AMITT/HTML_GENERATING_CODE/.ipynb_checkpoints/AMITT_create_incident_visualisations-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create AMITT incident visualisations\n",
    "\n",
    "Many thanks to https://python-graph-gallery.com/91-customize-seaborn-heatmap/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fb2e18c9880>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import generate_amitt_ttps\n",
    "\n",
    "# Check that heatmap works\n",
    "df = pd.DataFrame(np.random.random((10,12)), columns=[\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\",\"h\",\"i\",\"j\",\"k\",\"l\"])\n",
    "sns.heatmap(df, annot=True, annot_kws={\"size\": 7})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5,  0,  8, 11,  1, 12,  2,  3,  1,  4,  2,  0],\n",
       "       [ 2,  0,  1,  1,  1,  4,  2,  2,  1,  0,  0,  0],\n",
       "       [ 0,  0,  1,  0,  3,  4, 10,  0,  2,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  1, 10,  0,  2,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  2,  2,  1,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  1,  0,  2,  3,  1,  5,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  4,  3,  1, 14,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  3, 12, 15,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0,  1,  0,  0,  0],\n",
       "       [ 0,  0,  0,  0,  0,  0,  0,  0, 13,  0,  0,  0]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "amitt = generate_amitt_ttps.Amitt()\n",
    "redgrid = amitt.create_padded_framework_table('AMITT Red', 'technique_ids', False)\n",
    "\n",
    "techcounts = amitt.it[['id_incident','id_technique']].drop_duplicates().groupby('id_technique').count().to_dict()['id_incident']\n",
    "techlabels = redgrid[2:][:]\n",
    "nrows = len(techlabels)\n",
    "ncols = len(techlabels[0])\n",
    "techgrid = np.zeros([nrows, ncols], dtype = int)\n",
    "\n",
    "for row in range(nrows):\n",
    "    for col in range(ncols):\n",
    "        if techlabels[row][col] in techcounts:\n",
    "            techgrid[row][col] = techcounts[techlabels[row][col]]\n",
    "\n",
    "sns.heatmap(techgrid, annot=True, annot_kws={\"size\": 7})\n",
    "techgrid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>phase_id</th>\n",
       "      <th>rank</th>\n",
       "      <th>summary</th>\n",
       "      <th>longname</th>\n",
       "      <th>technique_ids</th>\n",
       "      <th>counter_ids</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TA01</td>\n",
       "      <td>Strategic Planning</td>\n",
       "      <td>P01</td>\n",
       "      <td>1</td>\n",
       "      <td>Define the desired end state, i.e. the set of ...</td>\n",
       "      <td>TA01 - Strategic Planning</td>\n",
       "      <td>[T0001, T0002, T0003, T0004]</td>\n",
       "      <td>[C00006, C00008, C00010, C00012, C00013, C0001...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TA02</td>\n",
       "      <td>Objective Planning</td>\n",
       "      <td>P01</td>\n",
       "      <td>2</td>\n",
       "      <td>Set clearly defined, measurable, and achievabl...</td>\n",
       "      <td>TA02 - Objective Planning</td>\n",
       "      <td>[T0005, T0006]</td>\n",
       "      <td>[C00009, C00011, C00028, C00029, C00030, C0003...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TA03</td>\n",
       "      <td>Develop People</td>\n",
       "      <td>P02</td>\n",
       "      <td>3</td>\n",
       "      <td>Develop online and offline users and agents, i...</td>\n",
       "      <td>TA03 - Develop People</td>\n",
       "      <td>[T0007, T0008, T0009]</td>\n",
       "      <td>[C00034, C00036, C00040, C00042, C00044, C0004...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TA04</td>\n",
       "      <td>Develop Networks</td>\n",
       "      <td>P02</td>\n",
       "      <td>4</td>\n",
       "      <td>Develop online and offline communities and tra...</td>\n",
       "      <td>TA04 - Develop Networks</td>\n",
       "      <td>[T0010, T0011, T0012, T0013, T0014, T0015]</td>\n",
       "      <td>[C00047, C00052, C00053, C00056, C00059, C0006...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TA05</td>\n",
       "      <td>Microtargeting</td>\n",
       "      <td>P02</td>\n",
       "      <td>5</td>\n",
       "      <td>Target very specific populations of people</td>\n",
       "      <td>TA05 - Microtargeting</td>\n",
       "      <td>[T0016, T0017, T0018]</td>\n",
       "      <td>[C00065, C00066, C00216]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TA06</td>\n",
       "      <td>Develop Content</td>\n",
       "      <td>P02</td>\n",
       "      <td>6</td>\n",
       "      <td>Create and acquire content used in incident</td>\n",
       "      <td>TA06 - Develop Content</td>\n",
       "      <td>[T0019, T0020, T0021, T0022, T0023, T0024, T00...</td>\n",
       "      <td>[C00014, C00032, C00071, C00072, C00074, C0007...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TA07</td>\n",
       "      <td>Channel Selection</td>\n",
       "      <td>P02</td>\n",
       "      <td>7</td>\n",
       "      <td>Set up specific delivery, amplification and ma...</td>\n",
       "      <td>TA07 - Channel Selection</td>\n",
       "      <td>[T0029, T0030, T0031, T0032, T0033, T0034, T00...</td>\n",
       "      <td>[C00097, C00098, C00099, C00100, C00101, C0010...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TA08</td>\n",
       "      <td>Pump Priming</td>\n",
       "      <td>P03</td>\n",
       "      <td>8</td>\n",
       "      <td>Release content on a targetted small scale, pr...</td>\n",
       "      <td>TA08 - Pump Priming</td>\n",
       "      <td>[T0039, T0040, T0041, T0042, T0043, T0044, T00...</td>\n",
       "      <td>[C00112, C00113, C00114, C00115, C00116, C0011...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>TA09</td>\n",
       "      <td>Exposure</td>\n",
       "      <td>P03</td>\n",
       "      <td>9</td>\n",
       "      <td>Release content to general public or push to l...</td>\n",
       "      <td>TA09 - Exposure</td>\n",
       "      <td>[T0047, T0048, T0049, T0050, T0051, T0052, T00...</td>\n",
       "      <td>[C00089, C00122, C00123, C00124, C00125, C0012...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TA10</td>\n",
       "      <td>Go Physical</td>\n",
       "      <td>P03</td>\n",
       "      <td>10</td>\n",
       "      <td>Move incident into offline world</td>\n",
       "      <td>TA10 - Go Physical</td>\n",
       "      <td>[T0057, T0061]</td>\n",
       "      <td>[C00129, C00130]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>TA11</td>\n",
       "      <td>Persistence</td>\n",
       "      <td>P03</td>\n",
       "      <td>11</td>\n",
       "      <td>Keep incident 'alive', beyond the incident cre...</td>\n",
       "      <td>TA11 - Persistence</td>\n",
       "      <td>[T0058, T0059, T0060]</td>\n",
       "      <td>[C00131, C00133, C00135, C00136, C00137, C0013...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>TA12</td>\n",
       "      <td>Measure Effectiveness</td>\n",
       "      <td>P04</td>\n",
       "      <td>12</td>\n",
       "      <td>Measure effectiveness of incident, for use in ...</td>\n",
       "      <td>TA12 - Measure Effectiveness</td>\n",
       "      <td>[T0062, T0063, T0064]</td>\n",
       "      <td>[C00090, C00140, C00147, C00148, C00149]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id                   name phase_id  rank  \\\n",
       "0   TA01     Strategic Planning      P01     1   \n",
       "1   TA02     Objective Planning      P01     2   \n",
       "2   TA03         Develop People      P02     3   \n",
       "3   TA04       Develop Networks      P02     4   \n",
       "4   TA05         Microtargeting      P02     5   \n",
       "5   TA06        Develop Content      P02     6   \n",
       "6   TA07      Channel Selection      P02     7   \n",
       "7   TA08           Pump Priming      P03     8   \n",
       "8   TA09               Exposure      P03     9   \n",
       "9   TA10            Go Physical      P03    10   \n",
       "10  TA11            Persistence      P03    11   \n",
       "11  TA12  Measure Effectiveness      P04    12   \n",
       "\n",
       "                                              summary  \\\n",
       "0   Define the desired end state, i.e. the set of ...   \n",
       "1   Set clearly defined, measurable, and achievabl...   \n",
       "2   Develop online and offline users and agents, i...   \n",
       "3   Develop online and offline communities and tra...   \n",
       "4          Target very specific populations of people   \n",
       "5         Create and acquire content used in incident   \n",
       "6   Set up specific delivery, amplification and ma...   \n",
       "7   Release content on a targetted small scale, pr...   \n",
       "8   Release content to general public or push to l...   \n",
       "9                    Move incident into offline world   \n",
       "10  Keep incident 'alive', beyond the incident cre...   \n",
       "11  Measure effectiveness of incident, for use in ...   \n",
       "\n",
       "                        longname  \\\n",
       "0      TA01 - Strategic Planning   \n",
       "1      TA02 - Objective Planning   \n",
       "2          TA03 - Develop People   \n",
       "3        TA04 - Develop Networks   \n",
       "4          TA05 - Microtargeting   \n",
       "5         TA06 - Develop Content   \n",
       "6       TA07 - Channel Selection   \n",
       "7            TA08 - Pump Priming   \n",
       "8                TA09 - Exposure   \n",
       "9             TA10 - Go Physical   \n",
       "10            TA11 - Persistence   \n",
       "11  TA12 - Measure Effectiveness   \n",
       "\n",
       "                                        technique_ids  \\\n",
       "0                        [T0001, T0002, T0003, T0004]   \n",
       "1                                      [T0005, T0006]   \n",
       "2                               [T0007, T0008, T0009]   \n",
       "3          [T0010, T0011, T0012, T0013, T0014, T0015]   \n",
       "4                               [T0016, T0017, T0018]   \n",
       "5   [T0019, T0020, T0021, T0022, T0023, T0024, T00...   \n",
       "6   [T0029, T0030, T0031, T0032, T0033, T0034, T00...   \n",
       "7   [T0039, T0040, T0041, T0042, T0043, T0044, T00...   \n",
       "8   [T0047, T0048, T0049, T0050, T0051, T0052, T00...   \n",
       "9                                      [T0057, T0061]   \n",
       "10                              [T0058, T0059, T0060]   \n",
       "11                              [T0062, T0063, T0064]   \n",
       "\n",
       "                                          counter_ids  \n",
       "0   [C00006, C00008, C00010, C00012, C00013, C0001...  \n",
       "1   [C00009, C00011, C00028, C00029, C00030, C0003...  \n",
       "2   [C00034, C00036, C00040, C00042, C00044, C0004...  \n",
       "3   [C00047, C00052, C00053, C00056, C00059, C0006...  \n",
       "4                            [C00065, C00066, C00216]  \n",
       "5   [C00014, C00032, C00071, C00072, C00074, C0007...  \n",
       "6   [C00097, C00098, C00099, C00100, C00101, C0010...  \n",
       "7   [C00112, C00113, C00114, C00115, C00116, C0011...  \n",
       "8   [C00089, C00122, C00123, C00124, C00125, C0012...  \n",
       "9                                    [C00129, C00130]  \n",
       "10  [C00131, C00133, C00135, C00136, C00137, C0013...  \n",
       "11           [C00090, C00140, C00147, C00148, C00149]  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amitt.df_tactics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>id_incident</th>\n",
       "      <th>name_incident</th>\n",
       "      <th>id_technique</th>\n",
       "      <th>name_technique</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I00002T001</td>\n",
       "      <td>buy FB targeted ads</td>\n",
       "      <td></td>\n",
       "      <td>I00002</td>\n",
       "      <td>#VaccinateUS</td>\n",
       "      <td>T0018</td>\n",
       "      <td>Paid targeted ads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I00005T003</td>\n",
       "      <td>Targeted FB paid ads</td>\n",
       "      <td></td>\n",
       "      <td>I00005</td>\n",
       "      <td>Brexit vote</td>\n",
       "      <td>T0018</td>\n",
       "      <td>Paid targeted ads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I00017T004</td>\n",
       "      <td>Targeted FB paid ads</td>\n",
       "      <td></td>\n",
       "      <td>I00017</td>\n",
       "      <td>US presidential elections</td>\n",
       "      <td>T0018</td>\n",
       "      <td>Paid targeted ads</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I00002T002</td>\n",
       "      <td>Promote \"funding\" campaign</td>\n",
       "      <td></td>\n",
       "      <td>I00002</td>\n",
       "      <td>#VaccinateUS</td>\n",
       "      <td>T0017</td>\n",
       "      <td>Promote online funding</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I00002T003</td>\n",
       "      <td>create web-site - information pollution</td>\n",
       "      <td></td>\n",
       "      <td>I00002</td>\n",
       "      <td>#VaccinateUS</td>\n",
       "      <td>T0019</td>\n",
       "      <td>Generate information pollution</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>I00044T008</td>\n",
       "      <td>Promote fake “experts” with impressive (and sc...</td>\n",
       "      <td></td>\n",
       "      <td>I00044</td>\n",
       "      <td>JadeHelm exercise</td>\n",
       "      <td>T0009</td>\n",
       "      <td>Create fake experts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>I00047T003</td>\n",
       "      <td>(Distort) Kremlin-controlled RT cited Russian ...</td>\n",
       "      <td></td>\n",
       "      <td>I00047</td>\n",
       "      <td>Sea of Azov</td>\n",
       "      <td>T0023</td>\n",
       "      <td>Distort facts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>I00053T002</td>\n",
       "      <td>Distorted, saccharine “news” about the Chinese...</td>\n",
       "      <td></td>\n",
       "      <td>I00053</td>\n",
       "      <td>China Huawei CFO Arrest</td>\n",
       "      <td>T0023</td>\n",
       "      <td>Distort facts</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>I00056T005</td>\n",
       "      <td>Memes... anti-Isreal/USA/West, conspiracy narr...</td>\n",
       "      <td></td>\n",
       "      <td>I00056</td>\n",
       "      <td>Iran Influence Operations</td>\n",
       "      <td>T0022</td>\n",
       "      <td>Conspiracy narratives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>I00056T006</td>\n",
       "      <td>Fake news/synthetic web-sites</td>\n",
       "      <td></td>\n",
       "      <td>I00056</td>\n",
       "      <td>Iran Influence Operations</td>\n",
       "      <td>T0008</td>\n",
       "      <td>Create fake or imposter news sites</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>186 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             id                                               name summary  \\\n",
       "0    I00002T001                                buy FB targeted ads           \n",
       "1    I00005T003                               Targeted FB paid ads           \n",
       "2    I00017T004                               Targeted FB paid ads           \n",
       "3    I00002T002                         Promote \"funding\" campaign           \n",
       "4    I00002T003            create web-site - information pollution           \n",
       "..          ...                                                ...     ...   \n",
       "181  I00044T008  Promote fake “experts” with impressive (and sc...           \n",
       "182  I00047T003  (Distort) Kremlin-controlled RT cited Russian ...           \n",
       "183  I00053T002  Distorted, saccharine “news” about the Chinese...           \n",
       "184  I00056T005  Memes... anti-Isreal/USA/West, conspiracy narr...           \n",
       "185  I00056T006                      Fake news/synthetic web-sites           \n",
       "\n",
       "    id_incident              name_incident id_technique  \\\n",
       "0        I00002               #VaccinateUS        T0018   \n",
       "1        I00005                Brexit vote        T0018   \n",
       "2        I00017  US presidential elections        T0018   \n",
       "3        I00002               #VaccinateUS        T0017   \n",
       "4        I00002               #VaccinateUS        T0019   \n",
       "..          ...                        ...          ...   \n",
       "181      I00044          JadeHelm exercise        T0009   \n",
       "182      I00047                Sea of Azov        T0023   \n",
       "183      I00053    China Huawei CFO Arrest        T0023   \n",
       "184      I00056  Iran Influence Operations        T0022   \n",
       "185      I00056  Iran Influence Operations        T0008   \n",
       "\n",
       "                         name_technique  \n",
       "0                     Paid targeted ads  \n",
       "1                     Paid targeted ads  \n",
       "2                     Paid targeted ads  \n",
       "3                Promote online funding  \n",
       "4        Generate information pollution  \n",
       "..                                  ...  \n",
       "181                 Create fake experts  \n",
       "182                       Distort facts  \n",
       "183                       Distort facts  \n",
       "184               Conspiracy narratives  \n",
       "185  Create fake or imposter news sites  \n",
       "\n",
       "[186 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amitt.it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tkinter as Tk\n",
    "import numpy as np\n",
    "\n",
    "class Begueradj(Tk.Frame):\n",
    "   def __init__(self,parent):\n",
    "      Tk.Frame.__init__(self, parent)\n",
    "      self.parent = parent\n",
    "      self.initialize()\n",
    "\n",
    "   def initialize(self):\n",
    "      '''\n",
    "      Draw the GUI\n",
    "      '''\n",
    "      self.parent.title(\"RUN ON START TEST\")       \n",
    "      self.parent.grid_rowconfigure(1,weight=1)\n",
    "      self.parent.grid_columnconfigure(1,weight=1)\n",
    "\n",
    "      self.frame = Tk.Frame(self.parent)  \n",
    "      self.frame.pack(fill=Tk.X, padx=5, pady=5)\n",
    "\n",
    "      # Create a 6x7 array of zeros as the one you used\n",
    "      self.a = np.zeros((6,7))\n",
    "      for i in range(0,self.a.shape[0]):\n",
    "          for j in range(0,self.a.shape[1]):\n",
    "               self.b = Tk.Button(self.frame, text = 'Hello')\n",
    "               self.b.grid(row=i,  column= j)\n",
    "\n",
    "# Start the main program here               \n",
    "if __name__ == \"__main__\": \n",
    "   root=Tk.Tk()\n",
    "   app = Begueradj(root)   \n",
    "   root.mainloop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>id</th>\n",
       "      <th>technique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>TA01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>TA06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>TA08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>C00165</td>\n",
       "      <td>T00025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>C00174</td>\n",
       "      <td>T0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>C00197</td>\n",
       "      <td>T0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>C00197</td>\n",
       "      <td>T0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>C00202</td>\n",
       "      <td>T0025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>263 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id technique_id\n",
       "1    C00008         TA01\n",
       "1    C00008         TA06\n",
       "1    C00008         TA08\n",
       "1    C00008        T0006\n",
       "1    C00008        T0009\n",
       "..      ...          ...\n",
       "120  C00165       T00025\n",
       "126  C00174        T0001\n",
       "138  C00197        T0007\n",
       "138  C00197        T0011\n",
       "140  C00202        T0025\n",
       "\n",
       "[263 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = amitt.cross_counterid_techniqueid\n",
    "ct[ct['technique_id'] != '']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>id</th>\n",
       "      <th>technique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>C00197</td>\n",
       "      <td>T0007</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         id technique_id\n",
       "138  C00197        T0007"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct[(ct['id'] == 'C00197') & (ct['technique_id'].isin(['T0002', 'T0007']))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>id</th>\n",
       "      <th>technique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>C00149</td>\n",
       "      <td>T0020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>C00174</td>\n",
       "      <td>T0001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>C00197</td>\n",
       "      <td>T0007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>C00197</td>\n",
       "      <td>T0011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>C00202</td>\n",
       "      <td>T0025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>190 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         id technique_id\n",
       "1    C00008        T0006\n",
       "1    C00008        T0009\n",
       "1    C00008        T0008\n",
       "1    C00008        T0013\n",
       "1    C00008        T0014\n",
       "..      ...          ...\n",
       "108  C00149        T0020\n",
       "126  C00174        T0001\n",
       "138  C00197        T0007\n",
       "138  C00197        T0011\n",
       "140  C00202        T0025\n",
       "\n",
       "[190 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ct = ct[ct['technique_id'].isin(amitt.df_techniques['id'].to_list()) & ct['id'].isin(amitt.df_counters['id'].to_list())]\n",
    "ct"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>id</th>\n",
       "      <th>technique_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>C00008</td>\n",
       "      <td>T0008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>C00042</td>\n",
       "      <td>T0022</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id technique_id\n",
       "1   C00008        T0008\n",
       "24  C00042        T0022"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "technique_id_list = ['T0007', 'T0008', 'T0022', 'T0023', 'T0043', 'T0052', 'T0036', 'T0037', 'T0038']\n",
    "counter_id_list = ['C00009', 'C00008', 'C00042', 'C00030', 'C00093', 'C00193', 'C00073', 'C000197', 'C00174', 'C00205']\n",
    "possible_counters_for_techniques = ct[ct['technique_id'].isin(technique_id_list)] \n",
    "possible_techniques_for_counters = ct[ct['technique_id'].isin(counter_id_list)] \n",
    "coverage = ct[(ct['id'].isin(counter_id_list)) & (ct['technique_id'].isin(technique_id_list))]\n",
    "coverage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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