "# reading and defining global parameters\n",
+    "config_file_path = base_path + 'config_ABPicb.ini'\n",
+    "global_params = GlobFile(config_file_path)    \n",
+    "\n",
+    "# Optional: Add \"time_now\" and \"save_name\" to avoid overwriting results\n",
+    "time_now = time.strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_name = 'test'\n",
+    "\n",
+    "# Create directory to save the outputs \n",
+    "global_params.result_path = global_params.result_path+ save_name+'_t' + time_now+'/'\n",
+    "os.makedirs(global_params.result_path)\n",
+    "\n",
+    "# Overwrite some parameters\n",
+    "global_params.config.filename = global_params.result_path + 'config_used.ini'\n",
+    "global_params.config['config_path']['result_path']=global_params.result_path\n",
+    "global_params.config.write()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Interpolate the grid\n",
+    "\n",
+    "Once everything is setup, we start by adapting the models and observations. \n",
+    "\n",
+    "The grid of models is interpolated for this, but you don't need to repeat this step once you've adapted the grid for a specific dataset. \n",
+    "\n",
+    "(Answer 'no' only the first time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Have you already interpolated the grids for this data? \n",
+    "y_n_par = 'yes'\n",
+    "#y_n_par = 'no' # Only answer no the first time, then comment to save time\n",
+    "\n",
+    "launch_adapt(global_params, justobs=y_n_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Lunch Nested Sampling \n",
+    "\n",
+    "Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Likelihood functions check-ups\n",
+      "\n",
+      "ABPicb_SINFONI_K will be computed with chi2_classic\n",
+      "\n",
+      "Done !\n",
+      "\n",
+      "\u001b[Kit=  1330 logz=-1071.92517042 \n",
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Nestle  \n",
+      " \n",
+      "The code spent 234.33751487731934 sec to run.\n",
+      "niter: 1331\n",
+      "ncall: 13109\n",
+      "nsamples: 1431\n",
+      "logz: -1071.101 +/-  0.347\n",
+      "h: 12.010\n",
+      "\n",
+      "\n",
+      " \n",
+      "-> Voilà, on est prêt\n"
+     ]
+    }
+   ],
+   "source": [
+    "launch_nested_sampling(global_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting the outcomes\n",
+    "\n",
+    "ForMoSA has been designed with a plotting class. Bellow we show 4 main features: \n",
+    "\n",
+    "- Plotting corner-plots\n",
+    "- Plotting spectra and residuals\n",
+    "- Plotting chains \n",
+    "- Accessing the different parameters\n",
+    "\n",
+    "All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc...\n",
+    "\n",
+    "We need to start by initializing the plotting class as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to output file created in the first step\n",
+    "config_file_path_pl = 'data_abpic/outputs/test_t20240807_005025'\n",
+    "\n",
+    "# Initialize the plotting class and set the color\n",
+    "plotForMoSA = PlottingForMoSA(config_file_path_pl+'/config_used.ini', 'red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Corner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Corner plot\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 970x970 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plotForMoSA.plot_corner(levels_sig=[0.997, 0.95, 0.68], bins=20, quantiles=(0.16, 0.5, 0.84), burn_in=1100)\n",
+    "#plt.savefig('') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Spectrum and Residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Best fit and residuals plot\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/plotting/plotting_class.py:723: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  axr2.legend(frameon=False,handlelength=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax, axr, axr2 = plotForMoSA.plot_fit(figsize=(10, 5), uncert='no')\n",
+    "\n",
+    "# You can modify the different axes and includ further plotting features\n",
+    "axr.set_ylim(-5,5)\n",
+    "\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Chains of posteriors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Posteriors chains for each parameter\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plotForMoSA.plot_chains(figsize=(10,6))\n",
+    "#axs[i, j] #i=cols, j=0,1\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access information\n",
+    "\n",
+    "You can access different parametes since we are working with a class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "posteriors_chains = plotForMoSA.posterior_to_plot\n",
+    "posteriors_names = plotForMoSA.posteriors_names"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/tutorials/exorem_info.html b/docs/_build/html/tutorials/exorem_info.html
new file mode 100644
index 0000000..208169e
--- /dev/null
+++ b/docs/_build/html/tutorials/exorem_info.html
@@ -0,0 +1,217 @@
+<!DOCTYPE html>
+<html class="writer-html5" lang="en" data-content_root="../">
+<head>
+  <meta charset="utf-8" /><meta name="viewport" content="width=device-width, initial-scale=1" />
+
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Exo-REM &mdash; ForMoSA 2.0.0 documentation</title>
+      <link rel="stylesheet" type="text/css" href="../_static/pygments.css?v=fa44fd50" />
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+    <link rel="index" title="Index" href="../genindex.html" />
+    <link rel="search" title="Search" href="../search.html" />
+    <link rel="next" title="demo AB Pic b" href="demoabpic.html" />
+    <link rel="prev" title="Tutorials" href="../demo.html" /> 
+</head>
+
+<body class="wy-body-for-nav"> 
+  <div class="wy-grid-for-nav">
+    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
+      <div class="wy-side-scroll">
+        <div class="wy-side-nav-search" >
+
+          
+          
+          <a href="../index.html" class="icon icon-home">
+            ForMoSA
+          </a>
+<div role="search">
+  <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
+    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
+    <input type="hidden" name="check_keywords" value="yes" />
+    <input type="hidden" name="area" value="default" />
+  </form>
+</div>
+        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
+              <p class="caption" role="heading"><span class="caption-text">Contents</span></p>
+<ul class="current">
+<li class="toctree-l1"><a class="reference internal" href="../installation.html">Installation</a></li>
+<li class="toctree-l1 current"><a class="reference internal" href="../demo.html">Tutorials</a><ul class="current">
+<li class="toctree-l2 current"><a class="reference internal" href="../demo.html#atmospheric-grids">Atmospheric grids</a><ul class="current">
+<li class="toctree-l3 current"><a class="current reference internal" href="#">Exo-REM</a><ul>
+<li class="toctree-l4"><a class="reference internal" href="#Imports">Imports</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Open-the-grid">Open the grid</a></li>
+<li class="toctree-l4"><a class="reference internal" href="#Get-a-spectrum-for-specific-values">Get a spectrum for specific values</a></li>
+</ul>
+</li>
+</ul>
+</li>
+<li class="toctree-l2"><a class="reference internal" href="../demo.html#list-of-tutorials">List of tutorials:</a></li>
+</ul>
+</li>
+<li class="toctree-l1"><a class="reference internal" href="../api.html">ForMoSA API</a></li>
+</ul>
+
+        </div>
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+          <a href="../index.html">ForMoSA</a>
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+          <li class="breadcrumb-item"><a href="../demo.html">Tutorials</a></li>
+      <li class="breadcrumb-item active">Exo-REM</li>
+      <li class="wy-breadcrumbs-aside">
+            <a href="../_sources/tutorials/exorem_info.ipynb.txt" rel="nofollow"> View page source</a>
+      </li>
+  </ul>
+  <hr/>
+</div>
+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="Exo-REM">
+<h1>Exo-REM<a class="headerlink" href="#Exo-REM" title="Link to this heading"></a></h1>
+<p>Example on how to access to the atmospheric grid info to check the parameters and their ranges.</p>
+<section id="Imports">
+<h2>Imports<a class="headerlink" href="#Imports" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[5]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
+<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Open-the-grid">
+<h2>Open the grid<a class="headerlink" href="#Open-the-grid" title="Link to this heading"></a></h2>
+<p>You can check the free parameters names, ranges and units together with the wavelength range and spectral resolution.</p>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[3]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">grid_path</span> <span class="o">=</span> <span class="s1">&#39;/EXOREM_native.nc&#39;</span>
+<span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">grid_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
+<span class="nb">print</span><span class="p">(</span><span class="n">ds</span><span class="p">)</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<div class="highlight"><pre>
+&lt;xarray.Dataset&gt;
+Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)
+Coordinates:
+  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6
+  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03
+  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0
+  * par3        (par3) float64 -0.5 0.0 0.5 1.0
+  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8
+Data variables:
+    grid        (wavelength, par1, par2, par3, par4) float64 ...
+Attributes:
+    key:      [&#39;par1&#39;, &#39;par2&#39;, &#39;par3&#39;, &#39;par4&#39;]
+    par:      [&#39;teff&#39;, &#39;logg&#39;, &#39;mh&#39;, &#39;co&#39;]
+    title:    [&#39;Teff&#39;, &#39;log(g)&#39;, &#39;[M/H]&#39;, &#39;C/O&#39;]
+    unit:     [&#39;(K)&#39;, &#39;(dex)&#39;, &#39;&#39;, &#39;&#39;]
+    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\n    ...
+</pre></div></div>
+</div>
+</section>
+<section id="Get-a-spectrum-for-specific-values">
+<h2>Get a spectrum for specific values<a class="headerlink" href="#Get-a-spectrum-for-specific-values" title="Link to this heading"></a></h2>
+<div class="nbinput docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[14]:
+</pre></div>
+</div>
+<div class="input_area highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Open the grid</span>
+<span class="n">grid</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
+
+<span class="c1"># get wavelength</span>
+<span class="n">wave</span> <span class="o">=</span> <span class="n">grid</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
+
+<span class="c1"># for Exo-REM par1=Teff, par2=logg, par3=[M/H], par4=C/O</span>
+<span class="c1"># get flux by selecting a value for each parameter</span>
+<span class="n">flux</span> <span class="o">=</span> <span class="n">grid</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="mi">1700</span><span class="p">,</span><span class="n">par2</span><span class="o">=</span><span class="mf">4.0</span><span class="p">,</span> <span class="n">par3</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">par4</span><span class="o">=</span><span class="mf">0.55</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s2">&quot;nearest&quot;</span><span class="p">)</span>
+
+<span class="c1"># Plot the spectrum</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">3</span><span class="p">))</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">wave</span><span class="p">,</span> <span class="n">flux</span><span class="o">*</span><span class="n">wave</span><span class="p">,</span> <span class="s1">&#39;k&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s1">&#39;Wavelength (µm)&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s1">&#39;Flux&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span><span class="mi">5</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">([])</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s1">&#39;Exo-REM at Teff=1700K, logg=4, and solar [M/H] and C/O&#39;</span><span class="p">)</span>
+<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
+</pre></div>
+</div>
+</div>
+<div class="nboutput nblast docutils container">
+<div class="prompt empty docutils container">
+</div>
+<div class="output_area docutils container">
+<img alt="../_images/tutorials_exorem_info_5_0.png" src="../_images/tutorials_exorem_info_5_0.png" />
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diff --git a/docs/_build/html/tutorials/exorem_info.ipynb b/docs/_build/html/tutorials/exorem_info.ipynb
new file mode 100644
index 0000000..4e848cc
--- /dev/null
+++ b/docs/_build/html/tutorials/exorem_info.ipynb
@@ -0,0 +1,140 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exo-REM\n",
+    "\n",
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges.\n",
+    "\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Open the grid\n",
+    "\n",
+    "You can check the free parameters names, ranges and units together with the wavelength range and spectral resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_path = '/EXOREM_native.nc'\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a spectrum for specific values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open the grid \n",
+    "grid = ds['grid']\n",
+    "\n",
+    "# get wavelength \n",
+    "wave = grid['wavelength'].values\n",
+    "\n",
+    "# for Exo-REM par1=Teff, par2=logg, par3=[M/H], par4=C/O\n",
+    "# get flux by selecting a value for each parameter\n",
+    "flux = grid.sel(par1=1700,par2=4.0, par3=0., par4=0.55, method=\"nearest\")\n",
+    "\n",
+    "# Plot the spectrum\n",
+    "plt.figure(figsize=(7,3))\n",
+    "plt.plot(wave, flux*wave, 'k')\n",
+    "plt.xlabel('Wavelength (µm)')\n",
+    "plt.ylabel('Flux')\n",
+    "plt.xlim(0.5,5) \n",
+    "plt.yticks([])\n",
+    "plt.title('Exo-REM at Teff=1700K, logg=4, and solar [M/H] and C/O')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/_build/html/tutorials/inputs.html b/docs/_build/html/tutorials/inputs.html
new file mode 100644
index 0000000..5244634
--- /dev/null
+++ b/docs/_build/html/tutorials/inputs.html
@@ -0,0 +1,127 @@
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+  <title>INPUTS &mdash; ForMoSA 2.0.0 documentation</title>
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+        <script>window.MathJax = {"tex": {"inlineMath": [["$", "$"], ["\\(", "\\)"]], "processEscapes": true}, "options": {"ignoreHtmlClass": "tex2jax_ignore|mathjax_ignore|document", "processHtmlClass": "tex2jax_process|mathjax_process|math|output_area"}}</script>
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+<li class="toctree-l1"><a class="reference internal" href="../demo.html">Tutorials</a></li>
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+      <li class="breadcrumb-item active">INPUTS</li>
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+            <a href="../_sources/tutorials/inputs.ipynb.txt" rel="nofollow"> View page source</a>
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+          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
+           <div itemprop="articleBody">
+             
+  <section id="INPUTS">
+<h1>INPUTS<a class="headerlink" href="#INPUTS" title="Link to this heading"></a></h1>
+<section id="Observations-(.fits)">
+<h2>Observations (.fits)<a class="headerlink" href="#Observations-(.fits)" title="Link to this heading"></a></h2>
+<div class="nbinput nblast docutils container">
+<div class="prompt highlight-none notranslate"><div class="highlight"><pre><span></span>[ ]:
+</pre></div>
+</div>
+<div class="input_area highlight-python notranslate"><div class="highlight"><pre><span></span>
+</pre></div>
+</div>
+</div>
+</section>
+<section id="Config-file-(.ini)">
+<h2>Config file (.ini)<a class="headerlink" href="#Config-file-(.ini)" title="Link to this heading"></a></h2>
+</section>
+</section>
+
+
+           </div>
+          </div>
+          <footer>
+
+  <hr/>
+
+  <div role="contentinfo">
+    <p>&#169; Copyright 2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin.</p>
+  </div>
+
+  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
+    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
+    provided by <a href="https://readthedocs.org">Read the Docs</a>.
+   
+
+</footer>
+        </div>
+      </div>
+    </section>
+  </div>
+  <script>
+      jQuery(function () {
+          SphinxRtdTheme.Navigation.enable(true);
+      });
+  </script> 
+
+</body>
+</html>
\ No newline at end of file
diff --git a/docs/_build/html/tutorials/inputs.ipynb b/docs/_build/html/tutorials/inputs.ipynb
new file mode 100644
index 0000000..6162c18
--- /dev/null
+++ b/docs/_build/html/tutorials/inputs.ipynb
@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INPUTS\n",
+    "\n",
+    "## Observations (.fits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Config file (.ini)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/source/adapt.rst b/docs/adapt.rst
similarity index 100%
rename from docs/source/adapt.rst
rename to docs/adapt.rst
diff --git a/docs/api.rst b/docs/api.rst
new file mode 100644
index 0000000..92bc8c4
--- /dev/null
+++ b/docs/api.rst
@@ -0,0 +1,12 @@
+.. _api:
+
+ForMoSA API
+===========
+
+.. toctree::
+   :titlesonly:
+   
+   main_utilities.rst
+   adapt.rst
+   nested_sampling.rst
+   plotting.rst
\ No newline at end of file
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diff --git a/docs/build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html b/docs/build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html
deleted file mode 100644
index f1d5d1f..0000000
--- a/docs/build/html/_modules/ForMoSA/adapt/adapt_obs_mod.html
+++ /dev/null
@@ -1,210 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="English" >
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>ForMoSA.adapt.adapt_obs_mod &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
-    <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
-  <!--[if lt IE 9]>
-    <script src="../../../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-        <script src="../../../_static/jquery.js"></script>
-        <script src="../../../_static/underscore.js"></script>
-        <script src="../../../_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="../../../_static/doctools.js"></script>
-        <script src="../../../_static/sphinx_highlight.js"></script>
-    <script src="../../../_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="../../../genindex.html" />
-    <link rel="search" title="Search" href="../../../search.html" /> 
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search" >
-
-          
-          
-          <a href="../../../index.html" class="icon icon-home">
-            ForMoSA
-          </a>
-              <div class="version">
-                1.0.3
-              </div>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../../demo.html">DEMO</a></li>
-<li class="toctree-l1"><a class="reference internal" href="../../../api.html">ForMoSA API</a></li>
-</ul>
-
-        </div>
-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="../../../index.html">ForMoSA</a>
-      </nav>
-
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="../../../index.html" class="icon icon-home" aria-label="Home"></a></li>
-          <li class="breadcrumb-item"><a href="../../index.html">Module code</a></li>
-      <li class="breadcrumb-item active">ForMoSA.adapt.adapt_obs_mod</li>
-      <li class="wy-breadcrumbs-aside">
-      </li>
-  </ul>
-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <h1>Source code for ForMoSA.adapt.adapt_obs_mod</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="c1"># import matplotlib.pyplot as plt</span>
-
-<span class="kn">from</span> <span class="nn">adapt.extraction_functions</span> <span class="kn">import</span> <span class="n">extract_observation</span>
-<span class="kn">from</span> <span class="nn">adapt.adapt_grid</span> <span class="kn">import</span> <span class="n">adapt_grid</span>
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="launch_adapt"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.adapt_obs_mod.launch_adapt">[docs]</a><span class="k">def</span> <span class="nf">launch_adapt</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">justobs</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Extract and adapt (wavelength resampling, resolution decreasing, continuum subtracting) the data and the synthetic</span>
-<span class="sd">    spectra from a model grid.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter</span>
-<span class="sd">        justobs: If the grid need to be adapted justobs=&#39;no&#39;</span>
-<span class="sd">    Returns:</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># Get back information from the config file</span>
-    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s2">&quot;netcdf4&quot;</span><span class="p">)</span>
-    <span class="n">wav_mod_nativ</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s2">&quot;wavelength&quot;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span>
-    <span class="n">attr</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">attrs</span>
-    <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-    <span class="c1"># Extract the data from the observation file</span>
-    <span class="n">obs_cut</span><span class="p">,</span> <span class="n">obs_pho</span><span class="p">,</span> <span class="n">obs_cut_ins</span><span class="p">,</span> <span class="n">obs_pho_ins</span> <span class="o">=</span> <span class="n">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">])</span>
-
-    <span class="c1"># Estimate and subtraction of the continuum (if needed)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">obs_cut_c</span><span class="p">,</span> <span class="n">obs_pho_c</span><span class="p">,</span> <span class="n">obs_cut_ins_c</span><span class="p">,</span> <span class="n">obs_pho_ins_c</span> <span class="o">=</span> <span class="n">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span>
-                                                                                 <span class="n">attr</span><span class="p">[</span><span class="s1">&#39;res&#39;</span><span class="p">],</span> <span class="s1">&#39;yes&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">):</span>
-            <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-=</span> <span class="n">obs_cut_c</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-    <span class="c1"># Merging of each sub-spectrum</span>
-    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">wav_obs_extract</span> <span class="o">=</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="n">flx_obs_extract</span> <span class="o">=</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">err_obs_extract</span> <span class="o">=</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
-            <span class="n">res_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-            <span class="n">ins_obs_extract</span> <span class="o">=</span> <span class="n">obs_cut_ins</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">wav_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_extract</span><span class="p">,</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
-            <span class="n">flx_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_extract</span><span class="p">,</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]))</span>
-            <span class="n">err_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_extract</span><span class="p">,</span> <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
-            <span class="n">res_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">res_obs_extract</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">3</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)))</span>
-            <span class="n">ins_obs_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">ins_obs_extract</span><span class="p">,</span> <span class="n">obs_cut_ins</span><span class="p">[</span><span class="n">c</span><span class="p">]))</span>
-
-    <span class="n">obs_merge</span> <span class="o">=</span> <span class="p">[</span><span class="n">wav_obs_extract</span><span class="p">,</span> <span class="n">flx_obs_extract</span><span class="p">,</span> <span class="n">err_obs_extract</span><span class="p">,</span> <span class="n">res_obs_extract</span><span class="p">]</span>
-
-    <span class="c1"># Save the new data spectrum</span>
-    <span class="n">np</span><span class="o">.</span><span class="n">savez</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/spectrum_obs&#39;</span><span class="p">,</span>
-             <span class="n">obs_merge</span><span class="o">=</span><span class="n">obs_merge</span><span class="p">,</span>
-             <span class="n">obs_cut</span><span class="o">=</span><span class="n">obs_cut</span><span class="p">,</span>
-             <span class="n">obs_cut_ins</span><span class="o">=</span><span class="n">obs_cut_ins</span><span class="p">,</span>
-             <span class="n">obs_merge_ins</span><span class="o">=</span><span class="n">ins_obs_extract</span><span class="p">,</span>
-             <span class="n">obs_pho</span><span class="o">=</span><span class="n">obs_pho</span><span class="p">,</span>
-             <span class="n">obs_pho_ins</span><span class="o">=</span><span class="n">obs_pho_ins</span><span class="p">)</span>
-
-    <span class="c1"># Adaptation of the model grid</span>
-    <span class="k">if</span> <span class="n">justobs</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
-        <span class="c1"># Creation of the repertory to store the adapted grid (if needed)</span>
-        <span class="k">if</span> <span class="n">os</span><span class="o">.</span><span class="n">path</span><span class="o">.</span><span class="n">isdir</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">):</span>
-            <span class="k">pass</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">os</span><span class="o">.</span><span class="n">mkdir</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span><span class="p">)</span>
-
-        <span class="nb">print</span><span class="p">()</span>
-        <span class="nb">print</span><span class="p">()</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;-&gt; To compare synthetic spectra with the observation we need to manage them. The following actions are performed:&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- extraction -&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- resizing on the observation&#39;s wavelength range -&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- adjustement of the spectral resolution -&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s2">&quot;- substraction of the continuum (if needed) -&quot;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">()</span>
-
-        <span class="n">adapt_grid</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">obs_merge</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">obs_pho</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
-
-    <span class="c1"># USER configuration path</span>
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Configuration of environment&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Where is your configuration file?&#39;</span><span class="p">)</span>
-    <span class="n">config_file_path</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">()</span>
-
-    <span class="c1"># CONFIG_FILE reading and defining global parameters</span>
-    <span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  <span class="c1"># To access any param.: global_params.parameter_name</span>
-
-    <span class="n">launch_adapt</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="s1">&#39;no&#39;</span><span class="p">)</span>
-</pre></div>
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-  <h1>Source code for ForMoSA.adapt.extraction_functions</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">division</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">astropy.io</span> <span class="kn">import</span> <span class="n">fits</span>
-<span class="kn">from</span> <span class="nn">scipy.ndimage</span> <span class="kn">import</span> <span class="n">gaussian_filter</span>
-<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
-<span class="kn">from</span> <span class="nn">spectres</span> <span class="kn">import</span> <span class="n">spectres</span>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="decoupe"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.decoupe">[docs]</a><span class="k">def</span> <span class="nf">decoupe</span><span class="p">(</span><span class="n">second</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Re-arranged a number of seconds in the hours-minutes-seconds format.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        second: number of second</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        hour, minute, second: hours-minutes-seconds format</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="n">hour</span> <span class="o">=</span> <span class="n">second</span> <span class="o">/</span> <span class="mi">3600</span>
-    <span class="n">second</span> <span class="o">%=</span> <span class="mi">3600</span>
-    <span class="n">minute</span> <span class="o">=</span> <span class="n">second</span> <span class="o">/</span> <span class="mi">60</span>
-    <span class="n">second</span> <span class="o">%=</span> <span class="mi">60</span>
-
-    <span class="k">return</span> <span class="n">hour</span><span class="p">,</span> <span class="n">minute</span><span class="p">,</span> <span class="n">second</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="find_nearest"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.find_nearest">[docs]</a><span class="k">def</span> <span class="nf">find_nearest</span><span class="p">(</span><span class="n">array</span><span class="p">,</span> <span class="n">value</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Return the indice of the closest values from a desire value in an array.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        array: Array to explore</span>
-<span class="sd">        value: Desire value</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        idx: Indice of the closest values from the desire value</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">idx</span> <span class="o">=</span> <span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">array</span> <span class="o">-</span> <span class="n">value</span><span class="p">))</span><span class="o">.</span><span class="n">argmin</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">idx</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="extract_observation"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.extract_observation">[docs]</a><span class="k">def</span> <span class="nf">extract_observation</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="n">cont</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Take back the extracted data spectrum from the function &#39;adapt_observation_range&#39; and decrease its spectral</span>
-<span class="sd">    resolution.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter</span>
-<span class="sd">        wav_mod_nativ: Wavelength grid of the model</span>
-<span class="sd">        res_mod_nativ: Spectral resolution of the model</span>
-<span class="sd">        cont: If the function is used to estimate the continuum cont=&#39;yes&#39;</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        obs_cut: List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot; with decreased resolution</span>
-<span class="sd">                 [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</span>
-<span class="sd">        obs_pho: List containing the photometry (&#39;P&#39; replace the spectral resolution here).</span>
-<span class="sd">                 [wav_phot, flx_phot, err_phot, &#39;P&#39;]</span>
-<span class="sd">        obs_cut_ins: List containing different instruments used for the data (1 per wavelength).</span>
-<span class="sd">                 [[instru_range_1], ..., [instru_range_n]]</span>
-<span class="sd">        obs_pho_ins: List containing different filters used for the data (1 per photometric point).</span>
-<span class="sd">                 [filter_phot_1, filter_phot_2, ..., filter_phot_n]</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-
-    <span class="c1"># Extract the wavelengths, flux, errors, spectral resolution, and instrument/filter names from the observation file.</span>
-    <span class="n">obs_cut</span><span class="p">,</span> <span class="n">obs_pho</span><span class="p">,</span> <span class="n">obs_cut_ins</span><span class="p">,</span> <span class="n">obs_pho_ins</span> <span class="o">=</span> <span class="n">adapt_observation_range</span><span class="p">(</span><span class="n">global_params</span><span class="p">)</span>
-
-    <span class="c1"># Reduce the spectral resolution for each sub-spectrum.</span>
-    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">):</span>
-        <span class="c1"># If we want to decrease the resolution of the data:</span>
-        <span class="k">if</span> <span class="n">cont</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_method</span> <span class="o">==</span> <span class="s1">&#39;by_sample&#39;</span><span class="p">:</span>
-                <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="p">[],</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="s1">&#39;obs&#39;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="k">pass</span>
-        <span class="c1"># If we want to estimate the continuum of the data:</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">obs_cut</span><span class="p">[</span><span class="n">c</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">),</span> <span class="s1">&#39;obs&#39;</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">obs_cut</span><span class="p">,</span> <span class="n">obs_pho</span><span class="p">,</span> <span class="n">obs_cut_ins</span><span class="p">,</span> <span class="n">obs_pho_ins</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="adapt_observation_range"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.adapt_observation_range">[docs]</a><span class="k">def</span> <span class="nf">adapt_observation_range</span><span class="p">(</span><span class="n">global_params</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Extract the wavelengths (um  -  vacuum), flux (W.m-2.um.1), errors (W.m-2.um.1), spectral resolution, and</span>
-<span class="sd">    instrument/filter names from the observation file. The wavelength range is define by the parameter &quot;wav_for_adapt&quot;.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        obs_cut: List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</span>
-<span class="sd">                 [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</span>
-<span class="sd">        obs_pho: List containing the photometry (&#39;P&#39; replace the spectral resolution here).</span>
-<span class="sd">                 [wav_phot, flx_phot, err_phot, &#39;P&#39;]</span>
-<span class="sd">        obs_cut_ins: List containing different instruments used for the data (1 per wavelength).</span>
-<span class="sd">                 [[instru_1], ..., [instru_n]]</span>
-<span class="sd">        obs_pho_ins: List containing different filters used for the data (1 per photometric point).</span>
-<span class="sd">                 [filter_phot_1, filter_phot_2, ..., filter_phot_n]</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Extraction</span>
-    <span class="k">with</span> <span class="n">fits</span><span class="o">.</span><span class="n">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">observation_path</span><span class="p">)</span> <span class="k">as</span> <span class="n">hdul</span><span class="p">:</span>
-        <span class="n">wav</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;WAV&#39;</span><span class="p">]</span>     <span class="c1"># wavelength (um  -  vacuum)</span>
-        <span class="n">flx</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;FLX&#39;</span><span class="p">]</span>     <span class="c1"># flux (W.m-2.um.1)</span>
-        <span class="n">err</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;ERR&#39;</span><span class="p">]</span>     <span class="c1"># error (W.m-2.um.1)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;RES&#39;</span><span class="p">]</span>     <span class="c1"># spectral resolution</span>
-        <span class="n">ins</span> <span class="o">=</span> <span class="n">hdul</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">data</span><span class="p">[</span><span class="s1">&#39;INS&#39;</span><span class="p">]</span>     <span class="c1"># instrument/filter name</span>
-
-        <span class="c1"># Filter the NaN values</span>
-        <span class="n">nan_mod_ind</span> <span class="o">=</span> <span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">flx</span><span class="p">)</span>
-        <span class="n">wav</span> <span class="o">=</span> <span class="n">wav</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
-        <span class="n">flx</span> <span class="o">=</span> <span class="n">flx</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
-        <span class="n">err</span> <span class="o">=</span> <span class="n">err</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">res</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
-        <span class="n">ins</span> <span class="o">=</span> <span class="n">ins</span><span class="p">[</span><span class="n">nan_mod_ind</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">res</span><span class="p">)):</span>
-            <span class="c1">#if res[i] == &#39;P&#39;:</span>
-            <span class="k">if</span> <span class="nb">float</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">:</span>
-                <span class="k">pass</span>
-            <span class="k">else</span><span class="p">:</span> 
-                <span class="n">res</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-    <span class="c1"># Select the wavelength range(s) for the extraction</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_adapt</span> <span class="o">==</span> <span class="s1">&#39;&#39;</span><span class="p">:</span>
-        <span class="n">wav_for_adapt_tab</span> <span class="o">=</span> <span class="p">[</span><span class="nb">str</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">wav</span><span class="p">))</span> <span class="o">+</span> <span class="s1">&#39;,&#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="nb">max</span><span class="p">(</span><span class="n">wav</span><span class="p">))]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">wav_for_adapt_tab</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_adapt</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-
-    <span class="k">for</span> <span class="n">range_ind</span><span class="p">,</span> <span class="n">rangee</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">wav_for_adapt_tab</span><span class="p">):</span>
-        <span class="n">rangee</span> <span class="o">=</span> <span class="n">rangee</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
-        <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="nb">float</span><span class="p">(</span><span class="n">rangee</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">wav</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav</span> <span class="o">&lt;=</span> <span class="nb">float</span><span class="p">(</span><span class="n">rangee</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-
-        <span class="c1"># Photometry part of the data</span>
-        <span class="c1">#ind_photometry = np.where(res[ind] == &#39;P&#39;)</span>
-        <span class="n">ind_photometry</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">==</span> <span class="mf">0.0</span><span class="p">)</span>
-        <span class="n">obs_pho</span> <span class="o">=</span> <span class="p">[</span><span class="n">wav</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="n">ind_photometry</span><span class="p">],</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="n">ind_photometry</span><span class="p">],</span> <span class="n">err</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="n">ind_photometry</span><span class="p">],</span>
-                   <span class="n">res</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="n">ind_photometry</span><span class="p">]]</span>
-        <span class="n">obs_pho_ins</span> <span class="o">=</span> <span class="n">ins</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="n">ind_photometry</span><span class="p">]</span>
-
-        <span class="c1"># Spectroscopy part of the data</span>
-        <span class="n">wav_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">wav</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
-        <span class="n">flx_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">flx</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
-        <span class="n">err_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">err</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
-        <span class="n">res_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">res</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
-        <span class="n">ins_spectro</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">delete</span><span class="p">(</span><span class="n">ins</span><span class="p">[</span><span class="n">ind</span><span class="p">],</span> <span class="n">ind_photometry</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">range_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">obs_cut</span> <span class="o">=</span> <span class="p">[[</span><span class="n">wav_spectro</span><span class="p">,</span> <span class="n">flx_spectro</span><span class="p">,</span> <span class="n">err_spectro</span><span class="p">,</span> <span class="n">res_spectro</span><span class="p">]]</span>
-            <span class="n">obs_cut_ins</span> <span class="o">=</span> <span class="p">[[</span><span class="n">ins_spectro</span><span class="p">]]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">obs_cut</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">wav_spectro</span><span class="p">,</span> <span class="n">flx_spectro</span><span class="p">,</span> <span class="n">err_spectro</span><span class="p">,</span> <span class="n">res_spectro</span><span class="p">])</span>
-            <span class="n">obs_cut_ins</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="n">ins_spectro</span><span class="p">])</span>
-
-    <span class="k">return</span> <span class="n">obs_cut</span><span class="p">,</span> <span class="n">obs_pho</span><span class="p">,</span> <span class="n">obs_cut_ins</span><span class="p">,</span> <span class="n">obs_pho_ins</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="adapt_model"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.adapt_model">[docs]</a><span class="k">def</span> <span class="nf">adapt_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are</span>
-<span class="sd">    calculated too. Then each sub-spectrum are merged.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter used in ForMoSA</span>
-<span class="sd">        wav_mod_nativ: Wavelength grid of the model</span>
-<span class="sd">        flx_mod_nativ: Flux of the model</span>
-<span class="sd">        res_mod_nativ: Spectral resolution of the model as a function of the wavelength grid</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        output_model: Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</span>
-<span class="sd">    </span>
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Extract the synthetic spectra from the model grid</span>
-    <span class="n">mod_cut</span><span class="p">,</span> <span class="n">mod_pho</span> <span class="o">=</span> <span class="n">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">)</span>
-
-    <span class="c1"># Estimate and subtraction of the continuum (if needed)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">mod_cut_c</span><span class="p">,</span> <span class="n">mod_pho_c</span> <span class="o">=</span> <span class="n">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="s1">&#39;yes&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mod_cut_c</span><span class="p">):</span>
-            <span class="n">mod_cut</span><span class="p">[</span><span class="n">c</span><span class="p">]</span> <span class="o">-=</span> <span class="n">mod_cut_c</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-
-    <span class="c1"># Merging of each sub-spectrum</span>
-    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">mod_cut</span><span class="p">):</span>
-        <span class="k">if</span> <span class="n">c</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">flx_mod_extract</span> <span class="o">=</span> <span class="n">mod_cut</span><span class="p">[</span><span class="n">c</span><span class="p">]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">flx_mod_extract</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_cut</span><span class="p">[</span><span class="n">c</span><span class="p">]))</span>
-
-    <span class="k">return</span> <span class="n">flx_mod_extract</span><span class="p">,</span> <span class="n">mod_pho</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="extract_model"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.extract_model">[docs]</a><span class="k">def</span> <span class="nf">extract_model</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="n">cont</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are</span>
-<span class="sd">    calculated too.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter used in ForMoSA</span>
-<span class="sd">        wav_mod_nativ: Wavelength grid of the model</span>
-<span class="sd">        flx_mod_nativ: Flux of the model</span>
-<span class="sd">        res_mod_nativ: Spectral resolution of the model as a function of the wavelength grid</span>
-<span class="sd">        cont: If the function is used to estimate the continuum cont=&#39;yes&#39;</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        mod_cut: List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</span>
-<span class="sd">                 [flx_1, ..., flx_n, err_n, reso_n]</span>
-<span class="sd">        mod_pho: List containing the photometry (&#39;P&#39; replace the spectral resolution here).</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Take back the extracted data.</span>
-    <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/spectrum_obs.npz&#39;</span><span class="p">,</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">obs_cut</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_cut&#39;</span><span class="p">]</span>
-    <span class="n">obs_pho_ins</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho_ins&#39;</span><span class="p">]</span>
-
-    <span class="c1"># Reduce the spectral resolution for each sub-spectrum.</span>
-    <span class="n">mod_cut</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">c</span><span class="p">,</span> <span class="n">cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_cut</span><span class="p">):</span>
-        <span class="c1"># If we want to decrease the resolution of the data:</span>
-        <span class="k">if</span> <span class="n">cont</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_method</span> <span class="o">==</span> <span class="s1">&#39;by_sample&#39;</span><span class="p">:</span>
-                <span class="n">mod_cut_flx</span> <span class="o">=</span> <span class="n">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span>
-                                                    <span class="s1">&#39;mod&#39;</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">mod_cut_flx</span> <span class="o">=</span> <span class="n">spectres</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">)</span>
-        <span class="c1"># If we want to estimate the continuum of the data:</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">mod_cut_flx</span> <span class="o">=</span> <span class="n">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="s1">&#39;mod&#39;</span><span class="p">)</span>
-        <span class="n">mod_cut</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">mod_cut_flx</span><span class="p">)</span>
-
-    <span class="c1"># Calculate each photometry point.</span>
-    <span class="n">mod_pho</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">for</span> <span class="n">pho_ind</span><span class="p">,</span> <span class="n">pho</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">obs_pho_ins</span><span class="p">):</span>
-        <span class="n">path_list</span> <span class="o">=</span> <span class="vm">__file__</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s2">&quot;/&quot;</span><span class="p">)[:</span><span class="o">-</span><span class="mi">2</span><span class="p">]</span>
-        <span class="n">separator</span> <span class="o">=</span> <span class="s1">&#39;/&#39;</span>
-        <span class="n">filter_pho</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">separator</span><span class="o">.</span><span class="n">join</span><span class="p">(</span><span class="n">path_list</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39;/phototeque/&#39;</span> <span class="o">+</span> <span class="n">pho</span> <span class="o">+</span> <span class="s1">&#39;.npz&#39;</span><span class="p">)</span>
-        <span class="n">x_filt</span> <span class="o">=</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;x_filt&#39;</span><span class="p">]</span>
-        <span class="n">y_filt</span> <span class="o">=</span> <span class="n">filter_pho</span><span class="p">[</span><span class="s1">&#39;y_filt&#39;</span><span class="p">]</span>
-        <span class="n">filter_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">x_filt</span><span class="p">,</span> <span class="n">y_filt</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
-        <span class="n">y_filt</span> <span class="o">=</span> <span class="n">filter_interp</span><span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">)</span>
-
-        <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">logical_and</span><span class="p">(</span><span class="n">wav_mod_nativ</span> <span class="o">&gt;</span> <span class="nb">min</span><span class="p">(</span><span class="n">x_filt</span><span class="p">),</span> <span class="n">wav_mod_nativ</span> <span class="o">&lt;</span> <span class="nb">max</span><span class="p">(</span><span class="n">x_filt</span><span class="p">)))</span>
-        <span class="n">flx_filt</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">flx_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="n">y_filt</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
-        <span class="n">y_filt_tot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">y_filt</span><span class="p">[</span><span class="n">ind</span><span class="p">]</span> <span class="o">*</span> <span class="p">(</span><span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
-        <span class="n">flx_filt</span> <span class="o">=</span> <span class="n">flx_filt</span> <span class="o">/</span> <span class="n">y_filt_tot</span>
-        <span class="n">mod_pho</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">flx_filt</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">mod_cut</span><span class="p">,</span> <span class="n">mod_pho</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="convolve_and_sample"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.convolve_and_sample">[docs]</a><span class="k">def</span> <span class="nf">convolve_and_sample</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">,</span> <span class="n">sigmas_wvs</span><span class="p">,</span> <span class="n">model_wvs</span><span class="p">,</span> <span class="n">model_fluxes</span><span class="p">,</span> <span class="n">num_sigma</span><span class="o">=</span><span class="mi">3</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Simulate the observations of a model. Convolves the model with a variable Gaussian LSF, sampled at each desired</span>
-<span class="sd">    spectral channel.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        wv_channels: the wavelengths desired (length of N_output)</span>
-<span class="sd">        sigmas_wvs: the LSF gaussian stddev of each wv_channels [IN UNITS OF model_wvs] (length of N_output)</span>
-<span class="sd">        model_wvs: the wavelengths of the model (length of N_model)</span>
-<span class="sd">        model_fluxes: the fluxes of the model (length of N_model)</span>
-<span class="sd">        num_sigma (float): number of +/- sigmas to evaluate the LSF to.</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        output_model: the fluxes in each of the wavelength channels (length of N_output)</span>
-<span class="sd">        </span>
-<span class="sd">    Author: Jason Wang</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">model_in_range</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">model_wvs</span> <span class="o">&gt;=</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">))</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">model_wvs</span> <span class="o">&lt;</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">wv_channels</span><span class="p">)))</span>
-    <span class="n">dwv_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">[</span><span class="n">model_in_range</span><span class="p">]</span> <span class="o">-</span> <span class="n">np</span><span class="o">.</span><span class="n">roll</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">[</span><span class="n">model_in_range</span><span class="p">],</span> <span class="mi">1</span><span class="p">))</span>
-    <span class="n">dwv_model</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">dwv_model</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">filter_size</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">((</span><span class="mi">2</span> <span class="o">*</span> <span class="n">num_sigma</span> <span class="o">*</span> <span class="n">sigmas_wvs</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">min</span><span class="p">(</span><span class="n">dwv_model</span><span class="p">))))</span>
-    <span class="n">filter_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">-</span><span class="n">num_sigma</span><span class="p">,</span> <span class="n">num_sigma</span><span class="p">,</span> <span class="n">filter_size</span><span class="p">)</span>
-    <span class="n">filter_coords</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">filter_coords</span><span class="p">,</span> <span class="p">[</span><span class="n">wv_channels</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="mi">1</span><span class="p">])</span>  <span class="c1"># shape of (N_output, filter_size)</span>
-
-    <span class="n">filter_wv_coords</span> <span class="o">=</span> <span class="n">filter_coords</span> <span class="o">*</span> <span class="n">sigmas_wvs</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span> <span class="o">+</span> <span class="n">wv_channels</span><span class="p">[:,</span> <span class="kc">None</span><span class="p">]</span>  <span class="c1"># model wavelengths we want</span>
-    <span class="n">lsf</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">filter_coords</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">/</span> <span class="mi">2</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="mi">2</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">lsf</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-
-        <span class="n">model_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">model_wvs</span><span class="p">,</span> <span class="n">model_fluxes</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">filter_model</span> <span class="o">=</span> <span class="n">model_interp</span><span class="p">(</span><span class="n">filter_wv_coords</span><span class="p">)</span>
-
-        <span class="n">output_model</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nansum</span><span class="p">(</span><span class="n">filter_model</span> <span class="o">*</span> <span class="n">lsf</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">lsf</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-
-        <span class="n">output_model</span> <span class="o">=</span> <span class="n">model_fluxes</span>
-
-    <span class="k">return</span> <span class="n">output_model</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="resolution_decreasing"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.resolution_decreasing">[docs]</a><span class="k">def</span> <span class="nf">resolution_decreasing</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">cut</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">,</span> <span class="n">res_mod_nativ</span><span class="p">,</span> <span class="n">obs_or_mod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the</span>
-<span class="sd">    wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one</span>
-<span class="sd">    for each wavelength (the lowest spectral resolution). It then calculates a sigma to decrease the resolution of the</span>
-<span class="sd">    spectrum to this lowest FWHM for each wavelength and resample it on the wavelength grid of the data using the</span>
-<span class="sd">    function &#39;convolve_and_sample&#39;.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter used in ForMoSA</span>
-<span class="sd">        cut: Sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</span>
-<span class="sd">             [[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</span>
-<span class="sd">        wav_mod_nativ: Wavelength grid of the model</span>
-<span class="sd">        flx_mod_nativ: Flux of the model</span>
-<span class="sd">        res_mod_nativ: Spectral resolution of the model as a function of the wavelength grid</span>
-<span class="sd">        obs_or_mod: Parameter to identify if you want to manage a data or a model spectrum. &#39;obs&#39; or &#39;mod&#39;</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        output_model: Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Estimate of the FWHM of the data as a function of the wavelength</span>
-    <span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">fwhm_obs</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">cut</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span>
-
-    <span class="c1"># Estimate of the FWHM of the model as a function of the wavelength</span>
-    <span class="n">ind_mod_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_mod_nativ</span> <span class="o">&lt;=</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_mod_nativ</span> <span class="o">&gt;</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]))</span>
-    <span class="n">wav_mod_obs</span> <span class="o">=</span> <span class="n">wav_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
-    <span class="n">res_mod_obs</span> <span class="o">=</span> <span class="n">res_mod_nativ</span><span class="p">[</span><span class="n">ind_mod_obs</span><span class="p">]</span>
-    <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_mod_obs</span><span class="p">,</span> <span class="n">res_mod_obs</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
-    <span class="n">res_mod_obs</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-    <span class="n">fwhm_mod</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="n">res_mod_obs</span>
-
-    <span class="c1"># Estimate of the FWHM of the custom resolution (if defined) as a function of the wavelength</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">custom_reso</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">fwhm_custom</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">custom_reso</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fwhm_custom</span> <span class="o">=</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-
-    <span class="c1"># Estimate of the sigma for the convolution as a function of the wavelength and decrease the resolution</span>
-    <span class="n">max_fwhm</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nanmax</span><span class="p">([</span><span class="n">fwhm_obs</span><span class="p">,</span> <span class="n">fwhm_mod</span><span class="p">,</span> <span class="n">fwhm_custom</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">obs_or_mod</span> <span class="o">==</span> <span class="s1">&#39;obs&#39;</span><span class="p">:</span>
-        <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">max_fwhm</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm_obs</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">sigma_conv</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="mf">2.355</span>
-        <span class="n">flx_obs_final</span> <span class="o">=</span> <span class="n">convolve_and_sample</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sigma_conv</span><span class="p">,</span> <span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">cut</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">max_fwhm</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm_mod</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-        <span class="n">sigma_conv</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="mf">2.355</span>
-        <span class="n">flx_obs_final</span> <span class="o">=</span> <span class="n">convolve_and_sample</span><span class="p">(</span><span class="n">cut</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">sigma_conv</span><span class="p">,</span> <span class="n">wav_mod_nativ</span><span class="p">,</span> <span class="n">flx_mod_nativ</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">flx_obs_final</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="continuum_estimate"><a class="viewcode-back" href="../../../adapt.html#ForMoSA.adapt.extraction_functions.continuum_estimate">[docs]</a><span class="k">def</span> <span class="nf">continuum_estimate</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">wav_cut</span><span class="p">,</span> <span class="n">wav</span><span class="p">,</span> <span class="n">flx</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">obs_or_mod</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the</span>
-<span class="sd">    wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease</span>
-<span class="sd">    the resolution of the spectrum to this custom FWHM for each wavelength using a gaussian filter and resample it on</span>
-<span class="sd">    the wavelength grid of the data.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter used in ForMoSA</span>
-<span class="sd">        wav_cut: Wavelength grid of the sub-spectrum data</span>
-<span class="sd">        wav: Wavelength grid of the spectrum for which you want to estimate the continuum</span>
-<span class="sd">        flx: Flux of the spectrum for which you want to estimate the continuum</span>
-<span class="sd">        res: Spectral resolution of the spectrum for which you want to estimate the continuum</span>
-<span class="sd">        obs_or_mod: Parameter to identify if you want to manage a data or a model spectrum. &#39;obs&#39; or &#39;mod&#39;</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        output_model: Estimated continuum of the spectrum re-sampled on the data wavelength grid</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Adapt the model to the data grid wavelength</span>
-    <span class="k">if</span> <span class="n">obs_or_mod</span> <span class="o">==</span> <span class="s1">&#39;mod&#39;</span><span class="p">:</span>
-        <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav</span><span class="p">,</span> <span class="n">flx</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">flx</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">wav_cut</span><span class="p">)</span>
-        <span class="n">interp_mod_to_obs</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav</span><span class="p">,</span> <span class="n">res</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;cubic&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">res</span> <span class="o">=</span> <span class="n">interp_mod_to_obs</span><span class="p">(</span><span class="n">wav_cut</span><span class="p">)</span>
-        <span class="n">wav</span> <span class="o">=</span> <span class="n">wav_cut</span>
-
-    <span class="c1"># Redifined a spectrum only composed by the wavelength ranges used to estimate the continuum</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_continuum</span> <span class="o">!=</span> <span class="s1">&#39;full&#39;</span><span class="p">:</span>
-        <span class="n">wav_for_continuum</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">wav_for_continuum</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">wav_for_cont_cut_ind</span><span class="p">,</span> <span class="n">wav_for_cont_cut</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">wav_for_continuum</span><span class="p">):</span>
-            <span class="n">wav_for_cont_cut</span> <span class="o">=</span> <span class="n">wav_for_cont_cut</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">wav_for_cont_cut_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">ind_cont_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">wav</span> <span class="o">&lt;=</span> <span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">0</span><span class="p">]))</span>
-            <span class="k">elif</span> <span class="n">wav_for_cont_cut_ind</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="n">wav_for_continuum</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-                <span class="n">ind_cont_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">wav</span><span class="p">)</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">ind_cont_cut</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span> <span class="o">&lt;=</span> <span class="n">wav</span><span class="p">)</span> <span class="o">&amp;</span>
-                                        <span class="p">(</span><span class="n">wav</span> <span class="o">&lt;=</span> <span class="nb">float</span><span class="p">(</span><span class="n">wav_for_cont_cut</span><span class="p">[</span><span class="mi">1</span><span class="p">])))</span>
-            <span class="k">if</span> <span class="n">wav_for_cont_cut_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">wav_for_cont_final</span> <span class="o">=</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]</span>
-                <span class="n">flx_for_cont_final</span> <span class="o">=</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">wav_for_cont_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_for_cont_final</span><span class="p">,</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]))</span>
-                <span class="n">flx_for_cont_final</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_for_cont_final</span><span class="p">,</span> <span class="n">flx</span><span class="p">[</span><span class="n">ind_cont_cut</span><span class="p">]))</span>
-
-        <span class="n">model_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_for_cont_final</span><span class="p">,</span> <span class="n">flx_for_cont_final</span><span class="p">,</span> <span class="n">kind</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">bounds_error</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">flx</span> <span class="o">=</span> <span class="n">model_interp</span><span class="p">(</span><span class="n">wav</span><span class="p">)</span>
-
-    <span class="c1"># To limit the computing time, the sigma for the convolution is not as a function of the wavelength but calculated</span>
-    <span class="c1"># from the median wavelength. We just want an estimate of the continuum here.</span>
-    <span class="n">wav_median</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">wav</span><span class="p">)</span>
-    <span class="n">ind_wav_median_obs</span> <span class="o">=</span> <span class="n">find_nearest</span><span class="p">(</span><span class="n">wav</span><span class="p">,</span> <span class="n">wav_median</span><span class="p">)</span>
-    <span class="n">dwav</span> <span class="o">=</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_wav_median_obs</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span> <span class="o">-</span> <span class="n">wav</span><span class="p">[</span><span class="n">ind_wav_median_obs</span><span class="p">]</span>
-
-    <span class="n">fwhm</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_median</span> <span class="o">/</span> <span class="n">np</span><span class="o">.</span><span class="n">median</span><span class="p">(</span><span class="n">res</span><span class="p">)</span>
-
-    <span class="n">fwhm_continuum</span> <span class="o">=</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">wav_median</span> <span class="o">/</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">continuum_sub</span><span class="p">)</span>
-
-    <span class="n">fwhm_conv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">fwhm_continuum</span><span class="o">**</span><span class="mi">2</span> <span class="o">-</span> <span class="n">fwhm</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-    <span class="n">sigma</span> <span class="o">=</span> <span class="n">fwhm_conv</span> <span class="o">/</span> <span class="p">(</span><span class="n">dwav</span> <span class="o">*</span> <span class="mf">2.355</span><span class="p">)</span>
-    <span class="n">continuum</span> <span class="o">=</span> <span class="n">gaussian_filter</span><span class="p">(</span><span class="n">flx</span><span class="p">,</span> <span class="n">sigma</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">continuum</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-</pre></div>
-
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-          </div>
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-
-  <hr/>
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-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
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diff --git a/docs/build/html/_modules/ForMoSA/main_utilities.html b/docs/build/html/_modules/ForMoSA/main_utilities.html
deleted file mode 100644
index 1a6806a..0000000
--- a/docs/build/html/_modules/ForMoSA/main_utilities.html
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-      <li class="breadcrumb-item active">ForMoSA.main_utilities</li>
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-           <div itemprop="articleBody">
-             
-  <h1>Source code for ForMoSA.main_utilities</h1><div class="highlight"><pre>
-<span></span><span class="kn">from</span> <span class="nn">configobj</span> <span class="kn">import</span> <span class="n">ConfigObj</span>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-<div class="viewcode-block" id="yesno"><a class="viewcode-back" href="../../main_utilities.html#ForMoSA.main_utilities.yesno">[docs]</a><span class="k">def</span> <span class="nf">yesno</span><span class="p">(</span><span class="n">text</span><span class="p">):</span>
-    <span class="sd">&#39;&#39;&#39; Function to interact with the terminal and decide for different options when running ForMoSA</span>
-<span class="sd">    Parameters:</span>
-<span class="sd">    - text (str)= (y/n) answer in the terminall in interactive mode</span>
-<span class="sd">    Returns:</span>
-<span class="sd">    - repeat yesno, if no match for y or n</span>
-<span class="sd">    - asw (str) = answer y or n</span>
-<span class="sd">    &#39;&#39;&#39;</span>
-    <span class="nb">print</span><span class="p">(</span><span class="n">text</span><span class="p">)</span>
-    <span class="n">asw</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
-    <span class="k">if</span> <span class="n">asw</span> <span class="ow">in</span> <span class="p">[</span><span class="s1">&#39;y&#39;</span><span class="p">,</span> <span class="s1">&#39;n&#39;</span><span class="p">]:</span>
-        <span class="k">return</span> <span class="n">asw</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">yesno</span><span class="p">()</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="GlobFile"><a class="viewcode-back" href="../../main_utilities.html#ForMoSA.main_utilities.GlobFile">[docs]</a><span class="k">class</span> <span class="nc">GlobFile</span><span class="p">:</span>
-    <span class="sd">&#39;&#39;&#39;Import config variables and make them GLOBAL FORMOSA VARIABLES</span>
-<span class="sd">    </span>
-<span class="sd">    Author: Paulina Palma-Bifani&#39;&#39;&#39;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config_file_path</span><span class="p">):</span>
-        <span class="c1"># Generate the confog object</span>
-        <span class="n">config</span> <span class="o">=</span> <span class="n">ConfigObj</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">,</span> <span class="n">encoding</span><span class="o">=</span><span class="s1">&#39;utf8&#39;</span><span class="p">)</span>
-        <span class="c1"># self.obsname = obsname</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">config</span><span class="o">=</span><span class="n">config</span>
-
-        <span class="c1">## Read CONFIG:</span>
-        <span class="c1"># [config_path] (4)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">observation_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;observation_path&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;adapt_store_path&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">result_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;result_path&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model_path</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;model_path&#39;</span><span class="p">]</span>
-        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_path&#39;</span><span class="p">][</span><span class="s1">&#39;model_path&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)</span>
-        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;.nc&#39;</span><span class="p">)</span>
-        <span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">=</span> <span class="n">grid_name</span>
-        <span class="n">model_name</span> <span class="o">=</span> <span class="n">grid_name</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;_&#39;</span><span class="p">)</span>
-        <span class="n">model_name</span> <span class="o">=</span> <span class="n">model_name</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">model_name</span> <span class="o">=</span> <span class="n">model_name</span>
-
-        <span class="c1"># [config_adapt] (5)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_adapt</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_adapt&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">adapt_method</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;adapt_method&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">custom_reso</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;custom_reso&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">continuum_sub</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;continuum_sub&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">wav_for_continuum</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_adapt&#39;</span><span class="p">][</span><span class="s1">&#39;wav_for_continuum&#39;</span><span class="p">]</span>
-
-        <span class="c1"># [config_inversion] (3)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">wav_fit</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;wav_fit&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;ns_algo&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">npoint</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_inversion&#39;</span><span class="p">][</span><span class="s1">&#39;npoint&#39;</span><span class="p">]</span>
-
-        <span class="c1"># [config_parameter] (11)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">par1</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">par2</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">par3</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">par4</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">par5</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">r</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;r&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">d</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;d&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">rv</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;rv&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">av</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;av&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">vsini</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;vsini&#39;</span><span class="p">]</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ld</span> <span class="o">=</span> <span class="n">config</span><span class="p">[</span><span class="s1">&#39;config_parameter&#39;</span><span class="p">][</span><span class="s1">&#39;ld&#39;</span><span class="p">]</span></div>
-
-        <span class="c1"># # [config_nestle] (10 but 3 relevant)  (n_ prefix for params)</span>
-        <span class="c1"># self.n_mechanic = config[&#39;config_nestle&#39;][&#39;mechanic&#39;]</span>
-        <span class="c1"># self.n_method = config[&#39;config_nestle&#39;][&#39;method&#39;]</span>
-        <span class="c1"># self.n_maxiter = int(config[&#39;config_nestle&#39;][&#39;maxiter&#39;])</span>
-        <span class="c1"># self.n_maxcall = eval(config[&#39;config_nestle&#39;][&#39;maxcall&#39;])</span>
-        <span class="c1"># self.n_dlogz = eval(config[&#39;config_nestle&#39;][&#39;dlogz&#39;])</span>
-        <span class="c1"># self.n_decline_factor = eval(config[&#39;config_nestle&#39;][&#39;decline_factor&#39;])</span>
-        <span class="c1"># self.n_update_interval = eval(config[&#39;config_nestle&#39;][&#39;update_interval&#39;])</span>
-        <span class="c1"># self.n_npdim = eval(config[&#39;config_nestle&#39;][&#39;npdim&#39;])</span>
-        <span class="c1"># self.n_rstate = eval(config[&#39;config_nestle&#39;][&#39;rstate&#39;])</span>
-
-        <span class="c1"># [config_dinesty] &amp; [config_ultranest] CHECK THIS</span>
-
-        <span class="c1"># ## create OUTPUTS Sub-Directories: interpolated grids and results</span>
-        <span class="c1"># stock_result_sub_dir = self.stock_result_raw + self.name_obs + &#39;_&#39; + self.model_name[</span>
-        <span class="c1">#                                                                      :-4] + self.data_type  # sub_directory: obsname_grid_datatype</span>
-        <span class="c1"># self.stock_interp_grid = stock_result_sub_dir + &#39;/interp_grid&#39;  # sub_sub directory to save interp grid (one interpolation for grid and data type, full wavelength covarage)</span>
-        <span class="c1">#</span>
-        <span class="c1"># self.path_grid_management = self.base_path + stock_result_sub_dir + &#39;/interp_grid/grid_management&#39;</span>
-        <span class="c1"># os.makedirs(self.base_path + stock_result_sub_dir + &#39;/interp_grid&#39; + &#39;/grid_management&#39;, exist_ok=True)</span>
-        <span class="c1">#</span>
-        <span class="c1"># subsub_dir_name = self.nest_samp_algo + &#39;_&#39; + self.NS_using_band + &#39;_Res&#39; + str(</span>
-        <span class="c1">#     self.R_by_wl[2]) + &#39;_&#39;  # subsub_directory: nestle_band_ResOBS_params_date</span>
-        <span class="c1"># subsub_dir_name = subsub_dir_name + self.free_params + &#39;_t&#39; + time.strftime(&quot;%Y%m%d_%H%M%S&quot;)</span>
-        <span class="c1"># stock_result_subsub_dir = stock_result_sub_dir + &#39;/&#39; + subsub_dir_name</span>
-        <span class="c1"># self.stock_result = self.base_path + stock_result_subsub_dir</span>
-        <span class="c1"># os.makedirs(self.base_path + stock_result_subsub_dir)</span>
-
-        <span class="c1"># ## Save CONFIG file with updated params for current run in OUTPUT subsub directory</span>
-        <span class="c1">#</span>
-        <span class="c1"># print(&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;)</span>
-        <span class="c1"># print(&#39;-&gt; Saving new configuration&#39;)</span>
-        <span class="c1"># print()</span>
-        <span class="c1">#</span>
-        <span class="c1"># config_current = self.result_path + &#39;/past_config.ini&#39;</span>
-        <span class="c1"># config.filename = config_current</span>
-        <span class="c1"># config[&#39;config_path&#39;][&#39;stock_interp_grid&#39;] = stock_interp_grid</span>
-        <span class="c1"># config[&#39;config_path&#39;][&#39;stock_result&#39;] = stock_result_subsub_dir</span>
-        <span class="c1"># config.write()</span>
-        <span class="c1">#</span>
-        <span class="c1"># print(&#39;Saved config: --- &#39; + config_current + &#39; ---&#39;)</span>
-        <span class="c1"># print()</span>
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
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diff --git a/docs/build/html/_modules/ForMoSA/nested_sampling/nested_modif_spec.html b/docs/build/html/_modules/ForMoSA/nested_sampling/nested_modif_spec.html
deleted file mode 100644
index 8dd278a..0000000
--- a/docs/build/html/_modules/ForMoSA/nested_sampling/nested_modif_spec.html
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-             
-  <h1>Source code for ForMoSA.nested_sampling.nested_modif_spec</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">extinction</span>
-<span class="kn">from</span> <span class="nn">scipy.interpolate</span> <span class="kn">import</span> <span class="n">interp1d</span>
-<span class="kn">from</span> <span class="nn">PyAstronomy.pyasl</span> <span class="kn">import</span> <span class="n">dopplerShift</span><span class="p">,</span> <span class="n">rotBroad</span>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="calc_ck"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.calc_ck">[docs]</a><span class="k">def</span> <span class="nf">calc_ck</span><span class="p">(</span><span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">,</span>
-            <span class="n">analytic</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum (from the radius</span>
-<span class="sd">    and distance or analytically).</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        flx_obs_merge: Flux of the data (spectroscopy)</span>
-<span class="sd">        err_obs_merge: Error of the data (spectroscopy)</span>
-<span class="sd">        new_flx_merge: Flux of the interpolated synthetic spectrum (spectroscopy)</span>
-<span class="sd">        flx_obs_phot: Flux of the data (photometry)</span>
-<span class="sd">        err_obs_phot: Error of the data (photometry)</span>
-<span class="sd">        new_flx_phot: Flux of the interpolated synthetic spectrum (photometry)</span>
-<span class="sd">        r_picked: Radius randomly picked by the nested sampling (in RJup)</span>
-<span class="sd">        d_picked: Distance randomly picked by the nested sampling (in pc)</span>
-<span class="sd">        analytic: = &#39;yes&#39; if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        new_flx_merge: Re-normalysed model spectrum</span>
-<span class="sd">        new_flx_phot: Re-normalysed model photometry</span>
-<span class="sd">        ck: Ck calculated</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Calculation of the dilution factor ck as a function of the radius and distance</span>
-    <span class="k">if</span> <span class="n">analytic</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
-        <span class="n">r_picked</span> <span class="o">*=</span> <span class="mi">69911</span>
-        <span class="n">d_picked</span> <span class="o">*=</span> <span class="mf">3.086e+13</span>
-        <span class="n">ck</span> <span class="o">=</span> <span class="p">(</span><span class="n">r_picked</span><span class="o">/</span><span class="n">d_picked</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span>
-    <span class="c1"># Calculation of the dilution factor ck analytically</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_merge</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">ck_top_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">new_flx_merge</span> <span class="o">*</span> <span class="n">flx_obs_merge</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">err_obs_merge</span> <span class="o">*</span> <span class="n">err_obs_merge</span><span class="p">))</span>
-            <span class="n">ck_bot_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">new_flx_merge</span> <span class="o">/</span> <span class="n">err_obs_merge</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ck_top_merge</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">ck_bot_merge</span> <span class="o">=</span> <span class="mi">0</span>
-        <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">flx_obs_phot</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">ck_top_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">new_flx_phot</span> <span class="o">*</span> <span class="n">flx_obs_phot</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">err_obs_phot</span> <span class="o">*</span> <span class="n">err_obs_phot</span><span class="p">))</span>
-            <span class="n">ck_bot_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">((</span><span class="n">new_flx_phot</span> <span class="o">/</span> <span class="n">err_obs_phot</span><span class="p">)</span><span class="o">**</span><span class="mi">2</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ck_top_phot</span> <span class="o">=</span> <span class="mi">0</span>
-            <span class="n">ck_bot_phot</span> <span class="o">=</span> <span class="mi">0</span>
-
-        <span class="n">ck</span> <span class="o">=</span> <span class="p">(</span><span class="n">ck_top_merge</span> <span class="o">+</span> <span class="n">ck_top_phot</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">ck_bot_merge</span> <span class="o">+</span> <span class="n">ck_bot_phot</span><span class="p">)</span>
-
-    <span class="c1"># Re-normalization of the interpolated synthetic spectra with ck</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_flx_merge</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">new_flx_merge</span> <span class="o">*=</span> <span class="n">ck</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">new_flx_phot</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">new_flx_phot</span> <span class="o">*=</span> <span class="n">ck</span>
-
-    <span class="k">return</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">ck</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="doppler_fct"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct">[docs]</a><span class="k">def</span> <span class="nf">doppler_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">rv_picked</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Application of a Doppler shifting to the interpolated synthetic spectrum using the function pyasl.dopplerShift.</span>
-<span class="sd">    Note: Observation can change due to side effects of the shifting.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        wav_obs_merge: Wavelength grid of the data</span>
-<span class="sd">        flx_obs_merge: Flux of the data</span>
-<span class="sd">        err_obs_merge: Error of the data</span>
-<span class="sd">        new_flx_merge: Flux of the interpolated synthetic spectrum</span>
-<span class="sd">        rv_picked: Radial velocity randomly picked by the nested sampling (in km.s-1)</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        wav_obs_merge: New wavelength grid of the data</span>
-<span class="sd">        flx_obs_merge: New flux of the data</span>
-<span class="sd">        err_obs_merge: New error of the data</span>
-<span class="sd">        flx_post_doppler: New flux of the interpolated synthetic spectrum</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># wav_doppler = wav_obs_merge*10000</span>
-    <span class="c1"># flx_post_doppler, wav_post_doppler = dopplerShift(wav_doppler, new_flx_merge, rv_picked)</span>
-    <span class="n">new_wav</span> <span class="o">=</span> <span class="n">wav_obs_merge</span> <span class="o">*</span> <span class="p">((</span><span class="n">rv_picked</span> <span class="o">/</span> <span class="mf">299792.458</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span>
-
-    <span class="n">rv_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">new_wav</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
-    <span class="n">flx_post_doppler</span> <span class="o">=</span> <span class="n">rv_interp</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">flx_post_doppler</span></div>
-
-    <span class="c1"># return Spectrum1d.from_array(new_wavelength, new_flux)</span>
-    <span class="c1"># # Side effects</span>
-    <span class="c1"># ind_nonan = np.argwhere(~np.isnan(flx_post_doppler))</span>
-    <span class="c1"># wav_obs_merge = wav_obs_merge[ind_nonan[:, 0]]</span>
-    <span class="c1"># flx_obs_merge = flx_obs_merge[ind_nonan[:, 0]]</span>
-    <span class="c1"># err_obs_merge = err_obs_merge[ind_nonan[:, 0]]</span>
-    <span class="c1"># flx_post_doppler = flx_post_doppler[ind_nonan[:, 0]]</span>
-
-    <span class="c1"># return wav_obs_merge, flx_obs_merge, err_obs_merge, flx_post_doppler</span>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="reddening_fct"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct">[docs]</a><span class="k">def</span> <span class="nf">reddening_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">wav_obs_phot</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function</span>
-<span class="sd">    extinction.fm07.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        wav_obs_merge: Wavelength grid of the data (spectroscopy)</span>
-<span class="sd">        wav_obs_phot: Wavelength of the data (photometry)</span>
-<span class="sd">        new_flx_merge: Flux of the interpolated synthetic spectrum (spectroscopy)</span>
-<span class="sd">        new_flx_phot: Flux of the interpolated synthetic spectrum (photometry)</span>
-<span class="sd">        av_picked: Extinction randomly picked by the nested sampling (in mag)</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        new_flx_merge: New flux of the interpolated synthetic spectrum (spectroscopy)</span>
-<span class="sd">        new_flx_phot: New flux of the interpolated synthetic spectrum (photometry)</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">dered_merge</span> <span class="o">=</span> <span class="n">extinction</span><span class="o">.</span><span class="n">fm07</span><span class="p">(</span><span class="n">wav_obs_merge</span> <span class="o">*</span> <span class="mi">10000</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;aa&#39;</span><span class="p">)</span>
-        <span class="n">new_flx_merge</span> <span class="o">*=</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">0.4</span><span class="o">*</span><span class="n">dered_merge</span><span class="p">)</span>
-    <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">)</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-        <span class="n">dered_phot</span> <span class="o">=</span> <span class="n">extinction</span><span class="o">.</span><span class="n">fm07</span><span class="p">(</span><span class="n">wav_obs_phot</span> <span class="o">*</span> <span class="mi">10000</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">,</span> <span class="n">unit</span><span class="o">=</span><span class="s1">&#39;aa&#39;</span><span class="p">)</span>
-        <span class="n">new_flx_phot</span> <span class="o">*=</span> <span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="o">-</span><span class="mf">0.4</span><span class="o">*</span><span class="n">dered_phot</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="vsini_fct"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct">[docs]</a><span class="k">def</span> <span class="nf">vsini_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function</span>
-<span class="sd">    extinction.fm07.</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        wav_obs_merge: Wavelength grid of the data</span>
-<span class="sd">        new_flx_merge: Flux of the interpolated synthetic spectrum</span>
-<span class="sd">        ld_picked: Limd darkening randomly picked by the nested sampling</span>
-<span class="sd">        vsini_picked: v.sin(i) randomly picked by the nested sampling (in km.s-1)</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        new_flx_merge: New flux of the interpolated synthetic spectrum</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Correct irregulatities in the wavelength grid</span>
-    <span class="n">wav_interval</span> <span class="o">=</span> <span class="n">wav_obs_merge</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">wav_obs_merge</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">wav_to_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="nb">min</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">wav_interval</span><span class="p">)</span> <span class="o">*</span> <span class="mi">2</span><span class="o">/</span><span class="mi">3</span><span class="p">)</span>
-    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
-    <span class="n">flx_to_vsini</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">)</span>
-    <span class="c1"># Apply the v.sin(i)</span>
-    <span class="n">new_flx</span> <span class="o">=</span> <span class="n">rotBroad</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">flx_to_vsini</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
-    <span class="n">vsini_interp</span> <span class="o">=</span> <span class="n">interp1d</span><span class="p">(</span><span class="n">wav_to_vsini</span><span class="p">,</span> <span class="n">new_flx</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s2">&quot;extrapolate&quot;</span><span class="p">)</span>
-    <span class="n">new_flx_merge</span> <span class="o">=</span> <span class="n">vsini_interp</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">new_flx_merge</span></div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<div class="viewcode-block" id="modif_spec"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_modif_spec.modif_spec">[docs]</a><span class="k">def</span> <span class="nf">modif_spec</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span>
-               <span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span>
-               <span class="n">wav_obs_phot</span><span class="p">,</span> <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    Modification of the interpolated synthetic spectra with the different extra-grid parameters:</span>
-<span class="sd">        - Re-calibration on the data</span>
-<span class="sd">        - Doppler shifting</span>
-<span class="sd">        - Application of a substellar extinction</span>
-<span class="sd">        - Application of a rotational velocity</span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        global_params: Class containing each parameter</span>
-<span class="sd">        theta: Parameter values randomly picked by the nested sampling</span>
-<span class="sd">        theta_index: Parameter index identificator</span>
-<span class="sd">        wav_obs_merge: Wavelength grid of the data (spectroscopy)</span>
-<span class="sd">        flx_obs_merge: Flux of the data (spectroscopy)</span>
-<span class="sd">        err_obs_merge: Error of the data (spectroscopy)</span>
-<span class="sd">        new_flx_merge: Flux of the interpolated synthetic spectrum (spectroscopy)</span>
-<span class="sd">        wav_obs_phot: Wavelength grid of the data (photometry)</span>
-<span class="sd">        flx_obs_phot: Flux of the data (photometry)</span>
-<span class="sd">        err_obs_phot: Error of the data (photometry)</span>
-<span class="sd">        new_flx_phot: Flux of the interpolated synthetic spectrum (photometry)</span>
-<span class="sd">    Returns:</span>
-<span class="sd">        wav_obs_merge: New wavelength grid of the data (may change with the Doppler shift)</span>
-<span class="sd">        flx_obs_merge: New flux of the data (may change with the Doppler shift)</span>
-<span class="sd">        err_obs_merge: New error of the data (may change with the Doppler shift)</span>
-<span class="sd">        new_flx_merge: New flux of the interpolated synthetic spectrum (spectroscopy)</span>
-<span class="sd">        wav_obs_phot: Wavelength grid of the data (photometry)</span>
-<span class="sd">        flx_obs_phot: Flux of the data (photometry)</span>
-<span class="sd">        err_obs_phot: Error of the data (photometry)</span>
-<span class="sd">        new_flx_phot: New flux of the interpolated synthetic spectrum (photometry)</span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum.</span>
-    <span class="c1"># From the radius and the distance.</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
-            <span class="n">r_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-            <span class="n">r_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
-            <span class="n">d_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span>
-            <span class="n">d_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span>
-                                                  <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">r_picked</span><span class="p">,</span> <span class="n">d_picked</span><span class="p">)</span>
-    <span class="c1"># Analytically</span>
-    <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">ck</span> <span class="o">=</span> <span class="n">calc_ck</span><span class="p">(</span><span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span>
-                                                  <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span>
-                                                  <span class="n">analytic</span><span class="o">=</span><span class="s1">&#39;yes&#39;</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;You need to define a radius AND a distance, or set them both to &quot;NA&quot;&#39;</span><span class="p">)</span>
-        <span class="n">exit</span><span class="p">()</span>
-
-    <span class="c1"># Correction of the radial velocity of the interpolated synthetic spectrum.</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">rv_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;rv&#39;</span><span class="p">)</span>
-            <span class="n">rv_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span> <span class="o">=</span> <span class="n">doppler_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span>
-                                                                                 <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span>
-                                                                                 <span class="n">rv_picked</span><span class="p">)</span>
-
-    <span class="c1"># Application of a synthetic interstellar extinction to the interpolated synthetic spectrum.</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">av_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_av</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;av&#39;</span><span class="p">)</span>
-            <span class="n">av_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span> <span class="o">=</span> <span class="n">reddening_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">wav_obs_phot</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">new_flx_phot</span><span class="p">,</span> <span class="n">av_picked</span><span class="p">)</span>
-
-
-    <span class="c1"># Correction of the rotational velocity of the interpolated synthetic spectrum.</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">vsini_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
-            <span class="n">vsini_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ld_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;ld&#39;</span><span class="p">)</span>
-            <span class="n">ld_picked</span> <span class="o">=</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]]</span>
-
-        <span class="n">new_flx_merge</span> <span class="o">=</span> <span class="n">vsini_fct</span><span class="p">(</span><span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">ld_picked</span><span class="p">,</span> <span class="n">vsini_picked</span><span class="p">)</span>
-
-    <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">==</span> <span class="s2">&quot;NA&quot;</span><span class="p">:</span>
-        <span class="k">pass</span>
-
-    <span class="k">else</span><span class="p">:</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;You need to define a v.sin(i) AND a limb darkening, or set them both to NA&#39;</span><span class="p">)</span>
-        <span class="n">exit</span><span class="p">()</span>
-
-    <span class="k">return</span> <span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">new_flx_merge</span><span class="p">,</span> <span class="n">wav_obs_phot</span><span class="p">,</span> <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">new_flx_phot</span></div>
-</pre></div>
-
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diff --git a/docs/build/html/_modules/ForMoSA/nested_sampling/nested_sampling.html b/docs/build/html/_modules/ForMoSA/nested_sampling/nested_sampling.html
deleted file mode 100644
index 8dd46b2..0000000
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-      <li class="breadcrumb-item active">ForMoSA.nested_sampling.nested_sampling</li>
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-  <h1>Source code for ForMoSA.nested_sampling.nested_sampling</h1><div class="highlight"><pre>
-<span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">nestle</span>
-<span class="kn">import</span> <span class="nn">time</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-
-<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">modif_spec</span>
-<span class="kn">from</span> <span class="nn">nested_sampling.nested_prior_function</span> <span class="kn">import</span> <span class="n">uniform_prior</span><span class="p">,</span> <span class="n">gaussian_prior</span>
-
-
-<div class="viewcode-block" id="loglike"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.loglike">[docs]</a><span class="k">def</span> <span class="nf">loglike</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">global_params</span><span class="p">,</span> <span class="n">for_plot</span><span class="o">=</span><span class="s1">&#39;no&#39;</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    </span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        </span>
-<span class="sd">    Returns:</span>
-<span class="sd">        </span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="c1"># Recovery of the spectroscopy and photometry data</span>
-    <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/spectrum_obs.npz&#39;</span><span class="p">,</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-    <span class="n">wav_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-    <span class="n">flx_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-    <span class="n">err_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
-    <span class="k">if</span> <span class="s1">&#39;obs_pho&#39;</span> <span class="ow">in</span> <span class="n">spectrum_obs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-        <span class="n">wav_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">flx_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">err_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">wav_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">flx_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">err_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-
-    <span class="c1"># Recovery of the spectroscopy and photometry model</span>
-    <span class="n">path_grid_m</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;adapted_grid_merge_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span>
-    <span class="n">path_grid_p</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;adapted_grid_phot_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span>
-    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_m</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-    <span class="n">grid_merge</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
-    <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_p</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-    <span class="n">grid_phot</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
-    <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-    <span class="c1"># Calculation of the likelihood for each sub-spectrum defined by the parameter &#39;wav_fit&#39;</span>
-    <span class="k">for</span> <span class="n">ns_u_ind</span><span class="p">,</span> <span class="n">ns_u</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">wav_fit</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;/&#39;</span><span class="p">)):</span>
-        <span class="n">min_ns_u</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ns_u</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)[</span><span class="mi">0</span><span class="p">])</span>
-        <span class="n">max_ns_u</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="n">ns_u</span><span class="o">.</span><span class="n">split</span><span class="p">(</span><span class="s1">&#39;,&#39;</span><span class="p">)[</span><span class="mi">1</span><span class="p">])</span>
-        <span class="n">ind_grid_merge_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
-        <span class="n">ind_grid_phot_sel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">]</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
-
-        <span class="c1"># Cutting of the grid on the wavelength grid defined by the parameter &#39;wav_fit&#39;</span>
-        <span class="n">grid_merge_cut</span> <span class="o">=</span> <span class="n">grid_merge</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">][</span><span class="n">ind_grid_merge_sel</span><span class="p">])</span>
-        <span class="n">grid_phot_cut</span> <span class="o">=</span> <span class="n">grid_phot</span><span class="o">.</span><span class="n">sel</span><span class="p">(</span><span class="n">wavelength</span><span class="o">=</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">][</span><span class="n">ind_grid_phot_sel</span><span class="p">])</span>
-
-        <span class="c1"># Interpolation of the grid at the theta parameters set</span>
-        <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="n">grid_merge_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="n">grid_phot_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="n">grid_merge_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="n">grid_phot_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">elif</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="n">grid_merge_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="n">grid_phot_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="n">grid_merge_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                          <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge_cut</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot_cut</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="n">grid_phot_cut</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                        <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot_cut</span> <span class="o">=</span> <span class="p">[]</span>
-
-
-        <span class="c1"># Re-merging of the data and interpolated synthetic spectrum to a wavelength grid defined by the parameter &#39;wav_fit&#39;</span>
-        <span class="n">ind_merge</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_obs_merge</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_obs_merge</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
-        <span class="n">wav_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">wav_obs_phot</span><span class="p">,</span><span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="n">ind_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">((</span><span class="n">wav_obs_phot</span> <span class="o">&gt;=</span> <span class="n">min_ns_u</span><span class="p">)</span> <span class="o">&amp;</span> <span class="p">(</span><span class="n">wav_obs_phot</span> <span class="o">&lt;=</span> <span class="n">max_ns_u</span><span class="p">))</span>
-        <span class="k">if</span> <span class="n">ns_u_ind</span> <span class="o">==</span> <span class="mi">0</span><span class="p">:</span>
-            <span class="n">wav_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">wav_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]</span>
-            <span class="n">flx_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">flx_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]</span>
-            <span class="n">err_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">err_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]</span>
-            <span class="n">flx_mod_merge_ns_u</span> <span class="o">=</span> <span class="n">flx_mod_merge_cut</span>
-            <span class="n">wav_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">wav_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]</span>
-            <span class="n">flx_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">flx_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]</span>
-            <span class="n">err_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">err_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]</span>
-            <span class="n">flx_mod_phot_ns_u</span> <span class="o">=</span> <span class="n">flx_mod_phot_cut</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">wav_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_merge_ns_u</span><span class="p">,</span> <span class="n">wav_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]))</span>
-            <span class="n">flx_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_merge_ns_u</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]))</span>
-            <span class="n">err_obs_merge_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_merge_ns_u</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">[</span><span class="n">ind_merge</span><span class="p">]))</span>
-            <span class="n">flx_mod_merge_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_mod_merge_ns_u</span><span class="p">,</span> <span class="n">flx_mod_merge_cut</span><span class="p">))</span>
-            <span class="n">wav_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">wav_obs_phot_ns_u</span><span class="p">,</span> <span class="n">wav_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]))</span>
-            <span class="n">flx_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_obs_phot_ns_u</span><span class="p">,</span> <span class="n">flx_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]))</span>
-            <span class="n">err_obs_phot_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">err_obs_phot_ns_u</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">[</span><span class="n">ind_phot</span><span class="p">]))</span>
-            <span class="n">flx_mod_phot_ns_u</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">flx_mod_phot_ns_u</span><span class="p">,</span> <span class="n">flx_mod_phot_cut</span><span class="p">))</span>
-
-    <span class="c1"># Modification of the synthetic spectrum with the extra-grid parameters</span>
-    <span class="n">modif_spec_chi2</span> <span class="o">=</span> <span class="n">modif_spec</span><span class="p">(</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span>
-                                 <span class="n">wav_obs_merge_ns_u</span><span class="p">,</span>  <span class="n">flx_obs_merge_ns_u</span><span class="p">,</span>  <span class="n">err_obs_merge_ns_u</span><span class="p">,</span>  <span class="n">flx_mod_merge_ns_u</span><span class="p">,</span>
-                                 <span class="n">wav_obs_phot_ns_u</span><span class="p">,</span>  <span class="n">flx_obs_phot_ns_u</span><span class="p">,</span> <span class="n">err_obs_phot_ns_u</span><span class="p">,</span>  <span class="n">flx_mod_phot_ns_u</span><span class="p">)</span>
-
-    <span class="c1">#print(modif_spec_chi2)</span>
-    <span class="c1"># Merging the spectroscopy with photometry in order to calculate the likelihood</span>
-    <span class="c1">#flx_obs_chi2 = np.array(np.concatenate((modif_spec_chi2[1], flx_obs_phot_ns_u)),dtype=float)</span>
-    <span class="c1">#err_obs_chi2 = np.array(np.concatenate((modif_spec_chi2[2], err_obs_phot_ns_u)),dtype=float)</span>
-    <span class="c1">#flx_mod_chi2 = np.array(np.concatenate((modif_spec_chi2[3], modif_spec_chi2[5])),dtype=float)</span>
-    <span class="n">flx_obs_chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">5</span><span class="p">])),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">err_obs_chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">6</span><span class="p">])),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="n">flx_mod_chi2</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">modif_spec_chi2</span><span class="p">[</span><span class="mi">7</span><span class="p">])),</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-    <span class="c1">#print(&#39;flx, mod, chi2&#39;, flx_mod_chi2)</span>
-    <span class="c1">#print(&#39;array-0&#39;,modif_spec_chi2[0])</span>
-    <span class="c1">#print(&#39;array-1&#39;,modif_spec_chi2[1])</span>
-    <span class="c1">#print(&#39;array-2&#39;,modif_spec_chi2[2])</span>
-    <span class="c1">#print(&#39;array-3&#39;,modif_spec_chi2[3])</span>
-    <span class="c1">#print(&#39;array-4&#39;,modif_spec_chi2[4])</span>
-    <span class="c1">#print(&#39;array-5&#39;,modif_spec_chi2[5])</span>
-    <span class="c1"># Calculation of the chi2 (used to calculate the likelihood)</span>
-    <span class="n">chisq</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(((</span><span class="n">flx_obs_chi2</span> <span class="o">-</span> <span class="n">flx_mod_chi2</span><span class="p">)</span> <span class="o">/</span> <span class="n">err_obs_chi2</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">)</span>
-    <span class="c1">#print(chisq)</span>
-
-    <span class="k">if</span> <span class="n">for_plot</span> <span class="o">==</span> <span class="s1">&#39;no&#39;</span><span class="p">:</span>
-        <span class="k">return</span> <span class="o">-</span><span class="n">chisq</span><span class="o">/</span><span class="mf">2.</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="k">return</span> <span class="n">modif_spec_chi2</span></div>
-
-
-<div class="viewcode-block" id="prior_transform"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.prior_transform">[docs]</a><span class="k">def</span> <span class="nf">prior_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">lim_param_grid</span><span class="p">,</span> <span class="n">global_params</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    </span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        </span>
-<span class="sd">    Returns:</span>
-<span class="sd">        </span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">prior</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_par1</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="n">prior_par1</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">prior_par1</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par1</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_par2</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="n">prior_par2</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">prior_par2</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par2</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_par3</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="n">prior_par3</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">prior_par3</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par3</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_par4</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="n">prior_par4</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">prior_par4</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par4</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">prior_par5</span> <span class="o">&lt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">0</span><span class="p">]:</span>
-                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-            <span class="k">elif</span> <span class="n">prior_par5</span> <span class="o">&gt;</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]:</span>
-                <span class="n">prior_par5</span> <span class="o">=</span> <span class="n">lim_param_grid</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_par5</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_r</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_r</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_r</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_d</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;d&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_d</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_d</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_d</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_d</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_rv</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;rv&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_rv</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_rv</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_rv</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_av</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;av&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_av</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_av</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_av</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_av</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_vsini</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_vsini</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_vsini</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">prior_law</span> <span class="o">=</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="k">if</span> <span class="n">prior_law</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">ind_theta_ld</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;ld&#39;</span><span class="p">)</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;uniform&#39;</span><span class="p">:</span>
-                <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">uniform_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="k">if</span> <span class="n">prior_law</span> <span class="o">==</span> <span class="s1">&#39;gaussian&#39;</span><span class="p">:</span>
-                <span class="n">prior_ld</span> <span class="o">=</span> <span class="n">gaussian_prior</span><span class="p">([</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">2</span><span class="p">])],</span> <span class="n">theta</span><span class="p">[</span><span class="n">ind_theta_ld</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">0</span><span class="p">]])</span>
-            <span class="n">prior</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">prior_ld</span><span class="p">)</span>
-
-    <span class="k">return</span> <span class="n">prior</span></div>
-
-
-<div class="viewcode-block" id="launch_nested_sampling"><a class="viewcode-back" href="../../../nested_sampling.html#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">[docs]</a><span class="k">def</span> <span class="nf">launch_nested_sampling</span><span class="p">(</span><span class="n">global_params</span><span class="p">):</span>
-    <span class="sd">&quot;&quot;&quot;</span>
-<span class="sd">    </span>
-
-<span class="sd">    Args:</span>
-<span class="sd">        </span>
-<span class="sd">    Returns:</span>
-<span class="sd">        </span>
-
-<span class="sd">    Author: Simon Petrus</span>
-<span class="sd">    &quot;&quot;&quot;</span>
-    <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-
-    <span class="c1"># Count the number of free parameters and identify the parameter position in theta</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span>
-        <span class="n">lim_param_grid</span> <span class="o">=</span> <span class="p">[[</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par1&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)]]</span>
-    <span class="k">else</span><span class="p">:</span>
-        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">lim_param_grid</span> <span class="o">=</span> <span class="p">[]</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par2&#39;</span><span class="p">)</span>
-        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par2&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par3&#39;</span><span class="p">)</span>
-        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par3&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par4&#39;</span><span class="p">)</span>
-        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par4&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par5&#39;</span><span class="p">)</span>
-        <span class="n">lim_param_grid</span><span class="o">.</span><span class="n">append</span><span class="p">([</span><span class="nb">min</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">),</span> <span class="nb">max</span><span class="p">(</span><span class="n">ds</span><span class="p">[</span><span class="s1">&#39;par5&#39;</span><span class="p">]</span><span class="o">.</span><span class="n">values</span><span class="p">)])</span>
-    <span class="n">n_free_parameters</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">ds</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;key&#39;</span><span class="p">])</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;rv&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;av&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-        <span class="n">n_free_parameters</span> <span class="o">+=</span> <span class="mi">1</span>
-        <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;ld&#39;</span><span class="p">)</span>
-    <span class="n">theta_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">theta_index</span><span class="p">)</span>
-
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">==</span> <span class="s1">&#39;nestle&#39;</span><span class="p">:</span>
-        <span class="n">tmpstot1</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span>
-        <span class="n">loglike_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">loglike</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">global_params</span><span class="p">)</span>
-        <span class="n">prior_transform_gp</span> <span class="o">=</span> <span class="k">lambda</span> <span class="n">theta</span><span class="p">:</span> <span class="n">prior_transform</span><span class="p">(</span><span class="n">theta</span><span class="p">,</span> <span class="n">theta_index</span><span class="p">,</span> <span class="n">lim_param_grid</span><span class="p">,</span> <span class="n">global_params</span><span class="p">)</span>
-        <span class="n">result</span> <span class="o">=</span> <span class="n">nestle</span><span class="o">.</span><span class="n">sample</span><span class="p">(</span><span class="n">loglike_gp</span><span class="p">,</span> <span class="n">prior_transform_gp</span><span class="p">,</span> <span class="n">n_free_parameters</span><span class="p">,</span> <span class="n">callback</span><span class="o">=</span><span class="n">nestle</span><span class="o">.</span><span class="n">print_progress</span><span class="p">,</span><span class="n">npoints</span><span class="o">=</span><span class="nb">int</span><span class="p">(</span><span class="nb">float</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">npoint</span><span class="p">)))</span>
-        <span class="n">tmpstot2</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">time</span><span class="p">()</span><span class="o">-</span><span class="n">tmpstot1</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n</span><span class="s1">&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;########     The code spent &#39;</span> <span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="n">tmpstot2</span><span class="p">)</span> <span class="o">+</span> <span class="s1">&#39; sec to run   ########&#39;</span><span class="p">)</span>
-        <span class="nb">print</span><span class="p">(</span><span class="n">result</span><span class="o">.</span><span class="n">summary</span><span class="p">())</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;</span><span class="se">\n\n</span><span class="s1">&#39;</span><span class="p">)</span>
-    
-    <span class="k">if</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">==</span> <span class="s1">&#39;dynesty&#39;</span><span class="p">:</span>
-        <span class="kn">from</span> <span class="nn">dynesty</span> <span class="kn">import</span> <span class="n">NestedSampler</span>
-
-        <span class="c1"># initialize our nested sampler</span>
-        <span class="c1">#sampler = NestedSampler(loglike, ptform, ndim)</span>
-
-    <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;wb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
-        <span class="n">pickle</span><span class="o">.</span><span class="n">dump</span><span class="p">(</span><span class="n">result</span><span class="p">,</span> <span class="n">f1</span><span class="p">)</span>
-    
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39; &#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Voilà, on est prêt&#39;</span><span class="p">)</span>
-
-    <span class="k">return</span> </div>
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">&#39;__main__&#39;</span><span class="p">:</span>
-    <span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
-
-    <span class="c1"># USER configuration path</span>
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Configuration of environment&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;Where is your configuration file?&#39;</span><span class="p">)</span>
-    <span class="n">config_file_path</span> <span class="o">=</span> <span class="nb">input</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">()</span>
-
-    <span class="c1"># CONFIG_FILE reading and defining global parameters</span>
-    <span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  <span class="c1"># To access any param.: global_params.parameter_name</span>
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;-&gt; Nested sampling&#39;</span><span class="p">)</span>
-    <span class="nb">print</span><span class="p">()</span>
-    <span class="n">launch_nested_sampling</span><span class="p">(</span><span class="n">global_params</span><span class="p">)</span>
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
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-  </script> 
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diff --git a/docs/build/html/_modules/ForMoSA/plotting/plotting_class.html b/docs/build/html/_modules/ForMoSA/plotting/plotting_class.html
deleted file mode 100644
index ba6027c..0000000
--- a/docs/build/html/_modules/ForMoSA/plotting/plotting_class.html
+++ /dev/null
@@ -1,816 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="English" >
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>ForMoSA.plotting.plotting_class &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="../../../_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="../../../_static/css/theme.css" type="text/css" />
-    <link rel="shortcut icon" href="../../../_static/favicon.ico"/>
-  <!--[if lt IE 9]>
-    <script src="../../../_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script data-url_root="../../../" id="documentation_options" src="../../../_static/documentation_options.js"></script>
-        <script src="../../../_static/jquery.js"></script>
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-    <link rel="index" title="Index" href="../../../genindex.html" />
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-              <div class="version">
-                1.0.3
-              </div>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="../../../search.html" method="get">
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-              <ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../installation.html">Installation</a></li>
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-<li class="toctree-l1"><a class="reference internal" href="../../../api.html">ForMoSA API</a></li>
-</ul>
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-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
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-      <li class="breadcrumb-item active">ForMoSA.plotting.plotting_class</li>
-      <li class="wy-breadcrumbs-aside">
-      </li>
-  </ul>
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-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <h1>Source code for ForMoSA.plotting.plotting_class</h1><div class="highlight"><pre>
-<span></span>
-<span class="kn">from</span> <span class="nn">__future__</span> <span class="kn">import</span> <span class="n">print_function</span><span class="p">,</span> <span class="n">division</span>
-<span class="kn">import</span> <span class="nn">sys</span><span class="o">,</span> <span class="nn">os</span><span class="o">,</span> <span class="nn">yaml</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">import</span> <span class="nn">matplotlib</span>
-<span class="kn">from</span> <span class="nn">matplotlib.figure</span> <span class="kn">import</span> <span class="n">Figure</span>
-<span class="kn">import</span> <span class="nn">corner</span>
-<span class="kn">import</span> <span class="nn">xarray</span> <span class="k">as</span> <span class="nn">xr</span>
-<span class="kn">import</span> <span class="nn">pickle</span>
-
-<span class="c1"># Import ForMoSA</span>
-<span class="kn">from</span> <span class="nn">main_utilities</span> <span class="kn">import</span> <span class="n">GlobFile</span>
-<span class="kn">from</span> <span class="nn">nested_sampling.nested_modif_spec</span> <span class="kn">import</span> <span class="n">modif_spec</span>
-
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-<div class="viewcode-block" id="ComplexRadar"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar">[docs]</a><span class="k">class</span> <span class="nc">ComplexRadar</span><span class="p">():</span>
-    <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    Original from Damian Cummins: https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb</span>
-<span class="sd">    </span>
-<span class="sd">    Adapted by: P. Palma-Bifani</span>
-<span class="sd">    &#39;&#39;&#39;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">fig</span><span class="p">,</span> <span class="n">variables</span><span class="p">,</span> <span class="n">ranges</span><span class="p">,</span> <span class="n">n_ordinate_levels</span><span class="o">=</span><span class="mi">6</span><span class="p">):</span>
-        <span class="n">angles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">360</span><span class="p">,</span> <span class="mf">360.</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">))</span>
-
-        <span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="n">fig</span><span class="o">.</span><span class="n">add_axes</span><span class="p">([</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.1</span><span class="p">,</span><span class="mf">0.9</span><span class="p">,</span><span class="mf">0.9</span><span class="p">],</span> <span class="n">polar</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s2">&quot;axes</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">i</span><span class="p">))</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">variables</span><span class="p">))]</span>
-
-        <span class="n">l</span><span class="p">,</span> <span class="n">text</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">set_thetagrids</span><span class="p">(</span><span class="n">angles</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">variables</span><span class="p">)</span>
-
-        <span class="p">[[</span><span class="n">txt</span><span class="o">.</span><span class="n">set_fontweight</span><span class="p">(</span><span class="s1">&#39;bold&#39;</span><span class="p">),</span>
-              <span class="n">txt</span><span class="o">.</span><span class="n">set_fontsize</span><span class="p">(</span><span class="mi">12</span><span class="p">),</span>
-              <span class="n">txt</span><span class="o">.</span><span class="n">set_position</span><span class="p">((</span><span class="mi">0</span><span class="p">,</span><span class="o">-</span><span class="mf">0.2</span><span class="p">))]</span> <span class="k">for</span> <span class="n">txt</span> <span class="ow">in</span> <span class="n">text</span><span class="p">]</span>
-
-        <span class="k">for</span> <span class="n">ax</span> <span class="ow">in</span> <span class="n">axes</span><span class="p">[</span><span class="mi">1</span><span class="p">:]:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">patch</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">grid</span><span class="p">(</span><span class="s2">&quot;off&quot;</span><span class="p">)</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">xaxis</span><span class="o">.</span><span class="n">set_visible</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">ax</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">axes</span><span class="p">):</span>
-            <span class="n">grid</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">linspace</span><span class="p">(</span><span class="o">*</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">num</span><span class="o">=</span><span class="n">n_ordinate_levels</span><span class="p">)</span>
-            <span class="n">gridlabel</span> <span class="o">=</span> <span class="p">[</span><span class="s2">&quot;</span><span class="si">{}</span><span class="s2">&quot;</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="nb">round</span><span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="mi">2</span><span class="p">))</span> <span class="k">for</span> <span class="n">x</span> <span class="ow">in</span> <span class="n">grid</span><span class="p">]</span>
-
-            <span class="n">gridlabel</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="s2">&quot;&quot;</span> <span class="c1"># clean up origin</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_rgrids</span><span class="p">(</span><span class="n">grid</span><span class="p">,</span> <span class="n">labels</span><span class="o">=</span><span class="n">gridlabel</span><span class="p">,</span><span class="n">angle</span><span class="o">=</span><span class="n">angles</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">(</span><span class="o">*</span><span class="n">ranges</span><span class="p">[</span><span class="n">i</span><span class="p">])</span>
-        
-        <span class="c1"># variables for plotting</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">angle</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">angles</span><span class="p">,</span> <span class="n">angles</span><span class="p">[</span><span class="mi">0</span><span class="p">]])</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span> <span class="o">=</span> <span class="n">ranges</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span> <span class="o">=</span> <span class="n">axes</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
-    <span class="k">def</span> <span class="nf">plot</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-        <span class="n">sdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata</span><span class="p">,</span> <span class="n">sdata</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
-
-    <span class="k">def</span> <span class="nf">fill</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-        <span class="n">sdata</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">data</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">fill</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata</span><span class="p">,</span> <span class="n">sdata</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
-    
-    <span class="k">def</span> <span class="nf">fill_between</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">list_down</span><span class="p">,</span> <span class="n">list_up</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">):</span>
-        <span class="n">sdata_down</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">list_down</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
-        <span class="n">sdata_up</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">scale_data</span><span class="p">(</span><span class="n">list_up</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ranges</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">angle</span><span class="p">,</span><span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata_down</span><span class="p">,</span><span class="n">sdata_down</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="n">np</span><span class="o">.</span><span class="n">r_</span><span class="p">[</span><span class="n">sdata_up</span><span class="p">,</span><span class="n">sdata_up</span><span class="p">[</span><span class="mi">0</span><span class="p">]],</span> <span class="o">*</span><span class="n">args</span><span class="p">,</span> <span class="o">**</span><span class="n">kw</span><span class="p">)</span>
-
-<div class="viewcode-block" id="ComplexRadar.scale_data"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.ComplexRadar.scale_data">[docs]</a>    <span class="k">def</span> <span class="nf">scale_data</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">,</span> <span class="n">ranges</span><span class="p">):</span>
-        <span class="sd">&quot;&quot;&quot;scales data[1:] to ranges[0]&quot;&quot;&quot;</span>
-        <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
-            <span class="k">assert</span> <span class="p">(</span><span class="n">y1</span> <span class="o">&lt;=</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">or</span> <span class="p">(</span><span class="n">y2</span> <span class="o">&lt;=</span> <span class="n">d</span> <span class="o">&lt;=</span> <span class="n">y1</span><span class="p">)</span>
-        <span class="n">x1</span><span class="p">,</span> <span class="n">x2</span> <span class="o">=</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">d</span> <span class="o">=</span> <span class="n">data</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">sdata</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span><span class="p">]</span>
-        <span class="k">for</span> <span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">ranges</span><span class="p">[</span><span class="mi">1</span><span class="p">:]):</span>
-            <span class="k">if</span> <span class="n">y1</span> <span class="o">&gt;</span> <span class="n">y2</span><span class="p">:</span>
-                <span class="n">d</span> <span class="o">=</span> <span class="n">_invert</span><span class="p">(</span><span class="n">d</span><span class="p">,</span> <span class="p">(</span><span class="n">y1</span><span class="p">,</span> <span class="n">y2</span><span class="p">))</span>
-                <span class="n">y1</span><span class="p">,</span> <span class="n">y2</span> <span class="o">=</span> <span class="n">y2</span><span class="p">,</span> <span class="n">y1</span>
-            <span class="n">sdata</span><span class="o">.</span><span class="n">append</span><span class="p">((</span><span class="n">d</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span> <span class="o">/</span> <span class="p">(</span><span class="n">y2</span><span class="o">-</span><span class="n">y1</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="n">x2</span> <span class="o">-</span> <span class="n">x1</span><span class="p">)</span> <span class="o">+</span> <span class="n">x1</span><span class="p">)</span>
-        <span class="k">return</span> <span class="n">sdata</span></div></div>
-
-
-
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-<span class="c1"># ----------------------------------------------------------------------------------------------------------------------</span>
-<div class="viewcode-block" id="PlottingForMoSA"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA">[docs]</a><span class="k">class</span> <span class="nc">PlottingForMoSA</span><span class="p">():</span>
-    <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">    Here all the plotting functionalities of ForMoSA to see the </span>
-
-<span class="sd">    Author: Paulina Palma-Bifani </span>
-<span class="sd">    &#39;&#39;&#39;</span>
-
-    <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">config_file_path</span><span class="p">,</span> <span class="n">color_out</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        Plotting class initializer</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        
-        <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span> <span class="o">=</span> <span class="n">GlobFile</span><span class="p">(</span><span class="n">config_file_path</span><span class="p">)</span>  
-        <span class="bp">self</span><span class="o">.</span><span class="n">color_out</span>     <span class="o">=</span> <span class="n">color_out</span>
-
-
-
-    <span class="k">def</span> <span class="nf">_get_posteriors</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        Function to get the posteriors, including luminosity derivation and corvengence parameters logz</span>
-
-<span class="sd">        (Adapted from Simon Petrus plotting functions)</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">open_pic</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">open_pic</span><span class="p">)</span>
-            <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">samples</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">weights</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">weights</span>
-
-            <span class="c1"># To test the quality of the fit</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sample_logz</span>    <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logz&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sample_logzerr</span> <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;logzerr&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">sample_h</span>       <span class="o">=</span> <span class="nb">round</span><span class="p">(</span><span class="n">result</span><span class="p">[</span><span class="s1">&#39;h&#39;</span><span class="p">],</span><span class="mi">1</span><span class="p">)</span>
-            <span class="bp">self</span><span class="o">.</span><span class="n">outputs_string</span> <span class="o">=</span> <span class="s1">&#39;logz = &#39;</span><span class="o">+</span> <span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_logz</span><span class="p">)</span><span class="o">+</span><span class="s1">&#39; ± &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_logzerr</span><span class="p">)</span><span class="o">+</span> <span class="s1">&#39; ; h = &#39;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sample_h</span><span class="p">)</span>
-        
-        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">model_path</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-        <span class="n">attrs</span> <span class="o">=</span> <span class="n">ds</span><span class="o">.</span><span class="n">attrs</span>
-        <span class="n">extra_parameters</span> <span class="o">=</span> <span class="p">[[</span><span class="s1">&#39;r&#39;</span><span class="p">,</span> <span class="s1">&#39;R&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(R$\mathrm{_</span><span class="si">{Jup}</span><span class="s1">}$)&#39;</span><span class="p">],</span>
-                            <span class="p">[</span><span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;d&#39;</span><span class="p">,</span> <span class="s1">&#39;(pc)&#39;</span><span class="p">],</span>
-                            <span class="p">[</span><span class="s1">&#39;rv&#39;</span><span class="p">,</span> <span class="s1">&#39;RV&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(km.s$\mathrm{^{-1}}$)&#39;</span><span class="p">],</span>
-                            <span class="p">[</span><span class="s1">&#39;av&#39;</span><span class="p">,</span> <span class="s1">&#39;Av&#39;</span><span class="p">,</span> <span class="s1">&#39;(mag)&#39;</span><span class="p">],</span>
-                            <span class="p">[</span><span class="s1">&#39;vsini&#39;</span><span class="p">,</span> <span class="s1">&#39;v.sin(i)&#39;</span><span class="p">,</span> <span class="sa">r</span><span class="s1">&#39;(km.s$\mathrm{^{-1}}$)&#39;</span><span class="p">],</span>
-                            <span class="p">[</span><span class="s1">&#39;ld&#39;</span><span class="p">,</span> <span class="s1">&#39;limb darkening&#39;</span><span class="p">,</span> <span class="s1">&#39;&#39;</span><span class="p">]</span>
-                            <span class="p">]</span>
-
-        <span class="n">tot_list_param_title</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">theta_index</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par1</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par1&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par2</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par2&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par3&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par4&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;title&#39;</span><span class="p">][</span><span class="mi">4</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;unit&#39;</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;par5&#39;</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;r&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">d</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;d&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">rv</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">2</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;rv&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">av</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">3</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;av&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">vsini</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">4</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;vsini&#39;</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span> <span class="ow">and</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ld</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">!=</span> <span class="s1">&#39;constant&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="s1">&#39; &#39;</span> <span class="o">+</span> <span class="n">extra_parameters</span><span class="p">[</span><span class="mi">5</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-            <span class="n">theta_index</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="s1">&#39;ld&#39;</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">theta_index</span><span class="p">)</span>
-
-        <span class="n">posterior_to_plot</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">res</span><span class="p">,</span> <span class="n">results</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="n">samples</span><span class="p">):</span>
-            <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-                <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">==</span> <span class="s2">&quot;constant&quot;</span><span class="p">:</span>
-                    <span class="n">r_picked</span> <span class="o">=</span> <span class="nb">float</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">ind_theta_r</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span> <span class="o">==</span> <span class="s1">&#39;r&#39;</span><span class="p">)</span>
-                    <span class="n">r_picked</span> <span class="o">=</span> <span class="n">results</span><span class="p">[</span><span class="n">ind_theta_r</span><span class="p">[</span><span class="mi">0</span><span class="p">]]</span>
-                <span class="n">lum</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">log10</span><span class="p">(</span><span class="mi">4</span> <span class="o">*</span> <span class="n">np</span><span class="o">.</span><span class="n">pi</span> <span class="o">*</span> <span class="p">(</span><span class="n">r_picked</span> <span class="o">*</span> <span class="mf">69911000.</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span> <span class="o">*</span> <span class="n">results</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">**</span> <span class="mi">4</span> <span class="o">*</span> <span class="mf">5.670e-8</span> <span class="o">/</span> <span class="mf">3.83e26</span><span class="p">)</span>
-                <span class="n">results</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">concatenate</span><span class="p">((</span><span class="n">results</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">lum</span><span class="p">)))</span>
-            <span class="n">posterior_to_plot</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">results</span><span class="p">)</span>
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">r</span> <span class="o">!=</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="n">tot_list_param_title</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;log(L/L$\mathrm{_{\odot}}$)&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">posterior_to_plot</span><span class="p">)</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span> <span class="o">=</span> <span class="n">tot_list_param_title</span>
-
-
-
-
-<div class="viewcode-block" id="PlottingForMoSA.plot_corner"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner">[docs]</a>    <span class="k">def</span> <span class="nf">plot_corner</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">levels_sig</span><span class="o">=</span><span class="p">[</span><span class="mf">0.997</span><span class="p">,</span> <span class="mf">0.95</span><span class="p">,</span> <span class="mf">0.68</span><span class="p">],</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span><span class="p">,</span> <span class="n">quantiles</span><span class="o">=</span><span class="p">(</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">),</span> <span class="n">burn_in</span><span class="o">=</span><span class="mi">0</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        See the corner plots</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Corner plot&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
-
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">corner</span><span class="o">.</span><span class="n">corner</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="n">burn_in</span><span class="p">:],</span>
-                        <span class="n">weights</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">weights</span><span class="p">[</span><span class="n">burn_in</span><span class="p">:],</span>
-                        <span class="n">labels</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">,</span>
-                        <span class="nb">range</span><span class="o">=</span><span class="p">[</span><span class="mf">0.999999</span> <span class="k">for</span> <span class="n">p</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">],</span>
-                        <span class="n">levels</span><span class="o">=</span><span class="n">levels_sig</span><span class="p">,</span>
-                        <span class="n">bins</span><span class="o">=</span><span class="n">bins</span><span class="p">,</span>
-                        <span class="n">smooth</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
-                        <span class="n">quantiles</span><span class="o">=</span><span class="n">quantiles</span><span class="p">,</span>
-                        <span class="n">top_ticks</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                        <span class="n">plot_datapoints</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
-                        <span class="n">plot_density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">plot_contours</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">fill_contours</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">show_titles</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
-                        <span class="n">title_fmt</span><span class="o">=</span><span class="s1">&#39;.2f&#39;</span><span class="p">,</span>
-                        <span class="n">title_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">),</span>
-                        <span class="n">contour_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">colors</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">linewidths</span><span class="o">=</span><span class="mf">0.7</span><span class="p">),</span>
-                        <span class="n">pcolor_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">color</span><span class="o">=</span><span class="s1">&#39;red&#39;</span><span class="p">),</span>
-                        <span class="n">label_kwargs</span><span class="o">=</span><span class="nb">dict</span><span class="p">(</span><span class="n">fontsize</span><span class="o">=</span><span class="mi">10</span><span class="p">))</span>
-
-        <span class="n">fig</span><span class="o">.</span><span class="n">supxlabel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">outputs_string</span><span class="p">,</span> <span class="n">va</span><span class="o">=</span><span class="s1">&#39;top&#39;</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">fig</span></div>
-
-
-<div class="viewcode-block" id="PlottingForMoSA.plot_chains"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains">[docs]</a>    <span class="k">def</span> <span class="nf">plot_chains</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">15</span><span class="p">)):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        To check the convergence of the chains</span>
-
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Posteriors chains for each parameter&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
-        
-        <span class="n">col</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">0</span><span class="p">][:])</span><span class="o">/</span><span class="mi">2</span><span class="p">)</span><span class="o">+</span><span class="nb">int</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">0</span><span class="p">][:])</span><span class="o">%</span><span class="mi">2</span><span class="p">)</span>
-        <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="n">col</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-
-        <span class="n">n</span><span class="o">=</span><span class="mi">0</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">col</span><span class="p">):</span>
-            <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">2</span><span class="p">):</span>
-                <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[:,</span><span class="n">n</span><span class="p">],</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
-                <span class="n">axs</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="n">j</span><span class="p">]</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">[</span><span class="n">n</span><span class="p">])</span>
-                <span class="k">if</span> <span class="n">n</span> <span class="o">==</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">)</span><span class="o">-</span><span class="mi">1</span><span class="p">:</span>
-                    <span class="k">break</span>
-                <span class="k">else</span><span class="p">:</span>
-                    <span class="n">n</span><span class="o">+=</span><span class="mi">1</span>
-        
-        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">axs</span></div>
-
-
-<div class="viewcode-block" id="PlottingForMoSA.plot_radar"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar">[docs]</a>    <span class="k">def</span> <span class="nf">plot_radar</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">ranges</span><span class="p">,</span><span class="n">label</span><span class="o">=</span><span class="s1">&#39;&#39;</span><span class="p">,</span><span class="n">quantiles</span><span class="o">=</span><span class="p">[</span><span class="mf">0.16</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">,</span> <span class="mf">0.84</span><span class="p">],</span><span class="n">chiffres</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">,</span><span class="mi">2</span><span class="p">]):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        To check overall the distribution of the parameters </span>
-
-<span class="sd">        Inputs:</span>
-<span class="sd">        ranges</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Radar plot&#39;</span><span class="p">)</span>
-
-        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
-
-        <span class="n">list_posteriors</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">list_uncert_down</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="n">list_uncert_up</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">l</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[</span><span class="mi">1</span><span class="p">,:])):</span>
-            <span class="n">q16</span><span class="p">,</span> <span class="n">q50</span><span class="p">,</span> <span class="n">q84</span> <span class="o">=</span> <span class="n">corner</span><span class="o">.</span><span class="n">quantile</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">posterior_to_plot</span><span class="p">[:,</span><span class="n">l</span><span class="p">],</span> <span class="n">quantiles</span><span class="p">)</span>
-            
-            <span class="n">list_posteriors</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">q50</span><span class="p">)</span>
-            <span class="n">list_uncert_down</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">q16</span><span class="p">)</span>
-            <span class="n">list_uncert_up</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">q84</span><span class="p">)</span>
-
-        <span class="n">fig1</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span> <span class="mi">6</span><span class="p">))</span>
-        <span class="n">radar</span> <span class="o">=</span> <span class="n">ComplexRadar</span><span class="p">(</span><span class="n">fig1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">posteriors_names</span><span class="p">,</span> <span class="n">ranges</span><span class="p">)</span>
-
-        <span class="n">radar</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">list_posteriors</span><span class="p">,</span> <span class="s1">&#39;o-&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="n">label</span><span class="p">)</span>
-        <span class="n">radar</span><span class="o">.</span><span class="n">fill_between</span><span class="p">(</span><span class="n">list_uncert_down</span><span class="p">,</span><span class="n">list_uncert_up</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-
-        <span class="n">radar</span><span class="o">.</span><span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">loc</span><span class="o">=</span><span class="s1">&#39;center&#39;</span><span class="p">,</span> <span class="n">bbox_to_anchor</span><span class="o">=</span><span class="p">(</span><span class="mf">0.5</span><span class="p">,</span> <span class="o">-</span><span class="mf">0.20</span><span class="p">),</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">ncol</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
-        
-        <span class="k">return</span> <span class="n">fig1</span><span class="p">,</span> <span class="n">radar</span><span class="o">.</span><span class="n">ax</span></div>
-
-
-    <span class="k">def</span> <span class="nf">_get_spectra</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">theta</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        To get the data and best model asociated </span>
-<span class="sd">        Use numba: https://numba.pydata.org/</span>
-
-<span class="sd">        (Adapted from Simon Petrus)</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">_get_posteriors</span><span class="p">()</span>
-        <span class="c1">#def get_spec(theta, theta_index, global_params, for_plot=&#39;no&#39;):</span>
-        <span class="c1"># Recovery of the spectroscopy and photometry data</span>
-        <span class="n">spectrum_obs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/spectrum_obs.npz&#39;</span><span class="p">,</span> <span class="n">allow_pickle</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
-        <span class="n">wav_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-        <span class="n">flx_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">err_obs_merge</span> <span class="o">=</span> <span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_merge&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
-        <span class="k">if</span> <span class="s1">&#39;obs_pho&#39;</span> <span class="ow">in</span> <span class="n">spectrum_obs</span><span class="o">.</span><span class="n">keys</span><span class="p">():</span>
-            <span class="n">wav_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">0</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-            <span class="n">flx_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">1</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-            <span class="n">err_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">(</span><span class="n">spectrum_obs</span><span class="p">[</span><span class="s1">&#39;obs_pho&#39;</span><span class="p">][</span><span class="mi">2</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="n">wav_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-            <span class="n">flx_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-            <span class="n">err_obs_phot</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">asarray</span><span class="p">([],</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">float</span><span class="p">)</span>
-
-        <span class="c1"># Recovery of the spectroscopy and photometry model</span>
-        <span class="n">path_grid_m</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;/adapted_grid_merge_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span>
-        <span class="n">path_grid_p</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">adapt_store_path</span> <span class="o">+</span> <span class="s1">&#39;/adapted_grid_phot_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">grid_name</span> <span class="o">+</span> <span class="s1">&#39;_nonan.nc&#39;</span>
-        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_m</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-        <span class="n">grid_merge</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-        <span class="n">ds</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_grid_p</span><span class="p">,</span> <span class="n">decode_cf</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">engine</span><span class="o">=</span><span class="s1">&#39;netcdf4&#39;</span><span class="p">)</span>
-        <span class="n">grid_phot</span> <span class="o">=</span> <span class="n">ds</span><span class="p">[</span><span class="s1">&#39;grid&#39;</span><span class="p">]</span>
-        <span class="n">ds</span><span class="o">.</span><span class="n">close</span><span class="p">()</span>
-
-        <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par3</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="n">grid_merge</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="n">grid_phot</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par4</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="n">grid_merge</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="n">grid_phot</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">elif</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">par5</span> <span class="o">==</span> <span class="s1">&#39;NA&#39;</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="n">grid_merge</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="n">grid_phot</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">else</span><span class="p">:</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_merge</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="n">grid_merge</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                          <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
-                                                          <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_merge</span> <span class="o">=</span> <span class="p">[]</span>
-            <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">grid_phot</span><span class="p">[</span><span class="s1">&#39;wavelength&#39;</span><span class="p">])</span> <span class="o">!=</span> <span class="mi">0</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="n">grid_phot</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">par1</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">par2</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">par3</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">par4</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span>
-                                                        <span class="n">par5</span><span class="o">=</span><span class="n">theta</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span>
-                                                        <span class="n">method</span><span class="o">=</span><span class="s2">&quot;linear&quot;</span><span class="p">,</span> <span class="n">kwargs</span><span class="o">=</span><span class="p">{</span><span class="s2">&quot;fill_value&quot;</span><span class="p">:</span> <span class="s2">&quot;extrapolate&quot;</span><span class="p">})</span>
-            <span class="k">else</span><span class="p">:</span>
-                <span class="n">flx_mod_phot</span> <span class="o">=</span> <span class="p">[]</span>
-
-        <span class="c1"># Modification of the synthetic spectrum with the extra-grid parameters</span>
-        <span class="n">modif_spec_chi2</span> <span class="o">=</span> <span class="n">modif_spec</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="p">,</span> <span class="n">theta</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">theta_index</span><span class="p">,</span>
-                                     <span class="n">wav_obs_merge</span><span class="p">,</span> <span class="n">flx_obs_merge</span><span class="p">,</span> <span class="n">err_obs_merge</span><span class="p">,</span> <span class="n">flx_mod_merge</span><span class="p">,</span>
-                                     <span class="n">wav_obs_phot</span><span class="p">,</span> <span class="n">flx_obs_phot</span><span class="p">,</span> <span class="n">err_obs_phot</span><span class="p">,</span> <span class="n">flx_mod_phot</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">modif_spec_chi2</span>
-
-
-    
-<div class="viewcode-block" id="PlottingForMoSA.plot_fit"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit">[docs]</a>    <span class="k">def</span> <span class="nf">plot_fit</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="mi">5</span><span class="p">),</span> <span class="n">uncert</span><span class="o">=</span><span class="s1">&#39;yes&#39;</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        Plot the best fit comparing with the data.</span>
-
-<span class="sd">        Args:</span>
-<span class="sd">            global_params: Class containing each parameter used in ForMoSA</span>
-<span class="sd">            save: If =&#39;yes&#39; save the figure in a pdf format</span>
-<span class="sd">        Returns:</span>
-
-<span class="sd">        Author: Paulina Palma-Bifani</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Best fit and residuals plot&#39;</span><span class="p">)</span>
-
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">fig</span><span class="o">.</span><span class="n">tight_layout</span><span class="p">()</span>
-        <span class="n">size</span> <span class="o">=</span> <span class="p">(</span><span class="mi">7</span><span class="p">,</span><span class="mi">11</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">5</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">axr</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">0</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">2</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
-        <span class="n">axr2</span><span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplot2grid</span><span class="p">(</span><span class="n">size</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span><span class="n">rowspan</span><span class="o">=</span><span class="mi">2</span> <span class="p">,</span><span class="n">colspan</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
-
-
-
-        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">ns_result</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">ns_result</span><span class="p">)</span>
-        <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">samples</span>
-        <span class="n">logl</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">logl</span>
-        <span class="n">ind</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">logl</span> <span class="o">==</span> <span class="nb">max</span><span class="p">(</span><span class="n">logl</span><span class="p">))</span>
-        <span class="n">theta_best</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="n">ind</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span>
-
-        <span class="n">spectra</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_get_spectra</span><span class="p">(</span><span class="n">theta_best</span><span class="p">)</span>
-
-        <span class="k">if</span> <span class="n">uncert</span><span class="o">==</span><span class="s1">&#39;yes&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">errorbar</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">yerr</span><span class="o">=</span><span class="n">spectra</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">label</span> <span class="o">=</span> <span class="s1">&#39;data&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">3</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;model&#39;</span><span class="p">)</span>
-
-
-        <span class="n">residuals</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">3</span><span class="p">]</span> <span class="o">-</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span>
-        <span class="n">sigma_res</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">std</span><span class="p">(</span><span class="n">residuals</span><span class="p">)</span>
-        <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">residuals</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;model-data&#39;</span><span class="p">)</span>
-        <span class="n">axr</span><span class="o">.</span><span class="n">axhline</span><span class="p">(</span><span class="n">y</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="s1">&#39;k&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">linestyle</span><span class="o">=</span><span class="s1">&#39;--&#39;</span><span class="p">)</span>
-        <span class="n">axr2</span><span class="o">.</span><span class="n">hist</span><span class="p">(</span><span class="n">residuals</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="n">bins</span><span class="o">=</span><span class="mi">100</span> <span class="p">,</span><span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.5</span><span class="p">,</span> <span class="n">density</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">orientation</span><span class="o">=</span><span class="s1">&#39;horizontal&#39;</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;density&#39;</span><span class="p">)</span>
-        <span class="n">axr2</span><span class="o">.</span><span class="n">axis</span><span class="p">(</span><span class="s1">&#39;off&#39;</span><span class="p">)</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span><span class="n">spectra</span><span class="p">[</span><span class="mi">5</span><span class="p">],</span><span class="s1">&#39;ko&#39;</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.7</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span><span class="n">spectra</span><span class="p">[</span><span class="mi">7</span><span class="p">],</span><span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">)</span>
-        <span class="n">residuals_phot</span> <span class="o">=</span> <span class="n">spectra</span><span class="p">[</span><span class="mi">7</span><span class="p">]</span><span class="o">-</span><span class="n">spectra</span><span class="p">[</span><span class="mi">5</span><span class="p">]</span>
-        <span class="n">axr</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">spectra</span><span class="p">[</span><span class="mi">4</span><span class="p">],</span> <span class="n">residuals_phot</span><span class="o">/</span><span class="n">sigma_res</span><span class="p">,</span> <span class="s1">&#39;o&#39;</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
-
-
-        <span class="n">axr</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Wavelength (µm)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Flux (W m-2 µm-1)&#39;</span><span class="p">)</span>
-        <span class="n">axr</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="sa">r</span><span class="s1">&#39;Residuals ($\sigma$)&#39;</span><span class="p">)</span>
-        
-        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">axr</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-        <span class="n">axr2</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span><span class="n">handlelength</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
-        <span class="c1"># define the data as global</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">spectra</span> <span class="o">=</span> <span class="n">spectra</span>
-        <span class="bp">self</span><span class="o">.</span><span class="n">residuals</span> <span class="o">=</span> <span class="n">residuals</span>
-
-        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">axr</span><span class="p">,</span> <span class="n">axr2</span></div>
-
-
-<div class="viewcode-block" id="PlottingForMoSA.plot_PT"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT">[docs]</a>    <span class="k">def</span> <span class="nf">plot_PT</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span><span class="n">path_temp_profile</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">),</span> <span class="n">model</span> <span class="o">=</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">):</span>
-        <span class="sd">&#39;&#39;&#39;</span>
-<span class="sd">        Plot the Pressure-Temperature profiles </span>
-<span class="sd">        Calculates the most probable temperature profile</span>
-
-<span class="sd">        Return: fig, ax</span>
-
-<span class="sd">        Author: Nathan Zimniak and Paulina Palma-Bifani</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Pressure-Temperature profile&#39;</span><span class="p">)</span>
-
-        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
-            <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">samples</span>
-
-        <span class="c1"># put nans where data is not realistic</span>
-        <span class="n">out</span><span class="o">=</span><span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">400</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2000</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">3.00</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">5.00</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.32</span> <span class="ow">or</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">10.00</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.10</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.80</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span>
-            <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
-        <span class="c1">#Crée une liste pour chaque paramètre</span>
-        <span class="n">Teffs</span><span class="p">,</span> <span class="n">loggs</span><span class="p">,</span> <span class="n">MHs</span><span class="p">,</span> <span class="n">COs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-        <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
-            <span class="n">gammas</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="n">Teffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">loggs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">:</span>
-                <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
-                <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
-            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
-                <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
-                <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">4</span><span class="p">])</span>
-                <span class="n">gammas</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
-
-        <span class="c1">#Charge la grille de profils de température</span>
-        <span class="n">temperature_grid_xa</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataarray</span><span class="p">(</span><span class="n">path_temp_profile</span><span class="p">)</span>
-        <span class="c1">#Crée les profils de température associés aux points de la grille</span>
-        <span class="n">P</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">]</span>
-        <span class="n">temperature_profiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ExoREM&#39;</span><span class="p">:</span>
-                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
-            <span class="k">elif</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
-                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">gamma</span><span class="o">=</span><span class="n">gammas</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
-        <span class="k">if</span> <span class="n">model</span> <span class="o">==</span> <span class="s1">&#39;ATMO&#39;</span><span class="p">:</span>
-            <span class="c1">#Calcule le 2eme facteur de robustesse (pour ATMO)</span>
-            <span class="n">nbNans</span> <span class="o">=</span> <span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">*</span><span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="mi">0</span><span class="p">,:])):</span>
-                <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="mi">0</span><span class="p">])):</span>
-                    <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="n">j</span><span class="p">,</span><span class="n">i</span><span class="p">])</span> <span class="o">==</span> <span class="s2">&quot;nan&quot;</span><span class="p">:</span>
-                        <span class="n">nbNans</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">nbNans</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">+</span><span class="mi">1</span>
-            <span class="n">FdR2</span> <span class="o">=</span> <span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">-</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">nbNans</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span>
-            <span class="n">FdR1</span> <span class="o">=</span> <span class="n">temperature_grid_xa</span><span class="o">.</span><span class="n">attrs</span><span class="p">[</span><span class="s1">&#39;Facteur de robustesse 1&#39;</span><span class="p">]</span>
-            <span class="n">FdR</span> <span class="o">=</span> <span class="n">FdR1</span><span class="o">*</span><span class="n">FdR2</span>
-            <span class="c1">#Extrapole les températures</span>
-            <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-                <span class="n">newT</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">DataArray</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]),</span> <span class="p">[(</span><span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="nb">list</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">P</span><span class="p">)))])</span>
-                <span class="n">newT</span> <span class="o">=</span> <span class="n">newT</span><span class="o">.</span><span class="n">interpolate_na</span><span class="p">(</span><span class="n">dim</span> <span class="o">=</span> <span class="s1">&#39;pressure&#39;</span><span class="p">,</span> <span class="n">method</span><span class="o">=</span><span class="s1">&#39;linear&#39;</span><span class="p">,</span> <span class="n">fill_value</span><span class="o">=</span><span class="s1">&#39;extrapolate&#39;</span><span class="p">)</span>
-                <span class="n">temperature_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">newT</span><span class="p">)</span>
-        <span class="c1">#Calcule le profil le plus probable</span>
-        <span class="n">Tfit</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
-            <span class="n">Tfit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">50</span><span class="p">))</span>
-        <span class="c1">#Calcule les percentiles 68 et 96 du profil le plus probable</span>
-        <span class="n">Tinf68</span><span class="p">,</span> <span class="n">Tsup68</span><span class="p">,</span> <span class="n">Tinf95</span><span class="p">,</span> <span class="n">Tsup95</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
-            <span class="n">Tinf68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">16</span><span class="p">))</span>
-            <span class="n">Tsup68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">84</span><span class="p">))</span>
-            <span class="n">Tinf95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
-            <span class="n">Tsup95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">temperature_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">98</span><span class="p">))</span>
-        <span class="c1">#Plot le profil le plus probable et les percentiles associés</span>
-
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">(</span><span class="n">figsize</span><span class="o">=</span><span class="n">figsize</span><span class="p">)</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Tinf95</span><span class="p">,</span> <span class="n">Tsup95</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;2 $\sigma$&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">Tinf68</span><span class="p">,</span> <span class="n">Tsup68</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;1 $\sigma$&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">Tfit</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">c</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best fit&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">max</span><span class="p">(</span><span class="n">P</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">P</span><span class="p">)])</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">minorticks_on</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (K)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (Pa)&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">fig</span></div>
-       
-
-    
-
-<div class="viewcode-block" id="PlottingForMoSA.plot_Clouds"><a class="viewcode-back" href="../../../plotting.html#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds">[docs]</a>    <span class="k">def</span> <span class="nf">plot_Clouds</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">cloud_prop</span><span class="p">,</span> <span class="n">path_cloud_profile</span><span class="p">,</span> <span class="n">figsize</span><span class="o">=</span><span class="p">(</span><span class="mi">6</span><span class="p">,</span><span class="mi">5</span><span class="p">)):</span>
-        <span class="sd">&#39;&#39;&#39; </span>
-<span class="sd">        Cloud profiles calculations</span>
-
-<span class="sd">        Inputs: </span>
-<span class="sd">        - cloud_prop (str) : choose the cloud </span>
-<span class="sd">               &#39;eddy_diffusion_coefficient&#39;,</span>
-<span class="sd">               &#39;vmr_CH4&#39;,</span>
-<span class="sd">               &#39;vmr_CO&#39;,</span>
-<span class="sd">               &#39;vmr_CO2&#39;,</span>
-<span class="sd">               &#39;vmr_FeH&#39;,</span>
-<span class="sd">               &#39;vmr_H2O&#39;,</span>
-<span class="sd">               &#39;vmr_H2S&#39;,</span>
-<span class="sd">               &#39;vmr_HCN&#39;,</span>
-<span class="sd">               &#39;vmr_K&#39;,</span>
-<span class="sd">               &#39;vmr_Na&#39;,</span>
-<span class="sd">               &#39;vmr_NH3&#39;,</span>
-<span class="sd">               &#39;vmr_PH3&#39;,</span>
-<span class="sd">               &#39;vmr_TiO&#39;,</span>
-<span class="sd">               &#39;vmr_VO&#39;,</span>
-<span class="sd">               &#39;cloud_opacity_Fe&#39;,</span>
-<span class="sd">               &#39;cloud_opacity_Mg2SiO4&#39;,</span>
-<span class="sd">               &#39;cloud_particle_radius_Fe&#39;,</span>
-<span class="sd">               &#39;cloud_particle_radius_Mg2SiO4&#39;,</span>
-<span class="sd">               &#39;cloud_vmr_Fe&#39;,</span>
-<span class="sd">               &#39;cloud_vmr_Mg2SiO4&#39;</span>
-<span class="sd">        </span>
-<span class="sd">        Return: fig, ax</span>
-
-<span class="sd">        Author: Nathan Zimniak and Paulina Palma-Bifani</span>
-<span class="sd">        &#39;&#39;&#39;</span>
-        <span class="nb">print</span><span class="p">(</span><span class="s1">&#39;ForMoSA - Cloud profile&#39;</span><span class="p">)</span>
-        
-        <span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">result_path</span> <span class="o">+</span> <span class="s1">&#39;/result_&#39;</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">global_params</span><span class="o">.</span><span class="n">ns_algo</span> <span class="o">+</span> <span class="s1">&#39;.pic&#39;</span><span class="p">,</span> <span class="s1">&#39;rb&#39;</span><span class="p">)</span> <span class="k">as</span> <span class="n">f1</span><span class="p">:</span>
-            <span class="n">result</span> <span class="o">=</span> <span class="n">pickle</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="n">f1</span><span class="p">)</span>
-            <span class="n">samples</span> <span class="o">=</span> <span class="n">result</span><span class="o">.</span><span class="n">samples</span>
-        
-        <span class="c1">#Supprime les points hors de la grille</span>
-        <span class="n">out</span><span class="o">=</span><span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="k">if</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mi">400</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mi">2000</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">3.00</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">5.00</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.32</span> <span class="ow">or</span> <span class="mi">10</span><span class="o">**</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">10.00</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-            <span class="k">elif</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&lt;</span> <span class="mf">0.10</span> <span class="ow">or</span> <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">]</span> <span class="o">&gt;</span> <span class="mf">0.80</span><span class="p">:</span>
-                <span class="n">out</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">out</span><span class="p">:</span>
-            <span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span>
-        <span class="n">samples</span> <span class="o">=</span> <span class="n">samples</span><span class="p">[</span><span class="o">~</span><span class="n">np</span><span class="o">.</span><span class="n">isnan</span><span class="p">(</span><span class="n">samples</span><span class="p">)</span><span class="o">.</span><span class="n">any</span><span class="p">(</span><span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)]</span>
-        <span class="c1">#Crée une liste pour chaque paramètre</span>
-        <span class="n">Teffs</span><span class="p">,</span> <span class="n">loggs</span><span class="p">,</span> <span class="n">MHs</span><span class="p">,</span> <span class="n">COs</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="n">Teffs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">0</span><span class="p">])</span>
-            <span class="n">loggs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">1</span><span class="p">])</span>
-            <span class="n">MHs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="mi">10</span><span class="o">**</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">2</span><span class="p">]))</span>
-            <span class="n">COs</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">samples</span><span class="p">[</span><span class="n">i</span><span class="p">][</span><span class="mi">3</span><span class="p">])</span>
-        <span class="c1">#Charge la grille de profils d&#39;une propriété d&#39;un nuage</span>
-        <span class="n">cloud_props_grids_xa</span> <span class="o">=</span> <span class="n">xr</span><span class="o">.</span><span class="n">open_dataset</span><span class="p">(</span><span class="n">path_cloud_profile</span><span class="p">)</span>
-        <span class="n">cloud_prop_grid_xa</span> <span class="o">=</span> <span class="n">cloud_props_grids_xa</span><span class="p">[</span><span class="s1">&#39;P_&#39;</span> <span class="o">+</span> <span class="n">cloud_prop</span><span class="p">]</span>
-        <span class="c1">#Crée les profils d&#39;une propriété d&#39;un nuage associés aux points de la grille</span>
-        <span class="n">P</span> <span class="o">=</span> <span class="n">cloud_prop_grid_xa</span><span class="o">.</span><span class="n">coords</span><span class="p">[</span><span class="s1">&#39;P&#39;</span><span class="p">]</span>
-        <span class="n">cloud_prop_profiles</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">full</span><span class="p">((</span><span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">),</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)),</span> <span class="n">np</span><span class="o">.</span><span class="n">nan</span><span class="p">)</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">samples</span><span class="p">)):</span>
-            <span class="n">cloud_prop_profiles</span><span class="p">[</span><span class="n">i</span><span class="p">][:]</span> <span class="o">=</span> <span class="n">cloud_prop_grid_xa</span><span class="o">.</span><span class="n">interp</span><span class="p">(</span><span class="n">Teff</span><span class="o">=</span><span class="n">Teffs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">logg</span><span class="o">=</span><span class="n">loggs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">MH</span><span class="o">=</span><span class="n">MHs</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="n">CO</span><span class="o">=</span><span class="n">COs</span><span class="p">[</span><span class="n">i</span><span class="p">])</span><span class="c1">#, kwargs={&#39;fill_value&#39;:&#39;extrapolate&#39;})</span>
-        <span class="c1">#Calcule le profil le plus probable</span>
-        <span class="n">propfit</span> <span class="o">=</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
-            <span class="n">propfit</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">50</span><span class="p">))</span>
-        <span class="c1">#Calcule les percentiles 68 et 96 du profil le plus probable</span>
-        <span class="n">propinf68</span><span class="p">,</span> <span class="n">propsup68</span><span class="p">,</span> <span class="n">propinf95</span><span class="p">,</span> <span class="n">propsup95</span> <span class="o">=</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[],</span> <span class="p">[]</span>
-        <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">P</span><span class="p">)):</span>
-            <span class="n">propinf68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">16</span><span class="p">))</span>
-            <span class="n">propsup68</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">84</span><span class="p">))</span>
-            <span class="n">propinf95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">2</span><span class="p">))</span>
-            <span class="n">propsup95</span><span class="o">.</span><span class="n">append</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">percentile</span><span class="p">(</span><span class="n">cloud_prop_profiles</span><span class="p">[:,</span><span class="n">i</span><span class="p">],</span> <span class="mi">98</span><span class="p">))</span>
-
-        <span class="c1">#Plot le profil le plus probable et les percentiles associés</span>
-        <span class="n">fig</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
-        <span class="n">ax</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">axes</span><span class="p">()</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">propinf95</span><span class="p">,</span> <span class="n">propsup95</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.1</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;2 $\sigma$&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">fill_betweenx</span><span class="p">(</span><span class="n">P</span><span class="p">,</span> <span class="n">propinf68</span><span class="p">,</span> <span class="n">propsup68</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;1 $\sigma$&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">propfit</span><span class="p">,</span> <span class="n">P</span><span class="p">,</span> <span class="n">color</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">color_out</span><span class="p">,</span> <span class="n">label</span><span class="o">=</span><span class="s1">&#39;Best fit&#39;</span><span class="p">)</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_yscale</span><span class="p">(</span><span class="s1">&#39;log&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">invert_yaxis</span><span class="p">()</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_xlim</span><span class="p">(</span><span class="n">left</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylim</span><span class="p">([</span><span class="nb">max</span><span class="p">(</span><span class="n">P</span><span class="p">),</span> <span class="nb">min</span><span class="p">(</span><span class="n">P</span><span class="p">)])</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">minorticks_on</span><span class="p">()</span>
-        <span class="k">if</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;T&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Temperature (K)&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;eddy_diffusion_coefficient&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Eddy diffusion coefficient ($m^2.s^{-1}$)&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CH4&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$CH_4$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CO&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;CO volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_CO2&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$CO_2$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_FeH&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;FeH volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_H2O&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$H_2O$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_H2S&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$H_2S$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_HCN&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;HCN volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_K&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;K volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_Na&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Na volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_NH3&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$NH_3$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_PH3&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$PH_3$ volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_TiO&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;TiO volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;vmr_VO&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;VO volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_opacity_Fe&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud opacity&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_opacity_Mg2SiO4&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud opacity&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_particle_radius_Fe&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud particle radius (m)&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_particle_radius_Mg2SiO4&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud particle radius (m)&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_vmr_Fe&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;Fe cloud volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="k">elif</span> <span class="n">cloud_prop</span> <span class="o">==</span> <span class="s1">&#39;cloud_vmr_Mg2SiO4&#39;</span><span class="p">:</span>
-            <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="s1">&#39;$Mg_2SiO_4$ cloud volume mixing ratio&#39;</span><span class="p">)</span>
-        <span class="n">ax</span><span class="o">.</span><span class="n">set_ylabel</span><span class="p">(</span><span class="s1">&#39;Pressure (Pa)&#39;</span><span class="p">)</span>
-
-        <span class="n">ax</span><span class="o">.</span><span class="n">legend</span><span class="p">(</span><span class="n">frameon</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
-        <span class="k">return</span> <span class="n">fig</span><span class="p">,</span> <span class="n">ax</span></div></div>
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-</pre></div>
-
-           </div>
-          </div>
-          <footer>
-
-  <hr/>
-
-  <div role="contentinfo">
-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
-  </div>
-
-  Built with <a href="https://www.sphinx-doc.org/">Sphinx</a> using a
-    <a href="https://github.com/readthedocs/sphinx_rtd_theme">theme</a>
-    provided by <a href="https://readthedocs.org">Read the Docs</a>.
-   
-
-</footer>
-        </div>
-      </div>
-    </section>
-  </div>
-  <script>
-      jQuery(function () {
-          SphinxRtdTheme.Navigation.enable(true);
-      });
-  </script> 
-
-</body>
-</html>
\ No newline at end of file
diff --git a/docs/build/html/_sources/api.rst.txt b/docs/build/html/_sources/api.rst.txt
deleted file mode 100644
index 0f4fb0c..0000000
--- a/docs/build/html/_sources/api.rst.txt
+++ /dev/null
@@ -1,12 +0,0 @@
-.. _api:
-
-ForMoSA API
-===========
-
-.. toctree::
-   :titlesonly:
-   
-   main_utilities
-   adapt
-   nested_sampling
-   plotting
\ No newline at end of file
diff --git a/docs/build/html/_sources/demo.rst.txt b/docs/build/html/_sources/demo.rst.txt
deleted file mode 100644
index 063ed2c..0000000
--- a/docs/build/html/_sources/demo.rst.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _demo:
-
-DEMO
-====
-
-There are several ways of using ForMoSA.
\ No newline at end of file
diff --git a/docs/build/html/_sources/index.rst.txt b/docs/build/html/_sources/index.rst.txt
deleted file mode 100644
index b56401a..0000000
--- a/docs/build/html/_sources/index.rst.txt
+++ /dev/null
@@ -1,41 +0,0 @@
-.. |br| raw:: html
-
-   <br />
-
-.. image:: ForMoSA.png
-   :width: 150px
-   :height: 105px
-   :align: center
-
-
-Forward Modeling Tool for Spectral Analysis
-===========================================
-
-Welcome to ForMoSA, an open-source Python package.
-Using a forward modeling approach, we designed this tool to model exoplanetary atmospheres.
-We encourage the community to exploit its capabilities!
-
-
-Attribution:
-++++++++++++
-
-When using ForMoSA please cite `Petrus et al (2023) <https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract>`_.
-
-
-User Guide:
-+++++++++++
-
-.. toctree::
-   :maxdepth: 2
-
-   installation
-   demo
-   api
-
-
-Version Track:
-++++++++++++++
-
-**1.0.0
-
-This is the first official release.
diff --git a/docs/build/html/_sources/install.rst.txt b/docs/build/html/_sources/install.rst.txt
deleted file mode 100644
index a425127..0000000
--- a/docs/build/html/_sources/install.rst.txt
+++ /dev/null
@@ -1,9 +0,0 @@
-Installation
-
-At the command line:
-
-easy_install crawler
-
-Or, if you have pip installed:
-
-pip install crawler
\ No newline at end of file
diff --git a/docs/build/html/_sources/installation.rst.txt b/docs/build/html/_sources/installation.rst.txt
deleted file mode 100644
index fc2a803..0000000
--- a/docs/build/html/_sources/installation.rst.txt
+++ /dev/null
@@ -1,34 +0,0 @@
-.. _installation:
-
-Installation
-============
-
-For all Users
-+++++++++++++
-
-We strongly recommend using a ``conda`` environment (learn mode 
-`here <https://conda.io/docs/user-guide/tasks/manage-environments.html>`_).
-To install and use our package follow the following sequence of commands.
-
-.. code-block:: bash
-	
-    $ conda create -n formosa_env python=3.7
-
-    $ conda activate formosa_env
-
-    $ pip install ForMoSA
-
-`ForMoSA PyPI <https://pypi.org/project/ForMoSA/>`_
-
-You may get some errors at this point when running ForMoSA. Run the following two lines on your environment to solve known bugs.
-
-.. code-block:: bash
-
-    $ pip install importlib-metadata==4.13.0
-
-    $ conda install xarray dask netCDF4 bottleneck
-
-Issues?
-+++++++
-
-If you run into any other problem, please create an issue on `GitHub <https://github.com/exoAtmospheres/ForMoSA/issues>`_.
diff --git a/docs/build/html/adapt.html b/docs/build/html/adapt.html
deleted file mode 100644
index c9db13b..0000000
--- a/docs/build/html/adapt.html
+++ /dev/null
@@ -1,422 +0,0 @@
-<!DOCTYPE html>
-<html class="writer-html5" lang="English" >
-<head>
-  <meta charset="utf-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Adapt the data &mdash; ForMoSA  documentation</title>
-      <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
-      <link rel="stylesheet" href="_static/css/theme.css" type="text/css" />
-    <link rel="shortcut icon" href="_static/favicon.ico"/>
-  <!--[if lt IE 9]>
-    <script src="_static/js/html5shiv.min.js"></script>
-  <![endif]-->
-  
-        <script data-url_root="./" id="documentation_options" src="_static/documentation_options.js"></script>
-        <script src="_static/jquery.js"></script>
-        <script src="_static/underscore.js"></script>
-        <script src="_static/_sphinx_javascript_frameworks_compat.js"></script>
-        <script src="_static/doctools.js"></script>
-        <script src="_static/sphinx_highlight.js"></script>
-    <script src="_static/js/theme.js"></script>
-    <link rel="index" title="Index" href="genindex.html" />
-    <link rel="search" title="Search" href="search.html" />
-    <link rel="next" title="Nested Sampling" href="nested_sampling.html" />
-    <link rel="prev" title="Main Utilities" href="main_utilities.html" /> 
-</head>
-
-<body class="wy-body-for-nav"> 
-  <div class="wy-grid-for-nav">
-    <nav data-toggle="wy-nav-shift" class="wy-nav-side">
-      <div class="wy-side-scroll">
-        <div class="wy-side-nav-search" >
-
-          
-          
-          <a href="index.html" class="icon icon-home">
-            ForMoSA
-          </a>
-<div role="search">
-  <form id="rtd-search-form" class="wy-form" action="search.html" method="get">
-    <input type="text" name="q" placeholder="Search docs" aria-label="Search docs" />
-    <input type="hidden" name="check_keywords" value="yes" />
-    <input type="hidden" name="area" value="default" />
-  </form>
-</div>
-        </div><div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="Navigation menu">
-              <ul class="current">
-<li class="toctree-l1"><a class="reference internal" href="installation.html">Installation</a></li>
-<li class="toctree-l1"><a class="reference internal" href="demo.html">DEMO</a></li>
-<li class="toctree-l1 current"><a class="reference internal" href="api.html">ForMoSA API</a><ul class="current">
-<li class="toctree-l2"><a class="reference internal" href="main_utilities.html">Main Utilities</a></li>
-<li class="toctree-l2 current"><a class="current reference internal" href="#">Adapt the data</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.adapt_grid">Adapt Grid:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.adapt_grid.adapt_grid"><code class="docutils literal notranslate"><span class="pre">adapt_grid()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.adapt_obs_mod">Adapt Observations and Models:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.adapt_obs_mod.launch_adapt"><code class="docutils literal notranslate"><span class="pre">launch_adapt()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.adapt.extraction_functions">Extraction Functions:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.adapt_model"><code class="docutils literal notranslate"><span class="pre">adapt_model()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.adapt_observation_range"><code class="docutils literal notranslate"><span class="pre">adapt_observation_range()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.continuum_estimate"><code class="docutils literal notranslate"><span class="pre">continuum_estimate()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.convolve_and_sample"><code class="docutils literal notranslate"><span class="pre">convolve_and_sample()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.decoupe"><code class="docutils literal notranslate"><span class="pre">decoupe()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.extract_model"><code class="docutils literal notranslate"><span class="pre">extract_model()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.extract_observation"><code class="docutils literal notranslate"><span class="pre">extract_observation()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.find_nearest"><code class="docutils literal notranslate"><span class="pre">find_nearest()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.adapt.extraction_functions.resolution_decreasing"><code class="docutils literal notranslate"><span class="pre">resolution_decreasing()</span></code></a></li>
-</ul>
-</li>
-</ul>
-</li>
-<li class="toctree-l2"><a class="reference internal" href="nested_sampling.html">Nested Sampling</a></li>
-<li class="toctree-l2"><a class="reference internal" href="plotting.html">Plotting Class</a></li>
-</ul>
-</li>
-</ul>
-
-        </div>
-      </div>
-    </nav>
-
-    <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap"><nav class="wy-nav-top" aria-label="Mobile navigation menu" >
-          <i data-toggle="wy-nav-top" class="fa fa-bars"></i>
-          <a href="index.html">ForMoSA</a>
-      </nav>
-
-      <div class="wy-nav-content">
-        <div class="rst-content">
-          <div role="navigation" aria-label="Page navigation">
-  <ul class="wy-breadcrumbs">
-      <li><a href="index.html" class="icon icon-home" aria-label="Home"></a></li>
-          <li class="breadcrumb-item"><a href="api.html">ForMoSA API</a></li>
-      <li class="breadcrumb-item active">Adapt the data</li>
-      <li class="wy-breadcrumbs-aside">
-            <a href="_sources/adapt.rst.txt" rel="nofollow"> View page source</a>
-      </li>
-  </ul>
-  <hr/>
-</div>
-          <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
-           <div itemprop="articleBody">
-             
-  <div class="section" id="adapt-the-data">
-<span id="adapt"></span><h1>Adapt the data<a class="headerlink" href="#adapt-the-data" title="Permalink to this heading"></a></h1>
-<div class="section" id="module-ForMoSA.adapt.adapt_grid">
-<span id="adapt-grid"></span><h2>Adapt Grid:<a class="headerlink" href="#module-ForMoSA.adapt.adapt_grid" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.adapt_grid.adapt_grid">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.adapt_grid.</span></span><span class="sig-name descname"><span class="pre">adapt_grid</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_spec</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_phot</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/adapt_grid.html#adapt_grid"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.adapt_grid.adapt_grid" title="Permalink to this definition"></a></dt>
-<dd><p>Adapt the synthetic spectra of a grid to make them comparable with data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter</p></li>
-<li><p><strong>wav_obs_spec</strong> -- Merged wavelength grid of the data</p></li>
-<li><p><strong>wav_obs_phot</strong> -- Wavelengths of the photometry points</p></li>
-</ul>
-</dd>
-</dl>
-<p>Returns:</p>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-</div>
-<div class="section" id="module-ForMoSA.adapt.adapt_obs_mod">
-<span id="adapt-observations-and-models"></span><h2>Adapt Observations and Models:<a class="headerlink" href="#module-ForMoSA.adapt.adapt_obs_mod" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.adapt_obs_mod.launch_adapt">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.adapt_obs_mod.</span></span><span class="sig-name descname"><span class="pre">launch_adapt</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">justobs</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/adapt_obs_mod.html#launch_adapt"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.adapt_obs_mod.launch_adapt" title="Permalink to this definition"></a></dt>
-<dd><p>Extract and adapt (wavelength resampling, resolution decreasing, continuum subtracting) the data and the synthetic
-spectra from a model grid.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter</p></li>
-<li><p><strong>justobs</strong> -- If the grid need to be adapted justobs='no'</p></li>
-</ul>
-</dd>
-</dl>
-<p>Returns:</p>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-</div>
-<div class="section" id="module-ForMoSA.adapt.extraction_functions">
-<span id="extraction-functions"></span><h2>Extraction Functions:<a class="headerlink" href="#module-ForMoSA.adapt.extraction_functions" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.adapt_model">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">adapt_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_nativ</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#adapt_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.adapt_model" title="Permalink to this definition"></a></dt>
-<dd><p>Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
-calculated too. Then each sub-spectrum are merged.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter used in ForMoSA</p></li>
-<li><p><strong>wav_mod_nativ</strong> -- Wavelength grid of the model</p></li>
-<li><p><strong>flx_mod_nativ</strong> -- Flux of the model</p></li>
-<li><p><strong>res_mod_nativ</strong> -- Spectral resolution of the model as a function of the wavelength grid</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>output_model</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.adapt_observation_range">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">adapt_observation_range</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#adapt_observation_range"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.adapt_observation_range" title="Permalink to this definition"></a></dt>
-<dd><p>Extract the wavelengths (um  -  vacuum), flux (W.m-2.um.1), errors (W.m-2.um.1), spectral resolution, and
-instrument/filter names from the observation file. The wavelength range is define by the parameter &quot;wav_for_adapt&quot;.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>global_params</strong> -- Class containing each parameter</p>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</dt><dd><p>[[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</p>
-</dd>
-<dt>obs_pho: List containing the photometry ('P' replace the spectral resolution here).</dt><dd><p>[wav_phot, flx_phot, err_phot, 'P']</p>
-</dd>
-<dt>obs_cut_ins: List containing different instruments used for the data (1 per wavelength).</dt><dd><p>[[instru_1], ..., [instru_n]]</p>
-</dd>
-<dt>obs_pho_ins: List containing different filters used for the data (1 per photometric point).</dt><dd><p>[filter_phot_1, filter_phot_2, ..., filter_phot_n]</p>
-</dd>
-</dl>
-</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>obs_cut</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.continuum_estimate">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">continuum_estimate</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_cut</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_or_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#continuum_estimate"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.continuum_estimate" title="Permalink to this definition"></a></dt>
-<dd><p>Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
-wavelengths of the custom spectral resolution (estimated for the continuum). It then calculates a sigma to decrease
-the resolution of the spectrum to this custom FWHM for each wavelength using a gaussian filter and resample it on
-the wavelength grid of the data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter used in ForMoSA</p></li>
-<li><p><strong>wav_cut</strong> -- Wavelength grid of the sub-spectrum data</p></li>
-<li><p><strong>wav</strong> -- Wavelength grid of the spectrum for which you want to estimate the continuum</p></li>
-<li><p><strong>flx</strong> -- Flux of the spectrum for which you want to estimate the continuum</p></li>
-<li><p><strong>res</strong> -- Spectral resolution of the spectrum for which you want to estimate the continuum</p></li>
-<li><p><strong>obs_or_mod</strong> -- Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod'</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Estimated continuum of the spectrum re-sampled on the data wavelength grid</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>output_model</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.convolve_and_sample">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">convolve_and_sample</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wv_channels</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">sigmas_wvs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_wvs</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model_fluxes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">num_sigma</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">3</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#convolve_and_sample"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.convolve_and_sample" title="Permalink to this definition"></a></dt>
-<dd><p>Simulate the observations of a model. Convolves the model with a variable Gaussian LSF, sampled at each desired
-spectral channel.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>wv_channels</strong> -- the wavelengths desired (length of N_output)</p></li>
-<li><p><strong>sigmas_wvs</strong> -- the LSF gaussian stddev of each wv_channels [IN UNITS OF model_wvs] (length of N_output)</p></li>
-<li><p><strong>model_wvs</strong> -- the wavelengths of the model (length of N_model)</p></li>
-<li><p><strong>model_fluxes</strong> -- the fluxes of the model (length of N_model)</p></li>
-<li><p><strong>num_sigma</strong> (<em>float</em>) -- number of +/- sigmas to evaluate the LSF to.</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>the fluxes in each of the wavelength channels (length of N_output)</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>output_model</p>
-</dd>
-</dl>
-<p>Author: Jason Wang</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.decoupe">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">decoupe</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">second</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#decoupe"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.decoupe" title="Permalink to this definition"></a></dt>
-<dd><p>Re-arranged a number of seconds in the hours-minutes-seconds format.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>second</strong> -- number of second</p>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>hours-minutes-seconds format</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>hour, minute, second</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.extract_model">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">extract_model</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cont</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#extract_model"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.extract_model" title="Permalink to this definition"></a></dt>
-<dd><p>Extracts a synthetic spectrum from a grid and decreases its spectral resolution. The photometry points are
-calculated too.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter used in ForMoSA</p></li>
-<li><p><strong>wav_mod_nativ</strong> -- Wavelength grid of the model</p></li>
-<li><p><strong>flx_mod_nativ</strong> -- Flux of the model</p></li>
-<li><p><strong>res_mod_nativ</strong> -- Spectral resolution of the model as a function of the wavelength grid</p></li>
-<li><p><strong>cont</strong> -- If the function is used to estimate the continuum cont='yes'</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.</dt><dd><p>[flx_1, ..., flx_n, err_n, reso_n]</p>
-</dd>
-</dl>
-<p>mod_pho: List containing the photometry ('P' replace the spectral resolution here).</p>
-</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>mod_cut</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.extract_observation">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">extract_observation</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cont</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#extract_observation"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.extract_observation" title="Permalink to this definition"></a></dt>
-<dd><p>Take back the extracted data spectrum from the function 'adapt_observation_range' and decrease its spectral
-resolution.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter</p></li>
-<li><p><strong>wav_mod_nativ</strong> -- Wavelength grid of the model</p></li>
-<li><p><strong>res_mod_nativ</strong> -- Spectral resolution of the model</p></li>
-<li><p><strong>cont</strong> -- If the function is used to estimate the continuum cont='yes'</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><dl class="simple">
-<dt>List containing the sub-spectra defined by the parameter &quot;wav_for_adapt&quot; with decreased resolution</dt><dd><p>[[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</p>
-</dd>
-<dt>obs_pho: List containing the photometry ('P' replace the spectral resolution here).</dt><dd><p>[wav_phot, flx_phot, err_phot, 'P']</p>
-</dd>
-<dt>obs_cut_ins: List containing different instruments used for the data (1 per wavelength).</dt><dd><p>[[instru_range_1], ..., [instru_range_n]]</p>
-</dd>
-<dt>obs_pho_ins: List containing different filters used for the data (1 per photometric point).</dt><dd><p>[filter_phot_1, filter_phot_2, ..., filter_phot_n]</p>
-</dd>
-</dl>
-</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>obs_cut</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.find_nearest">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">find_nearest</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">array</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">value</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#find_nearest"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.find_nearest" title="Permalink to this definition"></a></dt>
-<dd><p>Return the indice of the closest values from a desire value in an array.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>array</strong> -- Array to explore</p></li>
-<li><p><strong>value</strong> -- Desire value</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Indice of the closest values from the desire value</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>idx</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.adapt.extraction_functions.resolution_decreasing">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.adapt.extraction_functions.</span></span><span class="sig-name descname"><span class="pre">resolution_decreasing</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">cut</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">res_mod_nativ</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">obs_or_mod</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/adapt/extraction_functions.html#resolution_decreasing"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.adapt.extraction_functions.resolution_decreasing" title="Permalink to this definition"></a></dt>
-<dd><p>Decrease the resolution of a spectrum (data or model). The function calculates the FWHM as a function of the
-wavelengths for the data, the model, and for a custom spectral resolution (optional) and estimates the highest one
-for each wavelength (the lowest spectral resolution). It then calculates a sigma to decrease the resolution of the
-spectrum to this lowest FWHM for each wavelength and resample it on the wavelength grid of the data using the
-function 'convolve_and_sample'.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter used in ForMoSA</p></li>
-<li><p><strong>cut</strong> -- Sub-spectra defined by the parameter &quot;wav_for_adapt&quot;.
-[[wav_1, flx_1, err_1, reso_1], ..., [wav_n, flx_n, err_n, reso_n]]</p></li>
-<li><p><strong>wav_mod_nativ</strong> -- Wavelength grid of the model</p></li>
-<li><p><strong>flx_mod_nativ</strong> -- Flux of the model</p></li>
-<li><p><strong>res_mod_nativ</strong> -- Spectral resolution of the model as a function of the wavelength grid</p></li>
-<li><p><strong>obs_or_mod</strong> -- Parameter to identify if you want to manage a data or a model spectrum. 'obs' or 'mod'</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Flux of the spectrum with a decreased spectral resolution, re-sampled on the data wavelength grid</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>output_model</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-</div>
-</div>
-
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-<html class="writer-html5" lang="Python" >
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-  <title>&lt;no title&gt; &mdash; ForMoSA 1.0.0 documentation</title>
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-<span id="id1"></span><h1>Installation<a class="headerlink" href="#installation" title="Permalink to this heading"></a></h1>
-<div class="section" id="for-all-users">
-<h2>For all Users<a class="headerlink" href="#for-all-users" title="Permalink to this heading"></a></h2>
-<p>We strongly recommend using a <code class="docutils literal notranslate"><span class="pre">conda</span></code> environment (learn mode
-<a class="reference external" href="https://conda.io/docs/user-guide/tasks/manage-environments.html">here</a>).
-To install and use our package follow the following sequence of commands.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ conda create -n formosa_env <span class="nv">python</span><span class="o">=</span><span class="m">3</span>.7
-
-$ conda activate formosa_env
-
-$ pip install ForMoSA
-</pre></div>
-</div>
-<p><a class="reference external" href="https://pypi.org/project/ForMoSA/">ForMoSA PyPI</a></p>
-<p>You may get some errors at this point when running ForMoSA. Run the following two lines on your environment to solve known bugs.</p>
-<div class="highlight-bash notranslate"><div class="highlight"><pre><span></span>$ pip install importlib-metadata<span class="o">==</span><span class="m">4</span>.13.0
-
-$ conda install xarray dask netCDF4 bottleneck
-</pre></div>
-</div>
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-<div class="section" id="issues">
-<h2>Issues?<a class="headerlink" href="#issues" title="Permalink to this heading"></a></h2>
-<p>If you run into any other problem, please create an issue on <a class="reference external" href="https://github.com/exoAtmospheres/ForMoSA/issues">GitHub</a>.</p>
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-<li class="toctree-l2 current"><a class="current reference internal" href="#">Nested Sampling</a><ul>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_sampling">Nested Main:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling"><code class="docutils literal notranslate"><span class="pre">launch_nested_sampling()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.loglike"><code class="docutils literal notranslate"><span class="pre">loglike()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_sampling.prior_transform"><code class="docutils literal notranslate"><span class="pre">prior_transform()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_modif_spec">Modify Spectra:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.calc_ck"><code class="docutils literal notranslate"><span class="pre">calc_ck()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct"><code class="docutils literal notranslate"><span class="pre">doppler_fct()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.modif_spec"><code class="docutils literal notranslate"><span class="pre">modif_spec()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct"><code class="docutils literal notranslate"><span class="pre">reddening_fct()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct"><code class="docutils literal notranslate"><span class="pre">vsini_fct()</span></code></a></li>
-</ul>
-</li>
-<li class="toctree-l3"><a class="reference internal" href="#module-ForMoSA.nested_sampling.nested_prior_function">Prior Functions:</a><ul>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior"><code class="docutils literal notranslate"><span class="pre">gaussian_prior()</span></code></a></li>
-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.nested_sampling.nested_prior_function.uniform_prior"><code class="docutils literal notranslate"><span class="pre">uniform_prior()</span></code></a></li>
-</ul>
-</li>
-</ul>
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-  <div class="section" id="nested-sampling">
-<span id="id1"></span><h1>Nested Sampling<a class="headerlink" href="#nested-sampling" title="Permalink to this heading"></a></h1>
-<div class="section" id="module-ForMoSA.nested_sampling.nested_sampling">
-<span id="nested-main"></span><h2>Nested Main:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_sampling" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">launch_nested_sampling</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#launch_nested_sampling"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling" title="Permalink to this definition"></a></dt>
-<dd><p>Args:</p>
-<p>Returns:</p>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.loglike">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">loglike</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">for_plot</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#loglike"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.loglike" title="Permalink to this definition"></a></dt>
-<dd><p>Args:</p>
-<p>Returns:</p>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_sampling.prior_transform">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_sampling.</span></span><span class="sig-name descname"><span class="pre">prior_transform</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">lim_param_grid</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_sampling.html#prior_transform"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_sampling.prior_transform" title="Permalink to this definition"></a></dt>
-<dd><p>Args:</p>
-<p>Returns:</p>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-</div>
-<div class="section" id="module-ForMoSA.nested_sampling.nested_modif_spec">
-<span id="modify-spectra"></span><h2>Modify Spectra:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_modif_spec" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.calc_ck">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">calc_ck</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">flx_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">r_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">d_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">analytic</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'no'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#calc_ck"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.calc_ck" title="Permalink to this definition"></a></dt>
-<dd><p>Calculation of the dilution factor Ck and re-normalization of the interpolated synthetic spectrum (from the radius
-and distance or analytically).</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>flx_obs_merge</strong> -- Flux of the data (spectroscopy)</p></li>
-<li><p><strong>err_obs_merge</strong> -- Error of the data (spectroscopy)</p></li>
-<li><p><strong>new_flx_merge</strong> -- Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
-<li><p><strong>flx_obs_phot</strong> -- Flux of the data (photometry)</p></li>
-<li><p><strong>err_obs_phot</strong> -- Error of the data (photometry)</p></li>
-<li><p><strong>new_flx_phot</strong> -- Flux of the interpolated synthetic spectrum (photometry)</p></li>
-<li><p><strong>r_picked</strong> -- Radius randomly picked by the nested sampling (in RJup)</p></li>
-<li><p><strong>d_picked</strong> -- Distance randomly picked by the nested sampling (in pc)</p></li>
-<li><p><strong>analytic</strong> -- = 'yes' if Ck needs to be calculated analytically by the formula from Cushing et al. (2008)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>Re-normalysed model spectrum
-new_flx_phot: Re-normalysed model photometry
-ck: Ck calculated</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>new_flx_merge</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.doppler_fct">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">doppler_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">rv_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#doppler_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.doppler_fct" title="Permalink to this definition"></a></dt>
-<dd><p>Application of a Doppler shifting to the interpolated synthetic spectrum using the function pyasl.dopplerShift.
-Note: Observation can change due to side effects of the shifting.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>wav_obs_merge</strong> -- Wavelength grid of the data</p></li>
-<li><p><strong>flx_obs_merge</strong> -- Flux of the data</p></li>
-<li><p><strong>err_obs_merge</strong> -- Error of the data</p></li>
-<li><p><strong>new_flx_merge</strong> -- Flux of the interpolated synthetic spectrum</p></li>
-<li><p><strong>rv_picked</strong> -- Radial velocity randomly picked by the nested sampling (in km.s-1)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>New wavelength grid of the data
-flx_obs_merge: New flux of the data
-err_obs_merge: New error of the data
-flx_post_doppler: New flux of the interpolated synthetic spectrum</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>wav_obs_merge</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.modif_spec">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">modif_spec</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">global_params</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta_index</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">flx_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">err_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_phot</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#modif_spec"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.modif_spec" title="Permalink to this definition"></a></dt>
-<dd><dl class="simple">
-<dt>Modification of the interpolated synthetic spectra with the different extra-grid parameters:</dt><dd><ul class="simple">
-<li><p>Re-calibration on the data</p></li>
-<li><p>Doppler shifting</p></li>
-<li><p>Application of a substellar extinction</p></li>
-<li><p>Application of a rotational velocity</p></li>
-</ul>
-</dd>
-</dl>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter</p></li>
-<li><p><strong>theta</strong> -- Parameter values randomly picked by the nested sampling</p></li>
-<li><p><strong>theta_index</strong> -- Parameter index identificator</p></li>
-<li><p><strong>wav_obs_merge</strong> -- Wavelength grid of the data (spectroscopy)</p></li>
-<li><p><strong>flx_obs_merge</strong> -- Flux of the data (spectroscopy)</p></li>
-<li><p><strong>err_obs_merge</strong> -- Error of the data (spectroscopy)</p></li>
-<li><p><strong>new_flx_merge</strong> -- Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
-<li><p><strong>wav_obs_phot</strong> -- Wavelength grid of the data (photometry)</p></li>
-<li><p><strong>flx_obs_phot</strong> -- Flux of the data (photometry)</p></li>
-<li><p><strong>err_obs_phot</strong> -- Error of the data (photometry)</p></li>
-<li><p><strong>new_flx_phot</strong> -- Flux of the interpolated synthetic spectrum (photometry)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>New wavelength grid of the data (may change with the Doppler shift)
-flx_obs_merge: New flux of the data (may change with the Doppler shift)
-err_obs_merge: New error of the data (may change with the Doppler shift)
-new_flx_merge: New flux of the interpolated synthetic spectrum (spectroscopy)
-wav_obs_phot: Wavelength grid of the data (photometry)
-flx_obs_phot: Flux of the data (photometry)
-err_obs_phot: Error of the data (photometry)
-new_flx_phot: New flux of the interpolated synthetic spectrum (photometry)</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>wav_obs_merge</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.reddening_fct">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">reddening_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">wav_obs_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_phot</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">av_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#reddening_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.reddening_fct" title="Permalink to this definition"></a></dt>
-<dd><p>Application of a sythetic interstellar extinction to the interpolated synthetic spectrum using the function
-extinction.fm07.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>wav_obs_merge</strong> -- Wavelength grid of the data (spectroscopy)</p></li>
-<li><p><strong>wav_obs_phot</strong> -- Wavelength of the data (photometry)</p></li>
-<li><p><strong>new_flx_merge</strong> -- Flux of the interpolated synthetic spectrum (spectroscopy)</p></li>
-<li><p><strong>new_flx_phot</strong> -- Flux of the interpolated synthetic spectrum (photometry)</p></li>
-<li><p><strong>av_picked</strong> -- Extinction randomly picked by the nested sampling (in mag)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>New flux of the interpolated synthetic spectrum (spectroscopy)
-new_flx_phot: New flux of the interpolated synthetic spectrum (photometry)</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>new_flx_merge</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_modif_spec.vsini_fct">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_modif_spec.</span></span><span class="sig-name descname"><span class="pre">vsini_fct</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">wav_obs_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">new_flx_merge</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ld_picked</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">vsini_picked</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_modif_spec.html#vsini_fct"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_modif_spec.vsini_fct" title="Permalink to this definition"></a></dt>
-<dd><p>Application of a rotation velocity (line broadening) to the interpolated synthetic spectrum using the function
-extinction.fm07.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>wav_obs_merge</strong> -- Wavelength grid of the data</p></li>
-<li><p><strong>new_flx_merge</strong> -- Flux of the interpolated synthetic spectrum</p></li>
-<li><p><strong>ld_picked</strong> -- Limd darkening randomly picked by the nested sampling</p></li>
-<li><p><strong>vsini_picked</strong> -- v.sin(i) randomly picked by the nested sampling (in km.s-1)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>New flux of the interpolated synthetic spectrum</p>
-</dd>
-<dt class="field-odd">Return type</dt>
-<dd class="field-odd"><p>new_flx_merge</p>
-</dd>
-</dl>
-<p>Author: Simon Petrus</p>
-</dd></dl>
-
-</div>
-<div class="section" id="module-ForMoSA.nested_sampling.nested_prior_function">
-<span id="prior-functions"></span><h2>Prior Functions:<a class="headerlink" href="#module-ForMoSA.nested_sampling.nested_prior_function" title="Permalink to this heading"></a></h2>
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_prior_function.gaussian_prior">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_prior_function.</span></span><span class="sig-name descname"><span class="pre">gaussian_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_fct_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_prior_function.html#gaussian_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_prior_function.gaussian_prior" title="Permalink to this definition"></a></dt>
-<dd><p>Gaussian prior for nested sampling</p>
-</dd></dl>
-
-<dl class="py function">
-<dt class="sig sig-object py" id="ForMoSA.nested_sampling.nested_prior_function.uniform_prior">
-<span class="sig-prename descclassname"><span class="pre">ForMoSA.nested_sampling.nested_prior_function.</span></span><span class="sig-name descname"><span class="pre">uniform_prior</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">prior_fct_arg</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">theta</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/nested_sampling/nested_prior_function.html#uniform_prior"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.nested_sampling.nested_prior_function.uniform_prior" title="Permalink to this definition"></a></dt>
-<dd><p>Uniform prior for nested sampling</p>
-</dd></dl>
-
-</div>
-</div>
-
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-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.plotting.plotting_class.ComplexRadar.scale_data"><code class="docutils literal notranslate"><span class="pre">ComplexRadar.scale_data()</span></code></a></li>
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-<li class="toctree-l4"><a class="reference internal" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT"><code class="docutils literal notranslate"><span class="pre">PlottingForMoSA.plot_PT()</span></code></a></li>
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-  <div class="section" id="module-ForMoSA.plotting.plotting_class">
-<span id="plotting-class"></span><span id="plotting"></span><h1>Plotting Class<a class="headerlink" href="#module-ForMoSA.plotting.plotting_class" title="Permalink to this heading"></a></h1>
-<dl class="py class">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.plotting.plotting_class.</span></span><span class="sig-name descname"><span class="pre">ComplexRadar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">fig</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">variables</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">n_ordinate_levels</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">6</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar" title="Permalink to this definition"></a></dt>
-<dd><p>Original from Damian Cummins: <a class="reference external" href="https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb">https://github.com/DamianCummins/statsbomb-football-event-visualisations/blob/master/Statsbomb%20Womens%20World%20Cup%202019%20visualisation.ipynb</a></p>
-<p>Adapted by: P. Palma-Bifani</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.ComplexRadar.scale_data">
-<span class="sig-name descname"><span class="pre">scale_data</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">data</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#ComplexRadar.scale_data"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.ComplexRadar.scale_data" title="Permalink to this definition"></a></dt>
-<dd><p>scales data[1:] to ranges[0]</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA">
-<em class="property"><span class="pre">class</span><span class="w"> </span></em><span class="sig-prename descclassname"><span class="pre">ForMoSA.plotting.plotting_class.</span></span><span class="sig-name descname"><span class="pre">PlottingForMoSA</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">config_file_path</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">color_out</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA" title="Permalink to this definition"></a></dt>
-<dd><p>Here all the plotting functionalities of ForMoSA to see the</p>
-<p>Author: Paulina Palma-Bifani</p>
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds">
-<span class="sig-name descname"><span class="pre">plot_Clouds</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">cloud_prop</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">path_cloud_profile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(6,</span> <span class="pre">5)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_Clouds"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds" title="Permalink to this definition"></a></dt>
-<dd><p>Cloud profiles calculations</p>
-<p>Inputs:
-- cloud_prop (str) : choose the cloud</p>
-<blockquote>
-<div><p>'eddy_diffusion_coefficient',
-'vmr_CH4',
-'vmr_CO',
-'vmr_CO2',
-'vmr_FeH',
-'vmr_H2O',
-'vmr_H2S',
-'vmr_HCN',
-'vmr_K',
-'vmr_Na',
-'vmr_NH3',
-'vmr_PH3',
-'vmr_TiO',
-'vmr_VO',
-'cloud_opacity_Fe',
-'cloud_opacity_Mg2SiO4',
-'cloud_particle_radius_Fe',
-'cloud_particle_radius_Mg2SiO4',
-'cloud_vmr_Fe',
-'cloud_vmr_Mg2SiO4'</p>
-</div></blockquote>
-<p>Return: fig, ax</p>
-<p>Author: Nathan Zimniak and Paulina Palma-Bifani</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT">
-<span class="sig-name descname"><span class="pre">plot_PT</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">path_temp_profile</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(6,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">model</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'ExoREM'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_PT"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT" title="Permalink to this definition"></a></dt>
-<dd><p>Plot the Pressure-Temperature profiles
-Calculates the most probable temperature profile</p>
-<p>Return: fig, ax</p>
-<p>Author: Nathan Zimniak and Paulina Palma-Bifani</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains">
-<span class="sig-name descname"><span class="pre">plot_chains</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(7,</span> <span class="pre">15)</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_chains"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains" title="Permalink to this definition"></a></dt>
-<dd><p>To check the convergence of the chains</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner">
-<span class="sig-name descname"><span class="pre">plot_corner</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">levels_sig</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.997,</span> <span class="pre">0.95,</span> <span class="pre">0.68]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">bins</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">100</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(0.16,</span> <span class="pre">0.5,</span> <span class="pre">0.84)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">burn_in</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_corner"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner" title="Permalink to this definition"></a></dt>
-<dd><p>See the corner plots</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit">
-<span class="sig-name descname"><span class="pre">plot_fit</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">figsize</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">(10,</span> <span class="pre">5)</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">uncert</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">'yes'</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_fit"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit" title="Permalink to this definition"></a></dt>
-<dd><p>Plot the best fit comparing with the data.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>global_params</strong> -- Class containing each parameter used in ForMoSA</p></li>
-<li><p><strong>save</strong> -- If ='yes' save the figure in a pdf format</p></li>
-</ul>
-</dd>
-</dl>
-<p>Returns:</p>
-<p>Author: Paulina Palma-Bifani</p>
-</dd></dl>
-
-<dl class="py method">
-<dt class="sig sig-object py" id="ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar">
-<span class="sig-name descname"><span class="pre">plot_radar</span></span><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">ranges</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">label</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">''</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">quantiles</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0.16,</span> <span class="pre">0.5,</span> <span class="pre">0.84]</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">chiffres</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">[0,</span> <span class="pre">2,</span> <span class="pre">2,</span> <span class="pre">2]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/ForMoSA/plotting/plotting_class.html#PlottingForMoSA.plot_radar"><span class="viewcode-link"><span class="pre">[source]</span></span></a><a class="headerlink" href="#ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar" title="Permalink to this definition"></a></dt>
-<dd><p>To check overall the distribution of the parameters</p>
-<p>Inputs:
-ranges</p>
-</dd></dl>
-
-</dd></dl>
-
-</div>
-
-
-           </div>
-          </div>
-          <footer><div class="rst-footer-buttons" role="navigation" aria-label="Footer">
-        <a href="nested_sampling.html" class="btn btn-neutral float-left" title="Nested Sampling" accesskey="p" rel="prev"><span class="fa fa-arrow-circle-left" aria-hidden="true"></span> Previous</a>
-    </div>
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-
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-    <p>&#169; Copyright 2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al..</p>
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"plot_chains"], [7, 3, 1, "", "plot_corner"], [7, 3, 1, "", "plot_fit"], [7, 3, 1, "", "plot_radar"]]}, "objtypes": {"0": "py:module", "1": "py:function", "2": "py:class", "3": "py:method"}, "objnames": {"0": ["py", "module", "Python module"], "1": ["py", "function", "Python function"], "2": ["py", "class", "Python class"], "3": ["py", "method", "Python method"]}, "titleterms": {"adapt": 0, "data": 0, "grid": 0, "observ": 0, "model": [0, 3], "extract": 0, "function": [0, 6], "formosa": 1, "api": 1, "demo": 2, "forward": 3, "tool": 3, "spectral": 3, "analysi": 3, "attribut": 3, "user": [3, 4], "guid": 3, "version": 3, "track": 3, "instal": 4, "For": 4, "all": 4, "issu": 4, "main": [5, 6], "util": 5, "nest": 6, "sampl": 6, "modifi": 6, "spectra": 6, "prior": 6, "plot": 7, "class": 7}, "envversion": {"sphinx.domains.c": 2, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 8, "sphinx.domains.index": 1, "sphinx.domains.javascript": 2, "sphinx.domains.math": 2, "sphinx.domains.python": 3, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.todo": 2, "sphinx.ext.viewcode": 1, "sphinx": 57}, "alltitles": {"ForMoSA API": [[1, "formosa-api"]], "DEMO": [[2, "demo"]], "Forward Modeling Tool for Spectral Analysis": [[3, "forward-modeling-tool-for-spectral-analysis"]], "Attribution:": [[3, "attribution"]], "User Guide:": [[3, "user-guide"]], "Version Track:": [[3, "version-track"]], "Installation": [[4, "installation"]], "For all Users": [[4, "for-all-users"]], "Issues?": [[4, "issues"]], "Main Utilities": [[5, "module-ForMoSA"]], "Adapt the data": [[0, "adapt-the-data"]], "Adapt Grid:": [[0, "module-ForMoSA.adapt.adapt_grid"]], "Adapt Observations and Models:": [[0, "module-ForMoSA.adapt.adapt_obs_mod"]], "Extraction Functions:": [[0, "module-ForMoSA.adapt.extraction_functions"]], "Nested Sampling": [[6, "nested-sampling"]], "Nested Main:": [[6, "module-ForMoSA.nested_sampling.nested_sampling"]], "Modify Spectra:": [[6, "module-ForMoSA.nested_sampling.nested_modif_spec"]], "Prior Functions:": [[6, "module-ForMoSA.nested_sampling.nested_prior_function"]], "Plotting Class": [[7, "module-ForMoSA.plotting.plotting_class"]]}, "indexentries": {"formosa.adapt.adapt_grid": [[0, "module-ForMoSA.adapt.adapt_grid"]], "formosa.adapt.adapt_obs_mod": [[0, "module-ForMoSA.adapt.adapt_obs_mod"]], "formosa.adapt.extraction_functions": [[0, "module-ForMoSA.adapt.extraction_functions"]], "adapt_grid() (in module formosa.adapt.adapt_grid)": [[0, "ForMoSA.adapt.adapt_grid.adapt_grid"]], "adapt_model() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.adapt_model"]], "adapt_observation_range() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.adapt_observation_range"]], "continuum_estimate() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.continuum_estimate"]], "convolve_and_sample() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.convolve_and_sample"]], "decoupe() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.decoupe"]], "extract_model() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.extract_model"]], "extract_observation() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.extract_observation"]], "find_nearest() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.find_nearest"]], "launch_adapt() (in module formosa.adapt.adapt_obs_mod)": [[0, "ForMoSA.adapt.adapt_obs_mod.launch_adapt"]], "module": [[0, "module-ForMoSA.adapt.adapt_grid"], [0, "module-ForMoSA.adapt.adapt_obs_mod"], [0, "module-ForMoSA.adapt.extraction_functions"], [6, "module-ForMoSA.nested_sampling.nested_modif_spec"], [6, "module-ForMoSA.nested_sampling.nested_prior_function"], [6, "module-ForMoSA.nested_sampling.nested_sampling"], [7, "module-ForMoSA.plotting.plotting_class"]], "resolution_decreasing() (in module formosa.adapt.extraction_functions)": [[0, "ForMoSA.adapt.extraction_functions.resolution_decreasing"]], "formosa.nested_sampling.nested_modif_spec": [[6, "module-ForMoSA.nested_sampling.nested_modif_spec"]], "formosa.nested_sampling.nested_prior_function": [[6, "module-ForMoSA.nested_sampling.nested_prior_function"]], "formosa.nested_sampling.nested_sampling": [[6, "module-ForMoSA.nested_sampling.nested_sampling"]], "calc_ck() (in module formosa.nested_sampling.nested_modif_spec)": [[6, "ForMoSA.nested_sampling.nested_modif_spec.calc_ck"]], "doppler_fct() (in module formosa.nested_sampling.nested_modif_spec)": [[6, "ForMoSA.nested_sampling.nested_modif_spec.doppler_fct"]], "gaussian_prior() (in module formosa.nested_sampling.nested_prior_function)": [[6, "ForMoSA.nested_sampling.nested_prior_function.gaussian_prior"]], "launch_nested_sampling() (in module formosa.nested_sampling.nested_sampling)": [[6, "ForMoSA.nested_sampling.nested_sampling.launch_nested_sampling"]], "loglike() (in module formosa.nested_sampling.nested_sampling)": [[6, "ForMoSA.nested_sampling.nested_sampling.loglike"]], "modif_spec() (in module formosa.nested_sampling.nested_modif_spec)": [[6, "ForMoSA.nested_sampling.nested_modif_spec.modif_spec"]], "prior_transform() (in module formosa.nested_sampling.nested_sampling)": [[6, "ForMoSA.nested_sampling.nested_sampling.prior_transform"]], "reddening_fct() (in module formosa.nested_sampling.nested_modif_spec)": [[6, "ForMoSA.nested_sampling.nested_modif_spec.reddening_fct"]], "uniform_prior() (in module formosa.nested_sampling.nested_prior_function)": [[6, "ForMoSA.nested_sampling.nested_prior_function.uniform_prior"]], "vsini_fct() (in module formosa.nested_sampling.nested_modif_spec)": [[6, "ForMoSA.nested_sampling.nested_modif_spec.vsini_fct"]], "complexradar (class in formosa.plotting.plotting_class)": [[7, "ForMoSA.plotting.plotting_class.ComplexRadar"]], "formosa.plotting.plotting_class": [[7, "module-ForMoSA.plotting.plotting_class"]], "plottingformosa (class in formosa.plotting.plotting_class)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA"]], "plot_clouds() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_Clouds"]], "plot_pt() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_PT"]], "plot_chains() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_chains"]], "plot_corner() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_corner"]], "plot_fit() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_fit"]], "plot_radar() (formosa.plotting.plotting_class.plottingformosa method)": [[7, "ForMoSA.plotting.plotting_class.PlottingForMoSA.plot_radar"]], "scale_data() (formosa.plotting.plotting_class.complexradar method)": [[7, "ForMoSA.plotting.plotting_class.ComplexRadar.scale_data"]]}})
\ No newline at end of file
diff --git a/docs/conf.py b/docs/conf.py
new file mode 100644
index 0000000..5e7b7de
--- /dev/null
+++ b/docs/conf.py
@@ -0,0 +1,91 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# For the full list of built-in configuration values, see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Project information -----------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
+
+import os
+import sys
+sys.path.insert(0, os.path.abspath('../'))
+sys.path.insert(1, os.path.abspath('../ForMoSA/'))
+sys.path.insert(2, os.path.abspath('../ForMoSA/adapt/'))
+sys.path.insert(3, os.path.abspath('../ForMoSA/nested_sampling/'))
+
+
+project = 'ForMoSA'
+copyright = '2024, Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin'
+author = 'Simon Petrus, Paulina Palma-Bifani, Matthieu Ravet, Allan Denis, Mickaël Bonnefoy and Gaël Chauvin'
+release = '2.0.0'
+root_doc = 'index'
+
+language = 'en'
+
+# -- General configuration ---------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
+
+extensions = ['sphinx_rtd_theme',
+              'sphinx.ext.autodoc',
+              'sphinx.ext.doctest',
+              'sphinx.ext.todo',
+              'sphinx.ext.mathjax',    
+              'sphinx.ext.napoleon',
+              'sphinx.ext.viewcode',
+              'nbsphinx',
+              ]
+
+
+templates_path = []
+
+# Disable notebook timeout
+nbsphinx_timeout = -1
+
+# Allow errors from notebooks
+nbsphinx_allow_errors = True
+
+autoclass_content = 'both'
+
+
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store', 'tutorials/.ipynb_checkpoints/*']
+
+
+
+# -- Options for HTML output -------------------------------------------------
+# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
+
+
+pygments_style = 'sphinx'
+
+# If true, `todo` and `todoList` produce output, else they produce nothing.
+todo_include_todos = True
+
+import sphinx_rtd_theme
+import sphinx_rtd_theme
+
+
+#html_theme = 'bizstyle'
+html_theme = 'sphinx_rtd_theme'
+#html_theme = 'classic'
+
+#html_theme_options = {
+#    'path_to_docs': 'docs',
+#    'repository_url': 'https://github.com/exoAtmospheres/ForMoSA',
+#    'repository_branch': 'activ_dev',
+#    'launch_buttons': {
+#        'notebook_interface': 'jupyterlab',
+#    },
+#    'use_edit_page_button': True,
+#    'use_issues_button': True,
+#    'use_repository_button': True,
+#    'use_download_button': True,
+#}
+
+html_static_path = ['_static']
+
+# These paths are either relative to html_static_path
+# or fully qualified paths (eg. https://...)
+html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
+
+
+html_favicon = 'favicon.ico'
diff --git a/docs/demo.rst b/docs/demo.rst
new file mode 100644
index 0000000..a17aaf0
--- /dev/null
+++ b/docs/demo.rst
@@ -0,0 +1,64 @@
+.. _demo:
+
+Tutorials
+=========
+
+There are several ways of using ForMoSA.
+
+To get started, we recomend you to keep the following structure locally.
+
+.. code-block:: bash
+
+   ~/YOUR/PATH/formosa_desk/
+   ├── atm_grids/
+   ├── demo_ABPicb/      
+   │   ├── data.fits       
+   │   ├── config.ini      
+   │   ├── adapted_grid/ 
+   │   └── outputs/
+   ├── (ForMoSA/)
+   ├── (PyMultiNest/)
+   └── (MultiNest/)
+
+Depending on the way you installed ForMoSA, the ForMoSA, PyMultiNest, and MultiNest subfolders need to be cloned from GitHub. 
+Follow the :doc:`installation` 
+
+Atmospheric grids
++++++++++++++++++
+
+This is the list of the publically available atmospheric grids which we have formated for ForMoSA. 
+
+Download the grid you want to use by clicking over it's name. Ideally, save it inside the ``atm_grids/`` subdirectory.
+
+* `ATMO <https://drive.google.com/file/d/1S1dcBD7UiuUCZIcNBNnJi6LMymrnkagM/view?usp=share_link>`_ from `M.W. Phillips et al. 2020 <https://ui.adsabs.harvard.edu/abs/2020A%26A...637A..38P/abstract>`_
+
+
+* `BT-Settl <https://drive.google.com/file/d/1wvf4A-DupdVnYIpK_HmHE-fobqnYtvEz/view?usp=share_link>`_ from `Allard et al. 2013 <https://ui.adsabs.harvard.edu/abs/2013MSAIS..24..128A/abstract>`_
+
+
+* `ExoREM <https://drive.google.com/file/d/1k9SQjHLnMCwmGOHtraRnhCgiZ1-4J3Wk/view?usp=share_link>`_ from `B. Charnay et al. 2018 <https://ui.adsabs.harvard.edu/abs/2018ApJ...854..172C/abstract>`_
+
+
+Learn more about:
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/exorem_info.ipynb
+
+
+
+
+List of tutorials:
+++++++++++++++++++
+
+
+.. toctree::
+   :maxdepth: 1
+
+   tutorials/inputs
+
+   tutorials/demoabpic
+..
+   demo_mosaic.rst
+   demo_pymultinest.rst
\ No newline at end of file
diff --git a/docs/source/favicon.ico b/docs/favicon.ico
similarity index 100%
rename from docs/source/favicon.ico
rename to docs/favicon.ico
diff --git a/docs/index.rst b/docs/index.rst
new file mode 100644
index 0000000..a0a1c16
--- /dev/null
+++ b/docs/index.rst
@@ -0,0 +1,59 @@
+.. ForMoSA documentation master file, created by
+   sphinx-quickstart on Sat Jul 27 09:35:15 2024.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+.. |br| raw:: html
+
+   <br />
+
+.. image:: ForMoSA.png
+   :width: 150px
+   :height: 105px
+   :align: center
+
+Forward Modeling Tool for Spectral Analysis
+===========================================
+
+Welcome to the documentation of ForMoSA, an open-source Python package.
+Using a forward modeling approach, we designed this tool to model exoplanetary atmospheres.
+We encourage the community to exploit its capabilities!
+
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Contents
+
+   installation.rst
+   demo.rst
+   api.rst
+
+
+
+Attribution
++++++++++++
+
+Please cite `Petrus et al (2023) <https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract>`_.
+
+
+
+Issues (?)
+++++++++++
+
+If you run into any other problem, please create an issue on `GitHub <https://github.com/exoAtmospheres/ForMoSA/issues>`_.
+
+
+
+Version Track
++++++++++++++
+
+- ``1.1.2`` Version adapted for including multiple instruments and high spectral resolution observations.
+
+- ``1.0.13`` First version distributed, presented at `Cloud Academy 3. <https://alienearths.space/cloud-academy-3/>`_.
+
+- ``1.0.5`` First operational release.
+
+
+Acknowledgments
++++++++++++++++
+Our sincere thanks to `Code/Astro <https://semaphorep.github.io/codeastro/>`_. 
diff --git a/docs/installation.rst b/docs/installation.rst
new file mode 100644
index 0000000..a3818ed
--- /dev/null
+++ b/docs/installation.rst
@@ -0,0 +1,136 @@
+.. _installation:
+
+Installation
+============
+
+There are two possibilities to install ``ForMoSA``. 
+Installation of the package through ``PyPI`` or through ``GitHub``. 
+
+
+Create a conda environment
+++++++++++++++++++++++++++
+
+We strongly recommend using a ``conda`` environment. You can `learn more about conda here <https://conda.io/docs/user-guide/tasks/manage-environments.html>`_ .
+
+(For all users)
+
+You can create your environment as:
+
+.. code-block:: console
+
+    $ conda create -n env_formosa python=3.11
+    $ conda activate env_formosa 
+
+(For macOS users) 
+
+To install and use our package in macOS with an M1 chip, a few extra steps are required. 
+To use PyMultiNest to paralelize the processes, make sure you are building your conda environment under an OSX-ARM64 architecture. You can `learn more here <https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple>`_ .
+
+.. code-block:: console
+
+    $ CONDA_SUBDIR=osx-arm64 conda create -n env_formosa python=3.11 numpy -c conda-forge
+    $ conda activate env_formosa 
+    $ conda config --env --set subdir osx-arm64
+
+
+
+A. Installation though PyPI
++++++++++++++++++++++++++++
+
+PyPI is the `pip package manager <https://packaging.python.org/tutorials/installing-packages/>`_, which will automatically install for you almost all the required dependencies in your environment.
+To install the latest version run: 
+
+.. code-block:: console
+
+    $ pip install ForMoSA
+
+To solve a standard error, run the following line in your environment:
+
+.. code-block:: console
+
+    $ conda install xarray dask netCDF4 bottleneck
+
+
+B. Installation though GitHub
++++++++++++++++++++++++++++++
+
+Install the following packages in your environment with pip install: 
+
+.. code-block:: console  
+
+    $ numpy
+    $ matplotlib
+    $ corner
+    $ astropy
+    $ scipy
+    $ configobj
+    $ extinction
+    $ nestle
+    $ PyAstronomy
+    $ spectres
+    $ pyyaml
+    $ importlib-metadata==4.13.0
+    $ xarray==2023.10.1
+
+To solve a standard error, run the following line in your environment:
+
+.. code-block:: console
+
+    $ conda install xarray dask netCDF4 bottleneck
+
+The last instalation step is to actually get ForMoSA. 
+ForMoSA can be cloned from the main branch from our GitHub repository. 
+You can do it by moving to the desired directory on your terminal and writing:
+
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/exoAtmospheres/ForMoSA.git
+
+
+PyMultiNest users
++++++++++++++++++
+
+If you want to use Pymultinest to run your inversion, follow the installation instructions from `PyMultinest <https://johannesbuchner.github.io/PyMultiNest/install.html>`_, detailed below. 
+
+First, you need to clone PyMultinest from GitHub and install it.
+
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/JohannesBuchner/PyMultiNest/
+    $ cd PyMultiNest
+    $ python setup.py install
+
+Second, you need to corroborate that your system has a C++ and a Fortran interpreter. 
+If you need to install brew, follow `these instructions <https://brew.sh/>`_.
+
+.. code-block:: console
+
+    $ brew install cmake
+    $ brew install gcc
+    $ brew install open-mpi
+
+Next, in your ForMoSA environment, install mpi4pi as:
+
+.. code-block:: console
+
+    $ pip install mpi4py
+
+Then, install MultiNest by cloning the GitHub repository and building it.
+Make sure you empty the build folder if you run this step more than once.
+    
+.. code-block:: console
+
+    $ cd ~/YOUR_PATH/formosa_desk/
+    $ git clone https://github.com/JohannesBuchner/MultiNest
+    $ cd MultiNest/build
+    $ cmake ..
+    $ make
+
+Finally, copy the files that were generated by building MultiNest onto your conda environment by doing:
+
+.. code-block:: console
+
+    $ cp -v ~/YOUR_PATH/MultiNest/lib/* /YOUR_PATH/opt/anaconda3/envs/env_formosa/lib/
+	
diff --git a/docs/source/main_utilities.rst b/docs/main_utilities.rst
similarity index 77%
rename from docs/source/main_utilities.rst
rename to docs/main_utilities.rst
index 977df0f..c4f187e 100644
--- a/docs/source/main_utilities.rst
+++ b/docs/main_utilities.rst
@@ -4,5 +4,7 @@ Main Utilities
 ==============
 .. module:: ForMoSA
 
+Global functions:
++++++++++++++++++
 .. automodule:: ForMoSA.main_utilities
 	:members:
\ No newline at end of file
diff --git a/docs/make.bat b/docs/make.bat
index 747ffb7..32bb245 100644
--- a/docs/make.bat
+++ b/docs/make.bat
@@ -7,8 +7,8 @@ REM Command file for Sphinx documentation
 if "%SPHINXBUILD%" == "" (
 	set SPHINXBUILD=sphinx-build
 )
-set SOURCEDIR=source
-set BUILDDIR=build
+set SOURCEDIR=.
+set BUILDDIR=_build
 
 %SPHINXBUILD% >NUL 2>NUL
 if errorlevel 9009 (
diff --git a/docs/source/nested_sampling.rst b/docs/nested_sampling.rst
similarity index 75%
rename from docs/source/nested_sampling.rst
rename to docs/nested_sampling.rst
index 9253b5f..7261fb6 100644
--- a/docs/source/nested_sampling.rst
+++ b/docs/nested_sampling.rst
@@ -16,4 +16,9 @@ Modify Spectra:
 Prior Functions:
 ++++++++++++++++
 .. automodule:: ForMoSA.nested_sampling.nested_prior_function
+	:members:
+
+Likelihood Functions:
++++++++++++++++++++++
+.. automodule:: ForMoSA.nested_sampling.nested_logL_functions
 	:members:
\ No newline at end of file
diff --git a/docs/source/plotting.rst b/docs/plotting.rst
similarity index 61%
rename from docs/source/plotting.rst
rename to docs/plotting.rst
index 6e0520f..bb5a1a2 100644
--- a/docs/source/plotting.rst
+++ b/docs/plotting.rst
@@ -3,5 +3,7 @@
 Plotting Class
 ==============
 
+The plotting module of ForMoSA:
++++++++++++++++++++++++++++++++
 .. automodule:: ForMoSA.plotting.plotting_class
 	:members:
\ No newline at end of file
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 0000000..d864e6e
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1,6 @@
+sphinx>=1.4
+ipykernel
+nbsphinx
+jupyter
+pandoc
+sphinx_rtd_theme
\ No newline at end of file
diff --git a/docs/source/api.rst b/docs/source/api.rst
deleted file mode 100644
index 0f4fb0c..0000000
--- a/docs/source/api.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-.. _api:
-
-ForMoSA API
-===========
-
-.. toctree::
-   :titlesonly:
-   
-   main_utilities
-   adapt
-   nested_sampling
-   plotting
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
deleted file mode 100644
index 1768edc..0000000
--- a/docs/source/conf.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# Configuration file for the Sphinx documentation builder.
-#
-# For the full list of built-in configuration values, see the documentation:
-# https://www.sphinx-doc.org/en/master/usage/configuration.html
-
-# -- Project information -----------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#project-information
-
-
-import os
-import sys
-sys.path.insert(0, os.path.abspath('../..')) # location of orbitize files with docstrings
-sys.path.insert(0, os.path.abspath('../../ForMoSA')) # location of orbitize files with docstrings
-
-import ForMoSA
-
-# -- General configuration ---------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration
-
-# -- General configuration ------------------------------------------------
-
-# If your documentation needs a minimal Sphinx version, state it here.
-#
-# needs_sphinx = '1.0'
-
-# Add any Sphinx extension module names here, as strings. They can be
-# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
-# ones.
-extensions = [
-    'sphinx_rtd_theme',
-    'sphinx.ext.autodoc',
-    'sphinx.ext.doctest',
-    'sphinx.ext.todo',
-    'sphinx.ext.mathjax',
-    'sphinx.ext.napoleon', # allows Google style-guide docs to render more prettily
-    'sphinx.ext.viewcode'
-    ]
-
-# Disable notebook timeout
-#nbsphinx_timeout = -1
-
-# Allow notebook errors
-#nbsphinx_allow_errors = True
-
-# Add any paths that contain templates here, relative to this directory.
-
-templates_path = ['_templates']
-exclude_patterns = []
-
-# source_suffix = ['.rst', '.md']
-source_suffix = '.rst'
-
-# The master toctree document.
-master_doc = 'index'
-
-
-project = 'ForMoSA'
-copyright = '2023, S. Petrus, P. Palma-Bifani, M. Bonnefoy, G. Chauvin, et al.'
-author = 'Simon Petrus, Paulina Palma-Bifani, Mickaël Bonnefoy, Gaël Chauvin, et al.'
-
-#version = ForMoSA.__version__
-
-
-language = 'English'
-
-# -- Options for HTML output -------------------------------------------------
-# https://www.sphinx-doc.org/en/master/usage/configuration.html#options-for-html-output
-
-
-pygments_style = 'sphinx'
-
-# If true, `todo` and `todoList` produce output, else they produce nothing.
-todo_include_todos = True
-
-import sphinx_rtd_theme
-
-html_theme = 'sphinx_rtd_theme'
-html_static_path = ['_static']
-html_theme_path = [sphinx_rtd_theme.get_html_theme_path()]
-
-# The name of an image file (within the static path) to use as favicon of the
-# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
-# pixels large.
-html_favicon = 'favicon.ico'
diff --git a/docs/source/demo.rst b/docs/source/demo.rst
deleted file mode 100644
index 063ed2c..0000000
--- a/docs/source/demo.rst
+++ /dev/null
@@ -1,6 +0,0 @@
-.. _demo:
-
-DEMO
-====
-
-There are several ways of using ForMoSA.
\ No newline at end of file
diff --git a/docs/source/index.rst b/docs/source/index.rst
deleted file mode 100644
index 709eb1e..0000000
--- a/docs/source/index.rst
+++ /dev/null
@@ -1,51 +0,0 @@
-.. |br| raw:: html
-
-   <br />
-
-.. image:: ForMoSA.png
-   :width: 150px
-   :height: 105px
-   :align: center
-
-
-Forward Modeling Tool for Spectral Analysis
-===========================================
-
-Welcome to ForMoSA, an open-source Python package.
-Using a forward modeling approach, we designed this tool to model exoplanetary atmospheres.
-We encourage the community to exploit its capabilities!
-
-
-Attribution:
-++++++++++++
-
-When using ForMoSA please cite `Petrus et al (2023) <https://ui.adsabs.harvard.edu/abs/2023A%26A...670L...9P/abstract>`_.
-
-
-User Guide:
-+++++++++++
-
-.. toctree::
-   :maxdepth: 2
-
-   installation
-   demo
-   api
-
-
-Version Track:
-++++++++++++++
-
-**1.0.13**
-
-First version distributed, presented at `Cloud Academy 3. <https://alienearths.space/cloud-academy-3/>`_.
-
-**1.0.5**
-
-First operational release.
-
-
-Acknowledgments:
-++++++++++++++++
-Our sincere thanks to `Code/Astro <https://semaphorep.github.io/codeastro/>`_. 
-By attending that workshop in 2020 and being TA for three years, the dream of ForMoSA being an open source Python package became a reality.
\ No newline at end of file
diff --git a/docs/source/installation.rst b/docs/source/installation.rst
deleted file mode 100644
index 8bbe977..0000000
--- a/docs/source/installation.rst
+++ /dev/null
@@ -1,99 +0,0 @@
-.. _installation:
-
-Installation
-============
-
-We strongly recommend using a ``conda`` environment ([learn more here](https://conda.io/docs/user-guide/tasks/manage-environments.html)).
-
-There are two possibilities. Installation of the package through (a) 'pip' or through (b) 'git clone'. 
-
-a) Installation though 'pip'
-++++++++++++++++++++++++++++
-
-a1/ To run parallelization without warnings, make sure you are building your environment under an OSX-ARM64 architecture ([learn more here](https://stackoverflow.com/questions/65415996/how-to-specify-the-architecture-or-platform-for-a-new-conda-environment-apple)).
-
-    $ CONDA_SUBDIR=osx-arm64 conda create -n env_formosa python=3.11 numpy -c conda-forge
-
-    $ conda activate env_formosa 
-    
-    $ conda config --env --set subdir osx-arm64
-
-a2/ Install ForMoSA thourgh pip, which will install all the required dependencies.
-
-    $ pip install ForMoSA==2.0.0
-
-a3/ To solve a standard error, run the following line in your environment:
-
-    $ conda install xarray dask netCDF4 bottleneck
-
-a4/ If you want to use Pymultinest to run your inversion, follow the installation instructions from [PyMultinest](https://johannesbuchner.github.io/PyMultiNest/install.html), detailed below. 
-
-If you don't need PyMultinest, you can directly go to point 5/.
-
-First. Clone PyMultinest from GitHub and install it.
-
-    $ git clone https://github.com/JohannesBuchner/PyMultiNest/
-    $ cd PyMultiNest
-    $ python setup.py install
-
-Second. Make sure your system has a C++ and a Fortran interpreter. If you need to install brew, follow [these instructions](https://brew.sh/).
-
-    $ brew install cmake
-    $ brew install gcc
-    $ brew install open-mpi
-
-Third. In your ForMoSA environment, install mpi4pi as:
-    
-    $ pip install mpi4py
-
-Fourth. Install MultiNest by cloning the GitHub repository and building it. Make sure you empty the build folder if you run this step more than once.
-    
-    $ git clone https://github.com/JohannesBuchner/MultiNest
-    $ cd MultiNest/build
-    $ cmake ..
-    $ make
-
-Finally, copy the files that were generated by building MultiNest onto your conda (may be miniconda) environment by doing:
-
-    $ cp -v ~/YOUR_PATH/MultiNest/lib/* /YOUR_PATH/opt/anaconda3/envs/env_formosa/lib/
-	
-
-b) Installation though 'git clone'
-++++++++++++++++++++++++++++++++++
-
-To install and use our package in macOS with an M1 chip, proceed with the following sequence of commands:
-
-b1/ Follow a1.
-
-b2/ Install the following packages in your environment with pip install: 
-    
-    $ numpy
-    $ matplotlib
-    $ corner
-    $ astropy
-    $ scipy
-    $ configobj
-    $ extinction
-    $ nestle
-    $ PyAstronomy
-    $ spectres
-    $ pyyaml
-    $ importlib-metadata==4.13.0
-    $ xarray==2023.10.1
-
-
-b3/ To solve a standard error, run the following line in your environment:
-
-    $ conda install xarray dask netCDF4 bottleneck
-
-b4/ Follow a4.
-
-5/ The last instalation step is to actually get ForMoSA. ForMoSA can be installed throgh pip, however, to work on the final released version, please clone the main branch from our GitHub repository. You can do it by moving to the desired directory on your terminal and writing:
-
-    $ git clone https://github.com/exoAtmospheres/ForMoSA.git
-
-
-Issues?
-+++++++
-
-If you run into any other problem, please create an issue on `GitHub <https://github.com/exoAtmospheres/ForMoSA/issues>`_.
diff --git a/docs/tutorials/data_abpic/ABPicb_SINFONI_K.fits b/docs/tutorials/data_abpic/ABPicb_SINFONI_K.fits
new file mode 100644
index 0000000..39d6d52
Binary files /dev/null and b/docs/tutorials/data_abpic/ABPicb_SINFONI_K.fits differ
diff --git a/docs/tutorials/data_abpic/config_ABPicb.ini b/docs/tutorials/data_abpic/config_ABPicb.ini
new file mode 100644
index 0000000..a945c88
--- /dev/null
+++ b/docs/tutorials/data_abpic/config_ABPicb.ini
@@ -0,0 +1,112 @@
+[config_path]
+    # Path to the observed spectrum file
+    observation_path = 'data_abpic/ABPicb_SINFONI_K.fits'
+
+    # Path to store your interpolated grid
+    adapt_store_path = 'data_abpic/adapted_grid/'
+
+    # Path where you wish to store your results.
+    result_path = 'data_abpic/outputs/'
+
+    # Path of your initial grid of models
+    model_path = '/Users/ppalmabifani/Desktop/exoAtm/c0_ForMoSA/INPUTS/atm_grids/atm_grids_native/EXOREM_native.nc'
+
+
+[config_adapt]
+    # Wavelength range used for the extraction of data and adaptation of the grid (separated windows can be defined).
+    # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'
+    wav_for_adapt = '1.9, 2.5'
+
+    # Method used to adapt the synthetic spectra to the data.
+    # example : 'by_reso' : A Gaussian law is convolved to the spectrum to decrease the resolution as a function of the
+    # wavelength.
+    #           'by_sample' : The spectrum is directly re-sampled to the wavelength grid of the data, using the module
+    # python spectres.
+    adapt_method = 'by_reso'
+
+    # Custom target resolution to reach (optional). The final resolution will be the lowest between this custom resolution,
+    # the resolution of the data, and the resolution of the models, for each wavelength.
+    # Format : float or 'NA'
+    custom_reso = 'NA'
+
+    # Continuum subtraction. If a float is given, the value will give the approximated spectral resolution of the continuum.
+    # Format : 'float' or 'NA'
+    continuum_sub = 'NA'
+
+    # Wavelength range used for the estimate of the continuum (separated windows can be defined).
+    # Format : 'window1_max / window2_min,window2_max / ... / windown_min'
+    # Note : the 'window1_min' and 'windown_max' are defined as the edges of 'wav_for_adapt'.
+    # If the entire wavelength range defined by the parameter 'wav_for_adapt' needs to be continuum-subtracted: 'full'
+    wav_for_continuum = 'NA'
+    
+    # Whether to use least square to estimate planetary and stellar contributions
+    use_lsqr = 'False'
+
+
+[config_inversion]
+    # Method to calculate the loglikelihood function used in the nested sampling procedure
+    # Format : 'chi2_classic' or 'CCF_Brogi' or 'CCF_Lockwood' or 'CCF_custom' (or 'chi2_covariance')
+    # or /!\ 'logL_type1', 'logL_type2', ... for each observation when using MOSAIC
+    logL_type = 'chi2_classic'
+
+    # Wavelength range used for the fit during the nested sampling procedure (separated windows can be defined).
+    # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'
+    wav_fit = '1.9, 2.5'
+
+    # Nested sampling algorithm used.
+    # Format : 'nestle' or 'dynesty' or 'ultranest' (/!\ only nestle currently /!\)
+    ns_algo = 'nestle'
+
+    # Number of living points during the nested sampling procedure.
+    # Format : 'integer'
+    npoint = 100
+
+
+[config_parameter]
+    # Definition of the prior function of each parameter explored by the grid. Please refer to the documentation to check
+    # the parameter space explore by each grid.
+    # Format : "function", function_param1, function_param2
+    # Example : "uniform", min, max
+    #           "constant", value
+    #           "gaussian", mu, sigma
+    #           'NA' if the grid cover a lower number of parameters
+    par1 = "uniform", 800, 2000
+    par2 = "uniform", 2.5, 5.5
+    par3 = "uniform", -0.1, 0.8
+    par4 = "uniform", 0.3, 0.9
+    par5 = 'NA'
+
+    # Definition of the prior function of each extra-grid parameter. r is the radius (MJup, >0), d is the distance (pc, >0),
+    # rv is the radial velocity (km.s-1), av is the extinction (mag), vsini is the projected rotational velocity (km.s-1, >0),
+    # and ld is the limb darkening (0-1).
+    # Format : "function", function_param1, function_param2
+    # Example : "uniform", min, max
+    #           "constant", value
+    #           "gaussian", mu, sigma
+    #           'NA' if you do not want to constrain this parameter
+    r = 'NA' #"uniform", 0, 10
+    d = 'NA' #"constant", 50.1
+    rv = 'NA'
+    alpha = 'NA'
+    av = 'NA'
+    vsini = 'NA'
+    ld = 'NA'
+    bb_T = 'NA' 
+    bb_R = 'NA' 
+
+
+[config_nestle]
+    # For details on these parameters, please see: http://kylebarbary.com/nestle/index.html
+    mechanic = 'static'     # Sampler “super-classes” of dynesty
+    # e.g. 'static' / 'dynamic' # the number of living point is fixed / variates
+    method = 'multi'        # Reduction of the parameters space
+    # e.g. 'single' / 'multi' #single-ellipsoidal / multi-ellipsoidal
+    maxiter = None          # Stopping criterions
+    maxcall = None
+    dlogz = None
+    decline_factor = 0.1
+    update_interval = None  # Divers
+    npdim = None
+    rstate = None
+    callback = None
+
diff --git a/docs/tutorials/data_abpic/outputs/test_t20240807_005025/config_used.ini b/docs/tutorials/data_abpic/outputs/test_t20240807_005025/config_used.ini
new file mode 100644
index 0000000..4ffc8e1
--- /dev/null
+++ b/docs/tutorials/data_abpic/outputs/test_t20240807_005025/config_used.ini
@@ -0,0 +1,111 @@
+[config_path]
+    # Path to the observed spectrum file
+    observation_path = data_abpic/ABPicb_SINFONI_K.fits
+    
+    # Path to store your interpolated grid
+    adapt_store_path = data_abpic/adapted_grid/
+    
+    # Path where you wish to store your results.
+    result_path = data_abpic/outputs/test_t20240807_005025/
+    
+    # Path of your initial grid of models
+    model_path = /Users/ppalmabifani/Desktop/exoAtm/c0_ForMoSA/INPUTS/atm_grids/atm_grids_native/EXOREM_native.nc
+
+
+[config_adapt]
+    # Wavelength range used for the extraction of data and adaptation of the grid (separated windows can be defined).
+    # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'
+    wav_for_adapt = "1.9, 2.5"
+    
+    # Method used to adapt the synthetic spectra to the data.
+    # example : 'by_reso' : A Gaussian law is convolved to the spectrum to decrease the resolution as a function of the
+    # wavelength.
+    #           'by_sample' : The spectrum is directly re-sampled to the wavelength grid of the data, using the module
+    # python spectres.
+    adapt_method = by_reso
+    
+    # Custom target resolution to reach (optional). The final resolution will be the lowest between this custom resolution,
+    # the resolution of the data, and the resolution of the models, for each wavelength.
+    # Format : float or 'NA'
+    custom_reso = NA
+    
+    # Continuum subtraction. If a float is given, the value will give the approximated spectral resolution of the continuum.
+    # Format : 'float' or 'NA'
+    continuum_sub = NA
+    
+    # Wavelength range used for the estimate of the continuum (separated windows can be defined).
+    # Format : 'window1_max / window2_min,window2_max / ... / windown_min'
+    # Note : the 'window1_min' and 'windown_max' are defined as the edges of 'wav_for_adapt'.
+    # If the entire wavelength range defined by the parameter 'wav_for_adapt' needs to be continuum-subtracted: 'full'
+    wav_for_continuum = NA
+    
+    # Whether to use least square to estimate planetary and stellar contributions
+    use_lsqr = False
+
+
+[config_inversion]
+    # Method to calculate the loglikelihood function used in the nested sampling procedure
+    # Format : 'chi2_classic' or 'CCF_Brogi' or 'CCF_Lockwood' or 'CCF_custom' (or 'chi2_covariance')
+    # or /!\ 'logL_type1', 'logL_type2', ... for each observation when using MOSAIC
+    logL_type = chi2_classic
+    
+    # Wavelength range used for the fit during the nested sampling procedure (separated windows can be defined).
+    # Format : 'window1_min,window1_max / window2_min,window2_max / ... / windown_min,windown_max'
+    wav_fit = "1.9, 2.5"
+    
+    # Nested sampling algorithm used.
+    # Format : 'nestle' or 'dynesty' or 'ultranest' (/!\ only nestle currently /!\)
+    ns_algo = nestle
+    
+    # Number of living points during the nested sampling procedure.
+    # Format : 'integer'
+    npoint = 100
+
+
+[config_parameter]
+    # Definition of the prior function of each parameter explored by the grid. Please refer to the documentation to check
+    # the parameter space explore by each grid.
+    # Format : "function", function_param1, function_param2
+    # Example : "uniform", min, max
+    #           "constant", value
+    #           "gaussian", mu, sigma
+    #           'NA' if the grid cover a lower number of parameters
+    par1 = uniform, 800, 2000
+    par2 = uniform, 2.5, 5.5
+    par3 = uniform, -0.1, 0.8
+    par4 = uniform, 0.3, 0.9
+    par5 = NA
+    
+    # Definition of the prior function of each extra-grid parameter. r is the radius (MJup, >0), d is the distance (pc, >0),
+    # rv is the radial velocity (km.s-1), av is the extinction (mag), vsini is the projected rotational velocity (km.s-1, >0),
+    # and ld is the limb darkening (0-1).
+    # Format : "function", function_param1, function_param2
+    # Example : "uniform", min, max
+    #           "constant", value
+    #           "gaussian", mu, sigma
+    #           'NA' if you do not want to constrain this parameter
+    r = NA    #"uniform", 0, 10
+    d = NA    #"constant", 50.1
+    rv = NA
+    alpha = NA
+    av = NA
+    vsini = NA
+    ld = NA
+    bb_T = NA
+    bb_R = NA
+
+
+[config_nestle]
+    # For details on these parameters, please see: http://kylebarbary.com/nestle/index.html
+    mechanic = static    # Sampler “super-classes” of dynesty
+    # e.g. 'static' / 'dynamic' # the number of living point is fixed / variates
+    method = multi    # Reduction of the parameters space
+    # e.g. 'single' / 'multi' #single-ellipsoidal / multi-ellipsoidal
+    maxiter = None    # Stopping criterions
+    maxcall = None
+    dlogz = None
+    decline_factor = 0.1
+    update_interval = None    # Divers
+    npdim = None
+    rstate = None
+    callback = None
diff --git a/docs/tutorials/data_abpic/outputs/test_t20240807_005025/result_nestle.pic b/docs/tutorials/data_abpic/outputs/test_t20240807_005025/result_nestle.pic
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diff --git a/docs/tutorials/data_abpic/outputs/test_t20240807_005025/spectrum_obs_ABPicb_SINFONI_K.npz b/docs/tutorials/data_abpic/outputs/test_t20240807_005025/spectrum_obs_ABPicb_SINFONI_K.npz
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diff --git a/docs/tutorials/demoabpic.ipynb b/docs/tutorials/demoabpic.ipynb
new file mode 100644
index 0000000..bfffa13
--- /dev/null
+++ b/docs/tutorials/demoabpic.ipynb
@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# demo AB Pic b\n",
+    "\n",
+    "\n",
+    "This tutorial is intended as a quick start. \n",
+    "\n",
+    "\n",
+    "We will use medium resolution VLT/SINFONI K-band data of AB Pic b. These observations and example model were published in [P. Palma-Bifani et al (2023)](https://www.aanda.org/articles/aa/pdf/2023/02/aa44294-22.pdf).\n",
+    "\n",
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Generic packages\n",
+    "import sys, time, os\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# ForMoSA modules\n",
+    "sys.path.insert(0, os.path.abspath('/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/'))\n",
+    "# For the interpolation & sampling\n",
+    "from ForMoSA.main_utilities import GlobFile\n",
+    "from ForMoSA.adapt.adapt_obs_mod import launch_adapt\n",
+    "from ForMoSA.nested_sampling.nested_sampling import launch_nested_sampling\n",
+    "# For the plots\n",
+    "from ForMoSA.plotting.plotting_class import PlottingForMoSA"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 0. Setup\n",
+    "\n",
+    "You need to create a config file with extension <.ini> and modify the parameters. Learn more about our config files in it's specific tutorial.\n",
+    "\n",
+    "To initialize ForMoSA we need to read the config.ini file and setup the outputs directory and global parameters as follows"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "base_path = 'data_abpic/'\n",
+    "\n",
+    "# CONFIG_FILE \n",
+    "# reading and defining global parameters\n",
+    "config_file_path = base_path + 'config_ABPicb.ini'\n",
+    "global_params = GlobFile(config_file_path)    \n",
+    "\n",
+    "# Optional: Add \"time_now\" and \"save_name\" to avoid overwriting results\n",
+    "time_now = time.strftime(\"%Y%m%d_%H%M%S\")\n",
+    "save_name = 'test'\n",
+    "\n",
+    "# Create directory to save the outputs \n",
+    "global_params.result_path = global_params.result_path+ save_name+'_t' + time_now+'/'\n",
+    "os.makedirs(global_params.result_path)\n",
+    "\n",
+    "# Overwrite some parameters\n",
+    "global_params.config.filename = global_params.result_path + 'config_used.ini'\n",
+    "global_params.config['config_path']['result_path']=global_params.result_path\n",
+    "global_params.config.write()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Interpolate the grid\n",
+    "\n",
+    "Once everything is setup, we start by adapting the models and observations. \n",
+    "\n",
+    "The grid of models is interpolated for this, but you don't need to repeat this step once you've adapted the grid for a specific dataset. \n",
+    "\n",
+    "(Answer 'no' only the first time)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Have you already interpolated the grids for this data? \n",
+    "y_n_par = 'yes'\n",
+    "#y_n_par = 'no' # Only answer no the first time, then comment to save time\n",
+    "\n",
+    "launch_adapt(global_params, justobs=y_n_par)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Lunch Nested Sampling \n",
+    "\n",
+    "Once the grid is interpolated, we proceed with the nested sampling. For this case we are using the Python package nestle. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Likelihood functions check-ups\n",
+      "\n",
+      "ABPicb_SINFONI_K will be computed with chi2_classic\n",
+      "\n",
+      "Done !\n",
+      "\n",
+      "\u001b[Kit=  1330 logz=-1071.92517042 \n",
+      "- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -\n",
+      "-> Nestle  \n",
+      " \n",
+      "The code spent 234.33751487731934 sec to run.\n",
+      "niter: 1331\n",
+      "ncall: 13109\n",
+      "nsamples: 1431\n",
+      "logz: -1071.101 +/-  0.347\n",
+      "h: 12.010\n",
+      "\n",
+      "\n",
+      " \n",
+      "-> Voilà, on est prêt\n"
+     ]
+    }
+   ],
+   "source": [
+    "launch_nested_sampling(global_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Plotting the outcomes\n",
+    "\n",
+    "ForMoSA has been designed with a plotting class. Bellow we show 4 main features: \n",
+    "\n",
+    "- Plotting corner-plots\n",
+    "- Plotting spectra and residuals\n",
+    "- Plotting chains \n",
+    "- Accessing the different parameters\n",
+    "\n",
+    "All plotting functions return the fig object. Therefore you can edit the axes, overplot text/curves, save, etc...\n",
+    "\n",
+    "We need to start by initializing the plotting class as follows."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Path to output file created in the first step\n",
+    "config_file_path_pl = 'data_abpic/outputs/test_t20240807_005025'\n",
+    "\n",
+    "# Initialize the plotting class and set the color\n",
+    "plotForMoSA = PlottingForMoSA(config_file_path_pl+'/config_used.ini', 'red')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Corner"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Corner plot\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 970x970 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig = plotForMoSA.plot_corner(levels_sig=[0.997, 0.95, 0.68], bins=20, quantiles=(0.16, 0.5, 0.84), burn_in=1100)\n",
+    "#plt.savefig('') \n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Spectrum and Residuals"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Best fit and residuals plot\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/ppalmabifani/opt/anaconda3/envs/exo_formosa_multi_pip2/lib/python3.11/site-packages/ForMoSA/plotting/plotting_class.py:723: UserWarning: No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n",
+      "  axr2.legend(frameon=False,handlelength=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax, axr, axr2 = plotForMoSA.plot_fit(figsize=(10, 5), uncert='no')\n",
+    "\n",
+    "# You can modify the different axes and includ further plotting features\n",
+    "axr.set_ylim(-5,5)\n",
+    "\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### PLOT Chains of posteriors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ForMoSA - Posteriors chains for each parameter\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plotForMoSA.plot_chains(figsize=(10,6))\n",
+    "#axs[i, j] #i=cols, j=0,1\n",
+    "#plt.savefig('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Access information\n",
+    "\n",
+    "You can access different parametes since we are working with a class\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "posteriors_chains = plotForMoSA.posterior_to_plot\n",
+    "posteriors_names = plotForMoSA.posteriors_names"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "exo_formosa_multi3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/exorem_info.ipynb b/docs/tutorials/exorem_info.ipynb
new file mode 100644
index 0000000..23fd7dd
--- /dev/null
+++ b/docs/tutorials/exorem_info.ipynb
@@ -0,0 +1,141 @@
+{
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exo-REM\n",
+    "\n",
+    "Example on how to access to the atmospheric grid info to check the parameters and their ranges.\n",
+    "\n",
+    "### Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import xarray as xr\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Open the grid\n",
+    "\n",
+    "You can check the free parameters names, ranges and units together with the wavelength range and spectral resolution."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.Dataset>\n",
+      "Dimensions:     (wavelength: 29922, par1: 33, par2: 5, par3: 4, par4: 15)\n",
+      "Coordinates:\n",
+      "  * wavelength  (wavelength) float64 0.6667 0.6667 0.6667 ... 245.4 248.4 251.6\n",
+      "  * par1        (par1) float64 400.0 450.0 500.0 ... 1.9e+03 1.95e+03 2e+03\n",
+      "  * par2        (par2) float64 3.0 3.5 4.0 4.5 5.0\n",
+      "  * par3        (par3) float64 -0.5 0.0 0.5 1.0\n",
+      "  * par4        (par4) float64 0.1 0.15 0.2 0.25 0.3 ... 0.6 0.65 0.7 0.75 0.8\n",
+      "Data variables:\n",
+      "    grid        (wavelength, par1, par2, par3, par4) float64 ...\n",
+      "Attributes:\n",
+      "    key:      ['par1', 'par2', 'par3', 'par4']\n",
+      "    par:      ['teff', 'logg', 'mh', 'co']\n",
+      "    title:    ['Teff', 'log(g)', '[M/H]', 'C/O']\n",
+      "    unit:     ['(K)', '(dex)', '', '']\n",
+      "    res:      [29999.50000004 29998.49999991 29997.50000002 ...    80.5\\n    ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "grid_path = '/EXOREM_native.nc'\n",
+    "ds = xr.open_dataset(grid_path, decode_cf=False)\n",
+    "print(ds)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Get a spectrum for specific values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Open the grid \n",
+    "grid = ds['grid']\n",
+    "\n",
+    "# get wavelength \n",
+    "wave = grid['wavelength'].values\n",
+    "\n",
+    "# for Exo-REM par1=Teff, par2=logg, par3=[M/H], par4=C/O\n",
+    "# get flux by selecting a value for each parameter\n",
+    "flux = grid.sel(par1=1700,par2=4.0, par3=0., par4=0.55, method=\"nearest\")\n",
+    "\n",
+    "# Plot the spectrum\n",
+    "plt.figure(figsize=(7,3))\n",
+    "plt.plot(wave, flux*wave, 'k')\n",
+    "plt.xlabel('Wavelength (µm)')\n",
+    "plt.ylabel('Flux')\n",
+    "plt.xlim(0.5,5) \n",
+    "plt.yticks([])\n",
+    "plt.title('Exo-REM at Teff=1700K, logg=4, and solar [M/H] and C/O')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3.7.13 ('exo_formosa')",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  },
+  "orig_nbformat": 4,
+  "vscode": {
+   "interpreter": {
+    "hash": "9aec1e17db1a1c759b18bf85d14c335cdcfb4080f740d60b236f2747bc3cb68f"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/docs/tutorials/inputs.ipynb b/docs/tutorials/inputs.ipynb
new file mode 100644
index 0000000..6162c18
--- /dev/null
+++ b/docs/tutorials/inputs.ipynb
@@ -0,0 +1,43 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# INPUTS\n",
+    "\n",
+    "## Observations (.fits)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "vscode": {
+     "languageId": "plaintext"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Config file (.ini)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/requirements.txt b/requirements.txt
index b6973a1..9f71f33 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -9,5 +9,6 @@ nestle
 PyAstronomy
 spectres
 pyyaml
+tqdm
 importlib-metadata==4.13.0
 xarray==2023.10.1
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 3450d80..2ccc6a8 100644
--- a/setup.py
+++ b/setup.py
@@ -1,21 +1,39 @@
-from setuptools import setup
+from setuptools import setup, find_packages, Extension
+import re
+
 
 with open("README.md", "r") as fh:
     long_description = fh.read()
 
+
+# auto-updating version code stolen from RadVel
+def get_property(prop, project):
+    result = re.search(
+        r'{}\s*=\s*[\'"]([^\'"]*)[\'"]'.format(prop),
+        open(project + "/__init__.py").read(),
+    )
+    return result.group(1)
+
+
 def get_requires():
     reqs = []
     for line in open('requirements.txt', 'r').readlines():
         reqs.append(line)
     return reqs
 
+description = '''Welcome to ForMoSA, an open-source Python package. 
+We designed this tool to model exoplanetary atmospheres with a forward modeling approach. 
+
+We encourage the community to exploit its capabilities!'''
+
 setup(name='ForMoSA',
-      version='2.0.0',
+      version=get_property("__version__", "ForMoSA"),
       description='ForMoSA: Forward Modeling Tool for Spectral Analysis',
+      long_description=description,
       url='https://github.com/exoAtmospheres/ForMoSA',
       author='P. Palma-Bifani, S. Petrus, M. Ravet, A. Denis, M. Bonnefoy, G. Chauvin',
       author_email='paulina.palma-bifani@oca.eu',
-      license='BSD 2-Clause License',
+      license='BSD 3-Clause License',
       packages=['ForMoSA', 'ForMoSA.adapt', 'ForMoSA.nested_sampling', 'ForMoSA.plotting'],
       install_requires=get_requires(),
       include_package_data = True,