{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential processing took 3.6679270267486572s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parallel processing took 2.1396920680999756s\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from multiprocessing import Pool\n",
    "from functools import partial\n",
    "import matplotlib.pyplot as plt\n",
    "from diffusion_module import diffusion, parallel, sequential \n",
    "%matplotlib inline\n",
    "\n",
    "if __name__=='__main__':\n",
    "    param = {\n",
    "        \"dx\": 1,\n",
    "        \"dt\": 1,\n",
    "        \"L\": 100,\n",
    "        \"T\": 100,\n",
    "        \"D\": 0.249,\n",
    "        \"workers\": 4\n",
    "    }\n",
    "    \n",
    "    # initial heat distribution\n",
    "    grid = np.zeros(2 * [param[\"L\"] * int(param[\"dx\"]**-1)])\n",
    "    gridsize = grid.shape\n",
    "    grid[int(gridsize[0] / 2), int(gridsize[1] / 2)] = 70\n",
    "    grid[int(gridsize[0] / 4), int(gridsize[1] / 4)] = 35\n",
    "    grid[10, 90] = 100\n",
    "    \n",
    "    \n",
    "    grid_s = np.copy(grid)  # keep original grid unchanged\n",
    "    plt.imshow(sequential(grid_s, param), cmap=plt.cm.copper, extent=[-1,1,-1,1]);\n",
    "    plt.xticks([]); plt.yticks([]);\n",
    "    plt.show()\n",
    "    grid_p = np.copy(grid)  # keep original grid unchanged\n",
    "    plt.imshow(parallel(grid_p, param[\"workers\"], gridsize, param), cmap=plt.cm.copper, extent=[-1,1,-1,1]);\n",
    "    plt.xticks([]); plt.yticks([]);\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0\n",
      "100.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       ...,\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.],\n",
       "       [0., 0., 0., ..., 0., 0., 0.]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(np.min(grid_p))\n",
    "print(np.max(grid_p))\n",
    "grid_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}