docs/Notebooks/Tutorial1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Tutorial 1: Generating planetary systems\n",
    "\n",
    "In this tutorial, we will review how to use the Generator class to generate a sample of planetary systems, including how to add or replace steps in the process. \n",
    "\n",
    "## Setup\n",
    "\n",
    "Let's start by importing the necessary module from Bioverse."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# Import numpy\n",
    "import numpy as np\n",
    "\n",
    "# Import the Generator class\n",
    "from bioverse.generator import Generator\n",
    "from bioverse.constants import ROOT_DIR\n",
    "\n",
    "# Import pyplot (for making plots later) and adjust some of its settings\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.size'] = 20."
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Loading the Generator\n",
    "\n",
    "The first step in using Bioverse is to generate a simulated sample of planetary systems. This is accomplished with a Generator object, of which two come pre-installed. Let's open one of them and examine its contents."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "# Open the transit mode generator and display its properties\n",
    "generator = Generator('transit')\n",
    "generator"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Generator with 12 steps:\n",
       " 0: Function 'create_stars_Gaia' with 6 keyword arguments.\n",
       " 1: Function 'create_planets_SAG13' with 9 keyword arguments.\n",
       " 2: Function 'assign_orbital_elements' with 1 keyword arguments.\n",
       " 3: Function 'geometric_albedo' with 2 keyword arguments.\n",
       " 4: Function 'impact_parameter' with 1 keyword arguments.\n",
       " 5: Function 'assign_mass' with no keyword arguments.\n",
       " 6: Function 'compute_habitable_zone_boundaries' with no keyword arguments.\n",
       " 7: Function 'compute_transit_params' with no keyword arguments.\n",
       " 8: Function 'classify_planets' with no keyword arguments.\n",
       " 9: Function 'scale_height' with no keyword arguments.\n",
       "10: Function 'Example1_water' with 3 keyword arguments.\n",
       "11: Function 'Example2_oxygen' with 2 keyword arguments."
      ]
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The list above shows each of the steps the Generator runs through in producing the sample of planetary systems. For more information about an individual step, we can display it based on its index:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# Show more about the 'create_planets_SAG13' step\n",
    "generator.steps[1]"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Function 'create_planets_SAG13' with 9 keyword arguments.\n",
       "\n",
       "Description:\n",
       "    Generates planets with periods and radii according to SAG13 occurrence rate estimates, but incorporating\n",
       "    the dependence of occurrence rates on spectral type from Mulders+2015.\n",
       "\n",
       "    Parameters\n",
       "    ----------\n",
       "    d : Table\n",
       "        Table containing simulated host stars.\n",
       "    eta_Earth : float, optional\n",
       "        The number of Earth-sized planets in the habitable zones of Sun-like stars. All occurrence\n",
       "        rates are uniformly scaled to produce this estimate.\n",
       "    R_min : float, optional\n",
       "        Minimum planet radius, in Earth units.\n",
       "    R_max : float, optional\n",
       "        Maximum planet radius, in Earth units.\n",
       "    P_min : float, optional\n",
       "        Minimum orbital period, in years.\n",
       "    P_max : float, optional\n",
       "        Maximum orbital period, in years.\n",
       "    normalize_SpT : bool, optional\n",
       "        If True, modulate occurrence rates by stellar mass according to Mulders+2015. Otherwise, assume no\n",
       "        dependency on stellar mass.\n",
       "    transit_mode : bool, optional\n",
       "        If True, only transiting planets are simulated. Occurrence rates are modified to reflect the R_*/a transit probability.\n",
       "    optimistic : bool, optional\n",
       "        If True, extrapolate the results of Mulders+2015 by assuming rocky planets are much more common around late-type M dwarfs. If False,\n",
       "        assume that occurrence rates plateau with stellar mass for stars cooler than ~M3.\n",
       "    optimistic_factor : float, optional\n",
       "        If optimistic = True, defines how many times more common rocky planets are around late-type M dwarfs compared to Sun-like stars.\n",
       "\n",
       "\n",
       "    Returns\n",
       "    -------\n",
       "    d : Table\n",
       "        Table containing the sample of simulated planets. Replaces the input Table.\n",
       "\n",
       "Argument values:\n",
       "    eta_Earth      = 0.075     \n",
       "    R_min          = 0.5       \n",
       "    R_max          = 14.3      \n",
       "    P_min          = 0.01      \n",
       "    P_max          = 10.0      \n",
       "    normalize_SpT  = True      \n",
       "    transit_mode   = True      \n",
       "    optimistic     = False     \n",
       "    optimistic_factor = 5         "
      ]
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Here we see a short description of the planet generation step as well as its keyword arguments, including `eta_Earth`. This specifies the average number of Earth-sized planets in the habitable zones of Sun-like stars, and it is currently set to 7.5% (following Pascucci et al. 2020). Let's change this to a more optimistic value:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Set eta_Earth = 15%\n",
    "generator.set_arg('eta_Earth', 0.15)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Some arguments are shared by multiple functions - for example, the `transit_mode` argument tells a couple of functions that only transiting planets are being simulated. By default, it is set to `False`, but we can change that like so:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Set transit_mode = True for all functions that use it\n",
    "generator.set_arg('transit_mode', True)\n",
    "\n",
    "# We can also check on an argument's value as follows:\n",
    "val = str(generator.get_arg('transit_mode'))\n",
    "print(\"\\nThe current value of transit_mode is {:s}\".format(val))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\n",
      "The current value of transit_mode is True\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Running the Generator\n",
    "\n",
    "Now, let's use this Generator object to produce an ensemble of transiting planets within 100 parsecs."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "sample = generator.generate(d_max=100)\n",
    "print(\"Generated a sample of {:d} transiting planets including {:d} exo-Earth candidates.\".format(len(sample), sample['EEC'].sum()))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Generated a sample of 17081 transiting planets including 1115 exo-Earth candidates.\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's plot the period and radius distribution of this planet sample:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(16,8))\n",
    "\n",
    "# Period histogram\n",
    "P = sample['P']\n",
    "bins = np.logspace(0, 4, 100)\n",
    "ax[0].hist(P, bins=bins)\n",
    "ax[0].set_xscale('log')\n",
    "ax[0].set_yscale('log')\n",
    "ax[0].set_xlabel('Period (d)', fontsize=24)\n",
    "ax[0].set_ylabel('Number of planets', fontsize=24)\n",
    "\n",
    "# Radius histogram\n",
    "R = sample['R']\n",
    "bins = np.logspace(-0.5,1,100)\n",
    "ax[1].hist(R, bins=bins)\n",
    "ax[1].set_xscale('log')\n",
    "ax[1].set_yscale('log')\n",
    "ax[1].set_xlabel('Radius ($R_\\oplus$)', fontsize=24)\n",
    "ax[1].set_ylabel('Number of planets', fontsize=24)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "## Adding new steps\n",
    "\n",
    "Finally, suppose you want to simulate a new planetary property in Bioverse. For example, suppose you want to assign an ocean covering fraction to each planet (`f_ocean_min` < `f_ocean` < `f_ocean_max` for exo-Earth candidates and `f_ocean` = 0 for others). We can accomplish this like so:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Define a function that accepts and returns a Table object\n",
    "def oceans(d, f_ocean_min=0, f_ocean_max=1):\n",
    "    # First, assign zero for all planets\n",
    "    d['f_ocean'] = np.zeros(len(d))\n",
    "\n",
    "    # Next, assign a random non-zero value for exo-Earth candidates\n",
    "    EEC = d['EEC']\n",
    "    d['f_ocean'][EEC] = np.random.uniform(f_ocean_min, f_ocean_max, size=EEC.sum())\n",
    "\n",
    "    # Finally, return the Table with its new column\n",
    "    return d\n",
    "\n",
    "# Insert this function at the end of the Generator\n",
    "generator.insert_step(oceans)\n",
    "\n",
    "# Run the generator with f_ocean_min = 0.3 and f_ocean_max = 0.7\n",
    "sample = generator.generate(d_max=100, f_ocean_min=0.3, f_ocean_max=0.7)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, let's plot the distribution of ocean covering fractions for EECs and non-EECs:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "fig, ax = plt.subplots(ncols=2, figsize=(16,8))\n",
    "\n",
    "f_ocean = sample['f_ocean']\n",
    "EEC = sample['EEC']\n",
    "\n",
    "# EECs\n",
    "bins = np.linspace(0., 1, 100)\n",
    "ax[0].hist(f_ocean[EEC], bins=bins)\n",
    "ax[0].set_title('exo-Earth candidates')\n",
    "ax[0].set_ylabel('Number of planets')\n",
    "\n",
    "# Non-EECs\n",
    "ax[1].hist(f_ocean[~EEC], bins=bins)\n",
    "ax[1].set_title('non-habitable planets')\n",
    "\n",
    "plt.subplots_adjust(wspace=0.3)"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1152x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Of course, you may wish to change the `oceans` function later, and it would be tiring to add it to the Generator again each time. Instead, we can point the Generator to a Python file containing your function, which must be saved under ``bioverse/functions/``."
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "# Reload the generator to erase the previously-added step\n",
    "generator = Generator('transit')\n",
    "\n",
    "# Write the function definition as a string\n",
    "func = \"\"\"\n",
    "# Define a function that accepts and returns a Table object\n",
    "def oceans(d, f_ocean_min=0, f_ocean_max=1):\n",
    "    # First, assign zero for all planets\n",
    "    d['f_ocean'] = np.zeros(len(d))\n",
    "\n",
    "    # Next, assign a random non-zero value for exo-Earth candidates\n",
    "    EEC = d['EEC']\n",
    "    d['f_ocean'][EEC] = np.random.uniform(f_ocean_min, f_ocean_max, size=EEC.sum())\n",
    "\n",
    "    # Finally, return the Table with its new column\n",
    "    return d\n",
    "\"\"\"\n",
    "\n",
    "# Save the function to a .py file\n",
    "with open(ROOT_DIR+'/example_oceans.py', 'w') as f:\n",
    "    f.write(func)\n",
    "\n",
    "# Insert this function into the Generator and specify the filename\n",
    "generator.insert_step('oceans', filename='example_oceans.py')\n",
    "\n",
    "# Verify that the new step has been added\n",
    "generator"
   ],
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Generator with 13 steps:\n",
       " 0: Function 'create_stars_Gaia' with 6 keyword arguments.\n",
       " 1: Function 'create_planets_SAG13' with 9 keyword arguments.\n",
       " 2: Function 'assign_orbital_elements' with 1 keyword arguments.\n",
       " 3: Function 'geometric_albedo' with 2 keyword arguments.\n",
       " 4: Function 'impact_parameter' with 1 keyword arguments.\n",
       " 5: Function 'assign_mass' with no keyword arguments.\n",
       " 6: Function 'compute_habitable_zone_boundaries' with no keyword arguments.\n",
       " 7: Function 'compute_transit_params' with no keyword arguments.\n",
       " 8: Function 'classify_planets' with no keyword arguments.\n",
       " 9: Function 'scale_height' with no keyword arguments.\n",
       "10: Function 'Example1_water' with 3 keyword arguments.\n",
       "11: Function 'Example2_oxygen' with 2 keyword arguments.\n",
       "12: Function 'oceans' with 2 keyword arguments."
      ]
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "Now, any changes you make to the `oceans` function under `example_oceans.py` will automatically be applied. Finally, you will want to save this Generator under a new name, so that you don't have to re-add the new step every time you load Bioverse:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "source": [
    "# Save the new Generator\n",
    "generator.save('transit_oceans')\n",
    "\n",
    "# Reload it\n",
    "generator = Generator('transit_oceans')"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The following lines of code will clean up the files created during this exercise:"
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "import os\n",
    "trash = [ROOT_DIR+'/Objects/Generators/transit_oceans.pkl', ROOT_DIR+'/example_oceans.py']\n",
    "for filename in trash:\n",
    "    if os.path.exists(filename):\n",
    "        os.remove(filename)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "source": [
    "The next example will translate this simulated sample of planetary systems into a dataset from a transit spectroscopy survey."
   ],
   "metadata": {}
  }
 ],
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