04 - Displaying Core Data and Deriving a Poro Perm Relationship.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4 - Displaying Core Data and Deriving a Poro Perm Relationship"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Created By: Andy McDonald\n",
    "<br><br>\n",
    "The following short tutorial illustrates the process of loading in core data from a CSV file, creating poro-perm crossplots, and deriving a poro-perm relationship using numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_data = pd.read_csv(\"Data/15_9-19A-CORE.csv\", na_values=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>OrigDepth</th>\n",
       "      <th>CORE_NO</th>\n",
       "      <th>SAMPLE</th>\n",
       "      <th>CKHG</th>\n",
       "      <th>CKHL</th>\n",
       "      <th>CKVG</th>\n",
       "      <th>CKVL</th>\n",
       "      <th>CPOR</th>\n",
       "      <th>CPORV</th>\n",
       "      <th>So</th>\n",
       "      <th>Sw</th>\n",
       "      <th>CGD</th>\n",
       "      <th>CGDV</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3838.60</td>\n",
       "      <td>3837.00</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>13.80</td>\n",
       "      <td>11.500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.66</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3838.85</td>\n",
       "      <td>3837.25</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.67</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3839.15</td>\n",
       "      <td>3837.55</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.20</td>\n",
       "      <td>21.400</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.16</td>\n",
       "      <td>10.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.69</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3839.40</td>\n",
       "      <td>3837.80</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.694</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.70</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3839.48</td>\n",
       "      <td>3837.88</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.8</td>\n",
       "      <td>52.9</td>\n",
       "      <td>36.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     DEPTH  OrigDepth  CORE_NO  SAMPLE   CKHG    CKHL  CKVG  CKVL  CPOR  \\\n",
       "0  3838.60    3837.00        1       1  13.80  11.500   NaN   NaN  17.0   \n",
       "1  3838.85    3837.25        1       2    NaN     NaN   NaN   NaN  14.8   \n",
       "2  3839.15    3837.55        1       3  25.20  21.400  3.94  3.16  10.8   \n",
       "3  3839.40    3837.80        1       4   1.02   0.694   NaN   NaN  12.8   \n",
       "4  3839.48    3837.88        1       5    NaN     NaN   NaN   NaN   NaN   \n",
       "\n",
       "   CPORV    So    Sw   CGD  CGDV  \n",
       "0    NaN   NaN   NaN  2.66   NaN  \n",
       "1    NaN   NaN   NaN  2.67   NaN  \n",
       "2    NaN   NaN   NaN  2.69   NaN  \n",
       "3    NaN   NaN   NaN  2.70   NaN  \n",
       "4   18.8  52.9  36.4   NaN  2.68  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DEPTH</th>\n",
       "      <th>OrigDepth</th>\n",
       "      <th>CORE_NO</th>\n",
       "      <th>SAMPLE</th>\n",
       "      <th>CKHG</th>\n",
       "      <th>CKHL</th>\n",
       "      <th>CKVG</th>\n",
       "      <th>CKVL</th>\n",
       "      <th>CPOR</th>\n",
       "      <th>CPORV</th>\n",
       "      <th>So</th>\n",
       "      <th>Sw</th>\n",
       "      <th>CGD</th>\n",
       "      <th>CGDV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>728.000000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>728.00000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>557.000000</td>\n",
       "      <td>557.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>141.000000</td>\n",
       "      <td>593.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>594.000000</td>\n",
       "      <td>71.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3920.528338</td>\n",
       "      <td>3920.153338</td>\n",
       "      <td>3.93956</td>\n",
       "      <td>364.500000</td>\n",
       "      <td>649.801463</td>\n",
       "      <td>627.972549</td>\n",
       "      <td>490.086071</td>\n",
       "      <td>472.875284</td>\n",
       "      <td>16.829342</td>\n",
       "      <td>19.497183</td>\n",
       "      <td>56.943662</td>\n",
       "      <td>28.309859</td>\n",
       "      <td>2.653569</td>\n",
       "      <td>2.656901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>46.545695</td>\n",
       "      <td>46.902816</td>\n",
       "      <td>1.75976</td>\n",
       "      <td>210.299786</td>\n",
       "      <td>2160.127387</td>\n",
       "      <td>2116.307023</td>\n",
       "      <td>1973.601098</td>\n",
       "      <td>1929.420018</td>\n",
       "      <td>6.552523</td>\n",
       "      <td>5.657763</td>\n",
       "      <td>21.084319</td>\n",
       "      <td>19.203758</td>\n",
       "      <td>0.046735</td>\n",
       "      <td>0.035560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3838.600000</td>\n",
       "      <td>3837.000000</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>0.017000</td>\n",
       "      <td>2.900000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.500000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.620000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3882.580000</td>\n",
       "      <td>3881.980000</td>\n",
       "      <td>3.00000</td>\n",
       "      <td>182.750000</td>\n",
       "      <td>2.620000</td>\n",
       "      <td>2.030000</td>\n",
       "      <td>0.874000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>18.150000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>2.640000</td>\n",
       "      <td>2.630000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3921.725000</td>\n",
       "      <td>3921.125000</td>\n",
       "      <td>4.00000</td>\n",
       "      <td>364.500000</td>\n",
       "      <td>59.900000</td>\n",
       "      <td>52.300000</td>\n",
       "      <td>27.300000</td>\n",
       "      <td>23.300000</td>\n",
       "      <td>18.100000</td>\n",
       "      <td>21.200000</td>\n",
       "      <td>62.300000</td>\n",
       "      <td>22.800000</td>\n",
       "      <td>2.650000</td>\n",
       "      <td>2.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3960.287500</td>\n",
       "      <td>3960.487500</td>\n",
       "      <td>5.00000</td>\n",
       "      <td>546.250000</td>\n",
       "      <td>213.000000</td>\n",
       "      <td>194.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>96.300000</td>\n",
       "      <td>21.700000</td>\n",
       "      <td>23.500000</td>\n",
       "      <td>68.500000</td>\n",
       "      <td>29.950000</td>\n",
       "      <td>2.660000</td>\n",
       "      <td>2.660000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>3999.950000</td>\n",
       "      <td>3999.750000</td>\n",
       "      <td>7.00000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>20800.000000</td>\n",
       "      <td>20500.000000</td>\n",
       "      <td>14800.000000</td>\n",
       "      <td>14500.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>27.800000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>90.800000</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>2.810000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "             DEPTH    OrigDepth    CORE_NO      SAMPLE          CKHG  \\\n",
       "count   728.000000   728.000000  728.00000  728.000000    557.000000   \n",
       "mean   3920.528338  3920.153338    3.93956  364.500000    649.801463   \n",
       "std      46.545695    46.902816    1.75976  210.299786   2160.127387   \n",
       "min    3838.600000  3837.000000    1.00000    1.000000      0.018000   \n",
       "25%    3882.580000  3881.980000    3.00000  182.750000      2.620000   \n",
       "50%    3921.725000  3921.125000    4.00000  364.500000     59.900000   \n",
       "75%    3960.287500  3960.487500    5.00000  546.250000    213.000000   \n",
       "max    3999.950000  3999.750000    7.00000  728.000000  20800.000000   \n",
       "\n",
       "               CKHL          CKVG          CKVL        CPOR      CPORV  \\\n",
       "count    557.000000    141.000000    141.000000  593.000000  71.000000   \n",
       "mean     627.972549    490.086071    472.875284   16.829342  19.497183   \n",
       "std     2116.307023   1973.601098   1929.420018    6.552523   5.657763   \n",
       "min        0.010000      0.030000      0.017000    2.900000   2.700000   \n",
       "25%        2.030000      0.874000      0.590000   12.000000  18.150000   \n",
       "50%       52.300000     27.300000     23.300000   18.100000  21.200000   \n",
       "75%      194.000000    108.000000     96.300000   21.700000  23.500000   \n",
       "max    20500.000000  14800.000000  14500.000000   36.000000  27.800000   \n",
       "\n",
       "              So         Sw         CGD       CGDV  \n",
       "count  71.000000  71.000000  594.000000  71.000000  \n",
       "mean   56.943662  28.309859    2.653569   2.656901  \n",
       "std    21.084319  19.203758    0.046735   0.035560  \n",
       "min     0.000000   4.500000    2.250000   2.620000  \n",
       "25%    52.000000  18.200000    2.640000   2.630000  \n",
       "50%    62.300000  22.800000    2.650000   2.650000  \n",
       "75%    68.500000  29.950000    2.660000   2.660000  \n",
       "max    86.000000  90.800000    3.030000   2.810000  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can make a quick scatter plot by:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11198d400>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PUSmpiFSRXOVWMhNJ1IxjXQmAvMMz2r8yeKme41UZn9toSMhKKiJVLpUU0hNJNtmWELfuPcqVdyfvXz/a5gaKbbTOUSmpiFSx9AnjfDKHZ9Zu2MWVP91IV6Lv/Va0f6Vwo7FQpybqRapYtgnjTGProv0m9lPJKDOhwOiZG5ChUU9FpIplmzCGZCJJ9DhXX7iYpTMn9hueybbDHqC2RvtXqp2SikgVyzZhnCuRpMuWjGqjxq8/dTYLp40fidClTCmpiFS5oUwY51q9pIQiSioiMqQJ49G6ekmGR0lFRIZsNK5ekuHR6i8REQmNkopIlRiNZdal/Gj4S6QKqJRKZankWmpKKiKj3Ggusz4aVfofACUZ/jKz7Wb2lJltMLOWoG2ymT1gZtuC741Bu5nZjWbWamabzOy1aedZGRy/zcxWluK9iJS70VxmfbQZ7h06y0Ep51TOdfdl7t4c/P454EF3XwQ8GPwOcAGwKPhaBdwMySQErAGWA2cAa1KJSKSa5JsrCbvMeqXNzVRSvKPhD4ByGv66GDgn+Pk24BFgddB+u7s78LiZTTKz6cGxD7j7AQAzewA4H/jxyIYtUjqFDJWEWWa90oZmKi3e0XCflVIlFQd+a2YOfNvdbwGmufseAHffY2anBMfOBHamPbctaMvV3o+ZrSLZy2HOnDlhvg+RkhnMXEkYGxUrbW6m0uKF0XGflVIllRXuvjtIHA+Y2Z8HONaytPkA7f0bk0nrFoDm5uasx4hUmmxFHQcqOz/cjYqDfb1Sq7R4Uyq9UkFJ5lTcfXfw/SXg5yTnRPYGw1oE318KDm8DZqc9fRawe4B2kaow0kMllTY0U2nxpmsaV8fpsydVXEKBEiQVMxtrZuNTPwPnAZuBe4HUCq6VwNrg53uBy4NVYGcCh4NhsvuB88ysMZigPy9oE6kKqaGS+liE8XU1/e55UumvN1yVFu9oYcn57xF8QbMFJHsnkBx++5G7X29mTcBdwBxgB/A+dz9gZgbcRHIS/jjwEXdPLUP+W+ALwbmud/fv53v95uZmb2lpCfU9iZTSSG+Uq7SNeZUWb7kys/Vpq3VzHzfSSaXUlFRERAav0KSi2l8iIhIaJRURyamSNg5KeSinzY8iUkYqbeOglAf1VESkn+HUoFLvprqppyIi/Qx146B6N6KeikiFCrtHkH6+oWwcHA0VdmX41FMRqUB3PP4C1/5qK7VRI97jw+4RZOthDLYGVaWWRZFwKamIVJg7Hn+BL/5iMwBd8WTbcAol5iq8uG71m1m3+s0Fbxys5LIoEh4Nf4lUkPaOTq795ZZ+7dGIDfmeGwPdw2MwNahUFkVAPRWRitJ28ASxaISuRKJPe3fCh9wjCLOHUekVdmX41FMRqSCzGhtIZCmttOadi4d8AQ+7h1HJFXZl+NRTEakg6TdxiprRnehhzTuXcNnyucM6r3oYEhYlFZEKs2LhFG75m2bAWTJj4rATQHoV39NnTwonSKlaSioiFSTszYXarChh05yKSIUIe3OhNitKMSipiFSIgZb+DvV88UTfVV/xRE/v+VTDS4ZCw18iFSLszYXd8QTxvqcj3pNs17CYDJV6KiIVIuylv9vbj2dt37z78JCGxdSzEVBPRaSihLn0d1mOlV5TxtXnreGVed939WwkRUlFpAxlXrTTNY2rC2UfycJp47n8rDnc/tiO3rbLz5rDWac2DTjMlplArn7HYr70q639aocNtRaZVDYlFZEyM5J/9V938V9x+Znz2LDzEMtmT2LhtPEAOSsUZys+ee0vtxKLWJ/zqjpx9VJSESkjuSoGF/Ov/oXTxvcmk5Rcw2xtB0/gPRllYtzpTvRtU3Xi6qWJepEyEvay4WwKnVDPVsNrbG2UzowE0pVw/vG8V6k6sQDqqYiUlaEuGx5oDibdcIfWjnUlqI9FONn9coz1sQjLFzQN6t4rMnopqYgUQaEX+WzPufrCxXzpvq1577jY3tHJlt2HeezZdm5dt53aaP9EkR4HMOyhtVzJLfU+lUxESUUkZEPpDWRbUbV0xsScSWnthl189q4NfTYvdsb7JopHW/f3Oecnz1k47Nv9pldJLvQ2w1JdzLPcm2E0a25u9paWllKHIaNUe0cnK77yUL/hoXWr35zzwlvIczJ7HGdc/39J5PinO76uhm9e9hpW/XB9n3PW1UQApzP+8hPrYxF+dcXZHOtKDKlXpaGu6mFm6929Od9x6qmIhCg10T6Y3kC256RuD5xtY+FH3jAvZ0IBgjkZ63fO2miEVW9awDcfae091/tfN4t33PTooOdYRttQl5JkeJRURAZpoAvQYCfa2zs6OXyim66Mwo7HOhNs3nWYsbVRrvzpRroS3psgvv3753LGVleTXHm1ZMaErHFcunwOly6fQ9vBE4ytjfKOmx6t+k2LqgYQLiUVkUHIdwEazJxD6lxRM7ozKzsCV6/dTE3E6MrolmRuE0n5xH9dwMfeuKB3k+Inz1nITQ9vozYa7RdH07g6Nu48NOw5lkpXin1Bo52SikiBCr0AFVKfK/1cufQ4/RJKLp84ZwGrz3810DfxgbHqTQu4dPkcADbuPNQbU3c8wYl4os95qm3T4lCGK2VgSioiBRrMBSh9ziHbcFnbwRNErW9pk0LEIoBZnx3sdTURPnb2gmCJ8RGuunsTnfGXE983H2ll8tjaPsuUm+c28mhre+85aiJQE62+TYth305AlFRECjaUC9Adj7/Atb/aSm3UiPd473DZrMYGuhO5eynZRA1+8+k3sXXPkX7Da6nlwxGsd2lx7/MixrW/3NJnXiY9oUDyPip3fvwMmuc3DSqmSqcl0uFTUhEpQK6NiVdfuLi3hErmheiOx1/gi7/YDEBXPNmWGi4D+OjZ87n5d7kn3TNFIkbj2FouWjaTxdMn9BaBbBxb229JcrruhBOLRuhKJLI+nrK9/XjVJRUI93YCoqQiklX6kFXmJsLUxsTNuw732/mevpP9ml9u6XfeaMS444kd/FuwrNeAQneKRc3YsvsIB493vTzBn+jho2fP7zcsBzCmNkqPO1e/YzHX/XJr3vPnur9KNRhtS6RLSUlFJEP6RHdXIkGPJ//aT120r/vlVu771Nl86b7c9xC544kd/Sr3AnTHe/jmw9vojHu/JJDPyXgPf/uDJwHvs5P+5t89RzTL9MzKs+bysTcu4NHW/STShu1iUesXW8SgcWztoOIpJe0rKV9KKiJpsq3wytQZ7+H767bnnLQ/eKyL//PQX7I+96Nnz+ffH99BZzw+pPjiOdYTZ1skduu651kyYwJX/nRj33vRuzOuLkpH58vDYXU1EbbsPsybXnnKgK+fubN/y+7DgDFjYv2Au/LDTALF2FeiJBUeJRUZ1XJdLFLtY2ujfS6G2VZ4ZXPnkzvIXLzVlejhPzbv4buPPk93lumL1D6SW9c9H8Zby6sz7nzqxxv6Da/VRCN0ZUzmn+hO9oK+/v5lLJ4+gUdb9wVzMcbZC6fSOLaWO57YwU0PbcOw3s2a6eeuq4ngDue+airvb57FlPH1zGps4D82v5h1sULKYC7oQ9lXkuu/dYo2P4ar4pOKmZ0P/CsQBb7r7l8ucUgyRMOp7Jv+nFTbE8+189XfPkMsGiHhztUXLmb25Ibeqr5mcLK7h1gEzOBTb34lZy2YzMkCehEJp88V1Uj2YAaaeN++v4M1azdnHRYrlmyvdCLHhH68Bz59Z/8kBE9nzP1kjz+16uz+rXu5f+tejOTnknq11GKFf7x7E4unT2DhtPG9F/TUJs8171zMZcvn5nw/g91Xkjq/e7LmWW0ELBLhinMX9u7dGermx0rq3YxkrBVdUNLMosBfgLcCbcCTwAfdPees5FALSrbuPdpntc1vt7zIlt1HmNs0hnF1NWzbe5QjJ+PMbmxg4phals6YwItHOnmh/Rhzm8YSTyS4e/0uDp/oYvrEeqZPbGDnwRNMm1DHioVTOevUJg4e6+L+LS8CzvL5TcRqonTHE2zefYQp4+o469TkypzUkMOYWITt7cd7bwPbuvcoj7bup74mggNtB4/jwOzGMYyvjwHO0ZNx/rL3KPuOdjK+voZZjWNYPn8ysZooY2uj/PnFI7zQfpy5TWNpiEX4+Z/aOHw8zuIZEzhyspu2A8eZMr6O+liUjpNxotEIiUQP4+praIhFqa+NMrtxDDMbG1gyYyLP7+vgFxt2947pdyd6eNUrJgDw5xePMLE+xvRJDTy95wi/+NNuzJIX7AuWnsLY2hqmTajntXMaWb/jIEdOxGmeO4lDJ+LU1UR45sWj/OgPO4hFI3QnnLf/1TT2H+3i0Wfbs/0nlDISi8A1Fy3tnZdKd/0lS7nszOyJZTAFO7Mdm66uxrji3EXc8vvnONr58h8S4+tq+PePLef0ARYuVFLvJqxYCy0oWelJ5SzgGnd/W/D75wHc/V9yPWcoSeWffvEUtz++YzihDknE+pbkSN0GPNuw+qumjeWZvceG/FpRyz4uL1IsUYP6WHJIKl1t1Hjs82/J+Rf1vRt29dtXku0iuXHnIS79zuP9zp+uribZn0rf2xNGVelyEWas1VKleCawM+33NmB5mC/QuvdoSRIK9E8euWo+AcNKKKCEIiMv4dAZ73/Bj0UHLpNS6L6SQjaY1kaj/So359v8WEmlXUoRa6UnlWx1LvpdHs1sFbAKYM6cOYN6gQ07Dw0pMBHJ70PL5/KDx17o05Zwz1smpZB9JU3j6ljzziW9G1CzyazcXMicQyWVdilFrJGinXlktAGz036fBezOPMjdb3H3Zndvnjp16qBeoJo3hImEoTaanEPJrHUWixqfessirr9kKbVRY2xtlPpYuPXHLjtzLte/aym1NRHG1EapiRhRS86bpL9W07g6Tp89qaDXTZV2qY9F+p2n3JQi1kqfU6khOVH/FmAXyYn6S929/1bmwJDmVNY+xe2PaU5FwlcXhc4cQ/4RkiunaoAegzcubOKM+ZM5dDzO99Y9n3M4dG5TA6+YUEfL9kPgkHn6D581l2WzJ/LIX/axZMZEXjN7EtvbjzOvaQzHuxM88+JRNu08BDjTJ43BcZ596RgPPbOvz3liEejugVjEMIM1Fy0B4Jp7t2AYjnPlea9i+YImZjU2sK51P1fevYloxEj0OP/rvX0rEBRzdVLm/powXqvaVn9VxUQ9gJm9HfjfJJcU3+ru1w90vFZ/lefqr1g0Qo3Bo8+20zimhmkTGmg7eHzA1V/j62NMaKhhxsQGdh8+yTNB7AY8v/8YB4+dpMfhdXMb2X+sm4ZYhOmTGmgcU0tXvIfamgiHjnex+9BJDp/oZtEp45jQEKNpbC3j6mO9/+07TnZzMt7D7ODCuG3vUcY3xJg8to7muZPYdegkADMn1dPywiFqo8aiaePp6Iyzcech6mNRPvD62cyfOo7Hnm1nf8fJ3r0fqX/oB4918WjrPupqosxsbGDGxAaOdSVy7q1I33vx5PYDbNl9hCUzJnDeklfkvAXxcC8qmf8Gcu39GOgCVkkXYumrapLKYOke9SIig1doUqn0ORURESkjSioiIhIaJRUREQmNkoqIiIRGSUVEREJTdau/zGwf8ELeA4tvCrC/1EEMgeIeWYp7ZCnu3Oa6e97d41WXVMqFmbUUsjyv3CjukaW4R5biHj4Nf4mISGiUVEREJDRKKqVzS6kDGCLFPbIU98hS3MOkORUREQmNeioiIhIaJZUSMLPtZvaUmW0ws7Ktbmlmt5rZS2a2Oa1tspk9YGbbgu+NpYwxmxxxX2Nmu4LPfENQ3bqsmNlsM3vYzJ42sy1m9umgvWw/8wFiroTPu97M/mBmG4PYrw3a55vZE8Hn/RMzqy11rCkDxPwDM3s+7fNeVrIYNfw18sxsO9Ds7mW9Ht7M3gR0ALe7+9Kg7QbggLt/2cw+BzS6++pSxpkpR9zXAB3u/tVSxjYQM5sOTHf3P5rZeGA9cAnwYcr0Mx8g5vdT/p+3AWPdvcPMYsCjwKeB/wH8zN3vNLNvARvd/eZSxpoyQMx/D/zK3e8uaYCopyIDcPffAwcymi8Gbgt+vo3kBaSs5Ii77Ln7Hnf/Y/DzUeBpYCZl/JkPEHPZ86SO4NdY8OXAm4HUxbncPu9cMZcNJZXScOC3ZrbezFaBhtIQAAAD4ElEQVSVOphBmubueyB5QQFOKXE8g3GFmW0KhsfKZggpGzObB7wGeIIK+cwzYoYK+LzNLGpmG4CXgAeAZ4FD7h4PDmmjzJJkZszunvq8rw8+72+YWcnugKakUhor3P21wAXAJ4PhGimum4FTgWXAHuBrpQ0nNzMbB9wDfMbdj5Q6nkJkibkiPm93T7j7MmAWcAbw6myHjWxUA8uM2cyWAp8H/gvwemAyULLhUSWVEnD33cH3l4Cfk/yfuVLsDcbRU+PpL5U4noK4+97gH2MP8B3K9DMPxsnvAe5w958FzWX9mWeLuVI+7xR3PwQ8ApwJTDKzmuChWcDuUsU1kLSYzw+GId3dO4HvU8LPW0llhJnZ2GBCEzMbC5wHbB74WWXlXmBl8PNKYG0JYylY6qIceBdl+JkHk7DfA55296+nPVS2n3mumCvk855qZpOCnxuAvyY5J/Qw8N7gsHL7vLPF/Oe0PzqM5BxQyT5vrf4aYWa2gGTvBKAG+JG7X1/CkHIysx8D55CsgLoXWAP8ArgLmAPsAN7n7mU1KZ4j7nNIDsU4sB34u9Q8Rbkws7OB/wSeAnqC5i+QnKMoy898gJg/SPl/3qeRnIiPkvwD+y53vy74N3onyWGkPwEfCnoAJTdAzA8BUwEDNgB/nzahP7IxKqmIiEhYNPwlIiKhUVIREZHQKKmIiEholFRERCQ0SioiIhIaJRWRIjGzV5jZnWb2rJltNbNfm9krzexEUEl2q5l9y8wiwfFLzOwhM/tLUCH36mDfAWb2YTPbFzzvz2b2D6V9dyLZKamIFEGQDH4OPOLup7r7YpL7N6YBzwZlNk4DFgOXBBvZ7gW+7O6vBE4H3gD897TT/iR43grgi2Y2e+TekUhhlFREiuNcoNvdv5VqcPcNwM603+PA/wMWApcC69z9t8Fjx4ErgM9lntjd24FWYHrmYyKlpqQiUhxLSd5bJCczGwO8heRu9CWZx7v7s8A4M5uQ8bw5QD2wKcyARcKgpCIy8k4NSpevA+5z99+QLK+Rq7xFqv2/mdkW4DngX939ZPFDFRmcmvyHiMgQbOHlooSZUnMqmcf3uQVCUIOqw92PBvP1P3H3K8zsLOA+M/uNu78YduAiw6GeikhxPATUmdnHUw1m9npgbo7j7wDONrO/Do5tAG4Ebsg80N0fA35I8jayImVFSUWkCDxZqfVdwFuDJcVbgGvIcW8Odz9B8rbB/9PMniE5z/IkcFOOl/gK8JHUbRREyoWqFIuISGjUUxERkdAoqYiISGiUVEREJDRKKiIiEholFRERCY2SioiIhEZJRUREQqOkIiIiofn/BetB8vLZU0gAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_data.plot(kind=\"scatter\", x=\"CPOR\", y=\"CKHG\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is one main problem with previous plot. Poro-perm data is normally presented on semilog scale, where the y-axis is logarithmic and the x-axis set to linear. We can change this by using the yscale('log') method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Zra8N4/45lpskHDj953YqUVfhOtR3frd7ZnjcBDQClt3wSWxdMB03TBmd4o6pCmq4orE+5Rw3XXpuwZSGMLApJ4vjVQBjieg8AIcAfAnA3FxOIL2qhN6gsjLSpeZ2dEWwbNN+z3miRmpMZPakkRhRV4UvLn85owzOeIpTHlWgPaTrqKs+3S9KlenV9t4JT3NDQegtpUrHfQrANADDiKgdwCJm/iURzQfwW1jpuI8yc2su55VeVUK+qBTE1MZhaVNz24/1IKTryaI9m/nNjUlLpf1YD3Yf6sTi9Xt8azNs7HGuqkFKC68dnzFor1J8Q2tCGDt8MIbWhArwlATBoiSKg5m/7LN9A4AN+Z5XLA4hF+yFtiakKxXE8hsnp03NVWVghQOEuVPOTS78OhG6o+kzqwCgOqhhfvNHMWRQEG3vnfDIc89ze5JxDFXRYTYT+7JpxCgI2VBOrqpeIxaHkC3OhTYSN6BpqbkZVrorp33L96sgB063Ns+WnpiJ+39nub0CGkF3pYroRGgaUYetC6aj9XAnAEqOk1XVddy5eicAQiSefSNGQciWilIcYnEI2aCs6HYPLjJNTBhRl7G1yNTGYVh+42QAjAkj6lBfG8bOg8czFv5pAHSdENI1AKmurrjJri3WjIzdhzvxVke3x4pQjYHVSfPkKUq1tFAoKkpxiMUhZIMq0BzWCUyEoG5VbC+cOT7jGFe/wLnKheXGBBAk4M6/H4eut3d7vg/phKhLmd29bg+YTUQNpFgR6+df7rmewSbAqZpDqqWFQlFO6bi9hohmEdHyzs7OUosilDGqhZ00wnevGYdY3ERQI9yzfg/WthwCkJqaa+NOjz0VM3HXM7vQ0RVJqVSuCenwI6TrGFYbhqoIlwHUhFL/PJkZ7nBJUNPQHTU8qbj3z5mYTANWVZ/3B9KlQQulRSwOYcChik0svHY87nluD6IGJ9uEpIsJZOpp5bRUtr/Zgft/+1dP7ylbeakqX2++/Dz8x0tvpWxzWyDWNgMNQ6sxcdQQpWXUX2dq5DNhUeg7KkpxCEK2uF1QuXZQrQnpiMTTzw2vrw1jS9tR/PT5fQgHNDCbYGaEAzoMNvGFyQ24fVULbhvvle/6TzWg4cxB+P7/eN1YTuY3j01J/c1mNGy5k8+ExWzPK4OcCkNFKQ4Jjgu54F5Us21uaL8NaxoBBiOsE0jzNs5zLoBOGAyA8OT2A57KccBqod4dNTBqaDWqgxp6fLKzwgEtJfXX7+082/YpmSjUeTJRjDboMsipsFSU4hBXlZAv2Q5nUikDJsJz8y/3VGb7tVX3UwROdh/qxN3rWxGJq9+OwwEN989JTf1VvZ0XyuWjOs8Zrn0KpVgK3QZdBjkVnopSHIKQK87FLpvhTOqMLE1Z5JdNdpWbcEBLFvr5KY1BQR0P33gRrhx3FnYePA52uV7Y5GS/K1V9Ry4LZkdXBK2HP8Rdq3d5akKWNVcl9ytkTCKXCYvZIIOcCo8oDmHA4rfYpVtMsn0bthWSrQQ0IpxMU0GuE/Dtq8clZ4XrpAqZW5jgZPFfLG4g4gqaRwxGTUhXLpiROOPJ7Qdw21Vjfc/vbJVyz3N7AGaPEgtqWnKGejFiEtko8XSyO4+RQU6Fp6IUh8Q4hGzxW+xG1FXhrY6Tvk0B7bfhO1fvgq4RDNOq+XB2w1X1mQoHNPzzr3Yh6mOABHQtOUv8N7vf9W1TEgpoKf2s7nx6p2cfO0biN0hq6aa25LXcz+SJ7QewdON+6BqldalFDBPM1pjazp5oxjf6fNxYuQT2bdmXueav2y8CTgtGI6PfpSaXGxWlOCTGIThJt1ip3sZjcRNz/v10B9ubLj0Xd3/uE57zcuL/mYG4YeIHz+7GoFAgJa3X3Wdq/fzL0w+Q5dPupXue26PcJagBG267HENrQli/8xDufHqXMkUXQPKe5zePxYPP70v5LqRribYlgN26ZEvbUdy1eudpyyJDR0bTNPG397vwnT+8BBDgTlRyvtEXO7V2TcuhpCsNQLLppNPqcVowR/f/GVdJYLxXVJTiEAQb52IVNUzMb25MectWuS/ca+WKbQdw0yVjUiwP21KxFtjTB5yIWIvV4nWtCAVSC/eCmobDnadARCnHOEnnXkpChBUvv4WnXjnoqQmxcVokADB3yrlYuqktZWbIqbiBrz32ajKjSydA07wzztNhH2u7yXSyqt2DAQ2GyUkZipVaa3P69+F9Xm6rx7ZgNv/N3w0oZEdFVY4LAxO7wtjOz3dXdUfiJh58fh8uu/eFlGrwbKq7Ww4eT/ncfqwHAc1/4QnqmscKsBQUoyrgfx0A+PXud9MG1GMGY8W2A2kX+Ce/fnHKvJD2Yz34wazxyQrycECDaXJKGrDByElpqDDYmnsei1tWly2Daj6637CqfFCd30biGMVDLA6hX6PKz1c1/QOswLDKfbFp7xH8YM3rOBnzLp5VQT3ZRgSwC//8nU4GMxbN8rY/nzCiLmOG1c837sPEUXVJd5cGwslY5pbsNmGd8O6HEazecRAfdEfx09/vS8pw+9XjcGZNCFVBHXc9syttoD5f7JjIPc/twYymc3xbzxdyQfdTtM7ZJkLhqSjFIcHxgYVffr6q6Z+Nyn3RfMHZMNd43UgaAd/71S7EE64Xe76Fn71hL1azJ43EjAnneOIrdoBW1winYqangjlqAN94/E8w2Qq4jxo6CLes2JFWUaUez5j/1J9TttnK88e/3ouakIa4acVllM9GJ5gmZxw45UQnr4vP+YwLnVrrxn1+lVtSKDwVpTgkOD6w8MvPt5v+3bna6/tWve06Fx8N1pszwwr4dkWsN/M7V+8C4E1Ltbm26RzcfV2Tx5/uZGrjMHz1sjH45ZY3UBXQ0B31nsu2BO5e14qXvncVvvh3DVix7UDa5xDSLJWXqa6wO5HS5edpY2ZoGqBIxPKF2ao9cT5n9zPON7U2W4p9fsFLRSkOofwpZNsKVbqpvWjZTf+e3H4AS10pmqrr2otP6+FOfP0/X4XbQ6QRgUAA1KvqC389grtd25z3amUtnVZkMcdbv6ao2YjEGY/88Q2s2tGe9hl89bLROLMmjIde2Jd2Pyd+rZriJlAV0BBLn/+Vei4AX5s6Bv/x0lun049d6cn2f4u5oPfHnlz9GVEcQp9R6LTMLW1HUxZBAlIUQ31tGLddNTZZVFcT0tEdNVJiFk7qa8Ooqw5ZSsalkAzTRJqaPI8LzD1h0GRrQJObmrCOc+qCCOlxT1D9kS1vIpDuogBG1FXjwef/qux5lQ9RHzdWJrYumH66aNAV33EH68Uq6P9IVpXQJ6SbX9Gb8zmzgYgIUxuHefatrw3jrY5uzFy6BV95ZDsuu3cjfv7CfuW1G4ZWw1DMx1g0ewLunzPRysIKe7OjnO4Z971GDVYqDQAwTMaQ6iBum+6t5I4ZjJ4MGmHJb/b6us90QtoMMBWKW8/IL7e8CcB6dnYNi/t3vKblEKYu2YivPLIdU5dsTGa3Cf0TURxCn1DotEzV+Six3U026bk27jTdkE740XVNuGHKaMyeNBJbF0zHkzdfgh9d1+Q7JCldiqgTO5iua4S5U85FOJD+mAB54xPpAtmaRsohUenIJyk3alhtTPx+x62HPyzoS4NQesreVUVE1wG4FsDZAJYx8+9KLJKQB4VOy6wJ6Yi43CqcuI4bv6I6d3quTbpgq+1LnzhqCGY0eTOnOroi6OyJpXX5hHTCbdPHJjN/Nm/ej/raMO6fkwjQ+/S1umb82fh165Gsng8AxA3OSxHkw9JN+/GZpnOUv2OApclgH9CXrsCSKA4iehTATABHmLnJsX0GgJ8B0AE8wsz3MvOzAJ4loqEAHgAgiqMfUsi0TDt+QIm36aqg9ZbbMDSkPF+6ojq/BcwdbFX9Udr7dHRF8OK+I3jpbx34j61vIqTrMEwzOb/czXc//THM+38+mrKtoyuCIYOCeGDORADAd/77z54sqVyUBpCf9ZAvOlnV8arfsaqGRYrzCktfT0wslcXxGIClAFbYG4hIB7AMwDUA2gG8SkRrmdlu3PMvie+Ffkoh0iZV8zBMk7Hhm1eg/S+vKY9xNibMJj3XeS1nl1jVH+WalkP47tM7UxSE3SspHAC+euloPLbt7ZTz/vT3+3D9RQ3J++/sieGSn7yQPAcBaQPxxURT9J2yCeoEgnfuOQCcjBm4ZcUO3D/nwmSgXFXDUoxajoFOsdu6qKBcfaAFuzDRGADrbYuDiC4F8ENm/vvE539K7Hpv4n/PM/Pv05xvHoB5ADB8+PCLVq5cWTzhs6Srqwu1tbWlFiNnylnunpiBN9/vTglg60Q476waGJGetHIbJqOjO4r3T0Rgl/s1DK1GXXXQs29nTywZLzFdfyMaES44x+pftffdE57vnXINr6vCu52nUvax5a0O6jBMxgfHP8S7henA0StsXaW6GyLCqISCPfjBSTCA4dXAey657WejK4LyhsmIGiZCuqb8vq8o53/f6fCTO93fRHUwfZsbJ83Nza8x8+Rs9i2nGMdIAAcdn9sBTAFwG4CrAdQRUSMzP6w6mJmXA1gOAJMnT+Zp06YVV9os2Lx5M8pBjlwpN7mdbiIA+M6SjSkWR1VQw9bZl+P1HduyktvPF2xvj8UNzHtkO6KG+o9ucDiAx6/4JABg2eaXfdt3BDRA1wxE4qnnseWtrw3jxX3v48i72/Dg69n/gedLQEPatN2f/EMT7ln/F0+bk4BG+NcvTsLMiSPQ0RXBZfduRCRu4o5PxPHg66lLiP1sJo4aUoxbSEu2Pv5y+/edLX5yd3RFfP8mimVxlJPiUL2CMDM/BOChrE4gLUcqDpXvtrduD1WxmH0dNtkzGMlN1Djt3nK3DbEJ6QCDPK4xnSy3DQA89MJ+LN24H7eNL77Vf9Ol52Ly6DOTLU+6I6nKgQD83ZgzYbBXs8RNxh1Pt8Bkxuj6GoR0zbcNSqliF8Xy8feH2pNit3VRUU6Kox3AKMfnBgCHSySLUAb4+W63Lpiu9KPbx+QzNc4dN0nHly8elYx9GIqgOxEwd8porH6t3VNIqGmEE6fiybf2YvL9z16AoYNCKUOppjYOw4O/24cnX0ltY8KwuvPeP2cibl/V4rFM7Aw0vz5gNWE9pZ16X1IsH39fB5x7Q1+3XSknxfEqgLFEdB6AQwC+BGBuLieQXlWVRbpZ0RNHDfH8cXT2xDB1ycac/9DTzsBQ8F8vH8Dq19qTfazcMAOPvfS20o8f1AiL1+3Ju0I7F/7xUw3KBeSZPx1U7G2l1L70vauw/Z+vxrqdh3Hvr/fiVDz12dt9wO56Zhd0IlQFNSy8djyaRtaV7K1c9fvTNcKmvUfQfMHZvU7C6KuAc2/py7YrJSkAJKKnAGwD8DEiaieirzNzHMB8AL8F8BcAq5i5NcfzziKi5Z2dnZl3FsqeXGo/bEsjnyKzdOm6KgyTfZWGE1PhxorEzT5RGhqAY91R7Dx4POUZtB/rQUhXx1NCup5MTZ41cYTHeWw/e7sQ8ryzarB1wXTccMlopSLvK1S/v+6IgUVrW/OuUi/2HJH+TkkUBzN/mZk/wsxBZm5g5l8mtm9g5nHM/FFm/lEe513HzPPq6uoKL7TQ5ziruFUV2k7aj/V4gmTZ/qHb18lUuZ0rDODqC85GSCfUhHSEA/6ZRCGdfLvW5kNQJ3zmoT/iy8tfTqmQbxhajVNxtdKLGgY6e6LJXl7pnn19bRjVQb0s3r5Tqv0d7WC6o0beVerFniPS3yknV1WvkeB45ZGt77ZhaLUnjdQOYmcT95g9aSQ0AuY/1VJQ+f/YdhQbvnkFuqPWovyN//qTJ2uJAHzrqrFYtulvOQ1uSocd4LdjLLevasHUxmH4ze53lUWJeqJ+49Yn/pzi5usv7cqdQ7kWrW1FtyPTLZ8q9VIEnPsTFaU4JMZRmWTju7WnzQX1WHJhNEwTP/v9Pqx6rT2ruMcZ1aGcZfvqpaPx5KsHEfUJdId0Ky4wcdQQdHRFlFlLIMLPXtjv6Y6bD4kiek/VedwEtv2tA4vX7/EcE9YJcWbEDEbMsIoXnf78/rJY2kO5/mXN7pTt+VoK/Ulx9jXS5FCoGGrDgRR3T9wEVrx8IOu4x4QRZyAXb1U4oOG2q8bigTleN4lN1DARHxYgAAAgAElEQVTR2RNLXnN+81joDhmDOgHMOSsN3UfOmAlcen698rujXacQ0r3+sIjBcIdddKJ+6c/Pxb2Z7flKGb8pVyrK4hBX1cDGqkrWky0/VKRzW9TXhvHTL0zCnat3wjQ540S9r00dg/ZjPZjaOCx1HkWiPcmpuAHDNHHrE39CTywOIkJVQEdA1zBv6hhc+tF6AITXd2zL6v6CGiFm2taU/34vtnV4iv2COuHyxrNw72/+mtW1uqMGtr/ZAQD97m1bLIXiU1GKQ1xVAwt37CKkaxmzozJlZU1tHIaXvncV2o/14OiJU/j6CnX/Kw3WHIoV295GzDCxaNaEZHbRjKZz0Hq405oXbgAnIrYiO+0K+o+X3sLNV5wPANiV5f3G/JpIKZgyph47DhyDrhEMk3H/nAvROHxwytzzSNyETt4iRZsfb9iL2rCenLlerjUMKvqTi60/UlGKQxg4qIqzztDIE9D8wuQGrNrRnjbA6VfotfPgcVQFNWVhoAlrDoU9uvb7z+4GCLhhyuiUSYJ+1g+bnKxHGVFXBSBW0Ofz6tsfJIPyzrdu59t4TUjHzKVb0p7HTjvOpYahP1RbC72johSHuKpKR18uFn7FWcuaq5RuipsuGYOWg8dTKqjTnevO1TsxZFAQI+pyC6guXtuKGRPOSQbq01k/EYNRE7JiImfWhPCjf/gYFq/bg6BOiBuMuGGmHdKUiXBATwbl3Tjfxp3FfN0+PbeA7DOT+lO1tZA/FaU4xFVVGvpysejoimDT3iPQXX3Hg5qWLKxzLoxu2dxVzqqq40ic8Y3H/wSTOcViicQNaBr5tiaxJ+HddtXYlHRODeRJs60KaikL9Q1TRmPGhNODoba2HVW2gbepDmqImyb8snezzSRyKtrdh6154Solks35+mO1tZAfFaU4hL6nLxcLWwkENPXCFnKlGqlk+/6zu1P89lMbhyktA7vj7aod7Xj8axfjrY6TGFM/CF959JW0Mi7dtD852c9elFsPf2jFO1xKwL0QOxWefeyT2w/gX3+/z2N9MIAfzmpKCcQzM6qDgZxrDpxTDaeMORMtB4/jg5NR/PT5fTnVMKRrESOKo7IQxSHkhNsl1VeLhV8jwpqQDoMtJaAf35/ynV8PKqfffuuC6WktAzYZc3/5CsK6N2ZyMmZ4uuM623YA1qJ85bizkmNhc1mI62vDmDvlXCzdtB9GPPU6C2eOt6wUx/ha+57zdRd6rLOZ49E0IvseVFJtPXBIqziI6HIA5zPzisTn1QDOTHz9/zHzxiLLlxMS4yguKpeU6o29GIuFSgnUhHUsnjUh2chu8+ZUxZEpzmAruHSWgVWBzckCv1U72rF+/uXojhqoCem49ud/RMSxqPvde74pou3HejyTlUIa0DTCaqvjzh7KV1mrrLN71u/B1gXTc7JcpNp6YJCp3GkxgB2Ozx8DcCeAHwK4q0gy5Y30qioezoXFWUwHoKAFV36olIBhctrup/W1YSy8drzvOZ2LvNMyqApqyTnmbvSEm2xiItB+/5yJKfe+8NrxaD/WoywytN1BADzNB/2oCeme+SBRE8nAeqEoVFM/uwHi4zdPwdYF0yUwXqFkclWd4Zj5DQD7mfk1ACCinxRPLKHcSOeS6ouCq3zfZptG1qE2rHu62YZ0Uh4/e9JIjP/IGfjsQ39Unq87YmD3oc6kArD3bzl4HB3dUd/Z5DZOqy1qGJjfPBbj2MTOg8eVz647anhSgt2B9UJQSDeT1FBUPpkUR0ouHzP/o+Pj8MKLI5QrmRaWvlgs8lFQDUOrEXfHIQIaNtx2uSc116Y7aiAc0BE11DUY9zy3BzOarLRbWxE4M5H8kgRU7qAHn9+HOz4Rxzc3vQRK1KE4lY3fwl1oV6C4mYRcyOSq2ktE17o3EtFMANn1LhD6FR1dEfTEDI8bpdA9gPLF3TuooyuCnQePo+29E1nL/UCiitqPbGMjTkWgsgDcrh6VO8gmYrCyl1ZfPndxMwnZksni+A6A54hoDoA/JbZdBOAyADOLKVg+SHC8d9hvz9/8eAzfWbLR8/Zbbj2AbHkB4FTMxJ0XGvj2vS9gfvPYZEoskOpOUhUBOrGzxhbOHG/VNCjmc9uWVqbJgW5XTzYDo1QZaX353LO1HJ0vGKX+dyD0PWktDmZuA3AhgD8CGJP434sALmTmfcUWLlckOJ4/zrdng9Vvv0D5dAtNcfsk/P8mMyJxxoPP78Nl956e/Lam5RBmLt2Cxev2YObSLb4T4da0HMLUJRvxlUe24571e7Dw2vF48uZL8KPrmpRv/A1Dq5MtR5zUhHWlZVBfG8bCmeOtjrg++MUVyuW5A6ef05vvd+c9YU/o32Ss42DmCIBH+0AWoYT0t+KtTG/7kbil+MZ/5IysChRV8Ye71+/BS9+bnmxc6H7j39J2FM7wSUADFs9u8p2/vablEO5ZvwchXYNpWlXo9lzysE7JGEc5Pm8bvxcMqQ4fWGSq4zgBTxa59RUAZuYziiKV0Of0t+KtbN0+LQePZ6UQ1a1HzJQWIipF45ymp2saZjSdkzwfgJRYjLuAMUDA8hsvQs+B3Vh15afKwv2Xif72giEUh0yuqsHMfEZCQfzN/tne3kcyCn2AMwirE+UdhLWD1bnOeM4Vp7x+NReRuIEx9YOyUoiW28mriJZu2q+8F1WgO6RreGL7gaS7y+nGUe+vo646hHBAKxs3VCb62wuGUBxyaTnS+7mWeUBE5wP4PoA6Zp5TChkGCnYQ9pVtW7B19uU5L2R93RnV3SL8T6+8hHDABJgRMRhEhLm/fAXXTRqBtTsPp00zra8NY35zIx58PjV0p5OG1sMf4spxZ6VsVy2gUcPAsk1tiMS9brF0C+6xQj2QPsCZttubFwyhf1OS0bFE9CgRHSGi3a7tM4jor0TURkTfAwBmfoOZv14KOQci9bVhVAf1vCwNVWV5JssjWwsl035Da0I4e3AYz912OTjROTcSNxGNm1i1ox23Xz0uY5rp3Cnnwh23PhkzcMuKHZ4AsCpNdn7zWE+jRacbpxzSmfPB/ezttN3zzqqRtN0BSqYYh7Pgb4jrM5j5V3le9zEASwGscFxLB7AMwDUA2gG8SkRrXZXrQpmSj+87WwvFbz/39iWXBTA0aiCkE6Ku2r0HfvdXbPunqzIu1JpGMFwtPuxAuzsA7E6TBYCfvZBqsfTE4snvyi2dORv8nn2+LxhCZZDJVTXL8fMfXJ8ZQF6Kg5lfJKIxrs0XA2hj5jcAgIhWAvgcgH6rOAyTfVtJVBIdXRF09kQ9qakq37ddJ1ET0rPKdmp77wTufHonogan7Deirgp3rt6FqMMt1H6sBxNCOqKKCUhBXa3EnN1+24/1oCqgJ8e7phzvowTtz/Y9ERGcXl1yzQ3pT+04ZL6G4Acx+4cuiKiOmTt9vvs7Zn417wtbimM9MzclPs8BMIOZb058vhHAFACLAPwIliXyCDMre2QR0TwA8wBg+PDhF61cuTJf0QpCZ08M0Z6TeP8UgWH5xOuqgyWVKVu6urpQW1vr2W6YjKhhzb2w00g7e2JoP9YDgjVOFczQSH3P7n0JVu2FjU6E886qQXVQT+5/8FgP3P9G7fO7t59TDQSqBsEwTbzTecpzzAXnDE7K7ZaHAXykrgrvdJ5KkSnd8fnckwq/511qemIG3ny/G4bP/ZSr3JkQudU0Nze/xsyTs9k3k8XxAhFdw8wp8TsiugZWbceoPGVUoaqKYmbuAPCNTAcz83IAywFg8uTJPG3atAKKlhsdXRFMXbIRt14APPi6tWBUBePYuuDKfvGmtnnzZrifn19L9alLNuJU7PSiGA5oePDzF+KM6hAmjLAS7+y38W8s3ZKyr5uqoJYMytvPMN3+bu74RBwPtxiIxA0wB1MWvIAG/HT8uKQrTHX+qqCB26+egB//eq/n3P/8mQsw7Pz6FOsxGxmd9+SH6nmXAx1dEXxnyUZPg0X7fspV7kyI3L0nk+L4dwCbEsrjfQAgormwLABPD6te0o5URdQA4HAuJyiXliPpWlT3B8Xhxs9lsfzGizxxDSLg9lU7EQ7oKVPp7LGrTqqCGkyTEQ7onmyn9mM9CGjed4mABgR0zTPQyeZ0z6hUqyFuprpZ/GIyZ9aEUBPSU3pPhTTggef3JYc52X5+1TnS3VN/QxofCn6kVRzM/AsiOgVgIxF9GsAXYb39NzPzWwWW5VUAY4noPACHAHwJwNwCX6NPqJRcd9v/39kTVS6yAHnu017QnZ1lT0QSP7tiD8zAhm9ege6o4YkD7T7U6W2FHtDw5Ncv9oxv1Qm+tRxOnMpb1S4kZpqYNGpIiqUCWPMvYJrJYU7OFFtVyxG/e+qP9MeAvlB8Mv61MfN/AbgbwJ9hLeRTe6s0iOgpANsAfIyI2ono68wcBzAfwG8B/AXAKmZuzeW85dKryn5T04j6ReqlKtXV2bfplhU7cCruXWQnjDgjJcU0FNAQTtOHKaxTihVhmCb2vPOhp/itoyuCe57z5kQsmjUek8+rx8KZ41P+4Woa+VogTiKGmRyApGoXct/1F2JoTQi3TmtEOJC4J508SslWQH7naBw+uN8U9GVDOfXJEsqDTOm4r8Oy+QnAIAD1sFxXAABmvjCfizLzl322bwCwIZ9zAuXjqgKsN7UXPtiHx6/4ZFm/qaliFzUme1xTAc2KX4T0VJeF8400Fjcw95evwLdWlAiaw2pxu49slGNiQzqaRtRhTcsh/ODZ3SkdqmIuS4YABHSrD9SpmImAZl2LmDFz6RYsvHY87nluj6ddyIlTcUxdsjFhTTHmXXk+PtN0DmYu3ZJy/phpJrPC3OeY2jgsuwcvCP2YTDGOf4Q1sOmga/to5Bh/6AuYeR2AdZMnT76l1LIA1phRe1JcOeIXu3jg8qBn4a4OBrDshk+hrjroUYT1tWH8Zve7WLyu1fI/wXIdGSbDNBlVIR2Gybh1WiOWv/hGihtLFftRjollRk1Ix12rd7o9Xh5CAQ3P3WbNBY/FDcx9ZDsAq5ocBmPxulaEAqlWREAnLF7XmpL2u2xzG+ZOOVfp5++OGp5nFPJJ+RWESiOT4vg/AP6Zmd92biSisxLfzVIeVSLKyeLoD/gFiAEoYzQTRpyhXBSfePltfP/Z3an7G2Yi9qAjFjfx3U9/DBd8ZLBvrYeznsIvKNsdNaCTBiD92NSQriXngu88eNwzzS+oa55aj1jctFJtHdvTjcbt6IpURBxLEPIhk+IYw8y73BuZeYeigK/klJvFUe74BfGrgyHcd/3Hs8qm6eiKYPF6bzzCMK3l3VYUP/71XtSGdZhsub2qg4Hkebe0HVVWJ7sX67b3TiCmCEa7cS7gftbLolnWoKagpuFU3EjUqLiC4kb60bi3TmvE0k1tHvedIFQ6mRRHVZrvyu7VSiyO3PB7s9eP7886m8ayWoBoFtezs6TCAQ3LbvhUss5jaqJWQFWdbF83Ods7sUg70TWCYVqFh+5EBL97nD1pJGZMOAethztxy4odiCncX4ZpYmvb0ZQ2KB1dETyx/QCWbdqPkK7DjoU4Jw4KQqWTSXG8SkS3MPMvnBuJ6OsAXiueWPkxEC0Ot4snV1QKYvPm/QCya4+x+1AnuqPejCadPNm3ju8oef6dWczLUM6y0ACGpTCMRGoTM7B+/uWe0bDuLrrdUQNt751I1GoQQrqOSNzbZsQdvF/Tcgh3rd6FSCIt1z7GjoUIwkAhk+L4NoD/IaIbcFpRTAYQAvAPxRRMyEyh2pjn2z/JL232nz97Ac45oyrZettZTAec7jh7/xyr+lzlLqsJ6ck+X6pYTEDTcCqeehyDcbizRzlTvL42nHSJsWkFyquCGpgty8IPW4kBwIJnTisN1T5icQgDhUwFgO8BuIyImgE0JTY/x8wbiy5ZHgwkV1VfN6BTWTbKtNmwjinn1WPiqCHJt/zdhztx97pWROKnTRC74+zWBdNx3/UX4s7Vu5Iupy9MbsDMpVuSCnHhzPHeOIXvYq+uI1FZLfbPQZ0QDlgV6d0RdfA+3ahaCYoLA42sBjkx8yYAm4osS68ptauqt26jXCjmCE93V18/y0YZeDbZE1CeOGoIRg2txjce/xNOOqwPW96EowlgArOJp145iJgjLfae9XuStRdJZXLteCxauxtOA4CAZNwkm+dlUxXQseyGT6KuOoTdhzpTruOMl6hG1YYD5T8nXBAKTS4TAIU0qBbXYs7WLVZbkzUth9D+7gk8/IftyQX67vV7lFPtcullNGFEnafrrLOQzrJG1H2mgpqGppF12LpgeopiHlwVwJ2rd0InDXHTxNlnWLkcKgWebka5lWpcl1RyM5rO8RxfXxvGFyY3YMW2A8njrr7gLCyZM1GUhjDgEMVRAPzcRsua0yWl9Y5iNKDr6IrgrtW7MP/jnOwv9YM1uz1Bbqdlk232VbraDD9LwMZOi3XHYuxr21lOR09EMOXHvwcRWXM1XIOH7Os7YxwAPM9NFfPp6Ipg1Y72lG2/3/s+frP7XdxwyejMD1cQKoiKUhylinG0H+sBm6mrKydmVxSTqY3DsPzGyQA4+cbcG57YfsAT/FVlRjnrG4DsgusdXRGMrq/B+vmXpzQAVBXSuZnf3Jj2/P93cxsicYbBnHBdcXIYk9M6UmVXZetW9OvWu3hdK2Y0nSNWhzCgqCjFUaoYR01It9pZOIgYVl1BsShURpVNR1cEyzbtz2rfr00dk9NCqZLVbsXitARUGVjhAPmmunZ0RbBp75Fkeq8Kp3WUawzKub/VCTf7yYKCUMlUlOIoFd1RA1VBzTPwRjVJrhAUI6Oq/ViPsp4hoBHiLmvq0a1v4eMfOSMrRZWNrE5LYPfhzmRFdzr32xMvv43F61oR0ICTquq9BHbcJ1dFq9r/ukkjPO4qg1kyqoQBhyiOAuC3cIR0y4de6GyrYmRUqYLH4QDhB7Mm+KbSZqOoVLLqGmHT3iNovuDslOBzMjg9wRucduLsjRX16UBSE9JhMOO+660Gzm7l9d2nd2L8R85Q1nyolN2dq3dB1fV34bXjxdoQBhyZp98IGbHdLfZcCrvtha5RylyLqUs2Ym3LoV5frxgZVc570BOtO+6fMxE3TBmNX9w0GYNCqeNRnYVxucraHTHwgzW7fZ9HuvkPbe+dwKJ1mce0XDdpJLYumI6pjcOwae8RT3wiajA++/MtyuurJjjqGiUaLJ6mJqyjaWRpZ78IQikQi6NAqLKLXti4ryhFesUa6WnfwyvbtqTMyfZLpc2kqGxLy67BMB2NBE/Gcn8ea1oO4c7VuxDP1FcdwNOvHcD4EWfgnuf2KGMnABD1sZz86lPcFoezZkUQBhIVpThKXTnuzi7qiRnQXJXMhSrSK9ZIz/raMKqDuic9NVdF5Y4R3H71ONz/u7969tOJsnoetvsoqmj5oSJqqFOJ3ah+H373C0DmbwsCKkxxlCKryh2/sD/vPtSJ4x0ncTKW6uIpZHuKfHtM5UMuikoVI3jg+X0I6eSZ1hczsnse6Sq/VQF8wL/JYsr1fX4ffvcr87cFocIURzFIF9h2v1V/4aIGrHqtHQGN0BUxcMcnUleuQrWn6G2wvaMrgtbDHyLX+o9sFZUyeK8TYgprYdGsCSldcP3uK13lNwj44uQGIPJWVvcBAIOCOkxw2t+H6n77UlkLQrkiiiMN6VI4VW/VK14+4HuuQSEdD3/lU7hy3NlFkynb4+9Y1ZLs8RTUCQ9+fmKvakDc+MUIFs2aYMUcNMvy+O6nx6FpZB06uiK+w5xsbPfRHU/v9FgtcYPxqz+141tNqW5Be9a4m5BOePjGi3wnGgqCkB5RHD5kqj9QVYunw2Tr7T6X67srnAFvWmm2wWXLyujEnU+3pCymMYPTpqY6ZbFlyLe9yOxJI5N9oJzNBKOGCcM0ETeRNl2WgURRpfe5x0wAzAgHtOREvlunNeLfX/xbcoCUzW3Tx+LKcWdlfF7ikhIENWWvOIioBsD/hTVkbjMzP9EX181Uf6CqFlfhrCfIdgGyrQp3T6VbpzXmXL9xemJdG3SNlHUPUYPx2Yf+iAcUlofTwumJxUFECAesmd2LZo3HDVPUfZr8YgT2f7+4fFuKAlTK9PMteGCOpXBsRa6ah2GjEeEXN01GXXUwqeSWbW5L2Scc0DIOXSp0Vb4gVBolURxE9CiAmQCOMHOTY/sMAD8DoAN4hJnvBfCPAFYz8zoi+m8AfaI4/OoPFq1txb+s2Y1bpzV6qsUDGqBpGsKJN96RQ6rx5C0X5/TWmm5uxNJN++GeN5Eu2G5NrNuZUrznR9TgpPWikuX0As/Jud/f/5/dACPZ5M/9lu4XD/Dr++SRyZEumy44bsOAx/2UazZYX885EYT+SKksjscALAWwwt5ARDqAZQCuAdAOa2ztWgANAF5P7OZTJ1x4UnooaZQc8GPXA6gW8YCupTTxe33HtmRPJifp3CDpFsiQrmPeledj2ea2jAvh6Tf07N1p7qK+bBbrxetaMeW8M7Fh97vJOdyZ3tJ3H+r0uI8yyaRS5EGdoBGS12wYGvI8i1zTlos550QQKoWSKA5mfpGIxrg2XwygjZnfAAAiWgngc7CUSAOAFvRxpbu96GzaewSL1ramFJH5LeJ+cQKbTG6QTHMj5k45F3OnnJtxIUy36IcDGn44azx+sLY1JdBsWy/HspDFhgj4zEN/TJ7H7nXlfEt3Kspj3VH8cN3utOd037N9nyrrwakUXt+xTXmOXDKhijXnRBAqCeIiNeLLeGFLcay3XVVENAfADGa+OfH5RgBTACyAZZ2cArDFL8ZBRPMAzAOA4cOHX7Ry5cqCyWqYjL3vnkipntaIcME5lpKIGiZCugbd5X7p6upCbW1tVudxHtvZE0u++Zt8ustuw9Bq1FUH85bZvp59Hvs6drj5rMFh1NeEcLK7C4GqQQjpGroi8eQ+JrMiLK1GJ8J5Z9UgGjdTjgcIqrNQ4h7t/fzu2Ui0q8/meeeL+7nk8tzzoVBy9zUid99SbLmbm5tfY+bJ2exbTsFxldObmbkbwP/OdDAzLyeidwDMGjx48EXTpk0rqHDdLYc8b7tXZQiYbt68GU45dh48jof/sD05JAkABocDePyKT3pcWqqsqlxdJU6Zo4aB+c1j8aUp56acxxk8D+lAT+wkvt3E+Pcd8eR9fumK02/1v9n9Lhava0VQ1xAzGcSsTBKoCmpYf9XFmLl0C065iiBVhAManrvNcvPle8/u550tKtdhX2ZV5St3qRG5+5ZykrucFEc7gFGOzw0ADudygmJWjheixUcubhDbvdLRFUF3NHMzwd7IvGxTGyJxM5mxxHx6AuBdz+zC1gXTk4rthktGJ1Nqa0I6Zi7d4inRtgsdu6NGVkHwoE64f05mN18x8HMdSqGfIPhTTorjVQBjieg8AIcAfAnA3FxOUOxeVb1dTHLt+VSItNBMMqum/jnx6+Vkf3beT9QwMb+5MZnu+os/vpE2CB7UCN++eiz+fsI5JVEakkElCPlRqnTcpwBMAzCMiNoBLGLmXxLRfAC/hZWO+ygzZ+6f7aBUEwBzIVsrIJtFLZ07JZu2ItlM/csUGFbdT7o04KBuzQPvicUBAh7+wxv4+aa2ktRKSAaVIORHqbKqvuyzfQOADfmet9TdcbMlG8sl06KWzhrJtq2I39Q/AmFwOJAx3ddds2Fv90sDrgnp+LevXAQAuGXFDkTipnI2eF8hGVSCkB8VNciJmdcx87y6uv4/XCfdoua0Rk5E4jgVswrlOroi6OiK4K7VOz1tRe5cbX2f6RohHRh1ZjWW3fApbF0wXWkFpBtOpRqCZGMwY8KIM1BXHUxOR7TJdjBUIfEbwCXWhiCkp6IUBxHNIqLlnZ2dfXK9jq4Idh487lmQC4HfogZAOdHOXnjbj/V4JtUBVrsU98LsvkZAAxiEQ8d6MO+/dmBr21HPvba9d8JXaQH+tR/hwOlFuZze9GcnJgU+fvMUX0UpCEIq5RQc7zV9GePoi35G7vjBlrajmLpko3KinXPhNdi7cPtNq7Ov0Xr4w6T7yGBOKoSpjcNSOtdGDBPkqg1xxwVundaIpYkqcjsNeK4jDbhYEwzzRTKoBCE3Kkpx9FWMo7fZOLnUCDjTct09rABr7nXcYNw6rTG5//1zJuJ2V4zj/jnp507Y7iNnhlVQ09B6+ENFv6pUbKXlVKYAYd6V56coDCfFmmAoCELxqSjF0VcWR2+ycZ54+W0sXr8nMQ3PxPzmsfhM0zkZC95U16wOaPj78cPx3OvvYPmLb2DZ5tPZSeM/cga2tB3FsNoQLv1oeoXW0RVBZ08MUcPrPvqwJ5as4rapCmowTUY4cLovFeBt+b5sc1vaTrS2TLYLTZSHIPQPKkpx9BX5+uifePltfP9Zq09TNJHI9ODz+/Dg8/sQ1gmkka/LS3XNnriJX/3ZqpF09og6cSqenHWRyY3mtBIM00RQJ+hEqApq+MLkBty+6s/KVuwbvnlFirLbefC48vzplKm0LxeE/okEx/Mgn2ycjq4IFq/zL0uJGOwJNKuuGQ6kr8TWCPjhulbf4LVbJmegO25ax59bPwiPf+1iPPHyAY/SsIPcjcMHY+KoIcl7rgnpHjfaqZiJmpC63Ui6zDBBEMqbirI4iuWqUsUknD56u7eSveg5p+XZtB/rQVC3ekalI53La/akkdCI8N2nd+KUT7X3yah3u0aE1sOduHLc2Sn30n6sB6ZriiGz1eTvO7942d1JBADw4OcnYubEEZ7t3VEDYZ1S+laFdELLweMYWuNtdy7Fd4LQf6koxVEMbHeKTlZMYtGsCcnBRfW14ZSMo1NxA6ZpTeyLmcCPL9Gw8+BxNAytRsPQahhZdCLOdzDToJCmVBoAcDJq4JYVO/DFvxuFVTvak66hT48fjqhLO0QNxgfdMcRM9T+NM6rV2xuGVoM0SulbFTU4Ofgqm/bxUnwnCP2DinJVFRqnO6U7aiBqMKrSzNwAABG5SURBVL7/7G48sf1tz/cnInHEDIbBQHfURDRu4p3OU/j8v23F1CUbsbXtaNK9VRPSoVOi/UZiLGw48XOug5nCAQ0/+ocm3D27ydctBACROGPFtgMprqG1O9/J6XkEdfKdm+5039WET8vRHTWUbigpvhOE/ktFWRyFTse1ium8MYXF6/ZgxoRzspqQFzUBmGayy+zWBdNTXFnZtk5XXWtQUMfDN16EK8edhY6uCP5lTfYDkvLhm9PHpl3Y0w2+Urmh3Cm5AJIWmigQQShfKsriKHTLkYah1YgZXqUQ1Ml3nKkfzoXTDirbP7sDzb6yuK5lwmrhAVhv8AuvHY+QTqgJ6QgU+DcbDlDa1Fqb+towmi842+OWS9c+fuKoIcniRlUbE0EQyouKUhyFpr42jEWzJni221XYpzOdMj/Gnlg8a/+9qpVJJtfOmpZDuOe5PQgFrPbmpiKc8oXJDSnH33TpuUnXWUgnfGHySFQFNehECGiATla2VFVQw/1zJmZtBeTqhpIMK0HoX1SUq6oY3HDJaIAs91RQJxgmpyyCtrvlye0H8NDG/SkzvJ2QwuWlIl1tg1+1tV9VuZOakI4bpozGghkXpBz/ravGpXxeMOPjeGXbFmz/58txrDuKloPHMSlhFeVCLpXhkmElCP0LURxZcMOU0cmYhmoRrK8N47arrArwzz70R0+mEgBUBfSMC2E2rUxUfZWyibUYfNpK8hvKZH+uDuop2WLFGiJlIxlWgtC/qChXVTELAO2uru3HenxdKI3DB+OBz09UFullsxCqWpJn0248U6wlpFPabC23W8wwuU9dR5JhJQj9i4qyOIrZqyrb9hhO15X23l8yDkRyku+bt73w3rl6l2cMbCigYcNtlytdTe57uv3qcTizJoRgJO6xYJxFhMVAmh4KQv+hoiyOYpFr8NZ2XV1wzuCc5jz05s179qSReOl703HHNeMQDlDy+AfmXKhUGqp7+vGv9+K7q3fhwAcncTKWWuFuFxEWM9vJmXEmCEL5UlEWR7HIN3ira4SJo4akbMvUUr03b962wpo75dyMx2eKixgmI6RrKR1zI3EuyYhXQRDKC1EcWVCo4G227q5cBgvlMtvDeUxnTzRj36ybLh2NJ7cfSLE+JNtJEARRHFnQm4l19sJeE9J7NfxJhUoRMZBWOTmPMRkIaEAooOOkonf6jAnD8XiivYqNZDsJglD2ioOIzgfwfQB1zDynVHLk40Lq7Ilh6pKNWY9czQVV6u4dT7eAQIgarFROqmPCAQ0Pf+UirN91CKt2nI5ffGHySAQDOhZeO94z20OsDUEY2BRVcRDRowBmAjjCzE2O7TMA/AyADuARZr7X7xzM/AaArxPR6mLKmg25TKyzLY1TMT3jyNV8UMUoLI+Sv3JSHRPSNdRVB3HfnEmYd8VH0XLwOPDuHizb/g5+/fp7iJkmFs4cj6YRdZLtJAgCgOJbHI8BWApghb2BiHQAywBcA6AdwKtEtBaWEvmJ6/ivMfORIsuYNblMrGs/1gN3NYdq5Kqz+jsXaybbPllO5ZQpVtM4fDCG1oSwct+fUxTePev3YOuC6aI0BEEAUGTFwcwvEtEY1+aLAbQlLAkQ0UoAn2Pmn8CyTsqSbKq6nTQMrYaq+Yh75CqQ3whVO+7y3ad3KivVBwV1mEhtj5JNrEal8CQgLgiCE+Ishgv16gKW4lhvu6qIaA6AGcx8c+LzjQCmMPN8n+PrAfwIloXySELBqPabB2AeAAwfPvyilStXFvQ+emIG3ny/O6Xrq06E886qQXXw9PwJw2REDRMhXUNXVxfauxgEy4HUMLQaddXBlPMaJmPvuydgOs6rEeGCcwZD1zL3t4rETew/0gV2HT+6fhCqg7ryHE4Z3d8bJuOD4x/iXUexei7ylJKuri7U1taWWoycEbn7FpFbTXNz82vMPDmbfUsRHFetPr7ai5k7AHwj00mZeTkRvQNg1uDBgy+aNm1a/hIq6OiK4DtLNqY0EqwKatg6+/IUy+Gu1TuhkwaDTdw3tQpfmn1lWhfUzoPH8fAftuNEJJ7cNjgcwONXfNJTA+JHpOWQx4qYmWNfKSdrfv08lv05nnK+q3pxvr5i8+bNKPTvvS8QufsWkbv3lEJxtAMY5fjcAOBwIU5czJYjAHDrtEYs3bQfIV0do7hjVQusjh9WauvBD07iWHc07TkLUSOiyvjKp77Dpq46iK0L0is8QRAGLqVQHK8CGEtE5wE4BOBLAOYW4sSFngBo44xBAIR5V56PuVPOTVlQWw9/CFebKDCAzzz0R1Q5guHu2EVvakTc5+lNzCTd+QRBEJwUOx33KQDTAAwjonYAi5j5l0Q0H8BvYWVSPcrMrcWUozeoguLLNrcppuGpvW0xgxEzLDeUXzB9auMwLL9xMgDGhBF1vVqwcw3iC4Ig5Eqxs6q+7LN9A4ANRbhewV1VqtoHXSNs2nsEzRecnVyMJ4yoQ1An30FOgDo7qRDWQSZ5JStKEIRCIt1xM6CKQXRHDCxa25oyG3tL29GUzCadvFkA7thFMUamylAkQRCKTUUpjmIMcnK2Oq8Jn0677Y4ayYW+7b0TWPDMrpQYh8Gnx8WGdVK2SM93cFO28spQJEEQikHZ96rKhd66qvwykeyspU17j2DR2lZ0R1O7xbYcPK5sUW7XZjARnpvvHaZULOtAhiIJglBMxOJIsKblEKYu2YivPLI96YJyjlWtrw2j+YKzUwoAAWuhnzRqSNr2H0GdUpSNTTGtAxmKJAhCsRCLA36dZndCI6TUbMyeNDI5olXXCIZptfRoHD44bfuPmMG+VoRYB4Ig9DcqyuLIF1VMIWYwInH2BK0TzqdE9u1pJTF70khs+OYVCAW8j3TRrPFpFYJYB4Ig9CcqSnHk66qqCekprURUBDUNrYc7seCZXYjEGSdjRnKUqp0F1Th8MB6Ykwikh3QQEX50XRNumDI673sSBEEoN8RVBStDKqwTImlqMKwYBmWskXC6no7u/zOuukSUhiAIlUVFWRz50jC0GuTq/BrUCeEApQStJ4w4I6ssKNv1VO7dZAVBEPKhoiyOfPHrF6VqHJiu0aEgCMJAoKIUR2+aHPp1mLXJptGhIAjCQKCiFEdvCwCdHWGfePltLF7XiqCuIW4yDNNE3ESGRoeCIAiVj8Q4FDzx8tv4/rO7ETUY3VEDkbjpaZne29YggiAI/RVRHC46uiJYvH5Pxv2kcaAgCAOVinJVFYL2Yz0I6YRoPHW7rgEBjSQoLgjCgKeiFEchJgA2DK1G3PTWc1z/qQYsmHGBtAYRBGHAU1GuKmZex8zz6urq8j5HfW0YC68d79m+dqc1Fl1agwiCMNCpKMVRKJpG1qHWMXsDkGC4IAiCjSgOBSp3lQTDBUEQLERxKJApeoIgCP5UVHC8kMicDEEQBDVlrziI6DoA1wI4G8AyZv5dX13bWUkuCIIgWBTVVUVEjxLRESLa7do+g4j+SkRtRPS9dOdg5meZ+RYAXwXwxSKKKwiCIGRBsS2OxwAsBbDC3kBEOoBlAK4B0A7gVSJaC0AH8BPX8V9j5iOJn/8lcZwgCIJQQojZf3hRQS5ANAbAemZuSny+FMAPmfnvE5//CQCY2a007OMJwL0Anmfm36e5zjwA8wBg+PDhF61cubKAd5EfXV1dqK2tLbUYOSNy9y0id98icqtpbm5+jZknZ7NvKWIcIwEcdHxuBzAlzf63AbgaQB0RNTLzw6qdmHk5gOUAMHnyZJ42bVphpO0FmzdvRjnIkSsid98icvctInfvKYXiUI3F8zV7mPkhAA9ldeICtBwRBEEQ0lOKOo52AKMcnxsAHC6BHIIgCEIelEJxvApgLBGdR0QhAF8CsLYQJy5ErypBEAQhPcVOx30KwDYAHyOidiL6OjPHAcwH8FsAfwGwiplbC3S9WUS0vLOzsxCnEwRBEBQUNcbBzF/22b4BwIYiXK9Xo2MFQRCEzFRUryqxOARBEIpPRSkOiXEIgiAUn4pSHIIgCELxqSjFIa4qQRCE4lNRikNcVYIgCMWnohSHIAiCUHwqSnGIq0oQBKH4FL07bikgovcBvF1qOQAMA3C01ELkgcjdt4jcfYvIrWY0M5+VzY4VqTjKBSLakW2b4nJC5O5bRO6+ReTuPRXlqhIEQRCKjygOQRAEISdEcRSX5aUWIE9E7r5F5O5bRO5eIjEOQRAEISfE4hAEQRByQhRHkSCit4jodSJqIaIdpZbHDyJ6lIiOENFux7Yzieh5Itqf+O/QUsqowkfuHxLRocQzbyGiz5ZSRhVENIqINhHRX4iolYi+ldhets88jcz94XlXEdErRLQzIfvixPbziGh74nn/d2KoXFmQRubHiOhNx/OeVDIZxVVVHIjoLQCTmbms88WJ6EoAXQBWMHNTYtt9AD5g5nuJ6HsAhjLzglLK6cZH7h8C6GLmB0opWzqI6CMAPsLMfyKiwQBeA3AdgK+iTJ95Gpm/gPJ/3gSghpm7iCgIYAuAbwG4HcCvmHklET0MYCcz/1spZbVJI/M3AKxn5tUlFRBicQx4mPlFAB+4Nn8OwH8mfv5PWItEWeEjd9nDzO8w858SP5+ANQVzJMr4maeRuexhi67Ex2DifwxgOgB7AS635+0nc9kgiqN4MIDfEdFrRDSv1MLkyHBmfgewFg0AZ5dYnlyYT0S7Eq6ssnH3qCCiMQA+CWA7+skzd8kM9IPnTUQ6EbUAOALgeQB/A3A8McYaANpRZorQLTMz28/7R4nn/X+IKFwq+URxFI+pzPwpAJ8BcGvCtSIUl38D8FEAkwC8A+DB0orjDxHVAngGwLeZ+cNSy5MNCpn7xfNmZoOZJwFoAHAxgI+rdutbqdLjlpmImgD8E4ALAPwdgDMBlMyVKYqjSDDz4cR/jwD4H1j/YPsL7yX82rZ/+0iJ5ckKZn4v8QdnAvgFyvSZJ/zWzwB4gpl/ldhc1s9cJXN/ed42zHwcwGYAlwAYQkSBxFcNAA6XSq50OGSekXAZMjNHAPwHSvi8RXEUASKqSQQRQUQ1AD4NYHf6o8qKtQD+V+Ln/wVgTQllyRp74U3wDyjDZ54IfP4SwF+Y+aeOr8r2mfvJ3E+e91lENCTxczWAq2HFaDYBmJPYrdyet0rmvY4XC4IVkynZ85asqiJAROfDsjIAIADgSWb+UQlF8oWIngIwDVbnzfcALALwLIBVAM4FcADA55m5rALRPnJP+//bu2NQKa4wDMPvFwPGIHZiLNTCYKFBK4tEG1F7rSRiEYuQIDbpglEQq5hOsbAX4ZomIKhgcRFELSyUhCsacoNgExCrhCgY/C3mLF4X1zjr1bvF+3Q7c2b2zMDuNzNn5h+6yyYF3Ae+GYwbTIokW4GrwG/Aszb5EN2YwUTu89f0+Usmf39vpBv8XkR3oPxzVR1rv9Epuks+t4B97Uh+wb2mz9PAciDAbeDbOYPo77ePBockqQ8vVUmSejE4JEm9GBySpF4MDklSLwaHJKkXg0N6C0k+STKVZDbJnSQXk6xL8rhVML2T5HSSD1r7DUmmk/zeKrMeafflk+SrJA/bcneTfLewWye9msEhjan94f8CXKmqtVW1nu75hhXAbCsZsRFYD+xqD3OdB36sqnXAJuAL4MCc1Z5ry20Bfkiy6v1tkfRmDA5pfNuAp1V1ejChqm4DD+Z8/g+4DnwK7AWuVdXlNu9f4CDw/fCKq+oR8AewcnietNAMDml8n9G9m2KkJB8D2+meut4w3L6qZoGlSZYNLbca+Aj4dT47LM0Hg0N6N9a2stjXgAtVdYmuVMSoUg2D6XuSzAB/Aieq6sm776rUz4f/30TSCDO8KJQ3bDDGMdz+pfL6rWbSP1X1dxsjP1dVB5N8DlxIcqmq/prvjktvwzMOaXzTwOIkXw8mJNkMrBnR/iywNcmO1nYJcBL4abhhVd0AztC9MlSaKAaHNKbqKoTuBna223FngKOMeLdDVT2me0Xs4ST36MY9bgKnRnzFcWD/oES/NCmsjitJ6sUzDklSLwaHJKkXg0OS1IvBIUnqxeCQJPVicEiSejE4JEm9GBySpF6eAxMa9S7gUtW1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "core_data.plot(kind=\"scatter\", x=\"CPOR\", y=\"CKHG\")\n",
    "plt.yscale('log')\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can further refine the scatter plot by removing the scientific notation on the y-axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.axis([0, 40, 0.01, 100000])\n",
    "ax.plot(core_data['CPOR'], core_data['CKHG'], 'bo')\n",
    "ax.set_yscale('log')\n",
    "ax.grid(True)\n",
    "ax.set_ylabel('Core Perm (mD)')\n",
    "ax.set_xlabel('Core Porosity (%)')\n",
    "\n",
    "#Format the axes so that they show whole numbers\n",
    "for axis in [ax.yaxis, ax.xaxis]:\n",
    "    formatter = FuncFormatter(lambda y, _: '{:.16g}'.format(y))\n",
    "    axis.set_major_formatter(formatter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can derive a linear regression by using the polyfit function from numpy. As we are working with a semi-log plot, we need to calculate log base 10 of the core permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "x = core_data['CPOR']\n",
    "y = np.log10(core_data['CKHG'])\n",
    "\n",
    "model = sm.OLS(y, x, missing='drop')\n",
    "results = model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>          <td>CKHG</td>       <th>  R-squared (uncentered):</th>      <td>   0.793</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.793</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2129.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Thu, 05 Nov 2020</td> <th>  Prob (F-statistic):</th>          <td>2.80e-192</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:18:49</td>     <th>  Log-Likelihood:    </th>          <td> -726.48</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>   557</td>      <th>  AIC:               </th>          <td>   1455.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>   556</td>      <th>  BIC:               </th>          <td>   1459.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     1</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "    <td></td>      <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>CPOR</th> <td>    0.0949</td> <td>    0.002</td> <td>   46.145</td> <td> 0.000</td> <td>    0.091</td> <td>    0.099</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 9.398</td> <th>  Durbin-Watson:     </th> <td>   0.654</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.009</td> <th>  Jarque-Bera (JB):  </th> <td>   7.595</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.198</td> <th>  Prob(JB):          </th> <td>  0.0224</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.588</td> <th>  Cond. No.          </th> <td>    1.00</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:                   CKHG   R-squared (uncentered):                   0.793\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.793\n",
       "Method:                 Least Squares   F-statistic:                              2129.\n",
       "Date:                Thu, 05 Nov 2020   Prob (F-statistic):                   2.80e-192\n",
       "Time:                        23:18:49   Log-Likelihood:                         -726.48\n",
       "No. Observations:                 557   AIC:                                      1455.\n",
       "Df Residuals:                     556   BIC:                                      1459.\n",
       "Df Model:                           1                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "==============================================================================\n",
       "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------\n",
       "CPOR           0.0949      0.002     46.145      0.000       0.091       0.099\n",
       "==============================================================================\n",
       "Omnibus:                        9.398   Durbin-Watson:                   0.654\n",
       "Prob(Omnibus):                  0.009   Jarque-Bera (JB):                7.595\n",
       "Skew:                           0.198   Prob(JB):                       0.0224\n",
       "Kurtosis:                       2.588   Cond. No.                         1.00\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RankWarning: Polyfit may be poorly conditioned\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "x = np.polyfit(core_data['CPOR'], np.log10(core_data['CKHG']), 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, nan])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result of the regression is: $10^{(0.16911398  * CPOR - 1.61346487)}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using the code from the previous plot, we can now add a new line with our x-axis set to porosity and our y-axis set to an equation. Note that we have to use the equation above to reverse the log base 10 function used when deriving the function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.axis([0, 30, 0.01, 10000])\n",
    "ax.semilogy(core_data['CPOR'], core_data['CKH'], 'bo')\n",
    "\n",
    "ax.grid(True)\n",
    "ax.set_ylabel('Core Perm (mD)')\n",
    "ax.set_xlabel('Core Porosity (%)')\n",
    "\n",
    "ax.semilogy(core_data['CPOR'], 10**(x[0] * core_data['CPOR'] + x[1]), 'r-')\n",
    "\n",
    "\n",
    "#Format the axes so that they show whole numbers\n",
    "for axis in [ax.yaxis, ax.xaxis]:\n",
    "    formatter = FuncFormatter(lambda y, _: '{:.16g}'.format(y))\n",
    "    axis.set_major_formatter(formatter)\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our next step is to calculate a predicted permeability using our new equation and add it as a new column in the pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "core_data[\"PRED_PERM\"] = 10**(x[0] * core_data['CPOR'] + x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>DEPTH</th>\n",
       "      <th>SW</th>\n",
       "      <th>SO</th>\n",
       "      <th>CKH</th>\n",
       "      <th>CPOR</th>\n",
       "      <th>CGD</th>\n",
       "      <th>PRED_PERM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "      <td>108.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10761.782905</td>\n",
       "      <td>35.509717</td>\n",
       "      <td>40.728041</td>\n",
       "      <td>114.365102</td>\n",
       "      <td>19.677500</td>\n",
       "      <td>2.661935</td>\n",
       "      <td>106.406799</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>28.333169</td>\n",
       "      <td>12.124610</td>\n",
       "      <td>11.880550</td>\n",
       "      <td>116.074693</td>\n",
       "      <td>4.366594</td>\n",
       "      <td>0.015253</td>\n",
       "      <td>108.960627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10715.701150</td>\n",
       "      <td>21.489047</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.045000</td>\n",
       "      <td>2.360000</td>\n",
       "      <td>2.647000</td>\n",
       "      <td>0.061044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10738.079762</td>\n",
       "      <td>26.733086</td>\n",
       "      <td>36.095689</td>\n",
       "      <td>36.319000</td>\n",
       "      <td>18.607500</td>\n",
       "      <td>2.655000</td>\n",
       "      <td>34.145923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10760.648600</td>\n",
       "      <td>31.393546</td>\n",
       "      <td>42.411968</td>\n",
       "      <td>96.861500</td>\n",
       "      <td>20.330000</td>\n",
       "      <td>2.657000</td>\n",
       "      <td>66.788949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10783.054388</td>\n",
       "      <td>41.666675</td>\n",
       "      <td>47.502606</td>\n",
       "      <td>151.569250</td>\n",
       "      <td>22.137500</td>\n",
       "      <td>2.663000</td>\n",
       "      <td>135.011588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10811.411500</td>\n",
       "      <td>77.868532</td>\n",
       "      <td>99.226071</td>\n",
       "      <td>788.070000</td>\n",
       "      <td>25.280000</td>\n",
       "      <td>2.758000</td>\n",
       "      <td>458.919473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              DEPTH          SW          SO         CKH        CPOR  \\\n",
       "count    108.000000  108.000000  108.000000  108.000000  108.000000   \n",
       "mean   10761.782905   35.509717   40.728041  114.365102   19.677500   \n",
       "std       28.333169   12.124610   11.880550  116.074693    4.366594   \n",
       "min    10715.701150   21.489047    0.000000    0.045000    2.360000   \n",
       "25%    10738.079762   26.733086   36.095689   36.319000   18.607500   \n",
       "50%    10760.648600   31.393546   42.411968   96.861500   20.330000   \n",
       "75%    10783.054388   41.666675   47.502606  151.569250   22.137500   \n",
       "max    10811.411500   77.868532   99.226071  788.070000   25.280000   \n",
       "\n",
       "              CGD   PRED_PERM  \n",
       "count  108.000000  108.000000  \n",
       "mean     2.661935  106.406799  \n",
       "std      0.015253  108.960627  \n",
       "min      2.647000    0.061044  \n",
       "25%      2.655000   34.145923  \n",
       "50%      2.657000   66.788949  \n",
       "75%      2.663000  135.011588  \n",
       "max      2.758000  458.919473  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then make a quick plot of our prediction vs the original measurement. To get an idea of how well the prediction did, we can add a 1 to 1 line to aid the visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11bb11128>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "\n",
    "ax.axis([0.01, 10000, 0.01, 10000])\n",
    "ax.loglog(core_data['CKH'], core_data['PRED_PERM'], 'bo')\n",
    "ax.loglog([0.01, 10000], [0.01, 10000], 'r-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}