From d641fdcaa2979fa999c58ed51ce71129ed9ef6da Mon Sep 17 00:00:00 2001
From: sblunt <sarah.blunt.3@gmail.com>
Date: Thu, 22 Jun 2023 14:35:07 -0700
Subject: [PATCH 01/10] add coveralls integration to CI

---
 .github/workflows/python-package.yml | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index b046614a..6d6bafe1 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -28,7 +28,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest
+        python -m pip install flake8 pytest coverage coveralls
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Lint with flake8
       run: |
@@ -38,4 +38,5 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        pytest
+        pytest --cov-report= --cov=orbitize tests/
+        coveralls

From bbb23895650d4bea738f6cd17090af4bbfbc4183 Mon Sep 17 00:00:00 2001
From: sblunt <sarah.blunt.3@gmail.com>
Date: Thu, 22 Jun 2023 14:41:56 -0700
Subject: [PATCH 02/10] using rtd v2 yaml file

---
 .readthedocs.yaml | 17 +++++++++++++++++
 1 file changed, 17 insertions(+)
 create mode 100644 .readthedocs.yaml

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
new file mode 100644
index 00000000..ada27db1
--- /dev/null
+++ b/.readthedocs.yaml
@@ -0,0 +1,17 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+sphinx:
+  configuration: docs/conf.py
+
+formats:
+   - pdf
+   - epub
+
+python:
+   install:
+   - requirements: requirements.txt
\ No newline at end of file

From 9e70c58d915e58856d782963ffccc0df3cc2263e Mon Sep 17 00:00:00 2001
From: sblunt <sarah.blunt.3@gmail.com>
Date: Thu, 22 Jun 2023 14:45:07 -0700
Subject: [PATCH 03/10] remove old rtd yaml files

---
 docs/readthedocs-environment.yml | 12 ------------
 readthedocs.yml                  |  4 ----
 2 files changed, 16 deletions(-)
 delete mode 100644 docs/readthedocs-environment.yml
 delete mode 100644 readthedocs.yml

diff --git a/docs/readthedocs-environment.yml b/docs/readthedocs-environment.yml
deleted file mode 100644
index c2572aee..00000000
--- a/docs/readthedocs-environment.yml
+++ /dev/null
@@ -1,12 +0,0 @@
-channels:
-  - conda-forge
-dependencies:
-  - python==3.8
-  - sphinx>=1.4
-  - numpy
-  - pandoc
-  - nbconvert
-  - ipykernel
-  - pip:
-    - nbsphinx
-    - prompt-toolkit==2.0.1
diff --git a/readthedocs.yml b/readthedocs.yml
deleted file mode 100644
index f311c6aa..00000000
--- a/readthedocs.yml
+++ /dev/null
@@ -1,4 +0,0 @@
-conda:
-  file: docs/readthedocs-environment.yml
-python:
-  pip_install: true
\ No newline at end of file

From 60e2e518338bca72941ffd14c08a82fd11721c4d Mon Sep 17 00:00:00 2001
From: sblunt <sarah.blunt.3@gmail.com>
Date: Thu, 22 Jun 2023 14:55:23 -0700
Subject: [PATCH 04/10] fixing coveralls gh action

---
 .github/workflows/python-package.yml | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 6d6bafe1..ea68552a 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -28,7 +28,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest coverage coveralls
+        python -m pip install flake8 pytest
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Lint with flake8
       run: |
@@ -38,5 +38,8 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        pytest --cov-report= --cov=orbitize tests/
+        pytest
         coveralls
+    - name: Coveralls GitHub Action
+      uses: coverallsapp/github-action@v2
+      

From f6a8f3a730fcdfe06d804c90a3d563f235058809 Mon Sep 17 00:00:00 2001
From: sblunt <sarah.blunt.3@gmail.com>
Date: Thu, 22 Jun 2023 15:09:08 -0700
Subject: [PATCH 05/10] remove radvel util function calls so we don't need it
 as dep.

---
 ...ing_nonOrbitize_Posteriors_as_Priors.ipynb | 452 +++++++++++++-----
 1 file changed, 330 insertions(+), 122 deletions(-)

diff --git a/docs/tutorials/Using_nonOrbitize_Posteriors_as_Priors.ipynb b/docs/tutorials/Using_nonOrbitize_Posteriors_as_Priors.ipynb
index 145aed97..5050fa27 100644
--- a/docs/tutorials/Using_nonOrbitize_Posteriors_as_Priors.ipynb
+++ b/docs/tutorials/Using_nonOrbitize_Posteriors_as_Priors.ipynb
@@ -1,6 +1,7 @@
 {
  "cells": [
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -16,6 +17,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -28,30 +30,38 @@
    "metadata": {
     "scrolled": true
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/Users/bluez3303/miniconda3/envs/python3.10/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.17.3 and <1.25.0 is required for this version of SciPy (detected version 1.25.0\n",
+      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
+     ]
+    }
+   ],
    "source": [
-    "import sys\n",
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
-    "import orbitize\n",
     "from orbitize import system, priors, basis, read_input, DATADIR\n",
     "import pandas as pd\n",
-    "import radvel.utils as rv_ut\n",
-    "import radvel.orbit as rv_or\n",
     "\n",
     "# Read RadVel posterior chain\n",
-    "pdf_fromRadVel = pd.read_csv(\"{}/sample_radvel_chains.csv.bz2\".format(DATADIR), compression='bz2', index_col=0)\n",
+    "pdf_fromRadVel = pd.read_csv(\n",
+    "    \"{}/sample_radvel_chains.csv.bz2\".format(DATADIR), compression=\"bz2\", index_col=0\n",
+    ")\n",
     "\n",
-    "per1 = pdf_fromRadVel.per1 # Period\n",
-    "k1 = pdf_fromRadVel.k1 # Doppler semi-amplitude\n",
+    "per1 = pdf_fromRadVel.per1  # Period\n",
+    "k1 = pdf_fromRadVel.k1  # Doppler semi-amplitude\n",
     "secosw1 = pdf_fromRadVel.secosw1\n",
     "sesinw1 = pdf_fromRadVel.sesinw1\n",
-    "tc1 = pdf_fromRadVel.tc1 # time of conj.\n",
+    "tc1 = pdf_fromRadVel.tc1  # time of conj.\n",
     "\n",
     "len_pdf = len(pdf_fromRadVel)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -61,6 +71,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -76,30 +87,49 @@
    "outputs": [],
    "source": [
     "# From P to sma:\n",
-    "system_mass = 1 # [Msol]\n",
-    "system_mass_err = 0.01 # [Msol]\n",
-    "pdf_msys = np.random.normal(system_mass, system_mass_err, size=len_pdf) \n",
-    "sma_prior = rv_ut.semi_major_axis(per1, pdf_msys)\n",
+    "system_mass = 1  # [Msol]\n",
+    "system_mass_err = 0.01  # [Msol]\n",
+    "per_yr = per1 / 365.25\n",
+    "pdf_msys = np.random.normal(system_mass, system_mass_err, size=len_pdf)\n",
+    "sma_prior = (per_yr**2 * pdf_msys) ** (1 / 3)\n",
     "\n",
     "# ecc from RadVel parametrization\n",
     "ecc_prior = sesinw1**2 + secosw1**2\n",
     "\n",
     "# little omega, i.e. argument of the periastron, from RadVel parametrization\n",
-    "w_prior = np.arctan2(sesinw1, secosw1) + np.pi #+pi bc of RadVelvs Orbitize convention\n",
-    "w_prior[w_prior>=np.pi] = w_prior[w_prior>=np.pi]-2*np.pi\n",
+    "w_prior = (\n",
+    "    np.arctan2(sesinw1, secosw1) + np.pi\n",
+    ")  # +pi bc of RadVel vs Orbitize convention\n",
+    "w_prior[w_prior >= np.pi] = w_prior[w_prior >= np.pi] - 2 * np.pi\n",
+    "\n",
+    "\n",
+    "def Tc_to_Tp(tc, per, ecc, omega):  # stolen from radvel\n",
+    "    f = np.pi / 2 - omega\n",
+    "    ee = 2 * np.arctan(np.tan(f / 2) * np.sqrt((1 - ecc) / (1 + ecc)))\n",
+    "    tp = tc - per / (2 * np.pi) * (ee - ecc * np.sin(ee))\n",
+    "    return tp\n",
+    "\n",
     "\n",
     "# tau, i.e. fraction of elapsed time of node passage, from RadVel parametrization\n",
-    "tp1 = rv_or.timetrans_to_timeperi(tc1, per1, ecc_prior, w_prior)\n",
+    "tp1 = Tc_to_Tp(tc1, per1, ecc_prior, w_prior)\n",
     "tau_prior = basis.tp_to_tau(tp1, 55000, per1)\n",
     "\n",
     "# m1 prior\n",
-    "m1sini_prior = rv_ut.Msini(k1, per1, pdf_msys, ecc_prior, Msini_units='jupiter') * 1e-3\n",
+    "K_0 = 28.4329\n",
+    "m1sini_prior = (\n",
+    "    k1\n",
+    "    / K_0\n",
+    "    * np.sqrt(1.0 - ecc_prior**2.0)\n",
+    "    * pdf_msys ** (2.0 / 3.0)\n",
+    "    * per_yr ** (1 / 3.0)\n",
+    ") * 1e-3\n",
     "sini_prior = priors.SinPrior()\n",
     "i_prior_samples = sini_prior.draw_samples(len_pdf)\n",
-    "m1_prior = m1sini_prior/np.sin(i_prior_samples)\n"
+    "m1_prior = m1sini_prior / np.sin(i_prior_samples)"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -107,6 +137,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -130,12 +161,18 @@
    ],
    "source": [
     "# Initialize System object which stores data & sets priors\n",
-    "data_table = read_input.read_file(\"{}/test_val.csv\".format(DATADIR)) #read data\n",
+    "data_table = read_input.read_file(\"{}/test_val.csv\".format(DATADIR))  # read data\n",
     "num_secondary_bodies = 1\n",
     "\n",
     "system_orbitize = system.System(\n",
-    "    num_secondary_bodies, data_table, system_mass,\n",
-    "    1e1, mass_err=system_mass_err, plx_err=0.1, tau_ref_epoch=55000,fit_secondary_mass=True,\n",
+    "    num_secondary_bodies,\n",
+    "    data_table,\n",
+    "    system_mass,\n",
+    "    1e1,\n",
+    "    mass_err=system_mass_err,\n",
+    "    plx_err=0.1,\n",
+    "    tau_ref_epoch=55000,\n",
+    "    fit_secondary_mass=True,\n",
     ")\n",
     "\n",
     "param_idx = system_orbitize.param_idx\n",
@@ -144,6 +181,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -160,26 +198,39 @@
     "\n",
     "# The values go into a matrix\n",
     "total_params = 6\n",
-    "values = np.empty((total_params,len_pdf))\n",
-    "values[0,:] = sma_prior\n",
-    "values[1,:] = ecc_prior\n",
-    "values[2,:] = i_prior_samples\n",
-    "values[3,:] = w_prior\n",
-    "values[4,:] = tau_prior\n",
-    "values[5,:] = m1_prior\n",
-    "\n",
-    "kde = gaussian_kde(values, bw_method=None) # None indicates that the KDE bandwidth is set to default\n",
-    "kde_prior_obj = priors.KDEPrior(kde, total_params)#,bounds=bounds_priors,log_scale_arr=[False,False,False,False,False,False])\n",
-    "\n",
-    "system_orbitize.sys_priors[param_idx['sma1']] = kde_prior_obj#priors.GaussianPrior(np.mean(sma_prior), np.std(sma_prior))#priors.KDEPrior(gaussian_kde(values[0,:], bw_method=None),1)#kde_prior_obj#\n",
-    "system_orbitize.sys_priors[param_idx['ecc1']] = kde_prior_obj#priors.GaussianPrior(np.mean(ecc_prior), np.std(ecc_prior))#kde_prior_obj#priors.GaussianPrior(np.mean(pdf_fromRadVel['e1']), np.std(pdf_fromRadVel['e1']))#priors.KDEPrior(gaussian_kde(values[1,:], bw_method=None),1)#kde_prior_obj\n",
-    "system_orbitize.sys_priors[param_idx['inc1']] = kde_prior_obj\n",
-    "system_orbitize.sys_priors[param_idx['aop1']] = kde_prior_obj#priors.GaussianPrior(np.mean(w_prior), 0.1)#kde_prior_obj#kde_prior_obj\n",
-    "system_orbitize.sys_priors[param_idx['tau1']] = kde_prior_obj#priors.GaussianPrior(np.mean(tau_prior), 0.01)#np.std(tau_prior))#kde_prior_obj#kde_prior_obj#priors.KDEPrior(gaussian_kde(values[4,:], bw_method=None),1)#kde_prior_obj\n",
+    "values = np.empty((total_params, len_pdf))\n",
+    "values[0, :] = sma_prior\n",
+    "values[1, :] = ecc_prior\n",
+    "values[2, :] = i_prior_samples\n",
+    "values[3, :] = w_prior\n",
+    "values[4, :] = tau_prior\n",
+    "values[5, :] = m1_prior\n",
+    "\n",
+    "kde = gaussian_kde(\n",
+    "    values, bw_method=None\n",
+    ")  # None indicates that the KDE bandwidth is set to default\n",
+    "kde_prior_obj = priors.KDEPrior(\n",
+    "    kde, total_params\n",
+    ")  # ,bounds=bounds_priors,log_scale_arr=[False,False,False,False,False,False])\n",
+    "\n",
+    "system_orbitize.sys_priors[\n",
+    "    param_idx[\"sma1\"]\n",
+    "] = kde_prior_obj  # priors.GaussianPrior(np.mean(sma_prior), np.std(sma_prior))#priors.KDEPrior(gaussian_kde(values[0,:], bw_method=None),1)#kde_prior_obj#\n",
+    "system_orbitize.sys_priors[\n",
+    "    param_idx[\"ecc1\"]\n",
+    "] = kde_prior_obj  # priors.GaussianPrior(np.mean(ecc_prior), np.std(ecc_prior))#kde_prior_obj#priors.GaussianPrior(np.mean(pdf_fromRadVel['e1']), np.std(pdf_fromRadVel['e1']))#priors.KDEPrior(gaussian_kde(values[1,:], bw_method=None),1)#kde_prior_obj\n",
+    "system_orbitize.sys_priors[param_idx[\"inc1\"]] = kde_prior_obj\n",
+    "system_orbitize.sys_priors[\n",
+    "    param_idx[\"aop1\"]\n",
+    "] = kde_prior_obj  # priors.GaussianPrior(np.mean(w_prior), 0.1)#kde_prior_obj#kde_prior_obj\n",
+    "system_orbitize.sys_priors[\n",
+    "    param_idx[\"tau1\"]\n",
+    "] = kde_prior_obj  # priors.GaussianPrior(np.mean(tau_prior), 0.01)#np.std(tau_prior))#kde_prior_obj#kde_prior_obj#priors.KDEPrior(gaussian_kde(values[4,:], bw_method=None),1)#kde_prior_obj\n",
     "system_orbitize.sys_priors[-2] = kde_prior_obj"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -203,8 +254,7 @@
     },
     {
      "data": {
-      "image/png": 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AoAlVM3juvtbdR9x9ZHBwsBMxAYAk2sIBQKNogwcAAJAxtMED0DPyas80Z3nl6JgEIFPI4AEpMrM1ktZI0tDQUMrRdL9h5du2XW+4ZxMAdB+qaDOAMe96F21cAQDtQAleJwVBXWPcxdVRQXuiAQAAGUUGr5PiOWxrFFdH1f4OAAAAqmg7rhXVqVTHomfEM0MEQXjgNjBLS165zs4ukaPDBYDeRwkegPZJzgxRR+l10rDy8kLjdzVmdX50Pq/AbPJGqsGwASBVlOABAABkDBk8AACAjKGKNkW1tqWjzR1QO7NdOyZNVrd2PBoASAcleO0QBNNH3M/lJLO2jcIPoLwxBXKZXNPTYF65imkyft8uHTziDiPlOn7EnUoAIEWU4LVDoaBh+VRpQT4vSRqmJA7ouEAFWaLsLp6xotqsGPH7dungEQ93VK5oPdmxBABSQgavjOS5O9mLrlT1T7Vt0AsPAAB0ElW0ALpKN7c57ebYACCJDF6Pyys32b4oOYhsXgzWCgBAv6KKtscl2xF5fvryuK0RAADoL2TwOqCeap1mhk4p1+aPtoBIVanpyXKtmX4s7gUbqPmODXnlFNDRHUBGkMED0F5RL/JdlrWgQVtcgt2K0uph5aeVgtei7mnQAKBDaIPXrGpjYpVBGzkAANAulOA1q9qYWGXQRg4AALQLJXg1YGgEAADQS8jgAUhVXuFUfiU7Y3SRijd6cVONHvgeAPoDGbxm5HJTJ/Nc/W3q4jHs2nVBiC+ctPVDNxtWPpxKrFRnjF4RN9XwHv8eADKDNng1Knn3njyR5/MKGmiHJ0kqSK1sjheH0coehgAAoHdQggcAAJAxZPAAoIpKhfN0wgLQjcjgtVCvNBYH2i4IWjJTRSyv3OSsFZ14XyljCuSyqe010O4WADqFNnhN2PXOPR/+1/ysSUBvK7Q2ESTnXO7E+0oJVAg7g0jhvw20uwWATqlagmdma8xs1MxGx8fHOxETgAzLQp4oHhEFALpV1Qyeu6919xF3HxkcHOxETAAAAGgCbfDqNKagZW16UtHitlFoDiXkAIB2oA1enSbb4fRg9YyZ5DQQ7CruvlbSWkkaGRnxlMOpT/GNQvw8Hh+yWmejXE75Nh2OPX0TBgAtQAYPQGOKO1IUP48yeqXaqoXL8rssa1UOt9HOFXGsvZXTBoBdUUULAACQMWTwkmifBgAAMoAq2qQWj90FAACQBkrwAAAAMoYSPACNi3vKtqj0O6/Gpv9Kq9dsxXiLexUDQAeRwesD8UUo2bNwsrdg3F0wCJQvhL0PnS6EqFWceWnRtA7DyssbGIOolVOS1fu5ZeOlyQeAFJHB6wM1XTQLBaZNBwAgI2iDB6BuzMMKAN2NDB4AAEDGUEXbj3I5ecGihun5XV+ncTjqlZyWzKz6NGUVdMM0Y8kYGu34ISncD0FQPi0FQdhWL5cjvQFoKTJ4/Sifj6aFonE4mmMWddRJZE7MJBUkmRrqsJNWh4lyMTTa8UOSrBC+18rti0IhfIE6bwAtRhUtAABAxlCCp/6bYJzCAgAAso0SvDLGFMhlcpnGFEw+74b2QS0VBE23mQIAAN2FErwyAhVkUZle3P7GsljGF7cBkijaQ1N65fBp9U1avL2gpVsFgOaQwQPQV1rdiSPeXgZv/wD0MKpoUTOz3imlAQCgn5HBk6q2r8sr1/Nt7/LK7fId88rR9g4AgAyiilal29sldcO4XM0q9R2GlZfvuhgoixJcAOgNZPBKCAcB7g/JC3a/fGegXi0rwWeWGAAd0p8ZPE6y1eVyU8OnsJ/Q51pWis8sMQA6pD8zeJxkq4szddTJAQDQc+hkAQAdUO5eiXsoAO3Q1xk8hv1oDvsOAIDu1J9VtADQTvHwQ7QGAZCSqhk8M1sjaY0kDQ0NtT0gAOh5k21YU40CQB+rWkXr7mvdfcTdRwYHBzsRE9A3zGyNmY2a2ej4+Hja4VRUqQ0Z1fXt3Q/sYwD16os2eJwYm8P+ax9uoAAA7ZD9DF4QTkM2OfYdAABAxmW/k0UhnIbMCxRDAZ3Wj6W/rfzO/bj/ALRG9kvw0HK0BwIal1duMhFNToEWzxwT1zQEAbUOAJqS/RI8AOgiw8rLo4mfh+MbpeKZY5htB0CT+rYEL6+cXCaXTZtIPF7essnFe0ip7x4vk5nGFExbf0zB9FKHRlBSAQBAy/VtCV65ycNbNql4D0p+dy9a5i4FRfWygQrhC83U11JSAchMk6V6ZlPpr9J6AFBJZkrwamkXRtuxzmJfAwCQjsxk8ACgG+WV26V5QyVjCvqyiQiA1spGBi8ISp9Ag2ByTshybe7QmFouQmMKaF+HvjesfNicoUaBCn3dVARAa2SjDV6hUPr+uBC3EevvtnXtECgaX7DKOky2DgBA52WjBC+huN0X7cAaU9xesZb2jQAAoDtkLoMHAADQ73ovgxcEzY+9hoaNKaAdI9AtGEcSQBm91wZvsl1d+TrB+CWGi2q9uO1dK1Qa7wvpKJWsGHetizGOJIAyeq8EDwAAABWRwQMAAMgYMnjojHrbCsVtLWlviQwqNatOK3qi05sdQKxn2uAl29XFbbdoa5e+Uu3oSv4u9bYVittaRhuc3CY/NnpQPNB6PZ2T4vcUL1OpMT0nb4JKvAagL/VMBg8AelUjA62Xek9xhm8SnS0AFKGKFgAAIGN6KoNXPP9puTHZGKutPfLKVd+n5eYFrlXc9i7Hb9dp5dpvlWovhvTEvwe/CYBKurqKtvgEVjwGW/J5suqilWO1YUq1aqawPV6ZeYGL1iu3zFWgoR0AAE3qqRI8AAAAVEcGDwCyIBf1uo171CaHJqp1ikemPgMyo7UZvOjkULGNSBAob9PXMZv+3rwFkplcpjEFNbWpi4cUoO1d5+WVq9juLj4O8spN+10bUal6N/k8q+2Tamknt0vaKrEsq/unr+XzYdOUQiH8fQuFqd61hcL01xKmHQ/Re6odHxxDQPdrbRu8WrrqF8q00Uq8t1Tbumpt6hoZhgCtMax8+eEbitaL1bI+AABoDFW0AAAAGUMGDwAAIGOqVtGa2RpJa6KnW83svuqbtYWSNoXvL7tOmWc2WSlbvLzc+m0wGX8P6+h3iH+tXX/H5OPiX81KLFF80BTFn1izhnZ45ZYVSb2xZon09XtV+d0qfa969kOTbagWmrSpTemv1XolPUf7VCq3XyueFW36mdOKlifTYfXjpPQ6cZyqen6flHoaA/qVeRvGHDOzUXcfafmGO6TX45d6/zv0evyN6pXv3StxSr0TK3ECaCWqaAEAADKGDB4AAEDGtCuDt7ZN2+2UXo9f6v3v0OvxN6pXvnevxCn1TqzECaBl2tIGDwAAAOmhihYAACBj6s7gmdmeZnaLma0zs5+Z2QuLXn9v9No6Mxszs89FywMzu8HMbjazD7bqC9SrifgvN7M7o+XfSSf6muI/wMx+Er1+o5ktjpb3yv4vF39X7P92MLMFZnatmd1kZpeY7Tr4hJl91cx+Y2aXpRDfW6Lf7GYze37Ra3PN7FtR7N8ys7mdjq/GOI81s7vN7Kn4mEpLlTg/YGa3R6+VPBa6JM4/M7OfRmn1WjPbM604AZTh7nX9KcwUzoweHyDp5xXW/Q9JK6PHV0o6Pnp8vaQl9X52K/6aiP9yScelEXM98Uv6rKQ3R4/fIunTvbT/K8TfFfu/TfvkIklnRI+/KunkEuvsL2mVpMs6HNt8SXdImi1pWNJPi14/S9JHoscflXRWSvuwWpx7Sdpd0jpJi1P8ravFeVDi8b9IemGXxjk78fh8SX+R1j7ljz/+Sv/VXYLn7hPuviN6uqek/y61npkNShp299uiRUe4+03R4x9IekG9n90KTcQvSRdHJRWntTvOcmqI/15J86LHCyT9LnrcK/u/XPxSF+z/Nlkl6dro8fdV4rdx94c7GVDC0ZJucven3X1M0u5mNifx+ipVib1DKsbp7o+5+9aUYkuqFucDiXWflrSjeAMdUi3OpxPr7qYw3QLoIlVnsijFzPaXdJWkgyW9rcxqpyu8A40lM5OPStq3kc9uhQbjf5+7bzKzBZL+y8x+7u6/bHOoJVWJ/3pJPzKzt0uaI2lFtLxX9n+5+Ltm/7fBfIW/iaL/904xlmILJP0h8fyxaNlvSryeZuzV4uwWNcVpZqsk7SfpJx2LbLqqcUZp9D2SnpT06Y5GB6CqhjpZuPvD7n6cwovvpWVWe6OkbyaeTyQe7yVpcyOf3QqNxO/um6L/N0u6TtLydsdZTpX4Py3pw+5+uKTzJF0YLe+V/V8y/m7a/40wsxlmdluJv48ovJDuFa2a6m9TwmZNlahKu8aXfD3N2KvF2S2qxmlmh0v6lKTT3D2tYQ6qxunuX3H3ZZK+K+n9HYwNQA0a6WSRrJ55XNKWEuscLMmLqhvuMrNjo8enKKU700bjN7N50f+zJf2JpPvbHGpJNcRvmpp383cK77ql3tn/JePvlv3fKHff6e4rS/x9QtJ6SS+NVn1p9Lxb3C7pODObZWZDkra6+7bE690Se7U4u0XFOM3sQIXtMP8svqlJSbU4k51pHpX0RKcDBFBZ3ePgmdmRkj4vaaekWZI+Jun3kl7k7p+J1jlf0ri7X5J43wGSvqKw0e5/uvsFLfkGdWoi/h8pbKQ9S9I33f0LnY49iqNi/GZ2mKR/Uth2Z5akd7r7Pb2y/yvE3xX7vx3MbG9JX9dUm8S/dPcJM/s7SZ9093Ezu0BhxnxfSf8j6ZXu/scOxfc2Se+Q5JLerfC3iX+vZyjMkCyW9GtJb3X3pzoRV51xHizpS5KOlHSPpCvc/R+6MM5rJS1RuC8l6TPu/oMujPMjkuIe8Jslvc3dHy29JQBpYKBjAACAjGGgYwAAgIwhgwcAAJAxZPAAAAAyhgweAABAxpDBAwAAyBgyeAD6ipnNM7M/r/D630VTFZZ6bV8z+1z0eFU0KDEAdB0yeE0yswEz+yczuzmaJ/Vb0fK3mNnOaJDQeN0HzezyxPP9o3VWl9n2OjO7tWgba81sXeJ5YGbXF73vwej/d5lZ3sze1KrvC2TAPEklM3hmNsPd3+Pu46Ved/eN7n5u9HSVJDJ4ALoSGbzmnSxpprv/ibsfL+kvE6/9TOGctjKzlQpnZkh6g6QvKJwWrZzXuftD0TZmK5yia0sy01dONJDr5TV+DyB1ZvYpM1sf3di83MyGzOyH0bL/im6oSi17tpn9wMxuiP4fjLb3oJl9PFr3quhj3ivpyOgG6mVmdp6ZXW5m10h6fbR8cfT+i6JYbjSzl8Q3VBbOifwWSR9KbOfvE9/jOjPLdXbvAcCUmWkHkAFbJS01s+e6+/9Ec6XGfiTpxQrnV32jpCskjSRef4Wkl0i6zszm1DC10sskXSPpPoWZw4ta9B2A1JnZyZLmu/sJZrabpFsVTkl3sbv/2MwGohk+Plti2WckfcLdbzOzV0r6K0nvU3iO+3d3/5iZ/djMlkq6WNKh7n5S9LlHSdrm7qdGz98Z/f9SSUOSjnV3N7MZkp4thXMiR6XxD7r7N81sQNL5Fk7Ft0jSdncvdGTHAUAJlOA1yd1/onBqrS+Z2ZiZvSfx8g5J95nZEZKWSvpF/EJ0obnf3Z+U9D1JL6/h406X9A1J31c4bRWQJcsknRA1QfgPSXMkHSrpRkly94lovcNKLFsm6aLove+XtDBavsPd43T3kKS9y3z2LSWWLZV0o0fT/bj7znKBR3FcLelPJb1V4fRtAJAaSvBawN2/KumrZranpJ9EVT2xb0taq7DkLelNkpaZ2Q8lzZW0SdK/lvsMM9tL0p9E25KkwMyWS9oYvT9eb5akp5v7RkAq7pX0Y3d/tzTZJOGbCtu6XReX1kXrlVr2KXe/M/HeUkxh+ig+95XKvN2jsKT8y9E2i2+Ii7fzFYVNIhZI+kSV7woAbUUJXpPMbFGUsZOkLQqrbC2xyk8l3a3wQhW/xySdJOkYdz/Z3VdJmmdm8yt81GsVXsBOdveTJb1N0hvd/beS9o7aBEnhhe/O5r8Z0Fnu/h+SHo/atN2oMMP0PknvM7P1kn4cZbJKLTtX0sejNng3SHp9hY/aKOlJM/tXM3thlXgeidrg3SDpRUWrXCfpTDP7bpTRfETSkwpL/bY3tBMAoKoXlCkAAB/hSURBVEUsqn1Ag8xshaTPS5pQeDf/fXe/0MzeImmxu1+QWPc4Se9QWH2zxt3flHjtw5J+5+5rE8vWSXqTu/86usCscfe4h+wcSXcprMI6UdJ5krZLekrSOxMdM85T1E6oLTsAwCQz+3dJf+Xu96cdC4D+Rgavi5nZlZL2V1hS91AD73+XpHdKOs/dr251fABCUdOI70nKu/v/STseAKgrg7dw4UIPgqB90QAdtGHDhk3uXnJA2zSQvpA1pDGgvSqlsbo6WQRBoNHR0dZEBaTMzLpqGAvSF7KGNAa0V6U0RicLAACAjCGDBwAAkDFk8AAAADKGDB4AAEDGkMEDAADIGDJ4AAAAGUMGr5WCIPwD0DuCQDIj7QKNIg11pbrGwUMVha4a8glALQoFyT28QAGoH2moK1GCBwAAkDFk8AAAADKGDB6QIjNbY2ajZjY6Pj6edjgAgIwggwekyN3XuvuIu48MDnbNnOxAZnAThX5FBg8AkFncRKFfkcEDAADImKoZPIq3AQAAekvVDB7F2zUKAimXC/8Y7BEAAKSIgY5bJR7oUWKwRwAAkCra4AEAAGQMGTwAAICMIYMHAACQMWTwAAAAMoYMHgAAQMaQwQPQH4Ig7OHeyDBGQcDwRwB6CsOkAOgP8VBGjQxjVCi0Ph4AaCNK8AAAADKGDB4AAEDGkMEDAACVNdOGFamgDR4AAKismTasSAUleAC6S4d6rJpxrQKaFgRSLhc+zuUo4esilOAB6C70WAV6R1yyJ0n5PHdNXYQSPCBFZrbGzEbNbHR8fDztcAAAGUEGD0iRu6919xF3HxkcHEw7HABARpDBAwAAyBgyeAAAoHYMmdIT6GQBAMgsM1sjaY0kDQ0NpRxNRjBkSk+gBA8AkFm0c0W/ogQPAADUJh7zrvgxug4ZPAAAUJt8vuTjuLbWOxoMKqGKth0YzRsAAKSIDF475POMxg90Gj37AGASVbQAsoGefQAwiRI8AACAjCGDBwAAkDFk8AAAQHlBUP+QKEFAe9iU0QYPAACUF7dvrfc9SBUleADSU+ddfsn+E3HvWQZdBYBJlOABSE8r7vIbKV0AgIyrWoJnZmvMbNTMRsfHxzsREwAAAJpQNYPHRM0AAKAmuRxNJroEVbRAisxsjaQ1kjQ0NJRyNABQmlmNLSGSc9Uy6Hiq6GQBpIgScgBAO5DBA5A9nZiXlnG+0GfMphfKUUDX3aiiBZA9nZiXlnG+AHQxSvAAAAAyhgwegEyi+ghAPyODBwAAkDFk8AB0t3o7M+RylcfgyuUqb4/OE5nCYP1NKDMNYHFni0ooSU8PnSwAdLd6OzPE43CVu7Dk85WvOnSeyBR3XytprSSNjIwwp109ktMAklHrOZTgAeiocnmrekoFqm2rXpQyAMgaMngAAAAZQwYPAAAgY8jgAQAAZAwZPABdYUyBxhRMPq+lXdyYAuVVocdsLAgq96yt571BoLwFjW0LADqEXrQAukKg+nuvBirI5KraNTLZG7BexdOeFQqJbCiAquJhh+Ie7ugIMngAAKB9GHooFWTwAABAQxhiqHvRBg9Az6s0hl68vBUXolLbaGT8PqAf5JWTa9eZMNAZlOABAICWmbrhyUuSPC/uglJACR6AzojmtXRZY3O9BkH4Xmvw/VXkVWKO2hp6346pwbiY8xZAG1GC1y7xhOb0GkIFZrZG0hpJGhoaSjmaNot6o5pJXmjgbr4Q9Zh1taU0YFj5XeOqofftZE/eeuOi4Tl6AAVvvYsSvHbJ5zmBoyp3X+vuI+4+Mjg4mHY4AICMIIMHAACQMWTwAAAAPcIzhgwegNZpVceBXG56Z4xcruGhFmqezqwVciU6agBACuhkAaB1WtXuNJ+f3hkj7qzUQOlC3AmiI/J5ikDQ08YUSFZg7LoMIIMHoG/Eea9asntmta3XSAyNTosLtFugormX0bOoogUAAMgYMngAAAAZQwYPAAAgY2iDB2C6uBdoq2ZhaaBXaVrNf+LetoFKdBbJ5eQF61yPXLREX80WAyRUzeCROIA+0+oZWHpoRpfheHL0Ut11o5696C3uvlbSWkkaGRmhewv6RtUqWqZSAgAgw4JAMguHSInklWt5aTU3SJ1FFS0AAP2sEA6NEiRyYHFpNnoXGTwADevUmG7JO/92jU/XDpRYoFtNjgnZK4kJdaMXLQAAQMaQwWsn5qUE6lLc7qdiO6Cg8Tlm88pNa2/UTmMK5LLJv7Kf26p5fIEeYUYpdztRRdtOzEsJ1KW43U/FdkCFgoYbrKwdVr50T9k2KJ4Lt+zn9lBvY2RIEEzOOzs1TBCygAwegFS0c67XdmyPtkrIpKiDhZQcJghZQBVtKyTugIB6mNkaMxs1s9Hx8fG0wwHQh1p5U0SlVfcgg9cKhULrRv1HX2GcSQBpiNuGMjNLdpHBA9A+FTpCjClouOQ7r1zTpeZ55cpe4Cq9BmRB3DY0rpYtHooIvY82eO0W96SlhA/9KOoIUapjQaCClPeGribDysvzUjP9JCp14GCQVwC9jgxeu9GTFiirmY4WpZIVSQ0AQlTRAgAAZAwZPAAAgIwhg9cJuVxYd8Qo9QAAoAPI4HVCPh8OJMlI9ciYMQU13bzEU47FbeSKpyDrhbZztcaYV25yDqZapkPrhe8ONCvumU5BR+fQyQJAwwJFo+BXyaXUNQVZj0t+t05NhwbUIs1ZWSZnySiQJjqFEjwAAICMIYMHAEAf6XSzAJohpIMqWgB1m6zqSTeMvpFm1RqA3kQJHgAAQMaQweukeNoyIAPGVDTPbBBMzQ8bBJLVNpdrlud9Le4tXErcE7nu7x8EnE9qYGZrzGzUzEbHx8fTDgfoGKpoO4lpy5Ah8WTlk7WGhUSP2ujxcA2He7/0qC0n7ok8bHX2umXYpZq4+1pJayVpZGSESm70DUrwADSFe5ZQNPRdzesCQDuRwQMAAMgYMngAAAAZQxs8AE3LK6egC/tIZKXjxtSUZ/nwv7hDC+3wAJRBBg9IkZmtkbRGkoaGhlKOpnHDysvzaUexq6x04AhUlJFLdmgBKjDr/vETk4dxt8faS6iiBVLk7mvdfcTdRwYHB9MOp6XIe7ReqX3KfkatxhTIZYkS4XTF8XRTTFlCCR4AAH1gamijdO8KpmbCCeMJH9v01yjJaxoleGlggFIAQB/KK0dpXYdQgpcGGkYDAPrQsPKplyD2i6oleEzz0ph6Bj0FUheVKk8es7lceABT0txWJacyi6Z5i//yFkyuS8kHquHag1jVEjymeakiOf9mPeJ5afP5FgcENKC4VDk+LrlStFXcy3faiTXuIauoB2RU2kHJB1ol7eGDsjz/dDehirZZiZNxXZiXFgCQgrSHD2r083thyJduQicLAADQNcYU0DykBSjB67RGqnOBFNU6COnU0AeoVyOF+WH1bY3r8aOghwQqSAWGTGkWGbw22+XkSps79Ji4YX+t1Sq0q6luTEHJ/VRqyrfkennlFJhF05SV2HBc6sF5Buh7ZPBagLsMZNku02RVkXb7nl4QJAZ4TSo15Vtyfw4rP3WeKVXqxxBMSAoCjUkSabIvkcEDACCLCgUG1uljZPCAPlStXVa59nR0/E4fvwGAWpDBAwAAXYubmsYwTEpKJg9Y5qUFAAAtRgavBm2d+qVQqNgwmmln0DG52qfCKtcLFNWVnJ6sCWMKpk4U8TBM8XRn3Dz2/XSbeeWmTXnXzcYUMMNFC1FFW6dW9JiNtzE5JAI939AN8vlwCI4alOsFiupa3cs4UNFsOmZTM+xwd9j3021OTYfX/cdC8rzSC/F2OzJ4KZocEqHMSZhzM9C/SP8AmkEVbZFklWhx9WjbTri5HFUpfarfq48AtEhULV+umUWrmwag+5HBa4N6MoJ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",
       "text/plain": [
        "<Figure size 792x432 with 6 Axes>"
       ]
@@ -216,41 +266,112 @@
     }
    ],
    "source": [
-    "fig, axs = plt.subplots(2, 3,figsize=(11,6))\n",
+    "fig, axs = plt.subplots(2, 3, figsize=(11, 6))\n",
     "# sma\n",
-    "axs[0, 0].hist(kde.resample(len_pdf)[0,:],100,density=True,color='b',label='Posterior Draw',stacked=True)\n",
-    "axs[0, 0].hist(sma_prior,100,density=True,color='r',histtype = 'step',label='Prior Draw',stacked=True)#color='r')#\n",
-    "axs[0, 0].set_xlabel('SMA [AU]')\n",
+    "axs[0, 0].hist(\n",
+    "    kde.resample(len_pdf)[0, :],\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"b\",\n",
+    "    label=\"Posterior Draw\",\n",
+    "    stacked=True,\n",
+    ")\n",
+    "axs[0, 0].hist(\n",
+    "    sma_prior,\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"r\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Prior Draw\",\n",
+    "    stacked=True,\n",
+    ")  # color='r')#\n",
+    "axs[0, 0].set_xlabel(\"SMA [AU]\")\n",
     "axs[0, 0].set_yticklabels([])\n",
     "# ecc\n",
-    "axs[0, 1].hist(kde.resample(len_pdf)[1,:],100,density=True,color='b',label='Posterior Draw',stacked=True)\n",
-    "axs[0, 1].hist(ecc_prior,100,density=True,color='r',histtype = 'step',label='Prior Draw',stacked=True)#color='r')#\n",
-    "axs[0, 1].set_xlabel('eccentricity')\n",
+    "axs[0, 1].hist(\n",
+    "    kde.resample(len_pdf)[1, :],\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"b\",\n",
+    "    label=\"Posterior Draw\",\n",
+    "    stacked=True,\n",
+    ")\n",
+    "axs[0, 1].hist(\n",
+    "    ecc_prior,\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"r\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Prior Draw\",\n",
+    "    stacked=True,\n",
+    ")  # color='r')#\n",
+    "axs[0, 1].set_xlabel(\"eccentricity\")\n",
     "axs[0, 1].set_yticklabels([])\n",
     "# mass\n",
-    "masskde_arr = kde.resample(len_pdf)[-1,:]\n",
-    "masskde_arr = masskde_arr[masskde_arr<0.003]\n",
-    "m1_prior_draw = m1_prior[m1_prior<0.003]\n",
-    "axs[1,0].hist(masskde_arr,100,density=True,color='b',label='Posterior Draw',stacked=True)\n",
-    "axs[1,0].hist((m1_prior_draw),100,density=True,color='r',histtype = 'step',label='Prior Draw',stacked=True)#color='r')#\n",
-    "axs[1, 0].set_xlabel('$Mass_b$ [$M_{Jup}$]')\n",
+    "masskde_arr = kde.resample(len_pdf)[-1, :]\n",
+    "masskde_arr = masskde_arr[masskde_arr < 0.003]\n",
+    "m1_prior_draw = m1_prior[m1_prior < 0.003]\n",
+    "axs[1, 0].hist(\n",
+    "    masskde_arr, 100, density=True, color=\"b\", label=\"Posterior Draw\", stacked=True\n",
+    ")\n",
+    "axs[1, 0].hist(\n",
+    "    (m1_prior_draw),\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"r\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Prior Draw\",\n",
+    "    stacked=True,\n",
+    ")  # color='r')#\n",
+    "axs[1, 0].set_xlabel(\"$Mass_b$ [$M_{Jup}$]\")\n",
     "axs[1, 0].set_yticklabels([])\n",
     "# inc\n",
-    "axs[1,1].hist(np.rad2deg(kde.resample(len_pdf)[2,:]),100,density=True,color='b',label='Posterior Draw',stacked=True)\n",
-    "axs[1,1].hist(np.rad2deg(i_prior_samples),100,density=True,color='r',histtype = 'step',label='Prior Draw',stacked=True)#color='r')#\n",
-    "axs[1, 1].set_xlabel('inclination [deg]')\n",
+    "axs[1, 1].hist(\n",
+    "    np.rad2deg(kde.resample(len_pdf)[2, :]),\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"b\",\n",
+    "    label=\"Posterior Draw\",\n",
+    "    stacked=True,\n",
+    ")\n",
+    "axs[1, 1].hist(\n",
+    "    np.rad2deg(i_prior_samples),\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"r\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Prior Draw\",\n",
+    "    stacked=True,\n",
+    ")  # color='r')#\n",
+    "axs[1, 1].set_xlabel(\"inclination [deg]\")\n",
     "axs[1, 1].set_yticklabels([])\n",
     "# w\n",
-    "axs[1,2].hist(np.rad2deg(kde.resample(len_pdf)[3,:]),100,density=True,color='b',label='Posterior Draw',stacked=True)\n",
-    "axs[1,2].hist(np.rad2deg(w_prior),100,density=True,color='r',histtype = 'step',label='Prior Draw',stacked=True)#color='r')#\n",
-    "axs[1, 2].set_xlabel('Arg. of periastron [deg]')\n",
+    "axs[1, 2].hist(\n",
+    "    np.rad2deg(kde.resample(len_pdf)[3, :]),\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"b\",\n",
+    "    label=\"Posterior Draw\",\n",
+    "    stacked=True,\n",
+    ")\n",
+    "axs[1, 2].hist(\n",
+    "    np.rad2deg(w_prior),\n",
+    "    100,\n",
+    "    density=True,\n",
+    "    color=\"r\",\n",
+    "    histtype=\"step\",\n",
+    "    label=\"Prior Draw\",\n",
+    "    stacked=True,\n",
+    ")  # color='r')#\n",
+    "axs[1, 2].set_xlabel(\"Arg. of periastron [deg]\")\n",
     "axs[1, 2].set_yticklabels([])\n",
     "\n",
-    "axs[0, 1].legend(bbox_to_anchor=(1.25, 1), loc='upper left', ncol=1)\n",
-    "axs[0, 2].axis('off')\n"
+    "axs[0, 1].legend(bbox_to_anchor=(1.25, 1), loc=\"upper left\", ncol=1)\n",
+    "axs[0, 2].axis(\"off\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -258,6 +379,7 @@
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -271,10 +393,20 @@
    "execution_count": 6,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/y8/lw5f1dcj04g4txq4y2znyc200000gn/T/ipykernel_15758/100748084.py:15: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lnprior_arr1[idx_prior] = kde1.logpdf(\n",
+      "/var/folders/y8/lw5f1dcj04g4txq4y2znyc200000gn/T/ipykernel_15758/100748084.py:25: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lnprior_arr2[idx_prior] = kde2.logpdf(\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f151c35e8d0>"
+       "<matplotlib.legend.Legend at 0x7f9fe2a17d30>"
       ]
      },
      "execution_count": 6,
@@ -283,8 +415,7 @@
     },
     {
      "data": {
-      "image/png": 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bREaSONa3H8JbP4Pkk+Dad2zDtmraxGMTRGwSNAdKCp0kchDycmw3ZvE6pZHmtjQSTj3RqiAidtxRtJdkp+9GaamhoCRQnkgOF/s5UFAC2LaWxFgfibFRxEd7I36ql/D/CZ0AY8yzwLNgq6rciKG01JCZk2dLE06yyHbmIUqK9TE4LZmfDO7E4LRkTu7YvGF6M4XS19Pg3dugQ7otaZR1+VSqoijbI8sOkAzYqqzCg04yOeCcE2+TSFwLWzKJEB6P0CzGRzOnfdGWSgLkFvrJLfSTk1vM97lFeD1CQoxNIpFaGom0xLET6FThdaqzLywUlgT4YvM+Zq/dzbz1e9ibVwxAm8QYBndN5oyuyQxOS6ZX28Twa5eoi2XPwawpti1jwutHBrQpVR2P1yaHuBa2faQk/0iVVm627aXVoos9HoFsqcRHXLSPNkngLy3lsJNEcov8HHRKI7HHlEY8EVAaibTEsRzoISJdsQljPPBTNwM6VFjCgg05zF67mwXffs/h4gAJMT5G9UrhzJ4pnNE1mc7J8RFfNK2UMfD5P+HTP0PPi2HcS/avSaVqS8QZvd7MDur0F8MPW+3mbwcJ7cK6HSQYPo+H5vHRNI+PLm8jyS0qIbfQmecttwivCAmxvvJEEq6lkYhKHMYYv4jcAczGdsd9wRiztqHj+D63kLnr9jBn7R6WbN5LScDQOiGaS9M7cEG/dgzr1oqYsnkZGitjYO7/wJIn4OSfwOX/rvOgN6XK+aKhVQ/bjTt3t20badHZllIagYptJG0SIVBaSl6Rv7xay5ZGCkiI8dEiPprmcVF4w6iWIqISB4AxZhYwq6Hvu23vYWav3c2cdXv46rsfMAY6J8czcVgaF/Zrx6mdW4bVDzakSgPwwd3w1X9h8E1w8cPac0rVP4/HJouoWDtH2b4iaHmS02urcfF6PDSPi6Z5nFMa8ZdysKCEA/nFZP2Qz64DQlJcFC3io0iM8bleg6G/7VUwxrBm50H+OWcDFz76GaP+sYD//ehbCksC3H1eTz6+ayQLfz2Ke3/Ul0FpyU0nafiLYcZNNmmM/BVc8g9NGo1IQkJC+fNZs2bRs2dPtm/fzv3330/Hjh1JT0+nR48eXHnllaxbd6QX/KhRo+jVqxfp6emkp6dXOpHfSy+9REpKCunp6fTr14+xY8eSn5/P448/zl133VV+3i233MJ5551nX4jw5ItvcudfngZ/EezdAMWHa/11GWO488476d69OwMGDOCrr76q9Lx7772XTp06HfV9ODb29PT0KqdZLygo4KyzziIQqHq26JqI2HEh7ZJi6dU2kW4pCbSMjyK3sIRtew+zPjuXXQcKKCj2B71Y2JQpU/j0009POKZjRVyJoyEUFAc4/9GFZP1QgEdgcFoy/zO6L+f3bUun5Hi3w3NPcT68eR1kzoXz/gQj7nI7IhUin3zyCXfeeSezZ8+mSxc7Y+/dd9/NlClTADsK+5xzzmH16tWkpKQAtZvkEOCnP/0p06ZNY/jw4bz22mvl51S5HkfrnrB/C+zdZEsitei599FHH7Fp0yY2bdrEl19+yW233caXX3553HljxozhjjvuoEePHtXGXpUXXniBK6+8Eq+37lVqfr8fn89X3lMrJaGEAj8cyC9m32G75k2sz0uL+ChaxEdX2zPz5z//OZMmTeKcc86pc1ygiaNScdFeLjm5Pd3bJHBu7za0SoicUa4hU3gQpo63U4aPfgwG/cztiBq3j+6B3avr9z3bnQwX1zxDz2effcakSZOYNWtWldNiXHXVVXz44YdMnTqVX/ziF7UOxe/3c/jwYVq2bEl6ejobN26koKCA4uJi4uLi6N69O6tXryY9PZ0lS5bw0EMPQVQctO5lG8wPbLej0xPbB9Vo/t5773HdddchIgwZMoQDBw6QnZ1N+/ZHjzUaMmRIrb+Wil577TWmTp0KwIIFC7j//vtp3bo1a9as4bTTTuPVV19FRHjggQd4//33KSgoYNiwYTzzzDOICKNGjSI9PZ1FixYxYcIE3n///aNep6enM2XKFEr8fk5JH8i9Dz7CvM+W8/y/HuXZl1/ni08/ZtLEazl48CClpaX07duXLVu20KVLF/bt28fu3btp165dnb5G0KqqKv3+kj78ZFAnTRpg55367xjIWgZjn9ek0YiVrcfx7rvvHrcex7EGDhzIt99+W/766quvLq/K+fWvf13pNdOmTSM9PZ2OHTuyf/9+xowZg8/n49RTT2X58uUsXbqUM844gyFDhrBkyRJ27tyJMYZOnZxe+F6fXdskvhVXXTOR9AH9yu9Ztr388svH3Xfnzp1H3gNITU1l587a9eSfMWMGAwYMYOzYsezYseO448XFxWzZsoW0tLTyfStXruSxxx5j3bp1bNmyhcWLFwNwxx13sHz5ctasWUNBQQEffPDBUe+TkZHBr371q6NeT548mYkTJzJt2jTWrF6NVwxzZrzCZecNJ3P9GvwBw9xPF9KtVx/en/cZn362mDPOODI+euDAgeX3rystcajqHdwJr1wOB76D8a9DzwvcjqhpCKJkEApRUVEMGzaM559/nscff7zac+tjPY6HH36Ye+65h2HDhrFkyRIKCgoYOnRo+XocZdOqH0U80LwT015/zU5h4ou1sxWEcP6rMWPGMGHCBGJiYnjmmWe4/vrrj2sz2Lt3Ly1atDhq3+mnn05qaioA6enpbNu2jREjRjB//nweeugh8vPz2b9/P/369WPMmDHl36OKyl5v2LCBrl270rNnTwCuv/56/vWvf3HXXXfRs0d3Avt3sHntKm674xcs+vxzSktLGTFiRPn7tGnThl27dtXL90NLHKpq+zbDCxfZZU+veVuTRhPg8Xh48803WbZsGQ8++GC1565cufKEJ2UsW4/js88+A2D48OEsWbKEL774gqFDh9KnTx/WrVvHkiVLjk8c9g246oY7SL/4etLPvoL09FNIP2VAlSWOjh07HlVKyMrKomPHjkHH26pVK2JibGK66aabWLFixXHnxMXFHbcWR9k1AF6vF7/fT2FhIbfffjvTp09n9erVTJo06ajrmjU7ev2ZY19X5swzz+Tjjz8mJiaan1x2CRu+yWD9qmWceeaZ5ecUFhaWr9BYV5o4VOV2r7FJozgPJr4PacPdjkg1kPj4eD788ENee+01nn/++UrPmTFjBnPmzGHChAknfJ9j1+NYunQpOTk5tGnTBhEpX49j+PDK/+9NmzaNVV9/w6pVq1j1ydus+uhlVi2ex3XXXXfcuZdeeikvv/wyxhiWLl1K8+bNj2vfqE52dnb585kzZ1aaMFu2bEkgEKhxIaey461btyYvL4/p06cHFUOvXr3Ytm0bmZmZALzyyiucddZZAIwcOZLHHnuMoUOHkpKSwv59+9i0cSP9+/cvv37jMa/rQquq1PEOZsHLl9mFeyZ+ACm93I5INbCj1uNwek09+uijvPrqq3Y9jv79+fTTT8uPgW3jKPuLtnXr1sybN++49w3JehxRsZDS0y4WdnCHbTRP6nhUo/kll1zCrFmz6N69O/Hx8bz44ovlx9LT01m1ahUAv/nNb5g6dSr5+fmkpqZy0003cf/99/PEE08wc+ZMfD4fycnJ5XEf64ILLmDRokVHuhNXokWLFkyaNIn+/fvTrl07Bg8eXP3X54iNjeXFF19k3Lhx+P1+Bg8ezK233grAGWecwZ49e8pLGAMGDGD37t3l4z1KSkrIzMyssSoxWBG1HseJiKT1OMJCSYEtaezbDJM+tb+QqkFE/HocbjMGDu20qxBGJ0JyGnga9m/jr776ikcffZRXXnmlQe9bk3feeYevvvqKP//5z8cdO5H1OLSqSh1hDHzwS8heBVc+q0lDRRYRaJ5qlyMuzoO9meCsA95QBg4cyNlnn12nAYCh4Pf7y3tp1QetqlJHLHsOvp4Ko34HvS9xO5omyRjj+nQSEa9Zaztv2v4tcCjLDhZsQDfccEOD3i8Y48aNq3T/idY4aYlDWdsWw+zf2Vluz/yN29E0SbGxsezbt++Ef5lVBbHN7Zof+fvsEsbqOMYY9u3bR2xs7We01hKHsmM13roeWnaFK5/RuadckpqaSlZWFjk5OW6H0jgYA3kH4bsMSGzX4O0dkSA2NrZ8nElt6HeyqSsphGnX2MeJU+1fasoVUVFRdO3a1e0wGpcDCfD0SGiZBjfOCekgwaZE/7RsyoyBD38Fu76CK57WxnDV+LTobNeKyV5l149R9UITR1OW8TysetW2afQZ7XY0SoVG7x/BGbfBl0/D+g9qPl/VSBNHU7X9C/jot9DzItuLSqnG7Pw/Qft0eO92O++aqhNNHE3RoV12XY0WXeAKbQxXTYAvBsa9aKtnp98AgRK3I4po+onR1PiLYNq1UJIP46dCXAu3I1KqYSSfBGMeh6zl8OnxI6hV8DRxNCXGwKwpsDMDLv8/aFP9egtKNTr9r4RBN8Dix2HjHLejiViaOJqSFS/CVy/DyCnQ91K3o1HKHRc+CG37wzu32DFMqtY0cTQV330Js34D3c+Hs3/vdjRKuScqDsa+aKttZ9wEAb/bEUUcTRxNwaFsePNaOwHcj58Dj9ftiJRyV0pPGP0IfLcEFv7d7WgijiaOxs5fZHtQFeU5jeEt3Y5IqfBwynhIvwY+exi2LHA7moiiiaOx++i3kLXMjp5t29ftaJQKL5c8BK17woxJkLvH7WgihiaOxmzFS7ZBfMTd0O9yt6NRKvxEN4NxL0HRIXh7EpSG1zoa4UoTR2O1Yxl8OAW6nQvn/NHtaJQKX237wsUPwdaFsOgRt6OJCJo4GqPiw/DWz6B5R/jxf7QxXKmaDLwO+o+F+Q/C9iVuRxP2NHE0Rp8/Ylc+u+IZiE92Oxqlwp8IjHnMTr8+/UY4vM/tiMJa2CUOEblfRHaKyCpnu6TCsd+JSKaIbBCRC92MM2zt3wpLnoSTx0HnIW5Ho1TkiEm07R35e+HdWxt8vfJIEnaJw/GoMSbd2WYBiEhfYDzQD7gI+LeIaB3Mseb8wa50dv4DbkeiVORpf4odWb5pDnzxlNvRhK1wTRyVuQx4wxhTZIzZCmQCp7scU3jZsgC+/QBG/hKSOrgdjVKRafBN0OsSWPA3Xa+8CuGaOO4QkW9E5AURKRux1hHYUeGcLGffcUTkZhHJEJGMJrN+c8APH91j62iH3uF2NEpFLhE7LU/JYdulXR3HlcQhIvNEZE0l22XA/wHdgHQgG/hnbd/fGPOsMWaQMWZQSkpK/QYfrjKeh5z1cMFfISrW7WiUimztToauZ8GXz4C/2O1owo7PjZsaY84L5jwReQ4oW+txJ9CpwuFUZ586vA/m/xVOGmWXyVRK1d3QO2DqOFj3Lgz4idvRhJUaSxwi0qohAqlwv/YVXl4BrHGezwTGi0iMiHQFegDLGjK2sDX/L3Yuqov+bovZSqm6634etO5leyka43Y0YSWYqqqlIvKWiFwi0iCfSg+JyGoR+QY4G7gbwBizFngTWAd8DEw2xuj8ALtX23rY0yfpwkxK1SePB4beDru/gW2L3I4mrIipIZM6yeI84AZgMPbD+yVjzMbQh1d3gwYNMhkZGW6HERrGwEs/gu/Xw51f6cy3StW3kgJ4tD+kDoafvuF2NA1GRFYYYwZVdbzGEoex5hpjJgCTgOuBZSKyUESG1mOsqrbWvgPbF8O5f9SkoVQoRMXZ7rkbP4K9mW5HEzaCauMQkV+ISAYwBfg50Br4FTA1xPGpqhTnw5w/2t4fA693OxqlGq/BN4I3Bpb+y+1IwkYwbRxfAEnA5caYHxlj3jbG+I0xGcDToQ1PVWnx43Y+qosf0kkMlQqlhDa2V9Wq13UOK0cwieMPxpg/G2OyynaIyDgAY4yuueiGA9/B4seg35XQZZjb0SjV+A2dDP4CWPGC25GEhWASxz2V7PtdfQeiamHOHwGBC/7sdiRKNQ1t+tjuucues8sxN3FVDgAUkYuBS4COIvJEhUNJgD/UgakqbP3cDkga9Xtonup2NEo1HUMnwytXwOrpcOrVbkfjqupKHLuADKAQWFFhmwnolOZuCPjh43ugeWcYfqfb0SjVtJx0NrTpB1/8q8kPCKyyxGGM+Rr4WkReM8ZoCSMcrHgR9qyBcf+13QSVUg1HxJY63rvdzkTd7Wy3I3JNlSUOEXnTebrSman2qK2B4lNl8vfb+ajSRkLfy9yORqmm6eSx0KxNk1+ro7pJDn/hPI5uiEBUDeY/CIUH4aK/6XxUSrnFFwOn32znh/t+vW00b4KqLHEYY7Kdx+2VbQ0XomLPWjtt+qAboF1/t6NRqmkbdAP44mDpv92OxDXVVVXlisihSrZcETnUkEE2acbAR7+FmCQ4+163o1FKNWsF6RPg62mQ10QWijtGdSWORGNMUiVbojEmqSGDbNLWz4Rtn8M5f4D4ZLejUUoBDLkdAkWw/D9uR+KK6kocSc5jcmVbw4XYhJUUwOw/2C6Ap/3M7WiUUmVa94CeF9nEUVLgdjQNrrpxHGUTGK7AjueoOJajkc5THmaWPAkHv4OL/wZeVxZrVEpVZegdkL8Xvnmz5nMbmerGcYx2Hrs2XDiq3MEs+PwR6HMpdD3T7WiUUsdKGwHtBtgBgadeaxd+aiKC+kpF5EoReURE/ikil4c4JgUw9z7AwAV/cTsSpVRlRGypY+8G2PyJ29E0qGDW4/g3cCuwGrv+960iohPTh9LBLFgzA864BVp2cTsapVRV+l0Bie2b3IDAYCrOzwH6GGeNWRH5L7A2pFE1dV+/DhhtEFcq3Pmi7R948+6H3WuazDirYKqqMoHOFV53cvapUCgthZWv2qlFkrV5Samwd9pEiIq3bR1NRHXdcd8XkZlAIrBeRBaIyHxgvbNPhcL2xfDDNtvYppQKf3Et4dRrYPVbkLvb7WgaRHVVVf9osCjUEStftaPE+4xxOxKlVLCG3GYXeVr2HJz7R7ejCbnquuMubMhAFHYSw3XvwSnjITre7WiUUsFKPgl6/8jOKTfyV43+9zeYXlVDRGS5iOSJSLGIBHSuqhBZ87Zd11irqZSKPEPvgIIf4OupNZ8b4YJpHH8KmABsAuKAm4Cm0wrUkFa+Cil9oONAtyNRStVW5yHQ8TT44t+2k0sjFtQAQGNMJuA1xgSMMS8CF4U2rCbo+/WwMwMGXqvrbSgVicpWCNy/GTbNdjuakAomceSLSDSwSkQeEpG7g7xO1cbKV8HjgwFXuR2JUupE9bkMmneCJY17QGAwCeBa57w7gMPYcRw/DmVQTU6gBL5+A3pdDM1aux2NUupEeX12QOD2RbBrpdvRhEyNicNZ7a8USAPeBu5xqq5Ufdk4286yqY3iSkW+gddBdGKjHhAYTK+qHwGbgSewDeWZInJxXW4qIuNEZK2IlIrIoGOO/U5EMkVkg4hcWGH/Rc6+TBG5py73DzsrX4WEdtDtXLcjUUrVVWxzu0LguplQlOd2NCERTFXVP4GzjTGjjDFnAWcDj9bxvmuAK4HPKu4Ukb7AeKAftgH+3yLiFREvtifXxUBfYIJzbuTL3Q2b5tj/aLrmhlKNQ9/L7AqBmfPcjiQkgkkcucdUTW0BcutyU2PMemPMhkoOXQa8YYwpMsZsxc6JdbqzZRpjthhjioE3nHMj39dvgAlA+jVuR6KUqi+dh0J8a1j/vtuRhESVf+KKyJXO0wwRmQW8CRhgHLA8RPF0BJZWeJ3l7APYccz+M0IUQ8MxxlZTdR4Krbu7HY1Sqr54vND7EljzDviLwBfjdkT1qroSxxhniwX2AGcBo4AcZ1+1RGSeiKypZAt5SUFEbhaRDBHJyMnJCfXtTtyOZbBvk50gTSnVuPQeA8W5sPWzms+NMNXNVVWnxSCMMeedwGU7sd19y6Q6+6hmf2X3fhZ4FmDQoEHmBOJoGCtfgahm0PdytyNRStW3k86yvavWz4Qe57sdTb0KpldVqoi8IyLfO9sMEUkNUTwzgfEiEiMiXYEewDJs1VgPEenqDEYc75wbuYryYO070P8KiElwOxqlVH3zxUDPC+HbWVAacDuaehVM4/iL2A/pDs72vrPvhInIFSKSBQwFPhSR2QDGmLXYtpR1wMfAZGeaEz92AOJs7HogbzrnRq5170Fxno7dUKox6zPajtH6bmnN50aQYPp/pjjzU5V5SUTuqstNjTHvAO9UceyvwF8r2T8LmFWX+4aVla9Cq+7QKfLb+JVSVeh+PnhjbO+qtOFuR1Nvgilx7BORa8rGU4jINcC+UAfWqO3NhO+W2EZxndBQqcYrJgG6nwvffmB7UTYSwSSOG4CfALuBbGAsUKeG8yZv1WsgXjhlgtuRKKVCrfdoOLgDsle5HUm9qbaqyhmx/aAx5tIGiqfxC/jh69dtL4vEdm5Ho5QKtV4X2z8U178PHU51O5p6UW2JwxgTALo4PZlUfdj8KeRm69gNpZqK+GRIG9GoRpEH0zi+BVgsIjOx06oDYIx5JGRRNWYrX7FTEfS4sOZzlVKNQ58xMGsK5GyAlF5uR1NnwbRxbAY+cM5NrLCp2jq8FzZ8BKeMB58W4pRqMnr/yD42klJHjSUOY8yfAEQkyb40dZrgsEn75k0oLYH0q92ORCnVkJI6QOpgmzjOnOJ2NHUWzMjxQSKyGvgGWC0iX4vIaaEPrZEpm9Cw42nQtnHMCK+UqoXeo23PqgM7ajw13AVTVfUCcLsxJs0YkwZMpo4jx5ukXSvh+7XaKK5UU9VnjH389gN346gHwSSOgDHm87IXxphFgD90ITVSK18FXyz01+XalWqSWnWDNv0aRTtHMIljoYg8IyKjROQsEfk3sEBEBorIwFAH2CiUFMDq6XZVsNjmbkejlHJLn9Hw3ReQF8bLPQQhmO64pziP9x2z/1Tswk7n1GtEjdH6D6DooFZTKdXU9RkDC/8OG2bBade7Hc0JC6ZX1dkNEUijtvIVaNEFuoxwOxKllJva9oeWaba6KoITRzBVVaouftgOWxfa0oZHv91KNWkitnfV1oVQeNDtaE6YfpKF2qqpgED6T92ORCkVDvpcCoFi2DTX7UhOWLWJQ0Q8IjKsoYJpdEpL7Uy43c6B5qFaNFEpFVFSB0NCW7ukbISqaZLDUuBfDRRL47N1oZ1OWRvFlVJlPB47BcmmebbHZQQKpqrqExH5sYiuOFRrK1+FuJZH5qlRSimwvatKDsPm+W5HckKCSRy3AG8BxSJySERyReRQiOOKfAU/2J4TJ//ELlqvlFJl0kbaMV0ROhgwmO64OhPuiVg9HQJFWk2llDqeNwp6XgwbP4JAiX0dQYLqVSUil4rIP5xtdKiDahRWvgrtBkD7AW5HopQKR33G2JqJ7YvdjqTWgpkd92/AL4B1zvYLEfnfUAcW0Q5m2VkwTx7ndiRKqXDV7RzwxUVkdVUwJY5LgPONMS8YY14ALgK0tbc6mfPsY48L3I1DKRW+ouOhx3nw7Ye2634ECXYAYIsKz3WWvppsmgvNOzWKJSKVUiHU51LIzYadK9yOpFaCmeTwf4GVIjIfEOBM4J6QRhXJ/MWwZSGcPNZOL6CUUlXpcQF4fHYwYKfBbkcTtBpLHMaY14EhwNvADGCoMWZaqAOLWDu+hOJc6HG+25EopcJdXAvoepZd3MkYt6MJWpWJQ0R6O48DgfZAlrN10HU4qpE5FzxR0PVMtyNRSkWCPmNg/xb4fp3bkQStuqqqXwI3A/+s5Jiuw1GVTfOgy1CI0eEvSqkg9P4RfHC37V3Vtp/b0QSlysRhjLlZRDzAH4wxkdfR2A0Hd9p1xc//s9uRKKUiRUIb6DzELvg2KjKaj4OZ5PCp+r6piIwTkbUiUioigyrsTxORAhFZ5WxPVzh2moisFpFMEXkiLOfOKu+Gq+0bSqla6DMG9qy2VVYRwK1JDtcAVwKfVXJsszEm3dlurbD//4BJQA9nu6ge46kfmXMhKRVSersdiVIqkvR2JuRY/4G7cQTJlUkOjTHrjTEbgj1fRNoDScaYpcYYA7wMXF6XGOpdoMR2w+1xnnbDVUrVTssudoqibxtJ4jDGJBpjPMaYKGNMkvM6KYQxdRWRlSKyUERGOvs6Ynt0lcly9oWPHV9C0SHortVUSqkT0OdS+zmSu9vtSGoU7CSHV4rIIyLyTxG5PMhr5onImkq2y6q5LBvobIw5Fdura6qI1DpJicjNIpIhIhk5OTm1vfzEbHK64Z50VsPcTynVuPRxqqsioNRR48hxEfk30B143dl1q4icb4yZXN11xpjzahuMMaYIKHKerxCRzUBPYCdQce3VVGdfVe/zLPAswKBBgxpmVE3mPNszQrvhKqVOREpvaNXdtnMMvsntaKoVTInjHOBCY8yLxpgXsZMehmQMh4ikiIjXeX4SthF8izEmGzgkIkOcRvrrgPdCEcMJObQL9qzR3lRKqRMnYntXbfsc8ve7HU21gkkcmUDnCq87OftOmIhcISJZwFDgQxGZ7Rw6E/hGRFYB04FbjTFl38Hbgf84994MfFSXGOrVprn2Uds3lFJ10XsMlPph4+yaz3VRMJMcJgLrRWSZ83owkCEiMwGMMZfW9qbGmHeAdyrZPwM7H1Zl12QA/Wt7rwaROReSOkKbPm5HopSKZB1OtZ8l334A6RPcjqZKwSSO/wl5FJGsrBtuvyu0G65Sqm48Hjum46v/QvFhiG7mdkSVCqY77sKyDUis+NrZ17SVdcPV9g2lVH3oMxr8hUdmoghDwS7kVOaBkEQRyTbNtfPpd9VuuEqpetB5GMQlh/Uo8tomDq2LOVbmPOg8FGJDOSZSKdVkeH3Q62LYNBtKA25HU6naJo5bQhJFpCrrhtu91kNWlFKqal3PgsKDsGet25FUKpgBgFce8zoVOAisNsZ8H6rAIoLOhquUCoW04fZx+2JoP8DdWCoRTK+qG7HjLeY7r0cBK7BzSj1gjHklRLGFv01zIbEDtOnrdiRKqcakeSq0TINti2DIbW5Hc5xgqqp8QB9jzI+NMT8G+mJXADwD+G0ogwtrgRLYskBnw1VKhUaXEbbEUVrqdiTHCSZxdDLG7Knw+ntn336gJDRhRYAdy3Q2XKVU6KSNgIIfwnIt8mCqqhaIyAfYNTkAxjr7mgEHQhVY2Mt0uuGeNMrtSJRSjVFZO8e2RdAuvCbNCKbEMRl4EUh3tv8Ck40xh40xZ4cutDC3aR50GqLdcJVSodGis922L3I7kuPUWOIwxhgRWQQUY9s2ljmr8DVdh7Lt+sDn3e92JEqpxqzLCNj4sW3n8NR29ETo1BiJiPwEWIatovoJ8KWIjA11YGGtrBuutm8opUIpbQQU7Iecb92O5CjBtHHcCwwuG7MhIinAPOy0501TptMNt20/tyNRSjVmaSPs47ZF0DZ8uv0HU/bxHDPQb1+Q1zVOAT9sXgDdz9VuuEqp0GrZBZp3Crt2jmBKHB87Cy2VLR17FTArdCGFuaxlUHRQR4srpRpG2gg72NiYsPljNZhp1X+NXb97gLM9a4xpugP/Nmk3XKVUA+oyHPL3Qs4GtyMpF0yJo9qV+ZqczLnQ6QyIbe52JEqppqC8neNzaNPb3VgcVZY4RCRXRA5VsuWKyKGGDDJs5O6G3at1NlylVMNpmWaXk90WPu0cVZY4jDGJDRlIRNDZcJVSDU3Eljo2fxo27RxNt3fUidg0FxLbQ9vwGv6vlGrkugyHwzmwd6PbkQCaOIIX8MOW+doNVynV8CqO5wgDmjiClbXcrsilo8WVUg0t+SRb26GJI8JkzgXxajdcpVTDK2vn2L7YtnO4TBNHsDY53XDjWrgdiVKqKUobAXl7YF+m25Fo4ghK7h7Y/Y1d7U8ppdzQJXzaOTRxBENnw1VKua1VN0hop4kjYmTOtT+wdie7HYlSqqkSsasChkE7hyaOmgT8duBN9/O0G65Syl1pIyA3G/ZvcTUMTRw12Zlhu+Fq+4ZSym1dKsxb5SJXEoeIPCwi34rINyLyjoi0qHDsdyKSKSIbROTCCvsvcvZlisg9DRbsprJuuE13eXWlVJho3QOatYFti10Nw60Sx1ygvzFmALAR+B2AiPQFxgP9gIuAf4uIV0S8wL+Ai4G+wATn3NDLnAudTtduuEop95W1c2xb5Go7hyuJwxgzxxjjd14uBVKd55cBbxhjiowxW4FM4HRnyzTGbDHGFANvOOeGVu4eyP5aZ8NVSoWPtBGQuwt+2OpaCOHQxnED8JHzvCOwo8KxLGdfVfsrJSI3i0iGiGTk5OSceGSbP7GPOhuuUipchMF4jpAlDhGZJyJrKtkuq3DOvYAfeK0+722MedYYM8gYMyglJeXE32jTXEhoC+0G1F9wSilVFym9IL61q+0cQa0AeCKMMdXW74jIRGA0cK4x5ZV1O4FOFU5LdfZRzf7QKOuG2/tH2g1XKRU+yuatKmvncOHzya1eVRcBvwEuNcbkVzg0ExgvIjEi0hXoASwDlgM9RKSriERjG9BnhjTInSug8IC2byilwk/aCDiUBQe2u3L7kJU4avAUEAPMFZstlxpjbjXGrBWRN4F12CqsycaYAICI3AHMBrzAC8aYtSGNMHMuiAe6aTdcpVSYqbg+R8u0Br+9K4nDGNO9mmN/Bf5ayf5ZwKxQxnWUTXMh9XSIa9lgt1RKqaCk9Ib4VjZxnHpNg98+HHpVhZ+SAij4QUeLK6XCk4hdTtalBnK3qqrCW1Qc/OJrKPXXfK5SSrkhbQSsnwk/bIeWXRr01lriqIoIeKPcjkIppSpX1s6xveFLHZo4lFIqEqX0sW2wLgwE1MShlFKRyONx2jk0cSillApW2kg7luPAjprPrUeaOJRSKlKlDbePDdzOoYlDKaUiVZt+ENuiwRd20sShlFKRqrydQ0scSimlgpU2wq7NcTCrwW6piUMppSJZWTtHA5Y6NHEopVQka9sfYpvD9obrlquJQymlIpnHC52HNeh4Dk0cSikV6dJGwP4tcGhXg9xOE4dSSkW6Bm7n0MShlFKRrt0AiElqsHYOTRxKKRXpPF7o0nDtHJo4lFKqMegyHPZlQu7ukN9KE4dSSjUGFdchDzFNHEop1Ri0GwDRiQ0y4aEmDqWUagy8PugyVEscSimlaqHLcNi7EXL3hPQ2mjiUUqqxSBtpH0NcXaWJQymlGov2p0B0QsirqzRxKKVUY+H1QechWuJQSilVC12GQ863kJcTslto4lBKqcakAdo5NHEopVRj0iEdopqFtJ1DE4dSSjUm3ijofEbjK3GIyMMi8q2IfCMi74hIC2d/mogUiMgqZ3u6wjWnichqEckUkSdERNyIXSmlwl7fyyF1MJQGQvL2bpU45gL9jTEDgI3A7yoc22yMSXe2Wyvs/z9gEtDD2S5qsGiVUiqSnHY9XPqEnTU3BFxJHMaYOcYYv/NyKZBa3fki0h5IMsYsNcYY4GXg8tBGqZRSqjLh0MZxA/BRhdddRWSliCwUEad7AB2BrArnZDn7KiUiN4tIhohk5OSErkuaUko1Rb5QvbGIzAPaVXLoXmPMe8459wJ+4DXnWDbQ2RizT0ROA94VkX61vbcx5lngWYBBgwaZE4lfKaVU5UKWOIwx51V3XEQmAqOBc53qJ4wxRUCR83yFiGwGegI7Obo6K9XZp5RSqoG51avqIuA3wKXGmPwK+1NExOs8PwnbCL7FGJMNHBKRIU5vquuA91wIXSmlmryQlThq8BQQA8x1etUudXpQnQk8ICIlQClwqzFmv3PN7cBLQBy2TeSjY99UKaVU6LmSOIwx3avYPwOYUcWxDKB/KONSSilVs3DoVaWUUiqCiNMu3WiJSA6w3XnZGtjrYjg10fjqJtzjg/CPUeOrm3CPD4KLsYsxJqWqg40+cVQkIhnGmEFux1EVja9uwj0+CP8YNb66Cff4oH5i1KoqpZRStaKJQymlVK00tcTxrNsB1EDjq5twjw/CP0aNr27CPT6ohxibVBuHUkqpumtqJQ6llFJ1pIlDKaVUrUR84hCRWBFZJiJfi8haEflTJec8WmFVwY0icqDCsetFZJOzXR9O8YlIuoh84Vz3jYhcVd/x1TXGCseTRCRLRJ4Kt/hEpLOIzBGR9SKyTkTSwiy+h5zr1odqdcsgY+wsIvOdZQ2+EZFLKhz7nbP65gYRuTCc4hOR80VkhdgVQleIyDnhFN8xx/NEZEq4xSciAyp81qwWkdhqb2iMiegNECDBeR4FfAkMqeb8nwMvOM+TgS3OY0vnecswiq8n0MN53gE77XyLcPoeVtj3ODAVeCrc4gMWAOc7zxOA+HCJDxgGLAa8zvYFMMqN7yG20fQ253lfYFuF519j55frCmwGvGEU36lAB+d5f2BnOH3/KhyfDrwFTAmn+LBTT30DnOK8blXTzzfiSxzGynNeRjlbdS3+E4DXnecXAnONMfuNMT9gl7St1yVp6xKfMWajMWaT83wX8D1Q5WhON2IEux480BaYU9+x1TU+EekL+Iwxc533yjMVZmR2Oz7nvFggGvvBHAXsqc/4ahGjAZKc582BXc7zy4A3jDFFxpitQCZwerjEZ4xZ6fx+AKwF4kQkJlziAxCRy4GtTnz1ro7xXQB8Y4z52nmvfcaYahcrj/jEASAiXhFZhf1gnWuM+bKK87pg/2L61NnVEdhR4ZRqVxZ0Ib6Kx07Hfrhsru/46hKjiHiAfwL1Xvyuj/iwpbYDIvK2U0R/WJyp+8MhPmPMF8B8bGkyG5htjFlf3/EFGeP9wDUikgXMwpaMIHx+T6qKr6IfA18Zu7ZPWMQnIgnAb4Hjqo/CIT7s74gRkdki8pWI/KamezWKxGGMCRhj0rELPJ0uIlXNojsemF5TNq1vdY1P7JrrrwA/M8aUhlmMtwOzjDFZVZzvdnw+YCQ2sQ0GTgImhkt8ItId6ONc1xE4R44smdzQMU4AXjLGpAKXAK84fxg0iLrGJ3a10L8Dt4RZfPcDj1YoEYREHeLzASOAq53HK0Tk3Oru1SgSRxljzAHsX29VVTeNp0IVC3YVwU4VXod0ZcETiA8RSQI+xC65uzRUsZU5gRiHAneIyDbgH8B1IvK3MIovC1hljNlijPED7wIDwyi+K7Dr0eQ5HywfYb+nIVNNjDcCbzrnfIGtQmtN+PyeVBUfIpIKvANcZ4wJSam8DvGdATzk/I7cBfxeRO4Io/iygM+MMXudatxZ1PA7EvGJQ+yqgS2c53HA+cC3lZzXG9sA/kWF3bOBC0SkpYi0xNb1zQ6X+EQkGvvL8LIxZnp9xlVfMRpjrjbGdDbGpGH/qn/ZGHNPuMQHLAdaiEhZ29A5wLowiu874CwR8YlIFHAWUO9VVUHG+B1wrnNOH+wHSw4wExgvIjEi0hW7MueycInPue5D4B5jzOL6jKs+4jPGjDTGpDm/I48BDxpj6rX3YR1/vrOBk0UkXkR82P+D1f+OmHpu3W/oDRgArMT2ClgD/I+z/wHs0rRl590P/K2S62/ANvZlYquCwiY+4BqgBFhVYUsPpxiPeZ+JhKZXVV1/xuc7167GriIZHS7xYXtSPYNNFuuAR+r7+xdsjNieNouxPahWARdUuP5ebPvaBuDicIoP+ANw+JjfkzbhEt8x73M/oelVVdef7zXYhvs1wEM13U+nHFFKKVUrEV9VpZRSqmFp4lBKKVUrmjiUUkrViiYOpZRStaKJQymlVK1o4lBKKVUrmjiUqoaI3CtHprVfJSJnOPsXiMh3IkemQBeRd0Uk75jr7xKRQhFpXsX7jxKRgyIyq6brRGSiHDNtvRPHIOf5fLHTdg+q+1euVNU0cShVBREZCowGBhpjBgDncfRkfweA4c65LYD2lbzNBOzo9SurudXnxphLjtkXzHVHMcacDWQEe75SJ0oTh1JVaw/sNc5Mq8bO5bOrwvE3sHNPgf2Af7vixSLSDbv+xx+wiSAoJ3qdUg1FE4dSVZsDdBK7Yt+/ReSsY45/ApzpTNM+Hph2zPHx2OTyOdBLRNoGed8TvU6pBqGJQ6kqGDtb7WnAzdjJ4KaJyMQKpwSARdgP+jhjzLZj3mICdgGkUmAGMC7IW1d1XVXzA+m8QapB+dwOQKlwZuy6GQuABSKyGrgeO1FimTewMxjfX/E6ETkZO4vsXKf9PBq7Aly1s6LWcN0+7Oy6FSUDe2v7dSlVF1riUKoKItJLRHpU2JUObD/mtM+B/+WYdVSwpYb7jTOdtjGmA9BB7AqA1anuuuXAcBFp58Q3CLvc7I6q306p+qclDqWqlgA86fSY8mOn3r+54gnGTi/9j0quHY9dZa2id5z9f6/mnlVeZ4z5u4j8ApjlrNyWB0wwIVoVUqmq6LTqSrlIREZh12cYXU/vt8B5P+2Wq0JGq6qUclcx0P/YAYAnQkTmY9dUL6lzVEpVQ0scSimlakVLHEoppWpFE4dSSqla0cShlFKqVjRxKKWUqpX/B13fhNWH0MLiAAAAAElFTkSuQmCC",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -297,29 +428,50 @@
    ],
    "source": [
     "numtry_prior = 20\n",
-    "sma_arr = np.mean(sma_prior) * np.linspace(0.98,1.02,numtry_prior)\n",
+    "sma_arr = np.mean(sma_prior) * np.linspace(0.98, 1.02, numtry_prior)\n",
     "\n",
     "# Initialize KDE with default bandwidth\n",
-    "kde1 = gaussian_kde(values, bw_method=None) # None indicates that the KDE bandwidth is set to default\n",
+    "kde1 = gaussian_kde(\n",
+    "    values, bw_method=None\n",
+    ")  # None indicates that the KDE bandwidth is set to default\n",
     "# Initialize KDE with narrow bandwidth\n",
     "bw2 = 0.15\n",
     "kde2 = gaussian_kde(values, bw_method=bw2)\n",
     "\n",
     "lnprior_arr1 = np.zeros((numtry_prior))\n",
     "lnprior_arr2 = np.zeros((numtry_prior))\n",
-    "for idx_prior,sma in enumerate(sma_arr):\n",
-    "    lnprior_arr1[idx_prior] = kde1.logpdf([sma, np.mean(ecc_prior),np.pi/2,np.mean(w_prior),np.mean(tau_prior),np.mean(m1_prior)])\n",
-    "    lnprior_arr2[idx_prior] = kde2.logpdf([sma, np.mean(ecc_prior),np.pi/2,np.mean(w_prior),np.mean(tau_prior),np.mean(m1_prior)])\n",
+    "for idx_prior, sma in enumerate(sma_arr):\n",
+    "    lnprior_arr1[idx_prior] = kde1.logpdf(\n",
+    "        [\n",
+    "            sma,\n",
+    "            np.mean(ecc_prior),\n",
+    "            np.pi / 2,\n",
+    "            np.mean(w_prior),\n",
+    "            np.mean(tau_prior),\n",
+    "            np.mean(m1_prior),\n",
+    "        ]\n",
+    "    )\n",
+    "    lnprior_arr2[idx_prior] = kde2.logpdf(\n",
+    "        [\n",
+    "            sma,\n",
+    "            np.mean(ecc_prior),\n",
+    "            np.pi / 2,\n",
+    "            np.mean(w_prior),\n",
+    "            np.mean(tau_prior),\n",
+    "            np.mean(m1_prior),\n",
+    "        ]\n",
+    "    )\n",
     "\n",
     "plt.figure(100)\n",
-    "plt.plot(sma_arr,lnprior_arr1,label='KDE BW = Default')\n",
-    "plt.plot(sma_arr,lnprior_arr2,label='KDE BW = {} (narrow)'.format(bw2))\n",
-    "plt.xlabel('SMA [AU]')\n",
-    "plt.ylabel('log-prior probability')\n",
-    "plt.legend(loc='upper right')#, fontsize='x-large')\n"
+    "plt.plot(sma_arr, lnprior_arr1, label=\"KDE BW = Default\")\n",
+    "plt.plot(sma_arr, lnprior_arr2, label=\"KDE BW = {} (narrow)\".format(bw2))\n",
+    "plt.xlabel(\"SMA [AU]\")\n",
+    "plt.ylabel(\"log-prior probability\")\n",
+    "plt.legend(loc=\"upper right\")  # , fontsize='x-large')"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -337,6 +489,16 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/y8/lw5f1dcj04g4txq4y2znyc200000gn/T/ipykernel_15758/2442406081.py:24: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  lnprior_arr[idx_prior] = kde.logpdf(\n",
+      "/Users/bluez3303/miniconda3/envs/python3.10/lib/python3.10/site-packages/scipy/optimize/_minpack_py.py:833: OptimizeWarning: Covariance of the parameters could not be estimated\n",
+      "  warnings.warn('Covariance of the parameters could not be estimated',\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
@@ -349,8 +511,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -362,8 +523,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -375,8 +535,7 @@
     },
     {
      "data": {
-      "image/png": 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",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -389,71 +548,120 @@
    ],
    "source": [
     "from scipy.optimize import curve_fit\n",
-    "def gaussian(x, amp, cen, wid,bias):\n",
-    "    return amp * np.exp(-(x-cen)**2 / wid)+bias\n",
+    "\n",
+    "\n",
+    "def gaussian(x, amp, cen, wid, bias):\n",
+    "    return amp * np.exp(-((x - cen) ** 2) / wid) + bias\n",
+    "\n",
     "\n",
     "numtry_bw = 10\n",
-    "kde_bw_arr = np.linspace(0.05,0.1,numtry_bw)\n",
+    "kde_bw_arr = np.linspace(0.05, 0.1, numtry_bw)\n",
     "diff_mean_arr = np.zeros((numtry_bw))\n",
     "diff_p_arr = np.zeros((numtry_bw))\n",
     "diff_m_arr = np.zeros((numtry_bw))\n",
     "res_fit_prior2gauss = np.zeros((numtry_bw))\n",
     "\n",
     "numtry_prior = 20\n",
-    "sma_arr = np.mean(sma_prior) * np.linspace(0.98,1.02,numtry_prior)\n",
+    "sma_arr = np.mean(sma_prior) * np.linspace(0.98, 1.02, numtry_prior)\n",
     "\n",
-    "for idx_bw,kde_bw in enumerate(kde_bw_arr):\n",
+    "for idx_bw, kde_bw in enumerate(kde_bw_arr):\n",
     "    kde = gaussian_kde(values, bw_method=kde_bw)\n",
-    "    \n",
+    "\n",
     "    # Check pdf\n",
     "    lnprior_arr = np.zeros((numtry_prior))\n",
-    "    for idx_prior,sma in enumerate(sma_arr):\n",
-    "        lnprior_arr[idx_prior] = kde.logpdf([sma, np.mean(ecc_prior),np.pi/2,np.mean(w_prior),np.mean(tau_prior),np.mean(m1_prior)])\n",
-    "        \n",
+    "    for idx_prior, sma in enumerate(sma_arr):\n",
+    "        lnprior_arr[idx_prior] = kde.logpdf(\n",
+    "            [\n",
+    "                sma,\n",
+    "                np.mean(ecc_prior),\n",
+    "                np.pi / 2,\n",
+    "                np.mean(w_prior),\n",
+    "                np.mean(tau_prior),\n",
+    "                np.mean(m1_prior),\n",
+    "            ]\n",
+    "        )\n",
+    "\n",
     "    # Quarentiles comparison\n",
-    "    masskde_arr = kde.resample(len_pdf)[5,:]\n",
-    "    masskde_arr = masskde_arr[masskde_arr<0.004]\n",
-    "    masskde_quantiles = np.quantile(masskde_arr*1000,[(1-0.68), 0.5, 0.5+(1-0.68)])\n",
-    "    massprior_quantiles = np.quantile(m1_prior_draw*1000,[(1-0.68), 0.5, 0.5+(1-0.68)])\n",
-    "    diff_mean_arr[idx_bw] = (masskde_quantiles[1]-massprior_quantiles[1])/massprior_quantiles[1]\n",
-    "    diff_p_arr[idx_bw] =((masskde_quantiles[1]-masskde_quantiles[0])-(massprior_quantiles[1]-massprior_quantiles[0]))/massprior_quantiles[1]\n",
-    "    diff_m_arr[idx_bw] =np.abs(((masskde_quantiles[2]-masskde_quantiles[1])-(massprior_quantiles[2]-massprior_quantiles[1]))/massprior_quantiles[1])\n",
-    "    \n",
+    "    masskde_arr = kde.resample(len_pdf)[5, :]\n",
+    "    masskde_arr = masskde_arr[masskde_arr < 0.004]\n",
+    "    masskde_quantiles = np.quantile(\n",
+    "        masskde_arr * 1000, [(1 - 0.68), 0.5, 0.5 + (1 - 0.68)]\n",
+    "    )\n",
+    "    massprior_quantiles = np.quantile(\n",
+    "        m1_prior_draw * 1000, [(1 - 0.68), 0.5, 0.5 + (1 - 0.68)]\n",
+    "    )\n",
+    "    diff_mean_arr[idx_bw] = (\n",
+    "        masskde_quantiles[1] - massprior_quantiles[1]\n",
+    "    ) / massprior_quantiles[1]\n",
+    "    diff_p_arr[idx_bw] = (\n",
+    "        (masskde_quantiles[1] - masskde_quantiles[0])\n",
+    "        - (massprior_quantiles[1] - massprior_quantiles[0])\n",
+    "    ) / massprior_quantiles[1]\n",
+    "    diff_m_arr[idx_bw] = np.abs(\n",
+    "        (\n",
+    "            (masskde_quantiles[2] - masskde_quantiles[1])\n",
+    "            - (massprior_quantiles[2] - massprior_quantiles[1])\n",
+    "        )\n",
+    "        / massprior_quantiles[1]\n",
+    "    )\n",
+    "\n",
     "    # fit to Gaussian\n",
-    "    n = len(sma_arr) \n",
-    "    mean = np.mean(sma_prior)                 \n",
-    "    sigma = 0.04   #note this correction\n",
-    "    best_vals, covar = curve_fit(gaussian, sma_arr, lnprior_arr,\n",
-    "                                 p0=[np.abs(np.max(lnprior_arr)-np.min(lnprior_arr)),np.mean(sma_prior),0.04,np.mean(lnprior_arr)*4])#[np.mean(lnprior_arr),mean,sigma])\n",
-    "    gauss_fit = gaussian(sma_arr,*best_vals)\n",
-    "    if idx_bw%3==0:\n",
+    "    n = len(sma_arr)\n",
+    "    mean = np.mean(sma_prior)\n",
+    "    sigma = 0.04  # note this correction\n",
+    "    best_vals, covar = curve_fit(\n",
+    "        gaussian,\n",
+    "        sma_arr,\n",
+    "        lnprior_arr,\n",
+    "        p0=[\n",
+    "            np.abs(np.max(lnprior_arr) - np.min(lnprior_arr)),\n",
+    "            np.mean(sma_prior),\n",
+    "            0.04,\n",
+    "            np.mean(lnprior_arr) * 4,\n",
+    "        ],\n",
+    "    )  # [np.mean(lnprior_arr),mean,sigma])\n",
+    "    gauss_fit = gaussian(sma_arr, *best_vals)\n",
+    "    if idx_bw % 3 == 0:\n",
     "        plt.figure(101)\n",
-    "        plt.plot(sma_arr,lnprior_arr,label=\"KDE BW = {:.2f}\".format(kde_bw))\n",
-    "    \n",
-    "    res_fit_prior2gauss[idx_bw] = np.std(gauss_fit-lnprior_arr)\n",
+    "        plt.plot(sma_arr, lnprior_arr, label=\"KDE BW = {:.2f}\".format(kde_bw))\n",
+    "\n",
+    "    res_fit_prior2gauss[idx_bw] = np.std(gauss_fit - lnprior_arr)\n",
     "plt.figure(101)\n",
-    "plt.legend(loc='upper right')\n",
-    "plt.xlabel('SMA [AU]')\n",
-    "plt.ylabel('log-prior')\n",
-    "plt.title('1. Log-prior Variation around Median Prior SMA')\n",
+    "plt.legend(loc=\"upper right\")\n",
+    "plt.xlabel(\"SMA [AU]\")\n",
+    "plt.ylabel(\"log-prior\")\n",
+    "plt.title(\"1. Log-prior Variation around Median Prior SMA\")\n",
     "\n",
     "plt.figure(301)\n",
-    "plt.plot(kde_bw_arr,diff_mean_arr*100,color='k',label='diff median')\n",
-    "plt.plot(kde_bw_arr,diff_p_arr*100,color='k',linestyle='--',label='diff upper 68th interval limit')\n",
-    "plt.plot(kde_bw_arr,diff_m_arr*100,color='k',linestyle='-.',label='diff lower 68th interval limit')\n",
-    "plt.xlabel('KDE BW')\n",
-    "plt.ylabel('% of prior median')\n",
-    "plt.legend(loc='upper right')#, fontsize='x-large')\n",
-    "plt.title('2. Median and 68th Interv. Limits Changes w.r.t. Prior PDF')\n",
+    "plt.plot(kde_bw_arr, diff_mean_arr * 100, color=\"k\", label=\"diff median\")\n",
+    "plt.plot(\n",
+    "    kde_bw_arr,\n",
+    "    diff_p_arr * 100,\n",
+    "    color=\"k\",\n",
+    "    linestyle=\"--\",\n",
+    "    label=\"diff upper 68th interval limit\",\n",
+    ")\n",
+    "plt.plot(\n",
+    "    kde_bw_arr,\n",
+    "    diff_m_arr * 100,\n",
+    "    color=\"k\",\n",
+    "    linestyle=\"-.\",\n",
+    "    label=\"diff lower 68th interval limit\",\n",
+    ")\n",
+    "plt.xlabel(\"KDE BW\")\n",
+    "plt.ylabel(\"% of prior median\")\n",
+    "plt.legend(loc=\"upper right\")  # , fontsize='x-large')\n",
+    "plt.title(\"2. Median and 68th Interv. Limits Changes w.r.t. Prior PDF\")\n",
     "\n",
     "plt.figure(302)\n",
-    "plt.plot(kde_bw_arr,res_fit_prior2gauss,label='')\n",
-    "plt.xlabel('KDE BW')\n",
-    "plt.ylabel('log-prior RMS')\n",
-    "plt.title('3. Std Dev of the Residuals to a Gaussian Fit of Plot #1')\n"
+    "plt.plot(kde_bw_arr, res_fit_prior2gauss, label=\"\")\n",
+    "plt.xlabel(\"KDE BW\")\n",
+    "plt.ylabel(\"log-prior RMS\")\n",
+    "plt.title(\"3. Std Dev of the Residuals to a Gaussian Fit of Plot #1\")"
    ]
   },
   {
+   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -483,7 +691,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.5"
+   "version": "3.10.4"
   }
  },
  "nbformat": 4,

From 9253f0de95204ca93e4bb8fb317922541ba09848 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 22 Jun 2023 15:42:06 -0700
Subject: [PATCH 06/10] oops forgot to take old coveralls command out

---
 .github/workflows/python-package.yml | 1 -
 1 file changed, 1 deletion(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index ea68552a..1ae6020b 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -39,7 +39,6 @@ jobs:
     - name: Test with pytest
       run: |
         pytest
-        coveralls
     - name: Coveralls GitHub Action
       uses: coverallsapp/github-action@v2
       

From 3e9996a56269f0310608e8bc32fbc1a81a743835 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 22 Jun 2023 15:59:37 -0700
Subject: [PATCH 07/10] getting readthedocs to install orbitize

---
 .readthedocs.yaml | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index ada27db1..56ed041d 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -14,4 +14,6 @@ formats:
 
 python:
    install:
-   - requirements: requirements.txt
\ No newline at end of file
+   - requirements: requirements.txt
+   - method: pip
+     path: .

From 5565486a70a121d8dda7902adb0d4032901ac31b Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 22 Jun 2023 16:09:06 -0700
Subject: [PATCH 08/10] add rtd requirements file

---
 .readthedocs.yaml     | 5 +++--
 docs/requirements.txt | 1 +
 2 files changed, 4 insertions(+), 2 deletions(-)
 create mode 100644 docs/requirements.txt

diff --git a/.readthedocs.yaml b/.readthedocs.yaml
index 56ed041d..df65702e 100644
--- a/.readthedocs.yaml
+++ b/.readthedocs.yaml
@@ -14,6 +14,7 @@ formats:
 
 python:
    install:
-   - requirements: requirements.txt
+   - requirements: requirements.txt # orbitize requirements
+   - requirements: docs/requirements.txt # docs-building requirements
    - method: pip
-     path: .
+     path: . # install orbitize
diff --git a/docs/requirements.txt b/docs/requirements.txt
new file mode 100644
index 00000000..bfc3f807
--- /dev/null
+++ b/docs/requirements.txt
@@ -0,0 +1 @@
+nbsphinx
\ No newline at end of file

From 280a5ad2a3516e71b1e4f694d80cfa115a6be6e6 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 22 Jun 2023 16:20:25 -0700
Subject: [PATCH 09/10] using pytest-cov to get coverage communicated

---
 .github/workflows/python-package.yml | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml
index 1ae6020b..34af55d8 100644
--- a/.github/workflows/python-package.yml
+++ b/.github/workflows/python-package.yml
@@ -28,7 +28,7 @@ jobs:
     - name: Install dependencies
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest
+        python -m pip install flake8 pytest pytest-cov
         if [ -f requirements.txt ]; then pip install -r requirements.txt; fi
     - name: Lint with flake8
       run: |
@@ -38,7 +38,7 @@ jobs:
         flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
     - name: Test with pytest
       run: |
-        pytest
+        pytest --cov
     - name: Coveralls GitHub Action
       uses: coverallsapp/github-action@v2
       

From 6c05e639105eca7ab132452a214d4d10aa51a4c4 Mon Sep 17 00:00:00 2001
From: Sarah Blunt <sblunt@caltech.edu>
Date: Thu, 22 Jun 2023 17:03:52 -0700
Subject: [PATCH 10/10] more rtd dependencies

---
 docs/requirements.txt | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/docs/requirements.txt b/docs/requirements.txt
index bfc3f807..1a20a1f3 100644
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -1 +1,3 @@
-nbsphinx
\ No newline at end of file
+nbsphinx
+pandoc
+ipykernel
\ No newline at end of file