{
 "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 Data Declustering vs. Naive Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive demonstration of the imapct of declustering\n",
    "\n",
    "\n",
    "### Basic Univariate Summary Statistics and Data Distribution Representativity Plotting in Python with GeostatsPy\n",
    "\n",
    "Here's a simple workflow with some basic univariate statistics and distribution representativity. This should help you get started data declustering to address spatial sampling bias.\n",
    "\n",
    "#### Geostatistical Sampling Representativity\n",
    "\n",
    "In general, we should assume that all spatial data that we work with is biased.\n",
    "\n",
    "##### Source of Spatial Sampling Bias\n",
    "\n",
    "Data is collected to answer questions:\n",
    "* how far does the contaminant plume extend? – sample peripheries\n",
    "* where is the fault? – drill based on seismic interpretation\n",
    "* what is the highest mineral grade? – sample the best part\n",
    "* who far does the reservoir extend? – offset drilling\n",
    "and to maximize NPV directly:\n",
    "* maximize production rates\n",
    "\n",
    "**Random Sampling**: when every item in the population has a equal chance of being chosen. Selection of every item is independent of every other selection.\n",
    "Is random sampling sufficient for subsurface?  Is it available?\n",
    "* it is not usually available, would not be economic\n",
    "* data is collected answer questions\n",
    "    * how large is the reservoir, what is the thickest part of the reservoir \n",
    "* and wells are located to maximize future production\n",
    "    * dual purpose appraisal and injection / production wells!\n",
    "\n",
    "**Regular Sampling**: when samples are taken at regular intervals (equally spaced).  \n",
    "* less reliable than random sampling.\n",
    "* Warning: may resonate with some unsuspected environmental variable.\n",
    "\n",
    "What do we have?\n",
    "* we usually have biased, opportunity sampling \n",
    "* we must account for bias (debiasing will be discussed later)\n",
    "\n",
    "So if we were designing sampling for representativity of the sample set and resulting sample statistics, by theory we have 2 options, random sampling and regular sampling.\n",
    "\n",
    "* What would happen if you proposed random sampling in the Gulf of Mexico at $150M per well?\n",
    "\n",
    "We should not change current sampling methods as they result in best economics, we should address sampling bias in the data.\n",
    "\n",
    "Never use raw spatial data without access sampling bias / correcting.\n",
    "\n",
    "##### Mitigating Sampling Bias\n",
    "\n",
    "In this demonstration we will take a biased spatial sample data set and apply declustering using **GeostatsPy** functionality.\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_biased.csv at https://git.io/fh0CW\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"TQDM_DISABLE\"] = \"True\" \n",
    "\n",
    "import geostatspy.GSLIB as GSLIB          # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats    # GSLIB methods convert to Python        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np                        # ndarrys for gridded data\n",
    "import pandas as pd                       # DataFrames for tabular data\n",
    "import os                                 # set working directory, run executables\n",
    "import matplotlib.pyplot as plt           # for plotting\n",
    "from scipy import stats                   # summary statistics\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",
    "from matplotlib.ticker import (MultipleLocator, AutoMinorLocator) # control of axes ticks\n",
    "plt.rc('axes', axisbelow=True)            # set axes and grids in the background for all plots\n",
    "from matplotlib.patches import Rectangle  # drawing shapes on plots\n",
    "from statsmodels.stats.weightstats import DescrStatsW\n",
    "cmap = plt.cm.inferno\n",
    "burnt_orange = \"#BF5700\""
   ]
  },
  {
   "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",
    "These functions read in the multiple realizations and produce local statistical summaries that we will cover below.  They will shortly be added to GeostatsPy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 \n",
    "\n",
    "def weighted_percentile(data,perc,weights=None): # from Luca Jokull (https://stackoverflow.com/questions/21844024/weighted-percentile-using-numpy)\n",
    "    if weights is None:\n",
    "        return nanpercentile(data,perc)\n",
    "    else:\n",
    "        d=data[(~np.isnan(data))&(~np.isnan(weights))]\n",
    "        ix=np.argsort(d)\n",
    "        d=d[ix]\n",
    "        wei=weights[ix]\n",
    "        wei_cum=100.*np.cumsum(wei*1./sum(wei))\n",
    "        return np.interp(perc,wei_cum,d)"
   ]
  },
  {
   "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": 4,
   "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": 18,
   "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>Level</th>\n",
       "      <th>Porosity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Coarse</td>\n",
       "      <td>7.812796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Coarse</td>\n",
       "      <td>8.105355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Coarse</td>\n",
       "      <td>9.142711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5000.0</td>\n",
       "      <td>4000.0</td>\n",
       "      <td>Coarse</td>\n",
       "      <td>6.589220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>Coarse</td>\n",
       "      <td>11.833955</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        X       Y   Level   Porosity\n",
       "0  4000.0  4000.0  Coarse   7.812796\n",
       "1  1000.0  1000.0  Coarse   8.105355\n",
       "2  5000.0  3000.0  Coarse   9.142711\n",
       "3  5000.0  4000.0  Coarse   6.589220\n",
       "4  1000.0  3000.0  Coarse  11.833955"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/2level_cluster.csv') # load data from Dr. Pyrcz's GitHub \n",
    "pormin=4.0; pormax = 16.0\n",
    "xmin = 0.0; xmax = 10000.0; ymin = 0.0; ymax = 10000\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "truth_average = np.average(df.loc[:81,'Porosity'])  # from representative sampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture --no-display       \n",
    "\n",
    "n_sample = 121; pormin = 4.0; pormax = 16.0\n",
    "ncell = 1; cmin = 1000; cmax = 1000; noff = 5; iminmax = 1\n",
    "\n",
    "declus_por_mean = np.zeros(n_sample)\n",
    "\n",
    "df_subset = df.loc[:n_sample-1,:].copy(deep=True)\n",
    "por = df_subset[\"Porosity\"].values\n",
    "cumulative_avg = np.cumsum(por) / np.arange(1, len(por) + 1)\n",
    "df_subset[\"Porosity_Cumulative_Mean\"] = cumulative_avg\n",
    "\n",
    "for isample in range(0,n_sample):\n",
    "    wts, cell_sizes, dmeans = geostats.declus(df_subset.loc[:isample,:],'X','Y','Porosity',iminmax = iminmax,noff = noff,\n",
    "        ncell = ncell,cmin = cmin,cmax = cmax)                # GeostatsPy's declustering function\n",
    "    declus_por_mean[isample] = np.average(df_subset.loc[:isample,'Porosity'],weights = wts) \n",
    "\n",
    "df_subset[\"Porosity_Cumulative_Declus_Mean\"] = declus_por_mean\n",
    "\n",
    "plt.subplot(121)\n",
    "sc = plt.scatter(df_subset['X'],df_subset['Y'],c=df_subset['Porosity'],s=30,edgecolor='black',cmap = cmap, vmin = pormin,vmax = pormax)\n",
    "cbar = plt.colorbar(sc)\n",
    "cbar.set_label(\"Porosity (%)\")\n",
    "\n",
    "add_grid(); plt.xlim([xmin,xmax]); plt.ylim([ymin,ymax])\n",
    "plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Porosity Data')\n",
    "\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(np.arange(0,n_sample)+1,df_subset[\"Porosity_Cumulative_Mean\"],c='black',lw=2,label='Naive Average',zorder=3)\n",
    "plt.plot(np.arange(0,n_sample)+1,df_subset[\"Porosity_Cumulative_Declus_Mean\"],c=burnt_orange,lw=4,label='Declustered Average',zorder=2)\n",
    "plt.plot([0,len(df)],[truth_average,truth_average],c='red',lw=1,ls='--',label='Truth',zorder=1)\n",
    "plt.axvline(x=len(df_subset), linestyle=\"--\", color=\"black\", linewidth=1, zorder=0)\n",
    "plt.legend(loc='upper right')\n",
    "plt.xlim([1,len(df)]); plt.ylim([8.0,12.0]); plt.xlabel('Number of Samples'); plt.ylabel('Average Porosity (%)'); plt.title('Running Average Porosity')\n",
    "add_grid()\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2); plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Dashboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "import sys\n",
    "from contextlib import contextmanager\n",
    "\n",
    "@contextmanager\n",
    "def suppress_stdout():\n",
    "    with open(os.devnull, \"w\") as devnull:\n",
    "        old_stdout = sys.stdout\n",
    "        sys.stdout = devnull\n",
    "        try:\n",
    "            yield\n",
    "        finally:\n",
    "            sys.stdout = old_stdout\n",
    "\n",
    "pormin = 4.0; pormax = 16.0\n",
    "ncell = 1; cmin = 1000; cmax = 1000; noff = 5; iminmax = 1\n",
    "\n",
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "style = {'description_width': 'initial'}\n",
    "l = widgets.Text(value='                                              Cell-based Declustering, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nsample = widgets.IntSlider(min = 1, max = len(df), value = 5, step = 10, description = 'Cell Size',style=style,orientation='horizontal',\n",
    "                          layout=Layout(width='450px', height='50px'),continuous_update = False)\n",
    "nsample.style.handle_color = 'gray'\n",
    "\n",
    "ui = widgets.VBox([l,nsample],)\n",
    "\n",
    "def f_make_declus(nsample):\n",
    "\n",
    "    declus_por_mean = np.zeros(nsample)\n",
    "    df_subset = df.loc[:nsample-1,:].copy(deep=True)\n",
    "    por = df_subset[\"Porosity\"].values\n",
    "    cumulative_avg = np.cumsum(por) / np.arange(1, len(por) + 1)\n",
    "    df_subset[\"Porosity_Cumulative_Mean\"] = cumulative_avg\n",
    "    #for isample in range(0, nsample):\n",
    "    for isample in tqdm(range(nsample), disable=True):\n",
    "        with suppress_stdout():\n",
    "            wts, cell_sizes, dmeans = geostats.declus(\n",
    "                df_subset.loc[:isample,:],\n",
    "                'X','Y','Porosity',\n",
    "                iminmax=iminmax,\n",
    "                noff=noff,\n",
    "                ncell=ncell,\n",
    "                cmin=cmin,\n",
    "                cmax=cmax\n",
    "            )\n",
    "            declus_por_mean[isample] = np.average(\n",
    "                df_subset.loc[:isample,'Porosity'],\n",
    "                weights=wts\n",
    "            )\n",
    "        df_subset[\"Porosity_Cumulative_Declus_Mean\"] = declus_por_mean\n",
    "\n",
    "\n",
    "    \n",
    "    # declus_por_mean = np.zeros(nsample)\n",
    "    \n",
    "    # df_subset = df.loc[:nsample-1,:].copy(deep=True)\n",
    "    # por = df_subset[\"Porosity\"].values\n",
    "    # cumulative_avg = np.cumsum(por) / np.arange(1, len(por) + 1)\n",
    "    # df_subset[\"Porosity_Cumulative_Mean\"] = cumulative_avg\n",
    "    \n",
    "    # for isample in range(0,nsample):\n",
    "    #     wts, cell_sizes, dmeans = geostats.declus(df_subset.loc[:isample,:],'X','Y','Porosity',iminmax = iminmax,noff = noff,\n",
    "    #         ncell = ncell,cmin = cmin,cmax = cmax)                # GeostatsPy's declustering function\n",
    "    #     declus_por_mean[isample] = np.average(df_subset.loc[:isample,'Porosity'],weights = wts) \n",
    "    \n",
    "    # df_subset[\"Porosity_Cumulative_Declus_Mean\"] = declus_por_mean\n",
    "    \n",
    "    plt.subplot(121)\n",
    "    sc = plt.scatter(df_subset['X'],df_subset['Y'],c=df_subset['Porosity'],s=30,edgecolor='black',cmap = cmap, vmin = pormin,vmax = pormax)\n",
    "    cbar = plt.colorbar(sc)\n",
    "    cbar.set_label(\"Porosity (%)\")\n",
    "    \n",
    "    add_grid(); plt.xlim([xmin,xmax]); plt.ylim([ymin,ymax])\n",
    "    plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Porosity Data')\n",
    "    \n",
    "    \n",
    "    plt.subplot(122)\n",
    "    plt.plot(np.arange(0,nsample)+1,df_subset[\"Porosity_Cumulative_Mean\"],c='black',lw=2,label='Naive Average',zorder=3)\n",
    "    plt.plot(np.arange(0,nsample)+1,df_subset[\"Porosity_Cumulative_Declus_Mean\"],c=burnt_orange,lw=4,label='Declustered Average',zorder=2)\n",
    "    plt.plot([0,len(df)],[truth_average,truth_average],c='red',lw=1,ls='--',label='Truth',zorder=1)\n",
    "    plt.axvline(x=len(df_subset), linestyle=\"--\", color=\"black\", linewidth=1, zorder=0)\n",
    "    plt.legend(loc='upper right')\n",
    "    plt.xlim([1,len(df)]); plt.ylim([8.0,12.0]); plt.xlabel('Number of Samples'); plt.ylabel('Average Porosity (%)'); plt.title('Running Average Porosity')\n",
    "    add_grid()\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.2, hspace=0.2); plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make_declus, {'nsample':nsample})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Cell-based Declustering Demostration\n",
    "\n",
    "* select the cell size and number of cell mesh offsets and visualize the declustering method \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",
    "* **Cell Size**: the size the of the cells in the mesh, **Number of Offsets**: number of cell mesh offsets to average to calculate the data weights "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80e35889b4904020964d1b7c2a9b94ee",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                              Cell-based Declustering, Michael Pyrc…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e48893b9b36e417f9379582a3641270e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 640x480 with 3 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                            # display the interactive plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "dfcaae9200b54a109aeed0b4d4d8d2be",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(Text(value='                                              Comparison Cell-based Declustered vs.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "68caee3a480d42ee8b011f808364d32d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(layout=Layout(height='650px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%capture --no-display\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "import sys\n",
    "from contextlib import contextmanager\n",
    "\n",
    "@contextmanager\n",
    "def suppress_stdout():\n",
    "    with open(os.devnull, \"w\") as devnull:\n",
    "        old_stdout = sys.stdout\n",
    "        sys.stdout = devnull\n",
    "        try:\n",
    "            yield\n",
    "        finally:\n",
    "            sys.stdout = old_stdout\n",
    "\n",
    "pormin = 4.0; pormax = 16.0\n",
    "ncell = 1; cmin = 1000; cmax = 1000; noff = 5; iminmax = 1\n",
    "\n",
    "# interactive calculation of the sample set (control of source parametric distribution and number of samples)\n",
    "style = {'description_width': 'initial'}\n",
    "l = widgets.Text(value='                                              Comparison Cell-based Declustered vs. Naïve Statistics, Michael Pyrcz, Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "nsample = widgets.BoundedIntText(min = 1, max = len(df), value = 1, step = 3, description = 'Number of Samples',style=style,orientation='horizontal',\n",
    "                          layout=Layout(width='450px', height='50px'),continuous_update = False)\n",
    "nsample.style.handle_color = 'gray'\n",
    "\n",
    "ui = widgets.VBox([l,nsample],)\n",
    "\n",
    "def f_make_declus(nsample):\n",
    "\n",
    "\n",
    "    \n",
    "    declus_por_mean = np.zeros(nsample)\n",
    "    df_subset = df.loc[:nsample-1,:].copy(deep=True)\n",
    "    por = df_subset[\"Porosity\"].values\n",
    "    cumulative_avg = np.cumsum(por) / np.arange(1, len(por) + 1)\n",
    "    df_subset[\"Porosity_Cumulative_Mean\"] = cumulative_avg\n",
    "    #for isample in range(0, nsample):\n",
    "    for isample in tqdm(range(nsample), disable=True):\n",
    "        with suppress_stdout():\n",
    "            wts, cell_sizes, dmeans = geostats.declus(\n",
    "                df_subset.loc[:isample,:],\n",
    "                'X','Y','Porosity',\n",
    "                iminmax=iminmax,\n",
    "                noff=noff,\n",
    "                ncell=ncell,\n",
    "                cmin=cmin,\n",
    "                cmax=cmax\n",
    "            )\n",
    "            declus_por_mean[isample] = np.average(\n",
    "                df_subset.loc[:isample,'Porosity'],\n",
    "                weights=wts\n",
    "            )\n",
    "        df_subset[\"Porosity_Cumulative_Declus_Mean\"] = declus_por_mean\n",
    "\n",
    "    with out:\n",
    "        out.clear_output(wait=True)\n",
    "            \n",
    "        plt.subplot(121)\n",
    "        sc = plt.scatter(df_subset['X'],df_subset['Y'],c=df_subset['Porosity'],s=30,edgecolor='black',cmap = cmap, vmin = pormin,vmax = pormax)\n",
    "        cbar = plt.colorbar(sc)\n",
    "        cbar.set_label(\"Porosity (%)\")\n",
    "        \n",
    "        add_grid(); plt.xlim([xmin,xmax]); plt.ylim([ymin,ymax])\n",
    "        plt.xlabel('X (m)'); plt.ylabel('Y (m)'); plt.title('Porosity Data')\n",
    "        \n",
    "        plt.subplot(122)\n",
    "        plt.plot(np.arange(0,nsample)+1,df_subset[\"Porosity_Cumulative_Mean\"],c='black',lw=2,label='Naive Average',zorder=3)\n",
    "        plt.plot(np.arange(0,nsample)+1,df_subset[\"Porosity_Cumulative_Declus_Mean\"],c=burnt_orange,lw=4,label='Declustered Average',zorder=2)\n",
    "        plt.plot([0,len(df)],[truth_average,truth_average],c='red',lw=1,ls='--',label='Truth',zorder=1)\n",
    "        plt.axvline(x=len(df_subset), linestyle=\"--\", color=\"black\", linewidth=1, zorder=0)\n",
    "        plt.legend(loc='upper right')\n",
    "        plt.xlim([1,len(df)]); plt.ylim([7.0,12.0]); plt.xlabel('Number of Samples'); plt.ylabel('Average Porosity (%)'); plt.title('Running Average Porosity')\n",
    "        add_grid()\n",
    "        \n",
    "        plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.2, wspace=0.15, hspace=0.2); plt.show()\n",
    "        plt.figure(figsize=(12,5))\n",
    "\n",
    "\n",
    "out = widgets.Output(layout=widgets.Layout(height='650px'))  # fixed height\n",
    "\n",
    "interactive_plot = widgets.interactive_output(\n",
    "    f_make_declus,\n",
    "    {'nsample': nsample}\n",
    ")\n",
    "\n",
    "display(ui, out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This is an interactive demonstration of cell-based declustering to correct for sampling bias. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays and many other workflows availble at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy.\n",
    "\n",
    "I hope this was helpful,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "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"
   ]
  }
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