porosity_summary_plots.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import data_manager as dm\n",
    "import numpy as np\n",
    "import matplotlib.patches as mpatches\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "plt.rcParams.update({'font.size':15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   polimer_type  sample_number       date                 mean  \\\n",
       "0     PDLG-5002              1 2020-03-18             0.560957   \n",
       "1     PDLG-5002              1 2020-06-10             0.505069   \n",
       "2     PDLG-5002              1 2020-06-17             0.759593   \n",
       "3     PDLG-5002              1 2020-06-25             0.665487   \n",
       "4     PDLG-5002              1 2020-07-02             0.907848   \n",
       "5     PDLG-5002              2 2020-03-23             0.597490   \n",
       "6     PDLG-5002              2 2020-06-10             0.757816   \n",
       "7     PDLG-5002              2 2020-06-17             0.718972   \n",
       "8     PDLG-5002              2 2020-06-25             0.850005   \n",
       "9     PDLG-5002              2 2020-07-02             0.815039   \n",
       "10    PDLG-5002              3 2020-03-19             0.561606   \n",
       "11    PDLG-5002              3 2020-06-10             0.663526   \n",
       "12    PDLG-5002              3 2020-06-17             0.849456   \n",
       "13    PDLG-5002              3 2020-06-25             0.641847   \n",
       "14    PDLG-5002              3 2020-07-02             0.908288   \n",
       "15       PDL-05              1 2020-03-18  0.47196288723857394   \n",
       "16       PDL-05              1 2020-06-09  0.32691662752205397   \n",
       "17       PDL-05              1 2020-06-16   0.2600556235822302   \n",
       "18       PDL-05              1 2020-06-23  0.23960837320420755   \n",
       "19       PDL-05              1 2020-06-30  0.22695548803994273   \n",
       "20       PDL-05              2 2020-03-18   0.4610649370512863   \n",
       "21       PDL-05              2 2020-06-09   0.4040096985870701   \n",
       "22       PDL-05              2 2020-06-16  0.34784103481016193   \n",
       "23       PDL-05              2 2020-06-23  0.33849428894822897   \n",
       "24       PDL-05              2 2020-06-30  0.48467748584846265   \n",
       "25       PDL-05              3 2020-03-17  0.49589749460145854   \n",
       "26       PDL-05              3 2020-06-09  0.49293165206444844   \n",
       "27       PDL-05              3 2020-06-16  0.43488719562099626   \n",
       "28       PDL-05              3 2020-06-23   0.4249918661675345   \n",
       "29       PDL-05              3 2020-06-30   0.5692145444802362   \n",
       "\n",
       "                     std  \n",
       "0               0.086796  \n",
       "1               0.064357  \n",
       "2               0.146762  \n",
       "3               0.156959  \n",
       "4               0.079558  \n",
       "5               0.054316  \n",
       "6               0.144968  \n",
       "7               0.146480  \n",
       "8               0.131564  \n",
       "9               0.088892  \n",
       "10              0.085658  \n",
       "11              0.122627  \n",
       "12              0.152419  \n",
       "13              0.305319  \n",
       "14              0.061421  \n",
       "15   0.05164990494060934  \n",
       "16  0.043206490614198595  \n",
       "17  0.045744795980007226  \n",
       "18  0.035911763863623394  \n",
       "19  0.045513926707659254  \n",
       "20   0.06012647365887036  \n",
       "21   0.05650905650903809  \n",
       "22   0.07154562492800948  \n",
       "23  0.061769151927077506  \n",
       "24   0.06262817798422401  \n",
       "25  0.048992896388771016  \n",
       "26   0.09777697017970065  \n",
       "27   0.09309174236654827  \n",
       "28   0.07134664671114609  \n",
       "29   0.07628414915100416  "
      ],
      "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>polimer_type</th>\n      <th>sample_number</th>\n      <th>date</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-03-18</td>\n      <td>0.560957</td>\n      <td>0.086796</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-10</td>\n      <td>0.505069</td>\n      <td>0.064357</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-17</td>\n      <td>0.759593</td>\n      <td>0.146762</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-25</td>\n      <td>0.665487</td>\n      <td>0.156959</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-07-02</td>\n      <td>0.907848</td>\n      <td>0.079558</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-03-23</td>\n      <td>0.597490</td>\n      <td>0.054316</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-10</td>\n      <td>0.757816</td>\n      <td>0.144968</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-17</td>\n      <td>0.718972</td>\n      <td>0.146480</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-25</td>\n      <td>0.850005</td>\n      <td>0.131564</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-07-02</td>\n      <td>0.815039</td>\n      <td>0.088892</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-03-19</td>\n      <td>0.561606</td>\n      <td>0.085658</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-10</td>\n      <td>0.663526</td>\n      <td>0.122627</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-17</td>\n      <td>0.849456</td>\n      <td>0.152419</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-25</td>\n      <td>0.641847</td>\n      <td>0.305319</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-07-02</td>\n      <td>0.908288</td>\n      <td>0.061421</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-03-18</td>\n      <td>0.47196288723857394</td>\n      <td>0.05164990494060934</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-09</td>\n      <td>0.32691662752205397</td>\n      <td>0.043206490614198595</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-16</td>\n      <td>0.2600556235822302</td>\n      <td>0.045744795980007226</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-23</td>\n      <td>0.23960837320420755</td>\n      <td>0.035911763863623394</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-30</td>\n      <td>0.22695548803994273</td>\n      <td>0.045513926707659254</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-03-18</td>\n      <td>0.4610649370512863</td>\n      <td>0.06012647365887036</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-09</td>\n      <td>0.4040096985870701</td>\n      <td>0.05650905650903809</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-16</td>\n      <td>0.34784103481016193</td>\n      <td>0.07154562492800948</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-23</td>\n      <td>0.33849428894822897</td>\n      <td>0.061769151927077506</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-30</td>\n      <td>0.48467748584846265</td>\n      <td>0.06262817798422401</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-03-17</td>\n      <td>0.49589749460145854</td>\n      <td>0.048992896388771016</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-09</td>\n      <td>0.49293165206444844</td>\n      <td>0.09777697017970065</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-16</td>\n      <td>0.43488719562099626</td>\n      <td>0.09309174236654827</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-23</td>\n      <td>0.4249918661675345</td>\n      <td>0.07134664671114609</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-30</td>\n      <td>0.5692145444802362</td>\n      <td>0.07628414915100416</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 2
    }
   ],
   "source": [
    "df = dm.load_data(\"setup_culindric_porosities.csv\")\n",
    "df['date'] =  df.date.str.replace(\"_\", \"/\")\n",
    "df['date'] = pd.to_datetime(df.date)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   polimer_type  sample_number       date      mean       std\n",
       "0     PDLG-5002              1 2020-03-18  0.560957  0.086796\n",
       "1     PDLG-5002              1 2020-06-10  0.505069  0.064357\n",
       "2     PDLG-5002              1 2020-06-17  0.759593  0.146762\n",
       "3     PDLG-5002              1 2020-06-25  0.665487  0.156959\n",
       "4     PDLG-5002              1 2020-07-02  0.907848  0.079558\n",
       "5     PDLG-5002              2 2020-03-23  0.597490  0.054316\n",
       "6     PDLG-5002              2 2020-06-10  0.757816  0.144968\n",
       "7     PDLG-5002              2 2020-06-17  0.718972  0.146480\n",
       "8     PDLG-5002              2 2020-06-25  0.850005  0.131564\n",
       "9     PDLG-5002              2 2020-07-02  0.815039  0.088892\n",
       "10    PDLG-5002              3 2020-03-19  0.561606  0.085658\n",
       "11    PDLG-5002              3 2020-06-10  0.663526  0.122627\n",
       "12    PDLG-5002              3 2020-06-17  0.849456  0.152419\n",
       "13    PDLG-5002              3 2020-06-25  0.641847  0.305319\n",
       "14    PDLG-5002              3 2020-07-02  0.908288  0.061421\n",
       "15       PDL-05              1 2020-03-18  0.471963  0.051650\n",
       "16       PDL-05              1 2020-06-09  0.326917  0.043206\n",
       "17       PDL-05              1 2020-06-16  0.260056  0.045745\n",
       "18       PDL-05              1 2020-06-23  0.239608  0.035912\n",
       "19       PDL-05              1 2020-06-30  0.226955  0.045514\n",
       "20       PDL-05              2 2020-03-18  0.461065  0.060126\n",
       "21       PDL-05              2 2020-06-09  0.404010  0.056509\n",
       "22       PDL-05              2 2020-06-16  0.347841  0.071546\n",
       "23       PDL-05              2 2020-06-23  0.338494  0.061769\n",
       "24       PDL-05              2 2020-06-30  0.484677  0.062628\n",
       "25       PDL-05              3 2020-03-17  0.495897  0.048993\n",
       "26       PDL-05              3 2020-06-09  0.492932  0.097777\n",
       "27       PDL-05              3 2020-06-16  0.434887  0.093092\n",
       "28       PDL-05              3 2020-06-23  0.424992  0.071347\n",
       "29       PDL-05              3 2020-06-30  0.569215  0.076284"
      ],
      "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>polimer_type</th>\n      <th>sample_number</th>\n      <th>date</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-03-18</td>\n      <td>0.560957</td>\n      <td>0.086796</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-10</td>\n      <td>0.505069</td>\n      <td>0.064357</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-17</td>\n      <td>0.759593</td>\n      <td>0.146762</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-06-25</td>\n      <td>0.665487</td>\n      <td>0.156959</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>PDLG-5002</td>\n      <td>1</td>\n      <td>2020-07-02</td>\n      <td>0.907848</td>\n      <td>0.079558</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-03-23</td>\n      <td>0.597490</td>\n      <td>0.054316</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-10</td>\n      <td>0.757816</td>\n      <td>0.144968</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-17</td>\n      <td>0.718972</td>\n      <td>0.146480</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-06-25</td>\n      <td>0.850005</td>\n      <td>0.131564</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>PDLG-5002</td>\n      <td>2</td>\n      <td>2020-07-02</td>\n      <td>0.815039</td>\n      <td>0.088892</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-03-19</td>\n      <td>0.561606</td>\n      <td>0.085658</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-10</td>\n      <td>0.663526</td>\n      <td>0.122627</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-17</td>\n      <td>0.849456</td>\n      <td>0.152419</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-06-25</td>\n      <td>0.641847</td>\n      <td>0.305319</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>PDLG-5002</td>\n      <td>3</td>\n      <td>2020-07-02</td>\n      <td>0.908288</td>\n      <td>0.061421</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-03-18</td>\n      <td>0.471963</td>\n      <td>0.051650</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-09</td>\n      <td>0.326917</td>\n      <td>0.043206</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-16</td>\n      <td>0.260056</td>\n      <td>0.045745</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-23</td>\n      <td>0.239608</td>\n      <td>0.035912</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>PDL-05</td>\n      <td>1</td>\n      <td>2020-06-30</td>\n      <td>0.226955</td>\n      <td>0.045514</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-03-18</td>\n      <td>0.461065</td>\n      <td>0.060126</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-09</td>\n      <td>0.404010</td>\n      <td>0.056509</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-16</td>\n      <td>0.347841</td>\n      <td>0.071546</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-23</td>\n      <td>0.338494</td>\n      <td>0.061769</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>PDL-05</td>\n      <td>2</td>\n      <td>2020-06-30</td>\n      <td>0.484677</td>\n      <td>0.062628</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-03-17</td>\n      <td>0.495897</td>\n      <td>0.048993</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-09</td>\n      <td>0.492932</td>\n      <td>0.097777</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-16</td>\n      <td>0.434887</td>\n      <td>0.093092</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-23</td>\n      <td>0.424992</td>\n      <td>0.071347</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>PDL-05</td>\n      <td>3</td>\n      <td>2020-06-30</td>\n      <td>0.569215</td>\n      <td>0.076284</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "df[\"mean\"] = pd.to_numeric(df[\"mean\"])\n",
    "df[\"std\"] = pd.to_numeric(df[\"std\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "polymer_type = \"PDLG-5002\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(polymer_type, df=df):\n",
    "    df = df[df.polimer_type==polymer_type].reset_index()\n",
    "\n",
    "    fig, ax = plt.subplots(figsize=(7, 7), constrained_layout=True)\n",
    "\n",
    "    colors = [\"blue\", \"black\", \"red\"]\n",
    "    markers = [\"o\", \"^\", \"x\"]\n",
    "    labels = []\n",
    "    for sample_number, color, marker in zip(np.flip(df.sample_number.unique()), colors, markers):\n",
    "        df_for_1_sample = df[df.sample_number==sample_number]\n",
    "        print(df_for_1_sample.date)\n",
    "        df_for_1_sample.date = ['исходный \\n (сухой)'] + [f\"неделя {i+1}\" for i in range(4)]\n",
    "        s = 3000*np.pi*(df_for_1_sample[\"std\"].to_numpy()**2)\n",
    "        labels.append(mpatches.Patch(color=color, label=polymer_type+f\" образец {sample_number}\"))\n",
    "        df_for_1_sample.plot(kind=\"scatter\",\n",
    "                             x=\"date\",\n",
    "                             y=\"mean\",\n",
    "                             ax=ax,\n",
    "                             c=color,\n",
    "                             s=s,\n",
    "                             marker=marker\n",
    "                             )\n",
    "    \n",
    "    ax.set_ylabel(\"средняя пористость\")\n",
    "    ax.set_xlabel(\"дата измерений\")\n",
    "    ax.set_ylim(bottom=0, top=1)\n",
    "    ax.legend(loc=2, handles=labels)\n",
    "    plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10   2020-03-19\n",
      "11   2020-06-10\n",
      "12   2020-06-17\n",
      "13   2020-06-25\n",
      "14   2020-07-02\n",
      "Name: date, dtype: datetime64[ns]\n",
      "5   2020-03-23\n",
      "6   2020-06-10\n",
      "7   2020-06-17\n",
      "8   2020-06-25\n",
      "9   2020-07-02\n",
      "Name: date, dtype: datetime64[ns]\n",
      "0   2020-03-18\n",
      "1   2020-06-10\n",
      "2   2020-06-17\n",
      "3   2020-06-25\n",
      "4   2020-07-02\n",
      "Name: date, dtype: datetime64[ns]\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    },
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     },
     "metadata": {}
    }
   ],
   "source": [
    "plot(\"PDLG-5002\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "10   2020-03-17\n",
      "11   2020-06-09\n",
      "12   2020-06-16\n",
      "13   2020-06-23\n",
      "14   2020-06-30\n",
      "Name: date, dtype: datetime64[ns]\n",
      "5   2020-03-18\n",
      "6   2020-06-09\n",
      "7   2020-06-16\n",
      "8   2020-06-23\n",
      "9   2020-06-30\n",
      "Name: date, dtype: datetime64[ns]\n",
      "0   2020-03-18\n",
      "1   2020-06-09\n",
      "2   2020-06-16\n",
      "3   2020-06-23\n",
      "4   2020-06-30\n",
      "Name: date, dtype: datetime64[ns]\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n",
      "/home/ankhafizov/4dtomo_v2/porous_implants/porous_implants/venv/lib/python3.7/site-packages/pandas/core/generic.py:5491: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  self[name] = value\n"
     ]
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plot(\"PDL-05\")"
   ]
  },
  {
   "source": [
    "# Синхротрон"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  polimer_type  sample_type      date  mean   std\n",
       "0       PDL-05        центр  исходный  0.78  0.12\n",
       "1       PDL-05  поверхность  исходный  0.69  0.13\n",
       "2       PDL-05        центр  неделя 1  0.77  0.12\n",
       "3       PDL-05  поверхность  неделя 1  0.70  0.14\n",
       "4       PDL-05        центр  неделя 2  0.77  0.13\n",
       "5       PDL-05  поверхность  неделя 2  0.68  0.11"
      ],
      "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>polimer_type</th>\n      <th>sample_type</th>\n      <th>date</th>\n      <th>mean</th>\n      <th>std</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>PDL-05</td>\n      <td>центр</td>\n      <td>исходный</td>\n      <td>0.78</td>\n      <td>0.12</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>PDL-05</td>\n      <td>поверхность</td>\n      <td>исходный</td>\n      <td>0.69</td>\n      <td>0.13</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>PDL-05</td>\n      <td>центр</td>\n      <td>неделя 1</td>\n      <td>0.77</td>\n      <td>0.12</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>PDL-05</td>\n      <td>поверхность</td>\n      <td>неделя 1</td>\n      <td>0.70</td>\n      <td>0.14</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>PDL-05</td>\n      <td>центр</td>\n      <td>неделя 2</td>\n      <td>0.77</td>\n      <td>0.13</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>PDL-05</td>\n      <td>поверхность</td>\n      <td>неделя 2</td>\n      <td>0.68</td>\n      <td>0.11</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "df =  pd.DataFrame(columns = ['polimer_type', 'sample_type', 'date', 'mean', 'std'])\n",
    "polimers = [\"PDL-05\"] * 6\n",
    "sample_types = [\"центр\", \"поверхность\", \"центр\", \"поверхность\", \"центр\", \"поверхность\"]\n",
    "dates = [\"исходный\", \"исходный\", \"неделя 1\", \"неделя 1\", \"неделя 2\", \"неделя 2\"]\n",
    "means = [0.78, 0.69, 0.77, 0.70, 0.77, 0.68]\n",
    "stds = [0.12, 0.13, 0.12, 0.14, 0.13, 0.11]\n",
    "\n",
    "for polimer, sample_type, date, mean, std in zip(polimers, sample_types, dates, means, stds):\n",
    "    df = df.append({'polimer_type': polimer, \n",
    "                    'sample_type': sample_type,\n",
    "                    'date': date,\n",
    "                    'mean': mean,\n",
    "                    'std': std}, ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "([0, 1, 2], [Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')])"
      ]
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 504x504 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(7, 7), constrained_layout=True)\n",
    "\n",
    "colors = [\"blue\", \"red\"]\n",
    "markers = [\"o\", \"^\", \"x\"]\n",
    "labels = []\n",
    "for polymer_type, sample_type, color, marker in zip(df.polimer_type, np.flip(df.sample_type.unique()), colors, markers):\n",
    "    df_for_1_sample = df[df.sample_type==sample_type]\n",
    "    s = 3000*np.pi*(df_for_1_sample[\"std\"].to_numpy()**2)\n",
    "    labels.append(mpatches.Patch(color=color, label=polymer_type+f\" образец {sample_type}\"))\n",
    "    df_for_1_sample.plot(kind=\"scatter\",\n",
    "                         x=\"date\",\n",
    "                         y=\"mean\",\n",
    "                         ax=ax,\n",
    "                         c=color,\n",
    "                         s=40,\n",
    "                         yerr = df_for_1_sample[\"std\"].to_numpy(),\n",
    "                         marker=marker,\n",
    "                         alpha=0.5\n",
    "                         )\n",
    "\n",
    "ax.set_ylabel(\"средняя пористость\")\n",
    "ax.set_xlabel(\"дата измерений\")\n",
    "ax.legend(loc=8, handles=labels)\n",
    "ax.set_ylim(bottom=0, top=1)\n",
    "plt.xticks(rotation='vertical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from phase_contrast_restoration import get_img as get_bin_img\n",
    "import file_paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_id= 0\n",
    "polimer_type = [\"PDL-05\", \"PDLG-5002\"][0]\n",
    "paths = file_paths.get_benchtop_setup_paths(polimer_type)\n",
    "sample_name = list(paths.keys())[sample_id]\n",
    "img3d = ~get_bin_img(sample_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f0ce3cfd890>"
      ]
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "plt.imshow(img3d[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}