{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_function_name(function_name):\n",
    "    return function_name[10:].rsplit(\".c\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>function_name</th>\n",
       "      <th>used_virtual_mem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000456</td>\n",
       "      <td>1</td>\n",
       "      <td>cgrid</td>\n",
       "      <td>2877.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.180264</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001184</td>\n",
       "      <td>512</td>\n",
       "      <td>gasdev</td>\n",
       "      <td>2882.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>700</td>\n",
       "      <td>cov_value</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1</td>\n",
       "      <td>clean_real</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.077514</td>\n",
       "      <td>1</td>\n",
       "      <td>covariance</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.079166</td>\n",
       "      <td>1</td>\n",
       "      <td>fftma2</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100123</td>\n",
       "      <td>1</td>\n",
       "      <td>generate</td>\n",
       "      <td>2877.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>3</td>\n",
       "      <td>length</td>\n",
       "      <td>2877.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000037</td>\n",
       "      <td>702</td>\n",
       "      <td>ran2</td>\n",
       "      <td>2882.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>3</td>\n",
       "      <td>fourt</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>build_real</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>2877.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>3</td>\n",
       "      <td>maxfactor</td>\n",
       "      <td>2877.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed_time  executions   function_name  used_virtual_mem\n",
       "0       0.000456           1           cgrid            2877.4\n",
       "1       0.180264           1  Py_kgeneration            2877.9\n",
       "2       0.001184         512          gasdev            2882.6\n",
       "3       0.000081         700       cov_value            2877.9\n",
       "4       0.000015           1      clean_real            2877.9\n",
       "5       0.077514           1      covariance            2877.9\n",
       "6       0.079166           1          fftma2            2877.9\n",
       "7       0.100123           1        generate            2877.4\n",
       "8       0.000098           3          length            2877.4\n",
       "9       0.000034           1    Py_getvalues               0.0\n",
       "10      0.000037         702            ran2            2882.6\n",
       "11      0.000193           3           fourt            2877.9\n",
       "12      0.000033           1      build_real            2877.9\n",
       "13      0.000034           1    prebuild_gwn            2877.9\n",
       "14      0.000010           3       maxfactor            2877.4"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {}\n",
    "with open(\"log_8.txt\") as log_file:\n",
    "    lines = log_file.readlines()\n",
    "    \n",
    "    for line in lines:\n",
    "        if \"MEM\" in line:\n",
    "            split_line = line.split()\n",
    "            idx_used_mem = split_line.index(\"USED\") + 4\n",
    "            function_name, used_virtual_mem = get_function_name(split_line[2]), float(split_line[idx_used_mem])\n",
    "            val = data.get(function_name, (0, 0, used_virtual_mem))\n",
    "            data[function_name] = (val[0], val[1], max(used_virtual_mem, val[2]))    \n",
    "        if \"ELAPSED\" in line:\n",
    "            split_line = line.split()\n",
    "            idx_elapsed = split_line.index(\"ELAPSED\") + 2\n",
    "            function_name, elapsed = get_function_name(split_line[2]), float(split_line[idx_elapsed])\n",
    "            val = data.get(function_name, (elapsed, 0, 0))\n",
    "            data[function_name] = (max(elapsed, val[0]), val[1] + 1, val[2])\n",
    "\n",
    "values = data.values()\n",
    "new_data = {\"function_name\": data.keys(), \"elapsed_time\": map(lambda x: x[0], values), \"executions\": map(lambda x: x[1], values), \"used_virtual_mem\":  map(lambda x: x[2], values)}\n",
    "                \n",
    "df = pd.DataFrame(new_data)  \n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_time\"] = df[\"elapsed_time\"] * df[\"executions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>function_name</th>\n",
       "      <th>used_virtual_mem</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.180264</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.180264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100123</td>\n",
       "      <td>1</td>\n",
       "      <td>generate</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.100123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.079166</td>\n",
       "      <td>1</td>\n",
       "      <td>fftma2</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.079166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.077514</td>\n",
       "      <td>1</td>\n",
       "      <td>covariance</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.077514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001184</td>\n",
       "      <td>512</td>\n",
       "      <td>gasdev</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.606208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000456</td>\n",
       "      <td>1</td>\n",
       "      <td>cgrid</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>3</td>\n",
       "      <td>fourt</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>3</td>\n",
       "      <td>length</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>700</td>\n",
       "      <td>cov_value</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.056700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000037</td>\n",
       "      <td>702</td>\n",
       "      <td>ran2</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>build_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1</td>\n",
       "      <td>clean_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>3</td>\n",
       "      <td>maxfactor</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed_time  executions   function_name  used_virtual_mem  total_time\n",
       "1       0.180264           1  Py_kgeneration            2877.9    0.180264\n",
       "7       0.100123           1        generate            2877.4    0.100123\n",
       "6       0.079166           1          fftma2            2877.9    0.079166\n",
       "5       0.077514           1      covariance            2877.9    0.077514\n",
       "2       0.001184         512          gasdev            2882.6    0.606208\n",
       "0       0.000456           1           cgrid            2877.4    0.000456\n",
       "11      0.000193           3           fourt            2877.9    0.000579\n",
       "8       0.000098           3          length            2877.4    0.000294\n",
       "3       0.000081         700       cov_value            2877.9    0.056700\n",
       "10      0.000037         702            ran2            2882.6    0.025974\n",
       "9       0.000034           1    Py_getvalues               0.0    0.000034\n",
       "13      0.000034           1    prebuild_gwn            2877.9    0.000034\n",
       "12      0.000033           1      build_real            2877.9    0.000033\n",
       "4       0.000015           1      clean_real            2877.9    0.000015\n",
       "14      0.000010           3       maxfactor            2877.4    0.000030"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"elapsed_time\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>function_name</th>\n",
       "      <th>used_virtual_mem</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001184</td>\n",
       "      <td>512</td>\n",
       "      <td>gasdev</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.606208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.180264</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.180264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100123</td>\n",
       "      <td>1</td>\n",
       "      <td>generate</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.100123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.079166</td>\n",
       "      <td>1</td>\n",
       "      <td>fftma2</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.079166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.077514</td>\n",
       "      <td>1</td>\n",
       "      <td>covariance</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.077514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>700</td>\n",
       "      <td>cov_value</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.056700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000037</td>\n",
       "      <td>702</td>\n",
       "      <td>ran2</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>3</td>\n",
       "      <td>fourt</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000456</td>\n",
       "      <td>1</td>\n",
       "      <td>cgrid</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>3</td>\n",
       "      <td>length</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>build_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>3</td>\n",
       "      <td>maxfactor</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1</td>\n",
       "      <td>clean_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed_time  executions   function_name  used_virtual_mem  total_time\n",
       "2       0.001184         512          gasdev            2882.6    0.606208\n",
       "1       0.180264           1  Py_kgeneration            2877.9    0.180264\n",
       "7       0.100123           1        generate            2877.4    0.100123\n",
       "6       0.079166           1          fftma2            2877.9    0.079166\n",
       "5       0.077514           1      covariance            2877.9    0.077514\n",
       "3       0.000081         700       cov_value            2877.9    0.056700\n",
       "10      0.000037         702            ran2            2882.6    0.025974\n",
       "11      0.000193           3           fourt            2877.9    0.000579\n",
       "0       0.000456           1           cgrid            2877.4    0.000456\n",
       "8       0.000098           3          length            2877.4    0.000294\n",
       "9       0.000034           1    Py_getvalues               0.0    0.000034\n",
       "13      0.000034           1    prebuild_gwn            2877.9    0.000034\n",
       "12      0.000033           1      build_real            2877.9    0.000033\n",
       "14      0.000010           3       maxfactor            2877.4    0.000030\n",
       "4       0.000015           1      clean_real            2877.9    0.000015"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"total_time\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>function_name</th>\n",
       "      <th>used_virtual_mem</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.001184</td>\n",
       "      <td>512</td>\n",
       "      <td>gasdev</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.606208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000037</td>\n",
       "      <td>702</td>\n",
       "      <td>ran2</td>\n",
       "      <td>2882.6</td>\n",
       "      <td>0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.180264</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.180264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000081</td>\n",
       "      <td>700</td>\n",
       "      <td>cov_value</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.056700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>1</td>\n",
       "      <td>clean_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.077514</td>\n",
       "      <td>1</td>\n",
       "      <td>covariance</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.077514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.079166</td>\n",
       "      <td>1</td>\n",
       "      <td>fftma2</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.079166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000193</td>\n",
       "      <td>3</td>\n",
       "      <td>fourt</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000033</td>\n",
       "      <td>1</td>\n",
       "      <td>build_real</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>2877.9</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000456</td>\n",
       "      <td>1</td>\n",
       "      <td>cgrid</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.100123</td>\n",
       "      <td>1</td>\n",
       "      <td>generate</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.100123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000098</td>\n",
       "      <td>3</td>\n",
       "      <td>length</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000010</td>\n",
       "      <td>3</td>\n",
       "      <td>maxfactor</td>\n",
       "      <td>2877.4</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000034</td>\n",
       "      <td>1</td>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    elapsed_time  executions   function_name  used_virtual_mem  total_time\n",
       "2       0.001184         512          gasdev            2882.6    0.606208\n",
       "10      0.000037         702            ran2            2882.6    0.025974\n",
       "1       0.180264           1  Py_kgeneration            2877.9    0.180264\n",
       "3       0.000081         700       cov_value            2877.9    0.056700\n",
       "4       0.000015           1      clean_real            2877.9    0.000015\n",
       "5       0.077514           1      covariance            2877.9    0.077514\n",
       "6       0.079166           1          fftma2            2877.9    0.079166\n",
       "11      0.000193           3           fourt            2877.9    0.000579\n",
       "12      0.000033           1      build_real            2877.9    0.000033\n",
       "13      0.000034           1    prebuild_gwn            2877.9    0.000034\n",
       "0       0.000456           1           cgrid            2877.4    0.000456\n",
       "7       0.100123           1        generate            2877.4    0.100123\n",
       "8       0.000098           3          length            2877.4    0.000294\n",
       "14      0.000010           3       maxfactor            2877.4    0.000030\n",
       "9       0.000034           1    Py_getvalues               0.0    0.000034"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"used_virtual_mem\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import libraries\n",
    "from matplotlib_venn import venn3\n",
    "from matplotlib import pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "relations = {\n",
    "    \"generate\": [\"gasdev\"],\n",
    "    \"fftma2\": [\"covariance\", \"fourt\", \"prebuild_gwn\"]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "plt.figure(dpi=125)\n",
    "set_a = set([0.023829, 0.010957, 0.012503])\n",
    "\n",
    "set_b = set([0.010957])\n",
    "\n",
    "set_c = set([0.012503])\n",
    "\n",
    "venn3([set_a, set_b, set_c], ('Py_kgeneration', 'generate', 'fftma2'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "venn3([set(['Py_kgeneration', 'generate', 'fftma2']), set(['generate', 'fftma2']), set(['fftma2'])])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(dpi=125)\n",
    "plt.title('Py_kgeneration')\n",
    "plt.pie([0.010957, 0.012503], labels=[\"generate\", \"fftma2\"], normalize=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "# Creating dataset\n",
    "size = 6\n",
    "cars = ['AUDI', 'BMW', 'FORD',\n",
    "\t\t'TESLA', 'JAGUAR', 'MERCEDES']\n",
    "\n",
    "data = np.array([[23, 16], [17, 23],\n",
    "\t\t\t\t[35, 11], [29, 33],\n",
    "\t\t\t\t[12, 27], [41, 42]])\n",
    "\n",
    "# normalizing data to 2 pi\n",
    "norm = data / np.sum(data)*2 * np.pi\n",
    "\n",
    "# obtaining ordinates of bar edges\n",
    "left = np.cumsum(np.append(0,\n",
    "\t\t\t\t\t\tnorm.flatten()[:-1])).reshape(data.shape)\n",
    "\n",
    "# Creating color scale\n",
    "cmap = plt.get_cmap(\"tab20c\")\n",
    "outer_colors = cmap(np.arange(6)*4)\n",
    "inner_colors = cmap(np.array([1, 2, 5, 6, 9,\n",
    "\t\t\t\t\t\t\t10, 12, 13, 15,\n",
    "\t\t\t\t\t\t\t17, 18, 20 ]))\n",
    "\n",
    "# Creating plot\n",
    "fig, ax = plt.subplots(figsize =(10, 7),\n",
    "\t\t\t\t\tsubplot_kw = dict(polar = True))\n",
    "\n",
    "ax.bar(x = left[:, 0],\n",
    "\twidth = norm.sum(axis = 1),\n",
    "\tbottom = 1-size,\n",
    "\theight = size,\n",
    "\tcolor = outer_colors,\n",
    "\tedgecolor ='w',\n",
    "\tlinewidth = 1,\n",
    "\talign =\"edge\")\n",
    "\n",
    "ax.bar(x = left.flatten(),\n",
    "\twidth = norm.flatten(),\n",
    "\tbottom = 1-2 * size,\n",
    "\theight = size,\n",
    "\tcolor = inner_colors,\n",
    "\tedgecolor ='w',\n",
    "\tlinewidth = 1,\n",
    "\talign =\"edge\")\n",
    "\n",
    "ax.set(title =\"Nested pie chart\")\n",
    "ax.set_axis_off()\n",
    "\n",
    "# show plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.18"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}