petrel2python.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"petrel2python.png\" style=\"width:350px\" align=\"center\">\n",
    "\n",
    "<h1><center>Petrel to Python 1</center></h1>\n",
    "\n",
    "Notebook created by **Manuel David Soto**, E&P Technical Direction. Data, from a Demo project, provided by **Ernesto Tundidor**, Accenture. May 2021"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective\n",
    "\n",
    "The objective of this notebook is to compare thickness obtained in Petrel from tops and from a seismic surface, and then find the best regression between them.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import r2_score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data loading\n",
    "\n",
    "The data collected from the Petrel's spread sheets can be saved in the Clipboard, from there you two options:\n",
    "\n",
    "* Take the data directly from the Clipboard by the code bellow\n",
    "* Take the data from files (csv preferably) created from the Clipboard\n",
    "\n",
    "These data may or may not include the headers, you have the option of create them while loading as is do bellow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data directly from the Clipboard\n",
    "\n",
    "# df_thick_top = pd.read_clipboard(sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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       "      <th>t_tops</th>\n",
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       "      <th>0</th>\n",
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       "      <th>1</th>\n",
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       "      <td>6.784049e+06</td>\n",
       "      <td>131.527908</td>\n",
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       "      <th>8</th>\n",
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       "      <td>6.783002e+06</td>\n",
       "      <td>206.482803</td>\n",
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       "      <th>9</th>\n",
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       "      <th>12</th>\n",
       "      <td>457537.109618</td>\n",
       "      <td>6.785280e+06</td>\n",
       "      <td>184.024139</td>\n",
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      "text/plain": [
       "                x             y      t_tops\n",
       "0   457379.271643  6.786013e+06  155.526306\n",
       "1   455308.000000  6.784068e+06  195.771317\n",
       "2   456514.729518  6.784049e+06  131.527908\n",
       "3   457762.825164  6.787130e+06  153.312592\n",
       "4   453085.280980  6.786788e+06  161.561844\n",
       "5   454716.938301  6.787607e+06  164.967438\n",
       "6   456316.580185  6.788725e+06  171.330933\n",
       "7   454901.278237  6.786211e+06  183.970886\n",
       "8   453523.008381  6.783002e+06  206.482803\n",
       "9   451496.662828  6.781788e+06  164.978958\n",
       "10  452257.451628  6.784852e+06  197.737625\n",
       "11  457379.271643  6.786013e+06  155.526306\n",
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   "source": [
    "# CSV file with thickness from tops. Header ara not included so they are declare while creating the dateframe\n",
    "\n",
    "column_names = ['x', 'y', 't_tops']\n",
    "df_thick_tops = pd.read_csv('thickness_tops.txt', header=None, names=column_names, delimiter=' ')\n",
    "df_thick_tops"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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       "      <th>t_surf</th>\n",
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       "      <th>1</th>\n",
       "      <td>455308.000000</td>\n",
       "      <td>6.784068e+06</td>\n",
       "      <td>201.745178</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>456514.729518</td>\n",
       "      <td>6.784049e+06</td>\n",
       "      <td>128.291351</td>\n",
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       "      <td>204.824753</td>\n",
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       "      <th>8</th>\n",
       "      <td>453523.008381</td>\n",
       "      <td>6.783002e+06</td>\n",
       "      <td>222.614792</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>6.781788e+06</td>\n",
       "      <td>185.659836</td>\n",
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       "      <th>10</th>\n",
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       "      <td>6.784852e+06</td>\n",
       "      <td>217.254135</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <th>12</th>\n",
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       "                x             y      t_surf\n",
       "0   457379.271643  6.786013e+06  147.528824\n",
       "1   455308.000000  6.784068e+06  201.745178\n",
       "2   456514.729518  6.784049e+06  128.291351\n",
       "3   457762.825164  6.787130e+06  150.312195\n",
       "4   453085.280980  6.786788e+06  196.132965\n",
       "5   454716.938301  6.787607e+06  193.104492\n",
       "6   456316.580185  6.788725e+06  177.770599\n",
       "7   454901.278237  6.786211e+06  204.824753\n",
       "8   453523.008381  6.783002e+06  222.614792\n",
       "9   451496.662828  6.781788e+06  185.659836\n",
       "10  452257.451628  6.784852e+06  217.254135\n",
       "11  457379.271643  6.786013e+06  147.528824\n",
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   "source": [
    "# CSV file with thickness from a seismic surface. Header ara not included so they are declare while creating the dateframe\n",
    "\n",
    "column_names = ['x', 'y', 't_surf']\n",
    "df_thick_surf = pd.read_csv('thickness_surface.txt', header=None, names=column_names, delimiter=' ')\n",
    "df_thick_surf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "10    True\n",
       "11    True\n",
       "12    True\n",
       "Name: x, dtype: bool"
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     "execution_count": 5,
     "metadata": {},
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    }
   ],
   "source": [
    "# Verifing xs are equal\n",
    "\n",
    "df_thick_tops.x == df_thick_surf.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     True\n",
       "1     True\n",
       "2     True\n",
       "3     True\n",
       "4     True\n",
       "5     True\n",
       "6     True\n",
       "7     True\n",
       "8     True\n",
       "9     True\n",
       "10    True\n",
       "11    True\n",
       "12    True\n",
       "Name: y, dtype: bool"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifing ys are equal\n",
    "\n",
    "df_thick_tops.y == df_thick_surf.y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join dataframes\n",
    "\n",
    "As the x and y are the same in both dataframes we can join them together, do the difference between the thickness, and then show the characteristics of the join dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       "      <th>0</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
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       "      <td>147.528824</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>455308.000000</td>\n",
       "      <td>6.784068e+06</td>\n",
       "      <td>195.771317</td>\n",
       "      <td>201.745178</td>\n",
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       "      <th>2</th>\n",
       "      <td>456514.729518</td>\n",
       "      <td>6.784049e+06</td>\n",
       "      <td>131.527908</td>\n",
       "      <td>128.291351</td>\n",
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       "      <th>3</th>\n",
       "      <td>457762.825164</td>\n",
       "      <td>6.787130e+06</td>\n",
       "      <td>153.312592</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>453085.280980</td>\n",
       "      <td>6.786788e+06</td>\n",
       "      <td>161.561844</td>\n",
       "      <td>196.132965</td>\n",
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       "      <th>5</th>\n",
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       "      <td>164.967438</td>\n",
       "      <td>193.104492</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>456316.580185</td>\n",
       "      <td>6.788725e+06</td>\n",
       "      <td>171.330933</td>\n",
       "      <td>177.770599</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>454901.278237</td>\n",
       "      <td>6.786211e+06</td>\n",
       "      <td>183.970886</td>\n",
       "      <td>204.824753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>453523.008381</td>\n",
       "      <td>6.783002e+06</td>\n",
       "      <td>206.482803</td>\n",
       "      <td>222.614792</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>451496.662828</td>\n",
       "      <td>6.781788e+06</td>\n",
       "      <td>164.978958</td>\n",
       "      <td>185.659836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>452257.451628</td>\n",
       "      <td>6.784852e+06</td>\n",
       "      <td>197.737625</td>\n",
       "      <td>217.254135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>457537.109618</td>\n",
       "      <td>6.785280e+06</td>\n",
       "      <td>184.024139</td>\n",
       "      <td>98.401115</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                x             y      t_tops      t_surf\n",
       "0   457379.271643  6.786013e+06  155.526306  147.528824\n",
       "1   455308.000000  6.784068e+06  195.771317  201.745178\n",
       "2   456514.729518  6.784049e+06  131.527908  128.291351\n",
       "3   457762.825164  6.787130e+06  153.312592  150.312195\n",
       "4   453085.280980  6.786788e+06  161.561844  196.132965\n",
       "5   454716.938301  6.787607e+06  164.967438  193.104492\n",
       "6   456316.580185  6.788725e+06  171.330933  177.770599\n",
       "7   454901.278237  6.786211e+06  183.970886  204.824753\n",
       "8   453523.008381  6.783002e+06  206.482803  222.614792\n",
       "9   451496.662828  6.781788e+06  164.978958  185.659836\n",
       "10  452257.451628  6.784852e+06  197.737625  217.254135\n",
       "11  457379.271643  6.786013e+06  155.526306  147.528824\n",
       "12  457537.109618  6.785280e+06  184.024139   98.401115"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Join dataframe\n",
    "\n",
    "df_join = df_thick_tops\n",
    "df_join['t_surf'] = df_thick_surf.t_surf\n",
    "df_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>t_tops</th>\n",
       "      <th>t_surf</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "      <td>7.997482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>455308.000000</td>\n",
       "      <td>6.784068e+06</td>\n",
       "      <td>195.771317</td>\n",
       "      <td>201.745178</td>\n",
       "      <td>-5.973861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>456514.729518</td>\n",
       "      <td>6.784049e+06</td>\n",
       "      <td>131.527908</td>\n",
       "      <td>128.291351</td>\n",
       "      <td>3.236557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457762.825164</td>\n",
       "      <td>6.787130e+06</td>\n",
       "      <td>153.312592</td>\n",
       "      <td>150.312195</td>\n",
       "      <td>3.000397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>453085.280980</td>\n",
       "      <td>6.786788e+06</td>\n",
       "      <td>161.561844</td>\n",
       "      <td>196.132965</td>\n",
       "      <td>-34.571121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>454716.938301</td>\n",
       "      <td>6.787607e+06</td>\n",
       "      <td>164.967438</td>\n",
       "      <td>193.104492</td>\n",
       "      <td>-28.137054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>456316.580185</td>\n",
       "      <td>6.788725e+06</td>\n",
       "      <td>171.330933</td>\n",
       "      <td>177.770599</td>\n",
       "      <td>-6.439666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>454901.278237</td>\n",
       "      <td>6.786211e+06</td>\n",
       "      <td>183.970886</td>\n",
       "      <td>204.824753</td>\n",
       "      <td>-20.853867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>453523.008381</td>\n",
       "      <td>6.783002e+06</td>\n",
       "      <td>206.482803</td>\n",
       "      <td>222.614792</td>\n",
       "      <td>-16.131989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>451496.662828</td>\n",
       "      <td>6.781788e+06</td>\n",
       "      <td>164.978958</td>\n",
       "      <td>185.659836</td>\n",
       "      <td>-20.680878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>452257.451628</td>\n",
       "      <td>6.784852e+06</td>\n",
       "      <td>197.737625</td>\n",
       "      <td>217.254135</td>\n",
       "      <td>-19.516510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "      <td>7.997482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>457537.109618</td>\n",
       "      <td>6.785280e+06</td>\n",
       "      <td>184.024139</td>\n",
       "      <td>98.401115</td>\n",
       "      <td>85.623024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x             y      t_tops      t_surf      delta\n",
       "0   457379.271643  6.786013e+06  155.526306  147.528824   7.997482\n",
       "1   455308.000000  6.784068e+06  195.771317  201.745178  -5.973861\n",
       "2   456514.729518  6.784049e+06  131.527908  128.291351   3.236557\n",
       "3   457762.825164  6.787130e+06  153.312592  150.312195   3.000397\n",
       "4   453085.280980  6.786788e+06  161.561844  196.132965 -34.571121\n",
       "5   454716.938301  6.787607e+06  164.967438  193.104492 -28.137054\n",
       "6   456316.580185  6.788725e+06  171.330933  177.770599  -6.439666\n",
       "7   454901.278237  6.786211e+06  183.970886  204.824753 -20.853867\n",
       "8   453523.008381  6.783002e+06  206.482803  222.614792 -16.131989\n",
       "9   451496.662828  6.781788e+06  164.978958  185.659836 -20.680878\n",
       "10  452257.451628  6.784852e+06  197.737625  217.254135 -19.516510\n",
       "11  457379.271643  6.786013e+06  155.526306  147.528824   7.997482\n",
       "12  457537.109618  6.785280e+06  184.024139   98.401115  85.623024"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# New column with the differece of thickness\n",
    "\n",
    "df_join = df_thick_tops\n",
    "df_join['delta'] = df_join.t_tops - df_join.t_surf\n",
    "df_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>t_tops</th>\n",
       "      <th>t_surf</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>13.000000</td>\n",
       "      <td>1.300000e+01</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>455244.492933</td>\n",
       "      <td>6.785502e+06</td>\n",
       "      <td>171.286081</td>\n",
       "      <td>174.705312</td>\n",
       "      <td>-3.419231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2130.532323</td>\n",
       "      <td>1.932820e+03</td>\n",
       "      <td>21.283699</td>\n",
       "      <td>37.327225</td>\n",
       "      <td>30.130888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>451496.662828</td>\n",
       "      <td>6.781788e+06</td>\n",
       "      <td>131.527908</td>\n",
       "      <td>98.401115</td>\n",
       "      <td>-34.571121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>453523.008381</td>\n",
       "      <td>6.784068e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "      <td>-20.680878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>455308.000000</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>164.978958</td>\n",
       "      <td>185.659836</td>\n",
       "      <td>-6.439666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786788e+06</td>\n",
       "      <td>184.024139</td>\n",
       "      <td>201.745178</td>\n",
       "      <td>3.236557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>457762.825164</td>\n",
       "      <td>6.788725e+06</td>\n",
       "      <td>206.482803</td>\n",
       "      <td>222.614792</td>\n",
       "      <td>85.623024</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x             y      t_tops      t_surf      delta\n",
       "count      13.000000  1.300000e+01   13.000000   13.000000  13.000000\n",
       "mean   455244.492933  6.785502e+06  171.286081  174.705312  -3.419231\n",
       "std      2130.532323  1.932820e+03   21.283699   37.327225  30.130888\n",
       "min    451496.662828  6.781788e+06  131.527908   98.401115 -34.571121\n",
       "25%    453523.008381  6.784068e+06  155.526306  147.528824 -20.680878\n",
       "50%    455308.000000  6.786013e+06  164.978958  185.659836  -6.439666\n",
       "75%    457379.271643  6.786788e+06  184.024139  201.745178   3.236557\n",
       "max    457762.825164  6.788725e+06  206.482803  222.614792  85.623024"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_join.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory plots\n",
    "\n",
    "Let's do some plots to understand the differences between the two estimations of thickness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Histograms\n",
    "\n",
    "plt.figure(1, figsize=(11, 7))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(df_join.t_tops)\n",
    "plt.xlabel('Thickness from tops (feet)')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(df_join.t_surf)\n",
    "plt.xlabel('Thickness from surfase (feet)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Map with the location of the anomaly\n",
    "\n",
    "plt.figure(2, figsize=(11, 7))\n",
    "plt.scatter(df_join.x, df_join.y, c=df_join.delta, cmap='viridis_r')\n",
    "plt.xlabel('X (meter)')\n",
    "plt.ylabel('Y (meter)')\n",
    "plt.grid(True)\n",
    "plt.text(456800, 6785200, 'Anomaly');\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Delta thickness (feet)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sk3D3VicXfWaWSWPD+oxoek+9wzAzs35y0WdmZmb5lKOh2Upw0WdmZma55dm7q3mk28zMzKwA3NNnZmZm+ZWThZUrwUWfmZmZ5VLg4d1SHt41MzMzKwD39JmZmVl+uaevk4s+MzMzyyd5eLeUh3fNzMzMCsA9fWZmZpZf7unr5KLPzMzMcsvDu6t5eNfMMouIeodgZmb95J4+M+vT6yse5KGF32NRy6MM0UimjPkUW2/4LzRoaL1DMzPrnXv6OrnoM7NevdnyDH998Qu0xXIAWmMJzyy+hBVtr/CujU+vc3RmZr3z8O5qHt41s17NfeNc2qNljba2WMELS29iZeurdYrKzMzK1WdPn6RJwCHA7sBmwHLgYeC3wO8ior2qEZpZXS1ueZKgba32BoaxtPV5moZsVIeozMwyEB7eLdFr0SfpfGAicAPwfeAVYDiwFbAvcKqkkyLijmoHamb1MXbYNrzZ8tRahV87LYwcOrlOUZmZZRNy1dehr56+H0XEw920PwxcI2kYsHnlwzKzgWLL9T/PC0tv6rynD6BBw5k4cl+aGt3LZ2Y2WPR6T19HwSfpuK77JB0XES0RMbdawZlZ/Y0aNpX3bnou6zdtDzQwtGEMW4w9nHeM/1a9QzMz65sqtOVA1tm7hwNndWmb1U2bmeXQ+sPfzu4TL6l3GGZmZQk8e7dUX/f0HQp8Gpgq6fqSXaOB16oZmJmZmZlVTl89fX8BXgTGAT8qaX8TeLBaQZmZmZlVhHv6OvVa9EXEc8BzwHskvQWYFhG3ShoBjCAp/szMzMwGnhzdj1cJmRZnlvQF4CrgnLRpEnBtlWIyMzMzswrL+kSOo4HdgMUAEfEUsHG1gjIzMzOrhFBltjzIOnt3ZUS0KF3gUNIQkkkxZmZmZgNXTgq2Ssja0/dHSacAIyR9ALgS+E31wjIzMzOzSspa9J0ELAAeAr4I3Ah8s1pBmZmZmVWCh3dXyzS8GxHtki4G7oiIJ7IcI+k84ADglYh4e9q2A/A/JM/vbQX+JSLuVjJufBawH7AMmBUR95WbjJmZmdkaclKwVULW2bsHAg8Av0/f79BlsebuXADs26XtP4HvRMQOwLfS9wAfBqal25HA2VniMjMzM7Nssg7vngbsArwBEBEPAFN7OyAi7mDtp3YEMCZ9PRZ4IX09E7goEncC60vaNGNsZmZmZmur0NBuoYZ3gVURsahj9m6qP7N3vwLcJOmHJAXne9P2icDzJZ+bl7a92PUEko4k6Q1kwoQJNDc3lxXAkiVLyj5mMHO++VWkXKFY+RYpVyhWvkXKFZJ86y4nBVslZC36HpH0aaBR0jTgWJJHtJXrS8DxEXG1pIOBc4F9yjlBRMwB5gBMnz49ZsyYUVYAzc3NlHvMYOZ886tIuUKx8i1SrlCsfIuUK1CoArdWJG0AbAYsB56NiPasx2Yd3v0y8DZgJfC/wCKSXrtyHQ5ck76+kmTIGGA+MLnkc5PSNjOzPrW2txLhpUPNrBuq0FZHksZKOkXSQ8CdJE9IuwJ4TtKVkvbMcp5ee/ok/SoiPgt8ISJOBU5dx7hfAPYAmoG9gKfS9uuBYyRdBrwbWBQRaw3tmpmVuvvVv3LN/MtYtOp11mtcjw9vMpO9J+xLl1tRzKzAcnI/3lXARcDuEfFG6Q5J04HPSHprRJzb20n6Gt7dSdJmwBGSLqJLrRsRXSdqlAZxKTADGCdpHslkkC8AZ6VP9FhBem8eybp/+wFzSZZs+XwfcZlZwf39jfv41XO/ZFW0ALC0bSnXv3gV7bTzwU32r3N0ZmaVExEf6GXfPcA9Wc7TV9H3P8BtwFuBe1mz6Iu0vacgDu1h107dfDZInu9rZpbJdfOv7Cz4OrS0t/C7l65nnwkfpkFZ714xs1zLR08fAJJui4i9+2rrSa//VYyIn0bEtsB5EfHWiJhasvVY8JmZVdurLQu6bW9pX8nK9hU1jsbMBqKo4JaFpEZJ90u6IX0/VdJdkuZKulzSsP7kIWm4pA1JRk83kLRhuk0hWe0kk16LPkmjACLiS319xsysliYM734pz+ENI2hqGF7jaMxsQKrUJI7svYXHAY+VvP8+8OOI2BJ4HZjdz0y+SDLiug1wX/r6XuA64GdZT9LX+Md1kn4k6f2SRnY0SnqrpNmSbmLtp26YmVXdQRM/xdAu/2ge1jCMmRM/6aFdM6s5SZOA/YFfpu9FMmn1qvQjFwIf7c+5I+KsiJgKnNhl1PWdEVGZoi8dI76NpMJ8RNIiSa8CFwObAIdHxFW9ncPMrBq2HfN2vrTlV5g4YjJDNIRxw8Zz2OZH8P7xe9U7NDMbSCrX0zdO0j0l25FrXoifAF8HOtbN2wh4IyJa0/cdD55YF+dJ+qakOQCSpkk6IOvBfS7OHBE3ksyuNTMbULYbsz3bbbd9vcMwswGsgku2LIyI6d3tSAuvVyLiXkkzKnbFtZ1HMqzb8USz+STrHt+Q5eCsT+QwMzMzs+7tBhwoaT9gODAGOAtYX9KQtLevEg+e2CIiPiXpUICIWKYyFib1jS9mZmaWXzWYyBERJ0fEpIiYAhwC/CEiDgNuBz6RfuxwkokX66JF0gjSCcWStiB5WlomLvrMzMwst0KV2frpG8BXJc0lucev1ydmZHAa8HtgsqRLSOZdfD3rwZmHdyW9D5gWEedLGg+Miohnyo3WzMzMLK8iopnkcbNExNPALhU89y2S7gN2Jel/PC4iFmY9PlNPn6TTSKrVk9OmoSQzeM3MzMwGptqv01dV6f17HwZ2iogbgPUkZS4qsw7vHgQcCCwFiIgXgNFlxmpmZmZWWzkq+oCfA+8BOh51+ybw31kPzjq82xIRIanjxsGRfR1gZmZmZhX17ojYUdL9ABHxejmPdsta9F0h6RySqcdfAI4AflF+rGZmZma1U8F1+gaCVZIaWT17dzyrF4PuU6aiLyJ+KOkDwGJga+BbEXFLP4I1MzMzq518FX0/BX4NbCzpdJLlYL6Z9eBMRZ+kqcCfOgo9SSMkTYmIZ8uP18zMzMyykjQ1Ip6JiEsk3QvsTVLOfjQiHst6nqzDu1ey+pEfAG1p285ZL2RmZmZWc/no6bsK2EnSbRGxN/B4f06StegbEhEtHW8ioqWcGwfNzMzMam7dFlYeSBoknQJsJemrXXdGxJmZTpLxYgskHdjxRtJMIPNigGZmZmbWb4eQjLIOIVkyr+uWSdaevqOASyT9jKSj9Hngc+VEa2ZmZlZzOejpi4gngO9LejAiftff82SdvfsPYFdJo9L3S/p7QTMzM7NaCPIxvCvpM8D/9lTwSdoC2DQi/tzbecp59u7+wNuA4clTQCAi/i1zxGZmZmbWHxsB96czd+8FFgDDgS2BPUhuuTupr5NkXbLlf4D1gD2BX5KsC3N3v8I2MzMzq5Uc9PRFxFnpLXZ7AbsB7wCWA48Bn42If2Y5T9aevvdGxDvSseTvSPoR0O8xZTMzM7OayEHRBxARbcAt6dYvWWfvrkh/LpO0GbAK2LS/FzUzMzOz2sra0/cbSesDPwDuI7k30s/eNTMzs4ErP+v0VUSvRZ+kT0bElcDFEfEGcLWkG4DhEbGoFgGamZmZ9ZuLvk59De+enP68uqMhIla64DMzMzOrLUnHSRqjxLmS7pP0wazH9zW8+6qkm4G3Srq+686IOLCbY8zMzMys8o5IZ/J+CNgA+CzwK+DmLAf3VfTtD+yYnvBH6xKlmZmZWa3l7J6+jmz2A34VEY+oY/HkDHot+iKiRdLfgD9GxB/XIUgzMzMzWzf3piOwU4GTJY0G2rMe3Ofs3Yhok/S2dQjQzMzMrD7y1dM3G9gBeDoilknaEPh81oOzLtnyQHpP35XA0o7GiLimjEDNzMzMaitfRd97gAciYmn6PN4dgbOyHpx1cebhwKskj//4SLodUGagZmZmZtZ/Z5M8KOOdwAnAP4CLsh6cqacvIjJ3HZqZmZkNCPlbnLk1IkLSTOBnEXGupNlZD85U9Ek6n+QpHGuIiCOyx2lmZmZWY/kq+t6UdDLJUi27S2oAhmY9OOs9fTeUvB4OHAS8kDlEMzMzM1tXnwI+TbJe30uSNid5RG4mWYd3ry59L+lS4M/lRGlmZmZWa3ka3k0LvauBaWnTQuDXWY/POpGjq2nAxv081szMzKw2VKFtAJD0BeAq4Jy0aSJwbdbjs97T9yZr3tP3EvCNrBcxMzMzs3V2NLALcBdARDwlKXMnXNbh3dH9i83MzMysjrTWPNTBbGX6tDQAJA2hm4m2Pck0vCtpN0kj09efkXSmpLf0J1ozMzOzWghVbhsg/ijpFGCEpA+QPDTjN1kPznpP3zotBmhmZmZm6+wkYAHwEPBF4Ebgm1kPzrpkyzotBmhmZmZWFwOnl26dRUQ78It0K1vWoq9jMcDPAO8vdzFAMzMzs7rIUdEnaTfg28BbSGo4ARERb81yfNair2MxwNn9WQzQzMzMzNbZucDxwL1AW7kHZ529+xJwZsn7f+J7+szMzGyAG0CTMCphUUT8rr8HZ+3pMzMzMxt88lX03S7pB8A1wMqOxoi4L8vBVSv6JJ0HHAC8EhFvL2n/Msnigm3AbyPi62n7ycDstP3YiLipWrGZmZmZDULvTn9OL2kLYK8sB1ezp+8C4GeUDANL2hOYCbwzIlZ2rCItaTvgEOBtwGbArZK2ioiyx6vNzMzMgAH1CLUK+XBErChtkLRR1oOzLs58gKT7Jb0mabGkNyUt7u2YiLgDeK1L85eAMyJiZfqZV9L2mcBlEbEyIp4B5pI8ZsTMzMys/xSV2QaGq9OncAAgaRPg5qwHZ+3p+wnwMeChiFiXzLcCdpd0OrACODEi/kbywOA7Sz43L21bi6QjgSMBJkyYQHNzc1kBLFmypOxjBjPnm19FyhWKlW+RcoVi5VukXCHJ1yrqWuBKSZ8AJgPXAydmPThr0fc88PA6Fnwd19sQ2BXYGbhCUqa1ZTpExBxgDsD06dNjxowZZQXQ3NxMuccMZs43v4qUKxQr3yLlCsXKt0i5AgOiwM3T7N2I+IWkYSTF3xTgixHxl6zHZy36vg7cKOmPrDlb5MyeD+nWPOCatHi8W1I7MA6YT1KxdpiUtpmZmZkVmqSvlr4FNgceAHaVtGvWeizrs3dPB5YBw4HRJVu5rgX2BJC0FTAMWEjSPXmIpCZJU4FpwN39OL+ZmZnZaqrQVl+ltdcokiVb5lJmPZa1p2+z0mVXspB0KTADGCdpHnAacB5wnqSHgRbg8LTX7xFJVwCPAq3A0Z65a2ZmZgYR8Z1KnCdr0XejpA9GROYZIhFxaA+7PtPD508n6VE0MzMzq4g83dMn6RbgkxHxRvp+A5LVTz6U5fisRd+XgBMltQCr0raIiDFlxmtmZmZWG2IgLbdSCeM7Cj6AiHi9Y83jLLI+e7c/9++ZmZmZWeW0Sdo8Iv4JIOktJE/kyCTzEzkkHQi8P33bHBE3lBWmmZmZWa3laHgXOBX4c7qaioDdSdcuziJT0SfpDJJ19S5Jm46TtFtEnFxmsGZmZma1k6OiLyJ+L2lHkvWOAb4SEQuzHp+1p28/YIeIaAeQdCFwP+Ciz8zMzKyKJG0TEY+nBR/AC+nPzdPh3vuynCfz8C6wPqufpTu2jOPMzMzM6iMfPX1fJRnG/VE3+wLYK8tJshZ9/wHcL+l2kl/f+4GTMh5rZmZmVgeRi9m7EXFk+nPPdTlPn0WfpAagndXPywX4RkS8tC4XNjMzM8sLSZOBi4AJJL1vcyLiLEkbApeTPCv3WeDgiHh9Ha7z3vRcnTVcRFyU5dg+i76IaJf09Yi4guRxaWZmZmYDX20fodYKnBAR90kaDdybLqY8C7gtIs6QdBLJSOk3+nMBSb8CtiB57m7Hk8uCpNjsU9bh3VslnUhSqS7taIyI13o+xMzMzKy+avVEjoh4EXgxff2mpMeAicBMksfSAlwINNPPog+YDmyXPsK2bFmLvk+lP48uaQvgrf25qJmZmdkgM07SPSXv50TEnO4+KGkK8C7gLmBCWhACvEQy/NtfDwObkBaX5eq16JP0yYi4Etg7Ip7uzwXMzMzM6qZyEzkWRsT0Pi8njQKuJllDb7G0uqsxIkIqPyBJvyHpbBsNPCrpbmBlyXkPzHKevnr6TgauBK4Cduzjs2ZmZmYDSw2XbJE0lKTguyQirkmbX5a0aUS8KGlT4JV+nPqHlYivr6LvVUk3A1MlrTWJI2tlaWZmZpZnSrr0zgUei4gzS3ZdDxwOnJH+vK4fpz81Ij64rjH2VfTtT9LD9yu6XxDQzMzMbMBS7Xr6dgM+Czwk6YG07RSSYu8KSbOB54CD+3HucZUIsNeiLyJagDslvTciFlTigmZmZmY1IWq2OHNE/JmeB5P3XsfTry/pY71c+5qe9pXKNHvXBZ+ZmZlZ3YwFDqD7ojKAyhV9ZkWyYMUibnrpXl5d+SbTN5zGruO2oVEN9Q7LzMz6Ix/P3n0uIo5Y15O46DMrcc9rT3LK3y+gLYJV0cqNL97NtFETOXPHIxnW4L8uZmaDTj6Kvopkkan7QtJ/Shojaaik2yQtkPSZSgRgNlC0RTvffvgSVrSvYlW0ArC8rYUn3pzHDfPvqnN0ZmZWYJ+txEmyjll9MCIWk4wnPwtsCXytEgGYDRRPvTmf1va2tdpXtq/i5pfuq0NEZma27qJCW/1ExMOVOE/W8aqOz+0PXBkRi1TDOdBmtTBEjUQPf7GHqLHG0ZiZWUW4XOmUtafvBkmPAzsBt0kaD6yoXlhmtbfFqE0ZM3S9tdqHNwzjwEm71iEiMzOzyslU9EXEScB7gekRsQpYCsysZmBmtSaJ/3jHLEYPGcF6jU00NQyhqWEo79/47ewzYYd6h2dmZuUSSFGRbSCQNE3SVZIelfR0x5b1+EzDu5I+Cfw+ItokfZPkKR3fBV7qX9hmA9O00RO55n3/yl8WPsobq5byzvXfyltHbVLvsMzMrL/yNbx7PnAa8GNgT+DzZB+1zXxP379GxJWS3gfsA/wAOBt4d3mxmg18TY1D2XPCO8s65pp/3sNPH7+VxauWM75pFN94+/7stcl2VYrQzMwKakRE3CZJEfEc8G1J9wLfynJw1uqwY0rj/sCciPgtMKz8WM3y55wnb+c7D17H6y1LaYt2XlqxmOPvuZQb5j1Q79DMzAquMkO7A2V4F1gpqQF4StIxkg4CRmU9OGvRN1/SOcCngBslNZVxrFlutbe3c85Tzd3u+97DN9Q2GDMzW5sqtA0MxwHrAceSTK79DPC5rAdnLdwOBm4CPhQRbwAb4nX6zFjUupy2aO9235LWlTWOxszMcm5KRCyJiHkR8fmI+DiwedaDs87eXQa8ArwvbWoFnio7VLOcGTmkqcd9DV7L0sys7qTKbAPEyRnbupV19u5pwHRga5KZI0OBi4Hdsl7ILI+GNQxhu7Gb8eiiF9ba94FN3laHiMzMrJOAgXM/Xr9J+jCwHzBR0k9Ldo0h6YjLJOvw7kHAgSTr8xERLwCjs17ELM/Oe89spowct0bbDhtM5ox3fbJOEZmZWc68ANxL8mCMe0u264EPZT1J1iVbWiIilE5fkTSyvFjN8mvEkGFct+dxzF/2Oo8vepF3bjCZccP9byIzs3oTDKSZt/0WEX8H/i7p4ojI3LPXVdai74p09u76kr4AHAH8or8XNcujiettwMT1Nqh3GGZmVmIA3Y/Xb5IeAjo63tbaHxHvyHKeTEVfRPxQ0geAxST39X0rIm7JHK2ZmZmZ9dcBlThJ1p4+0iLPhZ6ZmZkNGjkZ3n2u47WktwDTIuJWSSMoo5bLNJFD0sckPSVpkaTFkt6UtLj8sM3MzMxqKEeLM6e32F0FnJM2TQKuzXp81tm7/wkcGBFjI2JMRIyOiDFlRWpmZmZm6+JokuXyFgNExFPAxlkPztol+HJEPFZ+bGZmZmZ1onwM75ZYGREtHZM5JA0hneCRRdai7x5Jl5N0IXY+Wyoirskep5mZmVltDZCR2Ur5o6RTgBHpBNt/AX6T9eCsRd8YYBnwwZK2AFz0mZmZmdXGScBs4CHgi8CNwC+zHpy16DshIl4rbZA0NetFzMzMzGpNRK6GdyOiXdK1wLURsaDc47NO5PiNpM6JG5K2pYzuRDMzM7N6kKIiW31zkCR9W9JC4AngCUkLJH2rnPNkLfr+g6TwGyVpJ5Lpwp8pL2QzMzMz64fjSWbt7hwRG0bEhsC7gd0kHZ/1JFmfyPFbSUOBm4HRwEER8WQ/gjYzMzOrmTw8hg34LPCBiFjY0RART0v6DElt9uMsJ+m16JP0X6w5FXgs8A/gGElExLFlh21mZmZWIw35uKdvaGnB1yEiFqSdcpn01dN3T5f392Y9sZmZmZlVREs/962h16IvIi4EkDQSWBERben7RqCpt2MlnUfygOBXIuLtXfadAPwQGB8RC5WsMngWsB/J0jCzIuK+rEmYmZmZdaX8LM78zh4efytgeNaTZJ3IcRswouT9CODWPo65ANi3a6OkySTr/f2zpPnDwLR0OxI4O2NcZmZmZj3Kw+zdiGhMH4PbdRsdEZmHd7MWfcMjYknJxZcA6/UR4B3Aa93s+jHwdda8V3AmcFEk7gTWl7RpxtjMzMzMrA9ZF2deKmnHjiHXdNmW5eVeTNJMYH5E/F1rTqeZCDxf8n5e2vZiN+c4kqQ3kAkTJtDc3FxWDEuWLCn7mMHM+eZXkXKFYuVbpFyhWPkWKVdI8q23nMzerYisRd9XgCslvUAyfrwJ8KlyLiRpPeAU1nyUW9kiYg4wB2D69OkxY8aMso5vbm6m3GMGM+ebX0XKFYqVb5FyhWLlW6RcgQFQ4EZeZu9WRNZ1+v4maRtg67TpiYhYVea1tgCmAh29fJOA+yTtAswHJpd8dlLaZmZmZtYvIjcTOSqir3X69oqIP0j6WJddW6Xr9F2T9UIR8RCwccm5nwWmp7N3rydZ++8ykhWmF0XEWkO7ZmZmZtY/ffX07QH8AfhIN/sC6LHok3QpMAMYJ2kecFpEnNvDx28kWa5lLsmSLZ/vIy4zMzOzPjXgnr4Ofa3Td1r6s+wiLCIO7WP/lJLXARxd7jXMzMzMeiRP5CiV6Z4+SU3Ax4EppcdExL9VJywzMzMzq6Sss3evAxaRPIZtZfXCMTMzM6sMkZtn71ZE1qJvUkSs9XQNMzMzs4HMs3dXy/pEjr9I2r6qkZiZmZlZ1fS1ZMvDQHv6uc9LeppkeFck8y/eUf0QzczMzPrDizOX6mt4dyKwQw3iMDMzM6s4D++u1lfR90xEPFeTSMzMzMysavoq+jaW9NWedkbEmRWOx8zMzKwiJM/eLdVX0dcIjCK5h8/MzMxsUPETOVbrq+h70Qswm5mZmQ1+fRV97uEzMzOzQcsTOVbrq+jbuyZRmJmZmVWYvGTLGnpdnDkiXqtVIGZmZmZWPVkfw2ZmZmY26Linb7Wsj2EzMzMzG1REUvRVYst0PWlfSU9ImivppOpmVz4XfWZmZmbrSFIj8N/Ah4HtgEMlbVffqNbk4V0zMzPLp9ouzrwLMDcingaQdBkwE3i0VgH0xUWfmZmZ5VYFF2ceJ+mekvdzImJOyfuJwPMl7+cB767UxSvBRZ+ZmZlZ3xZGxPR6B7EuXPSZmZlZLtV4nb75wOSS95PStgHDRZ+ZmZnlVg2Lvr8B0yRNJSn2DgE+XauLZ+Giz8zMzGwdRUSrpGOAm4BG4LyIeKTOYa3BRV8Xb7Qs4fllC5k4YiM2bBpd73DMzMysnzrW6auViLgRuLFmFyyTi75Ua3sbP3r8Gm55+T6Gaggt0cqM8e/g5O0OZkhDY73DMzMzs37wEzlW8+LMqV89+wduffl+WtpbWdq2glXtrdyx4CHm/ON39Q7NzMzMbJ256EtdM+/PrGxftUbbyvZVXDf/r0T4XwlmZmaDjUjW6avElgce3k0tbV3RbfuKthaCQKjGEZmZmdk6KeO5uUXgnr7UtmM277Z9y9Gb0SD/mszMzGxwczWTOnarmYxoHEZD2qPXgBjeMJSvbHVQnSMzMzOz/mpQe0W2PPDwbmrrMZP45S5f4X+fu50nFs9ny1Gb8um37MmUURPqHZqZmZn1Q62XbBnoXPSVmLzeeL6x7cH1DsPMzMys4lz0mZmZWW7lZeZtJbjoMzMzs1wSnr1byhM5zMzMzArAPX1mZmaWW3mZeVsJLvrMzMwslyRo9PBuJw/vmpmZmRWAe/rMzMwstzx7dzUXfWZmZpZT4Xv6Snh418zMzKwA3NNnZmZmueTHsK3JRZ+ZmZnlVqPv6evk4V0zMzOzAnBPn5mZmeWSPJFjDS76zMzMLLd8T99qHt41MzMzK4CqFX2SzpP0iqSHS9p+IOlxSQ9K+rWk9Uv2nSxprqQnJH2oWnGZmZlZMXQ8hq0SWx5Us6fvAmDfLm23AG+PiHcATwInA0jaDjgEeFt6zM8lNVYxNjMzMyuABtorsuVB1Yq+iLgDeK1L280R0Zq+vROYlL6eCVwWESsj4hlgLrBLtWIzMzMzK5p6TuQ4Arg8fT2RpAjsMC9tW4ukI4EjASZMmEBzc3NZF12yZEnZxwxmzje/ipQrFCvfIuUKxcq3SLlCkm89JbN38zE0Wwl1KfoknQq0ApeUe2xEzAHmAEyfPj1mzJhR1vHNzc2Ue8xg5nzzq0i5QrHyLVKuUKx8i5QrMCAK3MacDM1WQs2LPkmzgAOAvSOio/yeD0wu+diktM3MzMysX/wYtjXVdMkWSfsCXwcOjIhlJbuuBw6R1CRpKjANuLuWsZmZmZnlWdV6+iRdCswAxkmaB5xGMlu3CbhFEsCdEXFURDwi6QrgUZJh36Mjoq1asZmZmVkRBI1+IkenqhV9EXFoN83n9vL504HTqxWPmZmZFYuABjy828FP5DAzMzMrAD9718zMzHLLw7uruegzMzOzXJKCBhd9nTy8a2ZmZlYA7ukzMzOz3Gr0RI5OLvrMzMwsl5LFmT2828HDu2ZmZmYF4J4+MzMzy6nw8G4JF31mZmaWSx7eXZOHd83MzMwKwD19ZmZmlluNuKevg4s+MzMzyyURNMj39HXw8K6ZmZlZAbinz8zMzHLLw7uruegzMzOzXBLQ6Nm7nTy8a2ZmZlYA7ukzMzOznAoavDhzJxd9ZmZmlkuSh3dLeXjXzMzMrADc02dmZma51eDZu51c9JmZmVkuiaDRizN38vCumZmZWQG4p8/MzMxySXhx5lLu6TMzM7PcalB7RbZ1IekHkh6X9KCkX0tav2TfyZLmSnpC0ofWNd/euOgzMzMzq65bgLdHxDuAJ4GTASRtBxwCvA3YF/i5pMZqBeGiz8zMzHJJBI20V2RbFxFxc0S0pm/vBCalr2cCl0XEyoh4BpgL7LJOF+uF7+kzMzOz3Krg7N1xku4peT8nIub04zxHAJenryeSFIEd5qVtVeGiz8zMzKxvCyNiek87Jd0KbNLNrlMj4rr0M6cCrcAl1Qmxdy76zMzMLJdE7RZnjoh9eo1FmgUcAOwdER3dj/OBySUfm5S2VYWLPjMzM8upGBDP3pW0L/B1YI+IWFay63rgfyWdCWwGTAPurlYcLvrMzMzMqutnQBNwiySAOyPiqIh4RNIVwKMkw75HR0RbtYJw0WdmZma5NFAWZ46ILXvZdzpwei3icNFnZmZmudWAn73bwev0mZmZmRWAe/rMzMwsl6SBMZFjoHDRZ2ZmZrnV6OHdTh7eNTMzMysA9/SZmZlZLtVycebBwEWfmZmZ5ZTv6Svl4V0zMzOzAnBPn5mZmeVSsjizJ3J0cNFnZmZmudUgF30dPLxrZmZmVgDu6TMzM7NcEjEgnr07ULjoMzMzs1zyPX1r8vCumZmZWQG4p8/MzMxyyxM5VqtaT5+k8yS9IunhkrYNJd0i6an05wZpuyT9VNJcSQ9K2rFacZmZmVkxdAzvVmLLg2oO714A7Nul7STgtoiYBtyWvgf4MDAt3Y4Ezq5iXGZmZmaFU7WiLyLuAF7r0jwTuDB9fSHw0ZL2iyJxJ7C+pE2rFZuZWTmuvfZaJPH444/XLYZRo0bV7dpmg5l7+lar9T19EyLixfT1S8CE9PVE4PmSz81L216kC0lHkvQGMmHCBJqbm8sKYMmSJWUfM5g53/wqUq5Q33zPOusstt9+e773ve/x+c9/vurX6y7Xtra23H7fRfqzXKRcIcm3ngQ0qK4hDCh1m8gRESGVf3dlRMwB5gBMnz49ZsyYUdbxzc3NlHvMYOZ886tIuUL98l2yZAlPPvkkt99+Ox/5yEe48MILaW5u5tvf/jbjxo3j4YcfZqedduLiiy9GErfddhsnnngira2t7Lzzzpx99tk0NTUxZcoUDj30UH73u98xZMgQ5syZw8knn8zcuXP52te+xlFHHcWSJUuYOXMmzz//PE1NTXz3u99l5syZADQ2NjJjxgw+97nP8bGPfYyPfvSjABx22GEcfPDBnZ/rycrlK/nlSZdw0/m3s3J5C+94/7Yc87P/x1u2nVTtX2GfivRnuUi5AgOgwM1PL10l1HrJlpc7hm3Tn6+k7fOBySWfm5S2mZnV1XXXXce+++7LVlttxUYbbcS9994LwP33389PfvITHn30UZ5++mn+7//+jxUrVjBr1iwuv/xyHnroIVpbWzn77NW3KG+++eY88MAD7L777syaNYurrrqKO++8k9NOOw2A4cOH8+tf/5o5c+Zw++23c8IJJxCx5v+wZs+ezQUXXADAokWL+Mtf/sL+++/fZx6nHfQDfvuLW1m+ZAXtbe38vfkRjnvvqbz20usV+k2Z2UBX66LveuDw9PXhwHUl7Z9LZ/HuCiwqGQY2M6ubSy+9lEMOOQSAQw45hEsvvRSAXXbZhUmTJtHQ0MAOO+zAs88+yxNPPMHUqVPZaqutADj88MO54447Os914IEHArD99tvz7ne/m9GjRzN+/Hiampp44403iAhOOeUUZs+ezT777MP8+fN5+eWX14hnjz324KmnnmLBggVceumlfPzjH2fIkN4HbZ579Hke/tNjrFqxqrMtAlpWrOL6s29e91+S2QAlkkKnElseVG14V9KlwAxgnKR5wGnAGcAVkmYDzwEHpx+/EdgPmAssA6p/04yZWR9ee+01/vCHP/DQQw8hiba2NiSx//7709TU1Pm5xsZGWltb+zxfxzENDQ1rHN/Q0EBrayuXXHIJCxYs4JxzzmGfffZhypQprFixYq3zfO5zn+Piiy/msssu4/zzz+/zuv98/AUahzbC8jXbV61cxdz7nu7zeLNBS9Doe/o6Va3oi4hDe9i1dzefDeDoasViZtYfV111FZ/97Gc555xzOtv22GMP/vSnP3X7+a233ppnn32WuXPnsuWWW/KrX/2KPfbYI/P1Fi1axMYbb8yQIUO4/fbbee6557r93KxZs9hll13YZJNN2G677fo87+bbbEbbqra12oc2DWXLHd+aOT4zG9zy0mNpZlZxl156KQcddNAabR//+Mc7h3i7Gj58OOeffz6f/OQn2X777WloaOCoo47KfL3DDjuMe+65hyOOOIKLLrqIbbbZptvPTZgwgW233TbzTOK3bDeZt71vG4YNH9rZJsGw4UP5yFEfzByf2WAjRGOFtjzwY9jMzHpw++23r9V27LHHcuyxx67R9rOf/azz9d57783999+/1nHPPvts5+tZs2Yxa9asbvf99a9/7XaGZ+nSF8uWLeOpp57i0EN7GlBZ23d+/XV+edLFq2fv7rEdX/6v2Wy06QaZz2E2GLl3azUXfWZmg8itt97K7NmzOf744xk7dmzm44av18QxP53NMT+dTUQg5aPnwsyyc9FnZjaI7LPPPj3e65eVCz4rCgGN/vPeyUWfmZmZ5VZDTu7HqwQPdZuZmZkVgHv6zMzMLJcEuZl5Wwku+szMzCy3PLy7mod3zczMzArAPX1mZmaWS0KevVvCRZ+ZmZnlVoMHNTv5N2FmZmZWAO7pMzMzs1wSnshRykWfmZmZ5ZRolAc1O/g3YWZmZlYA7ukzMzOzXEqGd92/1UERUe8Y+k3SAqDcJ4+PAxZWIZyByvnmV5FyhWLlW6RcoVj5FilXSPIdGRHj63FxSb9PY6iEhRGxb4XOVReDuujrD0n3RMT0esdRK843v4qUKxQr3yLlCsXKt0i5QvHyHejc52lmZmZWAC76zMzMzAqgiEXfnHoHUGPON7+KlCsUK98i5QrFyrdIuULx8h3QCndPn5mZmVkRFbGnz8zMzKxwXPSZmZmZFUDuij5J50l6RdLDJW3/LulBSQ9IulnSZmm7JP1U0tx0/471i7x/usu3ZN8JkkLSuPT9oM63h+/225Lmp9/tA5L2K9l3cprrE5I+VJ+o+6+n71bSlyU9LukRSf9Z0j5o8+3hu7285Ht9VtIDJfsGba7QY747SLozzfceSbuk7Xn8e/tOSX+V9JCk30gaU7JvsH+3kyXdLunR9O/ocWn7hpJukfRU+nODtH3Qfr+95PrJ9H27pOldjhnU3++gFxG52oD3AzsCD5e0jSl5fSzwP+nr/YDfkSzavStwV73jr0S+aftk4CaSxavH5SHfHr7bbwMndvPZ7YC/A03AVOAfQGO9c6hAvnsCtwJN6fuN85BvT3+OS/b/CPhWHnLt5bu9Gfhw+no/oLnkdd7+3v4N2CN9fQTw7zn6bjcFdkxfjwaeTPP6T+CktP0k4PuD/fvtJddtga2BZmB6yecH/fc72Lfc9fRFxB3Aa13aFpe8HQl0zF6ZCVwUiTuB9SVtWptIK6O7fFM/Br7O6lxhkOfbS67dmQlcFhErI+IZYC6wS9WCq4Ie8v0ScEZErEw/80raPqjz7e27lSTgYODStGlQ5wo95htAR4/XWOCF9HUe/95uBdyRvr4F+Hj6Og/f7YsRcV/6+k3gMWAiSW4Xph+7EPho+nrQfr895RoRj0XEE90cMui/38Eud0VfTySdLul54DDgW2nzROD5ko/NS9sGNUkzgfkR8fcuu3KZL3BMOixyXseQCfnNdStgd0l3SfqjpJ3T9rzmC7A78HJEPJW+z2uuXwF+kP536ofAyWl7HvN9hKQAAPgkycgE5CxXSVOAdwF3ARMi4sV010vAhPR1LnLukmtPcpHrYFaYoi8iTo2IycAlwDH1jqdaJK0HnMLqwjbvzga2AHYAXiQZBsyzIcCGJMNAXwOuSHvC8uxQVvfy5dmXgOPT/04dD5xb53iq6QjgXyTdSzIs2FLneCpO0ijgauArXUabiIhgzVGYQa23XG1gKUzRV+ISVg8lzGf1vzABJqVtg9kWJPdK/F3SsyQ53SdpE3KYb0S8HBFtEdEO/ILVQwW5yzU1D7gmHQq6G2gneZh4LvOVNAT4GHB5SXMucwUOB65JX19Jjv8sR8TjEfHBiNiJpKD/R7orF7lKGkpSBF0SER3f6csdw7bpz45bMwZ1zj3k2pNBnWseFKLokzSt5O1M4PH09fXA59LZU7sCi0q63weliHgoIjaOiCkRMYWkSNgxIl4ih/l2ufflIKBjhuD1wCGSmiRNBaYBd9c6viq4lmQyB5K2AoYBC8lvvvsAj0fEvJK2vOb6ArBH+novoGM4O49/bzdOfzYA3wT+J9016L/btOf9XOCxiDizZNf1JIU96c/rStoH5ffbS649GfTf76BX75kkld5I/tX4IrCKpOCZTfKvkIeBB4HfkNxoCslsqf8m+VfmQ5TMMhosW3f5dtn/LKtn7w7qfHv4bn+V5vIgyX9QNi35/Klprk+QzoocTFsP+Q4DLk7/PN8H7JWHfHv6cwxcABzVzecHba69fLfvA+4lmd14F7BT+tk8/r09jmSm55PAGaRPh8rJd/s+kqHbB4EH0m0/YCPgNpJi/lZgw8H+/faS60Hpd70SeBm4KS/f72Df/Bg2MzMzswIoxPCumZmZWdG56DMzMzMrABd9ZmZmZgXgos/MzMysAFz0mZmZmRWAiz6zAULSRpIeSLeXJM1PX78h6dEejvk3Sfv0cs5Zkn5Wvah7l67HdWuax6eqeJ0pkj5drfOXXOcnkt6fvt5d0iNpbiP6ca5TSl4Pk3RHuhi1mVlVuOgzGyAi4tWI2CEidiBZrPbH6esdSJ680d0x34qIW2sWZPneBZDmVfpUDSQ1VvA6U4CqFn2SNgJ2jYg70qbDgO+luS3vxyk7i76IaCFZw61qhbGZmYs+s8GhUdIv0p6lmzt6liRdIOkT6eudJf1F0t8l3S1pdOkJJO0v6a+SxqXH/TT9/NMd50g/9zVJf5P0oKTvpG0jJf02PffDHb12ks6Q9Gj62R92ud7GJAtJ75z2hm0h6VlJ35d0H/BJSYdKeig95/dLjl0i6QdpvrdK2kVScxrrgd38fs4Adk+vc7yk4ZLOT899v6SOp5jMknRdeq6nJJ3WW35dfBz4ffr5/wccDPy7pEt6+r2l7Z9Jv48HJJ0jqVHSGcCItO2S9KPXkhSSZmZV4aEEs8FhGnBoRHxB0hUkBcjFHTslDSN5Pu2nIuJvksYAy0v2HwR8FdgvIl5Pnp7EpiQr6m9D8jSTqyR9ML3WLiRPCrg+Hc4cD7wQEfun5xub9nwdBGwTESFp/dKAI+KVtDg6MSIOSI8DeDUidpS0GXAnsBPwOnCzpI9GxLXASOAPEfE1Sb8Gvgt8ANgOuDCNt9RJXa5zQhJCbC9pm/TcW6Wf3QV4O7AM+Juk3wJv6ZpfN9/BbsBVaW6/lPQ+4IaI6O33toCk9263iFgl6efAYRFxkqRj0p7cDg8DO3dzXTOzinDRZzY4PBMRD6Sv7yUZziy1NfBiRPwNICIWQ2eRtRcwHfhgR3vq2ohoBx6VNCFt+2C63Z++H0VSzPwJ+FHaG3dDRPwpvf9sBXCupBuAGzLm0jHMuzPQHBEL0lgvAd5P0uPVQtqrRvJoqpVp0fRQN7l3533AfwFExOOSngM6ir5bIuLV9JrXpJ+9sWt+3ZxzU5Iirjs9/d7eQVLU/i39LkYAr3R3gohok9QiaXREvJkhRzOzsnh412xwWFnyuo3y/sH2D2A0q4ue7s6pkp8d96ntEBFbRsS5EfEksCNJAfZdSd+KiFaSnq2rgANYXaT1ZWmGz6yK1c+IbO+INS1S1/Ufq12fPRnd5dfNccuB4T2cs9vfW9p+YUn71hHx7V5iayIppM3MKs5Fn1k+PAFsKmlnAEmjtXom6HMkw8EXSXpbH+e5CThC0qj0PBMlbZwOxS6LiIuBHwA7pp8ZGxE3AscD7ywz5ruBPdJ7DBuBQ4E/lnmODm+SFLYd/kR6f1w6rLs5ye8I4AOSNlRyX+RHgf/rLr9urvEYsGUP1+/290YyOeMT6WvS674lPWaVpKEdJ0iHyxdGxKryUjczy8bDu2Y5EBEt6eSD/0qLmeXAPiX7H5d0GHClpI/0cp6bJW0L/DUdjlwCfIak2PmBpHZgFfAlkiLrOknDSXq0vlpmzC9KOgm4PT3+txFxXTnnKPEg0Cbp78AFwM+Bs9Ph4FZgVkSsTHO6G7gamARcHBH3SPpQN/l19Vvgi8Avu8ml299bRDwq6Zsk9xQ2pOc+mqQQnwM8KOm+iDgM2DO9hplZVWj1CIqZWb5JmgVMj4hj+nn8n4EDIuKNSsaVnvsa4KR0qNnMrOI8vGtmlt0JJEPFFZXOvr7WBZ+ZVZN7+szMzMwKwD19ZmZmZgXgos/MzMysAFz0mZmZmRWAiz4zMzOzAnDRZ2ZmZlYA/x/r9eynY9W6nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 792x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Crossplot\n",
    "\n",
    "plt.figure(3, figsize=(11, 7))\n",
    "plt.scatter(df_join.t_tops, df_join.t_surf, c=df_join.delta, cmap='viridis_r')\n",
    "plt.xlabel('Thickness from tops (feet)')\n",
    "plt.ylabel('Thickness from surface (feet)')\n",
    "plt.text(175, 98, 'Anomaly');\n",
    "plt.grid(True)\n",
    "cb = plt.colorbar()\n",
    "cb.set_label('Delta Thickness (feet)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Regression\n",
    "\n",
    "Functions `np.polyfit` and `np.poly1d` are powerful tools to do polynomial regressions of different degrees. Some time, as in the case of the permeability (k), a variable linearization (log(k)) is required but not in this case. Other important variable linearization are shown bellow:\n",
    "\n",
    "\n",
    "Relationship |  Example   |     General Eqn.     |  Altered Var.  |        Linearized Eqn.  \n",
    "-------------|------------|----------------------|----------------|------------------------------------------\n",
    "Linear       | x          | y =     B * x    + C | -              |        y =   C    + B * x\n",
    "Logarithmic  | log(x)     | y = A * log(B*x) + C | log(x)         |        y =   C    + A * (log(B) + log(x))\n",
    "Exponential  | 2^x, e^x | y = A * exp(B*x) + C | log(y)         | log(y-C) = log(A) + B * x\n",
    "Power        | x^2       | y =     B * x^N + C | log(x), log(y) | log(y-C) = log(B) + N * log(x)\n",
    "\n",
    "Table from: https://www.mmbyte.com/article/45748.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Definition of the regression function\n",
    "\n",
    "For later uses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def reg(x, y, degree):\n",
    "    \n",
    "    poly_coeff = np.polyfit(x, y, degree)\n",
    "#   poly_coeff = np.polyfit(x, np.log(y), degree)\n",
    "    \n",
    "    newy = np.poly1d(poly_coeff)\n",
    "    \n",
    "    e = y - newy(x)\n",
    "#   e = y - np.exp(newy(x))\n",
    "    \n",
    "    mse = mean_squared_error(y, newy(x))\n",
    "#   mse = mean_squared_error(y, np.exp(newy(x)))\n",
    "    \n",
    "    r2 = r2_score(y, newy(x))\n",
    "    \n",
    "    return newy, e, mse, r2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First degree regression and residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data CC: 0.5906184698181037\n",
      "\n",
      "Reggresion equation (y):  \n",
      "1.036 x - 2.717\n",
      "\n",
      "Regresion errors\n",
      "MSE: 837.4976011055413\n",
      "R2: 0.3488301768902783\n"
     ]
    }
   ],
   "source": [
    "print('Data CC:', df_join.t_tops.corr(df_join.t_surf))\n",
    "print()\n",
    "\n",
    "newy1, e, mse, r2= reg(df_join.t_tops, df_join.t_surf, 1)\n",
    "print('Reggresion equation (y):', newy1)\n",
    "print()\n",
    "\n",
    "print('Regresion errors')\n",
    "print('MSE:' , mse)\n",
    "print('R2:' , r2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression and residual plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 240, 1)\n",
    "\n",
    "plt.figure(3, figsize=(11, 11))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(df_join.t_tops, df_join.t_surf)\n",
    "plt.plot(x, newy1(x), color='black', label='Regression')\n",
    "plt.text(170, 160, 'y = '+str(newy1)[2:])\n",
    "plt.ylabel('Thickness from surface (feet)')\n",
    "plt.axis([100, 240, 80, 240])\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(df_join.t_tops, e)\n",
    "plt.axhline(y=np.mean(e), color='black', label=\"Mean of residual\")\n",
    "plt.xlim(100, 240)\n",
    "plt.xlabel('Thickness from tops (feet)')\n",
    "plt.ylabel('Residual')\n",
    "plt.grid(True)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Residuals** provide an excellent way to assess the quality of the regression:\n",
    "\n",
    " + The sum and mean of the residuals have to be close to zero\n",
    " + Residuals have to be randomly distributed around zero\n",
    " + Residuals do not have to show patterns\n",
    " + Patterns such as those bellow have different implications:\n",
    " \n",
    " <img src=\"https://condor.depaul.edu/sjost/it223/documents/resid-plots.gif\" style=\"float:center\" width=\"500px\" />\n",
    "\n",
    "Image from: https://condor.depaul.edu/sjost/it223/documents/regress.htm\n",
    "\n",
    "Another reference: https://www.qualtrics.com/support/stats-iq/analyses/regression-guides/interpreting-residual-plots-improve-regression/#x-unbalanced-header\n",
    "\n",
    "What efacie do you think has the best phi-k regression?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dropping the anomalous point\n",
    "\n",
    "The anomalous point is located at the end of the join dataframe, after that all the metrics and the shape of the residual improve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>t_tops</th>\n",
       "      <th>t_surf</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "      <td>7.997482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>455308.000000</td>\n",
       "      <td>6.784068e+06</td>\n",
       "      <td>195.771317</td>\n",
       "      <td>201.745178</td>\n",
       "      <td>-5.973861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>456514.729518</td>\n",
       "      <td>6.784049e+06</td>\n",
       "      <td>131.527908</td>\n",
       "      <td>128.291351</td>\n",
       "      <td>3.236557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>457762.825164</td>\n",
       "      <td>6.787130e+06</td>\n",
       "      <td>153.312592</td>\n",
       "      <td>150.312195</td>\n",
       "      <td>3.000397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>453085.280980</td>\n",
       "      <td>6.786788e+06</td>\n",
       "      <td>161.561844</td>\n",
       "      <td>196.132965</td>\n",
       "      <td>-34.571121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>454716.938301</td>\n",
       "      <td>6.787607e+06</td>\n",
       "      <td>164.967438</td>\n",
       "      <td>193.104492</td>\n",
       "      <td>-28.137054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>456316.580185</td>\n",
       "      <td>6.788725e+06</td>\n",
       "      <td>171.330933</td>\n",
       "      <td>177.770599</td>\n",
       "      <td>-6.439666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>454901.278237</td>\n",
       "      <td>6.786211e+06</td>\n",
       "      <td>183.970886</td>\n",
       "      <td>204.824753</td>\n",
       "      <td>-20.853867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>453523.008381</td>\n",
       "      <td>6.783002e+06</td>\n",
       "      <td>206.482803</td>\n",
       "      <td>222.614792</td>\n",
       "      <td>-16.131989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>451496.662828</td>\n",
       "      <td>6.781788e+06</td>\n",
       "      <td>164.978958</td>\n",
       "      <td>185.659836</td>\n",
       "      <td>-20.680878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>452257.451628</td>\n",
       "      <td>6.784852e+06</td>\n",
       "      <td>197.737625</td>\n",
       "      <td>217.254135</td>\n",
       "      <td>-19.516510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>457379.271643</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>147.528824</td>\n",
       "      <td>7.997482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                x             y      t_tops      t_surf      delta\n",
       "0   457379.271643  6.786013e+06  155.526306  147.528824   7.997482\n",
       "1   455308.000000  6.784068e+06  195.771317  201.745178  -5.973861\n",
       "2   456514.729518  6.784049e+06  131.527908  128.291351   3.236557\n",
       "3   457762.825164  6.787130e+06  153.312592  150.312195   3.000397\n",
       "4   453085.280980  6.786788e+06  161.561844  196.132965 -34.571121\n",
       "5   454716.938301  6.787607e+06  164.967438  193.104492 -28.137054\n",
       "6   456316.580185  6.788725e+06  171.330933  177.770599  -6.439666\n",
       "7   454901.278237  6.786211e+06  183.970886  204.824753 -20.853867\n",
       "8   453523.008381  6.783002e+06  206.482803  222.614792 -16.131989\n",
       "9   451496.662828  6.781788e+06  164.978958  185.659836 -20.680878\n",
       "10  452257.451628  6.784852e+06  197.737625  217.254135 -19.516510\n",
       "11  457379.271643  6.786013e+06  155.526306  147.528824   7.997482"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_join = df_join.drop(12)\n",
    "df_join"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "        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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>t_tops</th>\n",
       "      <th>t_surf</th>\n",
       "      <th>delta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.200000e+01</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>455053.441542</td>\n",
       "      <td>6.785521e+06</td>\n",
       "      <td>170.224576</td>\n",
       "      <td>181.063995</td>\n",
       "      <td>-10.839419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2105.747911</td>\n",
       "      <td>2.017564e+03</td>\n",
       "      <td>21.867723</td>\n",
       "      <td>30.766475</td>\n",
       "      <td>14.476348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>451496.662828</td>\n",
       "      <td>6.781788e+06</td>\n",
       "      <td>131.527908</td>\n",
       "      <td>128.291351</td>\n",
       "      <td>-34.571121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>453413.576531</td>\n",
       "      <td>6.784063e+06</td>\n",
       "      <td>155.526306</td>\n",
       "      <td>149.616352</td>\n",
       "      <td>-20.724125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>455104.639118</td>\n",
       "      <td>6.786013e+06</td>\n",
       "      <td>164.973198</td>\n",
       "      <td>189.382164</td>\n",
       "      <td>-11.285827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>456730.865049</td>\n",
       "      <td>6.786874e+06</td>\n",
       "      <td>186.920994</td>\n",
       "      <td>202.515072</td>\n",
       "      <td>3.059437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>457762.825164</td>\n",
       "      <td>6.788725e+06</td>\n",
       "      <td>206.482803</td>\n",
       "      <td>222.614792</td>\n",
       "      <td>7.997482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   x             y      t_tops      t_surf      delta\n",
       "count      12.000000  1.200000e+01   12.000000   12.000000  12.000000\n",
       "mean   455053.441542  6.785521e+06  170.224576  181.063995 -10.839419\n",
       "std      2105.747911  2.017564e+03   21.867723   30.766475  14.476348\n",
       "min    451496.662828  6.781788e+06  131.527908  128.291351 -34.571121\n",
       "25%    453413.576531  6.784063e+06  155.526306  149.616352 -20.724125\n",
       "50%    455104.639118  6.786013e+06  164.973198  189.382164 -11.285827\n",
       "75%    456730.865049  6.786874e+06  186.920994  202.515072   3.059437\n",
       "max    457762.825164  6.788725e+06  206.482803  222.614792   7.997482"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_join.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First degree regression and residual without anomalous point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data CC: 0.903107703194117\n",
      "\n",
      "Reggresion equation (y):  \n",
      "1.271 x - 35.23\n",
      "\n",
      "Regresion errors\n",
      "MSE: 159.99984065968795\n",
      "R2: 0.815603523568553\n"
     ]
    }
   ],
   "source": [
    "print('Data CC:', df_join.t_tops.corr(df_join.t_surf))\n",
    "print()\n",
    "\n",
    "newy1, e, mse, r2= reg(df_join.t_tops, df_join.t_surf, 1)\n",
    "print('Reggresion equation (y):', newy1)\n",
    "print()\n",
    "\n",
    "print('Regresion errors')\n",
    "print('MSE:' , mse)\n",
    "print('R2:' , r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(4, figsize=(11, 11))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(df_join.t_tops, df_join.t_surf)\n",
    "plt.plot(x, newy1(x), color='black', label='Regression')\n",
    "plt.text(170, 160, 'y = '+str(newy1)[2:])\n",
    "plt.ylabel('Thickness from surface (feet)')\n",
    "plt.axis([100, 240, 100, 240])\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(df_join.t_tops, e)\n",
    "plt.axhline(y=np.mean(e), color='black', label=\"Mean of residual\")\n",
    "plt.xlim(100, 240)\n",
    "plt.xlabel('Thickness from tops (feet)')\n",
    "plt.ylabel('Residual')\n",
    "plt.grid(True)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second degree polynomial\n",
    "\n",
    "You can see that the errors improves even more with a second degree polynomial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data CC: 0.903107703194117\n",
      "\n",
      "Reggresion equation (y):            2\n",
      "-0.008219 x + 4.096 x - 274.5\n",
      "\n",
      "Regresion errors\n",
      "MSE: 144.11349948564896\n",
      "R2: 0.8339122001509959\n"
     ]
    }
   ],
   "source": [
    "print('Data CC:', df_join.t_tops.corr(df_join.t_surf))\n",
    "print()\n",
    "\n",
    "newy2, e, mse, r2= reg(df_join.t_tops, df_join.t_surf, 2)\n",
    "print('Reggresion equation (y):', newy2)\n",
    "print()\n",
    "\n",
    "print('Regresion errors')\n",
    "print('MSE:' , mse)\n",
    "print('R2:' , r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.arange(0, 240, 1)\n",
    "\n",
    "plt.figure(3, figsize=(11, 11))\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.scatter(df_join.t_tops, df_join.t_surf)\n",
    "plt.plot(x, newy2(x), color='black', label='Regression')\n",
    "plt.text(170, 160, 'y = '+str(newy2)[2:])\n",
    "plt.ylabel('Thickness from surface (feet)')\n",
    "plt.axis([100, 240, 100, 240])\n",
    "plt.grid(True)\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.scatter(df_join.t_tops, e)\n",
    "plt.axhline(y=np.mean(e), color='black', label=\"Mean of residual\")\n",
    "plt.xlim(100, 240)\n",
    "plt.xlabel('Thickness from tops (feet)')\n",
    "plt.ylabel('Residual')\n",
    "plt.grid(True)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, base on the regression metrics, the second degree regression appears to be better. The regression and residual plots also suggest that. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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