test_jupyter_extensions.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pystencils.session import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dh = ps.create_data_handling(domain_size=(256, 256), periodicity=True)\n",
    "c_field = dh.add_array('c')\n",
    "dh.fill(\"c\", 0.0, ghost_layers=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "for x in range(129):\n",
    "    for y in range(258):\n",
    "        dh.cpu_arrays['c'][x, y] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5fa4f1cdc0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fa4f425b0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scalar_field(dh.cpu_arrays[\"c\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "ur = ps.Assignment(c_field[0, 0], c_field[1, 0])\n",
    "ast = ps.create_kernel(ur, target=dh.default_target, cpu_openmp=True)\n",
    "kernel = ast.compile()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "c_sync = dh.synchronization_function(['c'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timeloop(steps=10):\n",
    "    for i in range(steps):\n",
    "        c_sync()\n",
    "        dh.run_kernel(kernel)\n",
    "    return dh.gather_array('c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps.jupyter.set_display_mode('video')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video controls width=\"80%\">\n",
       " <source 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type=\"video/mp4\">\n",
       " Your browser does not support the video tag.\n",
       "</video>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ani = ps.plot.scalar_field_animation(timeloop, rescale=True, frames=12)\n",
    "ps.jupyter.display_animation(ani)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ps.jupyter.set_display_mode('image_update')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fa4e07160>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ani = ps.plot.scalar_field_animation(timeloop, rescale=True, frames=12)\n",
    "ps.jupyter.display_animation(ani)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def grid_update_function(image):\n",
    "    for i in range(40):\n",
    "        c_sync()\n",
    "        dh.run_kernel(kernel)\n",
    "    return dh.gather_array('c')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V1ZvWPaELxH74e9jVpuXTbLFp+7aHbjE2ev17vWH9maw75DvaqPli092PL5enknwuG/+MevK5fVCXy1Prm+HKbbfWi/7voXe5afkkW23ann3y2K9iw/ozWXfIv5bkQFVdX1UvTnI4G5s3X7Sq6qVV9bLnrif502xsXH13kluXw25N8vn1zPC82G6tF/3G3ftl0/LtNm3PPnjs17Jh/QXwCu/bsvGq7neTfGTd8zkP631NNl6h/maSB59bc5JXJDmW5DvL5RXrnuserfcz2fhn5C+y8czjPWdaa5KPLH8LjyR567rnv4K1/2OSbyW5f/kP+OqLdO1/lI3TA/cnuW/5ett+eOzPsPaVPfY+2Qkw3LpPrQCwS0IOMJyQAwwn5ADDCTnAcEIOMJyQAwwn5ADD/S+qRf44PxPM0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5fc03ea6a0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "animation = ps.jupyter.make_imshow_animation(dh.cpu_arrays[\"c\"], grid_update_function, frames=300)"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}