Interactive_Variogram_Calculation_Modeling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<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",
    "## Interactive Variogram Calculation and Modeling Demonstration\n",
    "\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",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "\n",
    "#### Variogram Modeling\n",
    "\n",
    "Spatial continuity can be modeled with nested, positive definate variogram structures:\n",
    "\n",
    "\\begin{equation}\n",
    "\\Gamma_x(\\bf{h}) = \\sum_{i=1}^{nst} \\gamma_i(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "where $\\Gamma_x(\\bf{h})$ is the nested variogram model resulting from the summation of $nst$ nested variograms  $\\gamma_i(\\bf{h})$.\n",
    "\n",
    "The types of structure commonly applied include:\n",
    "\n",
    "* spherical\n",
    "\n",
    "* exponential\n",
    "\n",
    "* gaussian\n",
    "\n",
    "* nugget\n",
    "\n",
    "Other less common models include:\n",
    "\n",
    "* hole effect\n",
    "\n",
    "* dampenned hole effect\n",
    "\n",
    "* power law\n",
    "\n",
    "these will not be covered here.\n",
    "\n",
    "Each one of these variogram structures, $\\gamma_i(\\bf{h})$, is based on a geometric anisotropy model parameterized by the orientation and range in the major and minor directions.  In 2D this is simply an azimuth and ranges, $azi$, $a_{maj}$ and $a_{min}$. Note, the range in the minor direction (orthogonal to the major direction).\n",
    "\n",
    "The geometric anisotropy model assumes that the range in all off-diagonal directions is based on an ellipse with the major and minor axes alligned with and set to the major and minor for the variogram.\n",
    "\n",
    "\\begin{equation}\n",
    "\\bf{h}_i = \\sqrt{\\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2 + \\left(\\frac{r_{maj}}{a_{maj_i}}\\right)^2}  \n",
    "\\end{equation}\n",
    "\n",
    "Therefore, if we know the major direction, range in major and minor directions, we may completely describe each nested componnent of the complete spatial continuity of the variable of interest, $i = 1,\\dots,nst$.\n",
    "\n",
    "Some comments on modeling nested variograms:\n",
    "\n",
    "* we can capture nugget, short and long range continuity structures\n",
    "\n",
    "* we rely on the geometric anisotropy model, so all structures must inform the same level of contribution (porportion of the sill) in all directions.\n",
    "\n",
    "* the geometric anisotropy model is based on azimuth of the major direction of continuity, range in the major direction and range in the minor direction (orthogonal to the major direction).  The range is interpolated between the major and minor azimuths with a ellipse model\n",
    "\n",
    "* we can vary the type of variogram, direction or azimuth of the major direction, and major and minor ranges by structure\n",
    "\n",
    "In this workflow we will explore methods to:\n",
    "\n",
    "1. detect directionality from a spatial dataset\n",
    "2. calculate the directional variograms in the major and minor directions \n",
    "3. build a consistent 2D model fit to the major and minor directions\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 file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\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. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "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": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### 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).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "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. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>909</td>\n",
       "      <td>8.947420</td>\n",
       "      <td>379.859420</td>\n",
       "      <td>4081.726625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170</td>\n",
       "      <td>929</td>\n",
       "      <td>8.650138</td>\n",
       "      <td>343.878040</td>\n",
       "      <td>4152.949017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825</td>\n",
       "      <td>875</td>\n",
       "      <td>6.577249</td>\n",
       "      <td>934.495444</td>\n",
       "      <td>4522.980121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>580</td>\n",
       "      <td>569</td>\n",
       "      <td>16.480268</td>\n",
       "      <td>201.505057</td>\n",
       "      <td>3608.928335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>775</td>\n",
       "      <td>225</td>\n",
       "      <td>14.964127</td>\n",
       "      <td>674.097711</td>\n",
       "      <td>3919.297374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y   Porosity        Perm           AI\n",
       "0  150  909   8.947420  379.859420  4081.726625\n",
       "1  170  929   8.650138  343.878040  4152.949017\n",
       "2  825  875   6.577249  934.495444  4522.980121\n",
       "3  580  569  16.480268  201.505057  3608.928335\n",
       "4  775  225  14.964127  674.097711  3919.297374"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = 4\n",
    "\n",
    "if data == 1:\n",
    "    df = pd.read_csv(\"https://raw.codeproxy.net/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:]                                      # remove first column\n",
    "elif data == 2:\n",
    "    df = pd.read_csv(\"https://raw.codeproxy.net/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v3.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s)\n",
    "    df = df.iloc[:,1:] \n",
    "elif data == 3:\n",
    "    df = pd.read_csv(\"https://raw.codeproxy.net/GeostatsGuy/GeoDataSets/master/12_sample_data.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df['Porosity'] = df['Porosity']*100.0\n",
    "    df = df.iloc[:,1:] \n",
    "else:\n",
    "    df = pd.read_csv(\"https://raw.codeproxy.net/GeostatsGuy/GeoDataSets/master/spatial_nonlinear_MV_facies_v5_sand_only.csv\") # load from Prof. Pyrcz's GitHub repository\n",
    "    df = df.rename(columns = {'Por':'Porosity'})            # rename feature(s) \n",
    "    df = df.iloc[:,1:] \n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The features:\n",
    "\n",
    "* **X** - x coordinate in meters\n",
    "* **Y** - y coordinate in meters\n",
    "* **Porosity** - rock porosity averaged over a specific rock unit from a vertical well\n",
    "* **Perm** - rock permeability averaged (scaled up) over a specific rock unit from a vertical well \n",
    "* **AI** - acoustic impedance from a seismic cube assigned at a specific rock unit and at the location of a vertical well \n",
    "* **facies** - facies, 0 - shale, 1 - sandstone\n",
    "\n",
    "Concerning facies:\n",
    "\n",
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "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>450.0</td>\n",
       "      <td>470.444444</td>\n",
       "      <td>288.791974</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>221.250000</td>\n",
       "      <td>475.000000</td>\n",
       "      <td>725.000000</td>\n",
       "      <td>980.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>450.0</td>\n",
       "      <td>516.160000</td>\n",
       "      <td>283.500794</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>275.000000</td>\n",
       "      <td>525.000000</td>\n",
       "      <td>756.500000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>450.0</td>\n",
       "      <td>11.626604</td>\n",
       "      <td>3.237444</td>\n",
       "      <td>4.378792</td>\n",
       "      <td>9.246387</td>\n",
       "      <td>11.283306</td>\n",
       "      <td>13.656570</td>\n",
       "      <td>20.835838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>450.0</td>\n",
       "      <td>442.290250</td>\n",
       "      <td>286.734825</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>226.181214</td>\n",
       "      <td>422.573712</td>\n",
       "      <td>628.742290</td>\n",
       "      <td>1437.594828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>450.0</td>\n",
       "      <td>4172.222166</td>\n",
       "      <td>290.509566</td>\n",
       "      <td>3493.755740</td>\n",
       "      <td>3973.941281</td>\n",
       "      <td>4158.552677</td>\n",
       "      <td>4370.203466</td>\n",
       "      <td>5071.276705</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count         mean         std          min          25%  \\\n",
       "X         450.0   470.444444  288.791974     0.000000   221.250000   \n",
       "Y         450.0   516.160000  283.500794     9.000000   275.000000   \n",
       "Porosity  450.0    11.626604    3.237444     4.378792     9.246387   \n",
       "Perm      450.0   442.290250  286.734825     0.000000   226.181214   \n",
       "AI        450.0  4172.222166  290.509566  3493.755740  3973.941281   \n",
       "\n",
       "                  50%          75%          max  \n",
       "X          475.000000   725.000000   980.000000  \n",
       "Y          525.000000   756.500000   999.000000  \n",
       "Porosity    11.283306    13.656570    20.835838  \n",
       "Perm       422.573712   628.742290  1437.594828  \n",
       "AI        4158.552677  4370.203466  5071.276705  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the Model Parameters\n",
    "\n",
    "See the the following model parameters:\n",
    "\n",
    "* **xmin**, **xmax**, **ymin** and **ymax** - extents of the dataset for plotting\n",
    "* **feature** and **feature_units** - feature of interest and associated units\n",
    "* **vmin** and **vmax** - minimum and maximum of the feature of interest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                                # spatial extents in x and y\n",
    "ymin = 0.0; ymax = 1000.0\n",
    "feature = 'Porosity'; feature_units = 'percentage'         # name and units of the feature of interest\n",
    "vmin = 0.0; vmax = 25.0                                  # min and max of the feature of interest\n",
    "cmap = plt.cm.inferno                                    # set the color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the feature to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['N' + feature], tvPor, tnsPor = geostats.nscore(df, feature) # nscore transform for all facies porosity "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPorosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150</td>\n",
       "      <td>909</td>\n",
       "      <td>8.947420</td>\n",
       "      <td>379.859420</td>\n",
       "      <td>4081.726625</td>\n",
       "      <td>-0.783500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>170</td>\n",
       "      <td>929</td>\n",
       "      <td>8.650138</td>\n",
       "      <td>343.878040</td>\n",
       "      <td>4152.949017</td>\n",
       "      <td>-0.853588</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>825</td>\n",
       "      <td>875</td>\n",
       "      <td>6.577249</td>\n",
       "      <td>934.495444</td>\n",
       "      <td>4522.980121</td>\n",
       "      <td>-1.737935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>580</td>\n",
       "      <td>569</td>\n",
       "      <td>16.480268</td>\n",
       "      <td>201.505057</td>\n",
       "      <td>3608.928335</td>\n",
       "      <td>1.354567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>775</td>\n",
       "      <td>225</td>\n",
       "      <td>14.964127</td>\n",
       "      <td>674.097711</td>\n",
       "      <td>3919.297374</td>\n",
       "      <td>0.971878</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y   Porosity        Perm           AI  NPorosity\n",
       "0  150  909   8.947420  379.859420  4081.726625  -0.783500\n",
       "1  170  929   8.650138  343.878040  4152.949017  -0.853588\n",
       "2  825  875   6.577249  934.495444  4522.980121  -1.737935\n",
       "3  580  569  16.480268  201.505057  3608.928335   1.354567\n",
       "4  775  225  14.964127  674.097711  3919.297374   0.971878"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df[feature], facecolor='red',bins=np.linspace(vmin,vmax,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([vmin,vmax]); plt.ylim([0,1.0])\n",
    "plt.xlabel(feature + '(' + feature_units + ')'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(122)  \n",
    "plt.hist(df['N'+feature], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Gaussian Transformed ' + feature); plt.ylabel('Frequency'); plt.title('Guassian Transformed ' + feature)\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the normal score transform has correctly transformed the feature to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the variogram calculation search template.\n",
    "\n",
    "Let's plot the location maps of the original feature and the normal score transforms of the feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "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": [
    "plt.subplot(121)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y',feature,0,1000,0,1000,vmin,vmax,feature,'X (m)','Y (m)',feature,cmap)\n",
    "\n",
    "plt.subplot(122)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','N'+feature,0,1000,0,1000,-3,3,'Gaussian Transformed ' + feature,'X (m)','Y (m)','Gaussian Transformed ' + feature,cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit!\n",
    "\n",
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation and Modeling\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating and modeling experimental variograms.\n",
    "\n",
    "* allowing you to calculate and model the variogram of the normal score transformed variogram interactively while changing (and exploring) the search template parameters.\n",
    "\n",
    "* first calculate the isotropic or directional variogram(s)\n",
    "\n",
    "* then fit the same isotropic or directional variogram(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 10, max = 500, value = 10, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 5, max = 500, value = 5, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 100, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 10, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    global lags,gammas,npps,lags2,gammas2,npps2\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    lags2, gammas2, npps2 = geostats.gamv(df,\"X\",\"Y\",\"N\"+feature,tmin,tmax,lag,lag_tol,nlag,azi+90.0,azi_tol,bandwidth,isill=1.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi), alpha = 0.8)\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi+90.0), alpha = 0.8)\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi) + ', Azi. Tol.' + str(azi_tol))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "    return\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demonstration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \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) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data. Note, size of the experimental point is scaled by the number of pairs.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ffcbe9deb6f4775998fee89e94662fb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "41682d6814b44f6d86df55af22c2952a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 1 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "l = widgets.Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nug = widgets.FloatSlider(min = 0, max = 1.0, value = 0.0, step = 0.1, description = 'nug',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='Type1:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c1 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.1, description = 'c1',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c1.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 25.0, description = 'hmaj1',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj1.style.handle_color = 'black'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 25.0, description = 'hmin1',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin1.style.handle_color = 'red'\n",
    "\n",
    "it2 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',\n",
    "    description='Type2:',disabled=False,layout=Layout(width='200px', height='30px'))\n",
    "c2 = widgets.FloatSlider(min=0.0, max = 1.0, value = 0.0, description = 'c2',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "c2.style.handle_color = 'gray'\n",
    "hmaj2 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmaj2',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmaj2.style.handle_color = 'black'\n",
    "hmin2 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 0.01, step = 100.0, description = 'hmin2',orientation='vertical',layout=Layout(width='60px', height='200px'))\n",
    "hmin2.style.handle_color = 'red'\n",
    "\n",
    "ui1 = widgets.HBox([nug,it1,c1,hmaj1,hmin1,it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "#ui2 = widgets.HBox([it2,c2,hmaj2,hmin2],)                   # basic widget formatting   \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def convert_type(it):\n",
    "    if it == 'Spherical': \n",
    "        return 1\n",
    "    elif it == 'Exponential':\n",
    "        return 2\n",
    "    else: \n",
    "        return 3\n",
    "\n",
    "def f_make(nug,it1,c1, hmaj1, hmin1, it2, c2, hmaj2, hmin2):                       # function to take parameters, make sample and plot\n",
    "    text_trap = io.StringIO()\n",
    "    sys.stdout = text_trap\n",
    "    \n",
    "    it1 = convert_type(it1); it2 = convert_type(it2)\n",
    "    if c2 > 0.0:\n",
    "        nst = 2\n",
    "    else:\n",
    "        nst = 1\n",
    "    \n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,0.0,hmaj1,hmin1,it2,c2,0.0,hmaj2,hmin2) # make model object\n",
    "    nlag = 100; xlag = 10;                                     \n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,0.0,vario)   # project the model in the major azimuth                                                  # project the model in the 135 azimuth\n",
    "    index_min,h_min,gam_min,cov_min,ro_min = geostats.vmodel(nlag,xlag,90.0,vario)   \n",
    "\n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.03,label = 'Major Azimuth ' +str(azi.value), alpha = 0.8)\n",
    "    plt.plot(h_maj,gam_maj,color = 'black')\n",
    "    plt.scatter(lags2,gammas2,color = 'red',s = npps*0.03,label = 'Minor Azimuth ' +str(azi.value+90.0), alpha = 0.8)\n",
    "    plt.plot(h_min,gam_min,color = 'red')\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    if azi_tol.value < 90.0:\n",
    "        plt.title('Directional NSCORE ' + feature + ' Variogram - Azi. ' + str(azi.value) + ', Azi. Tol.' + str(azi_tol.value))\n",
    "    else: \n",
    "        plt.title('Omni Directional NSCORE ' + feature + ' Variogram ')\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=1.5, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'nug':nug, 'it1':it1,'c1':c1, 'hmaj1':hmaj1, 'hmin1':hmin1, 'it2':it2, 'c2':c2, 'hmaj2':hmaj2, 'hmin2':hmin2})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Nested Variogram Modeling Demostration\n",
    "\n",
    "* select the nested structures and their types, contributions and major and minor ranges \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) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Fit a positive definite variogram model based on the addition of multiple structures each describing spatial components of the feature variance \n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1 / c2**: contributions of the sill\n",
    "\n",
    "* **hmaj1 / hmaj2**: range in the major direction\n",
    "\n",
    "* **hmin1 / hmin2**: range in the minor direction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "86e5546cca5b447492916e708958ce5b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Modeling, Michael Pyrcz, Associate Professo…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e9145d0415874d11ae45509140392df0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 1 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration / exercise of variogram calculation and modeling for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \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",
    "\n",
    "#### More Resources Available at: [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",
    "  "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}