sim_results_nb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import re\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "# my custom data frame intermediate between pandas and pickled data like saved lists\n",
    "import pkldf as pydf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## find all pkl files in target directory which make up our data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_test_dir = pydf.PklManager('run_2', pattern='results_*.pkl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_test_dir.sort(k=lambda x: int(re.search('results_(\\d+)', x).group(1)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['run_2\\\\results_1.pkl',\n",
       " 'run_2\\\\results_2.pkl',\n",
       " 'run_2\\\\results_3.pkl',\n",
       " 'run_2\\\\results_4.pkl',\n",
       " 'run_2\\\\results_5.pkl',\n",
       " 'run_2\\\\results_6.pkl',\n",
       " 'run_2\\\\results_7.pkl',\n",
       " 'run_2\\\\results_8.pkl',\n",
       " 'run_2\\\\results_9.pkl',\n",
       " 'run_2\\\\results_10.pkl',\n",
       " 'run_2\\\\results_11.pkl',\n",
       " 'run_2\\\\results_12.pkl',\n",
       " 'run_2\\\\results_13.pkl',\n",
       " 'run_2\\\\results_14.pkl',\n",
       " 'run_2\\\\results_15.pkl',\n",
       " 'run_2\\\\results_16.pkl',\n",
       " 'run_2\\\\results_17.pkl',\n",
       " 'run_2\\\\results_18.pkl',\n",
       " 'run_2\\\\results_19.pkl',\n",
       " 'run_2\\\\results_20.pkl',\n",
       " 'run_2\\\\results_21.pkl',\n",
       " 'run_2\\\\results_22.pkl',\n",
       " 'run_2\\\\results_23.pkl',\n",
       " 'run_2\\\\results_24.pkl',\n",
       " 'run_2\\\\results_25.pkl',\n",
       " 'run_2\\\\results_26.pkl',\n",
       " 'run_2\\\\results_27.pkl',\n",
       " 'run_2\\\\results_28.pkl',\n",
       " 'run_2\\\\results_29.pkl']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not necessary, but I like to see the files\n",
    "pkl_test_dir.get_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "pkl_test_dir.load_all()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>results_1</th>\n",
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       "      <th>99999</th>\n",
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       "</table>\n",
       "<p>100000 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       results_1  results_2  results_3  results_4  results_5  results_6  \\\n",
       "0              5          6          7          6          8          6   \n",
       "1              5          4          5          7          7          8   \n",
       "2              5          6          6          7          7          6   \n",
       "3              5          5          6          6          7          6   \n",
       "4              5          5          6          7          6          7   \n",
       "...          ...        ...        ...        ...        ...        ...   \n",
       "99995          5          6          6          6          7          8   \n",
       "99996          5          6          6          6          7          8   \n",
       "99997          5          5          6          8          8          8   \n",
       "99998          5          5          5          8          6          8   \n",
       "99999          5          6          6          6          6          8   \n",
       "\n",
       "       results_7  results_8  results_9  results_10  ...  results_20  \\\n",
       "0              8          9          8           8  ...          12   \n",
       "1              7          8         10           9  ...          12   \n",
       "2              8          8          9           9  ...          12   \n",
       "3              8          9          7           9  ...          12   \n",
       "4             10          8          8           9  ...          11   \n",
       "...          ...        ...        ...         ...  ...         ...   \n",
       "99995          7          9          8           8  ...          12   \n",
       "99996          9          9          9          10  ...          11   \n",
       "99997          7          7          9           8  ...          12   \n",
       "99998          7          7         10           9  ...          12   \n",
       "99999          6          9          7          10  ...          12   \n",
       "\n",
       "       results_21  results_22  results_23  results_24  results_25  results_26  \\\n",
       "0              12          12          12          12          12          12   \n",
       "1              12          12          12          12          11          12   \n",
       "2              12          12          12          12          12          12   \n",
       "3              12          12          12          12          12          12   \n",
       "4              12          11          12          12          12          12   \n",
       "...           ...         ...         ...         ...         ...         ...   \n",
       "99995          12          12          11          12          12          12   \n",
       "99996          12          11          12          12          12          12   \n",
       "99997          12          10          11          11          12          12   \n",
       "99998          12          12          12          12          12          12   \n",
       "99999          12          12          12          12          12          12   \n",
       "\n",
       "       results_27  results_28  results_29  \n",
       "0              12          12          12  \n",
       "1              12          12          12  \n",
       "2              12          12          12  \n",
       "3              12          12          12  \n",
       "4              12          12          12  \n",
       "...           ...         ...         ...  \n",
       "99995          12          12          12  \n",
       "99996          12          12          12  \n",
       "99997          12          12          12  \n",
       "99998          12          12          12  \n",
       "99999          12          12          12  \n",
       "\n",
       "[100000 rows x 29 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pkl_test_dir.as_df()\n",
    "test_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename columns trial_0, trial_1, etc\n",
    "test_df.columns = ['trial_' + str(i+1) for i in range(test_df.shape[1])]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "looking at a specific tier in the data set over all simulations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "trial_1     0.00000\n",
       "trial_2     0.00000\n",
       "trial_3     0.00000\n",
       "trial_4     0.00000\n",
       "trial_5     0.00000\n",
       "trial_6     0.00000\n",
       "trial_7     0.00000\n",
       "trial_8     0.00013\n",
       "trial_9     0.00172\n",
       "trial_10    0.00653\n",
       "trial_11    0.01946\n",
       "trial_12    0.04461\n",
       "trial_13    0.08889\n",
       "trial_14    0.15476\n",
       "trial_15    0.24000\n",
       "trial_16    0.34213\n",
       "trial_17    0.45901\n",
       "trial_18    0.56788\n",
       "trial_19    0.67119\n",
       "trial_20    0.76310\n",
       "trial_21    0.83893\n",
       "trial_22    0.89415\n",
       "trial_23    0.93409\n",
       "trial_24    0.96132\n",
       "trial_25    0.97772\n",
       "trial_26    0.98815\n",
       "trial_27    0.99423\n",
       "trial_28    0.99715\n",
       "trial_29    0.99855\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.apply(lambda x: np.mean(x == 12)) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting probability of a tier for each max attempt simulated, a.k.a the distribution of probability over all max attempts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>trial_1</th>\n",
       "      <td>0.70206</td>\n",
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       "    <tr>\n",
       "      <th>trial_2</th>\n",
       "      <td>0.51846</td>\n",
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       "    <tr>\n",
       "      <th>trial_3</th>\n",
       "      <td>0.23381</td>\n",
       "      <td>0.55154</td>\n",
       "      <td>0.20883</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_4</th>\n",
       "      <td>0.07487</td>\n",
       "      <td>0.42081</td>\n",
       "      <td>0.41993</td>\n",
       "      <td>0.08439</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_5</th>\n",
       "      <td>0.01676</td>\n",
       "      <td>0.23256</td>\n",
       "      <td>0.48591</td>\n",
       "      <td>0.23945</td>\n",
       "      <td>0.02532</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_6</th>\n",
       "      <td>0.00281</td>\n",
       "      <td>0.10159</td>\n",
       "      <td>0.40538</td>\n",
       "      <td>0.38664</td>\n",
       "      <td>0.09863</td>\n",
       "      <td>0.00495</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_7</th>\n",
       "      <td>0.00029</td>\n",
       "      <td>0.03440</td>\n",
       "      <td>0.27276</td>\n",
       "      <td>0.45249</td>\n",
       "      <td>0.21285</td>\n",
       "      <td>0.02605</td>\n",
       "      <td>0.00116</td>\n",
       "      <td>0.00000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_8</th>\n",
       "      <td>0.00004</td>\n",
       "      <td>0.01023</td>\n",
       "      <td>0.15318</td>\n",
       "      <td>0.42740</td>\n",
       "      <td>0.33225</td>\n",
       "      <td>0.07050</td>\n",
       "      <td>0.00627</td>\n",
       "      <td>0.00013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_9</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00243</td>\n",
       "      <td>0.07228</td>\n",
       "      <td>0.33773</td>\n",
       "      <td>0.41812</td>\n",
       "      <td>0.14584</td>\n",
       "      <td>0.02188</td>\n",
       "      <td>0.00172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_10</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00032</td>\n",
       "      <td>0.03029</td>\n",
       "      <td>0.23239</td>\n",
       "      <td>0.44775</td>\n",
       "      <td>0.22950</td>\n",
       "      <td>0.05322</td>\n",
       "      <td>0.00653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_11</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.01051</td>\n",
       "      <td>0.14040</td>\n",
       "      <td>0.41688</td>\n",
       "      <td>0.31266</td>\n",
       "      <td>0.09998</td>\n",
       "      <td>0.01946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_12</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00307</td>\n",
       "      <td>0.07739</td>\n",
       "      <td>0.34360</td>\n",
       "      <td>0.36481</td>\n",
       "      <td>0.16652</td>\n",
       "      <td>0.04461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_13</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00074</td>\n",
       "      <td>0.03646</td>\n",
       "      <td>0.25617</td>\n",
       "      <td>0.38649</td>\n",
       "      <td>0.23125</td>\n",
       "      <td>0.08889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_14</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00023</td>\n",
       "      <td>0.01664</td>\n",
       "      <td>0.17409</td>\n",
       "      <td>0.36214</td>\n",
       "      <td>0.29214</td>\n",
       "      <td>0.15476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_15</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00005</td>\n",
       "      <td>0.00647</td>\n",
       "      <td>0.10757</td>\n",
       "      <td>0.31385</td>\n",
       "      <td>0.33206</td>\n",
       "      <td>0.24000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_16</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00225</td>\n",
       "      <td>0.06078</td>\n",
       "      <td>0.25090</td>\n",
       "      <td>0.34394</td>\n",
       "      <td>0.34213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_17</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00086</td>\n",
       "      <td>0.03158</td>\n",
       "      <td>0.18261</td>\n",
       "      <td>0.32594</td>\n",
       "      <td>0.45901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_18</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00025</td>\n",
       "      <td>0.01574</td>\n",
       "      <td>0.12673</td>\n",
       "      <td>0.28940</td>\n",
       "      <td>0.56788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_19</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00719</td>\n",
       "      <td>0.08087</td>\n",
       "      <td>0.24072</td>\n",
       "      <td>0.67119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_20</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00316</td>\n",
       "      <td>0.04823</td>\n",
       "      <td>0.18550</td>\n",
       "      <td>0.76310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_21</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00116</td>\n",
       "      <td>0.02634</td>\n",
       "      <td>0.13357</td>\n",
       "      <td>0.83893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_22</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00035</td>\n",
       "      <td>0.01357</td>\n",
       "      <td>0.09193</td>\n",
       "      <td>0.89415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_23</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00017</td>\n",
       "      <td>0.00673</td>\n",
       "      <td>0.05901</td>\n",
       "      <td>0.93409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_24</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00318</td>\n",
       "      <td>0.03547</td>\n",
       "      <td>0.96132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_25</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00001</td>\n",
       "      <td>0.00134</td>\n",
       "      <td>0.02093</td>\n",
       "      <td>0.97772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_26</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00073</td>\n",
       "      <td>0.01112</td>\n",
       "      <td>0.98815</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_27</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00023</td>\n",
       "      <td>0.00554</td>\n",
       "      <td>0.99423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_28</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00011</td>\n",
       "      <td>0.00274</td>\n",
       "      <td>0.99715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trial_29</th>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00003</td>\n",
       "      <td>0.00142</td>\n",
       "      <td>0.99855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           p_of_5   p_of_6   p_of_7   p_of_8   p_of_9  p_of_10  p_of_11  \\\n",
       "trial_1   0.70206  0.00000  0.00000  0.00000  0.00000  0.00000  0.00000   \n",
       "trial_2   0.51846  0.42172  0.00000  0.00000  0.00000  0.00000  0.00000   \n",
       "trial_3   0.23381  0.55154  0.20883  0.00000  0.00000  0.00000  0.00000   \n",
       "trial_4   0.07487  0.42081  0.41993  0.08439  0.00000  0.00000  0.00000   \n",
       "trial_5   0.01676  0.23256  0.48591  0.23945  0.02532  0.00000  0.00000   \n",
       "trial_6   0.00281  0.10159  0.40538  0.38664  0.09863  0.00495  0.00000   \n",
       "trial_7   0.00029  0.03440  0.27276  0.45249  0.21285  0.02605  0.00116   \n",
       "trial_8   0.00004  0.01023  0.15318  0.42740  0.33225  0.07050  0.00627   \n",
       "trial_9   0.00000  0.00243  0.07228  0.33773  0.41812  0.14584  0.02188   \n",
       "trial_10  0.00000  0.00032  0.03029  0.23239  0.44775  0.22950  0.05322   \n",
       "trial_11  0.00000  0.00011  0.01051  0.14040  0.41688  0.31266  0.09998   \n",
       "trial_12  0.00000  0.00000  0.00307  0.07739  0.34360  0.36481  0.16652   \n",
       "trial_13  0.00000  0.00000  0.00074  0.03646  0.25617  0.38649  0.23125   \n",
       "trial_14  0.00000  0.00000  0.00023  0.01664  0.17409  0.36214  0.29214   \n",
       "trial_15  0.00000  0.00000  0.00005  0.00647  0.10757  0.31385  0.33206   \n",
       "trial_16  0.00000  0.00000  0.00000  0.00225  0.06078  0.25090  0.34394   \n",
       "trial_17  0.00000  0.00000  0.00000  0.00086  0.03158  0.18261  0.32594   \n",
       "trial_18  0.00000  0.00000  0.00000  0.00025  0.01574  0.12673  0.28940   \n",
       "trial_19  0.00000  0.00000  0.00000  0.00003  0.00719  0.08087  0.24072   \n",
       "trial_20  0.00000  0.00000  0.00000  0.00001  0.00316  0.04823  0.18550   \n",
       "trial_21  0.00000  0.00000  0.00000  0.00000  0.00116  0.02634  0.13357   \n",
       "trial_22  0.00000  0.00000  0.00000  0.00000  0.00035  0.01357  0.09193   \n",
       "trial_23  0.00000  0.00000  0.00000  0.00000  0.00017  0.00673  0.05901   \n",
       "trial_24  0.00000  0.00000  0.00000  0.00000  0.00003  0.00318  0.03547   \n",
       "trial_25  0.00000  0.00000  0.00000  0.00000  0.00001  0.00134  0.02093   \n",
       "trial_26  0.00000  0.00000  0.00000  0.00000  0.00000  0.00073  0.01112   \n",
       "trial_27  0.00000  0.00000  0.00000  0.00000  0.00000  0.00023  0.00554   \n",
       "trial_28  0.00000  0.00000  0.00000  0.00000  0.00000  0.00011  0.00274   \n",
       "trial_29  0.00000  0.00000  0.00000  0.00000  0.00000  0.00003  0.00142   \n",
       "\n",
       "          p_of_12  \n",
       "trial_1   0.00000  \n",
       "trial_2   0.00000  \n",
       "trial_3   0.00000  \n",
       "trial_4   0.00000  \n",
       "trial_5   0.00000  \n",
       "trial_6   0.00000  \n",
       "trial_7   0.00000  \n",
       "trial_8   0.00013  \n",
       "trial_9   0.00172  \n",
       "trial_10  0.00653  \n",
       "trial_11  0.01946  \n",
       "trial_12  0.04461  \n",
       "trial_13  0.08889  \n",
       "trial_14  0.15476  \n",
       "trial_15  0.24000  \n",
       "trial_16  0.34213  \n",
       "trial_17  0.45901  \n",
       "trial_18  0.56788  \n",
       "trial_19  0.67119  \n",
       "trial_20  0.76310  \n",
       "trial_21  0.83893  \n",
       "trial_22  0.89415  \n",
       "trial_23  0.93409  \n",
       "trial_24  0.96132  \n",
       "trial_25  0.97772  \n",
       "trial_26  0.98815  \n",
       "trial_27  0.99423  \n",
       "trial_28  0.99715  \n",
       "trial_29  0.99855  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the probabilities of each tier for each trial (trial = max attempts for simulation) \n",
    "probs = {}\n",
    "for tier in range(5, 13):\n",
    "    probs[f'p_of_{tier}'] = test_df.apply(lambda x: np.mean(x == tier))\n",
    "\n",
    "probs_df = pd.DataFrame(probs)\n",
    "probs_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sanity check of the probabilities on trial_8, which is the first simulation where its possible to reach tier 12; must be sum = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "probs_df.loc['trial_8'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Graphing the distribution of probability over all max attempts for all tiers in the simulation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for tier in range(5, 13):\n",
    "    plt.plot(probs_df.index, probs_df[f'p_of_{tier}'], label=f'Tier {tier}')\n",
    "# \n",
    "# make x axis labels properly spaced and legible\n",
    "xticks = np.arange(0, len(probs_df.index), step=4)  # adjust the step to change the spacing between labels\n",
    "plt.xticks(xticks)\n",
    "plt.xlabel('Trial')\n",
    "plt.ylabel('Probability')\n",
    "plt.title('Probability of Each Tier for Each Trial')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8     42740\n",
       "9     33225\n",
       "7     15318\n",
       "10     7050\n",
       "6      1023\n",
       "11      627\n",
       "12       13\n",
       "5         4\n",
       "Name: trial_8, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.trial_8.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# graph the results\n",
    "test_df.trial_8.hist(bins=range(5, 14), align='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parse & graph the markdown file for p values of specific tier\n",
    "\n",
    "# Open and read the markdown file\n",
    "with open('sim_results.txt', 'r') as f:\n",
    "    content = f.read()\n",
    "\n",
    "# Use regular expressions to find all max attempts and p values\n",
    "max_attempts = re.findall(r'max attempts per iteration: (\\d+)', content)\n",
    "p_values = re.findall(r'p = (\\d+\\.\\d+)', content)\n",
    "\n",
    "# Convert to integers and floats\n",
    "max_attempts = [int(x) for x in max_attempts]\n",
    "p_values = [float(x) for x in p_values]\n",
    "\n",
    "# Create the plot\n",
    "plt.plot(max_attempts, p_values, marker='o')\n",
    "plt.xlabel('Max Attempts simulated (100,000 trials for each simulation)')\n",
    "plt.ylabel('P Value')\n",
    "plt.title('P Value vs Max Attempts Per Simulation')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2\n",
       "0  0  1  2\n",
       "1  0  1  2\n",
       "2  0  1  2"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pkl_dir = pydf.PklManager('', pattern='2d_list.pkl')\n",
    "pkl_dir.load_all()\n",
    "df = pkl_dir.as_df()\n",
    "df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "bootCamp_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}