Interactive_PCA.ipynb

{
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   "cell_type": "markdown",
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    "\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 Workflow of Principal Component Analysis\n",
    "    \n",
    "#### The University of Texas at Austin, PGE 2020 SURI, Undergraduation Research Internship,\n",
    "    \n",
    " #### Arham Junaid, Undergraduate Student, The University of Texas at Austin,\n",
    " \n",
    " #### [LinkedIn](https://www.linkedin.com/in/arhamjunaid)\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",
    "#### Introduction\n",
    "\n",
    "Hello and welcome to my workflow relating to Principal Component Analysis. I built out this well-documented workflow with Prof. Pyrcz as part of a SURI undergraduate research project at The University of Texas at Austin.\n",
    "\n",
    "I am Arham Junaid, currently a student doing his undergraduate studies relating to the Energy industry while exploring various Machine Learning methodologies. Principal Component Analysis is an example of inferential machine learning, essentially a method in which your data can be analyzed for dimensional reduction, relationships between variables and making predictions based on the relationships determined. \n",
    "\n",
    "For more check out Prof. Pyrcz's YouTube channel [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig). For the walkthrough video of this workflow go here [walkthrough](TBD). Here's some basic concepts on Principal Component Analysis.\n",
    "\n",
    "#### Principal Component Analysis\n",
    "\n",
    "Principal Component Analysis one of a variety of methods for dimensional reduction:\n",
    "\n",
    "Dimensional reduction transforms the data to a lower dimension\n",
    "\n",
    "* Given features, $𝑋_1,\\dots,𝑋_𝑚$  we would require ${m \\choose 2}=\\frac{𝑚 \\cdot (𝑚−1)}{2}$ scatter plots to visualize just the two-dimensional scatter plots.\n",
    "\n",
    "* Once we have 4 or more variables understanding our data gets very hard.\n",
    "\n",
    "* Recall the curse of dimensionality, impact inference, modeling and visualization. \n",
    "\n",
    "One solution, is to find a good lower dimensional, $𝑝$,  representation of the original dimensions $𝑚$\n",
    "\n",
    "Benefits of Working in a Reduced Dimensional Representation:\n",
    "\n",
    "1. Data storage / Computational Time\n",
    "\n",
    "2. Easier visualization\n",
    "\n",
    "3. Also takes care of multicollinearity \n",
    "\n",
    "#### Orthogonal Transformation \n",
    "\n",
    "Convert a set of observations into a set of linearly uncorrelated variables known as principal components\n",
    "\n",
    "* The number of principal components ($k$) available are min⁡($𝑛−1,𝑚$) \n",
    "\n",
    "* Limited by the variables/features, $𝑚$, and the number of data\n",
    "\n",
    "Components are ordered\n",
    "\n",
    "* First component describes the larges possible variance / accounts for as much variability as possible\n",
    "* Next component describes the largest possible remaining variance \n",
    "* Up to the maximum number of principal components\n",
    "\n",
    "Eigen Values / Eigen Vectors\n",
    "\n",
    "* The Eigen values are the variance explained for each component. \n",
    "* The Eigen vectors of the data covariance matrix are the principal components and the Eigen  \n",
    "* Out of scope – just making the linkage\n",
    "\n",
    "#### Objective\n",
    "In this workflow, we will explore 4 basic aspects of PCA. These include:\n",
    "\n",
    "  - Orthogonal rotation and the incorporation of eigen math\n",
    "\n",
    "  - Forward and backward transformations\n",
    "\n",
    "  - Variance Explained\n",
    "\n",
    "  - Dimensionality Reduction\n",
    "\n",
    "PCA allows is to find good relationships within lower dimensions to allow for greater representation of a multivariate data set and has benefits including ease of visualization, multicollinearity and a reduction in data storage and computational analysis.\n",
    "\n",
    "It is hoped that this interactive version of PCA will provide greater understanding with regards to the benefit of the method in general application.\n",
    "\n",
    "#### Getting Started\n",
    "Here are the steps to get setup in Python with the GeostatsPy package:\n",
    "1.\tInstall Anaconda 3 on your machine (https://www.anaconda.com/download/).\n",
    "2.\tFrom Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal.\n",
    "3.\tIn the terminal type: pip install geostatspy.\n",
    "4.\tOpen 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",
    "   - Tabular data - unconv_MV_v2.csv at https://git.io/fjmBH.\n",
    "\n",
    "#### Install Packages\n",
    "\n",
    "For this interactive workflow to work, we need to install several packages relating to display features, widgets and data analysis interpretation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "from sklearn.decomposition import PCA                     # PCA program from scikit learn (package for machine learning)\n",
    "from sklearn.preprocessing import StandardScaler          # standardize variables to mean of 0.0 and variance of 1.0\n",
    "import pandas as pd                                       # DataFrames and plotting\n",
    "import pandas.plotting as pd_plot                         # matrix scatter plots\n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import matplotlib.pyplot as plt                           # plotting\n",
    "import seaborn as sns\n",
    "import ipywidgets as widgets\n",
    "from ipywidgets import interactive, interact              # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "import matplotlib.transforms as transforms\n",
    "import math\n",
    "from ipywidgets import VBox, HBox\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "cmap = plt.cm.inferno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Data Set\n",
    "Next, let’s go ahead and load the data set we are going to be working with. We’re able to view the variables relating to production.\n",
    "This dataset has variables from 1,000 unconventional wells including:\n",
    " -\twell average porosity\n",
    " - \tlog transform of permeability (to linearize the relationships with other variables)\n",
    " -\taccoustic impedance (kg/m^3 x m/s x 10^6)\n",
    " -\tbrittness ratio (%)\n",
    " -\ttotal organic carbon (%)\n",
    " -\tvitrinite reflectance (%)\n",
    " -\tinitial production 90 day average (MCFPD).\n",
    " -\tscaled production\n",
    " \n",
    "First copy the \"unconv_MV_v2.csv\" comma delimited file from https://github.com/GeostatsGuy/GeoDataSets to your working directory, then run this command to read the file into a DataFrame object (part of Pandas package)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sample</th>\n",
       "      <th>Por</th>\n",
       "      <th>LogPerm</th>\n",
       "      <th>AI</th>\n",
       "      <th>Brittle</th>\n",
       "      <th>TOC</th>\n",
       "      <th>VR</th>\n",
       "      <th>Production</th>\n",
       "      <th>Prod2Scaled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>15.91</td>\n",
       "      <td>1.67</td>\n",
       "      <td>3.06</td>\n",
       "      <td>14.05</td>\n",
       "      <td>1.36</td>\n",
       "      <td>1.85</td>\n",
       "      <td>177.381958</td>\n",
       "      <td>1897.657798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>15.34</td>\n",
       "      <td>1.65</td>\n",
       "      <td>2.60</td>\n",
       "      <td>31.88</td>\n",
       "      <td>1.37</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1479.767778</td>\n",
       "      <td>2745.732996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>20.45</td>\n",
       "      <td>2.02</td>\n",
       "      <td>3.13</td>\n",
       "      <td>63.67</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4421.221583</td>\n",
       "      <td>5835.130524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>1.14</td>\n",
       "      <td>3.90</td>\n",
       "      <td>58.81</td>\n",
       "      <td>0.40</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1488.317629</td>\n",
       "      <td>2132.237219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>19.53</td>\n",
       "      <td>1.83</td>\n",
       "      <td>2.57</td>\n",
       "      <td>43.75</td>\n",
       "      <td>1.40</td>\n",
       "      <td>2.11</td>\n",
       "      <td>5261.094919</td>\n",
       "      <td>6282.254735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Sample    Por  LogPerm    AI  Brittle   TOC    VR   Production  Prod2Scaled\n",
       "0       1  15.91     1.67  3.06    14.05  1.36  1.85   177.381958  1897.657798\n",
       "1       2  15.34     1.65  2.60    31.88  1.37  1.79  1479.767778  2745.732996\n",
       "2       3  20.45     2.02  3.13    63.67  1.79  2.53  4421.221583  5835.130524\n",
       "3       4  11.95     1.14  3.90    58.81  0.40  2.03  1488.317629  2132.237219\n",
       "4       5  19.53     1.83  2.57    43.75  1.40  2.11  5261.094919  6282.254735"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "#os.chdir(r'C:\\PGE383')\n",
    "#df=pd.read_csv(\"unconv_MV_v2.csv\")\n",
    "df=pd.read_csv(r\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/unconv_MV_v2.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are also able to view some basic summary statistics relating to our variables and transpose the data set to make it easier for us to work with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>14.950460</td>\n",
       "      <td>3.029634</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>12.857500</td>\n",
       "      <td>14.985000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>24.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogPerm</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.398880</td>\n",
       "      <td>0.405966</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.982610</td>\n",
       "      <td>0.577629</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>2.577500</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>4.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>49.769980</td>\n",
       "      <td>14.944955</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>39.722500</td>\n",
       "      <td>49.680000</td>\n",
       "      <td>59.170000</td>\n",
       "      <td>93.47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.003810</td>\n",
       "      <td>0.504978</td>\n",
       "      <td>-0.260000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>2.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.991170</td>\n",
       "      <td>0.308194</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.172500</td>\n",
       "      <td>2.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2247.295809</td>\n",
       "      <td>1464.256312</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>1191.369560</td>\n",
       "      <td>1976.487820</td>\n",
       "      <td>3023.594214</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod2Scaled</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3237.154325</td>\n",
       "      <td>1507.552730</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>2120.961071</td>\n",
       "      <td>2991.762748</td>\n",
       "      <td>4105.623405</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std       min          25%  \\\n",
       "Por          1000.0    14.950460     3.029634  5.400000    12.857500   \n",
       "LogPerm      1000.0     1.398880     0.405966  0.120000     1.130000   \n",
       "AI           1000.0     2.982610     0.577629  0.960000     2.577500   \n",
       "Brittle      1000.0    49.769980    14.944955  3.030000    39.722500   \n",
       "TOC          1000.0     1.003810     0.504978 -0.260000     0.640000   \n",
       "VR           1000.0     1.991170     0.308194  0.900000     1.810000   \n",
       "Production   1000.0  2247.295809  1464.256312  2.713535  1191.369560   \n",
       "Prod2Scaled  1000.0  3237.154325  1507.552730  2.713535  2120.961071   \n",
       "\n",
       "                     50%          75%          max  \n",
       "Por            14.985000    17.080000     24.65000  \n",
       "LogPerm         1.390000     1.680000      2.58000  \n",
       "AI              3.010000     3.360000      4.70000  \n",
       "Brittle        49.680000    59.170000     93.47000  \n",
       "TOC             0.995000     1.360000      2.71000  \n",
       "VR              2.000000     2.172500      2.90000  \n",
       "Production   1976.487820  3023.594214  12568.64413  \n",
       "Prod2Scaled  2991.762748  4105.623405  12568.64413  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.iloc[:,1:9]                                  # copy all rows and columns 1 through 9, note 0 column is removed\n",
    "df.describe().transpose()                            # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can go about ensuring the negative values are removed, namely for TOC and Brittleness, since those are not possible and we can set our data values into a single Data Frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Por</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>14.950460</td>\n",
       "      <td>3.029634</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>12.857500</td>\n",
       "      <td>14.985000</td>\n",
       "      <td>17.080000</td>\n",
       "      <td>24.65000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LogPerm</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.398880</td>\n",
       "      <td>0.405966</td>\n",
       "      <td>0.120000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>1.390000</td>\n",
       "      <td>1.680000</td>\n",
       "      <td>2.58000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2.982610</td>\n",
       "      <td>0.577629</td>\n",
       "      <td>0.960000</td>\n",
       "      <td>2.577500</td>\n",
       "      <td>3.010000</td>\n",
       "      <td>3.360000</td>\n",
       "      <td>4.70000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brittle</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>49.769980</td>\n",
       "      <td>14.944955</td>\n",
       "      <td>3.030000</td>\n",
       "      <td>39.722500</td>\n",
       "      <td>49.680000</td>\n",
       "      <td>59.170000</td>\n",
       "      <td>93.47000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TOC</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.006170</td>\n",
       "      <td>0.499838</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.995000</td>\n",
       "      <td>1.360000</td>\n",
       "      <td>2.71000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VR</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>1.991170</td>\n",
       "      <td>0.308194</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>1.810000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.172500</td>\n",
       "      <td>2.90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Production</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>2247.295809</td>\n",
       "      <td>1464.256312</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>1191.369560</td>\n",
       "      <td>1976.487820</td>\n",
       "      <td>3023.594214</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prod2Scaled</th>\n",
       "      <td>1000.0</td>\n",
       "      <td>3237.154325</td>\n",
       "      <td>1507.552730</td>\n",
       "      <td>2.713535</td>\n",
       "      <td>2120.961071</td>\n",
       "      <td>2991.762748</td>\n",
       "      <td>4105.623405</td>\n",
       "      <td>12568.64413</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              count         mean          std       min          25%  \\\n",
       "Por          1000.0    14.950460     3.029634  5.400000    12.857500   \n",
       "LogPerm      1000.0     1.398880     0.405966  0.120000     1.130000   \n",
       "AI           1000.0     2.982610     0.577629  0.960000     2.577500   \n",
       "Brittle      1000.0    49.769980    14.944955  3.030000    39.722500   \n",
       "TOC          1000.0     1.006170     0.499838  0.000000     0.640000   \n",
       "VR           1000.0     1.991170     0.308194  0.900000     1.810000   \n",
       "Production   1000.0  2247.295809  1464.256312  2.713535  1191.369560   \n",
       "Prod2Scaled  1000.0  3237.154325  1507.552730  2.713535  2120.961071   \n",
       "\n",
       "                     50%          75%          max  \n",
       "Por            14.985000    17.080000     24.65000  \n",
       "LogPerm         1.390000     1.680000      2.58000  \n",
       "AI              3.010000     3.360000      4.70000  \n",
       "Brittle        49.680000    59.170000     93.47000  \n",
       "TOC             0.995000     1.360000      2.71000  \n",
       "VR              2.000000     2.172500      2.90000  \n",
       "Production   1976.487820  3023.594214  12568.64413  \n",
       "Prod2Scaled  2991.762748  4105.623405  12568.64413  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num = df._get_numeric_data()                         # get the numerical values\n",
    "num[num < 0] = 0                                     # truncate negative values to 0.0\n",
    "df.describe().transpose()                            # calculate summary statistics for the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to go about dimensionality reduction, a useful first step is to develop a correlation matrix. The higher the magnitude of correlation between your 2 variables, the more likely it is that your principal component scores will be able to account for a greater variance of your data.\n",
    "\n",
    "* PCA assumes that there is linear correlation between features, this does exist in our dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": [
    "_=sns.heatmap(df.corr(),annot=True,cmap=cmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the correlation matrix above, can observe a mix of bivariate data, and linear correlation magnitudes and for additional visualization, we can take a look at the matrix scatter plot as we look to narrow down to 2 variables to allow for analysis of our data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 64 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd_plot.scatter_matrix(df, alpha = 0.1,              # pandas matrix scatter plot\n",
    "    figsize=(7, 7),color = 'black', hist_kwds={'color':['grey']})\n",
    "plt.suptitle(\"Matrix Scatter Plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interactive Exercise #1: Orthogonal Rotation\n",
    "\n",
    "\n",
    "For our first example, let’s explore the power of orthogonal rotation. \n",
    "* Eigen values and eigen vectors are based on the idea of the rotation of your axis. For our example, 2 variables, Por and TOC, with a relatively high correlation coefficient, were chosen to explore this idea further. \n",
    "* For reduced computational load, only 100 values have been selected but you’re welcome to incorporate more of the data by copying and changing the code. \n",
    "* For principle component scores to be calculated, the data first needs to be standardized. \n",
    "\n",
    "We can have a go at visualizing how the data points alter as the angle of rotation varies and how much of the variance is influenced by rotation coordinate 1 and rotational coordinate 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_data_por_perm = df[[\"Por\",\"TOC\"]]                # extract just por and TOC, 100 samples\n",
    "my_data_por_perm =my_data_por_perm.iloc[0:100]\n",
    "features = ['Por','TOC']\n",
    "x = my_data_por_perm.loc[:,features].values\n",
    "s = StandardScaler()\n",
    "s.fit(x)\n",
    "x = s.transform(x)                                  # standardize the data features to mean = 0, var = 1.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have our data standardized, we can take a look at a scatter plot of the 2 variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Standardized TOC')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "figure, a = plt.subplots()\n",
    "a.scatter(my_data_por_perm[\"Por\"],my_data_por_perm[\"TOC\"],s=None, c=\"darkorange\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.8, linewidths=1.0, edgecolors=\"black\")    \n",
    "a.set_title('Standardized Porosity vs Standardized TOC'); \n",
    "a.set_xlabel(\"Standardized Porosity\")\n",
    "a.set_ylabel(\"Standardized TOC\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, there seems to be a seemingly linear trend.\n",
    "\n",
    "We can now visualize how the data is rotated in order to understand the proportion of the variance for each rotated coordinate. The rotational coordinates are determined by simple trigonometric functions, sine and cosine to determine their new location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "global xdata                                        #create global values to allow for orthoganal rotation and visualization\n",
    "global ydata\n",
    "def pc_slider(Angle):\n",
    "    global xdata\n",
    "    global ydata\n",
    "    fig15, ((ax15,ax16)) = plt.subplots(1, 2,figsize=(15,6), constrained_layout=True)\n",
    "    fig15.subplots_adjust(wspace=.5,hspace = .5)\n",
    "    \n",
    "    \n",
    "    base = plt.gca().transData\n",
    "    #print(base)\n",
    "    rot = transforms.Affine2D().rotate_deg(int(Angle))\n",
    "    line=ax16.plot(x[:,0],x[:,1], 'o', transform= rot + base, c = 'black', alpha = 0.3)\n",
    "    \n",
    "    xdata=x[:,0]*math.cos(math.radians(int(Angle)))-x[:,1]*math.sin(math.radians(int(Angle)))\n",
    "    ydata=x[:,1]*math.cos(math.radians(int(Angle)))+x[:,0]*math.sin(math.radians(int(Angle)))\n",
    "    \n",
    "    eigen = np.zeros([2,2])\n",
    "    eigen[0,0] = math.cos(Angle*math.pi/180.0)\n",
    "    eigen[1,0] = math.sin(Angle*math.pi/180.0)\n",
    "    eigen[0,1] = -1*math.sin(Angle*math.pi/180.0)\n",
    "    eigen[1,1] = math.cos(Angle*math.pi/180.0)\n",
    "    \n",
    "    df2 = pd.DataFrame({'x':xdata, 'y':ydata})\n",
    "    data = df2.values\n",
    "    lists=[]\n",
    "    \n",
    "    ydataZeroed = np.zeros(len(ydata))\n",
    "\n",
    "    ax16.plot(xdata, ydataZeroed,\"or\", c = 'red', alpha = 0.3)\n",
    "    ax16.plot(ydataZeroed, ydata,\"or\", c= 'blue', alpha = 0.3)\n",
    "    ax16.set_xlim(left=-3.5, right=3.5)\n",
    "    ax16.set_ylim(bottom=-3.5, top=3.5)\n",
    "    \n",
    "    ax16.set_title(\"Arbitrary Data Projection\");ax16.set_xlabel('Projected Feature 1'); ax16.set_ylabel('Projected Feature 2')\n",
    "    labels = 'Feature 2 Variance', 'Feature 1 Variance'\n",
    "    sizes = []\n",
    "    \n",
    "    print('Your Estimated Principal Component/Eigen Vector #1 = ' + str(eigen[:,0]))\n",
    "    print('Your Estimated Principal Component/Eigen Vector #2 = ' + str(eigen[:,1]))\n",
    "    \n",
    "    sumOfVariance=df2.var()['x']+df2.var()['y']\n",
    "    sizes.append(df2.var()['x']/sumOfVariance)\n",
    "    sizes.append(df2.var()['y']/sumOfVariance)\n",
    "    n = ax15.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    ax15.axis('equal')\n",
    "    ax15.legend(sizes, labels=labels,loc='upper left')\n",
    "#    plt.tight_layout()\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.1)\n",
    "    plt.show()\n",
    "    \n",
    "x0 = widgets.Text(value='                                   Interactive Feature Projection - Orthogonal Rotation, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=0, max = 180, value = 0, description = 'Angle',orientation='horizontal')\n",
    "uik2 = widgets.VBox([x0,x1],)\n",
    "interactive_plot = widgets.interactive_output(pc_slider, {'Angle': x1})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Feature Projection with Arbitrary Rotation\n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Observed the partitioning of variance over 2 new features through orthogonal projection / rotation.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Angle**: data rotation angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0acc52bfe8e4adf8b7d96bfe98387d0",
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      "text/plain": [
       "VBox(children=(Text(value='                                   Interactive Feature Projection - Orthogonal Rota…"
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     "metadata": {},
     "output_type": "display_data"
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      "text/plain": [
       "Output(outputs=({'output_type': 'stream', 'text': 'Your Estimated Principal Component/Eigen Vector #1 = [1. 0.…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik2,interactive_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now convert our rotated coordinates back in the data frame to do some analysis.\n",
    "\n",
    "Now let's visualize the principal components in a more intuitive manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "global xdata                                        #create global values to allow for orthoganal rotation and visualization\n",
    "global ydata\n",
    "def pc_slider(Angle):\n",
    "    global xdata\n",
    "    global ydata\n",
    "    fig15, ((ax15,ax16)) = plt.subplots(1, 2,figsize=(15,6), constrained_layout=True)\n",
    "    fig15.subplots_adjust(wspace=.5,hspace = .5)\n",
    "    \n",
    "    base = plt.gca().transData\n",
    "    #print(base)\n",
    "    rot = transforms.Affine2D().rotate_deg(int(Angle))\n",
    "    #line=ax16.plot(x[:,0],x[:,1], 'o', transform= rot + base, c = 'black', alpha = 0.3)\n",
    "    line=ax16.plot(x[:,0],x[:,1], 'o', c = 'black', alpha = 0.3)\n",
    "    \n",
    "    xdata=x[:,0]*math.cos(math.radians(int(Angle)))-x[:,1]*math.sin(math.radians(int(Angle)))\n",
    "    ydata=x[:,1]*math.cos(math.radians(int(Angle)))+x[:,0]*math.sin(math.radians(int(Angle)))\n",
    "    \n",
    "    eigen = np.zeros([2,2])\n",
    "    eigen[0,0] = math.cos(Angle*math.pi/180.0)\n",
    "    eigen[1,0] = math.sin(Angle*math.pi/180.0)\n",
    "    eigen[0,1] = -1*math.sin(Angle*math.pi/180.0)\n",
    "    eigen[1,1] = math.cos(Angle*math.pi/180.0)\n",
    "    \n",
    "    df2 = pd.DataFrame({'x':xdata, 'y':ydata})\n",
    "    data = df2.values\n",
    "    lists=[]\n",
    "    \n",
    "    ydataZeroed = np.zeros(len(ydata))\n",
    "\n",
    "    rotinv = transforms.Affine2D().rotate_deg(int(-Angle)) \n",
    "    ax16.plot(xdata, ydataZeroed,\"or\", c = 'red', alpha = 0.3,transform= rotinv + base)\n",
    "    ax16.plot(ydataZeroed, ydata,\"or\", c= 'blue', alpha = 0.3,transform= rotinv + base)\n",
    "    ax16.set_xlim(left=-3.5, right=3.5)\n",
    "    ax16.set_ylim(bottom=-3.5, top=3.5)\n",
    "    \n",
    "    ax16.set_title(\"Data and Arbitrary Feature Projection Components\");ax16.set_xlabel('Standardized Porosity'); ax16.set_ylabel('Standardized TOC')\n",
    "    labels = 'Feature 2 Variance', 'Feature 1 Variance'\n",
    "    sizes = []\n",
    "    \n",
    "#     print('Your Estimated Principal Component/Eigen Vector #1 = ' + str(eigen[:,0]))\n",
    "#     print('Your Estimated Principal Component/Eigen Vector #2 = ' + str(eigen[:,1]))\n",
    "    \n",
    "    sumOfVariance=df2.var()['x']+df2.var()['y']\n",
    "    sizes.append(df2.var()['x']/sumOfVariance)\n",
    "    sizes.append(df2.var()['y']/sumOfVariance)\n",
    "    n = ax15.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    ax15.axis('equal')\n",
    "    ax15.legend(sizes, labels=labels,loc='upper left')\n",
    "    ax15.set_title('Variance for Arbitrary Feature Projection Components')\n",
    "#    plt.tight_layout()\n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.0, top=1.0, wspace=0.1, hspace=0.1)\n",
    "    plt.show()\n",
    "    \n",
    "x0 = widgets.Text(value='                                            Interactive Feature Projection - Orthogonal Rotation, Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=0, max = 180, value = 0, description = 'Angle',orientation='horizontal',continuous_update=False)\n",
    "uik2 = widgets.VBox([x0,x1],)\n",
    "interactive_plot = widgets.interactive_output(pc_slider, {'Angle': x1})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Feature Projection with Arbitrary Rotation\n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Observed the partitioning of variance over 2 new features through orthogonal projection / rotation.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Angle**: data rotation angle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "9b612e89a04c443a8b51f8d6f667bb6a",
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       "version_minor": 0
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      "text/plain": [
       "VBox(children=(Text(value='                                            Interactive Feature Projection - Orthog…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c953dacaa07b429d83296b3e6b51934e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 1080x432 with 2 Axes>', '…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik2,interactive_plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, we can see the calculation of the covariance matrix and the proportion of variance explained by each Principal Component Score as determined by PCA. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.707  0.707]\n",
      " [-0.707  0.707]]\n",
      "Variance explained by PC1 and PC2 = [0.8596 0.1404]\n"
     ]
    }
   ],
   "source": [
    "n_components = 2\n",
    "pca = PCA(n_components=n_components)\n",
    "pca.fit(x)\n",
    "print(np.round(pca.components_,3))\n",
    "print('Variance explained by PC1 and PC2 =', np.round(pca.explained_variance_ratio_,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "At angle 0, the covariance tells us that both variables have the same amount of influence on the variance. This changes with the angle of rotation. \n",
    "\n",
    "The proportion of variance explained by the first principal component score is 85.96%. \n",
    "\n",
    "**Exercise:** Play around with the rotation above and see what angle provides the variance determined by PCA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Interative Workflow #2: Forward and Backward Transformations\n",
    "\n",
    "Below you can choose an arbitrary value for a new data point, in blue. As you play around with where the new data point is placed, you notice that the data point can only go along the de-standardized line in graph 6. This is a key aspect of PCA relating to how the data is transformed based on the Principal Component scores calculated.\n",
    "\n",
    "Here is a flowchart of the transformations which take place:\n",
    "\n",
    "   ![alt text](flowChartFinal.png \"Title\")\n",
    "   \n",
    "   \n",
    "\n",
    "\n",
    "First, the PCA code is written to calculate the location of data points in each of these state spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mu = np.mean(x, axis=0)\n",
    "sd = np.std(x, axis=0)\n",
    "x_stand = StandardScaler().fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "\n",
    "nComp = 1\n",
    "\n",
    "n_components = 2                                                   # build principal component model with 2 components\n",
    "pca = PCA(n_components=n_components)\n",
    "pca.fit(x)\n",
    "\n",
    "x_trans = pca.transform(x)                                         # calculate principal component scores\n",
    "\n",
    "xtrans2 = x_trans.copy()\n",
    "x_trans[:,1] = 0.0                                                 # zero / remove the 2nd principal component \n",
    "\n",
    "xtrans2[:,0] = 0\n",
    "\n",
    "xhat = pca.inverse_transform(x_trans)                             # reverse the principal component scores to standardized values\n",
    "\n",
    "xhat = np.dot(pca.inverse_transform(xhat)[:,:nComp], pca.components_[:nComp,:])\n",
    "xhat = sd*xhat + mu                                                # remove the standardization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can then plot the data points in scatter form in each of the state spaces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    " def pc_slider(X, Y):\n",
    "    nComp = 1\n",
    "    f, ((ax201,ax202, ax203), (ax204, ax205, ax206)) = plt.subplots(2, 3,figsize=(13,8))\n",
    "    f.subplots_adjust(wspace=0.5,hspace = 0.3)\n",
    "\n",
    "    ax201.scatter(my_data_por_perm['Por'],my_data_por_perm['TOC'],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")    \n",
    "    ax201.set_title('1. Porosity vs. TOC'); \n",
    "    ax201.set_xlabel(\"Porosity\"); ax201.set_ylabel(\"TOC\")\n",
    "    ax201.set_xlim([5,25]), ax201.set_ylim([0.0,2.5])\n",
    "    features = ['Por','TOC']\n",
    "    \n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    stscalar = StandardScaler() \n",
    "    x_stand = stscalar.fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "    temp = np.array([float(X), float(Y)])\n",
    "    temp=temp.reshape(-1, 2)\n",
    "    temp=s.transform(temp)\n",
    "    \n",
    "\n",
    "    ax202.scatter(x_stand[:,0],x_stand[:,1], s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax202.set_title('2. Standardized Por vs TOC'); ax202.set_xlabel('Standardized Porosity'); ax202.set_ylabel('Standardized TOC')\n",
    "    ax202.set_xlim([-3,3]), ax202.set_ylim([-3,3])\n",
    "\n",
    "    n_components = 2                                                # build principal component model with 2 components\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "\n",
    "    x_trans = pca.transform(x)                                   # calculate principal component scores\n",
    "    ax203.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax203.set_title('3. Principal Component Scores'); ax203.set_xlabel('PC1'); ax203.set_ylabel('PC2')\n",
    "    ax203.set_xlim([-3,3]), ax203.set_ylim([-3,3])\n",
    "    \n",
    "    x_trans[:,1] = 0.0                                              # zero / remove the 2nd principal component \n",
    "\n",
    "    ax204.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax204.set_title('4. Only 1st Principal Component Scores'); ax204.set_xlabel('PC1'); ax204.set_ylabel('PC2')\n",
    "    ax204.set_xlim([-3,3]), ax204.set_ylim([-3,3])\n",
    "    \n",
    "    xhat = pca.inverse_transform(x_trans)                           # reverse the principal component scores to standardized values\n",
    "    ax205.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax205.set_title('5. Reverse PCA'); \n",
    "    ax205.set_xlabel('Standardized POR'); ax205.set_ylabel('Standardized TOC')\n",
    "    ax205.set_xlim([-3,3]), ax205.set_ylim([-3,3])                      \n",
    "\n",
    "    xhat = s.inverse_transform(xhat)\n",
    "    ax206.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax206.set_title('6. De-standardized Reverse PCA');\n",
    "    ax206.set_xlabel('Porosity'); ax206.set_ylabel('TOC')\n",
    "    ax206.set_xlim([5,25]), ax206.set_ylim([0.0,2.5])\n",
    "    \n",
    "    ax201.plot(float(X),float(Y),marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    ax202.plot(temp[:,0], temp[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    x_trans2= pca.transform(temp)\n",
    "    ax203.plot(x_trans2[:,0], x_trans2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    x_trans2[:,1] = 0.0 \n",
    "    ax204.plot(x_trans2[:,0], x_trans2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    xhat2 = pca.inverse_transform(x_trans2) \n",
    "    ax205.plot(xhat2[:,0],xhat2[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "    xhat21 = s.inverse_transform(xhat2)\n",
    "    ax206.plot(xhat21[:,0],xhat21[:,1],marker = \"+\", c = 'black',markersize = 20,markeredgewidth = 3,alpha = 0.8)\n",
    "\n",
    "x0 = widgets.Text(value='                                  Interactive PCA - Datum Transformations, Arham Junaid and Prof. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "x1 = widgets.IntSlider(min=5, max = 25, value = 0, description = 'Porosity',orientation='horizontal')\n",
    "x2 = widgets.FloatSlider(min=0, max = 2.5, step=0.1,value = 1, description = 'TOC',orientation='horizontal')\n",
    "uik3 = widgets.VBox([x0,x1,x2],)\n",
    "interactive_output = widgets.interactive_output(pc_slider, {'X': x1, 'Y': x2})\n",
    "interactive_output.clear_output(wait = True)                       # reduce flickering by delaying plot updating\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Datum Projection with PCA \n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Move around a datum in original feature space and observe its location over all steps of PCA forward and reverse transformation, including standardization.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Porosity (%), TOC (total organic carbon %)**: the new datum's values in the original features, prior to standardization/projection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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      "text/plain": [
       "VBox(children=(Text(value='                                  Interactive PCA - Datum Transformations, Arham Ju…"
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     "metadata": {},
     "output_type": "display_data"
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      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 936x576 with 6 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(uik3,interactive_output)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We notice the transformation, forward and backward, as we notice the  movement of the arbitrary point. In the first graph, the blue dot can move anywhere in the 2D space while in the 6th graph, the movement of the blue point is restricted to a single direction despite returning back to the lower dimensional original space.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "#### Interactive Workflow #3: Dimensionality Reduction\n",
    "\n",
    "PCA is all about dimensionality reduction. We can see this from the graphs detailed below and how analysis in reduced dimensions allows for a more compact visualization based on the calculation of the Principal Component scores. \n",
    "\n",
    "This is done by converting our data set into Principal Component scores and retaining *p* Principal Component scores. Here, you may choose your 2 variables to calculate your 2 Principal Component scores.\n",
    "\n",
    "The following can be seen from the output below:\n",
    "1. Visual Distribution of the 2 variables\n",
    "2. Descriptive statistics of the original and transformed data set\n",
    "3. The covariance matrix and the matrix displaying variance explained by each PC score\n",
    "4. Cross plots looking the same after PCA is run indicating the method is working correctly\n",
    "5. The transformation of the data into reduced dimensional space\n",
    "6. The variance explained statistics for each PC score  \n",
    "7. Total variance explained of the 2 selected variables based on the number of PC scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6d3bd9d05e184b6781f8f5bc4fc4b840",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='                                     Interactive PCA - Dimensionality Reduction, Arham Junaid and …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "var_por = np.var(my_data_por_perm['Por'])\n",
    "var_por_hat = np.var(xhat[:,0])\n",
    "var_logperm = np.var(my_data_por_perm['TOC'])\n",
    "var_logperm_hat = np.var(xhat[:,1])\n",
    "\n",
    "\n",
    "def firstvariable(feature1=\"Por\",\n",
    "                 feature2=\"LogPerm\"):\n",
    "    column1 = feature1; column2 = feature2\n",
    "    my_data_por_perm = df[[column1,column2]]                \n",
    "    my_data_por_perm =my_data_por_perm.iloc[0:100]\n",
    "    \n",
    "    print(\"1: Visual Distribution\")\n",
    "    \n",
    "    f, (ax1, ax2) = plt.subplots(1, 2, sharey=True)\n",
    "    ax1.hist(my_data_por_perm[column1], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "    ax1.set_title(column1)\n",
    "    ax2.hist(my_data_por_perm[column2], alpha = 0.2, color = 'red', edgecolor = 'black', bins=20)\n",
    "    ax2.set_title(column2)\n",
    "    plt.show()\n",
    "    \n",
    "\n",
    "    features = [column1,column2]\n",
    "    x = my_data_por_perm.loc[:,features].values\n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    x = StandardScaler().fit_transform(x)                     # standardize the data features to mean = 0, var = 1.0\n",
    "    \n",
    "    print(\"2: Original and Transformed Statistics\")\n",
    "    print(\" \")\n",
    "    print(\"Original Mean \"+column1+' = ', np.round(mu[0],2), ', Original Mean '+column2+' = ', np.round(mu[1],2)) \n",
    "    print(\"Original StDev \"+column1+' = ', np.round(sd[0],2), ', Original StDev '+column2+' = ', np.round(sd[1],2)) \n",
    "    print('Mean Transformed '+column1+' = ',np.round(np.mean(x[:,0]),2),', Mean Transformed '+column2+' = ',np.round(np.mean(x[:,1]),2))\n",
    "    print('Variance Transformed '+column1+' = ',np.round(np.var(x[:,0]),2),', Variance Transformed '+column2+' = ',np.var(x[:,1]))\n",
    "    \n",
    "\n",
    "    n_components = 2\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "    print(\" \")\n",
    "    print(\"3: Covariance and Variance Explained Matrices\")\n",
    "    print(\" \")\n",
    "    print(np.round(pca.components_,3))\n",
    "    print('Variance explained by PC1 and PC2 =', np.round(pca.explained_variance_ratio_,3))\n",
    "    print(\" \")\n",
    "    print(\"4: Original and PCA Cross-plots\")\n",
    "    \n",
    "    \n",
    "    f, (ax101, ax102, ax103) = plt.subplots(1, 3,figsize=(12,2.7))\n",
    "    f.subplots_adjust(wspace=0.7)\n",
    "\n",
    "    ax101.scatter(x[:,0],x[:,1], s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax101.set_title('Standardized '+column2+' vs. '+column1); ax101.set_xlabel('Standardized '+column1); ax101.set_ylabel('Standardized '+column2)\n",
    "\n",
    "    x_trans = pca.transform(x)                                # calculate the principal component scores\n",
    "    ax102.scatter(x_trans[:,0],x_trans[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax102.set_title('Principal Component Scores'); ax102.set_xlabel('PC1'); ax102.set_ylabel('PC2')\n",
    "\n",
    "    x_reverse = pca.inverse_transform(x_trans)                        # reverse the principal component scores to standardized values\n",
    "    ax103.scatter(x_reverse[:,0],x_reverse[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax103.set_title('Reverse PCA'); ax103.set_xlabel('Standardized '+column1); ax103.set_ylabel('Standardized '+column2)\n",
    "    plt.show()\n",
    "    \n",
    "    nComp = 1\n",
    "    \n",
    "   \n",
    "    print(\"5: PCA State Space Transformation\")\n",
    "\n",
    "    mu = np.mean(x, axis=0)\n",
    "    sd = np.std(x, axis=0)\n",
    "    x_stand = StandardScaler().fit_transform(x)               # standardize the data features to mean = 0, var = 1.0\n",
    "\n",
    "\n",
    "    n_components = 2                                          # build principal component model with 2 components\n",
    "    pca = PCA(n_components=n_components)\n",
    "    pca.fit(x)\n",
    "\n",
    "    x_trans = pca.transform(x)                                # calculate principal component scores\n",
    "\n",
    "    x_trans[:,1] = 0.0                                        # zero / remove the 2nd principal component \n",
    "\n",
    "    xhat = pca.inverse_transform(x_trans)                     # reverse the principal component scores to standardized values\n",
    "\n",
    "    xhat = np.dot(pca.inverse_transform(x)[:,:nComp], pca.components_[:nComp,:])\n",
    "    xhat = sd*xhat + mu                                       # remove the standardization\n",
    "     \n",
    "    f, (ax201, ax206) = plt.subplots(1, 2,figsize=(10,3))\n",
    "    f.subplots_adjust(wspace=0.5,hspace = 0.3)\n",
    "    \n",
    "    ax201.scatter(my_data_por_perm[column1],my_data_por_perm[column2],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax201.set_title(column2+' vs. '+column1); ax201.set_xlabel(column1); ax201.set_ylabel(column2)\n",
    "\n",
    "    ax206.scatter(xhat[:,0],xhat[:,1],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    ax206.set_title('De-standardized Reverse PCA'); ax206.set_xlabel(column1); ax206.set_ylabel(column2)\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "    var_por = np.var(my_data_por_perm[column1]); var_por_hat = np.var(xhat[:,0]);\n",
    "    var_logperm = np.var(my_data_por_perm[column2]); var_logperm_hat = np.var(xhat[:,1]);\n",
    "    #print('Variance '+column1+' = ',np.round(var_por,3),', Variance Reduced Dimensional '+column1+' =',np.round(var_por_hat,3),'Fraction = ',np.round(var_por_hat/var_por,3))\n",
    "    #print('Variance '+column2+' =',np.round(var_por,3),', Variance Reduced Dimensional '+column2+' =',np.round(var_logperm_hat,3),'Fraction = ',np.round(var_logperm_hat/var_logperm,3))\n",
    "\n",
    "    global my_data_f7\n",
    "    my_data_f7=df.copy(deep=True)\n",
    "    \n",
    "    np.set_printoptions(suppress=True)\n",
    "    features = ['Por','LogPerm','AI','Brittle','TOC','VR',\"Production\"]\n",
    "    x_f7 = my_data_f7.loc[:,features].values\n",
    "    mu_f7 = np.mean(x_f7, axis=0)\n",
    "    sd_f7 = np.std(x_f7, axis=0)\n",
    "    x_f7 = StandardScaler().fit_transform(x_f7)\n",
    "\n",
    "    #print(\"Original Means = \", features[:], np.round(mu_f7[:],2)) \n",
    "    #print(\"Original StDevs = \", features[:],np.round(sd_f7[:],2)) \n",
    "    #print('Mean Transformed = ',features[:],np.round(x.mean(axis=0),2))\n",
    "    #print('Variance Transformed = ',features[:],np.round(x.var(axis=0),2))\n",
    "    \n",
    "    print(\"6: Variance Explained by PC Score\")\n",
    "    n_components = 7\n",
    "    pca_f7 = PCA(n_components=n_components)\n",
    "    pca_f7.fit(x_f7)\n",
    "    print('\\n\\nVariance explained by PC1 thru PC7 =', np.round(pca_f7.explained_variance_ratio_,3))\n",
    "    fig5 = plt.figure(figsize=(2,2))\n",
    "    ax = fig5.add_axes([0,0,1,1])\n",
    "    langs = ['PC1', 'PC2', 'PC3', 'PC4', 'PC5', 'PC6', 'PC7']\n",
    "    ax.bar(langs,np.round(pca_f7.explained_variance_ratio_,3))\n",
    "    ax.set_title('Variance Explained by Principal Component'); ax.set_xlabel(\"Principal Component\"); ax.set_ylabel(\"Fraction of Variance\")\n",
    "    \n",
    "    nComp=7\n",
    "    x_hat_dim=[]\n",
    "    while nComp >=1: \n",
    "        temp_xhat_dim = np.dot(pca_f7.transform(x_f7)[:,:nComp], pca_f7.components_[:nComp,:])\n",
    "        temp_xhat_dim= sd_f7*temp_xhat_dim + mu_f7\n",
    "        nComp-=1\n",
    "        x_hat_dim.append(temp_xhat_dim)\n",
    "\n",
    "\n",
    "    f, axes2 = plt.subplots(1, 8, figsize=(20,20))\n",
    "    f.subplots_adjust(wspace=0.7)\n",
    "    columns=['Std. Porosity','Std. Log[Perm.]','Std. Acoustic Imped.','Std. Brittleness','Std. Total Organic C', 'Std. Vit. Reflectance', 'Std. Production']\n",
    "    axes2[0].scatter(my_data_f7[column1],my_data_f7[column2],s=None, c=\"red\",marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "    axes2[0].set_title('Original Data'); axes2[0].set_xlabel(column1); axes2[0].set_ylabel(column2)\n",
    "    axes2[0].set_ylim(0.0,3.0); axes2[0].set_xlim(8,22); axes2[0].set_aspect(4.0); \n",
    "    i=1\n",
    "    title=['7 Principal Component','6 Principal Components', '5 Principal Components','4 Principal Components', '3 Principal Components', '2 Principal Components', '1 Principal Components' ]\n",
    "    while i<len(axes2):\n",
    "           axes2[i].scatter(x_hat_dim[i-1][:,0],x_hat_dim[i-1][:,4],s=None, c=\"red\", marker=None, cmap=None, norm=None, vmin=None, vmax=None, alpha=0.2, linewidths=1.0, edgecolors=\"black\")\n",
    "           axes2[i].set_title(title[i-1]); axes2[i].set_xlabel(column1); axes2[i].set_ylabel(column2)\n",
    "           axes2[i].set_ylim(0.0,3.0); axes2[i].set_xlim(8,22); axes2[i].set_aspect(4.0)\n",
    "           i+=1\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"7: Total Variance of Chosen Features Explained by PC Score\")\n",
    "    print(\" \")\n",
    "    \n",
    "    i=len(x_hat_dim)-1\n",
    "    while i>=0:\n",
    "        print(title[i]+': Variance '+column1+' = ',np.round(np.var(x_hat_dim[i][:,0])/(sd_f7[0]*sd_f7[0]),2),' Variance '+column2+' = ',np.round(np.var(x_hat_dim[i][:,4])/(sd_f7[4]*sd_f7[4]),2))\n",
    "        i-=1\n",
    "\n",
    "    global df_dim\n",
    "    df_dim=[]\n",
    "    i=len(x_hat_dim)-1\n",
    "    while i>=0:\n",
    "        df_1d = pd.DataFrame(data=x_hat_dim[i],columns=features)  \n",
    "        i-=1\n",
    "        df_dim.append(df_1d)\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "x0 = widgets.Text(value='                                     Interactive PCA - Dimensionality Reduction, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "display(x0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Full PCA Workflow\n",
    "\n",
    "#### Arham Junaid, SURI Student, Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "Select two features from the dataset and perform a complete PCA workflow on these features.\n",
    "\n",
    "### The Inputs\n",
    "\n",
    "* **Feature1, Feature2**: the 2 features to apply to a complete PCA workflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "df9a7aab89084059bd29c7dca76c422d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(Dropdown(description='feature1', options=('Por', 'LogPerm', 'AI', 'Brittle', 'TOC', 'VR'…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<function __main__.firstvariable(feature1='Por', feature2='LogPerm')>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interact(firstvariable,feature1=[\"Por\", \"LogPerm\",\"AI\", \"Brittle\",\"TOC\",\"VR\"],feature2=[\"Por\", \"LogPerm\",\"AI\", \"Brittle\",\"TOC\",\"VR\"], continous_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observations:\n",
    "* You can see from the bar graph the proportion of the variance of the data which each principal component explains.\n",
    "\n",
    "* Moreover, you can see how the bivariate relationship of the 2 variables improves as more principal components are included in your analysis. \n",
    "\n",
    "* It is interesting to note however that the first principal component alone is able to describe a large percentage of the variance of that variable. For example, the first principal component captures 88% of the Porosity variance and 68% of the Permeability variance of the data set.\n",
    "\n",
    "\n",
    "#### Interactive Workflow #4: Variance Explained\n",
    "As can be seen from the data display above, the more principal components we include, the greater the resemblance of your original data. \n",
    "* That being said, even the first 2 principle components can tell us a significant amount in relation to the 2 variables and their variance. \n",
    "* From the interaction below, the number of principal components can be chosen, and the corresponding overall variance of the data set can be explained. \n",
    "* What isn’t explained is the variance lost, a percentage that is in the minority after selecting a mere 2 principal components alone.\n",
    "\n",
    "Let's start of with a reminder of the original matrix scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 49 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd_plot.scatter_matrix(my_data_f7.drop('Prod2Scaled',axis=1), alpha = 0.1,      # pandas matrix scatter plot\n",
    "figsize=(6, 4),color = 'black', hist_kwds={'color':['grey']})\n",
    "features = ['Por','LogPerm','AI','Brittle','TOC','VR',\"Production\"]\n",
    "x_f7 = my_data_f7.loc[:,features].values\n",
    "plt.suptitle('Original Data')\n",
    "x_f7 = StandardScaler().fit_transform(x_f7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we can see how the number of principle component scores affects the matrix scatter plot and the proportion of variance explained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9827b199db894ebabc8306c30e4b4f08",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Text(value='                                     Interactive PCA - Variance Explained, Arham Junaid and Dr. Mi…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6465e9eaf3b24618864e6bdad494c9fe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(IntSlider(value=4, description='PCs', max=7, min=1), Output()), _dom_classes=('widget-in…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def pc_slider(PCs):\n",
    "    pd_plot.scatter_matrix(df_dim[PCs-1], alpha = 0.1,                         # pandas matrix scatter plot\n",
    "    figsize=(5, 5),color = 'black', hist_kwds={'color':['grey']})\n",
    "    plt.suptitle(str(PCs)+' Principle Components')\n",
    "    n_components = PCs\n",
    "    pca_f7 = PCA(n_components=n_components)\n",
    "    pca_f7.fit(x_f7)\n",
    "    labels = 'Variance Explained', 'Variance Missing'\n",
    "    sizes = []\n",
    "    sizes.append(sum(np.round(pca_f7.explained_variance_ratio_,3)))\n",
    "    sizes.append(1-sum(np.round(pca_f7.explained_variance_ratio_,3)))\n",
    "    fig1, ax1 = plt.subplots(figsize=(3,3))\n",
    "    \n",
    "    n = ax1.pie(sizes, autopct='%1.2f%%',colors = ['red','blue'],shadow=True,startangle=90)\n",
    "    #ax1.legend(sizes, labels=labels,loc='lower right')\n",
    "    ax1.set_title('Variance for Arbitrary Feature Projection Components')\n",
    "    n[0][0].set_alpha(0.5); n[0][1].set_alpha(0.5)\n",
    "    \n",
    "    ax1.axis('equal')                                                          # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "x0 = widgets.Text(value='                                     Interactive PCA - Variance Explained, Arham Junaid and Dr. Michael Pyrcz, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "display(x0)\n",
    "_=interact(pc_slider,PCs=(1,7), continuous_update=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "This is an interactive workflow displaying the key aspects of Principal Components. Appreciation to **Arham Junaid** for writing this code and providing this documentation as part of the SURI program at The University of Texas at Austin.\n",
    "For more about in-depth workflows on PCA, check out INSERT LINK HERE the YouTube video associated with this interactive workflow.\n",
    "\n",
    "If you want to learn more about data analytics and machine learning in Python, The Texas Center for Geostatistics has many other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations, trend modeling and many other workflows available [here](https://github.com/GeostatsGuy/PythonNumericalDemos), along with a package for geostatistics in Python called [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy). \n",
    "\n",
    "We hope this was helpful.\n",
    "\n",
    "*Arham and Michael*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More on Arham Junaid:\n",
    "Arham is a world citizen who has resided in 5 countries and has spent time in various industries including Emergency Services, the Automotive industry and the Oil & Gas industry. He is currently pursuing a Mechanical Engineering degree and continues to seek experiences which combine his technical expertise with his leadership and business passion. Check out his LinkedIn below:\n",
    "#### [LinkedIn](https://www.linkedin.com/in/arhamjunaid)\n",
    "\n",
    "#### More on Michael Pyrcz and the Texas Center for Data Analytics and Geostatistics:\n",
    "\n",
    "##### Michael Pyrcz, Associate Professor, University of Texas at Austin\n",
    " \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "    \n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development.\n",
    "    \n",
    "For more about Michael check out these links,\n",
    "    \n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1),\n",
    "    \n",
    "   #### Want to Work Together?\n",
    "   \n",
    "   I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate,\n",
    "    \n",
    "   * Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you!\n",
    "    \n",
    "   * Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "   \n",
    "   * I can be reached at mpyrcz@austin.utexas.edu.\n",
    "   \n",
    "I'm always happy to discuss,\n",
    "   \n",
    "*Michael*\n",
    "  \n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "    \n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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