{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_function_name(function_name):\n",
    "    return function_name[10:].rsplit(\".c\")[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>function_name</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>used_virtual_mem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ran2</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>41552</td>\n",
       "      <td>240.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gasdev</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>32768</td>\n",
       "      <td>240.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>generate</td>\n",
       "      <td>2.055015</td>\n",
       "      <td>1</td>\n",
       "      <td>219.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>maxfactor</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>5</td>\n",
       "      <td>219.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>length</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>3</td>\n",
       "      <td>219.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cgrid</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>1</td>\n",
       "      <td>219.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cov_value</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>24624</td>\n",
       "      <td>234.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>covariance</td>\n",
       "      <td>0.839737</td>\n",
       "      <td>1</td>\n",
       "      <td>228.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fourt</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>3</td>\n",
       "      <td>227.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>build_real</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>clean_real</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fftma2</td>\n",
       "      <td>0.849271</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2.904626</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     function_name  elapsed_time  executions  used_virtual_mem\n",
       "0     Py_getvalues      0.000007           1               0.0\n",
       "1             ran2      0.000065       41552             240.5\n",
       "2           gasdev      0.000383       32768             240.5\n",
       "3         generate      2.055015           1             219.2\n",
       "4        maxfactor      0.000005           5             219.2\n",
       "5           length      0.000071           3             219.2\n",
       "6            cgrid      0.000265           1             219.2\n",
       "7        cov_value      0.000156       24624             234.7\n",
       "8       covariance      0.839737           1             228.0\n",
       "9            fourt      0.003451           3             227.9\n",
       "10    prebuild_gwn      0.000138           1             227.4\n",
       "11      build_real      0.000424           1             227.4\n",
       "12      clean_real      0.000158           1             227.4\n",
       "13          fftma2      0.849271           1             227.4\n",
       "14  Py_kgeneration      2.904626           1             227.4"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {}\n",
    "with open(\"log_32.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": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_time\"] = df[\"elapsed_time\"] * df[\"executions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>function_name</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</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>gasdev</td>\n",
       "      <td>0.000383</td>\n",
       "      <td>32768</td>\n",
       "      <td>240.5</td>\n",
       "      <td>12.550144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cov_value</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>24624</td>\n",
       "      <td>234.7</td>\n",
       "      <td>3.841344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>2.904626</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "      <td>2.904626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ran2</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>41552</td>\n",
       "      <td>240.5</td>\n",
       "      <td>2.700880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>generate</td>\n",
       "      <td>2.055015</td>\n",
       "      <td>1</td>\n",
       "      <td>219.2</td>\n",
       "      <td>2.055015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fftma2</td>\n",
       "      <td>0.849271</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "      <td>0.849271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>covariance</td>\n",
       "      <td>0.839737</td>\n",
       "      <td>1</td>\n",
       "      <td>228.0</td>\n",
       "      <td>0.839737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fourt</td>\n",
       "      <td>0.003451</td>\n",
       "      <td>3</td>\n",
       "      <td>227.9</td>\n",
       "      <td>0.010353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>build_real</td>\n",
       "      <td>0.000424</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "      <td>0.000424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cgrid</td>\n",
       "      <td>0.000265</td>\n",
       "      <td>1</td>\n",
       "      <td>219.2</td>\n",
       "      <td>0.000265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>length</td>\n",
       "      <td>0.000071</td>\n",
       "      <td>3</td>\n",
       "      <td>219.2</td>\n",
       "      <td>0.000213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>clean_real</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "      <td>0.000158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>0.000138</td>\n",
       "      <td>1</td>\n",
       "      <td>227.4</td>\n",
       "      <td>0.000138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>maxfactor</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>5</td>\n",
       "      <td>219.2</td>\n",
       "      <td>0.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     function_name  elapsed_time  executions  used_virtual_mem  total_time\n",
       "2           gasdev      0.000383       32768             240.5   12.550144\n",
       "7        cov_value      0.000156       24624             234.7    3.841344\n",
       "14  Py_kgeneration      2.904626           1             227.4    2.904626\n",
       "1             ran2      0.000065       41552             240.5    2.700880\n",
       "3         generate      2.055015           1             219.2    2.055015\n",
       "13          fftma2      0.849271           1             227.4    0.849271\n",
       "8       covariance      0.839737           1             228.0    0.839737\n",
       "9            fourt      0.003451           3             227.9    0.010353\n",
       "11      build_real      0.000424           1             227.4    0.000424\n",
       "6            cgrid      0.000265           1             219.2    0.000265\n",
       "5           length      0.000071           3             219.2    0.000213\n",
       "12      clean_real      0.000158           1             227.4    0.000158\n",
       "10    prebuild_gwn      0.000138           1             227.4    0.000138\n",
       "4        maxfactor      0.000005           5             219.2    0.000025\n",
       "0     Py_getvalues      0.000007           1               0.0    0.000007"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"total_time\", \"used_virtual_mem\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>function_name</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</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>ran2</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>333450</td>\n",
       "      <td>548.2</td>\n",
       "      <td>73.359000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>gasdev</td>\n",
       "      <td>0.000707</td>\n",
       "      <td>262144</td>\n",
       "      <td>548.2</td>\n",
       "      <td>185.335808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>generate</td>\n",
       "      <td>20.687490</td>\n",
       "      <td>1</td>\n",
       "      <td>425.6</td>\n",
       "      <td>20.687490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>maxfactor</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>4</td>\n",
       "      <td>425.6</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>length</td>\n",
       "      <td>0.000059</td>\n",
       "      <td>3</td>\n",
       "      <td>425.6</td>\n",
       "      <td>0.000177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>cgrid</td>\n",
       "      <td>0.000199</td>\n",
       "      <td>1</td>\n",
       "      <td>425.6</td>\n",
       "      <td>0.000199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>cov_value</td>\n",
       "      <td>0.000158</td>\n",
       "      <td>156816</td>\n",
       "      <td>425.6</td>\n",
       "      <td>24.776928</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>covariance</td>\n",
       "      <td>7.813795</td>\n",
       "      <td>1</td>\n",
       "      <td>313.0</td>\n",
       "      <td>7.813795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>clean_real</td>\n",
       "      <td>0.000966</td>\n",
       "      <td>1</td>\n",
       "      <td>311.3</td>\n",
       "      <td>0.000966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Py_kgeneration</td>\n",
       "      <td>28.676072</td>\n",
       "      <td>1</td>\n",
       "      <td>311.3</td>\n",
       "      <td>28.676072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>fourt</td>\n",
       "      <td>0.069088</td>\n",
       "      <td>3</td>\n",
       "      <td>311.1</td>\n",
       "      <td>0.207264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>prebuild_gwn</td>\n",
       "      <td>0.001624</td>\n",
       "      <td>1</td>\n",
       "      <td>308.7</td>\n",
       "      <td>0.001624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>build_real</td>\n",
       "      <td>0.004294</td>\n",
       "      <td>1</td>\n",
       "      <td>308.4</td>\n",
       "      <td>0.004294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>fftma2</td>\n",
       "      <td>7.988212</td>\n",
       "      <td>1</td>\n",
       "      <td>308.4</td>\n",
       "      <td>7.988212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     function_name  elapsed_time  executions  used_virtual_mem  total_time\n",
       "1             ran2      0.000220      333450             548.2   73.359000\n",
       "2           gasdev      0.000707      262144             548.2  185.335808\n",
       "3         generate     20.687490           1             425.6   20.687490\n",
       "4        maxfactor      0.000005           4             425.6    0.000020\n",
       "5           length      0.000059           3             425.6    0.000177\n",
       "6            cgrid      0.000199           1             425.6    0.000199\n",
       "7        cov_value      0.000158      156816             425.6   24.776928\n",
       "8       covariance      7.813795           1             313.0    7.813795\n",
       "12      clean_real      0.000966           1             311.3    0.000966\n",
       "14  Py_kgeneration     28.676072           1             311.3   28.676072\n",
       "9            fourt      0.069088           3             311.1    0.207264\n",
       "10    prebuild_gwn      0.001624           1             308.7    0.001624\n",
       "11      build_real      0.004294           1             308.4    0.004294\n",
       "13          fftma2      7.988212           1             308.4    7.988212\n",
       "0     Py_getvalues      0.000009           1               0.0    0.000009"
      ]
     },
     "execution_count": 21,
     "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": []
  },
  {
   "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": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "with open(\"log_64.txt\") as log_file:\n",
    "    lines = log_file.readlines()\n",
    "    \n",
    "    for line in lines:\n",
    "        row = {}\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",
    "\n",
    "            row[\"function\"] = function_name\n",
    "            row[\"memory\"] = used_virtual_mem \n",
    "\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].rsplit(\",\")[0])\n",
    "            #val = data.get(function_name, (elapsed, 0, 0))\n",
    "            #data[function_name] = (max(elapsed, val[0]), val[1] + 1, val[2])\n",
    "            #data.append({\"function\": function_name, \"time\": elapsed, \"memory\": 0})\n",
    "            row[\"function\"] = function_name\n",
    "            row[\"time\"] = elapsed\n",
    "\n",
    "        if row: data.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame(data)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.fillna(np.nan)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>function</th>\n",
       "      <th>time</th>\n",
       "      <th>memory</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Py_getvalues</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ran2</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ran2</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>gasdev</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>gasdev</td>\n",
       "      <td>0.000006</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       function      time  memory\n",
       "0  Py_getvalues  0.000008     NaN\n",
       "1          ran2  0.000003     0.0\n",
       "2          ran2  0.000023     0.0\n",
       "3        gasdev  0.000046     0.0\n",
       "4        gasdev  0.000006     0.0"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2_grouped = df2.groupby(['function']).agg({'time': ['min', 'max', 'mean', 'sum', 'count'], 'memory': ['min', 'max', 'median']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">time</th>\n",
       "      <th colspan=\"3\" halign=\"left\">memory</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>sum</th>\n",
       "      <th>count</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>function</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Py_kgeneration</th>\n",
       "      <td>11.316621</td>\n",
       "      <td>11.316621</td>\n",
       "      <td>11.316621</td>\n",
       "      <td>11.316621</td>\n",
       "      <td>1</td>\n",
       "      <td>-293.7</td>\n",
       "      <td>-293.7</td>\n",
       "      <td>-293.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>generate</th>\n",
       "      <td>7.424831</td>\n",
       "      <td>7.424831</td>\n",
       "      <td>7.424831</td>\n",
       "      <td>7.424831</td>\n",
       "      <td>1</td>\n",
       "      <td>-226.3</td>\n",
       "      <td>-226.3</td>\n",
       "      <td>-226.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gasdev</th>\n",
       "      <td>0.000003</td>\n",
       "      <td>0.000277</td>\n",
       "      <td>0.000021</td>\n",
       "      <td>5.484581</td>\n",
       "      <td>262144</td>\n",
       "      <td>-20.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fftma2</th>\n",
       "      <td>3.891431</td>\n",
       "      <td>3.891431</td>\n",
       "      <td>3.891431</td>\n",
       "      <td>3.891431</td>\n",
       "      <td>1</td>\n",
       "      <td>-69.5</td>\n",
       "      <td>-69.5</td>\n",
       "      <td>-69.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>covariance</th>\n",
       "      <td>3.765731</td>\n",
       "      <td>3.765731</td>\n",
       "      <td>3.765731</td>\n",
       "      <td>3.765731</td>\n",
       "      <td>1</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>-65.0</td>\n",
       "      <td>-65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cov_value</th>\n",
       "      <td>0.000007</td>\n",
       "      <td>0.000108</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>2.062640</td>\n",
       "      <td>156816</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ran2</th>\n",
       "      <td>0.000002</td>\n",
       "      <td>0.000114</td>\n",
       "      <td>0.000005</td>\n",
       "      <td>1.534732</td>\n",
       "      <td>333450</td>\n",
       "      <td>-19.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fourt</th>\n",
       "      <td>0.032778</td>\n",
       "      <td>0.051545</td>\n",
       "      <td>0.039664</td>\n",
       "      <td>0.118991</td>\n",
       "      <td>3</td>\n",
       "      <td>-1.9</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>build_real</th>\n",
       "      <td>0.004053</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>0.004053</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prebuild_gwn</th>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>0.001132</td>\n",
       "      <td>1</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.5</td>\n",
       "      <td>-2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clean_real</th>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>0.000830</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "      <td>-0.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cgrid</th>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>length</th>\n",
       "      <td>0.000015</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>0.000118</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>maxfactor</th>\n",
       "      <td>0.000004</td>\n",
       "      <td>0.000025</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Py_getvalues</th>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>0.000008</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     time                                          memory  \\\n",
       "                      min        max       mean        sum   count    min   \n",
       "function                                                                    \n",
       "Py_kgeneration  11.316621  11.316621  11.316621  11.316621       1 -293.7   \n",
       "generate         7.424831   7.424831   7.424831   7.424831       1 -226.3   \n",
       "gasdev           0.000003   0.000277   0.000021   5.484581  262144  -20.0   \n",
       "fftma2           3.891431   3.891431   3.891431   3.891431       1  -69.5   \n",
       "covariance       3.765731   3.765731   3.765731   3.765731       1  -65.0   \n",
       "cov_value        0.000007   0.000108   0.000013   2.062640  156816   -1.9   \n",
       "ran2             0.000002   0.000114   0.000005   1.534732  333450  -19.7   \n",
       "fourt            0.032778   0.051545   0.039664   0.118991       3   -1.9   \n",
       "build_real       0.004053   0.004053   0.004053   0.004053       1    0.0   \n",
       "prebuild_gwn     0.001132   0.001132   0.001132   0.001132       1   -2.5   \n",
       "clean_real       0.000830   0.000830   0.000830   0.000830       1   -0.7   \n",
       "cgrid            0.000148   0.000148   0.000148   0.000148       1    0.0   \n",
       "length           0.000015   0.000064   0.000039   0.000118       3    0.0   \n",
       "maxfactor        0.000004   0.000025   0.000011   0.000043       4    0.0   \n",
       "Py_getvalues     0.000008   0.000008   0.000008   0.000008       1    NaN   \n",
       "\n",
       "                              \n",
       "                  max median  \n",
       "function                      \n",
       "Py_kgeneration -293.7 -293.7  \n",
       "generate       -226.3 -226.3  \n",
       "gasdev            5.3    0.0  \n",
       "fftma2          -69.5  -69.5  \n",
       "covariance      -65.0  -65.0  \n",
       "cov_value         1.5    0.0  \n",
       "ran2              2.6    0.0  \n",
       "fourt            -0.0   -0.2  \n",
       "build_real        0.0    0.0  \n",
       "prebuild_gwn     -2.5   -2.5  \n",
       "clean_real       -0.7   -0.7  \n",
       "cgrid             0.0    0.0  \n",
       "length            0.0    0.0  \n",
       "maxfactor         0.0    0.0  \n",
       "Py_getvalues      NaN    NaN  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_grouped.sort_values(by=('time', 'sum'), ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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