examples/benchmarking/solvers/.ipynb_checkpoints/analyze-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import glob\n",
    "\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import pandas as pd\n",
    "import datreant.core as dtr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = dtr.discover('../../../solvers/solver_timing/Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(b.categories.all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x106b01690>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KCop9Xy9evIivry8uLi7xfvhlz56d5s2bs3DhQh4+fMi7775L9uzZTfMihRDP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\nOpHvJHqDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106aaf850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, library in df.groupby(\"library\"): \n",
    "    library = library.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(library[\"numberOfElements\"], library[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b = dtr.discover('../../../solvers/solver_timing/memory/Data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(b.categories.all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1087618d0>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OYMfglwfjV8uP/K75n3jMgwfwxRfw+bQHtL+xgKPOn5Cby6gm7WH0Sqhe3YKf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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x106e283d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for name, library in df.groupby(\"library\"): \n",
    "    library = library.sort_values(by=\"numberOfElements\")\n",
    "    plt.loglog(library[\"numberOfElements\"], library[\"elapsed\"], label=name)\n",
    "plt.legend(loc=\"upper left\")\n",
    "plt.xlabel(\"number of elements\")\n",
    "plt.ylabel(\"elapsed time / s\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Usage: __main__.py <command> <options> <arguments>\n",
      "\n",
      "Available commands:\n",
      "\n",
      "    run      run a given command or python file\n",
      "    rm       remove a given file generated by mprof\n",
      "    clean    clean the current directory from files created by mprof\n",
      "    list     display existing profiles, with indices\n",
      "    plot     plot memory consumption generated by mprof run\n",
      "\n",
      "Type mprof <command> --help for usage help on a specific command.\n",
      "For example, mprof plot --help will list all plotting options.\n",
      "\n"
     ]
    },
    {
     "ename": "SystemExit",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "An exception has occurred, use %tb to see the full traceback.\n",
      "\u001b[0;31mSystemExit\u001b[0m\u001b[0;31m:\u001b[0m 1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/guyer/anaconda/envs/condafipy/lib/python2.7/site-packages/IPython/core/interactiveshell.py:2889: UserWarning: To exit: use 'exit', 'quit', or Ctrl-D.\n",
      "  warn(\"To exit: use 'exit', 'quit', or Ctrl-D.\", stacklevel=1)\n"
     ]
    }
   ],
   "source": [
    "mprof = !which mprof\n",
    "execfile(mprof[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def parse_args(mprofile):\n",
    "    parser = argparse.ArgumentParser()\n",
    "    parser.add_argument(\"--output\", help=\"directory to store results in\")\n",
    "    parser.add_argument(\"--numberOfElements\", help=\"number of total cells in a Grid2D\",\n",
    "                        type=int, default=10000)\n",
    "    parser.add_argument(\"--solver\", help=\"solver class to use\",\n",
    "                        choices=(\"cg\", \"pcg\", \"cgs\", \"gmres\", \"lu\"), default=\"cg\")\n",
    "    parser.add_argument(\"--sweeps\", help=\"number of nonlinear sweeps to take\",\n",
    "                        type=int, default=10)\n",
    "    parser.add_argument(\"--iterations\", help=\"maximum number of linear iterations to take for each sweep\",\n",
    "                        type=int, default=1000)\n",
    "    parser.add_argument(\"--tolerance\", help=\"linear solver tolerance\",\n",
    "                        type=float, default=1e-10)\n",
    "    parser.add_argument(\"--writeFiles\", help=\"whether to write solution values and matrix to OUTPUT\",\n",
    "                        action='store_true')\n",
    "    \n",
    "    parser.add_argument(\"--pysparse\", help=\"use PySparse solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"pysparse\")\n",
    "    parser.add_argument(\"--trilinos\", help=\"use PyTrilinos solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"trilinos\")\n",
    "    parser.add_argument(\"--scipy\", help=\"use SciPy solvers\", dest=\"library\",\n",
    "                        action=\"store_const\", const=\"scipy\")\n",
    "    parser.set_defaults(library=\"pysparse\")\n",
    "\n",
    "    print mprofile['cmd_line']\n",
    "    return parser.parse_known_args(mprofile['cmd_line'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df['mem_usage'] = np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'iterations': 1000,\n",
       " 'library': 'pysparse',\n",
       " 'numberOfElements': 10000,\n",
       " 'output': None,\n",
       " 'solver': 'cg',\n",
       " 'sweeps': 10,\n",
       " 'tolerance': 1e-10,\n",
       " 'writeFiles': False}"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --trilinos --numberOfElements=100 --output Data/d94fbe7f-1517-4ab7-bbdf-c269401115f0 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --trilinos --numberOfElements=1000 --output Data/10f97a8e-c473-42ce-b260-c273b10ef7ae --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --trilinos --numberOfElements=10000000 --output Data/e91f2378-4d33-4ee6-96e1-a82ef5aef967 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --scipy --numberOfElements=100 --output Data/f1853e58-269d-4862-ae10-d293dbbfa684 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --scipy --numberOfElements=1000000 --output Data/b7871336-2ccc-44cb-944d-a6c4d8eb02fd --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --scipy --numberOfElements=10000 --output Data/277910c4-5c28-413c-87d7-6257dfda7055 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --pysparse --numberOfElements=1000 --output Data/50e332d4-761d-4ec4-977d-a4e73da63072 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --pysparse --numberOfElements=10000 --output Data/f7911aed-9081-44e2-b91c-1b81af7fdda9 --sweeps 3\n",
      "\n",
      "python /users/guyer/FiPy/fipy/examples/benchmarking/solvers/diffusion.py --pysparse --numberOfElements=10000000 --output Data/b966eaef-2105-486a-b4cb-5c5d558df8fc --sweeps 3\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 86.246094,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 100.804688,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 12352.59375,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 54.277344,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 10746.234375,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 181.398438,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 45.164062,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 57.195312,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False},\n",
       " {'iterations': 1000,\n",
       "  'library': 'pysparse',\n",
       "  'mem_usage': 11271.007812,\n",
       "  'numberOfElements': 10000,\n",
       "  'output': None,\n",
       "  'solver': 'cg',\n",
       "  'sweeps': 10,\n",
       "  'tolerance': 1e-10,\n",
       "  'writeFiles': False}]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "highwater = []\n",
    "\n",
    "for f in glob.glob('../../../solvers/solver_timing/memory/mprofile_*.dat'):\n",
    "    mprofile = read_mprofile_file(f)\n",
    "    \n",
    "    args, unknowns = parse_args(mprofile)\n",
    "    args.__dict__['mem_usage'] = max(mprofile['mem_usage'])\n",
    "    highwater.append(args.__dict__)\n",
    "#    df[(df[\"library\"] == args.library) \n",
    "#       & (df[\"solver\"] == args.solver) \n",
    "#       & (df[\"numberOfElements\"] == args.numberOfElements)]['mem_usage'] = max(mprofile['mem_usage'])\n",
    "\n",
    "highwater"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>iterations</th>\n",
       "      <th>library</th>\n",
       "      <th>mem_usage</th>\n",
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       "      <td>1000</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>cg</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
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       "      <td>1000</td>\n",
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       "      <td>10000</td>\n",
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       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1000</td>\n",
       "      <td>pysparse</td>\n",
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       "      <td>1.000000e-10</td>\n",
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       "      <th>7</th>\n",
       "      <td>1000</td>\n",
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       "      <td>57.195312</td>\n",
       "      <td>10000</td>\n",
       "      <td>None</td>\n",
       "      <td>cg</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1000</td>\n",
       "      <td>pysparse</td>\n",
       "      <td>11271.007812</td>\n",
       "      <td>10000</td>\n",
       "      <td>None</td>\n",
       "      <td>cg</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   iterations   library     mem_usage  numberOfElements output solver  sweeps  \\\n",
       "0        1000  pysparse     86.246094             10000   None     cg      10   \n",
       "1        1000  pysparse    100.804688             10000   None     cg      10   \n",
       "2        1000  pysparse  12352.593750             10000   None     cg      10   \n",
       "3        1000  pysparse     54.277344             10000   None     cg      10   \n",
       "4        1000  pysparse  10746.234375             10000   None     cg      10   \n",
       "5        1000  pysparse    181.398438             10000   None     cg      10   \n",
       "6        1000  pysparse     45.164062             10000   None     cg      10   \n",
       "7        1000  pysparse     57.195312             10000   None     cg      10   \n",
       "8        1000  pysparse  11271.007812             10000   None     cg      10   \n",
       "\n",
       "      tolerance writeFiles  \n",
       "0  1.000000e-10      False  \n",
       "1  1.000000e-10      False  \n",
       "2  1.000000e-10      False  \n",
       "3  1.000000e-10      False  \n",
       "4  1.000000e-10      False  \n",
       "5  1.000000e-10      False  \n",
       "6  1.000000e-10      False  \n",
       "7  1.000000e-10      False  \n",
       "8  1.000000e-10      False  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(highwater)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <th>sweep 0 - iterations</th>\n",
       "      <th>sweep 1 - iterations</th>\n",
       "      <th>sweep 2 - iterations</th>\n",
       "      <th>sweep 3 - iterations</th>\n",
       "      <th>sweep 4 - iterations</th>\n",
       "      <th>sweep 5 - iterations</th>\n",
       "      <th>sweep 6 - iterations</th>\n",
       "      <th>sweep 7 - iterations</th>\n",
       "      <th>sweep 8 - iterations</th>\n",
       "      <th>sweep 9 - iterations</th>\n",
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       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
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       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>10.0</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>cg</td>\n",
       "      <td>460.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>491.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>529.0</td>\n",
       "      <td>620.0</td>\n",
       "      <td>561.0</td>\n",
       "      <td>589.0</td>\n",
       "      <td>572.0</td>\n",
       "      <td>579.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>1.000000e-10</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed  iterations   library  numberOfElements  processes  \\\n",
       "0  18406.32      1000.0  trilinos         1000000.0        1.0   \n",
       "1      0.45      1000.0     scipy             100.0        1.0   \n",
       "2      0.42      1000.0  trilinos             100.0        1.0   \n",
       "3  25033.30      1000.0     scipy         1000000.0        1.0   \n",
       "4   3983.78      1000.0     scipy          100000.0        1.0   \n",
       "\n",
       "                                              script solver  \\\n",
       "0  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "1  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "2  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "3  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "4  /users/guyer/FiPy/fipy/examples/benchmarking/s...     cg   \n",
       "\n",
       "   sweep 0 - iterations  sweep 1 - iterations  sweep 2 - iterations  \\\n",
       "0                1000.0                1000.0                1000.0   \n",
       "1                  10.0                  15.0                  16.0   \n",
       "2                  10.0                  15.0                  16.0   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                 460.0                 793.0                 491.0   \n",
       "\n",
       "   sweep 3 - iterations  sweep 4 - iterations  sweep 5 - iterations  \\\n",
       "0                1000.0                1000.0                1000.0   \n",
       "1                  17.0                  16.0                  17.0   \n",
       "2                  17.0                  16.0                  17.0   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                 700.0                 529.0                 620.0   \n",
       "\n",
       "   sweep 6 - iterations  sweep 7 - iterations  sweep 8 - iterations  \\\n",
       "0                1000.0                1000.0                1000.0   \n",
       "1                  16.0                  17.0                  17.0   \n",
       "2                  16.0                  17.0                  16.0   \n",
       "3                1000.0                1000.0                1000.0   \n",
       "4                 561.0                 589.0                 572.0   \n",
       "\n",
       "   sweep 9 - iterations  sweeps     tolerance  mem_usage  \n",
       "0                1000.0    10.0  1.000000e-10        NaN  \n",
       "1                  16.0    10.0  1.000000e-10        NaN  \n",
       "2                  17.0    10.0  1.000000e-10        NaN  \n",
       "3                1000.0    10.0  1.000000e-10        NaN  \n",
       "4                 579.0    10.0  1.000000e-10        NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cmd_line', 'timestamp', 'func_timestamp', 'mem_usage', 'filename']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mprofile.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.246094"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(mprofile['mem_usage'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Namespace(iterations=1000, library='pysparse', numberOfElements=10000, output=None, solver='cg', sweeps=10, tolerance=1e-10, writeFiles=False)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "args"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['../../../solvers/solver_timing/memory/mprofile_20170210154924.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210154948.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210154955.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155010.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155024.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155026.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155038.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155039.dat',\n",
       " '../../../solvers/solver_timing/memory/mprofile_20170210155042.dat']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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