Population_evolution_OwWu17__04_06-2020-Copy1.ipynb 1.56 MB
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{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
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    "# last worked on 5.6.2020"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 31,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Number of processors:  8\n"
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    }
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   "source": [
    "import sys\n",
    "import importlib\n",
    "sys.path.append('../platypos_package/')\n",
    "# Planet Class\n",
    "from Planet_class_LoFo14 import planet_LoFo14\n",
    "from Planet_class_Ot20 import planet_Ot20\n",
    "from Lx_evo_and_flux import Lx_evo, flux_at_planet_earth, L_xuv_all\n",
    "import keplers_3rd_law as kepler3\n",
    "import notify\n",
    "importlib.reload(sys.modules['notify'])\n",
    "\n",
    "#importlib.reload(sys.modules['Mass_evolution_function'])\n",
    "from Lx_evo_and_flux import undo_what_Lxuv_all_does\n",
    "importlib.reload(sys.modules['Lx_evo_and_flux'])\n",
    "\n",
    "sys.path.append('../platypos_package/population_evolution/')\n",
    "from evolve_planet import evolve_one_planet, evolve_ensamble\n",
    "importlib.reload(sys.modules['evolve_planet'])\n",
    "from create_planet_chunks import create_planet_chunks\n",
    "from create_summary_files import create_summary_files_with_final_planet_parameters\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({'font.size': 18, 'legend.fontsize': 14})\n",
    "mpl.rcParams['axes.linewidth'] = 1.1 #set the value globally\n",
    "from matplotlib.ticker import ScalarFormatter, FormatStrFormatter, FuncFormatter\n",
    "import matplotlib.ticker as ticker\n",
    "import os\n",
    "import math\n",
    "from astroquery.nasa_exoplanet_archive import NasaExoplanetArchive\n",
    "from PyAstronomy import pyasl\n",
    "from astropy import constants as const\n",
    "# Use multiple cores for parallel processing\n",
    "import multiprocessing\n",
    "import multiprocessing as mp\n",
    "print(\"Number of processors: \", mp.cpu_count())\n",
    "import time\n",
    "import scipy\n",
    "from scipy import stats\n",
    "from sklearn.neighbors import KernelDensity\n",
    "import seaborn as sns\n",
    "import scipy.optimize as optimize\n",
    "from scipy.optimize import fsolve\n",
    "\n",
    "import random\n",
    "random.seed(42)\n",
    "\n",
    "#################################################################################################\n",
    "p = \"../supplementary_files/\"\n",
    "# Tu et al. (2015) model tracks\n",
    "blue = pd.read_csv(p+'Lx_blue_track.csv', header=None).sort_values(0)\n",
    "red = pd.read_csv(p+'Lx_red_track.csv', header=None).sort_values(0)\n",
    "green = pd.read_csv(p+'Lx_green_track.csv', header=None).sort_values(0).reset_index(drop=True)\n",
    "# Lx sample Jackson et al. (2012)\n",
    "jack = pd.read_csv(p+\"Jackson2012_Lx_clean.csv\")\n",
    "\n",
    "def read_results_file(path, filename):\n",
    "    # read in results files\n",
    "    df = pd.read_csv(path+filename)\n",
    "    t, M, R, Lx = df[\"Time\"].values, df[\"Mass\"].values, df[\"Radius\"].values, df[\"Lx\"].values\n",
    "    return t, M, R, Lx"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 32,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2025\n",
      "1298\n",
      "1290\n",
      "1074\n",
      "1017\n",
      "983\n",
      "921\n"
     ]
    }
   ],
   "source": [
    "# get dataset from CKS (California-Kepler-Survey): https://california-planet-search.github.io/cks-website/ \n",
    "# column explanations: https://www.astro.caltech.edu/~howard/cks/column-definitions.txt)\n",
    "df_cks_orig = pd.read_csv(\"../supplementary_files/cks_physical_merged.csv\")\n",
    "print(len(df_cks_orig))\n",
    "df_cks = df_cks_orig.copy()\n",
    "# sample selection based on Fulton 2017\n",
    "mask_confirmed = df_cks[\"koi_disposition\"] == \"CONFIRMED\" # only select confirmed planets\n",
    "df_cks = df_cks[mask_confirmed]\n",
    "print(len(df_cks))\n",
    "# restrict sample to only the magnitudelimited portion of the larger CKS sample (Kp <14.2):\n",
    "mask_magnitude = df_cks[\"kic_kmag\"] < 14.2\n",
    "df_cks = df_cks[mask_magnitude]\n",
    "print(len(df_cks))\n",
    "# planet-to-star radius ratio (R_pl/R_star) becomes uncertain at high impact parameters (b) due to degeneracies with limbdarkening. \n",
    "# excluded KOIs with b > 0.7 to minimize the impact of grazing geometries.\n",
    "mask_impactparam = df_cks[\"koi_impact\"] <= 0.7\n",
    "df_cks = df_cks[mask_impactparam]\n",
    "print(len(df_cks))\n",
    "# remove planets with orbital periods longer than 100 days in order to avoid domains of low completeness \n",
    "# (especially for planets smaller than about 4 R_earth) and low transit probability.\n",
    "mask_period = df_cks[\"koi_period\"] <= 100.\n",
    "df_cks = df_cks[mask_period]\n",
    "print(len(df_cks))\n",
    "# also excised planets orbiting evolved stars since they have somewhat lower detectability and less certain radii. \n",
    "# implemented using an ad hoc temperature-dependent stellar radius filter:\n",
    "mask_evolved = df_cks[\"koi_srad\"] <= 10**(0.00025*(df_cks[\"koi_steff\"]-5500)+0.20)\n",
    "df_cks = df_cks[mask_evolved]\n",
    "print(len(df_cks))\n",
    "# also restrict sample to planets orbiting stars within the temperature range where we can extract precise stellar parameters \n",
    "# from our high-resolution optical spectra (6500–4700 K).\n",
    "mask_temperature = (df_cks[\"koi_steff\"] >= 4700) & (df_cks[\"koi_steff\"] <= 6500)\n",
    "df_cks = df_cks[mask_temperature]\n",
    "print(len(df_cks))\n",
    "\n",
    "# drop columns which have missing stellar mass, planetary radius, semi-major axis or period\n",
    "df_cks = df_cks.dropna(axis=0, how=\"any\", subset=[\"koi_sma\", \"koi_period\", \"koi_smass\", \"koi_prad\"])\n",
    "df_cks.reset_index(inplace=True)\n",
    "\n",
    "\n",
    "# Gaussian kernel density estimation\n",
    "# actual data -> transform to log space\n",
    "log3P = np.log(df_cks[\"koi_period\"])/np.log(3)\n",
    "log2R = np.log(df_cks[\"koi_prad\"])/np.log(2)\n",
    "\n",
    "# make a grid (in log space - to match the data)\n",
    "n = np.arange(log3P.min(),log3P.max()+1,1)\n",
    "m = np.arange(log2R.min(),log2R.max()+1,1)\n",
    "nn, mm = np.mgrid[n.min():n.max():200j, m.min():m.max():200j] \n",
    "\n",
    "nm_sample = np.vstack([nn.ravel(), mm.ravel()]).T # now I have a grid of points covering my radii and periods\n",
    "nm_train  = np.vstack([log3P, log2R]).T # this is my data in grid-form\n",
    "\n",
    "# construct a Gaussian kernel density estimate of the distribution\n",
    "kde = KernelDensity(bandwidth=0.25, kernel='gaussian')\n",
    "kde.fit(nm_train) # fit/load real dataset\n",
    "\n",
    "# score_samples() returns the log-likelihood of the samples\n",
    "prob_density = np.exp(kde.score_samples(nm_sample))\n",
    "# nm_sample is the range/grid over which I compute the density based on the data (2-D)\n",
    "prob_density = np.reshape(prob_density, nn.shape)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 33,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# creating a distribution for the stellar mass\n",
    "# info: df_cks is the \n",
    "cks_mean = df_cks[\"koi_smass\"].mean()\n",
    "cks_std = df_cks[\"koi_smass\"].std()\n",
    "from scipy.stats import norm\n",
    "smass_dist_cks = norm(cks_mean, cks_std) # Gaussian\n",
    "\n",
    "# sample probabilities for a range of outcomes\n",
    "starmass_arr = np.linspace(df_cks[\"koi_smass\"].min(), df_cks[\"koi_smass\"].max(), 100)\n",
    "probabilities = [smass_dist_cks.pdf(value) for value in starmass_arr]\n",
    "\n",
    "# Owen & Wu 2017 paper:\n",
    "# Section 3.2. \"We adopt host star masses similar to those in the CKS sample (Fulton et al. 2017), \n",
    "# a Gaussian distribution in mass, centered at 1.3 M_sun with a variance of 0.3 M_sun.\"\n",
    "mean_OW17, variance_OW17 = 1.3, 0.3\n",
    "std_OW17 = np.sqrt(variance_OW17)\n",
    "smass_dist_OW17 = norm(mean_OW17, std_OW17)\n",
    "smass_dist_OW17_2 = norm(mean_OW17, variance_OW17)\n",
    "\n",
    "starmass_arr_OW17 = np.linspace(0,5,100)\n",
    "probabilities_OW17 = [smass_dist_OW17.pdf(value) for value in starmass_arr_OW17]\n",
    "probabilities_OW17_2 = [smass_dist_OW17_2.pdf(value) for value in starmass_arr_OW17]\n",
    "\n",
    "#plot \n",
    "fig, ax = plt.subplots(figsize=(10,5))\n",
    "ax.hist(df_cks[\"koi_smass\"], bins=20, histtype=\"step\", density=True, lw=2)\n",
    "\n",
    "ax.plot(starmass_arr, probabilities, ls=\"-\", lw=2, label=r\"$\\mu$={:.2f}, $\\sigma$={:.2f}\".format(cks_mean, cks_std))\n",
    "ax.plot(starmass_arr_OW17, probabilities_OW17, ls=\"-\", label=r\"$\\mu$={}, $\\sigma$={:.2f}\".format(mean_OW17, std_OW17))\n",
    "ax.plot(starmass_arr_OW17, probabilities_OW17_2, ls=\"-\", label=r\"$\\mu$={}, $\\sigma$={}\".format(mean_OW17, variance_OW17))\n",
    "ylim = ax.get_ylim()\n",
    "ax.vlines(cks_mean, ylim[0], ylim[1], linestyle=\":\", color=\"k\", lw=1.3)\n",
    "ax.vlines(mean_OW17, ylim[0], ylim[1], linestyle=\":\", color=\"k\", lw=1.3)\n",
    "ax.set_xlim(0,2.5)\n",
    "ax.xaxis.set_major_formatter(ticker.StrMethodFormatter('{x:.1f}'))\n",
    "ax.set_title(\"Stellar Mass Histogram\", fontsize=15)\n",
    "ax.set_xlabel(\"Stellar Mass [M$_\\odot$]\")\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 34,
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   "metadata": {},
   "outputs": [
    {
     "data": {
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      "image/png": 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\n",
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def L_high_energy(t_i, mass_star):\n",
    "    \"\"\" L_HE (UV to X-ray) in Owen & Wu (2017).\n",
    "    @param t_i - in Myr\n",
    "    @param mass_star - in solar masses\n",
    "    \"\"\"\n",
    "    t_sat = 100. # Myr\n",
    "    L_sat = 10**(-3.5)*(mass_star) * (const.L_sun.cgs).value\n",
    "    a_0 = 0.5\n",
    "    if t_i < t_sat:\n",
    "        L_HE = L_sat\n",
    "    elif t_i >= t_sat:\n",
    "        L_HE = L_sat*(t_i/t_sat)**(-1-a_0)\n",
    "    return L_HE # in erg/s\n",
    "\n",
    "# make test plot\n",
    "step_size, t_track_start, t_track_end = 1., 1., 3000.\n",
    "fig, ax = plt.subplots()\n",
    "for m in [0.7, 1.0, 1.3]:\n",
    "    t_arr = np.arange(t_track_start, t_track_end+step_size, step_size)\n",
    "    Lx_arr = np.array([L_high_energy(t_i, m) for t_i in t_arr])\n",
    "    ax.plot(t_arr, Lx_arr, label=\"{} M$_\\odot$\".format(m))\n",
    "ax.loglog()\n",
    "ax.legend()\n",
    "ax.set(xlabel=\"Time [Myr]\", ylabel=\"L$_{\\, \\mathrm{high\\ energy}}$\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Semi-major axis distribution"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 35,
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   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1000\n",
    "deltaP = 0.01\n",
    "period_values = np.arange(0.01, 100, deltaP)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "# this is dN/dP (dN/dlogP*dP/dP = dN/dP*dlogP/dP = dN/dP*P)\n",
    "\n",
    "class P_pdf_unnormalized(stats.rv_continuous):\n",
    "    def _pdf(self, P):\n",
    "        if P < 7.6:\n",
    "            return P**1.9/P\n",
    "        elif P >= 7.6:\n",
    "            return float(7.6**1.9)/P\n",
    "\n",
    "rv_P = P_pdf_unnormalized(a=0, b=100, name='P_pdf')\n",
    "pdfs = np.array([rv_P.pdf(p) for p in period_values])\n",
    "area_under_curve_P = scipy.integrate.trapz(pdfs, period_values)\n",
    "\n",
    "class P_pdf_normalized(stats.rv_continuous):\n",
    "    def _pdf(self, P):\n",
    "        if P < 7.6:\n",
    "            return (P**1.9)/P/area_under_curve_P\n",
    "        elif P >= 7.6:\n",
    "            return float(7.6**1.9)/P/area_under_curve_P\n",
    "\n",
    "rv_P = P_pdf_normalized(a=0, b=100, name='P_pdf')        \n",
    "pdfs = np.array([rv_P.pdf(p) for p in period_values])\n",
    "print(scipy.integrate.trapz(pdfs, dx=deltaP))\n",
    "\n",
    "# rvs = rv_P.rvs(size=N)\n",
    "# fig, ax = plt.subplots(figsize=(10,4))\n",
    "# ax.plot(period_values, pdfs)\n",
    "# ax.hist(rvs, bins=10**np.linspace(np.log10(np.min(period_values)), np.log10(np.max(period_values)), 50, endpoint=False), density=True, rwidth=0.9)\n",
    "# ax.set(xlabel=\"Period [d]\", ylabel=\"Probability density\")\n",
    "# plt.show()\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10,4))\n",
    "# ax.plot(period_values, pdfs)\n",
    "# ax.hist(rvs, bins=10**np.linspace(np.log10(np.min(period_values)), np.log10(np.max(period_values)), 50, endpoint=False), density=True, rwidth=0.9)\n",
    "# ax.set(xlabel=\"Period [d]\", ylabel=\"Probability density\")\n",
    "# ax.set_xscale(\"log\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Core-mass distribution"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 38,
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   "metadata": {},
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   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9999990738451694\n",
      "(array(3.75994241), array(3.86283306), array(0.63111066), array(0.2450893))\n"
     ]
    }
   ],
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   "source": [
    "from scipy.stats import rayleigh\n",
    "\n",
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    "rv_Mcore = rayleigh(scale=3) # 3, 5\n",
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    "\n",
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    "# The probability density above is defined in the “standardized” form. \n",
    "# To shift and/or scale the distribution use the loc and scale parameters. \n",
    "# Specifically, rayleigh.pdf(x, loc, scale) is identically equivalent to rayleigh.pdf(y) / scale with y = (x - loc) / scale.\n",
    "\n",
    "core_masses = np.arange(0,20,0.01)\n",
    "# generate N random variables\n",
    "rvs_ray = rv_Mcore.rvs(size=N)\n",
    "\n",
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    "# PLOT\n",
    "fig, axs = plt.subplots(1,2, figsize=(10,4))\n",
    "axs[0].set_title(\"PDF\")\n",
    "axs[0].plot(core_masses, rv_Mcore.pdf(core_masses), label=\"PDF\")\n",
    "axs[0].hist(rvs_ray, bins=20, density=True, rwidth=0.9)\n",
    "axs[0].set_xlabel(\"Core mass [M$_\\oplus$]\")\n",
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    "\n",
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    "axs[1].set_title(\"CDF\")\n",
    "axs[1].plot(core_masses, rv_Mcore.cdf(core_masses), label=\"CDF\")\n",
    "axs[1].set_xlabel(\"Core mass [M$_\\oplus$]\")\n",
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    "\n",
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    "plt.tight_layout()\n",
    "plt.show()\n",
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    "\n",
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    "print(scipy.integrate.trapz(rv_Mcore.pdf(core_masses), dx=0.01))\n",
    "print(rv_Mcore.stats(moments=\"mvsk\"))"
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   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initial envelope mass fraction distribution\n",
    "logarithmically flat initial envelope mass fraction [X=M_env/M_c] distribution; from X=0.01-0.3"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "deltaX = 0.001\n",
    "X_values = np.arange(0.01, 0.3, deltaX)"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.33986900521952396\n",
      "1.0000000000000009\n"
     ]
    }
   ],
   "source": [
    "class X_pdf_unnormalized(stats.rv_continuous):\n",
    "    def _pdf(self, X):\n",
    "        return 0.1/X\n",
    "\n",
    "rv_X = X_pdf_unnormalized(a=0.01, b=0.3, name='X_pdf')\n",
    "pdfs = np.array([rv_X.pdf(x) for x in X_values])\n",
    "area_under_curve_X = scipy.integrate.trapz(pdfs, X_values)\n",
    "print(area_under_curve_X)\n",
    "\n",
    "class X_pdf_normalized(stats.rv_continuous):\n",
    "    def _pdf(self, X):\n",
    "        return 0.1/X/area_under_curve_X\n",
    "    \n",
    "rv_X = X_pdf_normalized(a=0.01, b=0.3, name='X_pdf')        \n",
    "pdfs = np.array([rv_X.pdf(x) for x in X_values])\n",
    "print(scipy.integrate.trapz(pdfs, dx=deltaX))\n",
    "\n",
    "# rvs = rv_X.rvs(size=N)\n",
    "# fig, ax = plt.subplots(figsize=(10,4))\n",
    "# ax.plot(X_values, pdfs)\n",
    "# ax.hist(rvs, bins=10**np.linspace(np.log10(np.min(X_values)), np.log10(np.max(X_values)), 50, endpoint=False), density=True, rwidth=0.9)\n",
    "# ax.set(xlabel=\"Envelope mass fraction X\", ylabel=\"Probability density\")\n",
    "# plt.show()\n",
    "\n",
    "# fig, ax = plt.subplots(figsize=(10,4))\n",
    "# ax.plot(X_values, pdfs)\n",
    "# ax.hist(rvs, bins=10**np.linspace(np.log10(np.min(X_values)), np.log10(np.max(X_values)), 50, endpoint=False), density=True, rwidth=0.9)\n",
    "# ax.set(xlabel=\"Envelope mass fraction X\", ylabel=\"Probability density\")\n",
    "# ax.set_xscale(\"log\")\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the sample\n",
    "**Assumption**: planets are born with the same envelope mass fraction and core mass distribution across all periods!"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 41,
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   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "CPU times: user 11.4 s, sys: 0 ns, total: 11.4 s\n",
      "Wall time: 11.3 s\n"
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     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# for each planet, draw a period. Then draw envelope mass fraction and core mass from those two distributions.\n",
    "N_planets = 5000\n",
    "metallicity = \"solarZ\" \n",
    "periods = rv_P.rvs(size=N_planets)\n",
    "\n",
    "# I also need to draw a stellar mass, this gives me L_sat and let's me calculate the orbital separation\n",
    "# I have 3 distributions to draw from: my Gaussian fit to CKS sample, a Gaussian with Owen&Wu17 paper values, a Gaussian with Owen&Wu17 mean (1.3 M_sun) and sigma=0.3\n",
    "# smass_dist_cks.rvs(), smass_dist_OW17.rvs(), smass_dist_OW17_2.rvs()\n",
    "smass_distro_list = [smass_dist_cks, smass_dist_OW17, smass_dist_OW17_2]\n",
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    "smass_distro = smass_distro_list[0]"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 42,
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   "metadata": {},
   "outputs": [
    {
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total planets in population:  5000\n",
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      "CPU times: user 9.36 s, sys: 0 ns, total: 9.36 s\n",
      "Wall time: 9.3 s\n"
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    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# for each of the N planets, I create a planet-class instance\n",
    "list_planets = []\n",
    "for i, P in enumerate(periods):\n",
    "    # draw core mass\n",
    "    m_c = rv_Mcore.rvs()\n",
    "    # draw envelope mass fraction\n",
    "    X = rv_X.rvs()\n",
    "    #print(i, P, m_c, X)\n",
    "    # LoFo14 planet: {\"core_mass\": m_c, \"fenv\": f, \"distance\": a, \"metallicity\": metal}\n",
    "    # X = m_env/m_core, fenv = m_env/(m_core+m_env)\n",
    "    m_env = X*m_c\n",
    "    fenv = (m_env/(m_c+m_env))*100 # input in %\n",
    "    #pl_dict = {\"period\": P, \"core_mass\": m_c, \"fenv\": fenv, \"metallicity\": metallicity}\n",
    "    \n",
    "    # draw stellar mass & calculate necessary stellar parameters\n",
    "    while True:\n",
    "        rv_smass = smass_distro.rvs()\n",
    "        if rv_smass <= 0.:\n",
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    "            #print(\"stellar mass <= 0. Redraw. Needs to be > 0.\")\n",
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    "            continue\n",
    "        else:\n",
    "            break\n",
    "    \n",
    "    # star_dictionary: {'star_id': \"dummySun\", 'mass': mass_star, 'radius': radius_star, 'age': age_star, 'L_bol': L_bol, 'Lx_age': Lx_age}\n",
    "    age_star = 1.0 # OwWu17 starting value\n",
    "    Luvx_age = L_high_energy(age_star, rv_smass)\n",
    "    Lx_age = undo_what_Lxuv_all_does(Luvx_age)\n",
    "    radius_star = None # not important for calculation.\n",
    "    # for now I have been setting Lbol as a constant. (LoFo14 models have a small radius dependence on Lbol)\n",
    "    # set to 1. for now.\n",
    "    L_bol = 1. # solar L\n",
    "    star_id = \"star_age{:.1f}_mass{:.2f}\".format(age_star, rv_smass)\n",
    "    star_dictionary = {'star_id': star_id, 'mass': rv_smass, 'radius': radius_star, 'age': age_star, 'L_bol': L_bol, 'Lx_age': Lx_age}\n",
    "    \n",
    "    # now that I also have the host star, I can use Kepler's 3rd law to convert from period to semi-major axis\n",
    "    a = kepler3.get_a_from_period(M_star=rv_smass, P=P)\n",
    "    pl_dict = {\"distance\": a, \"core_mass\": m_c, \"fenv\": fenv, \"metallicity\": metallicity}\n",
    "    # create planet class instance & add to list\n",
    "    list_planets.append(planet_LoFo14(star_dictionary=star_dictionary, planet_dict=pl_dict))\n",
    "\n",
    "list_planets = np.array(list_planets)\n",
    "print(\"Total planets in population: \", len(list_planets))"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 Myr\n",
    "sample_a = np.array([pl.distance for pl in list_planets])\n",
    "sample_P = np.array([kepler3.get_period_from_a(pl.mass_star, pl.distance) for pl in list_planets])\n",
    "sample_radius = np.array([pl.radius for pl in list_planets])\n",
    "sample_Rcore = np.array([pl.core_radius for pl in list_planets])\n",
    "sample_Mcore = np.array([pl.core_mass for pl in list_planets])\n",
    "sample_fenv = np.array([pl.fenv for pl in list_planets])\n",
    "sample_mass = np.array([pl.mass for pl in list_planets])\n",
    "\n",
    "sample_smass = np.array([pl.mass_star for pl in list_planets])\n",
    "sample_Lsat = np.array([pl.Lx_age for pl in list_planets])"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "'/work2/lketzer/work/gitlab/platypos/population_evolution/result_files_OwWu17_smass_cks_Mcore3_5Gyr/'"
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      ]
     },
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     "execution_count": 44,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
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    "path_save, planet_chunks = create_planet_chunks(curr_path=os.getcwd()+\"/\", folder_name=\"result_files_OwWu17_smass_cks_Mcore3_5Gyr/\", list_planets=list_planets, chunk_size=9)\n",
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    "path_save"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evolve the ensemble"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 45,
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   "metadata": {},
   "outputs": [],
   "source": [
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    "t_final, init_step, eps = 5000., 0.1, 0.1\n",
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    "# for testing\n",
    "planets_chunks = planet_chunks[:]"
   ]
  },
  {
   "cell_type": "code",
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   "metadata": {},
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ·············\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "start\n",
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      "Planet:  planet_0788_track_1.0_100.0_5000.0_1.3513492712458256e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4404_track_1.0_100.0_5000.0_1.899437673113507e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_1867_track_1.0_100.0_5000.0_2.0113610226662734e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4219_track_1.0_100.0_5000.0_1.703487991588149e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_1715_track_1.0_100.0_5000.0_1.35024398545229e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0109_track_1.0_100.0_5000.0_1.981300101072589e+29_0.0_0.0.txt\n",
      "Planet:  planet_4662_track_1.0_100.0_5000.0_1.459636347416811e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
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      "Planet:  planet_3730_track_1.0_100.0_5000.0_1.56828299519671e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0077_track_1.0_100.0_5000.0_1.5409752559091946e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  83.89999999999951\n",
      "M_core =  3.4282668290282388\n",
      "R_core =  1.3607196428511072\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "That took 0.13764990170796712 minutes\n",
      "Planet:  planet_0550_track_1.0_100.0_5000.0_1.9596430478739385e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2225_track_1.0_100.0_5000.0_2.4600001862064055e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1839_track_1.0_100.0_5000.0_2.0251390153536563e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_0801_track_1.0_100.0_5000.0_1.9057764650129974e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3118_track_1.0_100.0_5000.0_2.224840469841218e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4247_track_1.0_100.0_5000.0_1.5353944020091115e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4065_track_1.0_100.0_5000.0_1.909474718367367e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2715_track_1.0_100.0_5000.0_1.6840384103567498e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4485_track_1.0_100.0_5000.0_2.0534199611107115e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  153.79999999999555\n",
      "M_core =  1.3635460721732657\n",
      "R_core =  1.0806061979775614\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "That took 0.09713194767634074 minutes\n",
      "Planet:  planet_0310_track_1.0_100.0_5000.0_1.336853581149191e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1172_track_1.0_100.0_5000.0_1.794166367189119e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1218_track_1.0_100.0_5000.0_2.47647111208368e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_0357_track_1.0_100.0_5000.0_1.7747495170638815e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4635_track_1.0_100.0_5000.0_1.5504290145932633e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3557_track_1.0_100.0_5000.0_1.815928998333817e+29_0.0_0.0.txt\n",
      "Planet:  planet_2413_track_1.0_100.0_5000.0_2.096820072843353e+29_0.0_0.0.txt\n",
      "Planet:  planet_2180_track_1.0_100.0_5000.0_1.8777935508285966e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_1208_track_1.0_100.0_5000.0_2.2332504185675066e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  50.020000000000415\n",
      "M_core =  1.8646975651096485\n",
      "R_core =  1.168562918547715\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  9.58999999999984\n",
      "M_core =  0.3270035210488749\n",
      "R_core =  0.7562024972963821\n",
      "Saved!\n",
      "Done!\n",
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      "Done!\n",
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  14.539999999999734\n",
      "M_core =  0.2089972347473894\n",
      "R_core =  0.676137673605843\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  201.49999999999284\n",
      "M_core =  2.8590172189710747\n",
      "R_core =  1.3003318277502456\n",
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      "Done!\n",
      "Saved!\n",
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      "That took 0.14425549507141114 minutes\n",
      "Planet:  planet_3451_track_1.0_100.0_5000.0_2.224586712754801e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3335_track_1.0_100.0_5000.0_1.4279086002355395e+29_0.0_0.0.txt\n",
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      "Planet:  planet_1075_track_1.0_100.0_5000.0_1.6409593239901215e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Start evolving.\n",
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      "Planet:  planet_1443_track_1.0_100.0_5000.0_1.6361030015202707e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Done!\n",
      "Saved!\n",
      "That took 0.18315760294596353 minutes\n",
      "Planet:  planet_0344_track_1.0_100.0_5000.0_2.163202150209593e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3293_track_1.0_100.0_5000.0_2.0386745961439072e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_0539_track_1.0_100.0_5000.0_1.6041036876992764e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2813_track_1.0_100.0_5000.0_2.254282983038066e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3657_track_1.0_100.0_5000.0_1.7979188046057312e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3831_track_1.0_100.0_5000.0_1.6535883169633055e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3193_track_1.0_100.0_5000.0_1.8054309084863768e+29_0.0_0.0.txt\n",
      "Planet:  planet_4822_track_1.0_100.0_5000.0_1.9069027727499506e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_1023_track_1.0_100.0_5000.0_2.2692569875842043e+29_0.0_0.0.txt\n",
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      "Start evolving.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  5.78999999999992\n",
      "M_core =  0.6129957817058762\n",
      "R_core =  0.8848392347584704\n",
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      "Saved!\n",
      "Done!\n",
      "Done!\n",
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      "Saved!\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  19.520000000000252\n",
      "M_core =  0.6636675786050626\n",
      "R_core =  0.9025840414647543\n",
      "Saved!\n"
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     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  434.4999999999927\n",
      "M_core =  4.231757559490862\n",
      "R_core =  1.4342676225446527\n",
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      "Done!\n",
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      "Saved!\n"
     ]
    },
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     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  49.31999999999876\n",
      "M_core =  2.285662019765897\n",
      "R_core =  1.2295692768609707\n",
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      "Saved!\n",
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      "That took 0.2910828749338786 minutes\n",
      "Planet:  planet_1579_track_1.0_100.0_5000.0_1.903226285665179e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0108_track_1.0_100.0_5000.0_1.5928069323809238e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2239_track_1.0_100.0_5000.0_1.95496133132746e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4066_track_1.0_100.0_5000.0_2.314932051433056e+29_0.0_0.0.txt\n",
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      "Planet:  planet_3588_track_1.0_100.0_5000.0_1.554656012089627e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_3681_track_1.0_100.0_5000.0_1.3885239317639516e+29_0.0_0.0.txt\n",
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      "Planet:  planet_2372_track_1.0_100.0_5000.0_1.5864764632378117e+29_0.0_0.0.txt\n",
      "Planet:  planet_4778_track_1.0_100.0_5000.0_2.0450921492423374e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
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      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  563.3000000000011\n",
      "M_core =  3.1622878665573158\n",
      "R_core =  1.3335225081616535\n",
      "Saved!\n",
      "That took 0.21367930968602497 minutes\n",
      "Planet:  planet_1361_track_1.0_100.0_5000.0_2.0505965968652753e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1001_track_1.0_100.0_5000.0_2.226460875726016e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3957_track_1.0_100.0_5000.0_1.7180389547834087e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4142_track_1.0_100.0_5000.0_1.975254897912572e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2701_track_1.0_100.0_5000.0_1.4509361258775402e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4449_track_1.0_100.0_5000.0_1.5999385471746146e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2885_track_1.0_100.0_5000.0_1.333074710153238e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_0347_track_1.0_100.0_5000.0_1.9741117289645173e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_3590_track_1.0_100.0_5000.0_2.403898268722096e+29_0.0_0.0.txt\n",
      "Start evolving.\n"
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    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  40.0000000000003\n",
      "M_core =  3.189854201795058\n",
      "R_core =  1.3364192058711473\n",
      "Saved!\n",
      "Done!\n",
      "Done!\n",
      "Saved!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  169.29999999999467\n",
      "M_core =  3.501075338357869\n",
      "R_core =  1.3678874469708713\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n",
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      "That took 0.19345107475916545 minutes\n",
      "Planet:  planet_0747_track_1.0_100.0_5000.0_1.8061326430894097e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_1042_track_1.0_100.0_5000.0_1.0335405099122564e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_4588_track_1.0_100.0_5000.0_2.2982761140603414e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_2550_track_1.0_100.0_5000.0_2.0577435260415526e+29_0.0_0.0.txt\n",
      "Planet:  planet_4753_track_1.0_100.0_5000.0_1.8636178756189953e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_0805_track_1.0_100.0_5000.0_2.092483453877429e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Planet:  planet_0663_track_1.0_100.0_5000.0_2.1517201626460664e+29_0.0_0.0.txt\n",
      "Planet:  planet_4490_track_1.0_100.0_5000.0_2.4165110109696122e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Planet:  planet_4354_track_1.0_100.0_5000.0_1.606393154116023e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
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Laura Ketzer committed
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      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  7.629999999999881\n",
      "M_core =  1.7438118076480835\n",
      "R_core =  1.1491451898435199\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  2610.799999999995\n",
      "M_core =  2.896934922064085\n",
      "R_core =  1.304621960878461\n",
      "Saved!\n",
      "Done!\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n",
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      "That took 0.20160624583562214 minutes\n",
      "Planet:  planet_0615_track_1.0_100.0_5000.0_2.5255916912037195e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1214
      "Planet:  planet_3088_track_1.0_100.0_5000.0_1.7645712454558793e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1216
      "Planet:  planet_3542_track_1.0_100.0_5000.0_2.124337777958898e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1218
      "Planet:  planet_1313_track_1.0_100.0_5000.0_2.09583038652941e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
1219
      "Start evolving.\n",
1220
      "Planet:  planet_1384_track_1.0_100.0_5000.0_2.2829023860357403e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1222
      "Planet:  planet_2309_track_1.0_100.0_5000.0_1.8922051458096532e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_2336_track_1.0_100.0_5000.0_2.0638281302780287e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1226
      "Planet:  planet_2789_track_1.0_100.0_5000.0_1.78626144065831e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_0230_track_1.0_100.0_5000.0_2.238640666345936e+29_0.0_0.0.txt\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  109.59999999999805\n",
      "M_core =  1.8407717647251927\n",
      "R_core =  1.1647963096839082\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  117.69999999999759\n",
      "M_core =  2.791708606120249\n",
      "R_core =  1.292610029526345\n",
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Laura Ketzer committed
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      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  24.330000000001004\n",
      "M_core =  1.0474095743416338\n",
      "R_core =  1.011647318854064\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  36.5500000000013\n",
      "M_core =  3.568001349879368\n",
      "R_core =  1.374378182966699\n",
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      "Saved!\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  4299.99999999999\n",
      "M_core =  1.1488623732741046\n",
      "R_core =  1.035301877249794\n",
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      "Saved!\n",
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      "That took 0.24259525934855145 minutes\n",
      "Planet:  planet_1604_track_1.0_100.0_5000.0_2.366873297889431e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2660_track_1.0_100.0_5000.0_1.4902373025484811e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_1680_track_1.0_100.0_5000.0_2.666195987263646e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Start evolving.\n",
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      "Planet:  planet_0170_track_1.0_100.0_5000.0_2.527384740266911e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0160_track_1.0_100.0_5000.0_1.298400191856379e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_3185_track_1.0_100.0_5000.0_2.219311301268071e+29_0.0_0.0.txt\n",
      "Planet:  planet_2049_track_1.0_100.0_5000.0_1.8357692198206832e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_3172_track_1.0_100.0_5000.0_2.0191724411058962e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  90.59999999999913\n",
      "M_core =  2.0630291234850593\n",
      "R_core =  1.198467716283179\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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    },
    {
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     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  190.59999999999346\n",
      "M_core =  2.2519195001845564\n",
      "R_core =  1.2250059987570558\n",
      "Saved!\n"
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     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  4003.9999999999936\n",
      "M_core =  2.9031521149369657\n",
      "R_core =  1.3053213696457768\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  457.89999999999947\n",
      "M_core =  2.6832585945486898\n",
      "R_core =  1.279869330729408\n",
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      "Saved!\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  1037.099999999998\n",
      "M_core =  2.4712768638359885\n",
      "R_core =  1.253806020310169\n",
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      "Saved!\n",
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      "That took 0.23362322251001993 minutes\n",
      "Planet:  planet_4967_track_1.0_100.0_5000.0_1.6528226190177783e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Start evolving.\n",
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      "Planet:  planet_2986_track_1.0_100.0_5000.0_2.854302642701749e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0648_track_1.0_100.0_5000.0_1.578742993326966e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_1658_track_1.0_100.0_5000.0_2.0609053302023026e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0601_track_1.0_100.0_5000.0_1.6427900414196456e+29_0.0_0.0.txt\n",
      "Planet:  planet_4942_track_1.0_100.0_5000.0_1.5045276444719256e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
      "Start evolving.\n",
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      "Planet:  planet_0840_track_1.0_100.0_5000.0_2.1685750135392472e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Done!\n",
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      "Saved!\n",
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      "Saved!\n",
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      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  772.2999999999904\n",
      "M_core =  1.910168614992159\n",
      "R_core =  1.1756225999552115\n",
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      "Saved!\n",
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      "That took 0.19588243166605632 minutes\n",
      "Planet:  planet_4927_track_1.0_100.0_5000.0_1.9379200053706086e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4362_track_1.0_100.0_5000.0_1.700227085305256e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2530_track_1.0_100.0_5000.0_1.7482674749792897e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2220_track_1.0_100.0_5000.0_2.640988057717363e+29_0.0_0.0.txt\n",
1517
      "Start evolving.\n",
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      "Planet:  planet_1326_track_1.0_100.0_5000.0_2.4567303138556667e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2845_track_1.0_100.0_5000.0_1.2524150886345176e+29_0.0_0.0.txt\n",
      "Planet:  planet_3113_track_1.0_100.0_5000.0_2.200976833009394e+29_0.0_0.0.txt\n",
      "Planet:  planet_2122_track_1.0_100.0_5000.0_1.929656344195592e+29_0.0_0.0.txt\n",
      "Planet:  planet_1593_track_1.0_100.0_5000.0_1.4386849563866154e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  3113.3999999999915\n",
      "M_core =  2.1518974367194734\n",
      "R_core =  1.2111708003919244\n",
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      "Saved!\n",
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      "That took 0.2170019229253133 minutes\n",
      "Planet:  planet_4765_track_1.0_100.0_5000.0_1.4833986039437414e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_2465_track_1.0_100.0_5000.0_1.9215395725663678e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0067_track_1.0_100.0_5000.0_1.8909707332117266e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_0463_track_1.0_100.0_5000.0_1.9978649820095824e+29_0.0_0.0.txt\n",
      "Planet:  planet_4244_track_1.0_100.0_5000.0_2.3765923805252638e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
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      "Planet:  planet_4660_track_1.0_100.0_5000.0_1.9157546181655443e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4901_track_1.0_100.0_5000.0_2.2624472478818015e+29_0.0_0.0.txt\n",
1577
      "Start evolving.\n",
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      "Planet:  planet_4581_track_1.0_100.0_5000.0_1.894579848829647e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_4906_track_1.0_100.0_5000.0_1.7661585301597117e+29_0.0_0.0.txt\n",
      "Start evolving.\n"
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  46.62000000000037\n",
      "M_core =  1.6811100248205018\n",
      "R_core =  1.1386730304808832\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  50.22000000000042\n",
      "M_core =  1.0364772201696526\n",
      "R_core =  1.0089971519804493\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  40.19000000000057\n",
      "M_core =  2.4651245174741003\n",
      "R_core =  1.2530249401955842\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n",
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      "That took 0.23927638133366902 minutes\n",
      "Planet:  planet_2600_track_1.0_100.0_5000.0_2.3444585719323794e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
1654
      "Planet:  planet_0413_track_1.0_100.0_5000.0_1.5455502825058148e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_1220_track_1.0_100.0_5000.0_2.3080585949462315e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_0901_track_1.0_100.0_5000.0_1.6950793577243755e+29_0.0_0.0.txt\n",
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Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_1405_track_1.0_100.0_5000.0_2.0847436928723294e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_2054_track_1.0_100.0_5000.0_2.1193302957684732e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
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      "Planet:  planet_1272_track_1.0_100.0_5000.0_2.102905852085866e+29_0.0_0.0.txt\n",
      "Planet:  planet_2004_track_1.0_100.0_5000.0_1.9417968186154966e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Start evolving.\n",
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      "Planet:  planet_2557_track_1.0_100.0_5000.0_1.4832975413755019e+29_0.0_0.0.txt\n",
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      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
      "t =  10.53999999999982\n",
      "M_core =  1.0114864896209799\n",
      "R_core =  1.002859335313532\n",
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      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n",
      "Done!\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "../platypos_package/Planet_models_LoFo14.py:20: RuntimeWarning: invalid value encountered in double_scalars\n",
      "  R_env = 2.06 * (M_p)**(-0.21) * (fenv/5)**0.59 * (F_p)**0.044 * ((age/1e3)/5)**(age_exponent[metallicity]) # R_earth\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Atmosphere has evaportated! Only bare rocky core left! STOP this madness!\n",
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      "t =  32.31000000000214\n",
      "M_core =  0.971765267379362\n",
      "R_core =  0.9928653239747777\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n",
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      "That took 0.1963588277498881 minutes\n",
      "Planet:  planet_4803_track_1.0_100.0_5000.0_2.125072799903701e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_4028_track_1.0_100.0_5000.0_2.1250373619613913e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_4084_track_1.0_100.0_5000.0_1.59101391888881e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_4879_track_1.0_100.0_5000.0_1.5927377970571073e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_3104_track_1.0_100.0_5000.0_1.8466433135441045e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
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      "Planet:  planet_2228_track_1.0_100.0_5000.0_1.9433012531689133e+29_0.0_0.0.txt\n",
      "Planet:  planet_4230_track_1.0_100.0_5000.0_1.7149223821455527e+29_0.0_0.0.txt\n",
      "Planet:  planet_0052_track_1.0_100.0_5000.0_2.2286441411492128e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
      "Start evolving.\n",
      "Start evolving.\n",
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      "Planet:  planet_2981_track_1.0_100.0_5000.0_2.5447915450270232e+29_0.0_0.0.txt\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Start evolving.\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
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      "Saved!\n",
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      "Done!\n",
Laura Ketzer's avatar
Laura Ketzer committed
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      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n",
      "Done!\n",
      "Saved!\n"