Interactive_Uncertainty_Checking.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",
    "## Uncertaint Model Checking 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 a simple workflow for checking the uncertainty model from simple kriging estimates and the estimation variance\n",
    "\n",
    "* we assume a Gaussian local uncertainty model\n",
    "\n",
    "#### Spatial Estimation\n",
    "\n",
    "Consider the case of making an estimate at some unsampled location, $𝑧(\\bf{u}_0)$, where $z$ is the property of interest (e.g. porosity etc.) and $𝐮_0$ is a location vector describing the unsampled location.\n",
    "\n",
    "How would you do this given data, $𝑧(\\bf{𝐮}_1)$, $𝑧(\\bf{𝐮}_2)$, and $𝑧(\\bf{𝐮}_3)$?\n",
    "\n",
    "It would be natural to use a set of linear weights to formulate the estimator given the available data.\n",
    "\n",
    "\\begin{equation}\n",
    "z^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} z(\\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "We could add an unbiasedness constraint to impose the sum of the weights equal to one.  What we will do is assign the remainder of the weight (one minus the sum of weights) to the global average; therefore, if we have no informative data we will estimate with the global average of the property of interest.\n",
    "\n",
    "\\begin{equation}\n",
    "z^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} z(\\bf{u}_{\\alpha}) + \\left(1-\\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} \\right) \\overline{z}\n",
    "\\end{equation}\n",
    "\n",
    "We will make a stationarity assumption, so let's assume that we are working with residuals, $y$. \n",
    "\n",
    "\\begin{equation}\n",
    "y^{*}(\\bf{u}) = z^{*}(\\bf{u}) - \\overline{z}(\\bf{u})\n",
    "\\end{equation}\n",
    "\n",
    "If we substitute this form into our estimator the estimator simplifies, since the mean of the residual is zero.\n",
    "\n",
    "\\begin{equation}\n",
    "y^{*}(\\bf{u}) = \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} y(\\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "while satisfying the unbaisedness constraint.  \n",
    "\n",
    "#### Kriging\n",
    "\n",
    "Now the next question is what weights should we use?  \n",
    "\n",
    "We could use equal weighting, $\\lambda = \\frac{1}{n}$, and the estimator would be the average of the local data applied for the spatial estimate. This would not be very informative.\n",
    "\n",
    "We could assign weights considering the spatial context of the data and the estimate:\n",
    "\n",
    "* **spatial continuity** as quantified by the variogram (and covariance function)\n",
    "* **redundancy** the degree of spatial continuity between all of the available data with themselves \n",
    "* **closeness** the degree of spatial continuity between the avaiable data and the estimation location\n",
    "\n",
    "The kriging approach accomplishes this, calculating the best linear unbiased weights for the local data to estimate at the unknown location.  The derivation of the kriging system and the resulting linear set of equations is available in the lecture notes.  Furthermore kriging provides a measure of the accuracy of the estimate!  This is the kriging estimation variance (sometimes just called the kriging variance).\n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^{2}_{E}(\\bf{u}) = C(0) - \\sum^{n}_{\\alpha = 1} \\lambda_{\\alpha} C(\\bf{u}_0 - \\bf{u}_{\\alpha})\n",
    "\\end{equation}\n",
    "\n",
    "What is 'best' about this estimate? Kriging estimates are best in that they minimize the above estimation variance. \n",
    "\n",
    "#### Properties of Kriging\n",
    "\n",
    "Here are some important properties of kriging:\n",
    "\n",
    "* **Exact interpolator** - kriging estimates with the data values at the data locations\n",
    "* **Kriging variance** can be calculated before getting the sample information, as the kriging estimation variance is not dependent on the values of the data nor the kriging estimate, i.e. the kriging estimator is homoscedastic. \n",
    "* **Spatial context** - kriging takes into account, furthermore to the statements on spatial continuity, closeness and redundancy we can state that kriging accounts for the configuration of the data and structural continuity of the variable being estimated.\n",
    "* **Scale** - kriging may be generalized to account for the support volume of the data and estimate. We will cover this later.\n",
    "* **Multivariate** - kriging may be generalized to account for multiple secondary data in the spatial estimate with the cokriging system. We will cover this later.\n",
    "* **Smoothing effect** of kriging can be forecast. We will use this to build stochastic simulations later.\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",
    "\n",
    "#### Accuracy Plots\n",
    "\n",
    "The accuracy plot was developed by Deutsch (1996) and described in Pyrcz and Deutsch (2014). \n",
    "\n",
    "* a method for checking uncertainty models\n",
    "\n",
    "* based on calculating the percentiles of withheld testing data in the estimated local uncertainty distributions, $Z(\\bf{u}_{\\alpha})$, describes by CDFs, $F_Z(z,\\bf{u}_{\\alpha})$, at all testing locations $\\alpha = 1,\\ldots,n_{test}$.\n",
    "\n",
    "The accuracy plot is the proportion of data within symmetric probability intervals vs. the probability interval, $p$.\n",
    "\n",
    "* for example, 20% of withheld testing data should fall between the P40 and P60 probability interval \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": 10,
   "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": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "from sklearn.model_selection import train_test_split    # train and test data split by random selection of a proportion\n",
    "from scipy.stats import norm                            # Gaussian distribution assumed for local uncertainty\n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io                                               # set the working directory\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 matplotlib.patches import Ellipse                  # plot an ellipse\n",
    "import math                                             # sqrt operator                    \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": [
    "#### Simple, Simple Kriging Function\n",
    "\n",
    "Let's write a fast Python function to take data points and unknown location and provide the:\n",
    "\n",
    "* **simple kriging estimate**\n",
    "\n",
    "* **simple kriging variance / estimation variance**\n",
    "\n",
    "* **simple kriging weights**\n",
    "\n",
    "This provides a fast method for small datasets, with less parameters (no search parameters) and the ability to see the simple kriging weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def simple_simple_krige(df,xcol,ycol,vcol,dfl,xlcol,ylcol,vario,skmean):\n",
    "# load the variogram\n",
    "    nst = vario['nst']; pmx = 9999.9\n",
    "    cc = np.zeros(nst); aa = np.zeros(nst); it = np.zeros(nst)\n",
    "    ang = np.zeros(nst); anis = np.zeros(nst)\n",
    "    nug = vario['nug']; sill = nug \n",
    "    cc[0] = vario['cc1']; sill = sill + cc[0]\n",
    "    it[0] = vario['it1']; ang[0] = vario['azi1']; \n",
    "    aa[0] = vario['hmaj1']; anis[0] = vario['hmin1']/vario['hmaj1'];\n",
    "    if nst == 2:\n",
    "        cc[1] = vario['cc2']; sill = sill + cc[1]\n",
    "        it[1] = vario['it2']; ang[1] = vario['azi2']; \n",
    "        aa[1] = vario['hmaj2']; anis[1] = vario['hmin2']/vario['hmaj2'];    \n",
    "\n",
    "# set up the required matrices\n",
    "    rotmat, maxcov = geostats.setup_rotmat(nug,nst,it,cc,ang,pmx)    \n",
    "    ndata = len(df); a = np.zeros([ndata,ndata]); r = np.zeros(ndata); s = np.zeros(ndata); rr = np.zeros(ndata)\n",
    "    nest = len(dfl)\n",
    "\n",
    "    est = np.zeros(nest); var = np.full(nest,sill); weights = np.zeros([nest,ndata])\n",
    "\n",
    "# Make and solve the kriging matrix, calculate the kriging estimate and variance \n",
    "    for iest in range(0,nest):\n",
    "        for idata in range(0,ndata):\n",
    "            for jdata in range(0,ndata):\n",
    "                a[idata,jdata] = geostats.cova2(df[xcol].values[idata],df[ycol].values[idata],df[xcol].values[jdata],df[ycol].values[jdata],\n",
    "                                        nst,nug,pmx,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            r[idata] = geostats.cova2(df[xcol].values[idata],df[ycol].values[idata],dfl[xlcol].values[iest],dfl[ylcol].values[iest],\n",
    "                                        nst,nug,pmx,cc,aa,it,ang,anis,rotmat,maxcov)\n",
    "            rr[idata] = r[idata]\n",
    "        \n",
    "        s = geostats.ksol_numpy(ndata,a,r)    \n",
    "        sumw = 0.0\n",
    "        for idata in range(0,ndata):                          \n",
    "            sumw = sumw + s[idata]\n",
    "            weights[iest,idata] = s[idata]\n",
    "            est[iest] = est[iest] + s[idata]*df[vcol].values[idata]\n",
    "            var[iest] = var[iest] - s[idata]*rr[idata]\n",
    "        est[iest] = est[iest] + (1.0-sumw)*skmean\n",
    "    return est,var,weights "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set Global Parameters\n",
    "\n",
    "These impact the look and results of this demonstration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 73073                                            # random number seed for train and test split and added error term\n",
    "cmap = plt.cm.plasma                                    # color map\n",
    "vmin = 0.0; vmax = 0.20                                 # feature min and max for plotting\n",
    "error_std = 0.0                                         # error standard deviation\n",
    "bins = 20                                               # number of bins for the accuracy plots"
   ]
  },
  {
   "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": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Load and Visualize the Spatial Data\n",
    "\n",
    "Here's the code to load our comma delimited data file in to a Pandas' DataFrame object and to visualize it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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": [
    "df = pd.read_csv(\"sample_data_biased.csv\")              # read a .csv file in as a DataFrame\n",
    "df['Porosity'] = df['Porosity']+norm.rvs(0.0,error_std,random_state = seed,size=len(df))\n",
    "\n",
    "plt.subplot(111)\n",
    "im = plt.scatter(df['X'],df['Y'],c=df['Porosity'],marker='o',cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,linewidths=0.8,\n",
    "        verts=None,edgecolors=\"black\",label=\"train\")\n",
    "plt.title(\"Subset of the Data for the Demonstration\")\n",
    "plt.xlim([0,1000]); plt.ylim([0,1000])\n",
    "plt.xlabel('X(m)'); plt.ylabel('Y(m)'); plt.legend()\n",
    "cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(vmin, vmax, 10),format='%.2f')\n",
    "cbar.set_label('Porosity (fraction)', rotation=270, labelpad=20)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Uncertainty Checking with Simple Kriging \n",
    "\n",
    "The following code includes:\n",
    "* dashboard with variogram model, number of data and the proportion of testing data \n",
    "* plots of variogram model, train and test data locations, accuracy plot and training data with testing data percentiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')\n",
    "\n",
    "# build the dashboard\n",
    "style = {'description_width': 'initial'}\n",
    "l = widgets.Text(value='                                              Simple Kriging, 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',continuous_update=False,\n",
    "                          layout=Layout(width='50px', height='200px'))\n",
    "nug.style.handle_color = 'gray'\n",
    "it1 = widgets.Dropdown(options=['Spherical', 'Exponential', 'Gaussian'],value='Spherical',continuous_update=False,\n",
    "    description='Type1:',disabled=False,layout=Layout(width='180px', height='30px'), style=style)\n",
    "\n",
    "azi = widgets.FloatSlider(min=0, max = 360, value = 0, step = 22.5, description = 'azi',continuous_update=False,\n",
    "                        orientation='vertical',layout=Layout(width='80px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "hmaj1 = widgets.FloatSlider(min=0.01, max = 10000.0, value = 100.0, step = 25.0, description = 'hmaj1',continuous_update=False,\n",
    "                        orientation='vertical',layout=Layout(width='80px', height='200px'))\n",
    "hmaj1.style.handle_color = 'gray'\n",
    "hmin1 = widgets.FloatSlider(min = 0.01, max = 10000.0, value = 100.0, step = 25.0, description = 'hmin1',continuous_update=False,\n",
    "                        orientation='vertical',layout=Layout(width='80px', height='200px'))\n",
    "hmin1.style.handle_color = 'gray'\n",
    "\n",
    "ptest = widgets.FloatSlider(min = 0.01, max = 0.9, value = 100.0, step = 0.1, description = 'prop test',continuous_update=False,\n",
    "                        orientation='vertical',layout=Layout(width='80px', height='200px'))\n",
    "ptest.style.handle_color = 'gray'\n",
    "\n",
    "ndata = widgets.IntSlider(min = 1, max = len(df), value = 100, step = 10, description = 'number data',continuous_update=False,\n",
    "                        orientation='vertical',layout=Layout(width='80px', height='200px'))\n",
    "ndata.style.handle_color = 'gray'\n",
    "\n",
    "uikvar = widgets.HBox([nug,it1,azi,hmaj1,hmin1,ptest,ndata],)  \n",
    "\n",
    "uipars = widgets.HBox([uikvar],) \n",
    "uik = widgets.VBox([l,uipars],)\n",
    "\n",
    "# convenience function ot convert variogram model type to a integer\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",
    "# calculate the kriging-based uncertainty distributions and match truth values to percentiles and product plots\n",
    "def f_make_krige(nug,it1,azi,hmaj1,hmin1,ptest,ndata):                      \n",
    "    text_trap = io.StringIO()\n",
    "    sys.stdout = text_trap\n",
    "    it1 = convert_type(it1)\n",
    "    \n",
    "    train, test = train_test_split(df.iloc[len(df)-ndata:,[0,1,3,]], test_size=ptest, random_state=73073)\n",
    "    \n",
    "    nst = 1; xlag = 10; nlag = int(hmaj1/xlag); c1 = 1.0-nug\n",
    "    vario = GSLIB.make_variogram(nug,nst,it1,c1,azi,hmaj1,hmin1) # make model object\n",
    "    index_maj,h_maj,gam_maj,cov_maj,ro_maj = geostats.vmodel(nlag,xlag,azi,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,azi+90.0,vario) # project the model in the minor azimuth\n",
    "    skmean = np.average(train['Porosity'])              # calculate the input mean and sill for simple kriging\n",
    "    sill = np.var(train['Porosity'])\n",
    "    \n",
    "    sk_est, sk_var, sk_weights =  simple_simple_krige(train,'X','Y','Porosity',test,'X','Y',vario,skmean=skmean) # data, esitmation locations\n",
    "    sk_std = np.sqrt(sk_var*sill)                       # standardize estimation variance by the sill and convert to std. dev.\n",
    "\n",
    "    \n",
    "    percentiles = norm.cdf(test['Porosity'],sk_est,sk_std) # calculate the percentiles of truth in the uncertainty models\n",
    "    test[\"Percentile\"] = percentiles\n",
    "    \n",
    "    xlag = 10.0; nlag = int(hmaj1/xlag)                 # lags for variogram plotting\n",
    "    \n",
    "    plt.subplot(221)                                    # plot variograms\n",
    "    plt.plot([0,hmaj1*1.5],[1.0,1.0],color = 'black')\n",
    "    plt.plot(h_maj,gam_maj,color = 'black',label = 'Major ' + str(azi))    \n",
    "    plt.plot(h_min,gam_min,color = 'black',label = 'Minor ' + str(azi+90.0))\n",
    "    deltas = [22.5, 45, 67.5]; \n",
    "    ndelta = len(deltas); hd = np.zeros(ndelta); gamd = np.zeros(ndelta);\n",
    "    color=iter(cm.plasma(np.linspace(0,1,ndelta)))\n",
    "    for delta in deltas:\n",
    "        index,hd,gamd,cov,ro = geostats.vmodel(nlag,xlag,azi+delta,vario);\n",
    "        c=next(color)\n",
    "        plt.plot(hd,gamd,color = c,label = 'Azimuth ' + str(azi+delta))\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Interpolated NSCORE Porosity Variogram Models')\n",
    "    plt.xlim([0,hmaj1*1.5])\n",
    "    plt.ylim([0,1.4])\n",
    "    plt.legend(loc='upper left')\n",
    "  \n",
    "    plt.subplot(222)                                    # plot the train and test data\n",
    "    im = plt.scatter(train['X'],train['Y'],c=train['Porosity'],marker='o',cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,linewidths=0.8,\n",
    "        verts=None,edgecolors=\"black\",label=\"train\")\n",
    "    plt.scatter(test['X'],test['Y'],c=test['Porosity'],marker='^',cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,linewidths=0.8,\n",
    "        verts=None,edgecolors=\"black\",label=\"test\")\n",
    "    plt.title(\"Training and Testing Data\")\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1000])\n",
    "    plt.xlabel('X(m)'); plt.ylabel('Y(m)'); plt.legend()\n",
    "    cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(vmin, vmax, 10),format='%.2f')\n",
    "    cbar.set_label('Porosity (fraction)', rotation=270, labelpad=20)\n",
    "\n",
    "    fraction_in = np.zeros(bins)                        # calculate and plot the accuracy plot\n",
    "    p_intervals = np.linspace(0.0,1.0,bins)\n",
    "    for i,p in enumerate(p_intervals): \n",
    "        test_result = (test['Percentile'] > 0.5-0.5*p) & (test['Percentile'] < 0.5+0.5*p)\n",
    "        fraction_in[i] = test_result.sum()/len(test)\n",
    "\n",
    "    plt.subplot(223)    \n",
    "    plt.scatter(p_intervals,fraction_in,c='black',marker='o',alpha=0.8)\n",
    "    plt.plot([0.0,1.0],[0.0,1.0],c='black')\n",
    "    plt.xlim([0.0,1.0]); plt.ylim([0,1.0])\n",
    "    plt.title('Uncertainty Model at Unknown Location')\n",
    "    plt.xlabel('Probability Interval'); plt.ylabel('Fraction In the Interval')\n",
    "\n",
    "    plt.subplot(224)                                      # plot the testing percentiles with the training data\n",
    "    plt.scatter(train['X'],train['Y'],s=20,c='black',marker='o',cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,linewidths=0.8,\n",
    "        verts=None,edgecolors=\"black\",label=\"train\")\n",
    "    im = plt.scatter(test['X'],test['Y'],s=80.0,c=test['Percentile'],marker='^',cmap=cmap,vmin=0.0,vmax=1.0,alpha=0.8,linewidths=0.8,\n",
    "        verts=None,edgecolors=\"black\",label=\"test\")\n",
    "    plt.title(\"Cross Validation Percentiles\")\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1000])\n",
    "    plt.xlabel('X(m)'); plt.ylabel('Y(m)'); plt.legend()\n",
    "    cbar = plt.colorbar(im, orientation=\"vertical\", ticks=np.linspace(0.0, 1.0, 10),format='%.2f')\n",
    "    cbar.set_label('Porosity (fraction)', rotation=270, labelpad=20)    \n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.2, top=2.2, 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_krige, {'nug':nug, 'it1':it1, 'azi':azi, 'hmaj1':hmaj1, 'hmin1':hmin1, 'ptest':ptest, 'ndata':ndata})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Uncertianty Checking Kriging Demostration\n",
    "\n",
    "* select the variogram model for simple kriging and observe the impact on the uncertainty model\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 Inputs\n",
    "\n",
    "Select the variogram model and the proportion of data withheld for testing.\n",
    "\n",
    "* **nug**: nugget effect\n",
    "\n",
    "* **c1**: contributions of the sill\n",
    "\n",
    "* **hmaj1 / hmin1**: range in the major and minor direction\n",
    "\n",
    "* **(x1, y1),...(x3,y3)**: spatial data locations  \n",
    "\n",
    "* **test proportion**: proportion of data withheld for testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8b017c666c6343eea4365347f026c4a7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                              Simple Kriging, Michael Pyrcz, Associ…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9dd139ed7eb347f9ab689bdaca53c1cb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 6 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik, interactive_plot)                            # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was an interactive demonstration of simple kriging for spatial data analytics. 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",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm.cdf(0,0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}