ipython notebook for hpic buffer_integrity_test

buffer_integrity_test.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import adios as ad\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import animation\n",
    "from matplotlib.colors import LogNorm\n",
    "from IPython.display import HTML\n",
    "\n",
    "import os\n",
    "%matplotlib inline\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create functions to load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "work_dir = '../../'\n",
    "os.chdir(work_dir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin\t\t     examples\t\t       libHPIC.a\r\n",
      "CHANGES\t\t     field_integrity_test.bp   Makefile\r\n",
      "CMakeCache.txt\t     hpic_output.bp\t       Makefile.gnu\r\n",
      "CMakeFiles\t     hpic_output_currents.xml  platforms\r\n",
      "cmake_install.cmake  hpic_output_noxml.bp      README.md\r\n",
      "CMakeLists.txt\t     hpic_output_noxml.bp.dir  src\r\n",
      "cmake_modules\t     hpic_output.xml\t       tests\r\n",
      "CTestTestfile.cmake  includes\t\t       TODO\r\n",
      "docs\t\t     input.par\t\t       two_stream_instability.bp\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle communication test\r\n",
      "nProcs:4, edge=3, n_levels=1\r\n",
      "time_start: 3.602e+04, total_time: 2.759e-03\r\n",
      "total time: 2.7590e-03\r\n"
     ]
    }
   ],
   "source": [
    "!mpiexec -n 4 bin/hpic_field_buffer_integrity_test  -edge 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def read_adios_file_no_xml(bp_path):\n",
    "    '''\n",
    "    Read adios variables in to pandas dictionary\n",
    "    '''\n",
    "    \n",
    "    #initialize and open adios file\n",
    "        \n",
    "    ad.init_noxml()\n",
    "    ad_file = ad.file(bp_path)\n",
    "    \n",
    "    #initialize variable dictionary\n",
    "    variable_dict = {}\n",
    "    \n",
    "    #read all variables from adios file\n",
    "    for var_name, var_value in ad_file.vars.items():\n",
    "        variable = {}\n",
    "        variable['n_dim'] = var_value.ndim\n",
    "        variable['is_scalar'] = False\n",
    "        variable['n_steps'] = var_value.nsteps\n",
    "        if variable['n_dim'] > 0:\n",
    "            variable['is_scalar'] = True\n",
    "        \n",
    "        variable['value'] = var_value.read()\n",
    "        variable_dict[var_name] = variable\n",
    "        \n",
    "    #close adios_file\n",
    "        \n",
    "    return variable_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "variable_dict = read_adios_file_no_xml('field_integrity_test.bp')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scalar_nx1_global',\n",
       " 'scalar_nx1_local',\n",
       " 'scalar_nx1_offset',\n",
       " 'scalar_nx2_global',\n",
       " 'scalar_nx2_local',\n",
       " 'scalar_nx2_offset',\n",
       " 'scalar_nx3_global',\n",
       " 'scalar_nx3_local',\n",
       " 'scalar_nx3_offset',\n",
       " 'test_send_edge_o',\n",
       " 'test_send_ghost_o']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(variable_dict.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plots eletric field"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbf4fbc2290>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf4fc7c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "edge_scalar = variable_dict['test_send_edge_o']['value'][0, :, :]\n",
    "plt.imshow(edge_scalar, interpolation = 'none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7fbf4db23850>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQEKEDSRE2EBChA0kRNhAQv8HNY6EwLgYqvwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf4fbe6350>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ghost_scalar = variable_dict['test_send_ghost_o']['value'][0, :, :]\n",
    "plt.imshow(ghost_scalar, interpolation = 'none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  1.,  1.,  1.],\n",
       "       [ 0.,  0.,  1.,  1.,  1.,  1.],\n",
       "       [ 2.,  2.,  6.,  6.,  4.,  4.],\n",
       "       [ 2.,  2.,  6.,  6.,  4.,  4.],\n",
       "       [ 2.,  2.,  5.,  5.,  3.,  3.],\n",
       "       [ 2.,  2.,  5.,  5.,  3.,  3.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ghost_scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "expected_answer = np.array([[0, 0, 2, 2, 2, 2],\n",
    "                            [0, 0, 2, 2, 2, 2],\n",
    "                            [1, 1, 6, 6, 5, 5],\n",
    "                            [1, 1, 6, 6, 5, 5],\n",
    "                            [1, 1, 4, 4, 3, 3],\n",
    "                            [1, 1, 4, 4, 3, 3]]).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(np.equal(expected_answer, ghost_scalar.astype(int)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Particle communication test\r\n",
      "nProcs:4, edge=3, n_levels=1\r\n",
      "time_start: 3.602e+04, total_time: 2.576e-03\r\n",
      "total time: 2.5760e-03\r\n"
     ]
    }
   ],
   "source": [
    "!mpiexec -n 4 bin/hpic_field_buffer_integrity_test  -edge 3 -period 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPYAAAD7CAYAAABZjGkWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBhIibCAhwgYSImwgof8CypV11yc5oGEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbf4fbe6e90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "variable_dict = read_adios_file_no_xml('field_integrity_test.bp')\n",
    "ghost_scalar = variable_dict['test_send_ghost_o']['value'][0, :, :]\n",
    "plt.imshow(ghost_scalar, interpolation = 'none')\n",
    "\n",
    "expected_answer = np.array([[6, 1, 6, 6, 5, 6],\n",
    "                            [2, 0, 2, 2, 2, 2],\n",
    "                            [6, 1, 6, 6, 5, 6],\n",
    "                            [6, 1, 6, 6, 5, 6],\n",
    "                            [4, 1, 4, 4, 3, 4],\n",
    "                            [6, 1, 6, 6, 5, 6]]).T\n",
    "\n",
    "np.all(np.equal(expected_answer, ghost_scalar.astype(int)))"
   ]
  }
 ],
 "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",
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 },
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