examples/PCA/PCA_on_descriptive_sensory_analysis_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principal component analysis (PCA) on descriptive sensory analysis data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook illustrates how to use the **hoggorm** package to carry out principal component analysis (PCA) on a multivariate descriptive sensory analysis data from cheese. Furthermore, we will learn how to visualise the results of the PCA using the **hoggormPlot** package."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import packages and prepare data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First import **hoggorm** for analysis of the data and **hoggormPlot** for plotting of the analysis results. We'll also import **pandas** such that we can read the data into a data frame. **numpy** is needed for checking dimensions of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import hoggorm as ho\n",
    "import hoggormplot as hop\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, load the cancer data that we are going to analyse using **hoggorm**. After the data has been loaded into the pandas data frame, we'll display it in the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>6.19</td>\n",
       "      <td>3.33</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.14</td>\n",
       "      <td>1.29</td>\n",
       "      <td>3.11</td>\n",
       "      <td>6.70</td>\n",
       "      <td>3.22</td>\n",
       "      <td>2.66</td>\n",
       "      <td>5.10</td>\n",
       "      <td>4.57</td>\n",
       "      <td>3.34</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1.89</td>\n",
       "      <td>1.23</td>\n",
       "      <td>3.15</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>6.55</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.32</td>\n",
       "      <td>2.52</td>\n",
       "      <td>1.24</td>\n",
       "      <td>3.91</td>\n",
       "      <td>6.68</td>\n",
       "      <td>2.57</td>\n",
       "      <td>2.42</td>\n",
       "      <td>4.87</td>\n",
       "      <td>4.75</td>\n",
       "      <td>4.13</td>\n",
       "      <td>3.09</td>\n",
       "      <td>2.29</td>\n",
       "      <td>1.51</td>\n",
       "      <td>3.93</td>\n",
       "      <td>4.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>6.23</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.42</td>\n",
       "      <td>2.03</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.93</td>\n",
       "      <td>6.61</td>\n",
       "      <td>3.39</td>\n",
       "      <td>2.56</td>\n",
       "      <td>5.00</td>\n",
       "      <td>4.73</td>\n",
       "      <td>3.44</td>\n",
       "      <td>3.08</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.37</td>\n",
       "      <td>3.19</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>6.14</td>\n",
       "      <td>2.93</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.13</td>\n",
       "      <td>1.08</td>\n",
       "      <td>3.12</td>\n",
       "      <td>6.51</td>\n",
       "      <td>2.98</td>\n",
       "      <td>2.50</td>\n",
       "      <td>4.66</td>\n",
       "      <td>4.68</td>\n",
       "      <td>3.92</td>\n",
       "      <td>2.93</td>\n",
       "      <td>1.99</td>\n",
       "      <td>1.19</td>\n",
       "      <td>3.13</td>\n",
       "      <td>4.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>6.70</td>\n",
       "      <td>1.97</td>\n",
       "      <td>4.72</td>\n",
       "      <td>2.43</td>\n",
       "      <td>1.13</td>\n",
       "      <td>4.60</td>\n",
       "      <td>7.01</td>\n",
       "      <td>2.07</td>\n",
       "      <td>2.32</td>\n",
       "      <td>5.29</td>\n",
       "      <td>5.19</td>\n",
       "      <td>4.52</td>\n",
       "      <td>3.14</td>\n",
       "      <td>2.47</td>\n",
       "      <td>1.34</td>\n",
       "      <td>4.67</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>6.19</td>\n",
       "      <td>5.28</td>\n",
       "      <td>1.59</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.13</td>\n",
       "      <td>6.42</td>\n",
       "      <td>5.18</td>\n",
       "      <td>2.82</td>\n",
       "      <td>5.02</td>\n",
       "      <td>4.49</td>\n",
       "      <td>2.05</td>\n",
       "      <td>2.54</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.18</td>\n",
       "      <td>1.29</td>\n",
       "      <td>4.11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>6.17</td>\n",
       "      <td>3.45</td>\n",
       "      <td>3.32</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.47</td>\n",
       "      <td>2.69</td>\n",
       "      <td>6.39</td>\n",
       "      <td>3.81</td>\n",
       "      <td>2.76</td>\n",
       "      <td>4.58</td>\n",
       "      <td>4.32</td>\n",
       "      <td>3.22</td>\n",
       "      <td>2.72</td>\n",
       "      <td>1.81</td>\n",
       "      <td>1.33</td>\n",
       "      <td>2.52</td>\n",
       "      <td>4.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>6.90</td>\n",
       "      <td>2.58</td>\n",
       "      <td>4.24</td>\n",
       "      <td>2.58</td>\n",
       "      <td>1.70</td>\n",
       "      <td>4.19</td>\n",
       "      <td>7.11</td>\n",
       "      <td>2.06</td>\n",
       "      <td>2.47</td>\n",
       "      <td>4.58</td>\n",
       "      <td>5.09</td>\n",
       "      <td>4.44</td>\n",
       "      <td>3.25</td>\n",
       "      <td>2.62</td>\n",
       "      <td>1.73</td>\n",
       "      <td>4.87</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>6.70</td>\n",
       "      <td>2.53</td>\n",
       "      <td>4.53</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1.22</td>\n",
       "      <td>4.16</td>\n",
       "      <td>6.91</td>\n",
       "      <td>2.42</td>\n",
       "      <td>2.41</td>\n",
       "      <td>4.52</td>\n",
       "      <td>4.96</td>\n",
       "      <td>4.49</td>\n",
       "      <td>3.37</td>\n",
       "      <td>2.47</td>\n",
       "      <td>1.64</td>\n",
       "      <td>4.54</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>6.35</td>\n",
       "      <td>3.14</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2.17</td>\n",
       "      <td>1.17</td>\n",
       "      <td>2.57</td>\n",
       "      <td>6.50</td>\n",
       "      <td>2.77</td>\n",
       "      <td>2.66</td>\n",
       "      <td>4.76</td>\n",
       "      <td>4.64</td>\n",
       "      <td>4.06</td>\n",
       "      <td>3.11</td>\n",
       "      <td>2.21</td>\n",
       "      <td>1.46</td>\n",
       "      <td>3.35</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>5.97</td>\n",
       "      <td>3.34</td>\n",
       "      <td>3.46</td>\n",
       "      <td>1.67</td>\n",
       "      <td>1.15</td>\n",
       "      <td>1.43</td>\n",
       "      <td>6.31</td>\n",
       "      <td>3.15</td>\n",
       "      <td>2.56</td>\n",
       "      <td>4.57</td>\n",
       "      <td>4.36</td>\n",
       "      <td>3.65</td>\n",
       "      <td>2.66</td>\n",
       "      <td>1.56</td>\n",
       "      <td>1.19</td>\n",
       "      <td>2.23</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>6.29</td>\n",
       "      <td>2.99</td>\n",
       "      <td>4.03</td>\n",
       "      <td>2.06</td>\n",
       "      <td>1.17</td>\n",
       "      <td>3.06</td>\n",
       "      <td>6.76</td>\n",
       "      <td>2.37</td>\n",
       "      <td>2.44</td>\n",
       "      <td>4.69</td>\n",
       "      <td>4.97</td>\n",
       "      <td>4.28</td>\n",
       "      <td>3.16</td>\n",
       "      <td>2.56</td>\n",
       "      <td>1.53</td>\n",
       "      <td>4.23</td>\n",
       "      <td>4.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>5.91</td>\n",
       "      <td>4.88</td>\n",
       "      <td>2.04</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.08</td>\n",
       "      <td>6.34</td>\n",
       "      <td>4.79</td>\n",
       "      <td>2.44</td>\n",
       "      <td>5.48</td>\n",
       "      <td>4.54</td>\n",
       "      <td>1.98</td>\n",
       "      <td>2.57</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.03</td>\n",
       "      <td>1.03</td>\n",
       "      <td>4.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>6.75</td>\n",
       "      <td>1.91</td>\n",
       "      <td>4.36</td>\n",
       "      <td>2.95</td>\n",
       "      <td>1.43</td>\n",
       "      <td>4.83</td>\n",
       "      <td>7.14</td>\n",
       "      <td>1.53</td>\n",
       "      <td>2.47</td>\n",
       "      <td>4.72</td>\n",
       "      <td>5.06</td>\n",
       "      <td>4.54</td>\n",
       "      <td>3.43</td>\n",
       "      <td>2.80</td>\n",
       "      <td>1.87</td>\n",
       "      <td>5.65</td>\n",
       "      <td>3.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Att 01  Att 02  Att 03  Att 04  Att 05  Att 06  Att 07  Att 08  \\\n",
       "Product                                                                   \n",
       "Pr 01      6.19    3.33    3.43    2.14    1.29    3.11    6.70    3.22   \n",
       "Pr 02      6.55    2.50    4.32    2.52    1.24    3.91    6.68    2.57   \n",
       "Pr 03      6.23    3.43    3.42    2.03    1.28    2.93    6.61    3.39   \n",
       "Pr 04      6.14    2.93    3.96    2.13    1.08    3.12    6.51    2.98   \n",
       "Pr 05      6.70    1.97    4.72    2.43    1.13    4.60    7.01    2.07   \n",
       "Pr 06      6.19    5.28    1.59    1.07    1.00    1.13    6.42    5.18   \n",
       "Pr 07      6.17    3.45    3.32    2.04    1.47    2.69    6.39    3.81   \n",
       "Pr 08      6.90    2.58    4.24    2.58    1.70    4.19    7.11    2.06   \n",
       "Pr 09      6.70    2.53    4.53    2.32    1.22    4.16    6.91    2.42   \n",
       "Pr 10      6.35    3.14    3.64    2.17    1.17    2.57    6.50    2.77   \n",
       "Pr 11      5.97    3.34    3.46    1.67    1.15    1.43    6.31    3.15   \n",
       "Pr 12      6.29    2.99    4.03    2.06    1.17    3.06    6.76    2.37   \n",
       "Pr 13      5.91    4.88    2.04    1.00    1.00    1.08    6.34    4.79   \n",
       "Pr 14      6.75    1.91    4.36    2.95    1.43    4.83    7.14    1.53   \n",
       "\n",
       "         Att 09  Att 10  Att 11  Att 12  Att 13  Att 14  Att 15  Att 16  \\\n",
       "Product                                                                   \n",
       "Pr 01      2.66    5.10    4.57    3.34    2.93    1.89    1.23    3.15   \n",
       "Pr 02      2.42    4.87    4.75    4.13    3.09    2.29    1.51    3.93   \n",
       "Pr 03      2.56    5.00    4.73    3.44    3.08    1.81    1.37    3.19   \n",
       "Pr 04      2.50    4.66    4.68    3.92    2.93    1.99    1.19    3.13   \n",
       "Pr 05      2.32    5.29    5.19    4.52    3.14    2.47    1.34    4.67   \n",
       "Pr 06      2.82    5.02    4.49    2.05    2.54    1.18    1.18    1.29   \n",
       "Pr 07      2.76    4.58    4.32    3.22    2.72    1.81    1.33    2.52   \n",
       "Pr 08      2.47    4.58    5.09    4.44    3.25    2.62    1.73    4.87   \n",
       "Pr 09      2.41    4.52    4.96    4.49    3.37    2.47    1.64    4.54   \n",
       "Pr 10      2.66    4.76    4.64    4.06    3.11    2.21    1.46    3.35   \n",
       "Pr 11      2.56    4.57    4.36    3.65    2.66    1.56    1.19    2.23   \n",
       "Pr 12      2.44    4.69    4.97    4.28    3.16    2.56    1.53    4.23   \n",
       "Pr 13      2.44    5.48    4.54    1.98    2.57    1.00    1.03    1.03   \n",
       "Pr 14      2.47    4.72    5.06    4.54    3.43    2.80    1.87    5.65   \n",
       "\n",
       "         Att 17  \n",
       "Product          \n",
       "Pr 01      4.07  \n",
       "Pr 02      4.07  \n",
       "Pr 03      4.16  \n",
       "Pr 04      4.29  \n",
       "Pr 05      4.03  \n",
       "Pr 06      4.11  \n",
       "Pr 07      4.26  \n",
       "Pr 08      3.98  \n",
       "Pr 09      4.01  \n",
       "Pr 10      3.93  \n",
       "Pr 11      4.01  \n",
       "Pr 12      4.03  \n",
       "Pr 13      4.16  \n",
       "Pr 14      3.98  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load data\n",
    "\n",
    "# Insert code for reading data from other folder in repository instead of directly from same repository.\n",
    "data_df = pd.read_csv('cheese_sensory.txt', sep='\\t', index_col=0)\n",
    "data_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a look at the dimensions of the data frame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 17)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(data_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are observations for 34 countries as well as all OECD countries together, which results in 14 rows. Furthermore, there are 17 columns where each column represents one type of cancer in men."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ``nipalsPCA`` class in hoggorm accepts only **numpy** arrays with numerical values and not pandas data frames. Therefore, the pandas data frame holding the imported data needs to be \"taken apart\" into three parts: \n",
    "* a numpy array holding the numeric values\n",
    "* a Python list holding variable (column) names\n",
    "* a Python list holding object (row) names. \n",
    "\n",
    "The array with values will be used as input for the ``nipalsPCA`` class for analysis. The Python lists holding the variable and row names will be used later in the plotting function from the **hoggormPlot** package when visualising the results of the analysis. Below is the code needed to access both data, variable names and object names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get the values from the data frame\n",
    "data = data_df.values\n",
    "\n",
    "# Get the variable or columns names\n",
    "data_varNames = list(data_df.columns)\n",
    "\n",
    "# Get the object or row names\n",
    "data_objNames = list(data_df.index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's have a quick look at the column or variable names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Att 01',\n",
       " 'Att 02',\n",
       " 'Att 03',\n",
       " 'Att 04',\n",
       " 'Att 05',\n",
       " 'Att 06',\n",
       " 'Att 07',\n",
       " 'Att 08',\n",
       " 'Att 09',\n",
       " 'Att 10',\n",
       " 'Att 11',\n",
       " 'Att 12',\n",
       " 'Att 13',\n",
       " 'Att 14',\n",
       " 'Att 15',\n",
       " 'Att 16',\n",
       " 'Att 17']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_varNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now show the object or row names."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Pr 01',\n",
       " 'Pr 02',\n",
       " 'Pr 03',\n",
       " 'Pr 04',\n",
       " 'Pr 05',\n",
       " 'Pr 06',\n",
       " 'Pr 07',\n",
       " 'Pr 08',\n",
       " 'Pr 09',\n",
       " 'Pr 10',\n",
       " 'Pr 11',\n",
       " 'Pr 12',\n",
       " 'Pr 13',\n",
       " 'Pr 14']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_objNames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply PCA to our data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's run PCA on the data using the ``nipalsPCA`` class. The documentation provides a [description of the input parameters](https://hoggorm.readthedocs.io/en/latest/pca.html). Using input paramter ``arrX`` we define which numpy array we would like to analyse. By setting input parameter ``Xstand=False`` we make sure that the variables are only mean centered, not scaled to unit variance. This is the default setting and actually doesn't need to expressed explicitly. Setting paramter ``cvType=[\"loo\"]`` we make sure that we compute the PCA model using full cross validation. ``\"loo\"`` means \"Leave One Out\". By setting paramter ``numpComp=4`` we ask for four principal components (PC) to be computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loo\n",
      "loo\n"
     ]
    }
   ],
   "source": [
    "model = ho.nipalsPCA(arrX=data, Xstand=False, cvType=[\"loo\"], numComp=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That's it, the PCA model has been computed. Now we would like to inspect the results by visualising them. We can do this using the taylor-made plotting function for PCA from the separate [**hoggormPlot** package](https://hoggormplot.readthedocs.io/en/latest/). If we wish to plot the results for component 1 and component 2, we can do this by setting the input argument ``comp=[1, 2]``. The input argument ``plots=[1, 2, 3, 4, 6]`` lets the user define which plots are to be plotted. If this list for example contains value ``1``, the function will generate the scores plot for the model. If the list contains value ``2``, then the loadings plot will be plotted. Value ``3`` stands for correlation loadings plot and value ``4`` stands for bi-plot and ``6`` stands for explained variance plot. The hoggormPlot documentation provides a [description of input paramters](https://hoggormplot.readthedocs.io/en/latest/mainPlot.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEWCAYAAABxMXBSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4xLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvDW2N/gAAIABJREFUeJzt3Xt4VOW5/vHvYzgJKCCejSEikIoYAwYFOWxRK9INqC1UxI1QRahotVgbKlR/3VZwQy21lCJo2UIpW+uhIiBoxROithI1IAgRLAEiHgDLSbEY8/z+mEmaQE6TZGbNZO7PdeViZp3mXqB5Zq13ve9r7o6IiEhNHRV0ABERSSwqHCIiEhEVDhERiYgKh4iIRESFQ0REIqLCISIiEVHhEGlAzGyUma0KOoc0bCoc0uCYWUszKzCz4WWWHWNm28xsSJDZ4omZvWJmo4POIYlHhUMaHHc/AIwBfmtmJ4QXTwNy3f3JIDKZWUoQnysSDSoc0iC5+1+BZ4EZZnYR8H3g5sq2D9/i+YeZ7TezLWZ2bZl1N5rZhvC6982sW3j5WeFv7XvMbL2ZDS6zzzwze9DMlpnZF0A/M2tqZveHr3w+NbPZZnZ0ePvjzWxp+Fifm9lrZlbh/59m5mZ2azjvLjP7VRXbXmhmq81sb/jPC8PLJwN9gJlmdsDMZkb2NyxJzd31o58G+QO0AT4GdgE/qGK7FsA+ICP8/hTg7PDrocBHQHfAgA5AO6AxsBmYCDQBLgb2lznGPGAv0IvQF7RmwAPAYuA44BhgCXBfePv7gNnh4zYm9EvdKsnrwMvh46QBHwCjw+tGAavCr48D/gmMABoB14Tftw2vf6VkP/3oJ5IfXXFIg+Xu/wTWA82Bv1SzeTHQxcyOdveP3X19ePloYJq7r/aQze6+FegBtAT+x90PuftLwFJCv5xLPOPur7t7MfAv4EZgvLt/7u77gSnAsPC2XxMqWO3c/Wt3f83dqxpIbmr4ONsIFaRrKtjmP4FN7r7A3Yvc/VFgIzComr8LkSqpcEiDZWb/BaQDK4CplW3n7l8AVwM/BD42s2fN7Fvh1acDH1aw26nA9nBRKLEVOK3M++1lXp9AqIC9Hb4dtQd4Lrwc4FeErmD+Gr4F9bNqTq/ssbeG81SUcethyw7PKBIxFQ5pkMzsROA3hL7ljwW+b2Z9K9ve3Z93928T+ta/EXg4vGo7cGYFu+wATj+sbSGN0G2t0sOWeb0LOEjoFljr8E8rd28Z/vz97v4Td29P6IrgdjO7pIpTPP2wz91RScZ2hy0rm1FDY0utqHBIQzUTWOTuL7v7x0AO8LCZNT18QzM7ycwGm1kLQreUDgDfhFf/AbjDzM6zkA5m1g74O/AFkGNmjcMN8IOAxyoKE74yeRj4TbioYWanmVn/8OuB4WMbofaWb8pkqMhPzayNmZ0O3Ab8uYJtlgGdzGy4mTUys6uBzoRuqQF8CrSv4jNEKqTCIQ2OmV0J9AZ+WrLM3f8AFAJ3V7DLUcBPCH1D/xz4D2BceL8ngMnA/xFq/F4EHOfuh4DBwABCVxOzgOvcfWMV0SYQuh31NzPbR+gWWkZ4Xcfw+wPAm8Asd3+limM9A7wN5BF6emzu4Ru4+25gYPjcdhMqngPdfVd4k98CQ8zsn2Y2o4rPEinHqm5/E5F4Y2YOdHT3zUFnkeSkKw4REYmICoeIiEREt6pERCQiuuIQEZGINAo6QDQcf/zxnp6eHnQMEZGE8fbbb+9y9xOq37KBFo709HRyc3ODjiESFTk5OUybNi3oGNLAmNnhowxUSreqRBLM3r17g44gSU6FQ0REIqLCIZJg0tLSgo4gSU6FQyTBTJo0KegIkuRUOEQSzIIFC4KOIElOhUMkwaxatSroCJLkVDhERCQiKhwiIhIRFQ6RBDN1aqWz4IrEhAqHSILZurXGHXxFoiLQwmFm/2tmn5nZukrWm5nNMLPNZrbWzLrFOqNIvJk1a1bQESTJBX3FMQ+4vIr1AwhNqdkRGAM8GINMIiJShUALh7uvJDTHc2WuAP7oIX8DWpvZKbFJJyIiFQn6iqM6pwHby7wvDC87gpmNMbNcM8vduXNnTMKJBOHaa68NOoIkuXgvHFbBsgqnLHT3h9w9292zTzihRkPKiySkvn37Bh1Bkly8F45C4PQy71OBHQFlEYkLY8eODTqCJLl4LxyLgevCT1f1APa6+8dBhxIRSWaBzgBoZo8CFwHHm1kh8P+AxgDuPhtYBnwH2Ax8CfwgmKQiIlIi0MLh7tdUs96Bm2MURyQhnHPOOUFHkCQX77eqROQwt9xyS9ARJMmpcIgkmJkzZwYdQZKcCkfAUlJSyMrKokuXLgwdOpQvv/yy2n22bNnCBRdcQMeOHbn66qs5dOhQ6brHH3+czp07c/bZZzN8+PBoRpeAvPfee0FHkCSnwhGwo48+mry8PNatW0eTJk2YPXt2ufXuTnFxcbllEyZMYPz48WzatIk2bdowd+5cADZt2sR9993H66+/zvr163nggQdidh4ikjxUOOJInz592Lx5MwUFBZx11lmMGzeObt26sX37vzvPuzsvvfQSQ4YMAWDkyJEsWrQIgIcffpibb76ZNm3aAHDiiSfG/iREpMFT4YgTRUVFLF++vPSJmfz8fK677jreffdd2rVrV7rd7t27ad26NY0ahR6IS01N5aOPPgLggw8+4IMPPqBXr1706NGD5557LvYnIlE3Z86coCNIklPhCNjBgwfJysoiOzubtLQ0brjhBgDatWtHjx49jtg+9IRyeWahkVmKiorYtGkTr7zyCo8++iijR49mz5490T0BibmVK1cGHUGSXKD9OJJZfn4+BQUFNGvWjLy8vCPWt2jRosL9jj/+ePbs2UNRURGNGjWisLCQU089FQhdffTo0YPGjRtzxhlnkJGRwaZNm+jevXtUz0Via+HChRqvSgKlK44A5Ofns3z5clq2bElRURH5+fk13tfM6NevH08++SQA8+fP54orrgDgyiuv5OWXXwZg165dfPDBB7Rv377+T0BEkpoKRwAKCgro3r07vXr1IiUlhYKCgoj2nzp1KtOnT6dDhw7s3r279PZW//79adu2LZ07d6Zfv3786le/om3btlE4AxFJZlbRPfNEl52d7bm5uUHHqFTJFUf37t1ZvXo1AwYMICMjI+hYkiDWrFnDueeeG3QMaWDM7G13z67JtmrjCEBJkSgoKFDRkIiVfcpOJAi6VRWQjIwM+vfvr6IhEZswYULQESTJqXCIiEhEVDhERCQiKhwiCaZ3795BR5Akp8IhkmBGjBgRdARJciocIglm8uTJQUeQJKfCIZJgtm3bFnQESXIqHCIiEhEVDpEE06pVq6AjSIRqM9PnzJkz6dChA2bGrl27jli/evVqUlJSSsetiyUVDpEEM23atKAjSIRqM9Nnr169WLFiRYUjBXzzzTdMmDCB/v37RzV3ZVQ4RBLMkiVLgo4gdVCTmT4BunbtSnp6eoXH+N3vfsf3vve9wGb5VOEQSTBLly4NOoLUUk1n+qzKRx99xNNPP80Pf/jDaEatkgqHiEiURTrTZ1V+/OMfM3XqVFJSUqIRtUY0Oq6ISJTUdqbPquTm5jJs2DAgNGHbsmXLaNSoEVdeeWWd89aUCodIgpk4cWLQEaQGys67UzLTZ32Mhr1ly5bS16NGjWLgwIExLRoQ8K0qM7vczPLNbLOZ/ayC9aPMbKeZ5YV/RgeRU0QkUnWd6XPGjBmkpqZSWFhIZmYmo0fHz6+/wGYANLMU4APg20AhsBq4xt3fL7PNKCDb3W+J5NjxPgOgSF2MHTuWOXPmBB1DqpFoM30mygyA5wOb3f0fAGb2GHAF8H6Ve4mIJICGPNNnkIXjNKDsg8uFwAUVbPc9M+tL6OpkvLtvr2AbzGwMMAYgLS2tnqOKiEQuIyOjQRWMEkG2cVgFyw6/b7YESHf3TGAFML+yg7n7Q+6e7e7ZJ5xwQj3GFIkvAwcODDqCJLkgC0chcHqZ96nAjrIbuPtud/9X+O3DwHkxyiYStwYNGhR0hIRQm/GhtmzZwgUXXEDHjh25+uqrOXToEBAakbhfv3507dqVzMxMli1bFu34cS3IwrEa6GhmZ5hZE2AYsLjsBmZ2Spm3g4ENMcwnEpdycnKCjpAQajM+1IQJExg/fjybNm2iTZs2zJ07F4B7772X73//+7z77rs89thjjBs3LmbnEY8CKxzuXgTcAjxPqCA87u7rzeweMxsc3uxWM1tvZmuAW4FRwaSNrfocSfOZZ54hMzOztNfqqlWrohldYmDv3r1BR0g4NRkfyt156aWXGDJkCAAjR45k0aJFAJgZ+/btA0J//6eeemrsTyKeuHuD+znvvPM8kbVo0aL09fDhw/3Xv/51ufXFxcX+zTfflFv2zjvv+JYtW7xdu3a+c+fO0uX79+/34uJid3dfs2aNZ2RkRDG5xMKYMWOCjpAQSv4/+vrrr33w4ME+a9Ys37Jli5uZv/nmm0dsv3PnTj/zzDNL32/bts3PPvtsd3ffsWOHd+nSxU877TRv3bq15+bmxuYkYgjI9Rr+jtVYVXGuriNptmzZErPQcwhffPFF6WtJXHpqsGYiHR/KK+jTVvL/y6OPPsqoUaMoLCxk2bJljBgx4ojbXMlEQ47EsZKRNC+//HIg1KHokUceYdasWREd5+mnn+bOO+/ks88+49lnn41GVImhSZMmBR0hrtV2fKjjjz+ePXv2UFRURKNGjSgsLCy9JTV37lyee+45AHr27MlXX33Frl27AhvWPGi64ohD9TmSJsBVV13Fxo0bWbRoEXfddVd9x5UYW7BgQdAR4lZJb+2WLVuWjg9VU2ZGv379SmfUmz9/PldccQUQusp78cUXAdiwYQNfffUVyfzYvwpHHMnPz+f5558v/aaUl5fH7373O5o0aQLUbiTNsvr27cuHH35Y4TSUkjj0gEPl6jo+1NSpU5k+fTodOnRg9+7dpV/afv3rX/Pwww9z7rnncs011zBv3rykvu2rwhEn6vJNqSqbN28uvXf7zjvvcOjQIdq2bVsvx5a6q80TdPv27auwr8HKlSvp1q0bjRo1CmQe6niQnp7O6tWref3115kyZcoR7X7p6emsW7eu0v3bt2/PW2+9xebNm3niiSdo2rQpAJ07d+b1119nzZo15OXlcdlll0XzNOKeCkeciNZImk899RRdunQhKyuLm2++mT//+c9J/U0p3tSmr8Fbb71VYV+DtLQ05s2bx/Dhw2OWP95kZGQwYMAADhw40ODGh4onahyPE+np6SxfvhygVt+Ubr31Vm699dYjlk+YMIEJEybUa1apm5SUFM455xyKior46quv+PLLL2nevDl9+vRh7dq1pYPi9evXjzfffJNFixZRXFzMsGHD+PzzzyksLGTw4FBXp2OPPZacnBzmzJlDy5YteeihhzjqqOT+PthQx4eKJ8n9X1gc0Tel5FH2KgNg9uzZ5eaidnc2btxYbi7qkh7Nb775Jo0bN2b+/NCwbaNGjaJdu3bk5eWRk5PD7bffHuSpSZJQ4YgjGRkZ9O/fX0UjiRQXFzNlyhQyMzN57bXXyMvLY+DAgaSmppY+QedlejSXdMAq6dF8zDHHqJ9OLdXnWFbz5s3jhBNOICsri6ysLP7whz9EO36gVDhEApCfn8+yZcs46qij+OUvf8myZcvYs2cPP/jBD3j22Wdp1apV6ba7d++mdevWNGrUiOOPP56ioiI++ugjAAoLCykqKuLMM88kJyeHGTNmBHVKCac+x7ICuPrqq0ufhoyn2fqiQYVDJMYOHjzIxRdfzG233Ya707t3b6BmPZrNjJNOOql03KT58+fzox/9iA8//JCpU6dy7733xuYkGpi6jmWVbFQ4RGKkpJ9O48aNefzxx9m0aRNNmzZlx47QbAI16dEMMHr0aPbu3XtEX4MzzzyTBQsW8MQTTzB27FjOPvvs2JxYgivbvgShf6ey7Uslyl75AaSmppZe+UHoCcbMzEyGDBlyxJBADY0Kh0gMlO2n4+688cYbNe5rcHiP5h07dnDfffexefNmpkyZUtrX4LPPPqNbt2588cUX7N69m/Xr18fs/BJRfY5lNWjQIAoKCli7di2XXnopI0eOjG74gOlxXJEYOLyfzumnn17uCbrq+u1MnTqVYcOG8fOf/5zi4mJ++9vfAqHh9FesWEHjxo1p06ZN6dNWUrlojGVVtlPtjTfe2OAfgVfhEImBw/vpdO3atdzTczXt0QwwduzY0quMkgIiNVNy5de9e/fSERpq+hRj2Su/YcOGlRvL6uOPP+aUU0Lzzi1evJizzjoraucQD1Q4RGKg5JdTSec+PXIdjIpGaIjk36LslV/Xrl1Lb2/NmDGDxYsX06hRI4477jjmzZsXpTOID1bRfbtEl52d7bm5uUHHEImKmTNncssttwQdIyGVveJYvXq1ingZZva2u2fXZFtdcYgkGBWN8sO2nHXWWcyfP5/mzZtXuc+WLVu47rrr+PTTT2nXrh0zZ84kIyODrVu3cv3117Nz506OO+44/vSnP5GamhqjM0lMeqpKJMHMnDkz6AiBq0vnvZK+GiXD099xxx1cd911rF27lrvvvps777wzZueRqFQ4RBLMe++9F3SEuFLXznvvv/8+l1xyCQD9+vXjmWeeif1JJBgVDhFJWPXRee/cc8/lqaeeAkLTLO/fv5/du3fH+EwSiwqHiCSc+uy8d//99/Pqq6/StWtXXn31VU477bTSAiMV09+OSIKZM2dO0BECE43Oe6eeeip/+ctfADhw4ABPPfVUuUEm5Ui64hBJMCtXrgw6QiDqMr3y4cO2lO28t2vXrtKG9Pvuu4/rr7++/sM3MCocIglm4cKFQUcIRF2nV546dSrTp08/YnDIV155hYyMDDp16sSnn37KpEmTopC+YdGtKhFJCHWdXrnssC1lDRkypPRpK6kZXXGISFTV10x7GRkZfPLJJwwfPpwHH3yQcePGsXXr1hicgRyuRoXDzE40s6vM7GYzu97MzjezOhcdM7vczPLNbLOZ/ayC9U3N7M/h9X83s/S6fqZIohs3blzQESJSnzPt9e/fnw0bNpCfn8+QIUPIycmJ2XnIv1X5y9/M+pnZ88CzwADgFKAz8HPgPTP7bzM7tjYfbGYpwO/Dx+0MXGNmnQ/b7Abgn+7eAfgNMLU2nyXSkJTtn5Bo6tpZr1+/fqVDi/To0YPCwsLYn4RUe8XxHeBGd+/u7mPc/efufoe7DwbOBd4Fvl3Lzz4f2Ozu/3D3Q8BjwBWHbXMFUDLBwJPAJVby8LVIlNXXLRaA8ePHk5WVRVZWFp06daJ169a1zpWocz3U10x7JebOncuAAQNiE17KqbJx3N1/WsW6IqAuE+6eBpSdX7EQuKCybdy9yMz2Am2BXYcfzMzGAGMA0tLSWLBgQelYNBB6omLr1q3MmjWrdNm1115L3759GTt2bOmyc845h1tuuYWZM2eWG9phzpw5rFy5stwTLePGjaNdu3bl/kfu3bs3I0aMYPLkyWzbtg2AVq1aMW3aNJYsWcLSpUtLt504cSIQaugrMXDgQAYNGkROTg579+6l5HwmTZqkc4rxOR111FFccMEFTJw4kdtuu41LLrmEzMzM0nMaOHAgOTk5pfN/p6WlsWbNGrKzsykqKuK1117jkksuYcmSJYwaNaq08Kxbt44OHToA1Pqc1qxZkzD/TgcPHiQ1NZWvvvqKk08+mbfffpsBAwZw0kkn8cgjj/DII4+U+3e6/fbb2blzJ2PHjqV3795cdNFF7Nq1q/S4rVq1IjMzkxdeeIE+ffqULm9I/+0F8e8UEXev8Q/QA3gJeB24KpJ9KzjWUOAPZd6PAH532DbrgdQy7z8E2lZ37PPOO89F6qpFixalrx988EG/6aabfMuWLf6tb33Lb7rpJs/KyvKCgoLSbYqLi71t27b+9ddfu7v7G2+84ZdddtkRx+3Zs6f/9a9/rXWuMWPG1HrfWNq4caM/99xz3rx58yPWbdmyxc8+++wK96vu7/GFF17wb33rW/7pp59GJ3iSAnK9hr+/q2vjOPmwRbcDg4HLgXsiK1FHKAROL/M+FdhR2TZm1ghoBXxex88ViUh93mLZunUrW7Zs4eKLL651nt69e9d631iJVme9d999l7Fjx7J48WJOPPHEqGSX6lXXxjHbzO4ys2bh93uA4cDVwL46fvZqoKOZnWFmTYBhwOLDtlkMlMz6PgR4KVwZRaKuPsdDKvHYY48xZMgQUlJSap1rxIgRtd43VqLVWe+nP/0pBw4cYOjQoWRlZTF48OAopJfqVNfGcaWZDQKWmtl84MeECkdz4Mq6fLCH2ixuAZ4HUoD/dff1ZnYPoUumxcBcYIGZbSZ0pTGsLp8pUhPRGA+pxGOPPcbvf//7OuWbPHly3PdujlZnvRUrVtRrTqmdavtiuPsSoD/QGvgLkO/uM9x9Z10/3N2XuXsndz/T3SeHl90dLhq4+1fuPtTdO7j7+e7+j7p+pkhVonWLpeTY//znP+nZs2edMpY0qMazjIwMBgwYwIEDBzQ9awNUXRvHYDNbRahBfB2hb/xXmdmjZnZmLAKKxFK0brEAPProowwbNuyI21cNVUZGBv3791fRaICqG6vqXqAncDSwzN3PB243s47AZHTrSBqYaN1iAfjFL35RLxk15LcErbrCsZdQcTga+KxkobtvQkVDGqCSb8cFBQVxe4tl2rRpQUeQJFddG8dVhBrCiwg1ios0ePF+i2XJkiVBR5AkV2XhcPddwDPAY+6+z8zSzWyImXWJTTwROVzZnsUiQaiucfxnwKvA38xsNPAcoUEJ/2xmt8cgn4iIxJnq2jhGEBq5tjlQALR3951m1gL4OzA9uvFERCTeVFc4vnH3g2Z2CDgI7AZw9y+S5ZFCkXhTMvCdSFCqKxzvmNn/AS2AF4H5ZvYccDHwfrTDiYhI/KnuqarRwBLgUUK3rR4k1K8jH/hBdKOJSEXKDrEtEoTqxqoqIlQ0SrwR/hERkSRV63nDzewX9ZhDRBJIbWZHnDlzJh06dMDM2LXr33OxLVy4kMzMTDIzM7nwwgtZs2ZNNKNLPah14QDerrcUIlJjAwcODDoCRx99NHl5eaxbt44mTZowe/bscuvdneLi4nLLevXqxYoVK46YM/2MM87g1VdfZe3atdx1112MGTMm6vmlbmpdOMKj5opIjA0aNCjoCOX06dOHzZs3U1BQwFlnncW4cePo1q0b27dvL7dd165djxj7C+DCCy+kTZs2APTo0YPCwsJYxJY6qK4DYHMzyzGzn5pZMzMbZWaLzWyambWMVUgR+becnJygI5Sq6eyINTV37lwGDBhQ3zGlnlX3OO48YDuhQQ6fBTYA9wODCD1hFf9TkYk0MHv37g06QunsiBC64rjhhhvYsWNHpbMj1sTLL7/M3LlzWbVqVX1GlSiornB0cvfvW6i338fApe7uZvYaoBYskSRT29kRq7N27VpGjx7N8uXLadu2bV1jSpTVqI0jPM/3spL5vsN/au5vkQCkpaUF8rl1mR2xKtu2beO73/0uCxYsoFOnTvVyTImu6gpHbklbhrtfX7IwPPvf/mgGE5GKBTXfeF1nR5wxYwapqakUFhaSmZnJ6NGjAbjnnnvYvXs348aNIysri+zs7Cikl/pk4YuIyHc0M6/tzlGWnZ3tubm5QccQiYoFCxYwYkTsmxdLrji6d+/O6tWr43aiK6kdM3vb3WtUtat7qqp3ZevCbR3Ham4OkdgKqvE4IyODAQMGcODAARWNJFdd4/j3zGwaoXk43gZ2As2ADkA/oB3wk6gmFJG4kZGRoYIh1c4AOB74T0JPVA0FfgncDnQE5rh7X3dfHfWUIlKp2gz/sWXLFi644AI6duzI1VdfzaFDh8qtf/LJJzEzdMtXKlLtU1Xu/k93f9jdR7l7f3e/0t3vdHc9bC0SgKlTp5Z7X5vhPyZMmMD48ePZtGkTbdq0Ye7cuaXr9u/fz4wZM7jggguidxKS0OoyVpWIBGDr1q2VrqvJ8B/uzksvvcSQIUMAGDlyJIsWLSpdf9ddd5GTk0OzZs2idxKS0FQ4RBLMrFmzKlxe0+E/du/eTevWrWnUKNTEmZqaykcffQTAu+++y/bt2+NiIEWJX9U1jotInIt0+I+KnqI3M4qLixk/fjzz5s2LdmRJcNUWDjM7FjjB3T88bHmmu6+NWjIRqVRKSgqdOnXi4MGDmBlvvPEGzZs3L7fN4cN/zJw5kwceeIAPP/yQNm3aUFRURKNGjdi+fTv79u2jQ4cObNu2jZ49e9K0aVM++eQTBg8ezOLFi9UpT8qprh/H94GNwFNmtt7MupdZPa+2H2pmx5nZC2a2Kfxnm0q2+8bM8sI/i2v7eSINybXXXkuzZs0YO3YsCxcuxN255557ym3j7kdcWZSdD6N37948+eSTANx77720bNmSDz/8kFWrVpGWlkZBQQE9evRQ0ZAKVdfGMRE4z92zCM0xvsDMvhteZ3X43J8BL7p7R+DF8PuKHHT3rPDP4Dp8nkiD0bdvX4qLi8sN/7F+/fpyDeIDBw7k66+/Lrdf2fkw7r77bqZPn06HDh3YuHEjEyZMwMzo0aMHe/bs4eOPPw7gzCRRVHerKsXdPwZw97fMrB+w1MxSqdsgh1cAF4VfzwdeASbU4XgiSWPs2LEcddRRrF69mqKiIjIyMjjvvPOAUIP4I488UmkDeon09HTeeustIDSjYPv27UvXlTSWv/LKK1E7B0ls1RWO/WZ2Zkn7hrt/bGYXAYuAs+vwuSeVKUgfm9mJlWzXzMxygSLgf9x9USXbYWZjgDEQ3OihIrHy1VdfMWfOHA4dOkTPnj2ZOHFirefDqKyxXKQy1RWOmzjslpS77zezy4HvV7Wjma0ATq5gVSRDe6a5+w4zaw+8ZGbvHd5IXybXQ8BDEBrkMILPEEkYJUOZN2vWjA0bNhyxvjbzYaSmppbr51FYWMipp55a+5DS4FXXxvEFcFIFy3sAf6tqR3e/1N27VPDzDPCpmZ0CEP7zs0qOsSP85z8I3c7qWk1ekQarZHTa1NTUep0PY/Dgwfzxj3/E3fnb3/5Gq1atOOWUU+rl2NIr4qfSAAAOKklEQVQwVVc4HqDieTcOhtfV1mJgZPj1SOCZwzcwszZm1jT8+nigF/B+HT5TJKGVzIdx11131et8GN/5zndo3749HTp04MYbb6y2fUSkyvk4zGydu1c4bHr4ttE5tfpQs7bA40AasA0Y6u6fm1k28EN3H21mFwJzgGJCBe4Bd59b6UHL0Hwc0hCVXHHs37+fY445RkObS72KZD6O6to4qhqs5uiaRyrP3XcDl1SwPBcYHX79BlCrwiTSEJUUienTp3P77beraEhgqrtVtdrMbjx8oZndQGh+DhGJoZJioaIhQaruiuPHwNNmdi3/LhTZQBPgqmgGExGR+FSjOcfDHf9K2jrWu/tLUU1VR2rjEBGJTH3OOd7MzH4MfA84BDwY70VDpKFbuXJl0BEkyVXXxjGf0K2p94ABwP1RTyQiVVq4cGHQESTJVdfG0bnkkVszmwu8Ff1IIiISz6q74igdXtPdi6KcRUREEkB1Vxznmtm+8GsDjg6/N8Dd/diophORI4wbNy7oCJLkqiwc7p4SqyAiUjNl5w8XCUJ1t6pEJM5MmKCpayRYKhwiIhIRFQ4REYmICodIgundu3fQESTJqXCIJJgRI0YEHUGSnAqHSIKZPHly0BEkyalwiCSYbdu2BR1BkpwKh4iIRESFQyTBtGrVKugIkuRUOEQSzLRp04KOIElOhUMkwSxZsiToCJLkVDhEEszSpUuDjiBJToVDREQiosIhIiIRUeEQSTATJ04MOoIkORUOERGJiAqHSIKZMmVK0BEkyalwiIhIRAIpHGY21MzWm1mxmWVXsd3lZpZvZpvN7GexzCgiIhUL6opjHfBdYGVlG5hZCvB7YADQGbjGzDrHJp5I/Bo4cGDQESTJNQriQ919A4CZVbXZ+cBmd/9HeNvHgCuA96MeUCSODRo0KOgIkuTiuY3jNGB7mfeF4WUVMrMxZpZrZrk7d+6MejiRoOTk5AQdQZJc1K44zGwFcHIFqya5+zM1OUQFy7yyjd39IeAhgOzs7Eq3E0l0e/fuDTqCJLmoFQ53v7SOhygETi/zPhXYUcdjiohIHcXzrarVQEczO8PMmgDDgMUBZxIJXFpaWtARJMkF9TjuVWZWCPQEnjWz58PLTzWzZQDuXgTcAjwPbAAed/f1QeQViSeTJk0KOoIkuUAKh7s/7e6p7t7U3U9y9/7h5Tvc/Ttltlvm7p3c/Ux3nxxEVpF4s2DBgqAjSJKL51tVIlKBVatWBR1BkpwKh4iIRESFQ0REIqLCIZJgpk6dGnQESXIqHCIJZuvWrUFHkCSnwiGSYGbNmhV0BElyKhwiIhIRFQ4REYmICodIgrn22muDjiBJToVDJMH07ds36AiS5FQ4RBLM2LFjg44gSU6FQ0REIqLCISIiEVHhEEkw55xzTtARJMmpcIgkmFtuuSXoCJLkVDgkqaSkpJCVlUWXLl0YOnQoX375ZbX7zJw5kw4dOmBm7Nq1q3T5xo0b6dmzJ02bNuX++++PZuwj8ogESYVDksrRRx9NXl4e69ato0mTJsyePbvcenenuLi43LJevXqxYsUK2rVrV275cccdx4wZM7jjjjuinrus9957L6afJ3I4FQ5JWn369GHz5s0UFBRw1llnMW7cOLp168b27dvLbde1a1fS09OP2P/EE0+ke/fuNG7cOEaJReKDCockpaKiIpYvX17a0Jyfn891113Hu+++e8SVhYiUp8IhSeXgwYNkZWWRnZ1NWloaN9xwAwDt2rWjR48eAaermTlz5gQdQZJco6ADiMRCfn4+BQUFNGvWjLy8vCPWt2jRIoBUtbNy5UoNOyKB0hWHNHj5+fksX76cli1bUlRURH5+ftCR6mThwoVBR5Akp8IhDV5BQQHdu3enV69epKSkUFBQENH+M2bMIDU1lcLCQjIzMxk9ejQAn3zyCampqUyfPp17772X1NRU9u3bF4UzEIkvulUlDV56ejrLly8HYMqUKUc8IZWens66desq3f/WW2/l1ltvPWL5ySefTGFhYb1mFUkEKhzS4GVkZAChK48BAwaUvk9U48aNCzqCJDkVDkkKGRkZCV8wSuhxYQma2jhEEsyECROCjiBJLpDCYWZDzWy9mRWbWXYV2xWY2XtmlmdmubHMKCIiFQvqVtU64LtATXoy9XP3XdVvJiIisRBI4XD3DQBmFsTHiyS03r17Bx1Bkly8t3E48Fcze9vMxlS1oZmNMbNcM8vduXNnjOKJxN6IESOCjiBJLmqFw8xWmNm6Cn6uiOAwvdy9GzAAuNnMKh1nwd0fcvdsd88+4YQT6pxfJF5Nnjw56AiS5KJ2q8rdL62HY+wI//mZmT0NnA+srOtxRRLZtm3bgo4gSS5ub1WZWQszO6bkNXAZoUZ1EREJUFCP415lZoVAT+BZM3s+vPxUM1sW3uwkYJWZrQHeAp519+eCyCsST1q1ahV0BEly5u5BZ6h32dnZnpurbh8iIjVlZm+7e6X96sqK21tVIlKxJUuWBB1BkpwKh0iCWbp0adARJMmpcIiISERUOEREJCIqHCIJZuLEiUFHkCSnwiEiIhFR4RBJMFOmTAk6giQ5FQ4REYmICoeIiEREhUMkwQwcODDoCJLkVDhEEsygQYOCjiBJToVDJMHk5OQEHUGSnAqHSILZu3dv0BEkyalwiIhIRFQ4RBJMWlpa0BEkyalwiCSYSZMmBR1BklyDnMjJzHYCW+t4mOOBXfUQJ1415PNryOcGOr9EF6/n187dT6jJhg2ycNQHM8ut6WxYiaghn19DPjfQ+SW6hnB+ulUlIiIRUeEQEZGIqHBU7qGgA0RZQz6/hnxuoPNLdAl/fmrjEBGRiOiKQ0REIqLCISIiEVHhqIKZ/cjM8s1svZlNCzpPNJjZHWbmZnZ80Fnqk5n9ysw2mtlaM3vazFoHnak+mNnl4f8mN5vZz4LOU5/M7HQze9nMNoT/n7st6Ez1zcxSzOxdM1sadJa6UOGohJn1A64AMt39bOD+gCPVOzM7Hfg2sC3oLFHwAtDF3TOBD4A7A85TZ2aWAvweGAB0Bq4xs87BpqpXRcBP3P0soAdwcwM7P4DbgA1Bh6grFY7K3QT8j7v/C8DdPws4TzT8BsgBGtwTEu7+V3cvCr/9G5AaZJ56cj6w2d3/4e6HgMcIfblpENz9Y3d/J/x6P6FfsKcFm6r+mFkq8J/AH4LOUlcqHJXrBPQxs7+b2atm1j3oQPXJzAYDH7n7mqCzxMD1wPKgQ9SD04DtZd4X0oB+sZZlZulAV+DvwSapVw8Q+qJWHHSQumoUdIAgmdkK4OQKVk0i9HfThtAlc3fgcTNr7wn0/HI15zcRuCy2iepXVefn7s+Et5lE6BbIwlhmixKrYFnC/PdYU2bWEngK+LG77ws6T30ws4HAZ+7+tpldFHSeukrqwuHul1a2zsxuAv4SLhRvmVkxocHJdsYqX11Vdn5mdg5wBrDGzCB0G+cdMzvf3T+JYcQ6qerfD8DMRgIDgUsSqeBXoRA4vcz7VGBHQFmiwswaEyoaC939L0HnqUe9gMFm9h2gGXCsmf3J3f8r4Fy1og6AlTCzHwKnuvvdZtYJeBFIayC/gMoxswIg293jccTOWjGzy4HpwH+4e8IU+6qYWSNCDf2XAB8Bq4Hh7r4+0GD1xELfYuYDn7v7j4POEy3hK4473H1g0FlqS20clftfoL2ZrSPUCDmyIRaNBmwmcAzwgpnlmdnsoAPVVbix/xbgeUINx483lKIR1gsYAVwc/jfLC39DlzijKw4REYmIrjhERCQiKhwiIhIRFQ4REYmICoeIiEREhUNERCKiwiFJycy+CT/uuc7MnjCz5uHlJ5vZY2b2oZm9b2bLwv14MLPnzGxPdSObmtkDZtY3/PpiM3sn/Dnzw30xMLNvmdmbZvYvM7ujimO9VubR1B1mtii8/HvhEWRfM7O24WVnmtljZfZtYmYrSz5TpL6ocEiyOujuWe7eBTgE/DDcAe1p4BV3P9PdOxMamuWk8D6/ItTPoFJmdhzQw91XmtlRhDq0DQt/zlZgZHjTz4FbqWbUZXfvE86ZBbwJlPSm/gmh4XD+CAwPL7sXuKvMvocIdVy9uuq/CpHIqHCIwGtAB6Af8LW7l3YWdPc8d38t/PpFYH81xxoCPBd+3Rb4l7t/EH7/AvC98LE+c/fVwNc1CWhmxwAXA4vCi4qBpkBz4Gsz6wN87O6bDtt1EXBtTT5DpKZUOCSphW/jDADeA7oAb9fxkL3KHGMX0NjMssPvh1B+rKlIXAW8WGbQv/8m1IP8UuBR4OfALyvYbx2hQTpF6o0KhySro80sD8glNJHV3Ho67imEB8IMD1EzDPiNmb1F6GqlqIp9q3INoQJB+NgvuPt57j4IuBJYBmSY2ZNm9nBJm427fwMcCl+xiNQLNZpJsjoYbjcoZWbrCV0V1Om4hEY/BcDd3wT6hI9/GaF5XiISbvw+n9BVx+HrmhNqN+kP/JXQxE7DCd2eeji8WVPgq0g/V6QyuuIQ+beXgKZmdmPJAjPrbmb/EcExNhBqLynZ/8Twn02BCUBtBlscCix194p++ecAv3X3r4GjCc3PUUyo7aOk6OwMrxepFyocImHhW0tXAd8OP467HvgF4TkvzOw14AngEjMrNLP+FRzmWeCiMu9/amYbgLXAEnd/KXysk82sELgd+Hn4eMeG1y0zs1PLHGMYZW5TlQhvk10yaRXwa0LT5I4E/i+8rB+h21gi9Uaj44rUMzNbBQx09z1xkOUvwJ3unh90Fmk4dMUhUv9+AqQFHcLMmgCLVDSkvumKQ0REIqIrDhERiYgKh4iIRESFQ0REIqLCISIiEVHhEBGRiPx/zFpFw5ykJYIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "hop.plot(model, comp=[1, 2], \n",
    "         plots=[1, 2, 3, 4, 6], \n",
    "         objNames=data_objNames, \n",
    "         XvarNames=data_varNames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Accessing numerical results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have visualised the PCA results, we may also want to access the numerical results. Below are some examples. For a complete list of accessible results, please see this part of the documentation.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>-0.501205</td>\n",
       "      <td>0.266029</td>\n",
       "      <td>-0.280707</td>\n",
       "      <td>0.055819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>1.531956</td>\n",
       "      <td>-0.036587</td>\n",
       "      <td>-0.417397</td>\n",
       "      <td>0.115648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>-0.627608</td>\n",
       "      <td>0.237580</td>\n",
       "      <td>-0.102188</td>\n",
       "      <td>0.016577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>0.080169</td>\n",
       "      <td>-0.480196</td>\n",
       "      <td>-0.416730</td>\n",
       "      <td>0.108057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>2.882254</td>\n",
       "      <td>0.214949</td>\n",
       "      <td>-0.636043</td>\n",
       "      <td>-0.536969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>-5.086867</td>\n",
       "      <td>0.783773</td>\n",
       "      <td>0.403236</td>\n",
       "      <td>0.080473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>-1.400832</td>\n",
       "      <td>-0.076834</td>\n",
       "      <td>-0.322059</td>\n",
       "      <td>0.730908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>2.584102</td>\n",
       "      <td>0.381835</td>\n",
       "      <td>0.461202</td>\n",
       "      <td>0.160848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>2.264157</td>\n",
       "      <td>0.084262</td>\n",
       "      <td>0.037005</td>\n",
       "      <td>0.073237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>-0.000691</td>\n",
       "      <td>-0.549657</td>\n",
       "      <td>0.384169</td>\n",
       "      <td>-0.026973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>-1.791861</td>\n",
       "      <td>-1.379898</td>\n",
       "      <td>0.146952</td>\n",
       "      <td>-0.059481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>1.146989</td>\n",
       "      <td>-0.328194</td>\n",
       "      <td>0.602016</td>\n",
       "      <td>-0.322401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>-4.925700</td>\n",
       "      <td>0.345724</td>\n",
       "      <td>-0.189081</td>\n",
       "      <td>-0.460261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>3.845137</td>\n",
       "      <td>0.537215</td>\n",
       "      <td>0.329624</td>\n",
       "      <td>0.064517</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            PC1       PC2       PC3       PC4\n",
       "Pr 01 -0.501205  0.266029 -0.280707  0.055819\n",
       "Pr 02  1.531956 -0.036587 -0.417397  0.115648\n",
       "Pr 03 -0.627608  0.237580 -0.102188  0.016577\n",
       "Pr 04  0.080169 -0.480196 -0.416730  0.108057\n",
       "Pr 05  2.882254  0.214949 -0.636043 -0.536969\n",
       "Pr 06 -5.086867  0.783773  0.403236  0.080473\n",
       "Pr 07 -1.400832 -0.076834 -0.322059  0.730908\n",
       "Pr 08  2.584102  0.381835  0.461202  0.160848\n",
       "Pr 09  2.264157  0.084262  0.037005  0.073237\n",
       "Pr 10 -0.000691 -0.549657  0.384169 -0.026973\n",
       "Pr 11 -1.791861 -1.379898  0.146952 -0.059481\n",
       "Pr 12  1.146989 -0.328194  0.602016 -0.322401\n",
       "Pr 13 -4.925700  0.345724 -0.189081 -0.460261\n",
       "Pr 14  3.845137  0.537215  0.329624  0.064517"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get scores and store in numpy array\n",
    "scores = model.X_scores()\n",
    "\n",
    "# Get scores and store in pandas dataframe with row and column names\n",
    "scores_df = pd.DataFrame(model.X_scores())\n",
    "scores_df.index = data_objNames\n",
    "scores_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_scores().shape[1])]\n",
    "scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_scores in module hoggorm.pca:\n",
      "\n",
      "X_scores(self)\n",
      "    Returns array holding scores T. First column holds scores for\n",
      "    component 1, second column holds scores for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14, 4)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dimension of the scores\n",
    "np.shape(model.X_scores())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see that the numpy array holds the scores for four components as required when computing the PCA model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Att 01</th>\n",
       "      <td>0.095756</td>\n",
       "      <td>0.213418</td>\n",
       "      <td>0.110989</td>\n",
       "      <td>0.034241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 02</th>\n",
       "      <td>-0.345165</td>\n",
       "      <td>0.280602</td>\n",
       "      <td>0.351717</td>\n",
       "      <td>-0.026955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 03</th>\n",
       "      <td>0.320400</td>\n",
       "      <td>-0.353435</td>\n",
       "      <td>-0.344550</td>\n",
       "      <td>-0.042383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 04</th>\n",
       "      <td>0.191426</td>\n",
       "      <td>0.020166</td>\n",
       "      <td>-0.070290</td>\n",
       "      <td>0.362712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 05</th>\n",
       "      <td>0.038677</td>\n",
       "      <td>0.063817</td>\n",
       "      <td>0.092521</td>\n",
       "      <td>0.345507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 06</th>\n",
       "      <td>0.434063</td>\n",
       "      <td>0.532828</td>\n",
       "      <td>-0.464027</td>\n",
       "      <td>0.206703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 07</th>\n",
       "      <td>0.090249</td>\n",
       "      <td>0.219497</td>\n",
       "      <td>0.099313</td>\n",
       "      <td>-0.127992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 08</th>\n",
       "      <td>-0.371582</td>\n",
       "      <td>0.300093</td>\n",
       "      <td>-0.214017</td>\n",
       "      <td>0.265250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 09</th>\n",
       "      <td>-0.032043</td>\n",
       "      <td>0.011523</td>\n",
       "      <td>0.080383</td>\n",
       "      <td>0.279356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 10</th>\n",
       "      <td>-0.043927</td>\n",
       "      <td>0.242686</td>\n",
       "      <td>-0.305197</td>\n",
       "      <td>-0.591165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 11</th>\n",
       "      <td>0.082753</td>\n",
       "      <td>0.172463</td>\n",
       "      <td>0.087512</td>\n",
       "      <td>-0.368893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 12</th>\n",
       "      <td>0.302646</td>\n",
       "      <td>-0.377508</td>\n",
       "      <td>0.138569</td>\n",
       "      <td>-0.037759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 13</th>\n",
       "      <td>0.098397</td>\n",
       "      <td>0.073569</td>\n",
       "      <td>0.129813</td>\n",
       "      <td>-0.047761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 14</th>\n",
       "      <td>0.195812</td>\n",
       "      <td>0.015657</td>\n",
       "      <td>0.237369</td>\n",
       "      <td>0.042827</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 15</th>\n",
       "      <td>0.072482</td>\n",
       "      <td>0.089597</td>\n",
       "      <td>0.260657</td>\n",
       "      <td>0.151411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 16</th>\n",
       "      <td>0.491182</td>\n",
       "      <td>0.281253</td>\n",
       "      <td>0.413507</td>\n",
       "      <td>-0.099732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 17</th>\n",
       "      <td>-0.018195</td>\n",
       "      <td>0.010280</td>\n",
       "      <td>-0.148933</td>\n",
       "      <td>0.117871</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PC1       PC2       PC3       PC4\n",
       "Att 01  0.095756  0.213418  0.110989  0.034241\n",
       "Att 02 -0.345165  0.280602  0.351717 -0.026955\n",
       "Att 03  0.320400 -0.353435 -0.344550 -0.042383\n",
       "Att 04  0.191426  0.020166 -0.070290  0.362712\n",
       "Att 05  0.038677  0.063817  0.092521  0.345507\n",
       "Att 06  0.434063  0.532828 -0.464027  0.206703\n",
       "Att 07  0.090249  0.219497  0.099313 -0.127992\n",
       "Att 08 -0.371582  0.300093 -0.214017  0.265250\n",
       "Att 09 -0.032043  0.011523  0.080383  0.279356\n",
       "Att 10 -0.043927  0.242686 -0.305197 -0.591165\n",
       "Att 11  0.082753  0.172463  0.087512 -0.368893\n",
       "Att 12  0.302646 -0.377508  0.138569 -0.037759\n",
       "Att 13  0.098397  0.073569  0.129813 -0.047761\n",
       "Att 14  0.195812  0.015657  0.237369  0.042827\n",
       "Att 15  0.072482  0.089597  0.260657  0.151411\n",
       "Att 16  0.491182  0.281253  0.413507 -0.099732\n",
       "Att 17 -0.018195  0.010280 -0.148933  0.117871"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get loadings and store in numpy array\n",
    "loadings = model.X_loadings()\n",
    "\n",
    "# Get loadings and store in pandas dataframe with row and column names\n",
    "loadings_df = pd.DataFrame(model.X_loadings())\n",
    "loadings_df.index = data_varNames\n",
    "loadings_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_loadings in module hoggorm.pca:\n",
      "\n",
      "X_loadings(self)\n",
      "    Returns array holding loadings P of array X. Rows represent variables\n",
      "    and columns represent components. First column holds loadings for\n",
      "    component 1, second column holds scores for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_loadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17, 4)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.shape(model.X_loadings())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we see that the array holds the loadings for the 10 variables in the data across four components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "      <th>PC4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Att 01</th>\n",
       "      <td>0.839489</td>\n",
       "      <td>0.380074</td>\n",
       "      <td>0.141458</td>\n",
       "      <td>0.033985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 02</th>\n",
       "      <td>-0.972762</td>\n",
       "      <td>0.160641</td>\n",
       "      <td>0.144112</td>\n",
       "      <td>-0.008618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 03</th>\n",
       "      <td>0.959356</td>\n",
       "      <td>-0.214974</td>\n",
       "      <td>-0.149988</td>\n",
       "      <td>-0.014375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 04</th>\n",
       "      <td>0.960568</td>\n",
       "      <td>0.020556</td>\n",
       "      <td>-0.051293</td>\n",
       "      <td>0.206192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 05</th>\n",
       "      <td>0.545112</td>\n",
       "      <td>0.182706</td>\n",
       "      <td>0.189557</td>\n",
       "      <td>0.551638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 06</th>\n",
       "      <td>0.957721</td>\n",
       "      <td>0.238822</td>\n",
       "      <td>-0.148867</td>\n",
       "      <td>0.051669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 07</th>\n",
       "      <td>0.862756</td>\n",
       "      <td>0.426249</td>\n",
       "      <td>0.138029</td>\n",
       "      <td>-0.138612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 08</th>\n",
       "      <td>-0.975683</td>\n",
       "      <td>0.160072</td>\n",
       "      <td>-0.081713</td>\n",
       "      <td>0.078893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 09</th>\n",
       "      <td>-0.602730</td>\n",
       "      <td>0.044025</td>\n",
       "      <td>0.219801</td>\n",
       "      <td>0.595248</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 10</th>\n",
       "      <td>-0.402761</td>\n",
       "      <td>0.452033</td>\n",
       "      <td>-0.406834</td>\n",
       "      <td>-0.613969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 11</th>\n",
       "      <td>0.806688</td>\n",
       "      <td>0.341512</td>\n",
       "      <td>0.124042</td>\n",
       "      <td>-0.407376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 12</th>\n",
       "      <td>0.962003</td>\n",
       "      <td>-0.243763</td>\n",
       "      <td>0.064047</td>\n",
       "      <td>-0.013606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 13</th>\n",
       "      <td>0.930770</td>\n",
       "      <td>0.141361</td>\n",
       "      <td>0.178531</td>\n",
       "      <td>-0.051193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 14</th>\n",
       "      <td>0.973107</td>\n",
       "      <td>0.015800</td>\n",
       "      <td>0.171507</td>\n",
       "      <td>0.024096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 15</th>\n",
       "      <td>0.823762</td>\n",
       "      <td>0.206837</td>\n",
       "      <td>0.430689</td>\n",
       "      <td>0.194909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 16</th>\n",
       "      <td>0.985137</td>\n",
       "      <td>0.114585</td>\n",
       "      <td>0.120578</td>\n",
       "      <td>-0.022664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Att 17</th>\n",
       "      <td>-0.460388</td>\n",
       "      <td>0.052859</td>\n",
       "      <td>-0.547921</td>\n",
       "      <td>0.337869</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             PC1       PC2       PC3       PC4\n",
       "Att 01  0.839489  0.380074  0.141458  0.033985\n",
       "Att 02 -0.972762  0.160641  0.144112 -0.008618\n",
       "Att 03  0.959356 -0.214974 -0.149988 -0.014375\n",
       "Att 04  0.960568  0.020556 -0.051293  0.206192\n",
       "Att 05  0.545112  0.182706  0.189557  0.551638\n",
       "Att 06  0.957721  0.238822 -0.148867  0.051669\n",
       "Att 07  0.862756  0.426249  0.138029 -0.138612\n",
       "Att 08 -0.975683  0.160072 -0.081713  0.078893\n",
       "Att 09 -0.602730  0.044025  0.219801  0.595248\n",
       "Att 10 -0.402761  0.452033 -0.406834 -0.613969\n",
       "Att 11  0.806688  0.341512  0.124042 -0.407376\n",
       "Att 12  0.962003 -0.243763  0.064047 -0.013606\n",
       "Att 13  0.930770  0.141361  0.178531 -0.051193\n",
       "Att 14  0.973107  0.015800  0.171507  0.024096\n",
       "Att 15  0.823762  0.206837  0.430689  0.194909\n",
       "Att 16  0.985137  0.114585  0.120578 -0.022664\n",
       "Att 17 -0.460388  0.052859 -0.547921  0.337869"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get loadings and store in numpy array\n",
    "loadings = model.X_corrLoadings()\n",
    "\n",
    "# Get loadings and store in pandas dataframe with row and column names\n",
    "loadings_df = pd.DataFrame(model.X_corrLoadings())\n",
    "loadings_df.index = data_varNames\n",
    "loadings_df.columns = ['PC{0}'.format(x+1) for x in range(model.X_corrLoadings().shape[1])]\n",
    "loadings_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_corrLoadings in module hoggorm.pca:\n",
      "\n",
      "X_corrLoadings(self)\n",
      "    Returns array holding correlation loadings of array X. First column\n",
      "    holds correlation loadings for component 1, second column holds\n",
      "    correlation loadings for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_corrLoadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>calibrated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>91.711092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>3.784519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>1.938654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>1.176851</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     calibrated explained variance\n",
       "PC1                      91.711092\n",
       "PC2                       3.784519\n",
       "PC3                       1.938654\n",
       "PC4                       1.176851"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get calibrated explained variance of each component\n",
    "calExplVar = model.X_calExplVar()\n",
    "\n",
    "# Get calibrated explained variance and store in pandas dataframe with row and column names\n",
    "calExplVar_df = pd.DataFrame(model.X_calExplVar())\n",
    "calExplVar_df.columns = ['calibrated explained variance']\n",
    "calExplVar_df.index = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "calExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_calExplVar in module hoggorm.pca:\n",
      "\n",
      "X_calExplVar(self)\n",
      "    Returns a list holding the calibrated explained variance for\n",
      "    each component. First number in list is for component 1, second number\n",
      "    for component 2, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_calExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative calibrated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>91.711092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>95.495611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>97.434265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>98.611116</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cumulative calibrated explained variance\n",
       "PC0                                  0.000000\n",
       "PC1                                 91.711092\n",
       "PC2                                 95.495611\n",
       "PC3                                 97.434265\n",
       "PC4                                 98.611116"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative calibrated explained variance\n",
    "cumCalExplVar = model.X_cumCalExplVar()\n",
    "\n",
    "# Get cumulative calibrated explained variance and store in pandas dataframe with row and column names\n",
    "cumCalExplVar_df = pd.DataFrame(model.X_cumCalExplVar())\n",
    "cumCalExplVar_df.columns = ['cumulative calibrated explained variance']\n",
    "cumCalExplVar_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumCalExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumCalExplVar in module hoggorm.pca:\n",
      "\n",
      "X_cumCalExplVar(self)\n",
      "    Returns a list holding the cumulative validated explained variance\n",
      "    for array X after each component. First number represents zero\n",
      "    components, second number represents component 1, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumCalExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>70.474211</td>\n",
       "      <td>94.626567</td>\n",
       "      <td>92.036353</td>\n",
       "      <td>92.269129</td>\n",
       "      <td>29.714715</td>\n",
       "      <td>91.723039</td>\n",
       "      <td>74.434732</td>\n",
       "      <td>95.195665</td>\n",
       "      <td>36.328289</td>\n",
       "      <td>16.221618</td>\n",
       "      <td>65.074545</td>\n",
       "      <td>92.544953</td>\n",
       "      <td>86.633263</td>\n",
       "      <td>94.693765</td>\n",
       "      <td>67.858463</td>\n",
       "      <td>97.049563</td>\n",
       "      <td>21.195723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>84.919941</td>\n",
       "      <td>97.207078</td>\n",
       "      <td>96.657667</td>\n",
       "      <td>92.311389</td>\n",
       "      <td>33.052898</td>\n",
       "      <td>97.426690</td>\n",
       "      <td>92.603667</td>\n",
       "      <td>97.757923</td>\n",
       "      <td>36.522101</td>\n",
       "      <td>36.654954</td>\n",
       "      <td>76.737650</td>\n",
       "      <td>98.486921</td>\n",
       "      <td>88.631578</td>\n",
       "      <td>94.718732</td>\n",
       "      <td>72.136672</td>\n",
       "      <td>98.362561</td>\n",
       "      <td>21.475124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>86.921297</td>\n",
       "      <td>99.284035</td>\n",
       "      <td>98.907511</td>\n",
       "      <td>92.574480</td>\n",
       "      <td>36.646288</td>\n",
       "      <td>99.642617</td>\n",
       "      <td>94.509232</td>\n",
       "      <td>98.425540</td>\n",
       "      <td>41.353398</td>\n",
       "      <td>53.205263</td>\n",
       "      <td>78.276541</td>\n",
       "      <td>98.897033</td>\n",
       "      <td>91.819062</td>\n",
       "      <td>97.660198</td>\n",
       "      <td>90.686484</td>\n",
       "      <td>99.816553</td>\n",
       "      <td>51.496651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>87.036847</td>\n",
       "      <td>99.291449</td>\n",
       "      <td>98.928198</td>\n",
       "      <td>96.825865</td>\n",
       "      <td>67.077823</td>\n",
       "      <td>99.909506</td>\n",
       "      <td>96.430354</td>\n",
       "      <td>99.047888</td>\n",
       "      <td>76.786759</td>\n",
       "      <td>90.903755</td>\n",
       "      <td>94.871511</td>\n",
       "      <td>98.915538</td>\n",
       "      <td>92.081035</td>\n",
       "      <td>97.718305</td>\n",
       "      <td>94.486308</td>\n",
       "      <td>99.867892</td>\n",
       "      <td>62.910246</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Att 01     Att 02     Att 03     Att 04     Att 05     Att 06  \\\n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "PC1  70.474211  94.626567  92.036353  92.269129  29.714715  91.723039   \n",
       "PC2  84.919941  97.207078  96.657667  92.311389  33.052898  97.426690   \n",
       "PC3  86.921297  99.284035  98.907511  92.574480  36.646288  99.642617   \n",
       "PC4  87.036847  99.291449  98.928198  96.825865  67.077823  99.909506   \n",
       "\n",
       "        Att 07     Att 08     Att 09     Att 10     Att 11     Att 12  \\\n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "PC1  74.434732  95.195665  36.328289  16.221618  65.074545  92.544953   \n",
       "PC2  92.603667  97.757923  36.522101  36.654954  76.737650  98.486921   \n",
       "PC3  94.509232  98.425540  41.353398  53.205263  78.276541  98.897033   \n",
       "PC4  96.430354  99.047888  76.786759  90.903755  94.871511  98.915538   \n",
       "\n",
       "        Att 13     Att 14     Att 15     Att 16     Att 17  \n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "PC1  86.633263  94.693765  67.858463  97.049563  21.195723  \n",
       "PC2  88.631578  94.718732  72.136672  98.362561  21.475124  \n",
       "PC3  91.819062  97.660198  90.686484  99.816553  51.496651  \n",
       "PC4  92.081035  97.718305  94.486308  99.867892  62.910246  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative calibrated explained variance for each variable\n",
    "cumCalExplVar_ind = model.X_cumCalExplVar_indVar()\n",
    "\n",
    "# Get cumulative calibrated explained variance for each variable and store in pandas dataframe with row and column names\n",
    "cumCalExplVar_ind_df = pd.DataFrame(model.X_cumCalExplVar_indVar())\n",
    "cumCalExplVar_ind_df.columns = data_varNames\n",
    "cumCalExplVar_ind_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumCalExplVar_ind_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumCalExplVar_indVar in module hoggorm.pca:\n",
      "\n",
      "X_cumCalExplVar_indVar(self)\n",
      "    Returns an array holding the cumulative calibrated explained variance\n",
      "    for each variable in X after each component. First row represents zero\n",
      "    components, second row represents one component, third row represents\n",
      "    two components, etc. Columns represent variables.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumCalExplVar_indVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>6.312007</td>\n",
       "      <td>3.334427</td>\n",
       "      <td>3.486557</td>\n",
       "      <td>1.983342</td>\n",
       "      <td>1.218472</td>\n",
       "      <td>2.840303</td>\n",
       "      <td>6.625481</td>\n",
       "      <td>3.208382</td>\n",
       "      <td>2.551060</td>\n",
       "      <td>4.867731</td>\n",
       "      <td>4.697810</td>\n",
       "      <td>3.566884</td>\n",
       "      <td>2.949254</td>\n",
       "      <td>1.949001</td>\n",
       "      <td>1.363672</td>\n",
       "      <td>3.166674</td>\n",
       "      <td>4.086977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>6.506694</td>\n",
       "      <td>2.632651</td>\n",
       "      <td>4.137981</td>\n",
       "      <td>2.372543</td>\n",
       "      <td>1.297108</td>\n",
       "      <td>3.722822</td>\n",
       "      <td>6.808972</td>\n",
       "      <td>2.452896</td>\n",
       "      <td>2.485912</td>\n",
       "      <td>4.778421</td>\n",
       "      <td>4.866059</td>\n",
       "      <td>4.182212</td>\n",
       "      <td>3.149312</td>\n",
       "      <td>2.347118</td>\n",
       "      <td>1.511039</td>\n",
       "      <td>4.165326</td>\n",
       "      <td>4.049983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>6.299903</td>\n",
       "      <td>3.378057</td>\n",
       "      <td>3.446057</td>\n",
       "      <td>1.959145</td>\n",
       "      <td>1.213583</td>\n",
       "      <td>2.785436</td>\n",
       "      <td>6.614073</td>\n",
       "      <td>3.255351</td>\n",
       "      <td>2.555110</td>\n",
       "      <td>4.873283</td>\n",
       "      <td>4.687349</td>\n",
       "      <td>3.528628</td>\n",
       "      <td>2.936816</td>\n",
       "      <td>1.924250</td>\n",
       "      <td>1.354510</td>\n",
       "      <td>3.104587</td>\n",
       "      <td>4.089276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>6.367677</td>\n",
       "      <td>3.133757</td>\n",
       "      <td>3.672829</td>\n",
       "      <td>2.094632</td>\n",
       "      <td>1.240958</td>\n",
       "      <td>3.092655</td>\n",
       "      <td>6.677949</td>\n",
       "      <td>2.992354</td>\n",
       "      <td>2.532431</td>\n",
       "      <td>4.842193</td>\n",
       "      <td>4.745920</td>\n",
       "      <td>3.742834</td>\n",
       "      <td>3.006460</td>\n",
       "      <td>2.062841</td>\n",
       "      <td>1.405811</td>\n",
       "      <td>3.452235</td>\n",
       "      <td>4.076398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>6.635993</td>\n",
       "      <td>2.166575</td>\n",
       "      <td>4.570617</td>\n",
       "      <td>2.631026</td>\n",
       "      <td>1.349333</td>\n",
       "      <td>4.308936</td>\n",
       "      <td>6.930835</td>\n",
       "      <td>1.951150</td>\n",
       "      <td>2.442644</td>\n",
       "      <td>4.719106</td>\n",
       "      <td>4.977801</td>\n",
       "      <td>4.590874</td>\n",
       "      <td>3.282178</td>\n",
       "      <td>2.611522</td>\n",
       "      <td>1.608912</td>\n",
       "      <td>4.828568</td>\n",
       "      <td>4.025414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>5.872903</td>\n",
       "      <td>4.917238</td>\n",
       "      <td>2.017312</td>\n",
       "      <td>1.105525</td>\n",
       "      <td>1.041115</td>\n",
       "      <td>0.849838</td>\n",
       "      <td>6.211629</td>\n",
       "      <td>4.912330</td>\n",
       "      <td>2.697998</td>\n",
       "      <td>5.069164</td>\n",
       "      <td>4.318333</td>\n",
       "      <td>2.179051</td>\n",
       "      <td>2.498037</td>\n",
       "      <td>1.051074</td>\n",
       "      <td>1.031293</td>\n",
       "      <td>0.914280</td>\n",
       "      <td>4.170413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>6.225862</td>\n",
       "      <td>3.644947</td>\n",
       "      <td>3.198317</td>\n",
       "      <td>1.811129</td>\n",
       "      <td>1.183678</td>\n",
       "      <td>2.449808</td>\n",
       "      <td>6.544291</td>\n",
       "      <td>3.542666</td>\n",
       "      <td>2.579887</td>\n",
       "      <td>4.907248</td>\n",
       "      <td>4.623363</td>\n",
       "      <td>3.294615</td>\n",
       "      <td>2.860733</td>\n",
       "      <td>1.772843</td>\n",
       "      <td>1.298465</td>\n",
       "      <td>2.724794</td>\n",
       "      <td>4.103345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>6.607443</td>\n",
       "      <td>2.269487</td>\n",
       "      <td>4.475088</td>\n",
       "      <td>2.573951</td>\n",
       "      <td>1.337801</td>\n",
       "      <td>4.179519</td>\n",
       "      <td>6.903927</td>\n",
       "      <td>2.061938</td>\n",
       "      <td>2.452198</td>\n",
       "      <td>4.732203</td>\n",
       "      <td>4.953128</td>\n",
       "      <td>4.500640</td>\n",
       "      <td>3.252840</td>\n",
       "      <td>2.553141</td>\n",
       "      <td>1.587301</td>\n",
       "      <td>4.682121</td>\n",
       "      <td>4.030839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>6.576806</td>\n",
       "      <td>2.379920</td>\n",
       "      <td>4.372578</td>\n",
       "      <td>2.512705</td>\n",
       "      <td>1.325427</td>\n",
       "      <td>4.040643</td>\n",
       "      <td>6.875052</td>\n",
       "      <td>2.180823</td>\n",
       "      <td>2.462450</td>\n",
       "      <td>4.746257</td>\n",
       "      <td>4.926651</td>\n",
       "      <td>4.403810</td>\n",
       "      <td>3.221359</td>\n",
       "      <td>2.490492</td>\n",
       "      <td>1.564111</td>\n",
       "      <td>4.524970</td>\n",
       "      <td>4.036661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>6.359934</td>\n",
       "      <td>3.161667</td>\n",
       "      <td>3.646922</td>\n",
       "      <td>2.079153</td>\n",
       "      <td>1.237830</td>\n",
       "      <td>3.057557</td>\n",
       "      <td>6.670652</td>\n",
       "      <td>3.022400</td>\n",
       "      <td>2.535022</td>\n",
       "      <td>4.845745</td>\n",
       "      <td>4.739229</td>\n",
       "      <td>3.718362</td>\n",
       "      <td>2.998503</td>\n",
       "      <td>2.047008</td>\n",
       "      <td>1.399950</td>\n",
       "      <td>3.412518</td>\n",
       "      <td>4.077870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>6.188419</td>\n",
       "      <td>3.779916</td>\n",
       "      <td>3.073031</td>\n",
       "      <td>1.736276</td>\n",
       "      <td>1.168554</td>\n",
       "      <td>2.280077</td>\n",
       "      <td>6.509001</td>\n",
       "      <td>3.687966</td>\n",
       "      <td>2.592417</td>\n",
       "      <td>4.924425</td>\n",
       "      <td>4.591004</td>\n",
       "      <td>3.176272</td>\n",
       "      <td>2.822257</td>\n",
       "      <td>1.696275</td>\n",
       "      <td>1.270122</td>\n",
       "      <td>2.532728</td>\n",
       "      <td>4.110460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>6.469831</td>\n",
       "      <td>2.765528</td>\n",
       "      <td>4.014638</td>\n",
       "      <td>2.298850</td>\n",
       "      <td>1.282219</td>\n",
       "      <td>3.555722</td>\n",
       "      <td>6.774229</td>\n",
       "      <td>2.595943</td>\n",
       "      <td>2.498247</td>\n",
       "      <td>4.795331</td>\n",
       "      <td>4.834202</td>\n",
       "      <td>4.065703</td>\n",
       "      <td>3.111432</td>\n",
       "      <td>2.271737</td>\n",
       "      <td>1.483136</td>\n",
       "      <td>3.976237</td>\n",
       "      <td>4.056988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>5.888335</td>\n",
       "      <td>4.861608</td>\n",
       "      <td>2.068950</td>\n",
       "      <td>1.136376</td>\n",
       "      <td>1.047348</td>\n",
       "      <td>0.919794</td>\n",
       "      <td>6.226174</td>\n",
       "      <td>4.852443</td>\n",
       "      <td>2.692834</td>\n",
       "      <td>5.062084</td>\n",
       "      <td>4.331670</td>\n",
       "      <td>2.227828</td>\n",
       "      <td>2.513895</td>\n",
       "      <td>1.082632</td>\n",
       "      <td>1.042975</td>\n",
       "      <td>0.993443</td>\n",
       "      <td>4.167480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>6.728195</td>\n",
       "      <td>1.834221</td>\n",
       "      <td>4.879124</td>\n",
       "      <td>2.815347</td>\n",
       "      <td>1.386574</td>\n",
       "      <td>4.726888</td>\n",
       "      <td>7.017734</td>\n",
       "      <td>1.593360</td>\n",
       "      <td>2.411790</td>\n",
       "      <td>4.676810</td>\n",
       "      <td>5.057482</td>\n",
       "      <td>4.882287</td>\n",
       "      <td>3.376923</td>\n",
       "      <td>2.800066</td>\n",
       "      <td>1.678704</td>\n",
       "      <td>5.301519</td>\n",
       "      <td>4.007895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Att 01    Att 02    Att 03    Att 04    Att 05    Att 06    Att 07  \\\n",
       "Pr 01  6.312007  3.334427  3.486557  1.983342  1.218472  2.840303  6.625481   \n",
       "Pr 02  6.506694  2.632651  4.137981  2.372543  1.297108  3.722822  6.808972   \n",
       "Pr 03  6.299903  3.378057  3.446057  1.959145  1.213583  2.785436  6.614073   \n",
       "Pr 04  6.367677  3.133757  3.672829  2.094632  1.240958  3.092655  6.677949   \n",
       "Pr 05  6.635993  2.166575  4.570617  2.631026  1.349333  4.308936  6.930835   \n",
       "Pr 06  5.872903  4.917238  2.017312  1.105525  1.041115  0.849838  6.211629   \n",
       "Pr 07  6.225862  3.644947  3.198317  1.811129  1.183678  2.449808  6.544291   \n",
       "Pr 08  6.607443  2.269487  4.475088  2.573951  1.337801  4.179519  6.903927   \n",
       "Pr 09  6.576806  2.379920  4.372578  2.512705  1.325427  4.040643  6.875052   \n",
       "Pr 10  6.359934  3.161667  3.646922  2.079153  1.237830  3.057557  6.670652   \n",
       "Pr 11  6.188419  3.779916  3.073031  1.736276  1.168554  2.280077  6.509001   \n",
       "Pr 12  6.469831  2.765528  4.014638  2.298850  1.282219  3.555722  6.774229   \n",
       "Pr 13  5.888335  4.861608  2.068950  1.136376  1.047348  0.919794  6.226174   \n",
       "Pr 14  6.728195  1.834221  4.879124  2.815347  1.386574  4.726888  7.017734   \n",
       "\n",
       "         Att 08    Att 09    Att 10    Att 11    Att 12    Att 13    Att 14  \\\n",
       "Pr 01  3.208382  2.551060  4.867731  4.697810  3.566884  2.949254  1.949001   \n",
       "Pr 02  2.452896  2.485912  4.778421  4.866059  4.182212  3.149312  2.347118   \n",
       "Pr 03  3.255351  2.555110  4.873283  4.687349  3.528628  2.936816  1.924250   \n",
       "Pr 04  2.992354  2.532431  4.842193  4.745920  3.742834  3.006460  2.062841   \n",
       "Pr 05  1.951150  2.442644  4.719106  4.977801  4.590874  3.282178  2.611522   \n",
       "Pr 06  4.912330  2.697998  5.069164  4.318333  2.179051  2.498037  1.051074   \n",
       "Pr 07  3.542666  2.579887  4.907248  4.623363  3.294615  2.860733  1.772843   \n",
       "Pr 08  2.061938  2.452198  4.732203  4.953128  4.500640  3.252840  2.553141   \n",
       "Pr 09  2.180823  2.462450  4.746257  4.926651  4.403810  3.221359  2.490492   \n",
       "Pr 10  3.022400  2.535022  4.845745  4.739229  3.718362  2.998503  2.047008   \n",
       "Pr 11  3.687966  2.592417  4.924425  4.591004  3.176272  2.822257  1.696275   \n",
       "Pr 12  2.595943  2.498247  4.795331  4.834202  4.065703  3.111432  2.271737   \n",
       "Pr 13  4.852443  2.692834  5.062084  4.331670  2.227828  2.513895  1.082632   \n",
       "Pr 14  1.593360  2.411790  4.676810  5.057482  4.882287  3.376923  2.800066   \n",
       "\n",
       "         Att 15    Att 16    Att 17  \n",
       "Pr 01  1.363672  3.166674  4.086977  \n",
       "Pr 02  1.511039  4.165326  4.049983  \n",
       "Pr 03  1.354510  3.104587  4.089276  \n",
       "Pr 04  1.405811  3.452235  4.076398  \n",
       "Pr 05  1.608912  4.828568  4.025414  \n",
       "Pr 06  1.031293  0.914280  4.170413  \n",
       "Pr 07  1.298465  2.724794  4.103345  \n",
       "Pr 08  1.587301  4.682121  4.030839  \n",
       "Pr 09  1.564111  4.524970  4.036661  \n",
       "Pr 10  1.399950  3.412518  4.077870  \n",
       "Pr 11  1.270122  2.532728  4.110460  \n",
       "Pr 12  1.483136  3.976237  4.056988  \n",
       "Pr 13  1.042975  0.993443  4.167480  \n",
       "Pr 14  1.678704  5.301519  4.007895  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get calibrated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from calibration using 1 component\n",
    "X_from_1_component = model.X_predCal()[1]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_1_component_df = pd.DataFrame(model.X_predCal()[1])\n",
    "X_from_1_component_df.index = data_objNames\n",
    "X_from_1_component_df.columns = data_varNames\n",
    "X_from_1_component_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>6.339538</td>\n",
       "      <td>3.308841</td>\n",
       "      <td>3.486884</td>\n",
       "      <td>2.028683</td>\n",
       "      <td>1.228764</td>\n",
       "      <td>3.123844</td>\n",
       "      <td>6.648852</td>\n",
       "      <td>3.363097</td>\n",
       "      <td>2.547155</td>\n",
       "      <td>4.984965</td>\n",
       "      <td>4.698533</td>\n",
       "      <td>3.425451</td>\n",
       "      <td>2.929720</td>\n",
       "      <td>1.888926</td>\n",
       "      <td>1.322790</td>\n",
       "      <td>3.119854</td>\n",
       "      <td>4.138097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>6.456519</td>\n",
       "      <td>2.472462</td>\n",
       "      <td>4.289825</td>\n",
       "      <td>2.443091</td>\n",
       "      <td>1.296112</td>\n",
       "      <td>3.920916</td>\n",
       "      <td>6.744686</td>\n",
       "      <td>2.561922</td>\n",
       "      <td>2.484245</td>\n",
       "      <td>4.828562</td>\n",
       "      <td>4.780561</td>\n",
       "      <td>4.133819</td>\n",
       "      <td>3.086913</td>\n",
       "      <td>2.252421</td>\n",
       "      <td>1.416474</td>\n",
       "      <td>3.970905</td>\n",
       "      <td>4.125403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>6.339832</td>\n",
       "      <td>3.408334</td>\n",
       "      <td>3.396595</td>\n",
       "      <td>1.977131</td>\n",
       "      <td>1.225018</td>\n",
       "      <td>2.962870</td>\n",
       "      <td>6.653951</td>\n",
       "      <td>3.352914</td>\n",
       "      <td>2.554265</td>\n",
       "      <td>4.952328</td>\n",
       "      <td>4.713265</td>\n",
       "      <td>3.424154</td>\n",
       "      <td>2.940238</td>\n",
       "      <td>1.904423</td>\n",
       "      <td>1.351670</td>\n",
       "      <td>3.127499</td>\n",
       "      <td>4.108892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>6.222642</td>\n",
       "      <td>2.849530</td>\n",
       "      <td>3.981552</td>\n",
       "      <td>2.153434</td>\n",
       "      <td>1.209091</td>\n",
       "      <td>3.052503</td>\n",
       "      <td>6.517331</td>\n",
       "      <td>2.966099</td>\n",
       "      <td>2.523586</td>\n",
       "      <td>4.788961</td>\n",
       "      <td>4.586774</td>\n",
       "      <td>3.862286</td>\n",
       "      <td>2.911874</td>\n",
       "      <td>1.961031</td>\n",
       "      <td>1.270524</td>\n",
       "      <td>3.134081</td>\n",
       "      <td>4.146264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>6.592887</td>\n",
       "      <td>2.017657</td>\n",
       "      <td>4.736553</td>\n",
       "      <td>2.485303</td>\n",
       "      <td>1.118676</td>\n",
       "      <td>4.607616</td>\n",
       "      <td>6.983576</td>\n",
       "      <td>2.009348</td>\n",
       "      <td>2.243988</td>\n",
       "      <td>5.282827</td>\n",
       "      <td>5.157294</td>\n",
       "      <td>4.441869</td>\n",
       "      <td>3.241071</td>\n",
       "      <td>2.440914</td>\n",
       "      <td>1.381078</td>\n",
       "      <td>4.679568</td>\n",
       "      <td>4.059059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>6.087684</td>\n",
       "      <td>5.276822</td>\n",
       "      <td>1.597953</td>\n",
       "      <td>1.122175</td>\n",
       "      <td>1.156245</td>\n",
       "      <td>1.096975</td>\n",
       "      <td>6.413412</td>\n",
       "      <td>5.082581</td>\n",
       "      <td>2.761924</td>\n",
       "      <td>5.088736</td>\n",
       "      <td>4.459106</td>\n",
       "      <td>1.936008</td>\n",
       "      <td>2.604200</td>\n",
       "      <td>1.162508</td>\n",
       "      <td>1.218807</td>\n",
       "      <td>1.293434</td>\n",
       "      <td>4.127900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>6.198747</td>\n",
       "      <td>3.490412</td>\n",
       "      <td>3.305460</td>\n",
       "      <td>2.097327</td>\n",
       "      <td>1.401511</td>\n",
       "      <td>2.709394</td>\n",
       "      <td>6.401891</td>\n",
       "      <td>3.782408</td>\n",
       "      <td>2.757297</td>\n",
       "      <td>4.554806</td>\n",
       "      <td>4.312301</td>\n",
       "      <td>3.251395</td>\n",
       "      <td>2.778364</td>\n",
       "      <td>1.726496</td>\n",
       "      <td>1.318301</td>\n",
       "      <td>2.497116</td>\n",
       "      <td>4.236673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>6.745629</td>\n",
       "      <td>2.534507</td>\n",
       "      <td>4.174410</td>\n",
       "      <td>2.607575</td>\n",
       "      <td>1.460414</td>\n",
       "      <td>4.202209</td>\n",
       "      <td>7.012955</td>\n",
       "      <td>2.120484</td>\n",
       "      <td>2.538604</td>\n",
       "      <td>4.589024</td>\n",
       "      <td>5.000005</td>\n",
       "      <td>4.414329</td>\n",
       "      <td>3.333120</td>\n",
       "      <td>2.675483</td>\n",
       "      <td>1.766083</td>\n",
       "      <td>4.964182</td>\n",
       "      <td>3.985036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>6.601404</td>\n",
       "      <td>2.414606</td>\n",
       "      <td>4.326943</td>\n",
       "      <td>2.538368</td>\n",
       "      <td>1.359532</td>\n",
       "      <td>4.083507</td>\n",
       "      <td>6.887849</td>\n",
       "      <td>2.217616</td>\n",
       "      <td>2.486855</td>\n",
       "      <td>4.712117</td>\n",
       "      <td>4.917405</td>\n",
       "      <td>4.374363</td>\n",
       "      <td>3.228864</td>\n",
       "      <td>2.503731</td>\n",
       "      <td>1.592395</td>\n",
       "      <td>4.556667</td>\n",
       "      <td>4.040648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>6.284342</td>\n",
       "      <td>3.143278</td>\n",
       "      <td>3.709968</td>\n",
       "      <td>2.031282</td>\n",
       "      <td>1.228977</td>\n",
       "      <td>2.580845</td>\n",
       "      <td>6.591609</td>\n",
       "      <td>2.768078</td>\n",
       "      <td>2.552034</td>\n",
       "      <td>4.611049</td>\n",
       "      <td>4.688003</td>\n",
       "      <td>3.980115</td>\n",
       "      <td>3.009224</td>\n",
       "      <td>2.128436</td>\n",
       "      <td>1.446755</td>\n",
       "      <td>3.419472</td>\n",
       "      <td>4.011824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>5.908197</td>\n",
       "      <td>3.446003</td>\n",
       "      <td>3.512625</td>\n",
       "      <td>1.676546</td>\n",
       "      <td>1.073538</td>\n",
       "      <td>1.464344</td>\n",
       "      <td>6.228324</td>\n",
       "      <td>3.226640</td>\n",
       "      <td>2.571712</td>\n",
       "      <td>4.579856</td>\n",
       "      <td>4.387826</td>\n",
       "      <td>3.719803</td>\n",
       "      <td>2.742656</td>\n",
       "      <td>1.707005</td>\n",
       "      <td>1.175785</td>\n",
       "      <td>2.211325</td>\n",
       "      <td>4.067377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>6.455566</td>\n",
       "      <td>2.893866</td>\n",
       "      <td>3.936873</td>\n",
       "      <td>2.132977</td>\n",
       "      <td>1.205582</td>\n",
       "      <td>3.034858</td>\n",
       "      <td>6.803244</td>\n",
       "      <td>2.283096</td>\n",
       "      <td>2.452793</td>\n",
       "      <td>4.722541</td>\n",
       "      <td>4.949216</td>\n",
       "      <td>4.285193</td>\n",
       "      <td>3.180835</td>\n",
       "      <td>2.395691</td>\n",
       "      <td>1.561836</td>\n",
       "      <td>4.165023</td>\n",
       "      <td>3.925952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>5.925373</td>\n",
       "      <td>4.904522</td>\n",
       "      <td>2.031414</td>\n",
       "      <td>0.989696</td>\n",
       "      <td>0.892894</td>\n",
       "      <td>1.096607</td>\n",
       "      <td>6.342191</td>\n",
       "      <td>4.874575</td>\n",
       "      <td>2.553043</td>\n",
       "      <td>5.475784</td>\n",
       "      <td>4.544534</td>\n",
       "      <td>2.088492</td>\n",
       "      <td>2.536767</td>\n",
       "      <td>1.023452</td>\n",
       "      <td>0.954976</td>\n",
       "      <td>1.058395</td>\n",
       "      <td>4.144944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>6.881639</td>\n",
       "      <td>2.099161</td>\n",
       "      <td>4.572947</td>\n",
       "      <td>2.826412</td>\n",
       "      <td>1.473645</td>\n",
       "      <td>4.873512</td>\n",
       "      <td>7.160130</td>\n",
       "      <td>1.701143</td>\n",
       "      <td>2.462500</td>\n",
       "      <td>4.668444</td>\n",
       "      <td>5.155178</td>\n",
       "      <td>4.722724</td>\n",
       "      <td>3.456154</td>\n",
       "      <td>2.889483</td>\n",
       "      <td>1.822524</td>\n",
       "      <td>5.582480</td>\n",
       "      <td>3.971930</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Att 01    Att 02    Att 03    Att 04    Att 05    Att 06    Att 07  \\\n",
       "Pr 01  6.339538  3.308841  3.486884  2.028683  1.228764  3.123844  6.648852   \n",
       "Pr 02  6.456519  2.472462  4.289825  2.443091  1.296112  3.920916  6.744686   \n",
       "Pr 03  6.339832  3.408334  3.396595  1.977131  1.225018  2.962870  6.653951   \n",
       "Pr 04  6.222642  2.849530  3.981552  2.153434  1.209091  3.052503  6.517331   \n",
       "Pr 05  6.592887  2.017657  4.736553  2.485303  1.118676  4.607616  6.983576   \n",
       "Pr 06  6.087684  5.276822  1.597953  1.122175  1.156245  1.096975  6.413412   \n",
       "Pr 07  6.198747  3.490412  3.305460  2.097327  1.401511  2.709394  6.401891   \n",
       "Pr 08  6.745629  2.534507  4.174410  2.607575  1.460414  4.202209  7.012955   \n",
       "Pr 09  6.601404  2.414606  4.326943  2.538368  1.359532  4.083507  6.887849   \n",
       "Pr 10  6.284342  3.143278  3.709968  2.031282  1.228977  2.580845  6.591609   \n",
       "Pr 11  5.908197  3.446003  3.512625  1.676546  1.073538  1.464344  6.228324   \n",
       "Pr 12  6.455566  2.893866  3.936873  2.132977  1.205582  3.034858  6.803244   \n",
       "Pr 13  5.925373  4.904522  2.031414  0.989696  0.892894  1.096607  6.342191   \n",
       "Pr 14  6.881639  2.099161  4.572947  2.826412  1.473645  4.873512  7.160130   \n",
       "\n",
       "         Att 08    Att 09    Att 10    Att 11    Att 12    Att 13    Att 14  \\\n",
       "Pr 01  3.363097  2.547155  4.984965  4.698533  3.425451  2.929720  1.888926   \n",
       "Pr 02  2.561922  2.484245  4.828562  4.780561  4.133819  3.086913  2.252421   \n",
       "Pr 03  3.352914  2.554265  4.952328  4.713265  3.424154  2.940238  1.904423   \n",
       "Pr 04  2.966099  2.523586  4.788961  4.586774  3.862286  2.911874  1.961031   \n",
       "Pr 05  2.009348  2.243988  5.282827  5.157294  4.441869  3.241071  2.440914   \n",
       "Pr 06  5.082581  2.761924  5.088736  4.459106  1.936008  2.604200  1.162508   \n",
       "Pr 07  3.782408  2.757297  4.554806  4.312301  3.251395  2.778364  1.726496   \n",
       "Pr 08  2.120484  2.538604  4.589024  5.000005  4.414329  3.333120  2.675483   \n",
       "Pr 09  2.217616  2.486855  4.712117  4.917405  4.374363  3.228864  2.503731   \n",
       "Pr 10  2.768078  2.552034  4.611049  4.688003  3.980115  3.009224  2.128436   \n",
       "Pr 11  3.226640  2.571712  4.579856  4.387826  3.719803  2.742656  1.707005   \n",
       "Pr 12  2.283096  2.452793  4.722541  4.949216  4.285193  3.180835  2.395691   \n",
       "Pr 13  4.874575  2.553043  5.475784  4.544534  2.088492  2.536767  1.023452   \n",
       "Pr 14  1.701143  2.462500  4.668444  5.155178  4.722724  3.456154  2.889483   \n",
       "\n",
       "         Att 15    Att 16    Att 17  \n",
       "Pr 01  1.322790  3.119854  4.138097  \n",
       "Pr 02  1.416474  3.970905  4.125403  \n",
       "Pr 03  1.351670  3.127499  4.108892  \n",
       "Pr 04  1.270524  3.134081  4.146264  \n",
       "Pr 05  1.381078  4.679568  4.059059  \n",
       "Pr 06  1.218807  1.293434  4.127900  \n",
       "Pr 07  1.318301  2.497116  4.236673  \n",
       "Pr 08  1.766083  4.964182  3.985036  \n",
       "Pr 09  1.592395  4.556667  4.040648  \n",
       "Pr 10  1.446755  3.419472  4.011824  \n",
       "Pr 11  1.175785  2.211325  4.067377  \n",
       "Pr 12  1.561836  4.165023  3.925952  \n",
       "Pr 13  0.954976  1.058395  4.144944  \n",
       "Pr 14  1.822524  5.582480  3.971930  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predicted X for a given number of components\n",
    "\n",
    "# Predicted X from calibration using 4 components\n",
    "X_from_4_component = model.X_predCal()[4]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_4_component_df = pd.DataFrame(model.X_predCal()[4])\n",
    "X_from_4_component_df.index = data_objNames\n",
    "X_from_4_component_df.columns = data_varNames\n",
    "X_from_4_component_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_predCal in module hoggorm.pca:\n",
      "\n",
      "X_predCal(self)\n",
      "    Returns a dictionary holding the predicted arrays Xhat from\n",
      "    calibration after each computed component. Dictionary key represents\n",
      "    order of component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_predCal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>validated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>89.695259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>2.367054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>3.000506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>1.642879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     validated explained variance\n",
       "PC1                     89.695259\n",
       "PC2                      2.367054\n",
       "PC3                      3.000506\n",
       "PC4                      1.642879"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated explained variance of each component\n",
    "valExplVar = model.X_valExplVar()\n",
    "\n",
    "# Get calibrated explained variance and store in pandas dataframe with row and column names\n",
    "valExplVar_df = pd.DataFrame(model.X_valExplVar())\n",
    "valExplVar_df.columns = ['validated explained variance']\n",
    "valExplVar_df.index = ['PC{0}'.format(x+1) for x in range(model.X_loadings().shape[1])]\n",
    "valExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_valExplVar in module hoggorm.pca:\n",
      "\n",
      "X_valExplVar(self)\n",
      "    Returns a list holding the validated explained variance for X after\n",
      "    each component. First number in list is for component 1, second number\n",
      "    for component 2, third number for component 3, etc.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_valExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cumulative validated explained variance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>89.695259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>92.062312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>95.062819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>96.705698</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     cumulative validated explained variance\n",
       "PC0                                 0.000000\n",
       "PC1                                89.695259\n",
       "PC2                                92.062312\n",
       "PC3                                95.062819\n",
       "PC4                                96.705698"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative validated explained variance\n",
    "cumValExplVar = model.X_cumValExplVar()\n",
    "\n",
    "# Get cumulative validated explained variance and store in pandas dataframe with row and column names\n",
    "cumValExplVar_df = pd.DataFrame(model.X_cumValExplVar())\n",
    "cumValExplVar_df.columns = ['cumulative validated explained variance']\n",
    "cumValExplVar_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumValExplVar_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumValExplVar in module hoggorm.pca:\n",
      "\n",
      "X_cumValExplVar(self)\n",
      "    Returns a list holding the cumulative validated explained variance\n",
      "    for array X after each component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumValExplVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PC0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC1</th>\n",
       "      <td>60.758218</td>\n",
       "      <td>92.903688</td>\n",
       "      <td>88.648599</td>\n",
       "      <td>90.936900</td>\n",
       "      <td>20.551169</td>\n",
       "      <td>90.682501</td>\n",
       "      <td>67.164466</td>\n",
       "      <td>94.265062</td>\n",
       "      <td>6.449322</td>\n",
       "      <td>-6.904037</td>\n",
       "      <td>55.909808</td>\n",
       "      <td>90.937199</td>\n",
       "      <td>85.073824</td>\n",
       "      <td>93.920674</td>\n",
       "      <td>59.922358</td>\n",
       "      <td>96.173785</td>\n",
       "      <td>17.008178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC2</th>\n",
       "      <td>75.091127</td>\n",
       "      <td>95.493089</td>\n",
       "      <td>93.766447</td>\n",
       "      <td>90.216558</td>\n",
       "      <td>19.057500</td>\n",
       "      <td>92.433499</td>\n",
       "      <td>89.587513</td>\n",
       "      <td>94.877221</td>\n",
       "      <td>-5.935075</td>\n",
       "      <td>-8.925615</td>\n",
       "      <td>68.801728</td>\n",
       "      <td>96.240976</td>\n",
       "      <td>86.890136</td>\n",
       "      <td>93.135298</td>\n",
       "      <td>58.558275</td>\n",
       "      <td>97.460565</td>\n",
       "      <td>-21.032417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC3</th>\n",
       "      <td>77.636112</td>\n",
       "      <td>97.998892</td>\n",
       "      <td>97.857451</td>\n",
       "      <td>87.979309</td>\n",
       "      <td>-3.212596</td>\n",
       "      <td>98.954792</td>\n",
       "      <td>90.045240</td>\n",
       "      <td>96.566838</td>\n",
       "      <td>-14.292728</td>\n",
       "      <td>7.228006</td>\n",
       "      <td>64.711140</td>\n",
       "      <td>97.724061</td>\n",
       "      <td>85.639562</td>\n",
       "      <td>94.339156</td>\n",
       "      <td>82.764976</td>\n",
       "      <td>99.526474</td>\n",
       "      <td>23.850685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC4</th>\n",
       "      <td>70.944990</td>\n",
       "      <td>98.067667</td>\n",
       "      <td>97.890550</td>\n",
       "      <td>93.806682</td>\n",
       "      <td>14.277290</td>\n",
       "      <td>99.614752</td>\n",
       "      <td>92.711142</td>\n",
       "      <td>97.456069</td>\n",
       "      <td>46.613553</td>\n",
       "      <td>82.547632</td>\n",
       "      <td>87.773777</td>\n",
       "      <td>96.749543</td>\n",
       "      <td>82.313227</td>\n",
       "      <td>93.824721</td>\n",
       "      <td>88.064581</td>\n",
       "      <td>99.697050</td>\n",
       "      <td>32.263310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Att 01     Att 02     Att 03     Att 04     Att 05     Att 06  \\\n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "PC1  60.758218  92.903688  88.648599  90.936900  20.551169  90.682501   \n",
       "PC2  75.091127  95.493089  93.766447  90.216558  19.057500  92.433499   \n",
       "PC3  77.636112  97.998892  97.857451  87.979309  -3.212596  98.954792   \n",
       "PC4  70.944990  98.067667  97.890550  93.806682  14.277290  99.614752   \n",
       "\n",
       "        Att 07     Att 08     Att 09     Att 10     Att 11     Att 12  \\\n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "PC1  67.164466  94.265062   6.449322  -6.904037  55.909808  90.937199   \n",
       "PC2  89.587513  94.877221  -5.935075  -8.925615  68.801728  96.240976   \n",
       "PC3  90.045240  96.566838 -14.292728   7.228006  64.711140  97.724061   \n",
       "PC4  92.711142  97.456069  46.613553  82.547632  87.773777  96.749543   \n",
       "\n",
       "        Att 13     Att 14     Att 15     Att 16     Att 17  \n",
       "PC0   0.000000   0.000000   0.000000   0.000000   0.000000  \n",
       "PC1  85.073824  93.920674  59.922358  96.173785  17.008178  \n",
       "PC2  86.890136  93.135298  58.558275  97.460565 -21.032417  \n",
       "PC3  85.639562  94.339156  82.764976  99.526474  23.850685  \n",
       "PC4  82.313227  93.824721  88.064581  99.697050  32.263310  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get cumulative validated explained variance for each variable\n",
    "cumCalExplVar_ind = model.X_cumCalExplVar_indVar()\n",
    "\n",
    "# Get cumulative validated explained variance for each variable and store in pandas dataframe with row and column names\n",
    "cumValExplVar_ind_df = pd.DataFrame(model.X_cumValExplVar_indVar())\n",
    "cumValExplVar_ind_df.columns = data_varNames\n",
    "cumValExplVar_ind_df.index = ['PC{0}'.format(x) for x in range(model.X_loadings().shape[1] + 1)]\n",
    "cumValExplVar_ind_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_cumValExplVar_indVar in module hoggorm.pca:\n",
      "\n",
      "X_cumValExplVar_indVar(self)\n",
      "    Returns an array holding the cumulative validated explained variance\n",
      "    for each variable in X after each component. First row represents\n",
      "    zero components, second row represents component 1, third row for\n",
      "    compnent 2, etc. Columns represent variables.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_cumValExplVar_indVar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>6.321932</td>\n",
       "      <td>3.334168</td>\n",
       "      <td>3.491657</td>\n",
       "      <td>1.971144</td>\n",
       "      <td>1.212817</td>\n",
       "      <td>2.819470</td>\n",
       "      <td>6.619674</td>\n",
       "      <td>3.206781</td>\n",
       "      <td>2.542288</td>\n",
       "      <td>4.849058</td>\n",
       "      <td>4.708178</td>\n",
       "      <td>3.585587</td>\n",
       "      <td>2.950969</td>\n",
       "      <td>1.954072</td>\n",
       "      <td>1.374494</td>\n",
       "      <td>3.168876</td>\n",
       "      <td>4.088299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>6.501670</td>\n",
       "      <td>2.648498</td>\n",
       "      <td>4.117002</td>\n",
       "      <td>2.355921</td>\n",
       "      <td>1.303013</td>\n",
       "      <td>3.700757</td>\n",
       "      <td>6.822306</td>\n",
       "      <td>2.442141</td>\n",
       "      <td>2.493082</td>\n",
       "      <td>4.768843</td>\n",
       "      <td>4.878060</td>\n",
       "      <td>4.186354</td>\n",
       "      <td>3.155193</td>\n",
       "      <td>2.352329</td>\n",
       "      <td>1.510873</td>\n",
       "      <td>4.188164</td>\n",
       "      <td>4.047898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>6.305708</td>\n",
       "      <td>3.373448</td>\n",
       "      <td>3.448529</td>\n",
       "      <td>1.953544</td>\n",
       "      <td>1.208190</td>\n",
       "      <td>2.774027</td>\n",
       "      <td>6.614489</td>\n",
       "      <td>3.243945</td>\n",
       "      <td>2.554678</td>\n",
       "      <td>4.862862</td>\n",
       "      <td>4.683939</td>\n",
       "      <td>3.536204</td>\n",
       "      <td>2.925206</td>\n",
       "      <td>1.933793</td>\n",
       "      <td>1.353315</td>\n",
       "      <td>3.098095</td>\n",
       "      <td>4.083473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>6.385159</td>\n",
       "      <td>3.149624</td>\n",
       "      <td>3.650552</td>\n",
       "      <td>2.091811</td>\n",
       "      <td>1.253332</td>\n",
       "      <td>3.090328</td>\n",
       "      <td>6.690836</td>\n",
       "      <td>2.993495</td>\n",
       "      <td>2.534945</td>\n",
       "      <td>4.856245</td>\n",
       "      <td>4.750954</td>\n",
       "      <td>3.729037</td>\n",
       "      <td>3.012297</td>\n",
       "      <td>2.068350</td>\n",
       "      <td>1.422392</td>\n",
       "      <td>3.476796</td>\n",
       "      <td>4.059960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>6.620673</td>\n",
       "      <td>2.215418</td>\n",
       "      <td>4.531467</td>\n",
       "      <td>2.662252</td>\n",
       "      <td>1.389410</td>\n",
       "      <td>4.238410</td>\n",
       "      <td>6.912736</td>\n",
       "      <td>1.941164</td>\n",
       "      <td>2.467034</td>\n",
       "      <td>4.612559</td>\n",
       "      <td>4.934899</td>\n",
       "      <td>4.594354</td>\n",
       "      <td>3.305507</td>\n",
       "      <td>2.631689</td>\n",
       "      <td>1.657194</td>\n",
       "      <td>4.841689</td>\n",
       "      <td>4.025028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>5.716833</td>\n",
       "      <td>4.686222</td>\n",
       "      <td>2.279631</td>\n",
       "      <td>1.144898</td>\n",
       "      <td>1.064576</td>\n",
       "      <td>0.746651</td>\n",
       "      <td>6.110567</td>\n",
       "      <td>4.729040</td>\n",
       "      <td>2.631455</td>\n",
       "      <td>5.086455</td>\n",
       "      <td>4.236178</td>\n",
       "      <td>2.285350</td>\n",
       "      <td>2.485418</td>\n",
       "      <td>1.005523</td>\n",
       "      <td>0.960328</td>\n",
       "      <td>0.769729</td>\n",
       "      <td>4.199640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>6.232702</td>\n",
       "      <td>3.660579</td>\n",
       "      <td>3.189808</td>\n",
       "      <td>1.790200</td>\n",
       "      <td>1.155120</td>\n",
       "      <td>2.430682</td>\n",
       "      <td>6.561056</td>\n",
       "      <td>3.511051</td>\n",
       "      <td>2.561228</td>\n",
       "      <td>4.939926</td>\n",
       "      <td>4.655168</td>\n",
       "      <td>3.305830</td>\n",
       "      <td>2.876202</td>\n",
       "      <td>1.771486</td>\n",
       "      <td>1.296169</td>\n",
       "      <td>2.751575</td>\n",
       "      <td>4.087225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>6.556990</td>\n",
       "      <td>2.224464</td>\n",
       "      <td>4.508297</td>\n",
       "      <td>2.569178</td>\n",
       "      <td>1.277206</td>\n",
       "      <td>4.168840</td>\n",
       "      <td>6.867850</td>\n",
       "      <td>2.069242</td>\n",
       "      <td>2.449770</td>\n",
       "      <td>4.758115</td>\n",
       "      <td>4.928710</td>\n",
       "      <td>4.505121</td>\n",
       "      <td>3.251167</td>\n",
       "      <td>2.538066</td>\n",
       "      <td>1.562009</td>\n",
       "      <td>4.640803</td>\n",
       "      <td>4.039662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>6.558221</td>\n",
       "      <td>2.361260</td>\n",
       "      <td>4.347290</td>\n",
       "      <td>2.538710</td>\n",
       "      <td>1.340199</td>\n",
       "      <td>4.019637</td>\n",
       "      <td>6.869212</td>\n",
       "      <td>2.149602</td>\n",
       "      <td>2.470240</td>\n",
       "      <td>4.779115</td>\n",
       "      <td>4.921091</td>\n",
       "      <td>4.388868</td>\n",
       "      <td>3.199166</td>\n",
       "      <td>2.491697</td>\n",
       "      <td>1.552562</td>\n",
       "      <td>4.518491</td>\n",
       "      <td>4.040623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>6.360698</td>\n",
       "      <td>3.163332</td>\n",
       "      <td>3.647456</td>\n",
       "      <td>2.072166</td>\n",
       "      <td>1.243048</td>\n",
       "      <td>3.095064</td>\n",
       "      <td>6.683779</td>\n",
       "      <td>3.041813</td>\n",
       "      <td>2.525408</td>\n",
       "      <td>4.852340</td>\n",
       "      <td>4.746862</td>\n",
       "      <td>3.692084</td>\n",
       "      <td>2.989927</td>\n",
       "      <td>2.034471</td>\n",
       "      <td>1.395331</td>\n",
       "      <td>3.417329</td>\n",
       "      <td>4.089244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>6.218828</td>\n",
       "      <td>3.814837</td>\n",
       "      <td>3.042897</td>\n",
       "      <td>1.753250</td>\n",
       "      <td>1.172705</td>\n",
       "      <td>2.400814</td>\n",
       "      <td>6.536903</td>\n",
       "      <td>3.733097</td>\n",
       "      <td>2.594650</td>\n",
       "      <td>4.963939</td>\n",
       "      <td>4.622144</td>\n",
       "      <td>3.135116</td>\n",
       "      <td>2.845963</td>\n",
       "      <td>1.721529</td>\n",
       "      <td>1.283082</td>\n",
       "      <td>2.592109</td>\n",
       "      <td>4.121268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>6.485638</td>\n",
       "      <td>2.748078</td>\n",
       "      <td>4.009985</td>\n",
       "      <td>2.319208</td>\n",
       "      <td>1.292297</td>\n",
       "      <td>3.597737</td>\n",
       "      <td>6.774672</td>\n",
       "      <td>2.620811</td>\n",
       "      <td>2.503993</td>\n",
       "      <td>4.805656</td>\n",
       "      <td>4.820732</td>\n",
       "      <td>4.042612</td>\n",
       "      <td>3.105912</td>\n",
       "      <td>2.242906</td>\n",
       "      <td>1.478028</td>\n",
       "      <td>3.947679</td>\n",
       "      <td>4.059718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>5.881300</td>\n",
       "      <td>4.834102</td>\n",
       "      <td>2.100872</td>\n",
       "      <td>1.212208</td>\n",
       "      <td>1.072666</td>\n",
       "      <td>0.859502</td>\n",
       "      <td>6.173853</td>\n",
       "      <td>4.862095</td>\n",
       "      <td>2.813877</td>\n",
       "      <td>4.854202</td>\n",
       "      <td>4.233191</td>\n",
       "      <td>2.365517</td>\n",
       "      <td>2.491343</td>\n",
       "      <td>1.132715</td>\n",
       "      <td>1.053101</td>\n",
       "      <td>0.998457</td>\n",
       "      <td>4.170150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>6.716475</td>\n",
       "      <td>1.824760</td>\n",
       "      <td>5.019389</td>\n",
       "      <td>2.767762</td>\n",
       "      <td>1.371599</td>\n",
       "      <td>4.676165</td>\n",
       "      <td>6.976906</td>\n",
       "      <td>1.626394</td>\n",
       "      <td>2.395899</td>\n",
       "      <td>4.665178</td>\n",
       "      <td>5.052267</td>\n",
       "      <td>4.971145</td>\n",
       "      <td>3.356691</td>\n",
       "      <td>2.791307</td>\n",
       "      <td>1.618959</td>\n",
       "      <td>5.176867</td>\n",
       "      <td>4.016990</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Att 01    Att 02    Att 03    Att 04    Att 05    Att 06    Att 07  \\\n",
       "Pr 01  6.321932  3.334168  3.491657  1.971144  1.212817  2.819470  6.619674   \n",
       "Pr 02  6.501670  2.648498  4.117002  2.355921  1.303013  3.700757  6.822306   \n",
       "Pr 03  6.305708  3.373448  3.448529  1.953544  1.208190  2.774027  6.614489   \n",
       "Pr 04  6.385159  3.149624  3.650552  2.091811  1.253332  3.090328  6.690836   \n",
       "Pr 05  6.620673  2.215418  4.531467  2.662252  1.389410  4.238410  6.912736   \n",
       "Pr 06  5.716833  4.686222  2.279631  1.144898  1.064576  0.746651  6.110567   \n",
       "Pr 07  6.232702  3.660579  3.189808  1.790200  1.155120  2.430682  6.561056   \n",
       "Pr 08  6.556990  2.224464  4.508297  2.569178  1.277206  4.168840  6.867850   \n",
       "Pr 09  6.558221  2.361260  4.347290  2.538710  1.340199  4.019637  6.869212   \n",
       "Pr 10  6.360698  3.163332  3.647456  2.072166  1.243048  3.095064  6.683779   \n",
       "Pr 11  6.218828  3.814837  3.042897  1.753250  1.172705  2.400814  6.536903   \n",
       "Pr 12  6.485638  2.748078  4.009985  2.319208  1.292297  3.597737  6.774672   \n",
       "Pr 13  5.881300  4.834102  2.100872  1.212208  1.072666  0.859502  6.173853   \n",
       "Pr 14  6.716475  1.824760  5.019389  2.767762  1.371599  4.676165  6.976906   \n",
       "\n",
       "         Att 08    Att 09    Att 10    Att 11    Att 12    Att 13    Att 14  \\\n",
       "Pr 01  3.206781  2.542288  4.849058  4.708178  3.585587  2.950969  1.954072   \n",
       "Pr 02  2.442141  2.493082  4.768843  4.878060  4.186354  3.155193  2.352329   \n",
       "Pr 03  3.243945  2.554678  4.862862  4.683939  3.536204  2.925206  1.933793   \n",
       "Pr 04  2.993495  2.534945  4.856245  4.750954  3.729037  3.012297  2.068350   \n",
       "Pr 05  1.941164  2.467034  4.612559  4.934899  4.594354  3.305507  2.631689   \n",
       "Pr 06  4.729040  2.631455  5.086455  4.236178  2.285350  2.485418  1.005523   \n",
       "Pr 07  3.511051  2.561228  4.939926  4.655168  3.305830  2.876202  1.771486   \n",
       "Pr 08  2.069242  2.449770  4.758115  4.928710  4.505121  3.251167  2.538066   \n",
       "Pr 09  2.149602  2.470240  4.779115  4.921091  4.388868  3.199166  2.491697   \n",
       "Pr 10  3.041813  2.525408  4.852340  4.746862  3.692084  2.989927  2.034471   \n",
       "Pr 11  3.733097  2.594650  4.963939  4.622144  3.135116  2.845963  1.721529   \n",
       "Pr 12  2.620811  2.503993  4.805656  4.820732  4.042612  3.105912  2.242906   \n",
       "Pr 13  4.862095  2.813877  4.854202  4.233191  2.365517  2.491343  1.132715   \n",
       "Pr 14  1.626394  2.395899  4.665178  5.052267  4.971145  3.356691  2.791307   \n",
       "\n",
       "         Att 15    Att 16    Att 17  \n",
       "Pr 01  1.374494  3.168876  4.088299  \n",
       "Pr 02  1.510873  4.188164  4.047898  \n",
       "Pr 03  1.353315  3.098095  4.083473  \n",
       "Pr 04  1.422392  3.476796  4.059960  \n",
       "Pr 05  1.657194  4.841689  4.025028  \n",
       "Pr 06  0.960328  0.769729  4.199640  \n",
       "Pr 07  1.296169  2.751575  4.087225  \n",
       "Pr 08  1.562009  4.640803  4.039662  \n",
       "Pr 09  1.552562  4.518491  4.040623  \n",
       "Pr 10  1.395331  3.417329  4.089244  \n",
       "Pr 11  1.283082  2.592109  4.121268  \n",
       "Pr 12  1.478028  3.947679  4.059718  \n",
       "Pr 13  1.053101  0.998457  4.170150  \n",
       "Pr 14  1.618959  5.176867  4.016990  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from validation using 1 component\n",
    "X_from_1_component_val = model.X_predVal()[1]\n",
    "\n",
    "# Predicted X from calibration using 1 component stored in pandas data frame with row and columns names\n",
    "X_from_1_component_val_df = pd.DataFrame(model.X_predVal()[1])\n",
    "X_from_1_component_val_df.index = data_objNames\n",
    "X_from_1_component_val_df.columns = data_varNames\n",
    "X_from_1_component_val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Att 01</th>\n",
       "      <th>Att 02</th>\n",
       "      <th>Att 03</th>\n",
       "      <th>Att 04</th>\n",
       "      <th>Att 05</th>\n",
       "      <th>Att 06</th>\n",
       "      <th>Att 07</th>\n",
       "      <th>Att 08</th>\n",
       "      <th>Att 09</th>\n",
       "      <th>Att 10</th>\n",
       "      <th>Att 11</th>\n",
       "      <th>Att 12</th>\n",
       "      <th>Att 13</th>\n",
       "      <th>Att 14</th>\n",
       "      <th>Att 15</th>\n",
       "      <th>Att 16</th>\n",
       "      <th>Att 17</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Pr 01</th>\n",
       "      <td>6.360633</td>\n",
       "      <td>3.315678</td>\n",
       "      <td>3.494673</td>\n",
       "      <td>1.983676</td>\n",
       "      <td>1.197300</td>\n",
       "      <td>3.092077</td>\n",
       "      <td>6.650614</td>\n",
       "      <td>3.357080</td>\n",
       "      <td>2.513557</td>\n",
       "      <td>4.994617</td>\n",
       "      <td>4.743662</td>\n",
       "      <td>3.451465</td>\n",
       "      <td>2.936340</td>\n",
       "      <td>1.893384</td>\n",
       "      <td>1.332340</td>\n",
       "      <td>3.131038</td>\n",
       "      <td>4.135418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 02</th>\n",
       "      <td>6.433230</td>\n",
       "      <td>2.481692</td>\n",
       "      <td>4.279644</td>\n",
       "      <td>2.364576</td>\n",
       "      <td>1.254152</td>\n",
       "      <td>3.875998</td>\n",
       "      <td>6.782745</td>\n",
       "      <td>2.511023</td>\n",
       "      <td>2.454280</td>\n",
       "      <td>4.908544</td>\n",
       "      <td>4.850385</td>\n",
       "      <td>4.143455</td>\n",
       "      <td>3.097305</td>\n",
       "      <td>2.243589</td>\n",
       "      <td>1.378508</td>\n",
       "      <td>4.007629</td>\n",
       "      <td>4.114168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 03</th>\n",
       "      <td>6.350347</td>\n",
       "      <td>3.406499</td>\n",
       "      <td>3.395414</td>\n",
       "      <td>1.964491</td>\n",
       "      <td>1.212921</td>\n",
       "      <td>2.958356</td>\n",
       "      <td>6.660384</td>\n",
       "      <td>3.341740</td>\n",
       "      <td>2.548721</td>\n",
       "      <td>4.955897</td>\n",
       "      <td>4.718020</td>\n",
       "      <td>3.425761</td>\n",
       "      <td>2.926651</td>\n",
       "      <td>1.914479</td>\n",
       "      <td>1.347584</td>\n",
       "      <td>3.123359</td>\n",
       "      <td>4.100673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 04</th>\n",
       "      <td>6.248422</td>\n",
       "      <td>2.854384</td>\n",
       "      <td>3.965634</td>\n",
       "      <td>2.100824</td>\n",
       "      <td>1.194557</td>\n",
       "      <td>2.990891</td>\n",
       "      <td>6.550625</td>\n",
       "      <td>2.912082</td>\n",
       "      <td>2.488705</td>\n",
       "      <td>4.912781</td>\n",
       "      <td>4.629963</td>\n",
       "      <td>3.848399</td>\n",
       "      <td>2.921869</td>\n",
       "      <td>1.956625</td>\n",
       "      <td>1.281168</td>\n",
       "      <td>3.177541</td>\n",
       "      <td>4.082263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 05</th>\n",
       "      <td>6.617568</td>\n",
       "      <td>2.145239</td>\n",
       "      <td>4.577304</td>\n",
       "      <td>2.764164</td>\n",
       "      <td>1.431089</td>\n",
       "      <td>4.546312</td>\n",
       "      <td>6.895807</td>\n",
       "      <td>2.113040</td>\n",
       "      <td>2.483129</td>\n",
       "      <td>4.661089</td>\n",
       "      <td>4.868409</td>\n",
       "      <td>4.456613</td>\n",
       "      <td>3.277748</td>\n",
       "      <td>2.557202</td>\n",
       "      <td>1.615825</td>\n",
       "      <td>4.739199</td>\n",
       "      <td>4.103596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 06</th>\n",
       "      <td>5.904038</td>\n",
       "      <td>5.177300</td>\n",
       "      <td>1.701867</td>\n",
       "      <td>1.112680</td>\n",
       "      <td>1.242584</td>\n",
       "      <td>1.004542</td>\n",
       "      <td>6.405788</td>\n",
       "      <td>4.850992</td>\n",
       "      <td>2.601372</td>\n",
       "      <td>5.366627</td>\n",
       "      <td>4.486668</td>\n",
       "      <td>1.824620</td>\n",
       "      <td>2.668043</td>\n",
       "      <td>1.093589</td>\n",
       "      <td>1.179794</td>\n",
       "      <td>1.253998</td>\n",
       "      <td>4.132811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 07</th>\n",
       "      <td>6.196952</td>\n",
       "      <td>3.578245</td>\n",
       "      <td>3.278874</td>\n",
       "      <td>1.789226</td>\n",
       "      <td>1.118990</td>\n",
       "      <td>2.473630</td>\n",
       "      <td>6.532822</td>\n",
       "      <td>3.520403</td>\n",
       "      <td>2.534853</td>\n",
       "      <td>5.002412</td>\n",
       "      <td>4.641057</td>\n",
       "      <td>3.307311</td>\n",
       "      <td>2.850065</td>\n",
       "      <td>1.722743</td>\n",
       "      <td>1.235574</td>\n",
       "      <td>2.658627</td>\n",
       "      <td>4.109321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 08</th>\n",
       "      <td>6.661184</td>\n",
       "      <td>2.485792</td>\n",
       "      <td>4.203633</td>\n",
       "      <td>2.526773</td>\n",
       "      <td>1.271773</td>\n",
       "      <td>4.173333</td>\n",
       "      <td>6.993977</td>\n",
       "      <td>2.083939</td>\n",
       "      <td>2.481596</td>\n",
       "      <td>4.770894</td>\n",
       "      <td>5.050723</td>\n",
       "      <td>4.412293</td>\n",
       "      <td>3.359974</td>\n",
       "      <td>2.659546</td>\n",
       "      <td>1.709469</td>\n",
       "      <td>4.970624</td>\n",
       "      <td>3.970245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 09</th>\n",
       "      <td>6.580679</td>\n",
       "      <td>2.397935</td>\n",
       "      <td>4.303408</td>\n",
       "      <td>2.538650</td>\n",
       "      <td>1.349410</td>\n",
       "      <td>4.050381</td>\n",
       "      <td>6.892116</td>\n",
       "      <td>2.167862</td>\n",
       "      <td>2.474206</td>\n",
       "      <td>4.790725</td>\n",
       "      <td>4.939382</td>\n",
       "      <td>4.359972</td>\n",
       "      <td>3.209846</td>\n",
       "      <td>2.501469</td>\n",
       "      <td>1.569439</td>\n",
       "      <td>4.558039</td>\n",
       "      <td>4.036350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 10</th>\n",
       "      <td>6.270425</td>\n",
       "      <td>3.138566</td>\n",
       "      <td>3.732409</td>\n",
       "      <td>2.009474</td>\n",
       "      <td>1.257453</td>\n",
       "      <td>2.624372</td>\n",
       "      <td>6.613778</td>\n",
       "      <td>2.795865</td>\n",
       "      <td>2.530413</td>\n",
       "      <td>4.562594</td>\n",
       "      <td>4.688713</td>\n",
       "      <td>3.942787</td>\n",
       "      <td>2.977472</td>\n",
       "      <td>2.099791</td>\n",
       "      <td>1.441950</td>\n",
       "      <td>3.425845</td>\n",
       "      <td>4.045377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 11</th>\n",
       "      <td>5.884421</td>\n",
       "      <td>3.630131</td>\n",
       "      <td>3.535049</td>\n",
       "      <td>1.767453</td>\n",
       "      <td>1.053620</td>\n",
       "      <td>1.712602</td>\n",
       "      <td>6.131250</td>\n",
       "      <td>3.492775</td>\n",
       "      <td>2.654989</td>\n",
       "      <td>4.485926</td>\n",
       "      <td>4.370812</td>\n",
       "      <td>3.735191</td>\n",
       "      <td>2.853655</td>\n",
       "      <td>1.907239</td>\n",
       "      <td>1.201831</td>\n",
       "      <td>2.207160</td>\n",
       "      <td>4.186027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 12</th>\n",
       "      <td>6.501589</td>\n",
       "      <td>2.786212</td>\n",
       "      <td>3.946993</td>\n",
       "      <td>2.360780</td>\n",
       "      <td>1.379932</td>\n",
       "      <td>3.265494</td>\n",
       "      <td>6.735913</td>\n",
       "      <td>2.461042</td>\n",
       "      <td>2.584698</td>\n",
       "      <td>4.509039</td>\n",
       "      <td>4.737268</td>\n",
       "      <td>4.211064</td>\n",
       "      <td>3.132669</td>\n",
       "      <td>2.317810</td>\n",
       "      <td>1.593561</td>\n",
       "      <td>4.011348</td>\n",
       "      <td>3.993320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 13</th>\n",
       "      <td>5.958723</td>\n",
       "      <td>4.900701</td>\n",
       "      <td>1.998717</td>\n",
       "      <td>1.252246</td>\n",
       "      <td>1.096185</td>\n",
       "      <td>1.189596</td>\n",
       "      <td>6.244858</td>\n",
       "      <td>5.062644</td>\n",
       "      <td>2.830428</td>\n",
       "      <td>4.981392</td>\n",
       "      <td>4.271392</td>\n",
       "      <td>2.174787</td>\n",
       "      <td>2.494224</td>\n",
       "      <td>1.099078</td>\n",
       "      <td>1.048505</td>\n",
       "      <td>1.038801</td>\n",
       "      <td>4.208040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pr 14</th>\n",
       "      <td>6.919159</td>\n",
       "      <td>2.153370</td>\n",
       "      <td>4.741480</td>\n",
       "      <td>2.719303</td>\n",
       "      <td>1.438620</td>\n",
       "      <td>4.870223</td>\n",
       "      <td>7.156610</td>\n",
       "      <td>1.770335</td>\n",
       "      <td>2.418096</td>\n",
       "      <td>4.713074</td>\n",
       "      <td>5.224105</td>\n",
       "      <td>4.831885</td>\n",
       "      <td>3.455983</td>\n",
       "      <td>2.903162</td>\n",
       "      <td>1.749773</td>\n",
       "      <td>5.488618</td>\n",
       "      <td>3.970054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Att 01    Att 02    Att 03    Att 04    Att 05    Att 06    Att 07  \\\n",
       "Pr 01  6.360633  3.315678  3.494673  1.983676  1.197300  3.092077  6.650614   \n",
       "Pr 02  6.433230  2.481692  4.279644  2.364576  1.254152  3.875998  6.782745   \n",
       "Pr 03  6.350347  3.406499  3.395414  1.964491  1.212921  2.958356  6.660384   \n",
       "Pr 04  6.248422  2.854384  3.965634  2.100824  1.194557  2.990891  6.550625   \n",
       "Pr 05  6.617568  2.145239  4.577304  2.764164  1.431089  4.546312  6.895807   \n",
       "Pr 06  5.904038  5.177300  1.701867  1.112680  1.242584  1.004542  6.405788   \n",
       "Pr 07  6.196952  3.578245  3.278874  1.789226  1.118990  2.473630  6.532822   \n",
       "Pr 08  6.661184  2.485792  4.203633  2.526773  1.271773  4.173333  6.993977   \n",
       "Pr 09  6.580679  2.397935  4.303408  2.538650  1.349410  4.050381  6.892116   \n",
       "Pr 10  6.270425  3.138566  3.732409  2.009474  1.257453  2.624372  6.613778   \n",
       "Pr 11  5.884421  3.630131  3.535049  1.767453  1.053620  1.712602  6.131250   \n",
       "Pr 12  6.501589  2.786212  3.946993  2.360780  1.379932  3.265494  6.735913   \n",
       "Pr 13  5.958723  4.900701  1.998717  1.252246  1.096185  1.189596  6.244858   \n",
       "Pr 14  6.919159  2.153370  4.741480  2.719303  1.438620  4.870223  7.156610   \n",
       "\n",
       "         Att 08    Att 09    Att 10    Att 11    Att 12    Att 13    Att 14  \\\n",
       "Pr 01  3.357080  2.513557  4.994617  4.743662  3.451465  2.936340  1.893384   \n",
       "Pr 02  2.511023  2.454280  4.908544  4.850385  4.143455  3.097305  2.243589   \n",
       "Pr 03  3.341740  2.548721  4.955897  4.718020  3.425761  2.926651  1.914479   \n",
       "Pr 04  2.912082  2.488705  4.912781  4.629963  3.848399  2.921869  1.956625   \n",
       "Pr 05  2.113040  2.483129  4.661089  4.868409  4.456613  3.277748  2.557202   \n",
       "Pr 06  4.850992  2.601372  5.366627  4.486668  1.824620  2.668043  1.093589   \n",
       "Pr 07  3.520403  2.534853  5.002412  4.641057  3.307311  2.850065  1.722743   \n",
       "Pr 08  2.083939  2.481596  4.770894  5.050723  4.412293  3.359974  2.659546   \n",
       "Pr 09  2.167862  2.474206  4.790725  4.939382  4.359972  3.209846  2.501469   \n",
       "Pr 10  2.795865  2.530413  4.562594  4.688713  3.942787  2.977472  2.099791   \n",
       "Pr 11  3.492775  2.654989  4.485926  4.370812  3.735191  2.853655  1.907239   \n",
       "Pr 12  2.461042  2.584698  4.509039  4.737268  4.211064  3.132669  2.317810   \n",
       "Pr 13  5.062644  2.830428  4.981392  4.271392  2.174787  2.494224  1.099078   \n",
       "Pr 14  1.770335  2.418096  4.713074  5.224105  4.831885  3.455983  2.903162   \n",
       "\n",
       "         Att 15    Att 16    Att 17  \n",
       "Pr 01  1.332340  3.131038  4.135418  \n",
       "Pr 02  1.378508  4.007629  4.114168  \n",
       "Pr 03  1.347584  3.123359  4.100673  \n",
       "Pr 04  1.281168  3.177541  4.082263  \n",
       "Pr 05  1.615825  4.739199  4.103596  \n",
       "Pr 06  1.179794  1.253998  4.132811  \n",
       "Pr 07  1.235574  2.658627  4.109321  \n",
       "Pr 08  1.709469  4.970624  3.970245  \n",
       "Pr 09  1.569439  4.558039  4.036350  \n",
       "Pr 10  1.441950  3.425845  4.045377  \n",
       "Pr 11  1.201831  2.207160  4.186027  \n",
       "Pr 12  1.593561  4.011348  3.993320  \n",
       "Pr 13  1.048505  1.038801  4.208040  \n",
       "Pr 14  1.749773  5.488618  3.970054  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get validated predicted X for a given number of components\n",
    "\n",
    "# Predicted X from validation using 3 components\n",
    "X_from_3_component_val = model.X_predVal()[3]\n",
    "\n",
    "# Predicted X from calibration using 3 components stored in pandas data frame with row and columns names\n",
    "X_from_3_component_val_df = pd.DataFrame(model.X_predVal()[3])\n",
    "X_from_3_component_val_df.index = data_objNames\n",
    "X_from_3_component_val_df.columns = data_varNames\n",
    "X_from_3_component_val_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_predVal in module hoggorm.pca:\n",
      "\n",
      "X_predVal(self)\n",
      "    Returns a dictionary holding the predicted arrays Xhat from\n",
      "    validation after each computed component. Dictionary key represents\n",
      "    order of component.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_predVal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PC0</th>\n",
       "      <th>PC1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>new object 0</th>\n",
       "      <td>0.283665</td>\n",
       "      <td>1.669553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 1</th>\n",
       "      <td>2.267250</td>\n",
       "      <td>0.886989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 2</th>\n",
       "      <td>0.154800</td>\n",
       "      <td>1.405315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new object 3</th>\n",
       "      <td>1.242297</td>\n",
       "      <td>0.570414</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   PC0       PC1\n",
       "new object 0  0.283665  1.669553\n",
       "new object 1  2.267250  0.886989\n",
       "new object 2  0.154800  1.405315\n",
       "new object 3  1.242297  0.570414"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get predicted scores for new measurements (objects) of X\n",
    "\n",
    "# First pretend that we acquired new X data by using part of the existing data and overlaying some noise\n",
    "import numpy.random as npr\n",
    "new_data = data[0:4, :] + npr.rand(4, np.shape(data)[1])\n",
    "np.shape(new_data)\n",
    "\n",
    "# Now insert the new data into the existing model and compute scores for two components (numComp=2)\n",
    "pred_scores = model.X_scores_predict(new_data, numComp=2)\n",
    "\n",
    "# Same as above, but results stored in a pandas dataframe with row names and column names\n",
    "pred_scores_df = pd.DataFrame(model.X_scores_predict(new_data, numComp=2))\n",
    "pred_scores_df.columns = ['PC{0}'.format(x) for x in range(2)]\n",
    "pred_scores_df.index = ['new object {0}'.format(x) for x in range(np.shape(new_data)[0])]\n",
    "pred_scores_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function X_scores_predict in module hoggorm.pca:\n",
      "\n",
      "X_scores_predict(self, Xnew, numComp=None)\n",
      "    Returns array of X scores from new X data using the exsisting model.\n",
      "    Rows represent objects and columns represent components.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ho.nipalsPCA.X_scores_predict)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}