pablomm · May 10, 2021 19:45
diff --git a/4-digits-bank-code.ipynb b/4-digits-bank-code.ipynb
 {
 "cells": [
  {
   "cell_type": "markdown",
   "id": "organic-savings",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T17:02:38.343926Z",
     "start_time": "2021-05-09T17:02:38.340893Z"
    }
   },
   "source": [
    "# Code of blog bank security code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "southeast-cement",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:42:00.991360Z",
     "start_time": "2021-05-10T13:42:00.987110Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "from matplotlib.colors import LogNorm\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import itertools\n",
    "\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "signal-springer",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T17:03:21.721903Z",
     "start_time": "2021-05-09T17:03:21.718643Z"
    }
   },
   "source": [
    "## Digits with uniform probability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "revolutionary-jackson",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:38:56.939064Z",
     "start_time": "2021-05-10T13:38:56.936346Z"
    }
   },
   "outputs": [],
   "source": [
    "digits = np.arange(10)[np.newaxis, :]\n",
    "product = digits.T*digits\n",
    "sumation = digits.T+digits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "duplicate-beast",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:38:56.947161Z",
     "start_time": "2021-05-10T13:38:56.942039Z"
    }
   },
   "outputs": [],
   "source": [
    "def compute_frequency(matrix: np.array) -> np.array:\n",
    "    \"\"\"\n",
    "    Return a matrix with the same shape and the frequency of each number\n",
    "    \"\"\"\n",
    "    frequency = np.empty_like(matrix, dtype=float)\n",
    "    unique, counts = np.unique(matrix, return_counts=True)\n",
    "    total = counts.sum()\n",
    "    for number, freq in zip(unique, counts):\n",
    "        frequency[matrix==number] = freq / total\n",
    "    \n",
    "    return frequency\n",
    "\n",
    "def plot_frequency_matrix(matrix, labels=None, ax=None, cbar=True):\n",
    "    ax = sns.heatmap(100*compute_frequency(matrix),\n",
    "                annot=matrix, \n",
    "                cmap='Blues',  \n",
    "                vmin=0,\n",
    "                vmax=20,\n",
    "                cbar=cbar, \n",
    "                ax=ax, \n",
    "                cbar_kws={'format': '%.0f%%'}\n",
    "    )\n",
    "    ax.set_aspect('equal')\n",
    "    if labels is None:\n",
    "        labels = ax.get_yticks().astype(int).tolist()\n",
    "    ax.set_yticks(labels)\n",
    "    ax.set_yticklabels(labels, rotation=0, fontsize=13, va='center')\n",
    "    ax.set_xticks(labels)\n",
    "    ax.set_xticklabels(labels, fontsize=13, ha='center')\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "medieval-saver",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T17:04:22.763883Z",
     "start_time": "2021-05-09T17:04:22.760590Z"
    }
   },
   "source": [
    "### Heatmap figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "electoral-halifax",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:38:58.821975Z",
     "start_time": "2021-05-10T13:38:56.949132Z"
    }
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(13,5))\n",
    "\n",
    "plot_frequency_matrix(sumation, ax=ax)\n",
    "plot_frequency_matrix(product, ax=ax2)\n",
    "plt.savefig('combinate-heatmap.png', dpi=180, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "freelance-suggestion",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T17:16:33.719639Z",
     "start_time": "2021-05-09T17:16:33.716287Z"
    }
   },
   "source": [
    "### Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ready-quarter",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:38:58.825751Z",
     "start_time": "2021-05-10T13:38:58.823383Z"
    }
   },
   "outputs": [],
   "source": [
    "combinations = np.concatenate([product, sumation]).flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "supported-retirement",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:38:59.224687Z",
     "start_time": "2021-05-10T13:38:58.827702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,9))\n",
    "\n",
    "# Histogram\n",
    "sns.histplot(combinations, bins=81, stat='probability', zorder=2, color='C0')\n",
    "\n",
    "# Ax settings\n",
    "ax = plt.gca()\n",
    "sns.despine(ax=ax)\n",
    "ax.set_aspect(300)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,0))\n",
    "ax.set_xlim(-1, 100)\n",
    "ax.set_ylabel('Probabilidad')\n",
    "\n",
    "# Base line\n",
    "ax.axhline(0.01, 0, 100, c='black', ls='--', zorder=1, lw=.7)\n",
    "ax.annotate(xy=(67,0.012), text='Probabilidad uniforme')\n",
    "\n",
    "plt.savefig('uniform-histogram.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "seeing-favor",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T17:32:32.808572Z",
     "start_time": "2021-05-09T17:32:32.805446Z"
    }
   },
   "source": [
    "## Digits with empiric frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "classical-efficiency",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.518590Z",
     "start_time": "2021-05-10T13:38:59.227946Z"
    }
   },
   "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>pin</th>\n",
       "      <th>frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0000</td>\n",
       "      <td>1874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0001</td>\n",
       "      <td>471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0002</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0003</td>\n",
       "      <td>182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0004</td>\n",
       "      <td>151</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    pin  frequency\n",
       "0  0000       1874\n",
       "1  0001        471\n",
       "2  0002        156\n",
       "3  0003        182\n",
       "4  0004        151"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://this-pin-can-be-easily-guessed.github.io/media/RockYou-4-digit.zip'\n",
    "df = pd.read_csv(url, header=None, names=['pin'])\n",
    "df = df.groupby(['pin']).size().reset_index(name='frequency')\n",
    "df.pin = df.pin.astype(str).str.zfill(4)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "floral-possibility",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.886333Z",
     "start_time": "2021-05-10T13:39:00.521259Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_sorted = df.sort_values('frequency', ascending=False)\n",
    "df_sorted.reset_index(inplace=True, drop=True)\n",
    "df_sorted.reset_index(inplace=True)\n",
    "df_sorted.rename(columns={'index': 'position'}, inplace=True)\n",
    "df_sorted['position'] /= len(df_sorted)\n",
    "\n",
    "df_sorted['cdf'] = (df_sorted.frequency / df_sorted.frequency.sum()).cumsum()\n",
    "\n",
    "cdf = np.concatenate([[0], df_sorted.cdf.to_numpy()])\n",
    "position = np.concatenate([df_sorted.position.to_numpy(), [1]])\n",
    "\n",
    "ax = plt.gca()\n",
    "ax.plot(position, cdf)\n",
    "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1, 0))\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1, 0))\n",
    "ax.set_ylim(0,1.01)\n",
    "ax.set_ylabel('% Utilización en RockYou')\n",
    "ax.set_xlabel('% Códigos 4 dígitos ')\n",
    "\n",
    " # location for the zoomed portion \n",
    "sub_axes = plt.axes([.45, .25, .4, .4]) \n",
    "sub_axes.xaxis.set_major_formatter(mtick.PercentFormatter(1, 0))\n",
    "sub_axes.yaxis.set_major_formatter(mtick.PercentFormatter(1, 0))\n",
    "\n",
    "index = position <=0.05\n",
    "sub_axes.plot(position[index], cdf[index])\n",
    "sub_axes.set_xlabel('')\n",
    "sub_axes.grid(True)\n",
    "sub_axes.set_xticks([0., 0.01, 0.02, 0.03, 0.04, 0.05])\n",
    "sub_axes.set_yticks([0., 0.2, 0.4, 0.6])\n",
    "sub_axes.set_xlim(-0.001, 0.05)\n",
    "sub_axes.set_ylim(0, 0.61)\n",
    "ax.set_aspect(2/(1+np.sqrt(5)))\n",
    "\n",
    "\n",
    "sns.despine(ax=ax)\n",
    "\n",
    "plt.savefig('distribución-contraseñas.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "economic-vatican",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.892377Z",
     "start_time": "2021-05-10T13:39:00.888541Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "276"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(df_sorted.cdf<=.5).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "breathing-morning",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.949765Z",
     "start_time": "2021-05-10T13:39:00.894101Z"
    }
   },
   "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>op</th>\n",
       "      <th>frequency</th>\n",
       "      <th>pdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1100590</td>\n",
       "      <td>0.206035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>231394</td>\n",
       "      <td>0.043318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>527936</td>\n",
       "      <td>0.098832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>276065</td>\n",
       "      <td>0.051681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>255360</td>\n",
       "      <td>0.047804</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   op  frequency       pdf\n",
       "0   0    1100590  0.206035\n",
       "1   1     231394  0.043318\n",
       "2   2     527936  0.098832\n",
       "3   3     276065  0.051681\n",
       "4   4     255360  0.047804"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# RockYou distribution\n",
    "df['op1'] = df.pin.str[0].astype(int) * df.pin.str[2].astype(int)\n",
    "df['op2'] = df.pin.str[1].astype(int) * df.pin.str[3].astype(int)\n",
    "df['op3'] = df.pin.str[0].astype(int) + df.pin.str[2].astype(int)\n",
    "df['op4'] = df.pin.str[1].astype(int) + df.pin.str[3].astype(int)\n",
    "\n",
    "df_concat = pd.concat(\n",
    "    [df[[f'op{i}', 'frequency']].rename(columns={f'op{i}':'op'}) \n",
    "     for i in range(1, 4)]\n",
    ")\n",
    "\n",
    "df_empirical = df_concat.groupby(['op']).sum().reset_index()\n",
    "df_empirical['pdf'] = df_empirical.frequency / df_empirical.frequency.sum()\n",
    "\n",
    "df_empirical.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ordered-interference",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.963730Z",
     "start_time": "2021-05-10T13:39:00.952232Z"
    }
   },
   "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>op</th>\n",
       "      <th>frequency</th>\n",
       "      <th>pdf_uniform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   op  frequency  pdf_uniform\n",
       "0   0         20        0.100\n",
       "1   1          3        0.015\n",
       "2   2          5        0.025\n",
       "3   3          6        0.030\n",
       "4   4          8        0.040"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Uniform digits distribution\n",
    "value, count = np.unique(combinations, return_counts=True)\n",
    "uniform_sample = np.array([value, count]).T\n",
    "df_uniform = pd.DataFrame(uniform_sample, columns=['op', 'frequency'])\n",
    "df_uniform['pdf_uniform'] =  df_uniform.frequency / df_uniform.frequency.sum()\n",
    "\n",
    "df_uniform.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "artistic-eligibility",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:00.977848Z",
     "start_time": "2021-05-10T13:39:00.965826Z"
    }
   },
   "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>op</th>\n",
       "      <th>pdf</th>\n",
       "      <th>pdf_uniform</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.206035</td>\n",
       "      <td>0.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.043318</td>\n",
       "      <td>0.015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.098832</td>\n",
       "      <td>0.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.051681</td>\n",
       "      <td>0.030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.047804</td>\n",
       "      <td>0.040</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   op       pdf  pdf_uniform\n",
       "0   0  0.206035        0.100\n",
       "1   1  0.043318        0.015\n",
       "2   2  0.098832        0.025\n",
       "3   3  0.051681        0.030\n",
       "4   4  0.047804        0.040"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_merge = df_empirical[['op', 'pdf']].merge(\n",
    "    df_uniform[['op', 'pdf_uniform']], on='op', how='outer')\n",
    "df_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "religious-mexico",
   "metadata": {},
   "source": [
    "### Probability comparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "funded-senator",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:01.698845Z",
     "start_time": "2021-05-10T13:39:00.979699Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "ax = plt.gca()\n",
    "df_merge.plot(kind='bar', x='op', y=['pdf_uniform', 'pdf'], ax=ax, width=.8, zorder=2)\n",
    "plt.legend(['Dígitos Uniformes', 'RockYou Dataset'])\n",
    "sns.despine()\n",
    "ax.set_ylabel('Probabilidad')\n",
    "ax.set_xlabel('')\n",
    "ax.axhline(0.01, 0, 100, c='black', ls='--', zorder=1, lw=1)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1, 0))\n",
    "\n",
    "plt.savefig('join-histogram.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "enhanced-cinema",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-09T20:40:27.583709Z",
     "start_time": "2021-05-09T20:40:27.580867Z"
    }
   },
   "source": [
    "## Estratégias con 1 solo intento\n",
    "\n",
    "Let $X$ ~ SecList distribution of digits\n",
    "\n",
    "### Estrategia 1 - Número aleatorio 0000-9999\n",
    "\n",
    "Estrategia $Y\\sim\\text{Unif}(0,99)$\n",
    "\n",
    "\n",
    "$$P(X=Y)=\n",
    "P(X^{(1,2)}=Y^{(1,2)}, X^{(3,4)}=Y^{(3,4)}) =\n",
    "P(X^{(1,2)}=Y^{(1,2)})P(X^{(3,4)}=Y^{(3,4)}) = \\\\\n",
    "\\frac{1}{100}P(X^{(3,4)}=Y^{(3,4)})=\\frac{1}{100} \\sum_{i=1}^{100}P(X^{(3,4)}=i)P(Y^{(3,4)}=i)= \\\\\n",
    "\\frac{\\sum_{i=1}^{100}P(X^{(3,4)}=i)}{10.000}=\n",
    "\\frac{1}{10.000}$$\n",
    "\n",
    "### Estrategia 2 - Imitar distribución empírica\n",
    "\n",
    "Estrategia $Y\\sim X$\n",
    "\n",
    "$$P(X=Y)=\n",
    "\\frac{1}{100}P(X^{(3,4)}=Y^{(3,4)})=\n",
    "\\frac{1}{100} \\sum_{i=1}^{100}P(X^{(3,4)}=i)P(Y^{(3,4)}=i)=\n",
    "\\frac{\\sum_{i=1}^{100}P(X^{(3,4)}=i)^2}{100} \\approx \\frac{1}{1.261}$$\n",
    "\n",
    "### Estrategia 3 - Usar valores más probables\n",
    "\n",
    "Estrategia $Y=0$\n",
    "\n",
    "$$P(X=Y)=\\frac{1}{100}P(X^{(3,4)}=Y^{(3,4)})=\n",
    "\\frac{P(X^{(3,4)}=0)}{100} \\approx \\frac{1}{485}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "comprehensive-filename",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:01.705410Z",
     "start_time": "2021-05-10T13:39:01.700393Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Strategy 1: 0.010%, 1/10000\n",
      "Strategy 2: 0.079%, 1/1261\n",
      "Strategy 3: 0.206%, 1/485\n"
     ]
    }
   ],
   "source": [
    "df_empirical_sorted = df_empirical.sort_values('pdf', ascending=False)\n",
    "pdf =df_empirical_sorted.pdf\n",
    "\n",
    "estrategia_1 = 1/10_000\n",
    "estrategia_2 = (df_empirical.pdf ** 2).sum() / 100\n",
    "estrategia_3 = pdf[0]/100\n",
    "\n",
    "for i, p in enumerate([estrategia_1, estrategia_2, estrategia_3]):\n",
    "    print(f\"Strategy {i+1}: {100*p:.3f}%, 1/{int(1/p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "hindu-nation",
   "metadata": {},
   "source": [
    "## Estrategias con múltiples intentos\n",
    "\n",
    "Sea $X$ la v.a. basado en la distribución.\n",
    "\n",
    "### Estrategia 4 - Número aleatorio 0000-9999\n",
    "\n",
    "Strategy $Y_1,Y_2, Y_3\\sim\\text{Unif}(0,99)$ (without repetitions)\n",
    "\n",
    "\n",
    "$$P(X=Y_1 \\lor X=Y_2 \\lor X=Y_3)= \\frac{1 - (1 -\\frac{1}{100})(1 - \\frac{1}{99})(1 -\\frac{1}{98})}{100} = \n",
    "\\frac{3}{10.000}\\approx \\frac{1}{3.333}$$\n",
    "\n",
    "\n",
    "### Estrategia 5 - Usar valores más probables\n",
    "\n",
    "Estrategia $Y_1=0, \\, Y_2=2 \\, | \\, Y_3=9$\n",
    "\n",
    "$$P(X=Y_1 \\lor X=Y_2 \\lor X=Y_3)=\n",
    "\\frac{P(X^{(3,4)}=0)+P(X^{(3,4)}=2)+P(X^{(3,4)}=9)}{100} \\approx \\frac{1}{257}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "amino-presentation",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T13:39:01.710905Z",
     "start_time": "2021-05-10T13:39:01.707072Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estrategia 4: 0.030%, 1/3333\n",
      "Estrategia 5: 0.388%, 1/257\n"
     ]
    }
   ],
   "source": [
    "estrategia_4 = 1/100*(1 - (1-1/100)*(1-1/99)*(1-1/98))\n",
    "estrategia_5 = 1/100 * (pdf[0:3].sum())\n",
    "\n",
    "for i, p in enumerate([estrategia_4, estrategia_5]):\n",
    "    print(f\"Estrategia {i+4}: {100*p:.3f}%, 1/{int(1/p)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "utility-duncan",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T10:02:47.299618Z",
     "start_time": "2021-05-10T10:02:47.297263Z"
    }
   },
   "source": [
    "## Pin code from verification code\n",
    "\n",
    "Compute combinations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "alpha-deficit",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:01.881338Z",
     "start_time": "2021-05-10T17:57:01.769691Z"
    }
   },
   "outputs": [],
   "source": [
    "combinations = {}\n",
    "combinations_list = []\n",
    "for code in range(0, 10000):\n",
    "    code = str(code).zfill(4)\n",
    "    a = str(int(str(code[0])) + int(str(code[2]))).zfill(2)\n",
    "    b = str(int(str(code[1])) + int(str(code[3]))).zfill(2)\n",
    "    c = str(int(str(code[0])) * int(str(code[2]))).zfill(2)\n",
    "    d = str(int(str(code[1])) * int(str(code[3]))).zfill(2)\n",
    "    #for cod_1, cod_2 in itertools.combinations([a,b,c,d], 2):\n",
    "    for cod_1, cod_2 in itertools.product((a,c), (b, d)):\n",
    "\n",
    "        if cod_1 > cod_2:\n",
    "            cod_1, cod_2 = cod_2, cod_1\n",
    "        key = f'{cod_1}-{cod_2}'\n",
    "        combinations.setdefault(key, set())\n",
    "        combinations[key].add(code)\n",
    "\n",
    "        combinations_list.append([cod_1 + '-' + cod_2, code])\n",
    "        \n",
    "df_combinaciones = pd.DataFrame(combinations_list, columns=['cod', 'pin'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "postal-developer",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:06.977757Z",
     "start_time": "2021-05-10T17:57:06.973359Z"
    }
   },
   "outputs": [],
   "source": [
    "labels = value\n",
    "translate = {}\n",
    "for i,v in enumerate(labels):\n",
    "    translate[str(v).zfill(2)] = i\n",
    "    \n",
    "matrix = np.zeros((len(translate), len(translate)))\n",
    "\n",
    "for key, val in combinations.items():\n",
    "    key0, key1 = key.split('-')\n",
    "    matrix[translate[key0], translate[key1]] = len(val)\n",
    "    matrix[translate[key1], translate[key0]] = len(val)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "relative-contract",
   "metadata": {},
   "source": [
    "### Heatmap pins per code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "dried-commitment",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:13.425842Z",
     "start_time": "2021-05-10T17:57:12.109992Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,8))\n",
    "ax = sns.heatmap(matrix, \n",
    "                 cmap='gist_heat',\n",
    "                 cbar_kws={'shrink': .8},\n",
    "                 norm=LogNorm(vmin=matrix.min(), vmax=501)\n",
    "                )\n",
    "\n",
    "ticks_labels = list(translate.keys())\n",
    "ticks = list(translate.values())\n",
    "\n",
    "ax.set_aspect('equal')\n",
    "ax.set_xticks(ticks)\n",
    "ax.set_xticklabels(ticks_labels, rotation=90, fontsize=10)\n",
    "ax.set_yticks(ticks)\n",
    "ax.set_yticklabels(ticks_labels, rotation=0, fontsize=10)\n",
    "\n",
    "plt.savefig('heatmap-codes-repetition.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "dirty-leonard",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:18.516711Z",
     "start_time": "2021-05-10T17:57:18.499933Z"
    }
   },
   "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>cod</th>\n",
       "      <th>n_pin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00-00</td>\n",
       "      <td>400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00-01</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00-02</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>00-03</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00-04</td>\n",
       "      <td>320</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cod  n_pin\n",
       "0  00-00    400\n",
       "1  00-01    120\n",
       "2  00-02    200\n",
       "3  00-03    240\n",
       "4  00-04    320"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cod_pin = df_combinaciones.groupby('cod').count().reset_index().rename(columns={'pin':'n_pin'})\n",
    "df_cod_pin.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "acknowledged-howard",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:23.608111Z",
     "start_time": "2021-05-10T17:57:23.555144Z"
    }
   },
   "outputs": [],
   "source": [
    "df_probability = df_combinaciones.pivot_table(\n",
    "    index=['cod', 'pin'], aggfunc='size'\n",
    ").reset_index().rename(\n",
    "    columns={0:'repetitions'}\n",
    ").merge(df_cod_pin, on='cod')\n",
    "\n",
    "df_probability['prob'] = df_probability.repetitions**2 / df_probability.n_pin / 4\n",
    "df_probability_agg = df_probability[['pin','prob']].groupby('pin').sum('prob').sort_values('prob', ascending=False).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "interpreted-tolerance",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:28.724707Z",
     "start_time": "2021-05-10T17:57:28.712149Z"
    }
   },
   "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>cod</th>\n",
       "      <th>pin</th>\n",
       "      <th>repetitions</th>\n",
       "      <th>n_pin</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>36917</th>\n",
       "      <td>18-18</td>\n",
       "      <td>9999</td>\n",
       "      <td>1</td>\n",
       "      <td>25</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37326</th>\n",
       "      <td>18-81</td>\n",
       "      <td>9999</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39007</th>\n",
       "      <td>81-81</td>\n",
       "      <td>9999</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         cod   pin  repetitions  n_pin  prob\n",
       "36917  18-18  9999            1     25  0.01\n",
       "37326  18-81  9999            2     10  0.10\n",
       "39007  81-81  9999            1      1  0.25"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_probability[df_probability.pin=='9999']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "collect-pregnancy",
   "metadata": {},
   "source": [
    "### Top-Bottom PIN codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "excessive-drove",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:57:34.398870Z",
     "start_time": "2021-05-10T17:57:33.796271Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, (ax, ax2) = plt.subplots(1, 2, figsize=(10,4))\n",
    "\n",
    "df_probability_agg.head(15).plot.bar('pin', 'prob', legend=False, ax=ax)\n",
    "df_probability_agg.tail(15).plot.bar('pin', 'prob', legend=False, ax=ax2, color='C1')\n",
    "\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,0))\n",
    "ax2.yaxis.set_major_formatter(mtick.PercentFormatter(1,2))\n",
    "ax.set_ylim(0,0.37)\n",
    "ax2.set_ylim(0,0.003)\n",
    "ax.set_xlabel('Pin menos seguros')\n",
    "ax2.set_xlabel('Pin más seguros')\n",
    "\n",
    "ax.set_ylabel('Probabilidad de inferir pin')\n",
    "sns.despine(ax=ax)\n",
    "sns.despine(ax=ax2)\n",
    "plt.savefig('bottom-top-probability.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "balanced-rescue",
   "metadata": {},
   "source": [
    "### Pin codes probability of inference"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "alternative-interest",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:58:19.156451Z",
     "start_time": "2021-05-10T17:58:10.803344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_probability_agg.sort_values('pin').plot(x='pin',y='prob', kind='scatter', legend=False, s=2)\n",
    "ax.yaxis.set_major_formatter(mtick.PercentFormatter(1,0))\n",
    "plt.xticks([], rotation=45)\n",
    "ax.set_ylabel('Probabilidad acierto pin')\n",
    "ax.set_xlabel('Código pin')\n",
    "sns.despine(ax=ax)\n",
    "ax.set_ylim(-0.001, 0.36)\n",
    "plt.savefig('probability-pin-from-codes.png', bbox_inches='tight', dpi=128)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "blind-concern",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T17:58:33.155589Z",
     "start_time": "2021-05-10T17:58:33.152809Z"
    }
   },
   "source": [
    "### Estrategia inferir el código más probable a partir de 2 códigos de verificación"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "north-creator",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:17:19.557448Z",
     "start_time": "2021-05-10T19:17:19.299165Z"
    }
   },
   "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>cod</th>\n",
       "      <th>pin</th>\n",
       "      <th>prob_adjust</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.130547</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.122785</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.101619</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.100490</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.078130</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             cod   pin  prob_adjust    prob\n",
       "index                                      \n",
       "0      XX00-XX00  2007     0.130547  0.0025\n",
       "0      XX00-XX00  2006     0.122785  0.0025\n",
       "0      XX00-XX00  2008     0.101619  0.0025\n",
       "0      XX00-XX00  2000     0.100490  0.0100\n",
       "0      XX00-XX00  2005     0.078130  0.0025"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_inference = df_probability.merge(df, on='pin', how='inner')[['pin', 'cod', 'prob', 'frequency', 'repetitions']].set_index('cod').sort_values(['prob', 'frequency'], ascending=False)\n",
    "df_inference.prob = 4*df_inference.prob\n",
    "df_inference['rep_frequency'] = df_inference.frequency * df_inference.repetitions\n",
    "\n",
    "df_adjust = df_probability.merge(df, on='pin', how='inner')[['pin', 'cod', 'prob', 'frequency', 'repetitions']]\n",
    "df_adjust['rep_frequency'] = df_adjust.frequency * df_adjust.repetitions\n",
    "df_adjust = df_adjust[['cod','rep_frequency']].groupby('cod').sum().reset_index().rename(columns={'rep_frequency':'total_frequency'})\n",
    "df_adjust\n",
    "df_inference = df_inference.merge(df_adjust, on='cod')\n",
    "\n",
    "df_inference['prob_adjust'] = df_inference.rep_frequency / df_inference.total_frequency \n",
    "df_inference['index'] = df_inference.cod.str.replace('-',  '').astype(int)\n",
    "df_inference['cod'] = 'XX' + df_inference.cod.str[:3] + 'XX' + df_inference.cod.str[3:]\n",
    "df_search = df_inference[['index', 'cod', 'pin', 'prob_adjust', 'prob']].sort_values(\n",
    "    ['index', 'prob_adjust', 'prob'], ascending=[True, False, False]\n",
    ")\n",
    "df_search.set_index('index', inplace=True)\n",
    "df_search.to_csv('pin_code_inference.csv')\n",
    "df_search.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "immediate-romantic",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:18:17.554266Z",
     "start_time": "2021-05-10T19:18:17.520039Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cod</th>\n",
       "      <th>pin</th>\n",
       "      <th>prob_adjust</th>\n",
       "      <th>prob</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.130547</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2006</td>\n",
       "      <td>0.122785</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.101619</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2000</td>\n",
       "      <td>0.100490</td>\n",
       "      <td>0.0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>XX00-XX00</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.078130</td>\n",
       "      <td>0.0025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>XX72-XX81</td>\n",
       "      <td>9989</td>\n",
       "      <td>0.370370</td>\n",
       "      <td>0.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>XX72-XX81</td>\n",
       "      <td>9899</td>\n",
       "      <td>0.367521</td>\n",
       "      <td>0.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>XX72-XX81</td>\n",
       "      <td>9998</td>\n",
       "      <td>0.153846</td>\n",
       "      <td>0.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7281</th>\n",
       "      <td>XX72-XX81</td>\n",
       "      <td>8999</td>\n",
       "      <td>0.108262</td>\n",
       "      <td>0.2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8181</th>\n",
       "      <td>XX81-XX81</td>\n",
       "      <td>9999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>39008 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             cod   pin  prob_adjust    prob\n",
       "index                                      \n",
       "0      XX00-XX00  2007     0.130547  0.0025\n",
       "0      XX00-XX00  2006     0.122785  0.0025\n",
       "0      XX00-XX00  2008     0.101619  0.0025\n",
       "0      XX00-XX00  2000     0.100490  0.0100\n",
       "0      XX00-XX00  2005     0.078130  0.0025\n",
       "...          ...   ...          ...     ...\n",
       "7281   XX72-XX81  9989     0.370370  0.2500\n",
       "7281   XX72-XX81  9899     0.367521  0.2500\n",
       "7281   XX72-XX81  9998     0.153846  0.2500\n",
       "7281   XX72-XX81  8999     0.108262  0.2500\n",
       "8181   XX81-XX81  9999     1.000000  1.0000\n",
       "\n",
       "[39008 rows x 4 columns]"
      ]
     },
     "execution_count": 346,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "lesbian-danish",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:22:47.228009Z",
     "start_time": "2021-05-10T19:22:47.201148Z"
    }
   },
   "outputs": [],
   "source": [
    "df_search = pd.read_csv('pin_code_inference.csv', index_col='index')\n",
    "\n",
    "def suggest_codes(cod1, cod2, n_tries=10000):\n",
    "    \"\"\"\n",
    "    Estrategia para probar números en base a las probabilidades de acierto y a su frecuencia en \n",
    "    el dataset RockYou\n",
    "    \"\"\"\n",
    "    cod1, cod2 = str(cod1).zfill(4)[2:], str(cod2).zfill(4)[2:]\n",
    "    if cod2 < cod1:\n",
    "        cod1, cod2 = cod2, cod1\n",
    "    key = int(cod1 + cod2)\n",
    "    \n",
    "    return df_search.loc[key]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 357,
   "id": "narrative-retailer",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:22:53.557948Z",
     "start_time": "2021-05-10T19:22:53.550465Z"
    }
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>cod</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>XX35-XX81</td>\n",
       "      <td>9597</td>\n",
       "      <td>0.436275</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>XX35-XX81</td>\n",
       "      <td>5979</td>\n",
       "      <td>0.215686</td>\n",
       "      <td>0.25</td>\n",
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       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>XX35-XX81</td>\n",
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       "      <td>0.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3581</th>\n",
       "      <td>XX35-XX81</td>\n",
       "      <td>7959</td>\n",
       "      <td>0.161765</td>\n",
       "      <td>0.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "             cod   pin  prob_adjust  prob\n",
       "index                                    \n",
       "3581   XX35-XX81  9597     0.436275  0.25\n",
       "3581   XX35-XX81  5979     0.215686  0.25\n",
       "3581   XX35-XX81  9795     0.186275  0.25\n",
       "3581   XX35-XX81  7959     0.161765  0.25"
      ]
     },
     "execution_count": 357,
     "metadata": {},
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    }
   ],
   "source": [
    "suggest_codes(4535,2381)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "embedded-munich",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-05-10T19:23:18.756411Z",
     "start_time": "2021-05-10T19:23:18.748103Z"
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    {
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       "      <th></th>\n",
       "      <th>cod</th>\n",
       "      <th>pin</th>\n",
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       "      <th>prob</th>\n",
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       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>XX49-XX81</td>\n",
       "      <td>7979</td>\n",
       "      <td>0.596439</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4981</th>\n",
       "      <td>XX49-XX81</td>\n",
       "      <td>9797</td>\n",
       "      <td>0.403561</td>\n",
       "      <td>0.5</td>\n",
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       "             cod   pin  prob_adjust  prob\n",
       "index                                    \n",
       "4981   XX49-XX81  7979     0.596439   0.5\n",
       "4981   XX49-XX81  9797     0.403561   0.5"
      ]
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     "execution_count": 358,
     "metadata": {},
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   ],
   "source": [
    "suggest_codes(3381,1949)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "recovered-disposal",
   "metadata": {},
   "source": [
    "\n",
    "## References\n",
    "* https://www.datagenetics.com/blog/september32012/index.html\n",
    "* https://github.com/danielmiessler/SecLists\n",
    "* https://www.cl.cam.ac.uk/~rja14/Papers/BPA12-FC-banking_pin_security.pdf\n",
    "* https://www.researchgate.net/publication/313823128_Understanding_Human-Chosen_PINs_Characteristics_Distribution_and_Security"
   ]
  }
 ],
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diff --git a/gistfile1.txt b/gistfile1.txt
 index,cod,pin,prob_adjust,prob
 0,XX00-XX00,2007,0.13054723748724867,0.0025
 0,XX00-XX00,2006,0.12278456020270492,0.0025
 0,XX00-XX00,2008,0.10161900687748857,0.0025
 0,XX00-XX00,2000,0.10049030899338576,0.01
 0,XX00-XX00,2005,0.07813024449636381,0.0025
 0,XX00-XX00,2004,0.04758300700911514,0.0025
 0,XX00-XX00,2009,0.04262397578071012,0.0025
 0,XX00-XX00,2003,0.03858303991575899,0.0025
 0,XX00-XX00,2002,0.03266971601566356,0.0025
 0,XX00-XX00,2001,0.02847411892461088,0.0025
 0,XX00-XX00,0000,0.02466682023100464,0.04
 0,XX00-XX00,1000,0.023626970285300603,0.01
 0,XX00-XX00,3000,0.014097206225937017,0.01
 0,XX00-XX00,1001,0.0056895587219059525,0.0025
 0,XX00-XX00,5000,0.005686268057520813,0.01
 0,XX00-XX00,1005,0.004274573036296028,0.0025
 0,XX00-XX00,1004,0.004208759748593241,0.0025
 0,XX00-XX00,1002,0.00400802922109974,0.0025
 0,XX00-XX00,1003,0.0037184507552074763,0.0025
 0,XX00-XX00,1007,0.0036921254401263617,0.0025
 0,XX00-XX00,1006,0.003488104248247721,0.0025
 0,XX00-XX00,1008,0.003149165816578367,0.0025
 0,XX00-XX00,0001,0.003099805850801277,0.01
 0,XX00-XX00,1009,0.0029484352890848664,0.0025
 0,XX00-XX00,0007,0.00272467011089539,0.01
 0,XX00-XX00,1200,0.002668728816348021,0.0025
 0,XX00-XX00,4000,0.002632531508111488,0.01
 0,XX00-XX00,9000,0.0026127875218006514,0.01
 0,XX00-XX00,0210,0.0024021850011517326,0.0025
 0,XX00-XX00,0420,0.002382441014840896,0.0025
 0,XX00-XX00,1500,0.0022541051038204615,0.0025
 0,XX00-XX00,0110,0.002224489124354207,0.0025
 0,XX00-XX00,1800,0.0021191878640297477,0.0025
 0,XX00-XX00,1100,0.002040211918786403,0.0025
 0,XX00-XX00,0610,0.0018822600282997137,0.0025
 0,XX00-XX00,0410,0.0018559347132185988,0.0025
 0,XX00-XX00,0910,0.0018526440488334595,0.0025
 0,XX00-XX00,0810,0.0018493533844483202,0.0025
 0,XX00-XX00,0710,0.0017868307611306723,0.0025
 0,XX00-XX00,0510,0.0017703774392049754,0.0025
 0,XX00-XX00,0310,0.0017308894665833032,0.0025
 0,XX00-XX00,0220,0.001628878870643983,0.0025
 0,XX00-XX00,6000,0.001619006877488565,0.01
 0,XX00-XX00,1300,0.0015762282404817533,0.0025
 0,XX00-XX00,3003,0.0015137056171641054,0.0025
 0,XX00-XX00,0330,0.0014807989733127119,0.0025
 0,XX00-XX00,0520,0.0014478923294613181,0.0025
 0,XX00-XX00,2500,0.0014281483431504822,0.0025
 0,XX00-XX00,3006,0.001401823028069367,0.0025
 0,XX00-XX00,0920,0.0013886603705288098,0.0025
 0,XX00-XX00,8000,0.0013754977129882523,0.01
 0,XX00-XX00,7000,0.001355753726677416,0.01
 0,XX00-XX00,0120,0.001299812432130047,0.0025
 0,XX00-XX00,2100,0.001299812432130047,0.0025
 0,XX00-XX00,0930,0.0012866497745894897,0.0025
 0,XX00-XX00,1900,0.0012866497745894897,0.0025
 0,XX00-XX00,0320,0.0012734871170489322,0.0025
 0,XX00-XX00,3001,0.0012471618019678173,0.0025
 0,XX00-XX00,4200,0.001243871137582678,0.0025
 0,XX00-XX00,0620,0.0012405804731975387,0.0025
 0,XX00-XX00,3004,0.0012339991444272598,0.0025
 0,XX00-XX00,0430,0.0012274178156569812,0.0025
 0,XX00-XX00,0003,0.001197801836190727,0.01
 0,XX00-XX00,0820,0.001197801836190727,0.0025
 0,XX00-XX00,3005,0.0011780578498798908,0.0025
 0,XX00-XX00,3007,0.0011681858567244726,0.0025
 0,XX00-XX00,0630,0.0011648951923393333,0.0025
 0,XX00-XX00,0800,0.001151732534798776,0.01
 0,XX00-XX00,0830,0.001145151206028497,0.0025
 0,XX00-XX00,0010,0.0011319885484879398,0.01
 0,XX00-XX00,3008,0.001089209911481128,0.0025
 0,XX00-XX00,0530,0.0010727565895554312,0.0025
 0,XX00-XX00,1600,0.0010694659251702919,0.0025
 0,XX00-XX00,0720,0.001053012603244595,0.0025
 0,XX00-XX00,1400,0.001053012603244595,0.0025
 0,XX00-XX00,0011,0.001043140610089177,0.0025
 0,XX00-XX00,0130,0.001033268616933759,0.0025
 0,XX00-XX00,0002,0.0010266872881634801,0.01
 0,XX00-XX00,5200,0.0010036526374675046,0.0025
 0,XX00-XX00,0004,0.0009937806443120866,0.01
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 1,XX00-XX01,0014,0.002548863427486061,0.008333333333333333
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 1,XX00-XX01,0191,0.0012254151093682986,0.008333333333333333
 1,XX00-XX01,0190,0.0011641443538998836,0.008333333333333333
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 1,XX00-XX01,0081,0.0005881992524967833,0.008333333333333333
 1,XX00-XX01,0150,0.0005881992524967833,0.008333333333333333
 1,XX00-XX01,0051,0.0005759451014031003,0.008333333333333333
 1,XX00-XX01,0181,0.0005636909503094174,0.008333333333333333
 1,XX00-XX01,0041,0.0005391826481220514,0.008333333333333333
 1,XX00-XX01,0170,0.0005391826481220514,0.008333333333333333
 1,XX00-XX01,0141,0.0005146743459346854,0.008333333333333333
 1,XX00-XX01,0140,0.0005024201948410024,0.008333333333333333
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 1,XX00-XX01,0061,0.0004166411371852215,0.008333333333333333
 1,XX00-XX01,0161,0.0004043869860915385,0.008333333333333333
 2,XX00-XX02,2007,0.11509505670328905,0.005
 2,XX00-XX02,2006,0.1082512061597556,0.005
 2,XX00-XX02,2008,0.08959090658535665,0.005
 2,XX00-XX02,2000,0.08859580665469452,0.02
 2,XX00-XX02,2005,0.0688823838300613,0.005
 2,XX00-XX02,2002,0.057605551671216663,0.02
 2,XX00-XX02,2010,0.04488103768904723,0.02
 2,XX00-XX02,2004,0.041950860050654355,0.005
 2,XX00-XX02,2009,0.037578802920893904,0.005
 2,XX00-XX02,2003,0.03401617109916476,0.005
 2,XX00-XX02,2001,0.02510378921288466,0.005
 2,XX00-XX02,1010,0.0217935588312943,0.02
 2,XX00-XX02,1020,0.01265488599868287,0.02
 2,XX00-XX02,2011,0.008903678388344275,0.005
 2,XX00-XX02,2012,0.008648375782226877,0.005
 2,XX00-XX02,1012,0.006594350269373261,0.005
 2,XX00-XX02,1210,0.006008314741694687,0.005
 2,XX00-XX02,1023,0.005700791147962366,0.005
 2,XX00-XX02,0101,0.00557023867892506,0.02
 2,XX00-XX02,1011,0.005535424687181778,0.005
 2,XX00-XX02,0102,0.005198889433663389,0.02
 2,XX00-XX02,1024,0.005100249790390758,0.005
 2,XX00-XX02,1013,0.005094447458433545,0.005
 2,XX00-XX02,1022,0.005056732300711656,0.005
 2,XX00-XX02,1220,0.0049638949893962385,0.005
 2,XX00-XX02,1021,0.004960993823417631,0.005
 2,XX00-XX02,1025,0.004691185387407199,0.005
 2,XX00-XX02,1014,0.0046534702296853105,0.005
 2,XX00-XX02,1015,0.0045026095987977565,0.005
 2,XX00-XX02,1029,0.004459092109118655,0.005
 2,XX00-XX02,1017,0.004328539640081348,0.005
 2,XX00-XX02,1120,0.004258911656594785,0.005
 2,XX00-XX02,1016,0.004160272013322155,0.005
 2,XX00-XX02,1028,0.004157370847343547,0.005
 2,XX00-XX02,1018,0.004116754523643052,0.005
 2,XX00-XX02,1026,0.004084841697878377,0.005
 2,XX00-XX02,1027,0.004012312548413207,0.005
 2,XX00-XX02,2013,0.00393688223296943,0.005
 2,XX00-XX02,1019,0.003780219270124663,0.005
 2,XX00-XX02,1201,0.003675777294894818,0.005
 2,XX00-XX02,1102,0.0035713353196649734,0.005
 2,XX00-XX02,1002,0.0035336201619430847,0.005
 2,XX00-XX02,1101,0.003490102672263983,0.005
 2,XX00-XX02,0220,0.002872154318820734,0.02
 2,XX00-XX02,1110,0.002561729559109806,0.005
 2,XX00-XX02,0201,0.002407967762243646,0.02
 2,XX00-XX02,1200,0.0023528456086501165,0.005
 2,XX00-XX02,2310,0.0022629094633133057,0.005
 2,XX00-XX02,2210,0.002152665156126247,0.005
 2,XX00-XX02,1310,0.002135258160254606,0.005
 2,XX00-XX02,0210,0.0021178511643829656,0.005
 2,XX00-XX02,0420,0.0021004441685113246,0.005
 2,XX00-XX02,2014,0.0020917406705755043,0.005
 2,XX00-XX02,1920,0.0019553858695809845,0.005
 2,XX00-XX02,1410,0.0018074264046720376,0.005
 2,XX00-XX02,2510,0.0017465019191212948,0.005
 2,XX00-XX02,0211,0.0017029844294421928,0.005
 2,XX00-XX02,2410,0.001700083263463586,0.005
 2,XX00-XX02,2110,0.0016652692717203045,0.005
 2,XX00-XX02,1510,0.0016536646078058772,0.005
 2,XX00-XX02,2200,0.00164205994389145,0.02
 2,XX00-XX02,1910,0.0015057051428969304,0.005
 2,XX00-XX02,1810,0.001465088819196435,0.005
 2,XX00-XX02,0121,0.0014389783253889738,0.005
 2,XX00-XX02,2610,0.0014389783253889738,0.005
 2,XX00-XX02,1610,0.0013838561717954446,0.005
 2,XX00-XX02,2710,0.0013838561717954446,0.005
 2,XX00-XX02,1520,0.001349042180052163,0.005
 2,XX00-XX02,0112,0.0013403386821163425,0.005
 2,XX00-XX02,1420,0.0013316351841805222,0.005
 2,XX00-XX02,1320,0.0013287340182019153,0.005
 2,XX00-XX02,2810,0.0013229316862447018,0.005
 2,XX00-XX02,1710,0.001305524690373061,0.005
 2,XX00-XX02,2102,0.0013026235243944542,0.005
 2,XX00-XX02,2201,0.0013026235243944542,0.005
 2,XX00-XX02,0520,0.001276513030586993,0.005
 2,XX00-XX02,2101,0.0012707106986297792,0.005
 2,XX00-XX02,2500,0.0012591060347153522,0.005
 2,XX00-XX02,2015,0.0012358967068864977,0.005
 2,XX00-XX02,0920,0.0012242920429720704,0.005
 2,XX00-XX02,0221,0.001218489711014857,0.005
 2,XX00-XX02,2910,0.0012068850471004297,0.005
 2,XX00-XX02,1820,0.0012010827151432161,0.005
 2,XX00-XX02,0122,0.0011691698893785412,0.005
 2,XX00-XX02,0120,0.0011459605615496869,0.005
 2,XX00-XX02,2100,0.0011459605615496869,0.005
 2,XX00-XX02,0320,0.0011227512337208324,0.005
 2,XX00-XX02,4200,0.001096640739913371,0.005
 2,XX00-XX02,0620,0.0010937395739347643,0.005
 2,XX00-XX02,0131,0.0010763325780631236,0.005
 2,XX00-XX02,0820,0.001056024416212876,0.005
 2,XX00-XX02,1620,0.001012506926533774,0.005
 2,XX00-XX02,0022,0.0009747917688118855,0.02
 2,XX00-XX02,3101,0.0009515824409830311,0.005
 2,XX00-XX02,0720,0.0009283731131541767,0.005
 2,XX00-XX02,0002,0.0009051637853253222,0.02
 2,XX00-XX02,5200,0.0008848556234750747,0.005
 2,XX00-XX02,0111,0.0008587451296676134,0.005
 2,XX00-XX02,0012,0.0008384369678173658,0.005
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 2,XX00-XX02,3200,0.0007485008224805549,0.005
 2,XX00-XX02,2016,0.0007485008224805549,0.005
 2,XX00-XX02,2017,0.0006962798348656325,0.005
 2,XX00-XX02,4201,0.0006788728389939916,0.005
 2,XX00-XX02,0023,0.0005976401915930012,0.005
 2,XX00-XX02,2600,0.0005976401915930012,0.005
 2,XX00-XX02,2018,0.0005889366936571808,0.005
 2,XX00-XX02,2019,0.0005599250338711128,0.005
 2,XX00-XX02,0021,0.0005338145400636515,0.005
 2,XX00-XX02,0020,0.0005222098761492244,0.02
 2,XX00-XX02,3002,0.0005193087101706175,0.005
 2,XX00-XX02,2800,0.00044968072668405434,0.005
 2,XX00-XX02,0200,0.0004293725648338067,0.02
 2,XX00-XX02,2700,0.0004061632370049523,0.005
 2,XX00-XX02,0024,0.0004003609050477387,0.005
 2,XX00-XX02,3201,0.0003945585730905251,0.005
 2,XX00-XX02,0025,0.0003626457473258503,0.005
 2,XX00-XX02,2900,0.00035974458134724346,0.005
 2,XX00-XX02,0192,0.00035974458134724346,0.005
 2,XX00-XX02,3102,0.00034523875145420945,0.005
 2,XX00-XX02,6002,0.00030752359373232104,0.005
 2,XX00-XX02,5002,0.0002988200957965006,0.005
 2,XX00-XX02,8200,0.0002988200957965006,0.005
 2,XX00-XX02,0027,0.0002959189298178938,0.005
 2,XX00-XX02,6101,0.0002959189298178938,0.005
 2,XX00-XX02,5201,0.00029301776383928703,0.005
 2,XX00-XX02,0191,0.0002901165978606802,0.005
 2,XX00-XX02,4101,0.0002698084360104326,0.005
 2,XX00-XX02,4102,0.0002698084360104326,0.005
 2,XX00-XX02,9002,0.00026690727003182577,0.005
 2,XX00-XX02,0231,0.00026690727003182577,0.005
 2,XX00-XX02,8201,0.00026690727003182577,0.005
 2,XX00-XX02,0028,0.000264006104053219,0.005
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 2,XX00-XX02,0291,0.0002611049380746122,0.005
 2,XX00-XX02,0230,0.00025530260611739857,0.005
 2,XX00-XX02,4002,0.000249500274160185,0.005
 2,XX00-XX02,0026,0.0002465991081815782,0.005
 2,XX00-XX02,9101,0.0002465991081815782,0.005
 2,XX00-XX02,0029,0.00024369794220297137,0.005
 2,XX00-XX02,6200,0.00024369794220297137,0.005
 2,XX00-XX02,0032,0.00024079677622436456,0.005
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 2,XX00-XX02,0182,0.00023789561024575777,0.005
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 2,XX00-XX02,5101,0.00022629094633133055,0.005
 2,XX00-XX02,8002,0.00022338978035272376,0.005
 2,XX00-XX02,0290,0.00021758744839551016,0.005
 2,XX00-XX02,0280,0.00021178511643829656,0.005
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 2,XX00-XX02,0281,0.00020308161850247615,0.005
 2,XX00-XX02,7201,0.00019727928654526255,0.005
 2,XX00-XX02,7102,0.00018567462263083533,0.005
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 2,XX00-XX02,9201,0.00017406995871640813,0.005
 2,XX00-XX02,0092,0.00016536646078058772,0.005
 2,XX00-XX02,0151,0.00015956412882337412,0.005
 2,XX00-XX02,8101,0.0001566629628447673,0.005
 2,XX00-XX02,0052,0.0001508606308875537,0.005
 2,XX00-XX02,0132,0.0001508606308875537,0.005
 2,XX00-XX02,7101,0.0001479594649089469,0.005
 2,XX00-XX02,0250,0.0001450582989303401,0.005
 2,XX00-XX02,8102,0.0001392559669731265,0.005
 2,XX00-XX02,0181,0.00013345363501591288,0.005
 2,XX00-XX02,0240,0.00012765130305869929,0.005
 2,XX00-XX02,0042,0.0001247501370800925,0.005
 2,XX00-XX02,0141,0.00012184897110148569,0.005
 2,XX00-XX02,0142,0.00011894780512287889,0.005
 2,XX00-XX02,0062,0.00011024430718705847,0.005
 2,XX00-XX02,0172,0.00010734314120845168,0.005
 2,XX00-XX02,0171,0.00010444197522984488,0.005
 2,XX00-XX02,0260,0.00010154080925123808,0.005
 2,XX00-XX02,0270,9.863964327263128e-05,0.005
 2,XX00-XX02,0241,9.863964327263128e-05,0.005
 2,XX00-XX02,0082,9.573847729402446e-05,0.005
 2,XX00-XX02,0161,9.573847729402446e-05,0.005
 2,XX00-XX02,0271,8.993614533681087e-05,0.005
 2,XX00-XX02,0152,8.413381337959727e-05,0.005
 2,XX00-XX02,0162,8.123264740099045e-05,0.005
 2,XX00-XX02,0251,8.123264740099045e-05,0.005
 2,XX00-XX02,0261,6.962798348656326e-05,0.005
 3,XX00-XX03,2010,0.12760762511238874,0.016666666666666666
 3,XX00-XX03,2003,0.0967161864539598,0.004166666666666667
 3,XX00-XX03,1020,0.03598089597545182,0.016666666666666666
 3,XX00-XX03,3000,0.03533749618496919,0.016666666666666666
 3,XX00-XX03,2011,0.025315307140912804,0.004166666666666667
 3,XX00-XX03,2012,0.02458942019780419,0.004166666666666667
 3,XX00-XX03,1030,0.02179310572378352,0.016666666666666666
 3,XX00-XX03,1230,0.016513927955720896,0.004166666666666667
 3,XX00-XX03,1023,0.016208725491004775,0.004166666666666667
 3,XX00-XX03,0123,0.015185884798442642,0.004166666666666667
 3,XX00-XX03,0102,0.014781697750575348,0.016666666666666666
 3,XX00-XX03,1024,0.014501241431647022,0.004166666666666667
 3,XX00-XX03,1022,0.014377510702708053,0.004166666666666667
 3,XX00-XX03,1220,0.014113551814304922,0.004166666666666667
 3,XX00-XX03,1021,0.014105303099042324,0.004166666666666667
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 406,X
No results found