{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['12:30:24', 'INFO', './lib_src/generate.c:61:', 'TOTAL', 'VIRTUAL', 'MEM', '=', '7683.0', 'MB,', 'USED', 'VIRTUAL', 'MEM', '=', '304.2', 'MB,', 'USED', 'VIRTUAL', 'MEM', 'BY', 'CURRENT', 'PROCESS', '=', '1072', 'MB']\n",
      "['12:30:24', 'INFO', './lib_src/Py_kgeneration.c:64:', 'TOTAL', 'VIRTUAL', 'MEM', '=', '7683.0', 'MB,', 'USED', 'VIRTUAL', 'MEM', '=', '303.6', 'MB,', 'USED', 'VIRTUAL', 'MEM', 'BY', 'CURRENT', 'PROCESS', '=', '1072', 'MB']\n"
     ]
    },
    {
     "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./lib_src/Py_getvalues.c:157:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./lib_src/ran2.c:68:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./lib_src/gasdev.c:35:</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./lib_src/gasdev.c:43:</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./lib_src/generate.c:75:</td>\n",
       "      <td>0.010957</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./lib_src/maxfactor.c:42:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./lib_src/length.c:47:</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./lib_src/cgrid.c:50:</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./lib_src/cov_value.c:60:</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./lib_src/covariance.c:86:</td>\n",
       "      <td>0.012162</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./lib_src/fourt.c:593:</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./lib_src/prebuild_gwn.c:57:</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./lib_src/build_real.c:50:</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./lib_src/clean_real.c:48:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./lib_src/fftma2.c:106:</td>\n",
       "      <td>0.012503</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./lib_src/Py_kgeneration.c:74:</td>\n",
       "      <td>0.023829</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     function_name  elapsed_time  executions\n",
       "0    ./lib_src/Py_getvalues.c:157:      0.000007           1\n",
       "1             ./lib_src/ran2.c:68:      0.000004         142\n",
       "2           ./lib_src/gasdev.c:35:      0.000023         151\n",
       "3           ./lib_src/gasdev.c:43:      0.000003         202\n",
       "4         ./lib_src/generate.c:75:      0.010957           1\n",
       "5        ./lib_src/maxfactor.c:42:      0.000004           1\n",
       "6           ./lib_src/length.c:47:      0.000013           1\n",
       "7            ./lib_src/cgrid.c:50:      0.000060           1\n",
       "8        ./lib_src/cov_value.c:60:      0.000011          76\n",
       "9       ./lib_src/covariance.c:86:      0.012162           1\n",
       "10          ./lib_src/fourt.c:593:      0.000065           2\n",
       "11    ./lib_src/prebuild_gwn.c:57:      0.000009           1\n",
       "12      ./lib_src/build_real.c:50:      0.000019           1\n",
       "13      ./lib_src/clean_real.c:48:      0.000007           1\n",
       "14         ./lib_src/fftma2.c:106:      0.012503           1\n",
       "15  ./lib_src/Py_kgeneration.c:74:      0.023829           1"
      ]
     },
     "execution_count": 3,
     "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",
    "            print(split_line)\n",
    "        if \"ELAPSED\" in line:\n",
    "            split_line = line.split()\n",
    "            idx_elapsed = split_line.index(\"ELAPSED\") + 2\n",
    "            function_name, elapsed = split_line[2], float(split_line[idx_elapsed])\n",
    "            if function_name not in data or (function_name in data and elapsed > data[function_name][0]):\n",
    "                val = data.get(function_name, (elapsed, 0))\n",
    "                data[function_name] = (val[0], val[1] + 1)\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)}\n",
    "                \n",
    "df = pd.DataFrame(new_data)  \n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_time\"] = df[\"elapsed_time\"] * df[\"executions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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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_name</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>executions</th>\n",
       "      <th>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./lib_src/Py_kgeneration.c:74:</td>\n",
       "      <td>0.023829</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./lib_src/fftma2.c:106:</td>\n",
       "      <td>0.012503</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./lib_src/covariance.c:86:</td>\n",
       "      <td>0.012162</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./lib_src/generate.c:75:</td>\n",
       "      <td>0.010957</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./lib_src/fourt.c:593:</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./lib_src/cgrid.c:50:</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./lib_src/gasdev.c:35:</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>151</td>\n",
       "      <td>0.003473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./lib_src/build_real.c:50:</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./lib_src/length.c:47:</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./lib_src/cov_value.c:60:</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>76</td>\n",
       "      <td>0.000836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./lib_src/prebuild_gwn.c:57:</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./lib_src/Py_getvalues.c:157:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./lib_src/clean_real.c:48:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./lib_src/ran2.c:68:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>142</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./lib_src/maxfactor.c:42:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./lib_src/gasdev.c:43:</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>202</td>\n",
       "      <td>0.000606</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     function_name  elapsed_time  executions  total_time\n",
       "15  ./lib_src/Py_kgeneration.c:74:      0.023829           1    0.023829\n",
       "14         ./lib_src/fftma2.c:106:      0.012503           1    0.012503\n",
       "9       ./lib_src/covariance.c:86:      0.012162           1    0.012162\n",
       "4         ./lib_src/generate.c:75:      0.010957           1    0.010957\n",
       "10          ./lib_src/fourt.c:593:      0.000065           2    0.000130\n",
       "7            ./lib_src/cgrid.c:50:      0.000060           1    0.000060\n",
       "2           ./lib_src/gasdev.c:35:      0.000023         151    0.003473\n",
       "12      ./lib_src/build_real.c:50:      0.000019           1    0.000019\n",
       "6           ./lib_src/length.c:47:      0.000013           1    0.000013\n",
       "8        ./lib_src/cov_value.c:60:      0.000011          76    0.000836\n",
       "11    ./lib_src/prebuild_gwn.c:57:      0.000009           1    0.000009\n",
       "0    ./lib_src/Py_getvalues.c:157:      0.000007           1    0.000007\n",
       "13      ./lib_src/clean_real.c:48:      0.000007           1    0.000007\n",
       "1             ./lib_src/ran2.c:68:      0.000004         142    0.000568\n",
       "5        ./lib_src/maxfactor.c:42:      0.000004           1    0.000004\n",
       "3           ./lib_src/gasdev.c:43:      0.000003         202    0.000606"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"elapsed_time\"], ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</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>total_time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>./lib_src/Py_kgeneration.c:74:</td>\n",
       "      <td>0.023829</td>\n",
       "      <td>1</td>\n",
       "      <td>0.023829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>./lib_src/fftma2.c:106:</td>\n",
       "      <td>0.012503</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>./lib_src/covariance.c:86:</td>\n",
       "      <td>0.012162</td>\n",
       "      <td>1</td>\n",
       "      <td>0.012162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>./lib_src/generate.c:75:</td>\n",
       "      <td>0.010957</td>\n",
       "      <td>1</td>\n",
       "      <td>0.010957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>./lib_src/gasdev.c:35:</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>151</td>\n",
       "      <td>0.003473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>./lib_src/cov_value.c:60:</td>\n",
       "      <td>0.000011</td>\n",
       "      <td>76</td>\n",
       "      <td>0.000836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>./lib_src/gasdev.c:43:</td>\n",
       "      <td>0.000003</td>\n",
       "      <td>202</td>\n",
       "      <td>0.000606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>./lib_src/ran2.c:68:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>142</td>\n",
       "      <td>0.000568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>./lib_src/fourt.c:593:</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>./lib_src/cgrid.c:50:</td>\n",
       "      <td>0.000060</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>./lib_src/build_real.c:50:</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>./lib_src/length.c:47:</td>\n",
       "      <td>0.000013</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>./lib_src/prebuild_gwn.c:57:</td>\n",
       "      <td>0.000009</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>./lib_src/Py_getvalues.c:157:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>./lib_src/clean_real.c:48:</td>\n",
       "      <td>0.000007</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>./lib_src/maxfactor.c:42:</td>\n",
       "      <td>0.000004</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000004</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     function_name  elapsed_time  executions  total_time\n",
       "15  ./lib_src/Py_kgeneration.c:74:      0.023829           1    0.023829\n",
       "14         ./lib_src/fftma2.c:106:      0.012503           1    0.012503\n",
       "9       ./lib_src/covariance.c:86:      0.012162           1    0.012162\n",
       "4         ./lib_src/generate.c:75:      0.010957           1    0.010957\n",
       "2           ./lib_src/gasdev.c:35:      0.000023         151    0.003473\n",
       "8        ./lib_src/cov_value.c:60:      0.000011          76    0.000836\n",
       "3           ./lib_src/gasdev.c:43:      0.000003         202    0.000606\n",
       "1             ./lib_src/ran2.c:68:      0.000004         142    0.000568\n",
       "10          ./lib_src/fourt.c:593:      0.000065           2    0.000130\n",
       "7            ./lib_src/cgrid.c:50:      0.000060           1    0.000060\n",
       "12      ./lib_src/build_real.c:50:      0.000019           1    0.000019\n",
       "6           ./lib_src/length.c:47:      0.000013           1    0.000013\n",
       "11    ./lib_src/prebuild_gwn.c:57:      0.000009           1    0.000009\n",
       "0    ./lib_src/Py_getvalues.c:157:      0.000007           1    0.000007\n",
       "13      ./lib_src/clean_real.c:48:      0.000007           1    0.000007\n",
       "5        ./lib_src/maxfactor.c:42:      0.000004           1    0.000004"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"total_time\"], 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": []
  }
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