{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Setting up a custom model\n",
    "In principle, any custom model can be used in ALPro. This tutorial allows shows how to set up a model with any given profile of magnetic field and density without using ALPro's inbuilt models. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt \n",
    "import numpy as np \n",
    "import alpro \n",
    "alpro.util.set_default_plot_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Custom model specified - make sure get_B & density methods are populated or domain set manually!\n"
     ]
    }
   ],
   "source": [
    "s = alpro.Survival(\"custom\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are two ways to set up a custom model. Minimally, ALPRO needs a domain instance initialised, and then the following variables need to be initialised:\n",
    "\n",
    "- ne: the electron density\n",
    "- B: the perpendicular magnetic field \n",
    "- phi: the angle the magnetic field vector makes with the $y$ axis\n",
    "- deltaL: the size of each domain\n",
    "\n",
    "So one can easily just specify these manually. As an example, let's set up a perpendicular field that varies randomly and makes a random angle with the $y$-axis. We'll give a characteristic field strength of $10\\, \\mu {\\rm G}$ and typical cell sizes of kpc. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(42)\n",
    "s.initialise_domain()\n",
    "s.domain.ne = np.random.random(size=10) * 0.01 \n",
    "s.domain.B = np.random.random(size=10) * 1e-5\n",
    "s.domain.deltaL = np.random.random(size=10) * 10.0\n",
    "redge = np.cumsum(s.domain.deltaL)\n",
    "s.domain.phi = np.random.random(size=10) * 2.0 * np.pi\n",
    "_ = plt.step(redge, s.domain.B, where=\"pre\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then compute the survival probability "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true,
    "tags": [
     "nbsphinx-thumbnail"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = np.logspace(3,4,100)\n",
    "s.set_params(g = 1e-12 * 1e-9, mass = 1e-11)\n",
    "s.propagate(energies=energies, pol=\"y\")\n",
    "fig = s.default_plot(mode=\"survival\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, you may want to set up a cell-based model with a given density and magnetic field strength with radius, then initialise a random cell model. In this case we're just going to use some arbitrary functions, but you could use, for example, power-law profiles or a universal pressure profile to set these up. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Custom model specified - make sure get_B & density methods are populated or domain set manually!\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def get_B(r):\n",
    "    return (1e-5 * (r/25.0)**-0.5)\n",
    "\n",
    "def get_ne(r):\n",
    "    return (0.1 * (r/25.0)**-2)\n",
    "\n",
    "# this controls the size of each random cell. Normally chosen using a power-law, but you can use\n",
    "# any function or supply a float for a constant cell size \n",
    "def coherence():\n",
    "    return (np.random.random() * 10.0)\n",
    "\n",
    "s = alpro.Survival(\"custom\")      # initialise model \n",
    "\n",
    "# set up get_B and density methods \n",
    "s.cluster.get_B = get_B\n",
    "s.cluster.density = get_ne\n",
    "\n",
    "# initialise domain subclass using this profile\n",
    "s.initialise_domain(s.cluster.profile)\n",
    "\n",
    "# create a random cell model\n",
    "s.domain.create_box_array(500.0, 1, coherence = coherence) \n",
    "\n",
    "# plot the model \n",
    "_ = plt.step(s.domain.r, s.domain.Bx, where=\"pre\")\n",
    "_ = plt.step(s.domain.r, s.domain.By, where=\"pre\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "energies = np.logspace(3,4,1000)\n",
    "s.set_params(g = 1e-12 * 1e-9, mass = 1e-13)\n",
    "s.propagate(energies=energies, pol=\"both\")\n",
    "fig = s.default_plot(mode=\"survival\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
