Updates to the thermal yield:

- The cooling curves don't go below 10 M_earth, so we just assigned a thermal equilibrium model to those planets with albedo=0.5
- For all other planets, we pick which ever is larger: cooling curves or thermal equilibrium.

Tutorials/example_thermal_yield.py

@@ -1,27 +1,36 @@
 from psisim import telescope,instrument,observation,spectrum,universe,plots
 import numpy as np
 import matplotlib.pylab as plt
-
+import copy
 import time
 
 tmt = telescope.TMT()
 psi_red = instrument.PSI_Red()
-psi_red.set_observing_mode(3600,2,'M',10, np.linspace(4.2,4.8,3)) #60s, 40 exposures,z-band, R of 10
+psi_red.set_observing_mode(3600,2,'M',10, np.linspace(4.2,4.8,3)) #3600s, 2 exposures,M-band, R of 10
+
+######################################
+######## Generate the universe #######
+######################################
 
 exosims_config_filename = "forBruceandDimitri_EXOCAT1.json" #Some filename here
 uni = universe.ExoSims_Universe(exosims_config_filename)
 uni.simulate_EXOSIMS_Universe()
 
+##############################
+######## Lots of setup #######
+##############################
+
 min_iwa = np.min(psi_red.current_wvs)*1e-6/tmt.diameter*206265
 planet_table = uni.planets
-planet_table = planet_table[np.where(planet_table['PlanetMass'] > 10)]
+# planet_table = planet_table[np.where(planet_table['PlanetMass'] > 10)]
 planet_table = planet_table[planet_table['AngSep']/1000 > min_iwa]
 planet_table = planet_table[planet_table['Flux Ratio'] > 1e-10]
 
 n_planets = len(planet_table)
 
 planet_types = []
-planet_spectra = []
+planet_spectra = [] #The spectrum from the cooling tracks
+planet_eq_spectra = [] #The spectrum from the equilibrium thermal emission
 planet_ages = []
 
 n_planets_now = n_planets
@@ -45,37 +54,70 @@
 #Generate the model wavelenths
 model_wvs = np.linspace(model_wv_low, model_wv_high, n_model_wv) #Choose some wavelengths
 
+#####################################
+######## Generate the Spectra #######
+#####################################
+
+
 print("\n Starting to generate planet spectra")
 for planet in planet_table[rand_planets]:
+
     #INSERT PLANET SELECTION RULES HERE
-    planet_type = "Gas"
-    planet_types.append(planet_type)
 
-    age = np.random.random() * 5e9 # between 0 and 5 Gyr
-    planet_ages.append(age)
+    if planet['PlanetMass'] < 10:
+        #If the planet is < 10 M_Earth, we don't trust bex. So we'll be pessimistic and just report its thermal equilibrium. 
+        planet_type = "blackbody"
+        planet_types.append(planet_type)
+
+        #The bond albedo
+        atmospheric_parameters = 0.5
+        planet_spectrum = spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters, package='blackbody')
+        planet_spectra.append(planet_spectrum)
+        planet_eq_spectra.append(planet_spectrum)
+
+    else:
+        planet_type = "Gas"
+        planet_types.append(planet_type)
+
+        age = np.random.random() * 5e9 # between 0 and 5 Gyr
+        planet_ages.append(age)
 
-    time1 = time.time()
+        time1 = time.time()
 
-    #Here we're going to generate the spectrum as the addition of cooling models and a blackbody (i.e. equilibrium Temperature)
-	#Generate the spectrum from cooling models and downsample to intermediate resolution
-    atmospheric_parameters = age, 'M', True
-    planet_spectrum = spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters, package='bex-cooling')
+        ### Here we're going to generate the spectrum as the addition of cooling models and a blackbody (i.e. equilibrium Temperature)
+    	## Generate the spectrum from cooling models and downsample to intermediate resolution
+        atmospheric_parameters = age, 'M', True
+        planet_spectrum = spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters, package='bex-cooling')
 
-    #The bond albedo
-    atmospheric_parameters = 0.5
-    planet_spectrum += np.array(spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters, package='blackbody'))
 
-    planet_spectra.append(planet_spectrum)
-
-    time2 = time.time()
-    print('Spectrum took {0:.3f} s'.format((time2-time1)))
+        ## Generate the spectrum from a blackbody
+        atmospheric_parameters = 0.5#The bond albedo
+        planet_eq_spectrum = np.array(spectrum.simulate_spectrum(planet, model_wvs, intermediate_R, atmospheric_parameters, package='blackbody'))
+
+        planet_spectra.append(planet_spectrum)
+        planet_eq_spectra.append(planet_eq_spectrum)
+
+        time2 = time.time()
+        print('Spectrum took {0:.3f} s'.format((time2-time1)))
 
 print("Done generating planet spectra")
 print("\n Starting to simulate observations")
 
+#Here we take the biggest of either the planet cooling spectrum or the planet equilibrium spectrum
+#Kind of hacky, but is a good start
+final_spectra = np.array(copy.deepcopy(planet_spectra))
+planet_eq_spectra = np.array(planet_eq_spectra)
+planet_spectra = np.array(planet_spectra)
+final_spectra[planet_eq_spectra > planet_spectra] = planet_eq_spectra[planet_eq_spectra > planet_spectra]
+
+
+##########################################
+######## Simulate the observations #######
+##########################################
+
 post_processing_gain=10
 sim_F_lambda, sim_F_lambda_errs,sim_F_lambda_stellar, noise_components = observation.simulate_observation_set(tmt, psi_red,
-	planet_table[rand_planets], planet_spectra, model_wvs, intermediate_R, inject_noise=False,
+	planet_table[rand_planets], final_spectra, model_wvs, intermediate_R, inject_noise=False,
 	post_processing_gain=post_processing_gain,return_noise_components=True)
 
 speckle_noises = np.array([s[0] for s in noise_components])
@@ -87,6 +129,11 @@
 
 detected = psi_red.detect_planets(planet_table[rand_planets],snrs,tmt)
 
+
+########################################
+######## Make the contrast Plot ########
+########################################
+
 #Choose which wavelength you want to plot the detections at:
 wv_index = 1
 
@@ -101,9 +148,124 @@
 print("Planets detected: {}".format(len(np.where(detected[:,wv_index])[0])))
 print("Planets not detected: {}".format(len(np.where(~detected[:,wv_index])[0])))
 print("Post-processing gain: {}".format(post_processing_gain))
+plt.show()
+
+########################################
+######## Make the magnitude Plot #######
+########################################
+
+## Choose which wavelength you want to plot the detections at:
+# wv_index = 1
 
+# fig, ax = plots.plot_detected_planet_magnitudes(planet_table[rand_planets],wv_index,
+#     detected,flux_ratios,psi_red,tmt,show=False)
 
+# #The user can now adjust the plot as they see fit. 
+# #e.g. Annotate the plot
+# # ax.text(4e-2,1e-5,"Planets detected: {}".format(len(np.where(detected[:,wv_index])[0])),color='k')
+# # ax.text(4e-2,0.5e-5,"Planets not detected: {}".format(len(np.where(~detected[:,wv_index])[0])),color='k')
+# # ax.text(4e-2,0.25e-5,"Post-processing gain: {}".format(post_processing_gain),color='k')
+# print("Planets detected: {}".format(len(np.where(detected[:,wv_index])[0])))
+# print("Planets not detected: {}".format(len(np.where(~detected[:,wv_index])[0])))
+# print("Post-processing gain: {}".format(post_processing_gain))
+# plt.show()
 
+###########################################
+######## Recalculate the magnitudes #######
+###########################################
+
+
+# dMags = -2.5*np.log10(flux_ratios[:,wv_index])    
+
+# band = psi_red.current_filter
+# if band == 'R':
+#     bexlabel = 'CousinsR'
+#     starlabel = 'StarRmag'
+# elif band == 'I':
+#     bexlabel = 'CousinsI'
+#     starlabel = 'StarImag'
+# elif band == 'J':
+#     bexlabel = 'SPHEREJ'
+#     starlabel = 'StarJmag'
+# elif band == 'H':
+#     bexlabel = 'SPHEREH'
+#     starlabel = 'StarHmag'
+# elif band == 'K':
+#     bexlabel = 'SPHEREKs'
+#     starlabel = 'StarKmag'
+# elif band == 'L':
+#     bexlabel = 'NACOLp'
+#     starlabel = 'StarKmag'
+# elif band == 'M':
+#     bexlabel = 'NACOMp'
+#     starlabel = 'StarKmag'
+# else:
+#     raise ValueError("Band needs to be 'R', 'I', 'J', 'H', 'K', 'L', 'M'. Got {0}.".format(band))
+
+# stellar_mags = planet_table[starlabel]
+# stellar_mags = np.array(stellar_mags)
+
+# planet_mag = stellar_mags+dMags
+
+# plt.figure()
+# plt.plot(planet_mag,np.log10(masses),'o',alpha=0.3)   
+# plt.xlabel("M-band Magnitude")
+# plt.ylabel("Log10(Mass)")
+# plt.show()
+
+# plt.figure()
+# plt.plot(np.log10(flux_ratios[:,wv_index]),np.log10(masses),'o',alpha=0.3) 
+# plt.xlabel("log10(Flux Ratios")
+# plt.ylabel("Log10(Mass)")
+# plt.show()
+
+# sim_F_lambda
+
+# plt.figure()
+# plt.plot((sim_F_lambda[:,wv_index]),np.log10(masses),'o',alpha=0.3) 
+# plt.xlabel("log10(sim_F_lambda)")
+# plt.ylabel("Log10(Mass)")
+# plt.xlim(0,1e8)
+# plt.show()
+
+
+############################################################
+######## Histogram of detections and non-detections ########
+############################################################
+
+### And now we'll make a simple histogram of detected vs. generated planet mass. 
+masses = [planet['PlanetMass'] for planet in planet_table]
+detected_masses = [planet['PlanetMass'] for planet in planet_table[detected[:,1]]] #Picking 4.5 micron
+
+bins = np.logspace(np.log10(1),np.log10(1000),20)
+fig,axes = plt.subplots(2,1)
+hist_detected = axes[0].hist(detected_masses,bins=bins,color='darkturquoise',histtype='step',label="Detected",linewidth=3.5)
+hist_all = axes[0].hist(masses,color='k',bins=bins,histtype='step',label="Full Sample",linewidth=3.5)
+axes[0].set_xscale("log")
+axes[0].set_ylabel("Number of planets")
+axes[0].set_xlabel(r"Planet Mass [$M_{\oplus}$]")
+axes[0].legend()
+
+efficiency = hist_detected[0]/hist_all[0]
+efficiency[efficiency!=efficiency]=0
+axes[1].step(np.append(hist_detected[1][0],hist_detected[1]),np.append(0,np.append(efficiency,0)),where='post',linewidth=3)
+axes[1].set_ylabel("Detection Efficiency")
+#Stupid hack
+axes[1].set_xscale("log")
+axes[1].set_xlabel(r"Planet Mass [$M_{\oplus}$]")
+fig.suptitle(r"Thermal Emission Detections at {:.1f}$\mu m$".format(psi_red.current_wvs[1]))
 plt.show()
 
-import pdb; pdb.set_trace()
+##################################
+######## Save the results ########
+##################################
+
+from astropy.io import fits
+planet_table[rand_planets].write("thermal_planet_table.csv")
+ps_hdu = fits.PrimaryHDU(planet_spectra)
+ps_hdu.writeto("thermal_planet_spectra.fits",overwrite=True)
+flux_hdu = fits.PrimaryHDU([sim_F_lambda, sim_F_lambda_errs,np.array(sim_F_lambda_stellar)])
+flux_hdu.writeto("thermal_Observation_set.fits",overwrite=True)
+noise_components_hdu = fits.PrimaryHDU(noise_components)
+noise_components_hdu.writeto("thermal_noise_components.fits",overwrite=True)
+