PythonDataBasics_Univariate_Visualization.ipynb

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   "source": [
    "<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",
    "# Data Science Basics in Python Series\n",
    "\n",
    "## Chapter III: Matplotlib for Univariate Data Visualization in Python \n",
    "\n",
    "### Michael Pyrcz, Associate Professor, The University of Texas at Austin \n",
    "\n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Data Visualization with MatPlotLib in Python for Engineers and Geoscientists \n",
    "\n",
    "This is a tutorial for / demonstration of **Univariate Data Visualization in Python**. In Python, a common tool for dealing with Data Visualization is the **Matplotlib Python package**\n",
    "\n",
    "* Initiated by John Hunter along with many contributors\n",
    "\n",
    "* Opensource project is a sponsored project of [NumFocus](https://numfocus.org/) \n",
    "\n",
    "This tutorial includes the methods and operations that would commonly be required for Engineers and Geoscientists working with Data Visualization for the purpose of:\n",
    "\n",
    "1. Data Checking and Cleaning\n",
    "2. Data Mining / Inferential Data Analysis\n",
    "3. Predictive Modeling\n",
    "\n",
    "for Data Analytics, Geostatistics and Machine Learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "##### Data Visualization\n",
    "\n",
    "Data visualization includes any graphical representation of the data. \n",
    "\n",
    "We will demonstate basic concepts with only:\n",
    "\n",
    "* univariate distributions, histograms\n",
    "\n",
    "We will start simple and add more complexity and customization."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Project Goal\n",
    "\n",
    "Learn the basics for working with Univariate Data Visualization in Python to build practical spatial data analytics, geostatistics and machine learning workflows.\n",
    "\n",
    "* Focus on customization and not a survey of available plot times\n",
    "\n",
    "#### Caveats\n",
    "\n",
    "I included methods that I have found useful for building my geoscience and engineering workflows for subsurface modeling. I think they should be accessible to most geoscientists and engineers. Certainly, there are more advanced, more compact, more efficient methods to accomplish the same tasks. I tried to keep the methods simple. I appreciate feedback and I will use it to improve this tutorial periodically."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Load and Configure the Required Libraries\n",
    "\n",
    "The following code loads the required libraries and sets a plotting default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
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   "outputs": [],
   "source": [
    "import os                                       # operating system\n",
    "import numpy as np                              # arrays and matrix math\n",
    "import pandas as pd                             # tabular data\n",
    "import matplotlib.pyplot as plt                 # plotting\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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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     "slide_type": "-"
    }
   },
   "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": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "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).  Also, in this case make sure to place the required (see below) data file in this directory.  When we are done with this tutorial we will write our new dataset back to this directory.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE383\")                           # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Loading the Datasets\n",
    "\n",
    "Let's load a tabular dataset from another .csv file, [spatial_nonlinear_MV_facies_v1.csv](https://github.com/GeostatsGuy/GeoDataSets/blob/master/spatial_nonlinear_MV_facies_v1.csv)\n",
    "\n",
    "We cover loading data in these previous **Data Science Basics in Python** lectures, [tabular data](https://www.youtube.com/watch?v=rku5rZxS0AA) and [gridded data](https://www.youtube.com/watch?v=uCRkFwQqdJo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The tabular data is a <class 'pandas.core.frame.DataFrame'>\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Facies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>765</td>\n",
       "      <td>325</td>\n",
       "      <td>17.235937</td>\n",
       "      <td>470.678224</td>\n",
       "      <td>3875.081273</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>900</td>\n",
       "      <td>619</td>\n",
       "      <td>20.362213</td>\n",
       "      <td>638.279517</td>\n",
       "      <td>3903.808828</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>645</td>\n",
       "      <td>205</td>\n",
       "      <td>7.341895</td>\n",
       "      <td>214.431925</td>\n",
       "      <td>4683.320716</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "     X    Y   Porosity        Perm           AI  Facies\n",
       "0  765  325  17.235937  470.678224  3875.081273       1\n",
       "1  900  619  20.362213  638.279517  3903.808828       1\n",
       "2  645  205   7.341895  214.431925  4683.320716       0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table = pd.read_csv('spatial_nonlinear_MV_facies_v1.csv') # load the tabular dataset\n",
    "table = table.iloc[:,1:]                        # remove the first feature (column)\n",
    "print('The tabular data is a ' + str(type(table)))     \n",
    "table.head(n=3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Extract the Feature from the Table\n",
    "\n",
    "I do this for concise and readable code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The por is a <class 'numpy.ndarray'> of shape (457,).\n"
     ]
    }
   ],
   "source": [
    "por = table['Porosity'].values                  # extract porosity feature a a 1D ndarray\n",
    "print('The por is a ' + str(type(por)) + ' of shape ' + str(por.shape) + '.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### Histograms\n",
    "\n",
    "Here's the basic histogram plot.\n",
    "\n",
    "* Quite a plain plot\n",
    "* Axes unlabeled"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
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   "outputs": [
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=table['Porosity'].values)            # basic histogram\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Design the Plot Space\n",
    "\n",
    "Let's improve the plot by considering and designing the plot space.\n",
    "    \n",
    "* Label the axes (.xlabel(),.ylabel()), always the 'right' answer!\n",
    "* Add a grid (.grid()) to improve our ability to perform 'ocular inspection'\n",
    "* We explicity control the plot size, start considering readability\n",
    "* Consider color (color = string) to separate elements, i.e. foreground and background"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=por,color='red')                     # histogram\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.grid()                                      # add grid\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compose the Elements\n",
    "\n",
    "Let's think more about how we can combine all the elements to improve clarity\n",
    "\n",
    "* Outline the histogram bars (edgecolor = string) to separate the binning of the data\n",
    "* Use transparency (alpha < 1.0) to further improve 'ocular inspection' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(alpha = 0.2,edgecolor='black',x=por,color='red'); plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.grid()                                      # add grid\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Improve the Consistency Between Elements\n",
    "\n",
    "Let's improve the consistency of the plot elements.\n",
    "\n",
    "* Specify the axes' extents (.xlim(),.ylim()) and align yaxes increments with integer frequency\n",
    "* Only show grid on y and add a minor grid and ticks for readibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,20),alpha = 0.2,edgecolor='black',x=por,color='red'); plt.xlabel('Porosity (%)'); plt.ylabel('Frequency'); plt.title('Porosity Histogram') # axes labels\n",
    "plt.xlim(0,30); plt.ylim(0,80)                      # constrain the axes' extents                           \n",
    "plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.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",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram Ultimate Control\n",
    "\n",
    "\n",
    "Let's further improve the consistency between our plot elements and add hierarchy to the labels\n",
    "\n",
    "* Specify the histogram bins (bins = list), grid and ticks to align with histogram bins \n",
    "* Adjust the font sizes (fontsize = float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red'); plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4); plt.gca().yaxis.grid(True, which='major',linewidth = 1.0); plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y grid\n",
    "plt.xlabel('Porosity (%)',fontsize=15); plt.ylabel('Frequency',fontsize=15); plt.title('Porosity Histogram',fontsize=20) # axes labels\n",
    "plt.xlim(0,30); plt.ylim(0,45)                      # constrain the axes' extents                           \n",
    "plt.gca().xaxis.set_major_locator(MultipleLocator(1)) # set the major ticks aligned with the bins\n",
    "plt.gca().yaxis.set_minor_locator(MultipleLocator(1)); plt.gca().yaxis.set_major_locator(MultipleLocator(5)) # set the minor ticks for integer frequency\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make a Custom Plot Formatting Function\n",
    "\n",
    "This is a lot of code, let's make a custom function to format our histogram\n",
    "\n",
    "* We could make our function more flexible with the addition of function arguments\n",
    "\n",
    "* This is helpful for concise workflows, especially if similar plots are reused\n",
    "\n",
    "* We will not discuss the definition and use of **styles**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_hist():                                  # function declaration\n",
    "    plt.xlabel('Porosity (%)',fontsize=15); plt.ylabel('Frequency',fontsize=15); plt.title('Porosity Histogram',fontsize=20) # axes labels\n",
    "    plt.xlim(0,30); plt.ylim(0,45)                      # constrain the axes' extents                           \n",
    "    plt.gca().yaxis.grid(True, which='major',linewidth = 1.0) # add y major grid\n",
    "    plt.gca().yaxis.grid(True, which='minor',linewidth = 0.2) # add y minor grid\n",
    "    plt.gca().tick_params(which='major',length=7); plt.gca().tick_params(which='minor', length=4)\n",
    "    plt.gca().xaxis.set_major_locator(MultipleLocator(1)) # set the major ticks aligned with the bins\n",
    "    plt.gca().yaxis.set_minor_locator(MultipleLocator(1)) # set the minor ticks for integer frequency\n",
    "    plt.gca().yaxis.set_major_locator(MultipleLocator(5)) # set the minor ticks for integer frequency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Histogram with Custom Formatting Function\n",
    "\n",
    "Let's demonstrate the application of the format function\n",
    "\n",
    "* Same result with much less code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                       # insert the custom plot formatting                                    \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add a Custom Legend\n",
    "\n",
    "We may want to communicate key statistics with a custom legend by adding shapes and annotation to our plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.5,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5))\n",
    "plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1)))\n",
    "plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Highlight Data Features\n",
    "\n",
    "Let's highlight the summary statistics on the plot with lines."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.5,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5)); plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1))); plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "p10 = np.percentile(por,10); avg = np.average(por); p90 = np.percentile(por,90)\n",
    "plt.plot([p10,p10],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.plot([avg,avg],[0.0,45],color = 'black')\n",
    "plt.plot([p90,p90],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From Start to End\n",
    "\n",
    "Let's compare our original and final plots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "plt.subplot(121)\n",
    "plt.hist(por)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.hist(bins=np.linspace(0,30,31),alpha = 0.4,edgecolor='black',x=por,color='red')\n",
    "format_hist()                                        # insert the custom plot formatting \n",
    "plt.gca().add_patch(Rectangle((23.7,33.5),4.5,9,facecolor='white',edgecolor='black',linewidth=0.5)); plt.text(24,40.5,'Mean ' + str(round(np.average(por),1))); plt.text(24,38.5,'P90    ' + str(round(np.percentile(por,90),1))); plt.text(24,36.5,'P10    ' + str(round(np.percentile(por,10),1))); plt.text(24,34.5,'n        ' + str(por.shape[0]))\n",
    "p10 = np.percentile(por,10); avg = np.average(por); p90 = np.percentile(por,90)\n",
    "plt.plot([p10,p10],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.plot([avg,avg],[0.0,45],color = 'black')\n",
    "plt.plot([p90,p90],[0.0,45],color = 'black',linestyle='dashed')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple Plots\n",
    "\n",
    "Now let's separate the samples by facies. For more information see the [tabular data](https://www.youtube.com/watch?v=rku5rZxS0AA) lecture.\n",
    "\n",
    "* Facies 0 - shalestone\n",
    "* Facies 1 - sandstone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 360 sandstone samples.\n",
      "There are 97 shalestone samples.\n"
     ]
    }
   ],
   "source": [
    "sand = table[table['Facies'] == 1]['Porosity'].values # extract sandstone samples\n",
    "shale = table[table['Facies'] == 0]['Porosity'].values # extract shalestone samples\n",
    "print('There are ' + str(len(sand)) + ' sandstone samples.')\n",
    "print('There are ' + str(len(shale)) + ' shalestone samples.')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine Multiple Plots with Subplots\n",
    "\n",
    "We can combine plots with subplots.\n",
    "\n",
    "* the subplot specification is [$n_{rows}$][$n_{columns}$][current index], current index is from 1 to $n_{rows} \\times n_{columns}$\n",
    "* index is from top left accross columns and then down (like reading a page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "plt.subplot(121)                                    # 1 row, 2 columns, 1st plot\n",
    "plt.hist(x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.legend(); format_hist()                         # add a legend and formatting\n",
    "plt.subplot(122)                                    # 1 row, 2 columns, 2nd plot\n",
    "plt.hist(x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend(); format_hist()                         # add a legend and formatting\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,wspace=0.2,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Combine Multiple Plots with in Same Plot Space\n",
    "\n",
    "We can combine plots in the same plot space. We do this by adding plots in sequence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    "                                   \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Specifying Plot Order \n",
    "\n",
    "We can use the zorder argument to specify the plotting order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": [
    "plt.subplot(121)\n",
    "plt.hist(zorder=1,x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(zorder=2,x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    " \n",
    "plt.subplot(122)\n",
    "plt.hist(zorder=2,x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Sand') # improved histogram\n",
    "plt.hist(zorder=1,x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,31),label='Shale') # improved histogram\n",
    "plt.legend()                                        # add a legend\n",
    "format_hist()                                       # custom formatting\n",
    "    \n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=2.0,top=1.1,wspace=0.2); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Adding PDF to the Histogram\n",
    "\n",
    "We can combine multiple types of plots. Here we add the PDFs estimated with kernel density estimation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns                               # plotting\n",
    "plt.hist(zorder=1,x=sand,color='yellow',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Sand',density=True) # improved histogram\n",
    "plt.hist(zorder=2,x=shale,color='brown',alpha=0.5,edgecolor='black',bins=np.linspace(0,30,31),label='Shale',density=True) # improved histogram\n",
    "sns.kdeplot(x=sand,color = 'orange',alpha = 0.1,levels = 1)\n",
    "sns.kdeplot(x=shale,color = 'black',alpha = 0.1,levels = 1)\n",
    "plt.legend()                                        # add a legend\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Density / Probability'); plt.title('Normalized Histograms and PDFs')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1,wspace=0.2); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting a Cumulative Distribution Function\n",
    "\n",
    "It is quite easy to switch from histogram to CDF.\n",
    "\n",
    "* We set the cumulative argument to True.\n",
    "* We switch the histtype argument to step or stepfilled."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(cumulative=True,histtype='stepfilled',x=shale,color='brown',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,400),label='Shale',density=True) # improved histogram\n",
    "plt.hist(cumulative=True,histtype='stepfilled',x=sand,color='yellow',alpha=1.0,edgecolor='black',bins=np.linspace(0,30,400),label='Sand',density=True) # improved histogram\n",
    "plt.legend(loc='upper left'); plt.xlim(0,30); plt.ylim(0,1) # add a legend\n",
    "plt.xlabel('Porosity (%)'); plt.ylabel('Cumulative Probabilities'); plt.title('Cumulative Distribution Function')\n",
    "plt.subplots_adjust(left=0.0,bottom=0.0,right=1.0,top=1.1); plt.show() # set plot size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "I hope this we helpful,\n",
    "\n",
    "Michael\n",
    "\n",
    "#### More About The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate 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)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "-"
    }
   },
   "source": [
    "#### 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"
   ]
  }
 ],
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