GeostatsPy_variogram_calculation.ipynb

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    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/intro/title_page.png\" style=\"display: block; margin: 0 auto; width: 100%;\">\n",
    "</figure>\n",
    "\n",
    "### Variogram Calculation\n",
    "\n",
    "Michael J. Pyrcz, Professor, The 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) | [Geostatistics Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [Applied Geostats in Python e-book](https://geostatsguy.github.io/GeostatsPyDemos_Book/intro.html) | [Applied Machine Learning in Python e-book](https://geostatsguy.github.io/MachineLearningDemos_Book/) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "Chapter of e-book \"Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy\". \n",
    "\n",
    "```{admonition} Cite this e-Book as:\n",
    ":class: remove-from-content-only\n",
    "\n",
    "Pyrcz, M.J., 2024, Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy, https://geostatsguy.github.io/GeostatsPyDemos_Book.  \n",
    "```\n",
    "\n",
    "The workflows in this book and more are available here:\n",
    "\n",
    "```{admonition} Cite the GeostatsPyDemos GitHub Repository as:\n",
    ":class: remove-from-content-only\n",
    "\n",
    "Pyrcz, M.J., 2024, GeostatsPyDemos: GeostatsPy Python Package for Spatial Data Analytics and Geostatistics Demonstration Workflows Repository (0.0.1). Zenodo. https://zenodo.org/doi/10.5281/zenodo.12667035\n",
    "\n",
    "[![DOI](https://zenodo.org/badge/777871341.svg)](https://zenodo.org/doi/10.5281/zenodo.12667035)\n",
    "\n",
    "```\n",
    "\n",
    "By Michael J. Pyrcz <br />\n",
    "&copy; Copyright 2024."
   ]
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    "This chapter is a tutorial for / demonstration of  **Calculating Variograms** with GeostatsPy. \n",
    "\n",
    "**YouTube Lecture**: check out my lectures on:\n",
    "\n",
    "* [Stationarity](https://youtu.be/QwxQ9xuUHlU?si=7GK0qTeskd1VsqQQ)\n",
    "* [Spatial Continuity](https://youtu.be/j0I5SGFm00c?si=6rQJQenNvvUGzuvs)        \n",
    "* [Variogram Introduction](https://youtu.be/jVRLGOsnYuw?si=RD5exyGl_I_LAl4H)\n",
    "* [Variogram Calculation](https://youtu.be/mzPLicovE7Q?si=-ZPgFK52h2xp1cER)\n",
    "* [Variogram Parameters](https://youtu.be/NE4xfhIHAm4?si=dF18zj5_M2yVwxoD)\n",
    "* [Variogram Directions](https://youtu.be/bryRCrtf3hk?si=058uFy-eTtTqwNPr)\n",
    "* [Variogram Modeling](https://youtu.be/-Bi63Y3u6TU?si=-kwvFLicNTbg85xL)\n",
    "\n",
    "For your convenience here's a summary of salient points.\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",
    "#### Stationarity\n",
    "\n",
    "Any statistic requires replicates, repeated sampling (e.g., air or water samples from a monitoring station). In our geo-spatial problems repeated samples are not available at a location in the subsurface.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/core_sample.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Core sample (left), what remains after the sample is taken (right).\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "The decision of the stationary domain for sampling is an expert choice. Without it we are stuck in the “hole” and cannot calculate any statistics nor say anything about the behavior of the subsurface between the sample data.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/core_samples.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Core samples at locations, \\(u_1, u_2, \\ldots, u_7\\) within the area of interest.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "From the above image we can see two important components for stationarity,\n",
    "\n",
    "1. **Import License** - choice to pool specific samples to evaluate a statistic.\n",
    "\n",
    "2. **Export License** - choice of where in the subsurface this statistic is applicable.\n",
    "\n",
    "How do we define stationarity? Here's a geological and an engineering or statistical definition of stationarity,\n",
    "\n",
    "**Geological Definition** - for example, The rock over the stationary domain is sourced, deposited, preserved, and post-depositionally altered in a similar manner, the domain is map-able and may be used for local prediction or as information for analogous locations within the subsurface; therefore, it is useful to pool information over this expert mapped volume of the subsurface.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/outcrop.png\" style=\"display: block; margin: 0 auto; width: 90%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Outcrop with mapped units, \\(S3, S4, \\ldots, S6\\). Each unit is deemed to be stationary by the geological definition (image from Fildani et al., 2009).\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "**Engineering or Statistical Definition**: The metrics of interest are invariant under translation over the domain.  For example, one point stationarity indicates the that histogram and associated statistics do not rely on location, $\\bf{u}$.  Statistical stationarity for some common statistics:\n",
    "\n",
    "Stationary mean,  \n",
    "\n",
    "$$\n",
    "𝐸{𝑍(\\bf{u})} = m, \\quad \\forall \\quad \\bf{u} \n",
    "$$\n",
    "\n",
    "Stationary Distribution,\n",
    "\n",
    "$$\n",
    "𝐹(\\bf{𝐮};𝑧)=𝐹(𝑧), \\quad \\forall \\quad \\bf{u} \n",
    "$$\n",
    "\n",
    "Stationary Semivariogram,\n",
    "\n",
    "$$\n",
    "\\gamma_𝑧 (\\bf{𝐮};\\bf{𝐡})= \\gamma_𝑧(\\bf{h}), \\quad \\forall \\bf{u} \n",
    "$$\n",
    "\n",
    "May be extended to any statistic of interest including, \n",
    "\n",
    "* facies proportions\n",
    "\n",
    "* bivariate distributions\n",
    "\n",
    "* multiple point statistics\n",
    "\n",
    "* correlation\n",
    "\n",
    "* etc.\n",
    "\n",
    "Note for engineering or statistical definition of stationarity there are two questions the we must answer,\n",
    "\n",
    "1. What metric / statistic?\n",
    "  \n",
    "2. Over what volume?\n",
    "\n",
    "Some further comments on stationarity,\n",
    "\n",
    "* **Stationarity is a decision, not a hypothesis**; therefore, it cannot be tested.  Data may demonstrate that it is inappropriate. \n",
    "\n",
    "* **The stationarity assessment depends on scale.** This choice of modeling scale(s) should be based on the specific problem and project needs.\n",
    "\n",
    "* **instead of time, we must pool samples over space** to calculate our statistics. This decision to pool is the decision of stationarity.  It is the decision that the subset of the subsurface is all the “same stuff”.\n",
    "\n",
    "#### Stationarity Exercise\n",
    "\n",
    "Let's test our knowledge by staring at stone wall on the 40 acres, campus of The University of Texas at Austin.\n",
    "\n",
    "Is this stone wall stationary?\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/wall.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Stone wall from north side of Patton Hall. The stone is Indiana limestone from Bedford, Indiana.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "While you stare at this, focus on the entire image, and see how it changes over the image? What if we zoom in here?\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/wall2a.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Stone wall from north side of Patton Hall. The stone is Indiana limestone from Bedford, Indiana.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Now let's look at only the center, is this more or less stationary?\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/wall2b.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Stone wall from north side of Patton Hall. The stone is Indiana limestone from Bedford, Indiana.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Now let's zoom in here,\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/wall3a.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Stone wall from north side of Patton Hall. The stone is Indiana limestone from Bedford, Indiana.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Now only looking at this part, is this more or less stationary?\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/wall3b.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Stone wall from north side of Patton Hall. The stone is Indiana limestone from Bedford, Indiana.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Let's return to the statistical definition of stationarity, what were you looking at?\n",
    "\n",
    "* brick color\n",
    "\n",
    "* brick size\n",
    "\n",
    "* brick aspect ratio\n",
    "\n",
    "* fossils (bivalves)\n",
    "\n",
    "Did you notice that the (1) entire image appears stationary, the (2) zoomed in appears less stationary, (3) second zoomed in image appears more stationary again!\n",
    "\n",
    "* remember, stationarity is not homogeneity, it is ok to have change by there is not a consistent change over the area of investigation.\n",
    "\n",
    "#### Comments on Stationarity\n",
    "\n",
    "**We cannot avoid a decision of stationarity**. No stationarity decision and we cannot move beyond the data.  Conversely, assuming broad stationarity over all the data and over large volumes of the earth is naïve.\n",
    "\n",
    "\n",
    "**Geomodeling stationarity is the decision**: (1) over what region to pool data (import license) and (2) over what region to use the resulting statistics (export license).\n",
    "\n",
    "\n",
    "**Nonstationary trends** may be mapped, and the remaining stationary residual modelled statistically / stochastically, trends may be treated uncertain.  \n",
    "\n",
    "**Good geological mapping and data integration is essential!** It is the framework of any subsurface model. \n",
    "\n",
    "Now that we understand stationarity we can calculate and model spatial continuity.\n",
    "\n",
    "#### Spatial Continuity\n",
    "\n",
    "Let's make a simple example,\n",
    "\n",
    "Area of interest\n",
    "2D 1,000 m x 1,000 m\n",
    "1 Injector and 4 producers \n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/example.png\" style=\"display: block; margin: 0 auto; width: 50%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example 2D water flood model over 1,000 x 1,000 m with 1 injector and 4 producers.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Note, for all models the porosity and permeability distributions are held constant, porosity and permeability are highly correlated and I will only show the permeability models for brevity.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/distributions.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example 2D water flood model porosity and permeability distributions.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Now consider these two models, remember they have the same univariate distributions,\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/model1.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example 2D water flood model over 1,000 x 1,000 m with 1 injector and 4 producers.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "What's the difference? The spatial arrangement of the permeability, how far can we go and the permeability is still similar, this is spatial continuity. The model on the left has no spatial continuity and the model on the right has short spatial continuity.\n",
    "\n",
    "Does spatial continuity impact our transfer functions? Let's run the water flood and looks at travel time (earliest arrival) of water.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/marching1.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example water flood travel earliest arrival times for no spatial continuity (left) and short spatial continuity (right).\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "The model with no spatial continuity has better, more uniform, piston-like sweep for better recovery. What if we increase the spatial continuity?\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/marching2.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example water flood travel earliest arrival times for no spatial continuity (left) and long spatial continuity (right).\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "With greater spatial continuity the sweep is even worse. One more example,\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/marching3.png\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Example water flood travel earliest arrival times for no spatial continuity (left) and long, anisotropic spatial continuity (right).\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "This long, anisotropic spatial continuity is resulting in preferred pathways due to high permeability streaks. Clearly spatial continuity matters, so we must calculate it and model it. \n",
    "\n",
    "* the semivariogram is one of our tools to calculate and model spatial continuity\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",
    "$$\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "$$\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",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/variogram.png\" style=\"display: block; margin: 0 auto; width: 30%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Tail \\(\\bf{u}\\) and head \\(\\bf{u} + \\bf{h}\\) locations separated by lag vector \\(\\bf{h}\\). \n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/variogram2.png\" style=\"display: block; margin: 0 auto; width: 50%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Experimental variogram points, variogram value vs. lag \\(\\bf{h}\\). \n",
    "</figcaption>\n",
    "</figure>\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",
    "$$\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "$$\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "$$\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "$$\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "$$\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "$$\n",
    "\n",
    "To help my students visualize the relationship between variogram and correlation of the $\\bf{h}-\\bf{h}$ scatter plot I build an interactive Python [variogram h-h scatter plot dashboard](https://github.com/GeostatsGuy/DataScienceInteractivePython/blob/main/Interactive_Variogram_h_Scatter.ipynb),\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/interactive_hscatter.png\" style=\"display: block; margin: 0 auto; width: 100%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Interactive Python dashboard for variogram and h-h scatter plot correlation. \n",
    "</figcaption>\n",
    "</figure>\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 some lag 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",
    "$$\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "$$\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",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/variogram_cov.png\" style=\"display: block; margin: 0 auto; width: 30%;\">\n",
    "  <figcaption style=\"text-align: center;\"> The relationship between variogram, sill and covariance function. \n",
    "</figcaption>\n",
    "</figure>\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",
    "$$\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "$$\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 nugget 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",
    "To help my students visualize the nugget I build an interactive Python [variogram nugget effect dashboard](https://github.com/GeostatsGuy/DataScienceInteractivePython/blob/main/Interactive_Variogram_Nugget_Effect.ipynb),\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/interactive_nugget.png\" style=\"display: block; margin: 0 auto; width: 100%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Interactive Python dashboard for variogram nugget effect. \n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "For commonly encountered irregularly space spatial data samples, if we specific a specific lag vector, $\\bf{h}$,\n",
    "\n",
    "$$\n",
    "\\bf{h} = 100.00000 \\text{ m at } 90.0000 \\text{ degrees azimuth}\n",
    "$$\n",
    "\n",
    "we are unlikely to find data pairs sperated by exactly that lag distance and direction. We need to use a search template to identify the pairs for a specific lag distance and direction.\n",
    "\n",
    "1. First step is to select the direction as an **azimuth**, here 045 azimuth, 45 degrees from north towards east.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/template.png\" style=\"display: block; margin: 0 auto; width: 60%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Variogram calculation search template, azimuth parameter. \n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "2. Set unit lag distance to at least the ‘common’ minimum data spacing $\\rightarrow$ no data pairs available at shorter lag distances.\n",
    "\n",
    "* lag distance controls the number/frequency of experimental variogram points\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/template2.png\" style=\"display: block; margin: 0 auto; width: 60%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Variogram calculation search template, unit lag distance.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "3. Set the lag tolerance, default to $\\frac{1}{2}$ the unit lag distance.\n",
    "\n",
    "* lower lag tolerance will omit possible pairs\n",
    "\n",
    "* larger lag tolerance will overlap the lag bins, smooth the results.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/template3.png\" style=\"display: block; margin: 0 auto; width: 60%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Variogram calculation search template, lag tolerance.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "4. Set the azimuth tolerance to 90.0 for isotropic variograms. Set azimuth tolerance, default 22.5 degrees for directional variograms.\n",
    "\n",
    "* lower azimuth tolerance is more direction specific and noisier\n",
    "\n",
    "* larger azimuth tolerance is more isotropic and smoother\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/template4.png\" style=\"display: block; margin: 0 auto; width: 60%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Variogram calculation search template, lag tolerance.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "5. Set a bandwidth limit is needed to limit the distance of investigation away from the lag vector.\n",
    "\n",
    "* set bandwidth very large if isotropic to remove its influence and avoid artifacts\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/template5.png\" style=\"display: block; margin: 0 auto; width: 60%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Variogram calculation search template, lag tolerance.\n",
    "</figcaption>\n",
    "</figure>\n",
    " \n",
    "#### Isotropic and Anisotropic Experimental Variograms \n",
    "\n",
    "**Isotropic or Omnidirectional** – azimuth tolerance $= 90^{o}$,\n",
    "\n",
    "* all pairs lag distance ± lag distance tolerance. \n",
    " \n",
    "**Anisotropic or Directional** – azimuth Tolerance $\\lt 90^{o}$. \n",
    "\n",
    "* only considering pairs in a specified direction, larger azimuth tolerance will approach isotropic.\n",
    "\n",
    "* calculate directional, experimental variograms in the major and minor directions to support interpretation and variogram modeling\n",
    "\n",
    "* do not extend the azimuth tolerance $\\gt 90^{o}$, results in a bias as some of the data pairs are counted twice in a lag due to arbitrary orientation.\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/directional.png\" style=\"display: block; margin: 0 auto; width: 50%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Azimuth tolerance and identified data pairs.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "With this information you are ready to calculate experimental variograms.\n",
    "\n",
    "#### Calculating Experimental Variograms\n",
    "\n",
    "While the workflow below demonstrates the workflow for calculating experimental variogram, to assist my students with the variogram calculation process, I build an interactive Python [variogram calculation dashboard](https://github.com/GeostatsGuy/DataScienceInteractivePython/blob/main/Interactive_Variogram_Calculation.ipynb),\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/variogram_calculate/interactive_variogram_calculate.png\" style=\"display: block; margin: 0 auto; width: 100%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Interactive Python dashboard for calculating experimental variograms. \n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "#### Load the Required Libraries\n",
    "\n",
    "The following code loads the required libraries. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GeostatsPy version: 0.0.71\n"
     ]
    }
   ],
   "source": [
    "import geostatspy.GSLIB as GSLIB                              # GSLIB utilities, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                        # GSLIB methods convert to Python      \n",
    "import geostatspy\n",
    "print('GeostatsPy version: ' + str(geostatspy.__version__)) "
   ]
  },
  {
   "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": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                     # set working directory, run executables\n",
    "\n",
    "from tqdm import tqdm                                         # suppress the status bar\n",
    "from functools import partialmethod\n",
    "tqdm.__init__ = partialmethod(tqdm.__init__, disable=True)\n",
    "\n",
    "ignore_warnings = True                                        # ignore warnings?\n",
    "import numpy as np                                            # ndarrays for gridded data\n",
    "import pandas as pd                                           # DataFrames for tabular data\n",
    "import matplotlib.pyplot as plt                               # for plotting\n",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "from scipy import stats                                       # summary statistics\n",
    "import math                                                   # trig etc.\n",
    "import scipy.signal as signal                                 # kernel for moving window calculation\n",
    "import random\n",
    "plt.rc('axes', axisbelow=True)                                # plot all grids below the plot elements\n",
    "if ignore_warnings == True:                                   \n",
    "    import warnings\n",
    "    warnings.filterwarnings('ignore')\n",
    "cmap = plt.cm.inferno                                         # color map"
   ]
  },
  {
   "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.  \n",
    "\n",
    "#### Declare Functions\n",
    "\n",
    "This is a convenience function to add major and minor gridlines to our plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_grid():\n",
    "    plt.gca().grid(True, which='major',linewidth = 1.0); plt.gca().grid(True, which='minor',linewidth = 0.2) # add y grids\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_minor_locator(AutoMinorLocator()); plt.gca().yaxis.set_minor_locator(AutoMinorLocator()) # turn on minor ticks   "
   ]
  },
  {
   "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": 9,
   "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": 11,
   "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>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity\n",
       "0  100  900       1  0.115359\n",
       "1  100  800       1  0.136425\n",
       "2  100  600       1  0.135810"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv\") # load data\n",
    "df = df[['X','Y','Facies','Porosity']]                        # retain only the required features\n",
    "df.head(n=3)                                                  # DataFrame preview to check  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work by-facies, that is separating sand and shale facies and working with them separately.\n",
    "\n",
    "* This command extracts the sand and shale 'Facies\" into new DataFrames for our analysis.\n",
    "\n",
    "* Note, we use deep copies to ensure that edits to the new DataFrames won't change the original DataFrame.\n",
    "\n",
    "* We use the drop parameter to avoid making an new index column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.154648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>0.153113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity\n",
       "0  100  900       1  0.115359\n",
       "1  100  800       1  0.136425\n",
       "2  100  600       1  0.135810\n",
       "3  200  800       1  0.154648\n",
       "4  200  700       1  0.153113"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index(drop = True) # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index(drop = True) # copy only 'Facies' = shale records\n",
    "df_sand.head()                                                # preview the sand only DataFrame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "Let's look at and compare the summary statistics for sand and shale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>Porosity</th>\n",
       "      <td>235.0</td>\n",
       "      <td>0.144298</td>\n",
       "      <td>0.035003</td>\n",
       "      <td>0.08911</td>\n",
       "      <td>0.118681</td>\n",
       "      <td>0.134647</td>\n",
       "      <td>0.16212</td>\n",
       "      <td>0.22879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std      min       25%       50%      75%  \\\n",
       "Porosity  235.0  0.144298  0.035003  0.08911  0.118681  0.134647  0.16212   \n",
       "\n",
       "              max  \n",
       "Porosity  0.22879  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand[['Porosity']].describe().transpose()                  # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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>Porosity</th>\n",
       "      <td>54.0</td>\n",
       "      <td>0.093164</td>\n",
       "      <td>0.012882</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.084734</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.101563</td>\n",
       "      <td>0.12277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          count      mean       std       min       25%       50%       75%  \\\n",
       "Porosity   54.0  0.093164  0.012882  0.058548  0.084734  0.094569  0.101563   \n",
       "\n",
       "              max  \n",
       "Porosity  0.12277  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shale[['Porosity']].describe().transpose()                 # summary table of shale only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The facies have significant differences in their summary statistics. \n",
    "\n",
    "* Looks like separation by facies is a good idea for modeling.\n",
    "\n",
    "#### Set Limits for Plotting, Colorbars and Map Specification\n",
    "\n",
    "Limits are applied for data and model visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0                                     # spatial limits\n",
    "ymin = 0.0; ymax = 1000.0\n",
    "\n",
    "pormin = 0.05; pormax = 0.23                                  # feature limits\n",
    "npormin = -3.0; npormax = 3.0                                 # feature limits\n",
    "\n",
    "vario_min = 0.0; vario_max = 1.6                              # variogram limits\n",
    "\n",
    "tmin = -9999.9; tmax = 9999.9                                 # triming limits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaussian Transformation\n",
    "\n",
    "Let's transform the data grouped overall both facies (sand and shale) and separated by facies to normal score values (Gaussian distributed with a mean of 0.0 and variance of 1.0). \n",
    "\n",
    "* This is required for sequential Gaussian simulation (common target for our variogram models) \n",
    "\n",
    "* Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "The following command will transform the Porosity and to standard normal.\n",
    "\n",
    "* Gaussian distributed with a mean of 0.0 and standard deviation and variance of 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity')   # all \n",
    "df_sand['NPor'], tvPorSand, tnsPorSand = geostats.nscore(df_sand, 'Porosity') # sand \n",
    "df_shale['NPor'], tvPorShale, tnsPorShale = geostats.nscore(df_shale, 'Porosity') # shale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once again we check the DataFrame, see the new Gaussian transformed porosity."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>NPor</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>100</td>\n",
       "      <td>900</td>\n",
       "      <td>1</td>\n",
       "      <td>0.115359</td>\n",
       "      <td>-0.804208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.136425</td>\n",
       "      <td>0.074735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>100</td>\n",
       "      <td>600</td>\n",
       "      <td>1</td>\n",
       "      <td>0.135810</td>\n",
       "      <td>0.042679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>200</td>\n",
       "      <td>800</td>\n",
       "      <td>1</td>\n",
       "      <td>0.154648</td>\n",
       "      <td>0.512201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>200</td>\n",
       "      <td>700</td>\n",
       "      <td>1</td>\n",
       "      <td>0.153113</td>\n",
       "      <td>0.476045</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     X    Y  Facies  Porosity      NPor\n",
       "0  100  900       1  0.115359 -0.804208\n",
       "1  100  800       1  0.136425  0.074735\n",
       "2  100  600       1  0.135810  0.042679\n",
       "3  200  800       1  0.154648  0.512201\n",
       "4  200  700       1  0.153113  0.476045"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sand.head()                                                # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! \n",
    "\n",
    "* 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 porosity.  \n",
    "* Also, the normal score transform of values close to the original distribution's mean 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": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)                                              # plot original sand and shale porosity histograms\n",
    "plt.hist(df_sand['Porosity'], facecolor='gold',bins=np.linspace(0.0,0.4,50),alpha=0.6,density=True,edgecolor='black',\n",
    "         label='Sand')\n",
    "plt.hist(df_shale['Porosity'], facecolor='lightgrey',bins=np.linspace(0.0,0.4,50),alpha=0.6,density=True,edgecolor='black',\n",
    "         label = 'Shale')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,40.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left'); add_grid()\n",
    "\n",
    "plt.subplot(122)                                              # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df_shale['NPor'], facecolor='grey',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.4,density=True,\n",
    "         cumulative=False,edgecolor='black',label='Shale')\n",
    "plt.hist(df_sand['NPor'], facecolor='gold',bins=np.linspace(-3.0,3.0,40),histtype=\"stepfilled\",alpha=0.4,density=True,\n",
    "         cumulative=False,edgecolor='black',label='Sand')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,0.50])\n",
    "plt.xlabel('Nscore Porosity'); plt.ylabel('Density'); plt.title('Gaussian Transformed Porosity Sand and Shale')\n",
    "plt.legend(loc='upper left'); add_grid()\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Location Maps\n",
    "\n",
    "The normal score transform has correctly transformed the porosity over sand and shale facies to standard normal.  Let's plot the location maps of normal score transforms of porosity and permeability for all facies, sand facies and shale facies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(131)                                              # location map all facies\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)                                              # location map sand only\n",
    "GSLIB.locmap_st(df_sand,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Sand Facies','X (m)','Y (m)',\n",
    "                'Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(133)                                              # location map shale only\n",
    "GSLIB.locmap_st(df_shale,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - Shale Facies','X (m)','Y (m)',\n",
    "                'Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.1, wspace=0.2, hspace=0.3); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variogram Calculation\n",
    "\n",
    "Let's see the parameters for the gamv, irregular data, GeostatsPy's experimental variogram calculation function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.gamv(df, xcol, ycol, vcol, tmin, tmax, xlag, xltol, nlag, azm, atol, bandwh, isill)>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.gamv                                                 # see the input parameters required by the gamv function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the location maps to help determine good variogram calculation parameters.\n",
    "\n",
    "We are ready to calculate variogram! Let's calculate isotropic variograms for the transformed normal score porosity and permeability for sand, shale and mixed (without separating sand and shale).  Some information on the parameters that I chose:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = 0; atol = 90.0; 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 additional 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 nscore transform sets the variance to 1.0\n",
    "\n",
    "Let's try running these variograms and visualizing them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lag_dist = 100.0; lag_tol = 100.0; nlag = 10; bandh = 9999.9; azi = 0; atol = 90.0; isill = 1\n",
    "\n",
    "lag, por_sand_gamma, por_sand_npair = geostats.gamv(df_sand,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,\n",
    "            bandh,isill)\n",
    "lag, por_shale_gamma, por_shale_npair = geostats.gamv(df_shale,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,\n",
    "            bandh,isill)\n",
    "lag, por_gamma, por_npair = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag_dist,lag_tol,nlag,azi,atol,bandh,isill)\n",
    "\n",
    "plt.subplot(111)\n",
    "plt.scatter(lag,por_gamma,color = 'white',edgecolor='black',s=50,marker='o',label = 'All',zorder=10)\n",
    "plt.scatter(lag,por_sand_gamma,color = 'gold',edgecolor='black',s=50,marker='o',label = 'Sand',zorder=9)\n",
    "plt.scatter(lag,por_shale_gamma,color = 'grey',edgecolor='black',s=50,marker='o',label = 'Shale',zorder=8)\n",
    "plt.plot([0,2000],[1.0,1.0],color = 'black',zorder=1); plt.annotate('Sill',(15,1.02))\n",
    "plt.xlabel(r'Lag Distance $\\bf(h)$, (m)'); plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "plt.title('Isotropic NSCORE Porosity Variogram')\n",
    "plt.xlim([0,1000]); plt.ylim([0,1.8]); plt.legend(loc='upper left'); add_grid()\n",
    "\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The experimental variograms have some interesting features:\n",
    "\n",
    "* the range of the sand porosity is greater than the shale porosity range\n",
    "* although the shale short range experimental points may be noisy due to sparse shale data\n",
    "\n",
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of variogram calculation with GeostatsPy. Much more can be done, I have other demonstrations for modeling workflows with GeostatsPy in the GitHub repository [GeostatsPy_Demos](https://github.com/GeostatsGuy/GeostatsPy_Demos/tree/main).\n",
    "\n",
    "I hope this is helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "#### About the Author\n",
    "\n",
    "<figure style=\"text-align: center;\">\n",
    "  <img src=\"_static/intro/michael_pyrcz_officeshot_jacket.jpg\" style=\"display: block; margin: 0 auto; width: 70%;\">\n",
    "  <figcaption style=\"text-align: center;\"> Professor Michael Pyrcz in his office on the 40 acres, campus of The University of Texas at Austin.\n",
    "</figcaption>\n",
    "</figure>\n",
    "\n",
    "Michael Pyrcz is a professor in the [Cockrell School of Engineering](https://cockrell.utexas.edu/faculty-directory/alphabetical/p), and the [Jackson School of Geosciences](https://www.jsg.utexas.edu/researcher/michael_pyrcz/), at [The University of Texas at Austin](https://www.utexas.edu/), where he researches and teaches subsurface, spatial data analytics, geostatistics, and machine learning. Michael is also,\n",
    "\n",
    "* the principal investigator of the [Energy Analytics](https://fri.cns.utexas.edu/energy-analytics) freshmen research initiative and a core faculty in the Machine Learn Laboratory in the College of Natural Sciences, The University of Texas at Austin\n",
    "\n",
    "* an associate editor for [Computers and Geosciences](https://www.sciencedirect.com/journal/computers-and-geosciences/about/editorial-board), and a board member for [Mathematical Geosciences](https://link.springer.com/journal/11004/editorial-board), the International Association for Mathematical Geosciences. \n",
    "\n",
    "Michael has written over 70 [peer-reviewed publications](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en), a [Python package](https://pypi.org/project/geostatspy/) for spatial data analytics, co-authored a textbook on spatial data analytics, [Geostatistical Reservoir Modeling](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) and author of two recently released e-books, [Applied Geostatistics in Python: a Hands-on Guide with GeostatsPy](https://geostatsguy.github.io/GeostatsPyDemos_Book/intro.html) and [Applied Machine Learning in Python: a Hands-on Guide with Code](https://geostatsguy.github.io/MachineLearningDemos_Book/intro.html).\n",
    "\n",
    "All of Michael’s university lectures are available on his [YouTube Channel](https://www.youtube.com/@GeostatsGuyLectures) with links to 100s of Python interactive dashboards and well-documented workflows in over 40 repositories on his [GitHub account](https://github.com/GeostatsGuy), to support any interested students and working professionals with evergreen content. To find out more about Michael’s work and shared educational resources visit his [Website](www.michaelpyrcz.com).\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-PI is Professor John Foster)? 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. Professor, Cockrell School of Engineering and 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) | [Geostatistics Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [Applied Geostats in Python e-book](https://geostatsguy.github.io/GeostatsPyDemos_Book/intro.html) | [Applied Machine Learning in Python e-book](https://geostatsguy.github.io/MachineLearningDemos_Book/) | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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