GeostatsPy_datadistributions.ipynb

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   "source": [
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Subsurface Data Analytics \n",
    "\n",
    "### Basic Univerariate Distributions in Python \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Univariate Data Distribution Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic univariate statistics and distribution plotting of tabular (easily extended to gridded) data summary statistics and distributions. This should help you get started data visualization and interpretation.\n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data files to your working directory.  They are avaiable here:\n",
    "\n",
    "1. Tabular data - sample_data.csv at https://git.io/fh4gm\n",
    "2. Gridded data - AI_grid.csv at https://git.io/fh4gU\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")             # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  For fun try misspelling the name. You will get an ugly, long error.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] File sample_data_cow.csv does not exist: 'sample_data_cow.csv'",
     "output_type": "error",
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      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
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      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36mparser_f\u001b[1;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, squeeze, prefix, mangle_dupe_cols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, dialect, error_bad_lines, warn_bad_lines, delim_whitespace, low_memory, memory_map, float_precision)\u001b[0m\n\u001b[0;32m    674\u001b[0m         )\n\u001b[0;32m    675\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 676\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    677\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    678\u001b[0m     \u001b[0mparser_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_read\u001b[1;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[0;32m    446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    447\u001b[0m     \u001b[1;31m# Create the parser.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 448\u001b[1;33m     \u001b[0mparser\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfp_or_buf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    449\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    450\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[0;32m    878\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"has_index_names\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    879\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 880\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    881\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    882\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[1;34m(self, engine)\u001b[0m\n\u001b[0;32m   1112\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_make_engine\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"c\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1113\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"c\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1114\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mCParserWrapper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptions\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1115\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1116\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mengine\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"python\"\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\io\\parsers.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, src, **kwds)\u001b[0m\n\u001b[0;32m   1889\u001b[0m         \u001b[0mkwds\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"usecols\"\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0musecols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1890\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1891\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparsers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTextReader\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msrc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1892\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_reader\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnamed_cols\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1893\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader.__cinit__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\parsers.pyx\u001b[0m in \u001b[0;36mpandas._libs.parsers.TextReader._setup_parser_source\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] File sample_data_cow.csv does not exist: 'sample_data_cow.csv'"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('sample_data_cow.csv')     # load our data table (wrong name!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's Python, but there's method to the madness. In general the error shows a trace from the initial command into all the nested programs involved until the actual error occured. If you are debugging code (I know, I'm getting ahead of myself now), this is valuable for the detective work of figuring out what went wrong. I've spent days in C++ debugging one issue, this helps. So since you're working in Jupyter Notebook, the program just assumes you code. Fine. If you scroll to the bottom of the error you often get a summary statement *FileNotFoundError: File b'sample_data_cow.csv' does not exist*. Ok, now you know that you don't have a file iwth that name in the working directory.  \n",
    "\n",
    "Painful to leave that error in our workflow, eh? Everytime I passes it while making this documented I wanted to fix it. Its a coder thing... go ahead and erase it if you like. Just select the block and click on the scissors above in the top bar of this window. While we are at it, notice if you click the '+' you can add in a new block anywhere. Ok, let's spell the file name correctly and get back to work, already."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('sample_data.csv')     # load our data table (wrong name!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No error now! It worked, we loaded our file into our DataFrame called 'df'. But how do you really know that it worked? Visualizing the DataFrame would be useful and we already leard about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       X      Y  Facies  Porosity       Perm           AI\n",
      "0  100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
      "1  100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
      "2  100.0  700.0     0.0  0.085357   5.984520  6127.548006\n",
      "3  100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
      "4  100.0  500.0     0.0  0.102468   1.952264  3835.270322\n"
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>100.0</td>\n",
       "      <td>400.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.110579</td>\n",
       "      <td>3.691908</td>\n",
       "      <td>5295.267191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>100.0</td>\n",
       "      <td>300.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.088936</td>\n",
       "      <td>1.073582</td>\n",
       "      <td>6744.996106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.102094</td>\n",
       "      <td>2.396189</td>\n",
       "      <td>5947.338115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137453</td>\n",
       "      <td>5.727603</td>\n",
       "      <td>5823.241783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>200.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>14.771314</td>\n",
       "      <td>5621.146994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>200.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.125984</td>\n",
       "      <td>10.675436</td>\n",
       "      <td>4292.700500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>200.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.121754</td>\n",
       "      <td>3.085825</td>\n",
       "      <td>5397.400218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>200.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.095147</td>\n",
       "      <td>0.962565</td>\n",
       "      <td>4619.786478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X      Y  Facies  Porosity       Perm           AI\n",
       "0   100.0  900.0     1.0  0.100187   1.363890  5110.699751\n",
       "1   100.0  800.0     0.0  0.107947  12.576845  4671.458560\n",
       "2   100.0  700.0     0.0  0.085357   5.984520  6127.548006\n",
       "3   100.0  600.0     0.0  0.108460   2.446678  5201.637996\n",
       "4   100.0  500.0     0.0  0.102468   1.952264  3835.270322\n",
       "5   100.0  400.0     0.0  0.110579   3.691908  5295.267191\n",
       "6   100.0  300.0     0.0  0.088936   1.073582  6744.996106\n",
       "7   100.0  200.0     0.0  0.102094   2.396189  5947.338115\n",
       "8   100.0  100.0     1.0  0.137453   5.727603  5823.241783\n",
       "9   200.0  900.0     1.0  0.137062  14.771314  5621.146994\n",
       "10  200.0  800.0     1.0  0.125984  10.675436  4292.700500\n",
       "11  200.0  700.0     0.0  0.121754   3.085825  5397.400218\n",
       "12  200.0  600.0     0.0  0.095147   0.962565  4619.786478"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 2 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), permeability as Perm (mDarcy) and acoustic impedance as AI (kg/m2s*10^6). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>261.0</td>\n",
       "      <td>629.823755</td>\n",
       "      <td>341.200403</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>241.000000</td>\n",
       "      <td>700.000000</td>\n",
       "      <td>955.000000</td>\n",
       "      <td>1005.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>261.0</td>\n",
       "      <td>488.344828</td>\n",
       "      <td>166.669352</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>416.000000</td>\n",
       "      <td>479.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>989.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>261.0</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.486148</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>261.0</td>\n",
       "      <td>0.150357</td>\n",
       "      <td>0.049783</td>\n",
       "      <td>0.058871</td>\n",
       "      <td>0.104893</td>\n",
       "      <td>0.137062</td>\n",
       "      <td>0.199108</td>\n",
       "      <td>0.242298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>261.0</td>\n",
       "      <td>183.711554</td>\n",
       "      <td>344.959449</td>\n",
       "      <td>0.033611</td>\n",
       "      <td>2.186525</td>\n",
       "      <td>19.977020</td>\n",
       "      <td>246.215865</td>\n",
       "      <td>2642.999829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>261.0</td>\n",
       "      <td>4203.657220</td>\n",
       "      <td>1317.753146</td>\n",
       "      <td>1844.166880</td>\n",
       "      <td>2947.867713</td>\n",
       "      <td>4204.150893</td>\n",
       "      <td>5397.400218</td>\n",
       "      <td>7881.898531</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean          std          min          25%  \\\n",
       "X         261.0   629.823755   341.200403    40.000000   241.000000   \n",
       "Y         261.0   488.344828   166.669352    29.000000   416.000000   \n",
       "Facies    261.0     0.620690     0.486148     0.000000     0.000000   \n",
       "Porosity  261.0     0.150357     0.049783     0.058871     0.104893   \n",
       "Perm      261.0   183.711554   344.959449     0.033611     2.186525   \n",
       "AI        261.0  4203.657220  1317.753146  1844.166880  2947.867713   \n",
       "\n",
       "                  50%          75%          max  \n",
       "X          700.000000   955.000000  1005.000000  \n",
       "Y          479.000000   539.000000   989.000000  \n",
       "Facies       1.000000     1.000000     1.000000  \n",
       "Porosity     0.137062     0.199108     0.242298  \n",
       "Perm        19.977020   246.215865  2642.999829  \n",
       "AI        4204.150893  5397.400218  7881.898531  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also use a wide variety of statistical summaries built into NumPy's ndarrays.  When we use the command:\n",
    "```p\n",
    "df['Porosity']                       # returns an Pandas series\n",
    "df['Porosity'].values                # returns an ndarray\n",
    "```\n",
    "Panda's DataFrame returns all the porosity data as a series and if we add 'values' it returns a NumPy ndarray and we have access to a lot of NumPy methods. I also like to use the round function to round the answer to a limited number of digits for accurate reporting of precision and ease of reading.\n",
    "\n",
    "For example, now we could use commands. like this one:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum is 0.06.\n",
      "The maximum is 0.24.\n",
      "The standard deviation is 0.05.\n",
      "The standard deviation is 0.05.\n"
     ]
    }
   ],
   "source": [
    "print('The minimum is ' + str(round((df['Porosity'].values).min(),2)) + '.')\n",
    "print('The maximum is ' + str(round((df['Porosity'].values).max(),2)) + '.')\n",
    "print('The standard deviation is ' + str(round((df['Porosity'].values).std(),2)) + '.')\n",
    "print('The standard deviation is ' + str(round((df['Porosity'].values).std(),2)) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's some of the NumPy statistical functions that take ndarrays as an inputs.  With these methods if you had a multidimensional array you could calculate the average by row (axis = 1) or by column (axis = 0) or over the entire array (no axis specified). We just have a 1D ndarray so this is not applicable here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The minimum is 0.06\n",
      "The maximum is 0.24\n",
      "The range (maximum - minimum) is 0.18\n",
      "The P10 is 0.092\n",
      "The P50 is 0.137\n",
      "The P90 is 0.212\n",
      "The P13 is 0.095\n",
      "The media (P50) is 0.137\n",
      "The mean is 0.15\n"
     ]
    }
   ],
   "source": [
    "print('The minimum is ' + str(round(np.amin(df['Porosity'].values),2)))\n",
    "print('The maximum is ' + str(round(np.amax(df['Porosity'].values),2)))\n",
    "print('The range (maximum - minimum) is ' + str(round(np.ptp(df['Porosity'].values),2)))\n",
    "print('The P10 is ' + str(round(np.percentile(df['Porosity'].values,10),3)))\n",
    "print('The P50 is ' + str(round(np.percentile(df['Porosity'].values,50),3)))\n",
    "print('The P90 is ' + str(round(np.percentile(df['Porosity'].values,90),3)))\n",
    "print('The P13 is ' + str(round(np.percentile(df['Porosity'].values,13),3)))\n",
    "print('The media (P50) is ' + str(round(np.median(df['Porosity'].values),3)))\n",
    "print('The mean is ' + str(round(np.mean(df['Porosity'].values),3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later in the ocurse we will talke about weights statistics. The NumPy command average allows for weighted averages as in the case of statistical expectation and declutered statistics. For demonstration, lets make a weighting array and apply it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The equal weighted average is 0.15, the same as the mean above.\n"
     ]
    }
   ],
   "source": [
    "nd = len(df)                              # get the number of data values\n",
    "wts = np.ones(nd)                         # make an array of nd length of 1's\n",
    "print('The equal weighted average is ' + str(round(np.average(df['Porosity'].values,weights = wts),3)) + ', the same as the mean above.')            "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's get fancy, we will modify the weights to be 0.5 if the porosity is greater than 13% and retain 1.0 if the porosity is less than or equal to 13%. The results should be a lower weighted average.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The equal weighted average is 0.112, lower than the equal weighted average above.\n"
     ]
    }
   ],
   "source": [
    "porosity = df['Porosity'].values\n",
    "wts[porosity > 0.13] *= 0.1\n",
    "print('The equal weighted average is ' + str(round(np.average(df['Porosity'].values,weights = wts),3)) + ', lower than the equal weighted average above.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I should note that SciPy stats functions provide a handy summary statistics function. The output is a 'list' of values (actually it is a SciPy.DescribeResult ojbect). One can extract any one of them to use in a workflow as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DescribeResult(nobs=261, minmax=(0.05887104264089544, 0.2422978845362024), mean=0.1503570616019656, variance=0.0024783238419715937, skewness=0.08071652694566994, kurtosis=-1.5618166076333853)\n",
      "Porosity kurtosis is -1.56\n"
     ]
    }
   ],
   "source": [
    "print(stats.describe(df['Porosity'].values))                # summary statistics   \n",
    "por_stats = stats.describe(df['Porosity'].values)           # store as an array\n",
    "print('Porosity kurtosis is ' + str(round(por_stats[5],2))) # extract a statistic "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting Distributions\n",
    "\n",
    "Let's display some histograms. I reimplimented the hist function from GSLIB. See the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.hist(array, xmin, xmax, log, cumul, bins, weights, xlabel, title, fig_name)>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.hist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's make a histogram for porosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pormin = 0.05; pormax = 0.25\n",
    "GSLIB.hist(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=10,weights = None, xlabel='Porosity (fraction)',title='Porosity Well Data',fig_name='hist_Porosity')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What's going on here? Looks quite bimodal. Let's explore with a couple bins sizes to check."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(131)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=5,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data')\n",
    "\n",
    "plt.subplot(132)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=10,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data')\n",
    "\n",
    "plt.subplot(133)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=20,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.5, wspace=0.1, hspace=0.2)\n",
    "plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What about cumulative plots? This method makes a cumulative histogram, but the axis remains in frequency. To be a true cumulative distribution function we would need to standardize the Y-axis to be from 0.0 to 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "GSLIB.hist(df['Porosity'].values,pormin,pormax,log=False,cumul = True,bins=100,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data',fig_name='hist_Porosity_CDF')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I don't want to suggest that matplotlib is hard to use. The GSLIB visualizations provide convenience and once again use the same parameters as the GSLIB methods. Particularly, the 'hist' function is pretty easy to use.  Here's how we can make a pretty nice looking CDF from our data.  Note after the initial hist command we can add a variety of features such as labels to our plot as shown below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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": [
    "plt.hist(df['Porosity'].values,density=True, cumulative=True, label='CDF',\n",
    "           histtype='stepfilled', alpha=0.2, bins = 100, color='red', edgecolor = 'black', range=[0.0,0.25])\n",
    "plt.xlabel('Porosity (fraction)')\n",
    "plt.title('Porosity CDF')\n",
    "plt.ylabel('Cumulation Probability')\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.2)\n",
    "plt.savefig('cdf_Porosity.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculating and Plotting a CDF by Hand\n",
    "\n",
    "Let's demonstrate the calculation and plotting of a non-parametric CDF by hand\n",
    "\n",
    "1. make a copy of the feature as a 1D array (ndarray from NumPy)\n",
    "2. sort the data in ascending order\n",
    "3. assign cumulative probabilities based on the tail assumptions\n",
    "4. plot cumuative probability vs. value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The ndarray has a shape of (261,).\n"
     ]
    },
    {
     "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": [
    "por = df['Porosity'].copy(deep = True).values # make a deepcopy of the feature from the DataFrame\n",
    "print('The ndarray has a shape of ' + str(por.shape) + '.')\n",
    "\n",
    "por = np.sort(por)                           # sort the data in ascending order\n",
    "n = por.shape[0]                             # get the number of data samples\n",
    "\n",
    "cprob = np.zeros(n)\n",
    "for i in range(0,n):\n",
    "    index = i + 1\n",
    "    cprob[i] = index / n                     # known upper tail\n",
    "    # cprob[i] = (index - 1)/n               # known lower tail\n",
    "    # cprob[i] = (index - 1)/(n - 1)         # known upper and lower tails\n",
    "    # cprob[i] = index/(n+1)                 # unknown tails  \n",
    "\n",
    "plt.subplot(111)\n",
    "plt.plot(por,cprob, alpha = 0.2, c = 'black') # plot piecewise linear interpolation\n",
    "plt.scatter(por,cprob,s = 10, alpha = 1.0, c = 'red', edgecolor = 'black') # plot the CDF points\n",
    "plt.grid(); plt.xlim([0.05,0.25]); plt.ylim([0.0,1.0])\n",
    "plt.xlabel(\"Porosity (fraction)\"); plt.ylabel(\"Cumulative Probability\"); plt.title(\"Non-parametric Porosity Cumulative Distribution Function\")\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.7, wspace=0.1, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's finish with the histograms of all our properties of interest as a finale!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "permmin = 0.01; permmax = 3000;                # user specified min and max\n",
    "AImin = 1000.0; AImax = 8000\n",
    "Fmin = 0; Fmax = 1\n",
    "\n",
    "plt.subplot(221)\n",
    "GSLIB.hist_st(df['Facies'].values,Fmin,Fmax,log=False,cumul = False,bins=20,weights = None,xlabel='Facies (1-sand, 0-shale)',title='Facies Well Data')\n",
    "\n",
    "plt.subplot(222)\n",
    "GSLIB.hist_st(df['Porosity'].values,pormin,pormax,log=False,cumul = False,bins=20,weights = None,xlabel='Porosity (fraction)',title='Porosity Well Data')\n",
    "\n",
    "plt.subplot(223)\n",
    "GSLIB.hist_st(df['Perm'].values,permmin,permmax,log=False,cumul = False,bins=20,weights = None,xlabel='Permeaiblity (mD)',title='Permeability Well Data')\n",
    "\n",
    "plt.subplot(224)\n",
    "GSLIB.hist_st(df['AI'].values,AImin,AImax,log=False,cumul = False,bins=20,weights = None,xlabel='Acoustic Impedance (kg/m2s*10^6)',title='Acoustic Impedance Well Data')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=3.5, wspace=0.1, hspace=0.2)\n",
    "plt.savefig('hist_Porosity_Multiple_bins.tif',dpi=600,bbox_inches=\"tight\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of calculating univariate statistics and visualizing data distributions. Much more could be done, I have other demosntrations on basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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