scripts for paper

examples/paper/55_Cnc_mask.py

@@ -715,7 +715,7 @@ def get_mask_from_neural_network(sme):
 
     # Set radial velocity and continuum settings
     # Set RV and Continuum flags
-    sme.vrad_flag = "each"
+    sme.vrad_flag = "fix"
     sme.cscale_flag = 3
     sme.cscale_type = "match+mask"
 
@@ -755,5 +755,7 @@ def get_mask_from_neural_network(sme):
     sme.save(out_file)
 
     # Plot results
+    sme.synth *= sme.telluric
     fig = plot_plotly.FinalPlot(sme)
     fig.save(filename=plot_file)
+    print(f"Finished: {target}")

examples/paper/HD_22049.py

@@ -649,12 +649,12 @@ def get_mask_from_neural_network(sme):
     #     raise ValueError("No data file found")
 
     # in_file = os.path.join(data_dir, fname)
-    in_file = os.path.join(examples_dir, f"results/{target}_mask.sme")
+    in_file = os.path.join(examples_dir, f"results/{target}_mask_new.sme")
 
     vald_file = os.path.join(examples_dir, f"data/hd22049.lin")
 
-    out_file = os.path.join(examples_dir, f"results/{target}_mask_out.sme")
-    plot_file = os.path.join(examples_dir, f"results/{target}_mask_out.html")
+    out_file = os.path.join(examples_dir, f"results/{target}_mask_new_out.sme")
+    plot_file = os.path.join(examples_dir, f"results/{target}_mask_new_out.html")
     date_string = datetime.datetime.now().isoformat().replace(":", ".")
     log_file = os.path.join(examples_dir, f"results/{target}_{date_string}.log")
 
@@ -781,7 +781,7 @@ def get_mask_from_neural_network(sme):
 
     # Set radial velocity and continuum settings
     # Set RV and Continuum flags
-    sme.vrad_flag = "each"
+    sme.vrad_flag = "fix"
     sme.cscale_flag = 2
     sme.cscale_type = "match+mask"
 
@@ -808,19 +808,19 @@ def get_mask_from_neural_network(sme):
     # sme.linelist = sme.linelist.trim(wmin, wmax)
 
     # Start SME solver
-    # sme = synthesize_spectrum(sme, segments=[6])
+    # sme = synthesize_spectrum(sme, segments=np.arange(6, 31))
     # sme.cscale_flag = "fix"
 
     # sme.save(out_file)
-
-    sme = solve(sme, fitparameters, segments=np.arange(2, 31))
+    sme = solve(sme, fitparameters, segments=np.arange(6, 31))
 
     print(sme.citation())
 
     # Save results
     sme.save(out_file)
 
     # Plot results
+    sme.synth *= sme.telluric
     fig = plot_plotly.FinalPlot(sme)
     fig.save(filename=plot_file)
     print(f"Finished: {target}")

examples/paper/L_98-59.py

@@ -811,5 +811,6 @@ def get_mask_from_neural_network(sme):
     sme.save(out_file)
 
     # Plot results
+    sme.synth *= sme.telluric
     fig = plot_plotly.FinalPlot(sme)
     fig.save(filename=plot_file)

examples/paper/L_98-59_mask.py

@@ -626,7 +626,9 @@ def get_mask_from_neural_network(sme):
     # Define the location of all your files
     # this will put everything into the example dir
     target = "L_98-59"
-    star = StellarDB().load(target)
+    sdb = StellarDB()
+    # sdb.auto_fill(target)
+    star = sdb.load(target)
     alias = [re.sub(r"[-_ ]", "", s).lower() for s in star["id"]]
 
     examples_dir = dirname(realpath(__file__))
@@ -666,7 +668,7 @@ def get_mask_from_neural_network(sme):
 
     # Get first guess from literature values
     sme.teff = star["t_eff"].to_value("K") if "t_eff" in star else 6000
-    sme.logg = star["logg"].to_value(1) if "logg" in star else 4
+    sme.logg = star["logg"].to_value(1) if "logg" in star else 4.9
     monh = star["metallicity"].to_value(1) if "metallicity" in star else 0
     sme.abund = Abund(monh, "asplund2009")
     sme.vmic = (
@@ -754,5 +756,7 @@ def get_mask_from_neural_network(sme):
     sme.save(out_file)
 
     # Plot results
+    sme.synth *= sme.telluric
     fig = plot_plotly.FinalPlot(sme)
     fig.save(filename=plot_file)
+    print(f"Finished: {target}")

examples/paper/L_98-59_predict.py

@@ -0,0 +1,95 @@
+""" Minimum working example of an SME script 
+"""
+import os
+import os.path
+import re
+from os.path import dirname, join, realpath
+import datetime
+
+import matplotlib.pyplot as plt
+import numpy as np
+from astropy import constants as const
+from astropy import coordinates as coord
+from astropy.io import fits
+from astropy.time import Time
+from data_sources.StellarDB import StellarDB
+from pysme import sme as SME
+from pysme import util
+from pysme.abund import Abund
+from pysme.gui import plot_plotly
+from pysme.iliffe_vector import Iliffe_vector
+from pysme.linelist.vald import ValdFile
+from pysme.persistence import save_as_idl
+from pysme.solve import solve
+from pysme.synthesize import synthesize_spectrum
+from scipy.linalg import lstsq, solve_banded
+from scipy.ndimage.filters import gaussian_filter1d, median_filter
+from scipy.optimize import least_squares
+from tqdm import tqdm
+
+if __name__ == "__main__":
+    # Define the location of all your files
+    # this will put everything into the example dir
+    target = "L_98-59"
+    sdb = StellarDB()
+    # sdb.auto_fill(target)
+    star = sdb.load(target)
+    alias = [re.sub(r"[-_ ]", "", s).lower() for s in star["id"]]
+
+    examples_dir = dirname(realpath(__file__))
+    data_dir = join(examples_dir, "data")
+
+    # Find the correct data file for this target
+    # fname = "ADP.2019-01-30T01:13:58.172.fits"
+    fname = "L_98-59_mask_out.sme"
+    in_file = os.path.join(examples_dir, "results", fname)
+    # in_file = os.path.join(examples_dir, f"results/{target}_mask.sme")
+
+    vald_file = os.path.join(examples_dir, f"data/hd22049.lin")
+
+    out_file = os.path.join(examples_dir, f"results/{target}_mask_out.sme")
+    plot_file = os.path.join(examples_dir, f"results/{target}.html")
+    date_string = datetime.datetime.now().isoformat().replace(":", ".")
+    log_file = os.path.join(examples_dir, f"results/{target}_{date_string}.log")
+
+    # Start the logging to the file
+    util.start_logging(log_file)
+
+    # err = hdu[1].data["ERR"]
+    sme = SME.SME_Structure.load(in_file)
+
+    # Set radial velocity and continuum settings
+    # Set RV and Continuum flags
+    sme.vrad_flag = "fix"
+    sme.cscale_flag = "none"
+    sme.cscale_type = "match"
+    sme.vrad = -14
+
+    # Reset observations
+    sme.wave = None
+    sme.spec = None
+    sme.synth = None
+    sme.telluric = None
+    # Set new Wavelength ranges of CRIRES+
+    # This is for setting L3426
+    sme.wran = [
+        [2885, 2935],
+        [3037, 3090],
+        [3206, 3261],
+        [3394, 3453],
+        [3607, 3669],
+        [3847, 3914],
+        [4122, 4193],
+    ]
+
+    # Perfrom the calculation
+    sme = synthesize_spectrum(sme)
+    print(sme.citation())
+
+    # Save results
+    sme.save(out_file)
+
+    # Plot results
+    fig = plot_plotly.FinalPlot(sme)
+    fig.save(filename=plot_file)
+    print(f"Finished: {target}")