evolved some more populations & worked on notebook to visualize the results

platypos_package/Mass_evolution_function.py

@@ -95,19 +95,16 @@ def mass_planet_RK4_forward_LO14(epsilon, K_on, beta_on, planet_object, initial_
         M_05k1 = M + 0.5*k1     
         M_env_05k1 = M_05k1 - M_core
         f_env_05k1 = (M_env_05k1/M_05k1)*100 # new mass fraction  
-        
         Mdot2 = mass_loss_rate_forward_LO14(t+0.5*dt, epsilon, K_on, beta_on, planet_object, f_env_05k1, track_dict)
         k2 = (dt*Myr_to_sec * Mdot2)/M_earth
         M_05k2 = M + 0.5*k2
         M_env_05k2 = M_05k2 - M_core
         f_env_05k2 = (M_env_05k2/M_05k2)*100 # new mass fraction
-        
         Mdot3 = mass_loss_rate_forward_LO14(t+0.5*dt, epsilon, K_on, beta_on, planet_object, f_env_05k2, track_dict) 
         k3 = (dt*Myr_to_sec * Mdot3)/M_earth
         M_k3 = M + k3
         M_env_k3 = M_k3 - M_core
         f_env_k3 = (M_env_k3/M_k3)*100 # new mass fraction
-        
         Mdot4 = mass_loss_rate_forward_LO14(t+dt, epsilon, K_on, beta_on, planet_object, f_env_k3, track_dict) 
         k4 = (dt*Myr_to_sec * Mdot4)/M_earth
 

population_evolution/Platypos_RUNS.md

+**result_files:**
+
+- OwWu17_smass_cks
+
+- OwWu17_smass_cks_lowerLx 
+    (here I fixed an issue with Lx and Lxuv -> Owen Lsat is XUV, so I needed to convert to Lx first, before passing into Platypos, since Platypos automatically converts from X-ray to XUV)
+    t_start = 1 Myr
+    t_final = 3 Gyr
+    smass distr: mu=1.0, sigma=0.3
+    Mcore = 3
+    
+- OwWu17_smass_OwWusmass
+    t_start = 1 Myr
+    t_final = 3 Gyr
+    smass distr: mu=1.3, sigma^2=0.3
+    Mcore = 3
+
+- OwWu17_smass_cks_Mcore5:
+    t_start = 1 Myr
+    t_final = 3 Gyr
+    smass distr: mu=1.0, sigma=0.3
+    Mcore = 5
+    
+- OwWu17_smass_cks_Mcore3_5Gyr
+    t_start = 1 Myr
+    t_final = 5 Gyr
+    smass distr: mu=1.0, sigma=0.3
+    Mcore = 3
\ No newline at end of file

population_evolution/get_ExoplanetEU_data.py

+from PyAstronomy import pyasl
+import pandas as pd
+import numpy as np
+
+from sklearn.neighbors import KernelDensity
+import astropy.units as u
+from astropy import constants as const
+
+
+def get_ExoplanetEU_data():
+    """use PyAstronomy to access the data provided by exoplanet.eu
+    https://pyastronomy.readthedocs.io/en/latest/pyaslDoc/resBasedDoc/exoplanetEU.html
+    
+    Return: dataframe, array for period, radius and distance
+    """
+
+    # Instantiate exoplanetEU2 object (download all the planets)
+    v = pyasl.ExoplanetEU2()
+    # Export all data as a pandas DataFrame
+    df_planets = v.getAllDataPandas()
+
+    mask_nan = ~(np.isnan(df_planets["orbital_period"]) | np.isnan(df_planets["radius"]))
+    mask_P = df_planets["orbital_period"]<300
+    df_planets_ = df_planets[mask_nan & mask_P] # dataframe only with planets which have P & R
+
+    RJ_to_RE = (const.R_jup/const.R_earth).value
+    period = df_planets_["orbital_period"].values
+    radius = df_planets_["radius"].values*RJ_to_RE
+    distance = df_planets_["semi_major_axis"].values
+    
+    return df_planets_, period, radius, distance
+    
+def gaussian_kernel_density_estimation(X_data, Y_data, baseX, baseY):
+    """
+    Input:
+    @ X_data
+    @ Y_data
+    @ baseX
+    @ baseY
+    
+    Return: X_arr and Y_arr for the grid, and the corresponding probability density grid, not in log-scale!
+    
+    Plot command: ax.contourf(X_grid, Y_grid, prob_density, cmap="binary", levels=15, alpha=0.5)
+    """
+    # transform actual data to log space before proceeding
+    baseX_data = np.log(X_data)/np.log(baseX)
+    baseY_data = np.log(Y_data)/np.log(baseY)
+
+    # make a grid (in log space - to match the data)
+    X = np.arange(baseX_data.min(),baseX_data.max()+1,1)
+    Y = np.arange(baseY_data.min(),baseY_data.max()+1,1)
+    # for now the density of the grid is fixed in here/ hardcoded!
+    XX, YY = np.mgrid[X.min():X.max():200j, Y.min():Y.max():200j] 
+
+    XY_sample = np.vstack([XX.ravel(), YY.ravel()]).T # now I have a grid of points covering my radii and periods
+    XY_train  = np.vstack([baseX_data, baseY_data]).T # this is my data in grid-form
+
+    # construct a Gaussian kernel density estimate of the distribution
+    # for now the bandwidth is also hardcoded!
+    kde = KernelDensity(bandwidth=0.25, kernel='gaussian')
+    kde.fit(XY_train) # fit/load real dataset
+
+    # score_samples() returns the log-likelihood of the samples
+    prob_density = np.exp(kde.score_samples(XY_sample))
+    # nm_sample is the range/grid over which I compute the density based on the data (2-D)
+    prob_density = np.reshape(prob_density, XX.shape)
+    
+    return baseX**XX, baseY**YY, prob_density
+