GeostatsPy_bootstrap.ipynb

{
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    "\n",
    "## GeostatsPy: Bootstrap for Subsurface Data Analytics in Python \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"
   ]
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   "cell_type": "markdown",
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   "source": [
    "### PGE 383 Exercise: Bootstrap for Subsurface Data Analytics in Python \n",
    "\n",
    "Here's a simple workflow, demonstration of bootstrap for subsurface modeling workflows. This should help you get started with building subsurface models that integrate uncertainty in the sample statistics.  \n",
    "\n",
    "#### Bootstrap\n",
    "\n",
    "Uncertainty in the sample statistics\n",
    "* one source of uncertainty is the paucity of data.\n",
    "* do 200 or even less wells provide a precise (and accurate estimate) of the mean? standard deviation? skew? P13?\n",
    "\n",
    "Would it be useful to know the uncertainty in these statistics due to limited sampling?\n",
    "* what is the impact of uncertainty in the mean porosity e.g. 20%+/-2%?\n",
    "\n",
    "**Bootstrap** is a method to assess the uncertainty in a sample statistic by repeated random sampling with replacement.\n",
    "\n",
    "Assumptions\n",
    "* sufficient, representative sampling, identical, idependent samples\n",
    "\n",
    "Limitations\n",
    "1. assumes the samples are representative \n",
    "2. assumes stationarity\n",
    "3. only accounts for uncertainty due to too few samples, e.g. no uncertainty due to changes away from data\n",
    "4. does not account for boundary of area of interest \n",
    "5. assumes the samples are independent\n",
    "6. does not account for other local information sources\n",
    "\n",
    "The Bootstrap Approach (Efron, 1982)\n",
    "\n",
    "Statistical resampling procedure to calculate uncertainty in a calculated statistic from the data itself.\n",
    "* Does this work?  Prove it to yourself, for uncertainty in the mean solution is standard error: \n",
    "\n",
    "\\begin{equation}\n",
    "\\sigma^2_\\overline{x} = \\frac{\\sigma^2_s}{n}\n",
    "\\end{equation}\n",
    "\n",
    "Extremely powerful - could calculate uncertainty in any statistic!  e.g. P13, skew etc.\n",
    "* Would not be possible access general uncertainty in any statistic without bootstrap.\n",
    "* Advanced forms account for spatial information and sampling strategy (game theory and Journel’s spatial bootstrap (1993).\n",
    "\n",
    "Steps: \n",
    "\n",
    "1. assemble a sample set, must be representative, reasonable to assume independence between samples\n",
    "\n",
    "2. optional: build a cumulative distribution function (CDF)\n",
    "    * may account for declustering weights, tail extrapolation\n",
    "    * could use analogous data to support\n",
    "\n",
    "3. For $\\ell = 1, \\ldots, L$ realizations, do the following:\n",
    "\n",
    "    * For $i = \\alpha, \\ldots, n$ data, do the following:\n",
    "\n",
    "        * Draw a random sample with replacement from the sample set or Monte Carlo simulate from the CDF (if available). \n",
    "\n",
    "6. Calculate a realization of the sammary statistic of interest from the $n$ samples, e.g. $m^\\ell$, $\\sigma^2_{\\ell}$. Return to 3 for another realization.\n",
    "\n",
    "7. Compile and summarize the $L$ realizations of the statistic of interest.\n",
    "\n",
    "This is a very powerful method.  Let's try it out.\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 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",
    "import math                               # trig etc.\n",
    "import scipy.signal as signal             # kernel for moving window calculation\n",
    "import random"
   ]
  },
  {
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                     # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('sample_data_biased.csv') # load our data table (wrong name!)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's drop some samples so that we increase the variations in bootstrap samples for our demonstration below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 260 number of samples\n"
     ]
    }
   ],
   "source": [
    "df = df.sample(frac = 0.9)                  # extract 50 random samples to reduce the size of the dataset   \n",
    "print('Using ' + str(len(df)) + ' number of samples')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizing the DataFrame would be useful and we already learned about these methods in this demo (https://git.io/fNgRW). \n",
    "\n",
    "We can preview the DataFrame by printing a slice or by utilizing the 'head' DataFrame member function (with a nice and clean format, see below). With the slice we could look at any subset of the data table and with the head command, add parameter 'n=13' to see the first 13 rows of the dataset.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       X    Y  Facies  Porosity        Perm\n",
      "209  530  789       1  0.154871   51.991055\n",
      "146  670   49       0  0.118435   11.076648\n",
      "196  500  699       1  0.121085    9.033307\n",
      "57   420  874       1  0.205473  954.697202\n",
      "76   840  949       1  0.141307   18.635578\n"
     ]
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       "      <th></th>\n",
       "      <th>X</th>\n",
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       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
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       "       X    Y  Facies  Porosity         Perm\n",
       "209  530  789       1  0.154871    51.991055\n",
       "146  670   49       0  0.118435    11.076648\n",
       "196  500  699       1  0.121085     9.033307\n",
       "57   420  874       1  0.205473   954.697202\n",
       "76   840  949       1  0.141307    18.635578\n",
       "273  820  919       1  0.104364     2.766127\n",
       "90   430  809       1  0.181352   212.452297\n",
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     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(df.iloc[0:5,:])                   # display first 4 samples in the table as a preview\n",
    "df.head(n=13)                           # we could also use this command for a table preview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Summary Statistics for Tabular Data\n",
    "\n",
    "The table includes X and Y coordinates (meters), Facies 1 and 0 (1 is sandstone and 0 interbedded sand and mudstone), Porosity (fraction), and permeability as Perm (mDarcy). \n",
    "\n",
    "There are a lot of efficient methods to calculate summary statistics from tabular data in DataFrames. The describe command provides count, mean, minimum, maximum, and quartiles all in a nice data table. We use transpose just to flip the table so that features are on the rows and the statistics are on the columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
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       "      <th>X</th>\n",
       "      <td>260.0</td>\n",
       "      <td>474.769231</td>\n",
       "      <td>255.099997</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>430.000000</td>\n",
       "      <td>670.000000</td>\n",
       "      <td>990.000000</td>\n",
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       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>260.0</td>\n",
       "      <td>530.242308</td>\n",
       "      <td>304.354191</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>544.000000</td>\n",
       "      <td>839.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>260.0</td>\n",
       "      <td>0.807692</td>\n",
       "      <td>0.394874</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>260.0</td>\n",
       "      <td>0.134177</td>\n",
       "      <td>0.038388</td>\n",
       "      <td>0.058548</td>\n",
       "      <td>0.106235</td>\n",
       "      <td>0.124065</td>\n",
       "      <td>0.153088</td>\n",
       "      <td>0.228790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>260.0</td>\n",
       "      <td>215.012711</td>\n",
       "      <td>576.830397</td>\n",
       "      <td>0.075819</td>\n",
       "      <td>3.416283</td>\n",
       "      <td>12.826386</td>\n",
       "      <td>62.273419</td>\n",
       "      <td>5308.842566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "          count        mean         std       min         25%         50%  \\\n",
       "X         260.0  474.769231  255.099997  0.000000  300.000000  430.000000   \n",
       "Y         260.0  530.242308  304.354191  9.000000  269.000000  544.000000   \n",
       "Facies    260.0    0.807692    0.394874  0.000000    1.000000    1.000000   \n",
       "Porosity  260.0    0.134177    0.038388  0.058548    0.106235    0.124065   \n",
       "Perm      260.0  215.012711  576.830397  0.075819    3.416283   12.826386   \n",
       "\n",
       "                 75%          max  \n",
       "X         670.000000   990.000000  \n",
       "Y         839.000000   999.000000  \n",
       "Facies      1.000000     1.000000  \n",
       "Porosity    0.153088     0.228790  \n",
       "Perm       62.273419  5308.842566  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualizing Tabular Data with Location Maps \n",
    "\n",
    "It is natural to set the x and y coordinate and feature ranges manually. e.g. do you want your color bar to go from 0.05887 to 0.24230 exactly? Also, let's pick a color map for display. I heard that plasma is known to be friendly to the color blind as the color and intensity vary together (hope I got that right, it was an interesting Twitter conversation started by Matt Hall from Agile if I recall correctly). We will assume a study area of 0 to 1,000m in x and y and omit any data outside this area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "xmin = 0.0; xmax = 1000.0               # range of x values\n",
    "ymin = 0.0; ymax = 1000.0               # range of y values\n",
    "pormin = 0.05; pormax = 0.25;           # range of porosity values\n",
    "nx = 100; ny = 100; csize = 10.0\n",
    "cmap = plt.cm.plasma                    # color map"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's try out locmap. This is a reimplementation of GSLIB's locmap program that uses matplotlib. I hope you find it simpler than matplotlib, if you want to get more advanced and build custom plots lock at the source. If you improve it, send me the new code. Any help is appreciated. To see the parameters, just type the command name:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.GSLIB.locmap(df, xcol, ycol, vcol, xmin, xmax, ymin, ymax, vmin, vmax, title, xlabel, ylabel, vlabel, cmap, fig_name)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "GSLIB.locmap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can populate the plotting parameters and visualize the porosity data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(111)\n",
    "GSLIB.locmap_st(df,'X','Y','Porosity',xmin,xmax,ymin,ymax,pormin,pormax,'Well Data - Porosity','X(m)','Y(m)','Porosity (fraction)',cmap)\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": [
    "#### Declustering\n",
    "\n",
    "Let's calculate some declustering weights. There is a demonstration on declustering here https://git.io/fhgJl if you need more information. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 260 data with:\n",
      "   mean of      0.13417733651923078 \n",
      "   min and max  0.058547873 and 0.228790002\n",
      "   standard dev 0.03831442273384987 \n",
      "Declustered mean = 0.121 and declustered standard deviation = 0.032\n"
     ]
    }
   ],
   "source": [
    "wts, cell_sizes, dmeans = geostats.declus(df,'X','Y','Porosity',iminmax = 1, noff= 10, ncell=100,cmin=10,cmax=2000)\n",
    "df['Wts'] = wts                            # add weights to the sample data DataFrame\n",
    "df.head()                                  # preview to check the sample data DataFrame\n",
    "\n",
    "def weighted_avg_and_std(values, weights): # function to calculate weighted mean and st. dev., from Eric O Lebigot, stack overflow,\n",
    "    average = np.average(values, weights=weights)\n",
    "    variance = np.average((values-average)**2, weights=weights)\n",
    "    return (average, math.sqrt(variance))\n",
    "\n",
    "sample_avg, sample_stdev = weighted_avg_and_std(df['Porosity'],df['Wts'])\n",
    "print('Declustered mean = ' + str(round(sample_avg,3)) + ' and declustered standard deviation = ' + str(round(sample_stdev,3)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### A Couple of Bootstrap Realizations\n",
    "\n",
    "We will attempt boostrap by-hand and manually loop over $L$ realizations and draw $n$ samples to calculate the summary statistics of interest, mean and variance. The choice function from the random package simplifies sampling with replacement from a set of samples with weights.\n",
    "\n",
    "This command returns a ndarray with k samples with replacment from the 'Porosity' column of our DataFrame (df) accounting for the data weights in column 'Wts'.\n",
    "```p\n",
    "samples1 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "```\n",
    "\n",
    "It is instructive to look at a couple of these realizations from the original declustered data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bootstrap means, realization 1 = 0.12022662484999999 and realization 2 = 0.12104601473076923\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "samples1 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "samples2 = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "\n",
    "print('Bootstrap means, realization 1 = ' + str(np.average(samples1)) + ' and realization 2 = ' + str(np.average(samples2)))\n",
    "\n",
    "plt.subplot(131)\n",
    "GSLIB.hist_st(df['Porosity'],pormin,pormax,False,False,20,df['Wts'],'Porosity (fraction)','Declustered Porosity')\n",
    "\n",
    "plt.subplot(132)\n",
    "GSLIB.hist_st(samples1,pormin,pormax,False,False,20,None,'Bootstrap Sample - Realizaton 1','Bootstrap Porosity 1')\n",
    "\n",
    "plt.subplot(133)\n",
    "GSLIB.hist_st(samples2,pormin,pormax,False,False,20,None,'Bootstrap Sample - Realizaton 2','Bootstrap Porosity 2')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the bootstrap distributions vary quite a bit from the original.\n",
    "\n",
    "#### Summarizations Over Bootstrap Realizations\n",
    "\n",
    "Let's make a loop to conduct $L$ resamples and calculate the average and standard deviation for each  ($m^\\ell$, $\\sigma^2_{\\ell}$,  for $\\ell = 0,\\dots,L-1$). We then summarization over these $L$ realizations.  \n",
    "\n",
    "I did not find any built-in, concise functions to accomplish this, i.e. with a single line of code, so we are going to do it by hand.  \n",
    "\n",
    "To understand this code there are just a couple of Python concepts that you need to add to your Python arsenal.\n",
    "\n",
    "1. declaring arrays - NumPy has a lot of great array (ndarray) functionality. There are build in functions to make a ndarray of any length (and dimension). This includes 'zeros', 'ones' and 'rand', so when we use this code:\n",
    "\n",
    "```p\n",
    "mean = np.zeros(L); stdev = np.zeros(L)\n",
    "```\n",
    "\n",
    "    we're making arrays of length $L$ pre-populated with zeros.\n",
    "\n",
    "2. For Loops - when we are using the command below, we are instructing the computer to loop over all the indented code below the command for $l = 0,1,2,\\ldots,L-1$ times. For each loop the $l$ variable increments, so we can use this to save each result to a different index in the arrays mean and stdev. Note, Python arrays index starting at 0 and stop at the length - 1.\n",
    "\n",
    "```p\n",
    "for l in range(0, L): \n",
    "```\n",
    "\n",
    "    we are running each bootstrap resampled realization, calculating the average and standard deviation and storing them in the arrays that we already declared."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Summary Statistics for Bootstrap Porosity Mean Realizations:\n",
      "DescribeResult(nobs=1000, minmax=(0.11519294999615383, 0.12744777477307692), mean=0.12106128366703461, variance=3.856622529753912e-06, skewness=0.01606438448013973, kurtosis=-0.1616858320295793)\n",
      "P10 0.119, P50 0.121, P90 0.124\n",
      "\n",
      "Summary Statistics for Bootstrap Porosity Standard Deviation Realizations:\n",
      "DescribeResult(nobs=1000, minmax=(0.026360820376657122, 0.037208224893171694), mean=0.03174381496886473, variance=2.9489058710525928e-06, skewness=-0.017058035550200187, kurtosis=-0.06831430135393113)\n",
      "P10 0.029, P50 0.032, P90 0.034\n"
     ]
    }
   ],
   "source": [
    "L = 1000                                 # set the number of realizations\n",
    "mean = np.zeros(L); stdev = np.zeros(L)    # declare arrays to hold the realizations of the statistics\n",
    "for l in range(0, L):                      # loop over realizations\n",
    "    samples = random.choices(df['Porosity'].values, weights=df['Wts'].values, cum_weights=None, k=len(df))\n",
    "    mean[l] = np.average(samples)\n",
    "    stdev[l] = np.std(samples)\n",
    "    \n",
    "plt.subplot(121)\n",
    "GSLIB.hist_st(mean,0.11,0.15,False,False,50,None,'Average Porosity (fraction)','Bootstrap Uncertainty in Porosity Average')\n",
    "\n",
    "plt.subplot(122)\n",
    "GSLIB.hist_st(stdev,0.015,0.045,False,False,50,None,'Standard Deviation Porosity (fraction)','Bootstrap Uncertainty in Porosity Standard Deviation')\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=3.0, top=1.2, wspace=0.2, hspace=0.2)\n",
    "plt.show()   \n",
    "    \n",
    "print('Summary Statistics for Bootstrap Porosity Mean Realizations:')\n",
    "print(stats.describe(mean))\n",
    "print('P10 ' + str(round(np.percentile(mean,10),3)) + ', P50 ' + str(round(np.percentile(mean,50),3)) + ', P90 ' + str(round(np.percentile(mean,90),3))) \n",
    "\n",
    "print('\\nSummary Statistics for Bootstrap Porosity Standard Deviation Realizations:')\n",
    "print(stats.describe(stdev))\n",
    "print('P10 ' + str(round(np.percentile(stdev,10),3)) + ', P50 ' + str(round(np.percentile(stdev,50),3)) + ', P90 ' + str(round(np.percentile(stdev,90),3))) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of bootstrap. Much more could be done, you could replace the statistics, average and standard deviation with any other statistics, for example P90, kurtosis, P13 etc. I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available 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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