{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "### Interactive Spatial Uncertainty Model Checking with Accuracy Plots\n",
    "\n",
    "\n",
    "#### Michael 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) | [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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uncertainty Checking\n",
    "\n",
    "Here is an interactive dashboard demonstrating spatial uncertainty model checking with the accuracy plot and uncertainty goodness cross validation-based approach proposed by Deutsch (1996) and included in Pyrcz and Deutsch (2014).\n",
    "\n",
    "I have recorded a walk-through of this interactive dashboard in my TBA series on my [YouTube](https://www.youtube.com/@GeostatsGuyLectures) channel. I'm stoked to guide you and share observations and things to try out!  \n",
    "\n",
    "* I have a lecture on [Model Checking](https://www.youtube.com/watch?v=AVms8JoUWXc&list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ&index=49) as part of my [Data Analytics and Geostatistics](https://www.youtube.com/playlist?list=PLG19vXLQHvSB-D4XKYieEku9GQMQyAzjJ). Note, for all my recorded lectures the interactive and well-documented workflow demonstrations are available in my GitHub repositorie:\n",
    "\n",
    "    * [GeostatsGuy's Python Numerical Demos](https://github.com/GeostatsGuy/PythonNumericalDemos)\n",
    "    * [GeostatsGuy's Data Science Interactive Python](https://github.com/GeostatsGuy/DataScience_Interactive_Python)\n",
    "\n",
    "### Interactive Uncertainty Checking\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",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "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\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "cmap = plt.cm.inferno"
   ]
  },
  {
   "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": 3,
   "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": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "seed = 73073                                            # random number seed for train and test split and added error term\n",
    "cmap = plt.cm.inferno                                   # 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": 5,
   "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": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data_biased.csv\")              # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_biased.csv\") # load the data from Dr. Pyrcz's github repository\n",
    "\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",
    "        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": 7,
   "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 = 45, 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 = 5000.0, step = 500.0, 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 = 3000.0, step = 500.0, 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",
    "    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',s=30,cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,\n",
    "        linewidths=2.0,edgecolors=\"black\",label=\"train\",zorder=50)\n",
    "    plt.scatter(test['X']+12.0,test['Y'],c=sk_est,marker='>',s=50,cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,\n",
    "        linewidths=0.5,edgecolors=\"black\",label=\"test\",zorder=10)\n",
    "    plt.scatter(test['X']-12.0,test['Y'],c=test['Porosity'],marker='<',s=50,cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,\n",
    "        linewidths=0.5,edgecolors=\"black\",label=\"truth\",zorder=10)\n",
    "    plt.scatter(test['X']-1,test['Y'],c='black',edgecolor='black',marker='o',s=7,cmap=cmap,vmin=vmin,vmax=vmax,alpha=0.8,\n",
    "        linewidths=0.5,edgecolors=\"black\",label=\"truth\",zorder=100)\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)')\n",
    "    legend = plt.legend(loc='lower left',ncols=4,fancybox=True,facecolor='white',framealpha=1, frameon=True).set_zorder(10000)\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",
    "    plt.grid(True)\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='grey',edgecolor='black',marker='o',alpha=0.8,zorder=100)\n",
    "    plt.plot([0.0,1.0],[0.0,1.0],c='grey',zorder=100,ls='--')\n",
    "    plt.fill_between([0.1,1],[0,0.9],[0,0],color='red',alpha=0.2,zorder=1)\n",
    "    plt.fill_between([0,0.9],[0.1,1.0],[1.0,1.0],color='yellow',alpha=0.2,zorder=1)\n",
    "    plt.xlim([0.0,1.0]); plt.ylim([0,1.0])\n",
    "    plt.annotate('Accurate and Precise',xy=[0.3,0.3],rotation=40,fontsize=16)\n",
    "    plt.annotate('Inaccurate and Imprecise',xy=[0.4,0.1],rotation=40,fontsize=16)\n",
    "    plt.annotate('Accurate and Imprecise',xy=[0.2,0.5],rotation=40,fontsize=16)\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",
    "        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",
    "        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 Truth Percentile (fraction)', rotation=270, labelpad=20)    \n",
    "    plt.grid(True) \n",
    "\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.8, 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": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "79db16a82acd4607b1f8bfa34dd2bac6",
       "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": "e7ea453f379847c39804b574ff1ebd83",
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      "text/plain": [
       "Output()"
      ]
     },
     "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 uncertainty modeling checking with accuracy plots (Deutsch, 1996; Pyrcz and Deutsch, 2014). 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, Professor, The 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. 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) | [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",
    "  "
   ]
  },
  {
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   "metadata": {},
   "source": [
    "<i>&copy; Copyright daytum 2021. All Rights Reserved</i>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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