map_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import umap\n",
    "import umap.plot\n",
    "from sklearn.datasets import load_digits"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "digits = load_digits()\n",
    "\n",
    "mapper = umap.UMAP().fit(digits.data)\n",
    "umap.plot.points(mapper, labels=digits.target)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% Lets just see umap do its thing\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "   Unnamed: 0  LocationID    SessionID GroupID  RecordID Language  Lockdown  \\\n0           0  CamdenTown  CamdenTown1   CT101     525.0  English         0   \n1           1  CamdenTown  CamdenTown1   CT101     561.0  English         0   \n2           2  CamdenTown  CamdenTown1   CT101     526.0  English         0   \n3           3  CamdenTown  CamdenTown1   CT102     560.0  English         0   \n4           4  CamdenTown  CamdenTown1   CT103     527.0  English         0   \n\n            start_time             end_time   latitude  ...  \\\n0  2019-05-02 11:40:00  2019-05-02 11:43:00  51.539124  ...   \n1  2019-05-02 11:40:00  2019-05-02 11:43:00  51.539124  ...   \n2  2019-05-02 11:41:00  2019-05-02 11:44:00  51.539124  ...   \n3  2019-05-02 11:50:00  2019-05-02 11:53:00  51.539124  ...   \n4  2019-05-02 11:49:00  2019-05-02 11:54:00  51.539124  ...   \n\n   FS_Avg,arith(vacil)  I_HM_Avg,arith(iu)  Ton_HM_Avg,arith(tuHMS)  \\\n0              0.01700               0.358                    0.309   \n1              0.01700               0.358                    0.309   \n2              0.01700               0.358                    0.309   \n3              0.01790               0.362                    0.268   \n4              0.00889               0.370                    0.193   \n\n   LZeq_L(dB(SPL))  LAeq_L(A)(dB(SPL))  LA10_LA90(dB(SPL))  \\\n0            79.72               70.07                7.78   \n1            79.72               70.07                7.78   \n2            79.72               70.07                7.78   \n3            80.27               71.26                8.38   \n4            76.79               66.71                4.23   \n\n   LCeq_LAeq(dB(SPL))  LC10_LC90(dB(SPL))  RA_2D_cp(cPa)  PA(Zwicker)  \n0                7.81                8.11           14.3    36.019181  \n1                7.81                8.11           14.3    36.019181  \n2                7.81                8.11           14.3    36.019181  \n3                6.25                7.82           14.7    40.523268  \n4                7.61                3.99           13.4    27.108635  \n\n[5 rows x 79 columns]",
      "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>Unnamed: 0</th>\n      <th>LocationID</th>\n      <th>SessionID</th>\n      <th>GroupID</th>\n      <th>RecordID</th>\n      <th>Language</th>\n      <th>Lockdown</th>\n      <th>start_time</th>\n      <th>end_time</th>\n      <th>latitude</th>\n      <th>...</th>\n      <th>FS_Avg,arith(vacil)</th>\n      <th>I_HM_Avg,arith(iu)</th>\n      <th>Ton_HM_Avg,arith(tuHMS)</th>\n      <th>LZeq_L(dB(SPL))</th>\n      <th>LAeq_L(A)(dB(SPL))</th>\n      <th>LA10_LA90(dB(SPL))</th>\n      <th>LCeq_LAeq(dB(SPL))</th>\n      <th>LC10_LC90(dB(SPL))</th>\n      <th>RA_2D_cp(cPa)</th>\n      <th>PA(Zwicker)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>CamdenTown</td>\n      <td>CamdenTown1</td>\n      <td>CT101</td>\n      <td>525.0</td>\n      <td>English</td>\n      <td>0</td>\n      <td>2019-05-02 11:40:00</td>\n      <td>2019-05-02 11:43:00</td>\n      <td>51.539124</td>\n      <td>...</td>\n      <td>0.01700</td>\n      <td>0.358</td>\n      <td>0.309</td>\n      <td>79.72</td>\n      <td>70.07</td>\n      <td>7.78</td>\n      <td>7.81</td>\n      <td>8.11</td>\n      <td>14.3</td>\n      <td>36.019181</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>CamdenTown</td>\n      <td>CamdenTown1</td>\n      <td>CT101</td>\n      <td>561.0</td>\n      <td>English</td>\n      <td>0</td>\n      <td>2019-05-02 11:40:00</td>\n      <td>2019-05-02 11:43:00</td>\n      <td>51.539124</td>\n      <td>...</td>\n      <td>0.01700</td>\n      <td>0.358</td>\n      <td>0.309</td>\n      <td>79.72</td>\n      <td>70.07</td>\n      <td>7.78</td>\n      <td>7.81</td>\n      <td>8.11</td>\n      <td>14.3</td>\n      <td>36.019181</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>CamdenTown</td>\n      <td>CamdenTown1</td>\n      <td>CT101</td>\n      <td>526.0</td>\n      <td>English</td>\n      <td>0</td>\n      <td>2019-05-02 11:41:00</td>\n      <td>2019-05-02 11:44:00</td>\n      <td>51.539124</td>\n      <td>...</td>\n      <td>0.01700</td>\n      <td>0.358</td>\n      <td>0.309</td>\n      <td>79.72</td>\n      <td>70.07</td>\n      <td>7.78</td>\n      <td>7.81</td>\n      <td>8.11</td>\n      <td>14.3</td>\n      <td>36.019181</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>CamdenTown</td>\n      <td>CamdenTown1</td>\n      <td>CT102</td>\n      <td>560.0</td>\n      <td>English</td>\n      <td>0</td>\n      <td>2019-05-02 11:50:00</td>\n      <td>2019-05-02 11:53:00</td>\n      <td>51.539124</td>\n      <td>...</td>\n      <td>0.01790</td>\n      <td>0.362</td>\n      <td>0.268</td>\n      <td>80.27</td>\n      <td>71.26</td>\n      <td>8.38</td>\n      <td>6.25</td>\n      <td>7.82</td>\n      <td>14.7</td>\n      <td>40.523268</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>CamdenTown</td>\n      <td>CamdenTown1</td>\n      <td>CT103</td>\n      <td>527.0</td>\n      <td>English</td>\n      <td>0</td>\n      <td>2019-05-02 11:49:00</td>\n      <td>2019-05-02 11:54:00</td>\n      <td>51.539124</td>\n      <td>...</td>\n      <td>0.00889</td>\n      <td>0.370</td>\n      <td>0.193</td>\n      <td>76.79</td>\n      <td>66.71</td>\n      <td>4.23</td>\n      <td>7.61</td>\n      <td>3.99</td>\n      <td>13.4</td>\n      <td>27.108635</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 79 columns</p>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database = pd.read_csv('SSID_Data.csv')\n",
    "database.head(5)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "        Unnamed: 0      RecordID     Lockdown     latitude    longitude  \\\ncount  1909.000000   1302.000000  1909.000000  1302.000000  1302.000000   \nmean    954.000000    758.415515     0.299109    50.911195     1.128755   \nstd     551.225151    910.521989     0.457988     1.832944     3.752435   \nmin       0.000000      1.000000     0.000000    45.431837    -0.153024   \n25%     477.000000    334.250000     0.000000    51.514220    -0.132300   \n50%     954.000000    700.500000     0.000000    51.523373    -0.126202   \n75%    1431.000000   1048.750000     1.000000    51.526900    -0.100162   \nmax    1908.000000  10018.000000     1.000000    51.539196    12.354908   \n\n           Traffic        Other        Human      Natural  ISOPleasant  ...  \\\ncount  1301.000000  1301.000000  1301.000000  1301.000000  1302.000000  ...   \nmean      2.652575     2.329746     3.112221     2.492698     0.272583  ...   \nstd       1.104175     1.141369     0.978859     1.184899     0.405349  ...   \nmin       1.000000     1.000000     1.000000     1.000000    -1.000000  ...   \n25%       2.000000     1.000000     2.000000     2.000000     0.000000  ...   \n50%       3.000000     2.000000     3.000000     2.000000     0.310700  ...   \n75%       4.000000     3.000000     4.000000     3.000000     0.603600  ...   \nmax       5.000000     5.000000     5.000000     5.000000     1.000000  ...   \n\n       FS_Avg,arith(vacil)  I_HM_Avg,arith(iu)  Ton_HM_Avg,arith(tuHMS)  \\\ncount          1743.000000         1743.000000              1743.000000   \nmean              0.017222            0.395924                 0.199941   \nstd               0.016673            0.078790                 0.154362   \nmin               0.002530            0.197000                 0.017300   \n25%               0.007170            0.340000                 0.099100   \n50%               0.013300            0.384000                 0.161000   \n75%               0.021150            0.432500                 0.257000   \nmax               0.229000            1.110000                 1.440000   \n\n       LZeq_L(dB(SPL))  LAeq_L(A)(dB(SPL))  LA10_LA90(dB(SPL))  \\\ncount      1743.000000         1743.000000         1743.000000   \nmean         72.847636           61.302932            5.989954   \nstd           5.762566            7.630255            3.514965   \nmin          52.990000           42.270000            0.440000   \n25%          69.110000           55.060000            3.760000   \n50%          73.200000           61.930000            5.430000   \n75%          76.585000           67.100000            7.490000   \nmax          97.310000           86.310000           22.970000   \n\n       LCeq_LAeq(dB(SPL))  LC10_LC90(dB(SPL))  RA_2D_cp(cPa)  PA(Zwicker)  \ncount         1743.000000         1743.000000    1743.000000  1743.000000  \nmean             8.405542            5.888726      12.313373    22.656203  \nstd              4.222677            3.109413       2.114922    15.541479  \nmin             -0.290000            0.520000       7.420000     4.335480  \n25%              5.460000            3.580000      10.900000    11.878576  \n50%              8.080000            4.980000      12.200000    18.857770  \n75%             10.625000            7.535000      13.600000    30.945373  \nmax             26.280000           21.960000      33.000000   199.155517  \n\n[8 rows x 65 columns]",
      "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>Unnamed: 0</th>\n      <th>RecordID</th>\n      <th>Lockdown</th>\n      <th>latitude</th>\n      <th>longitude</th>\n      <th>Traffic</th>\n      <th>Other</th>\n      <th>Human</th>\n      <th>Natural</th>\n      <th>ISOPleasant</th>\n      <th>...</th>\n      <th>FS_Avg,arith(vacil)</th>\n      <th>I_HM_Avg,arith(iu)</th>\n      <th>Ton_HM_Avg,arith(tuHMS)</th>\n      <th>LZeq_L(dB(SPL))</th>\n      <th>LAeq_L(A)(dB(SPL))</th>\n      <th>LA10_LA90(dB(SPL))</th>\n      <th>LCeq_LAeq(dB(SPL))</th>\n      <th>LC10_LC90(dB(SPL))</th>\n      <th>RA_2D_cp(cPa)</th>\n      <th>PA(Zwicker)</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1909.000000</td>\n      <td>1302.000000</td>\n      <td>1909.000000</td>\n      <td>1302.000000</td>\n      <td>1302.000000</td>\n      <td>1301.000000</td>\n      <td>1301.000000</td>\n      <td>1301.000000</td>\n      <td>1301.000000</td>\n      <td>1302.000000</td>\n      <td>...</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n      <td>1743.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>954.000000</td>\n      <td>758.415515</td>\n      <td>0.299109</td>\n      <td>50.911195</td>\n      <td>1.128755</td>\n      <td>2.652575</td>\n      <td>2.329746</td>\n      <td>3.112221</td>\n      <td>2.492698</td>\n      <td>0.272583</td>\n      <td>...</td>\n      <td>0.017222</td>\n      <td>0.395924</td>\n      <td>0.199941</td>\n      <td>72.847636</td>\n      <td>61.302932</td>\n      <td>5.989954</td>\n      <td>8.405542</td>\n      <td>5.888726</td>\n      <td>12.313373</td>\n      <td>22.656203</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>551.225151</td>\n      <td>910.521989</td>\n      <td>0.457988</td>\n      <td>1.832944</td>\n      <td>3.752435</td>\n      <td>1.104175</td>\n      <td>1.141369</td>\n      <td>0.978859</td>\n      <td>1.184899</td>\n      <td>0.405349</td>\n      <td>...</td>\n      <td>0.016673</td>\n      <td>0.078790</td>\n      <td>0.154362</td>\n      <td>5.762566</td>\n      <td>7.630255</td>\n      <td>3.514965</td>\n      <td>4.222677</td>\n      <td>3.109413</td>\n      <td>2.114922</td>\n      <td>15.541479</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>1.000000</td>\n      <td>0.000000</td>\n      <td>45.431837</td>\n      <td>-0.153024</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>1.000000</td>\n      <td>-1.000000</td>\n      <td>...</td>\n      <td>0.002530</td>\n      <td>0.197000</td>\n      <td>0.017300</td>\n      <td>52.990000</td>\n      <td>42.270000</td>\n      <td>0.440000</td>\n      <td>-0.290000</td>\n      <td>0.520000</td>\n      <td>7.420000</td>\n      <td>4.335480</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>477.000000</td>\n      <td>334.250000</td>\n      <td>0.000000</td>\n      <td>51.514220</td>\n      <td>-0.132300</td>\n      <td>2.000000</td>\n      <td>1.000000</td>\n      <td>2.000000</td>\n      <td>2.000000</td>\n      <td>0.000000</td>\n      <td>...</td>\n      <td>0.007170</td>\n      <td>0.340000</td>\n      <td>0.099100</td>\n      <td>69.110000</td>\n      <td>55.060000</td>\n      <td>3.760000</td>\n      <td>5.460000</td>\n      <td>3.580000</td>\n      <td>10.900000</td>\n      <td>11.878576</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>954.000000</td>\n      <td>700.500000</td>\n      <td>0.000000</td>\n      <td>51.523373</td>\n      <td>-0.126202</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>3.000000</td>\n      <td>2.000000</td>\n      <td>0.310700</td>\n      <td>...</td>\n      <td>0.013300</td>\n      <td>0.384000</td>\n      <td>0.161000</td>\n      <td>73.200000</td>\n      <td>61.930000</td>\n      <td>5.430000</td>\n      <td>8.080000</td>\n      <td>4.980000</td>\n      <td>12.200000</td>\n      <td>18.857770</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1431.000000</td>\n      <td>1048.750000</td>\n      <td>1.000000</td>\n      <td>51.526900</td>\n      <td>-0.100162</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>4.000000</td>\n      <td>3.000000</td>\n      <td>0.603600</td>\n      <td>...</td>\n      <td>0.021150</td>\n      <td>0.432500</td>\n      <td>0.257000</td>\n      <td>76.585000</td>\n      <td>67.100000</td>\n      <td>7.490000</td>\n      <td>10.625000</td>\n      <td>7.535000</td>\n      <td>13.600000</td>\n      <td>30.945373</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1908.000000</td>\n      <td>10018.000000</td>\n      <td>1.000000</td>\n      <td>51.539196</td>\n      <td>12.354908</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>5.000000</td>\n      <td>1.000000</td>\n      <td>...</td>\n      <td>0.229000</td>\n      <td>1.110000</td>\n      <td>1.440000</td>\n      <td>97.310000</td>\n      <td>86.310000</td>\n      <td>22.970000</td>\n      <td>26.280000</td>\n      <td>21.960000</td>\n      <td>33.000000</td>\n      <td>199.155517</td>\n    </tr>\n  </tbody>\n</table>\n<p>8 rows × 65 columns</p>\n</div>"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "database.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
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