Interactive_Variogram_Calculation.ipynb

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    "\n",
    "<p align=\"center\">\n",
    "    <img src=\"https://github.com/GeostatsGuy/GeostatsPy/blob/master/TCG_color_logo.png?raw=true\" width=\"220\" height=\"240\" />\n",
    "\n",
    "</p>\n",
    "\n",
    "## Interactive Variogram Calculation Demonstration\n",
    "\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "\n",
    "### The Interactive Workflow\n",
    "\n",
    "Here's an interactive workflow for calculating directional experimental variograms in 2D. \n",
    "\n",
    "* setting the variogram calculation parameters for identifying spatial data pairs \n",
    "\n",
    "This approach is essential for quantifying spatial continuity with sparsely sampled, irregular spatial data.\n",
    "\n",
    "I have more comprehensive workflows for variogram calculation:\n",
    "\n",
    "* [Experimental Variogram Calculation in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_variogram_calculation.ipynb)\n",
    "\n",
    "* [Determination of Major and Minor Spatial Continuity Directions in Python with GeostatsPy](https://github.com/GeostatsGuy/PythonNumericalDemos/blob/master/GeostatsPy_spatial_continuity_directions.ipynb)\n",
    "\n",
    "#### Spatial Continuity \n",
    "\n",
    "**Spatial Continuity** is the correlation between values over distance.\n",
    "\n",
    "* No spatial continuity – no correlation between values over distance, random values at each location in space regardless of separation distance.\n",
    "\n",
    "* Homogenous phenomenon have perfect spatial continuity, since all values as the same (or very similar) they are correlated. \n",
    "\n",
    "We need a statistic to quantify spatial continuity! A convenient method is the Semivariogram.\n",
    "\n",
    "#### The Semivariogram\n",
    "\n",
    "Function of difference over distance.\n",
    "\n",
    "* The expected (average) squared difference between values separated by a lag distance vector (distance and direction), $h$:\n",
    "\n",
    "\\begin{equation}\n",
    "\\gamma(\\bf{h}) = \\frac{1}{2 N(\\bf{h})} \\sum^{N(\\bf{h})}_{\\alpha=1} (z(\\bf{u}_\\alpha) - z(\\bf{u}_\\alpha + \\bf{h}))^2  \n",
    "\\end{equation}\n",
    "\n",
    "where $z(\\bf{u}_\\alpha)$ and $z(\\bf{u}_\\alpha + \\bf{h})$ are the spatial sample values at tail and head locations of the lag vector respectively.\n",
    "\n",
    "* Calculated over a suite of lag distances to obtain a continuous function.\n",
    "\n",
    "* the $\\frac{1}{2}$ term converts a variogram into a semivariogram, but in practice the term variogram is used instead of semivariogram.\n",
    "* We prefer the semivariogram because it relates directly to the covariance function, $C_x(\\bf{h})$ and univariate variance, $\\sigma^2_x$:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "Note the correlogram is related to the covariance function as:\n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\frac{C_x(\\bf{h})}{\\sigma^2_x}\n",
    "\\end{equation}\n",
    "\n",
    "The correlogram provides of function of the $\\bf{h}-\\bf{h}$ scatter plot correlation vs. lag offset $\\bf{h}$.  \n",
    "\n",
    "\\begin{equation}\n",
    "-1.0 \\le \\rho_x(\\bf{h}) \\le 1.0\n",
    "\\end{equation}\n",
    "\n",
    "#### Variogram Observations\n",
    "\n",
    "The following are common observations for variograms that should assist with their practical use.\n",
    "\n",
    "##### Observation \\#1 - As distance increases, variability increase (in general).\n",
    "\n",
    "This is common since in general, over greater distance offsets, there is often more difference between the head and tail samples.\n",
    "\n",
    "In some cases, such as with spatial cyclicity of the hole effect variogram model the variogram may have negative slope over somelag distance intervals\n",
    "\n",
    "Negative slopes at lag distances greater than half the data extent are often caused by too few pairs for a reliable variogram calculation\n",
    "\n",
    "##### Observation \\#2 - Calculated with over all possible pairs separated by lag vector, $\\bf{𝐡}$.\n",
    "\n",
    "We scan through the entire data set, searching for all possible pair combinations with all other data.  We then calculate the variogram as one half the expectation of squared difference between all pairs.\n",
    "\n",
    "More pairs results in a more reliable measure.\n",
    "\n",
    "##### Observation \\#3 - Need to plot the sill to know the degree of correlation.\n",
    "\n",
    "**Sill** is the variance, $\\sigma^2_x$\n",
    "\n",
    "Given stationarity of the variance, $\\sigma^2_x$, and variogram $\\gamma(\\bf{h})$:\n",
    "\n",
    "we can define the covariance function:\n",
    "\n",
    "\\begin{equation}\n",
    "C_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "The covariance measure is a measure of similarity over distance (the mirror image of the variogram as shown by the equation above).\n",
    "\n",
    "Given a standardized distribution $\\sigma^2_x = 1.0$, the covariance, $C_x(\\bf{h})$, is equal to the correlogram, $\\rho_x(\\bf{h})$: \n",
    "\n",
    "\\begin{equation}\n",
    "\\rho_x(\\bf{h}) = \\sigma^2_x - \\gamma(\\bf{h})\n",
    "\\end{equation}\n",
    "\n",
    "##### Observation \\#4 - The lag distance at which the variogram reaches the sill is know as the range.\n",
    "\n",
    "At the range, knowing the data value at the tail location provides no information about a value at the head location of the lag distance vector.\n",
    "\n",
    "##### Observation \\#5 - The nugget effect, a discontinuity at the origin\n",
    "\n",
    "Sometimes there is a discontinuity in the variogram at distances less than the minimum data spacing.  This is known as **nugget effect**.\n",
    "\n",
    "The ratio of nugget / sill, is known as relative nugget effect (%). Modeled as a discontinuity with no correlation structure that at lags, $h \\gt \\epsilon$, an infinitesimal lag distance, and perfect correlation at $\\bf{h} = 0$.\n",
    "Caution when including nuggect effect in the variogram model as measurement error, mixing populations cause apparent nugget effect\n",
    "\n",
    "This exercise demonstrates the semivariogram calculation with GeostatsPy. The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. sample from the simulation\n",
    "3. calculate and visualize experimental semivariograms\n",
    "\n",
    "#### Variogram Calculation Parameters\n",
    "\n",
    "The variogram calculation parameters include:\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth (isotropic variograms are calculated with an azimuth tolerance of to 90.0)\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance, usually set to the minimum data spacing\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance, commonly set to 50% of unit lag distanceonal smoothing\n",
    "\n",
    "* **number of lags** - set based on the spatial extent of the dataset, we can typically calculate reliable variograms up to 1/2 the extent of the dataset\n",
    "\n",
    "* **bandwidth** is the maximum offset allowable from the lag vector \n",
    "\n",
    "#### Objective \n",
    "\n",
    "In the PGE 383: Stochastic Subsurface Modeling class I want to provide hands-on experience with building subsurface modeling workflows. Python provides an excellent vehicle to accomplish this. I have coded a package called GeostatsPy with GSLIB: Geostatistical Library (Deutsch and Journel, 1998) functionality that provides basic building blocks for building subsurface modeling workflows. \n",
    "\n",
    "The objective is to remove the hurdles of subsurface modeling workflow construction by providing building blocks and sufficient examples. This is not a coding class per se, but we need the ability to 'script' workflows working with numerical methods.    \n",
    "\n",
    "#### Getting Started\n",
    "\n",
    "Here's the steps to get setup in Python with the GeostatsPy package:\n",
    "\n",
    "1. Install Anaconda 3 on your machine (https://www.anaconda.com/download/). \n",
    "2. From Anaconda Navigator (within Anaconda3 group), go to the environment tab, click on base (root) green arrow and open a terminal. \n",
    "3. In the terminal type: pip install geostatspy. \n",
    "4. Open Jupyter and in the top block get started by copy and pasting the code block below from this Jupyter Notebook to start using the geostatspy functionality. \n",
    "\n",
    "You will need to copy the data file to your working directory.  They are available here:\n",
    "\n",
    "* Tabular data - sample_data.csv at https://git.io/fh4gm.\n",
    "\n",
    "There are exampled below with these functions. You can go here to see a list of the available functions, https://git.io/fh4eX, other example workflows and source code. \n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geostatspy.GSLIB as GSLIB                       # GSLIB utilies, visualization and wrapper\n",
    "import geostatspy.geostats as geostats                 # GSLIB methods convert to Python    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will also need some standard packages. These should have been installed with Anaconda 3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import os                                               # to set current working directory \n",
    "import sys                                              # supress output to screen for interactive variogram modeling\n",
    "import io\n",
    "import numpy as np                                      # arrays and matrix math\n",
    "import pandas as pd                                     # DataFrames\n",
    "import matplotlib.pyplot as plt                         # plotting\n",
    "from matplotlib.pyplot import cm                        # color maps\n",
    "from ipywidgets import interactive                      # widgets and interactivity\n",
    "from ipywidgets import widgets                            \n",
    "from ipywidgets import Layout\n",
    "from ipywidgets import Label\n",
    "from ipywidgets import VBox, HBox"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#os.chdir(\"d:/PGE383\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Loading Tabular Data\n",
    "\n",
    "Here's the command to load our comma delimited data file in to a Pandas' DataFrame object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "      <td>100.0</td>\n",
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       "      <td>2534.551236</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154\n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666\n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513\n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787\n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df = pd.read_csv(\"sample_data_MV_biased.csv\")           # read a .csv file in as a DataFrame\n",
    "df = pd.read_csv(\"https://raw.githubusercontent.com/GeostatsGuy/GeoDataSets/master/sample_data_MV_biased.csv\")\n",
    "#print(df.iloc[0:5,:])                                  # display first 4 samples in the table as a preview\n",
    "df.head()                                               # we could also use this command for a table preview "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will work with all facies pooled together. I wanted to simplify this workflow and focus more on spatial continuity direction detection. Finally, by not using facies we do have more samples to support our statistical inference. Most often facies are essential in the subsurface model. Don't worry we will check if this is reasonable in a bit.   \n",
    "\n",
    "You are welcome to repeat this workflow on a by-facies basis.  The following code could be used to build DataFrames ('df_sand' and 'df_shale') for each facies.\n",
    "\n",
    "```p\n",
    "df_sand = pd.DataFrame.copy(df[df['Facies'] == 1]).reset_index()  # copy only 'Facies' = sand records\n",
    "df_shale = pd.DataFrame.copy(df[df['Facies'] == 0]).reset_index() # copy only 'Facies' = shale records\n",
    "```\n",
    "\n",
    "Let's look at summary statistics for all facies combined:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <td>368.0</td>\n",
       "      <td>293.260870</td>\n",
       "      <td>169.058258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>150.500000</td>\n",
       "      <td>296.000000</td>\n",
       "      <td>439.500000</td>\n",
       "      <td>586.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>X</th>\n",
       "      <td>368.0</td>\n",
       "      <td>499.565217</td>\n",
       "      <td>289.770794</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>762.500000</td>\n",
       "      <td>990.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Y</th>\n",
       "      <td>368.0</td>\n",
       "      <td>520.644022</td>\n",
       "      <td>277.412187</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>269.000000</td>\n",
       "      <td>539.000000</td>\n",
       "      <td>769.000000</td>\n",
       "      <td>999.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Facies</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.597826</td>\n",
       "      <td>0.491004</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Porosity</th>\n",
       "      <td>368.0</td>\n",
       "      <td>0.127026</td>\n",
       "      <td>0.030642</td>\n",
       "      <td>0.041122</td>\n",
       "      <td>0.103412</td>\n",
       "      <td>0.125842</td>\n",
       "      <td>0.148623</td>\n",
       "      <td>0.210258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Perm</th>\n",
       "      <td>368.0</td>\n",
       "      <td>85.617362</td>\n",
       "      <td>228.362654</td>\n",
       "      <td>0.094627</td>\n",
       "      <td>2.297348</td>\n",
       "      <td>10.377292</td>\n",
       "      <td>50.581288</td>\n",
       "      <td>1991.097723</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AI</th>\n",
       "      <td>368.0</td>\n",
       "      <td>4791.736646</td>\n",
       "      <td>974.560569</td>\n",
       "      <td>1981.177309</td>\n",
       "      <td>4110.728374</td>\n",
       "      <td>4713.325533</td>\n",
       "      <td>5464.043562</td>\n",
       "      <td>7561.250336</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            count         mean         std          min          25%  \\\n",
       "Unnamed: 0  368.0   293.260870  169.058258     0.000000   150.500000   \n",
       "X           368.0   499.565217  289.770794     0.000000   240.000000   \n",
       "Y           368.0   520.644022  277.412187     9.000000   269.000000   \n",
       "Facies      368.0     0.597826    0.491004     0.000000     0.000000   \n",
       "Porosity    368.0     0.127026    0.030642     0.041122     0.103412   \n",
       "Perm        368.0    85.617362  228.362654     0.094627     2.297348   \n",
       "AI          368.0  4791.736646  974.560569  1981.177309  4110.728374   \n",
       "\n",
       "                    50%          75%          max  \n",
       "Unnamed: 0   296.000000   439.500000   586.000000  \n",
       "X            500.000000   762.500000   990.000000  \n",
       "Y            539.000000   769.000000   999.000000  \n",
       "Facies         1.000000     1.000000     1.000000  \n",
       "Porosity       0.125842     0.148623     0.210258  \n",
       "Perm          10.377292    50.581288  1991.097723  \n",
       "AI          4713.325533  5464.043562  7561.250336  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().transpose()                               # summary table of sand only DataFrame statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's transform the porosity and permeaiblity data to standard normal (mean = 0.0, standard deviation = 1.0, Gaussian shape). This is required for sequential Gaussian simulation (common target for our variogram models) and the Gaussian transform assists with outliers and provides more interpretable variograms. \n",
    "\n",
    "Let's look at the inputs for the GeostatsPy nscore program.  Note the output include an ndarray with the transformed values (in the same order as the input data in Dataframe 'df' and column 'vcol'), and the transformation table in original values and also in normal score values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function geostatspy.geostats.nscore(df, vcol, wcol=None, ismooth=False, dfsmooth=None, smcol=0, smwcol=0)>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geostats.nscore                                         # see the input parameters required by the nscore function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following command will transform the Porosity and Permeabilty to standard normal. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Transform to Gaussian by Facies\n",
    "df['NPor'], tvPor, tnsPor = geostats.nscore(df, 'Porosity') # nscore transform for all facies porosity \n",
    "df['NPerm'], tvPermSand, tnsPermSand = geostats.nscore(df, 'Perm')  # nscore transform for all facies permeability"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the updated DataFrame to make sure that we now have the normal score porosity and permeability."
   ]
  },
  {
   "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>Unnamed: 0</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Facies</th>\n",
       "      <th>Porosity</th>\n",
       "      <th>Perm</th>\n",
       "      <th>AI</th>\n",
       "      <th>NPor</th>\n",
       "      <th>NPerm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>900.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.101319</td>\n",
       "      <td>1.996868</td>\n",
       "      <td>5590.417154</td>\n",
       "      <td>-0.749088</td>\n",
       "      <td>-0.767247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>100.0</td>\n",
       "      <td>800.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.147676</td>\n",
       "      <td>10.711789</td>\n",
       "      <td>3470.845666</td>\n",
       "      <td>0.653263</td>\n",
       "      <td>0.017030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>100.0</td>\n",
       "      <td>700.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.145912</td>\n",
       "      <td>17.818143</td>\n",
       "      <td>3586.988513</td>\n",
       "      <td>0.611663</td>\n",
       "      <td>0.336607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>100.0</td>\n",
       "      <td>600.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.186167</td>\n",
       "      <td>217.109365</td>\n",
       "      <td>3732.114787</td>\n",
       "      <td>1.993601</td>\n",
       "      <td>1.211919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>100.0</td>\n",
       "      <td>500.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.146088</td>\n",
       "      <td>16.717367</td>\n",
       "      <td>2534.551236</td>\n",
       "      <td>0.628172</td>\n",
       "      <td>0.279461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0      X      Y  Facies  Porosity        Perm           AI  \\\n",
       "0           0  100.0  900.0     0.0  0.101319    1.996868  5590.417154   \n",
       "1           1  100.0  800.0     1.0  0.147676   10.711789  3470.845666   \n",
       "2           2  100.0  700.0     1.0  0.145912   17.818143  3586.988513   \n",
       "3           3  100.0  600.0     1.0  0.186167  217.109365  3732.114787   \n",
       "4           4  100.0  500.0     1.0  0.146088   16.717367  2534.551236   \n",
       "\n",
       "       NPor     NPerm  \n",
       "0 -0.749088 -0.767247  \n",
       "1  0.653263  0.017030  \n",
       "2  0.611663  0.336607  \n",
       "3  1.993601  1.211919  \n",
       "4  0.628172  0.279461  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()                                               # preview sand DataFrame with nscore transforms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "That looks good! One way to check is to see if the relative magnitudes of the normal score transformed values match the original values.  e.g. that the normal score transform of 0.10 porosity normal score is less than the normal score transform of 0.14 porsity.  Also, the normal score transform of values close to the mean value should be close to 0.0 \n",
    "\n",
    "Let's also check the original and transformed sand and shale porosity distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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+Cb/f7+YEiKRILBbj1KFDLL3wArs8Hu5va+PmtjY1lSICwMjICPv37+c3fuM3uO+++5idneXRRx/lFa94BXfeeScf+9jHLmzb1dXFY489xr333suBAwcYGhoC4jX77rvv5p577uG+++7D5/Nd7uNEJAesr28wNjbFCy+c4sc/nge6aG6+h+7u+6mqqlVTmSFqLFOkv7+f++9/6Te5NTU19PT0EIlEeO655/jiF7/ID3/4w5ds85nPfIb6+npOnjzJ7//+73PkyKW/wR0eHuZ973sf/f391NXV8a1vfQuAN73pTRw6dIgTJ05w++2387d/+7fpOUCRNFuYnaXvRz/i5LPPwtwc+7u6aKqtdR1LRLLQwMAA73znOzl27BidnZ380R/9EYcPH+bEiRN8//vfZ2Bg4MK2ra2tHDt2jHe961386Z/+KQCPP/44TzzxBMePH+fpp5/Gq6WCRHJONBplenqWH/2oj+efH+bUqSihUA9dXfupq2t1Ha8gqbFMEWvtJWeRO//8q1/9ahoaGl72+rPPPstb3/pWAPbv38+BAwcuuf89e/Zwzz3xG4zvv/9+JiYmADh16hQ/+7M/y1133cVXv/pV+vv7U3REIum3ubnJ6uoq0WiUuelpGjc32efxcKCrS7Myishl3XzzzTzwwAMXHn/961/nvvvu47777uP06dMvaSzf9KY3AS+tna961av4wAc+wF/+5V+ysbGBx+PJaH4RuX6BQIDZ2Vl++MPjHD06QyDQQXX1nXR27qG+vlm/PziUl/dYunDnnXdeuIp43sbGBmfPnsXj8VBZWXnJ99nEUglXU1b2k0v4Ho/nwlDYRx55hG9/+9vcfffdfOELX6C3t/f6DkAkg6y1TE9NsTAwQH1REdFgkMpQiKb6ei0VIiJXtbOmDg8P8+d//ue8+OKL1NXV8fa3v51AIHDh9fP10+PxEIlEAPjoRz/Kww8/zD/90z/xwAMP0Nvby969ezN7ECJyTcLhMNPTc0xOLrCxUU5V1S46O5tcx5IddMUyRR566CH8fj9f+tKXgPjl+Q9+8IM88sgjVFRUXPZ9P/MzP8OTTz4JxIf29PX1XdPnbm5u0t7eTjgc5qtf/er1H4BIBi0tLrJw9Ci3VlZyU1MTpcXF3Kz1J0XkOmxsbFBdXU1NTQ2zs7N873vfu+p7RkdHOXDgAB/5yEe49957GRwczEBSEbke0WiUkycHefbZkxw9uk40upvOzjuorVVTmW3y8oplaV1dUjO5Xsv+rsYYw9///d/z3ve+l49//OPEYjFe//rX84lPfIKvf/3rl33fe9/7Xn7913+dAwcOXJhcoPYa7iv7+Mc/zoMPPsiuXbu466672NzcTPq9Iq5sb23RXl5O9WWu5ItI9qmrK73qTK7Xur9UuO+++7jjjjvYv38/N910E6961auu+p5PfvKTPPPMMxQVFXHgwAFe85rXpCSLiKTOzhle19eLaG29nZ4er2aBzmJ52Vgms+ZkOnR3d/Pd7373Zc8/8sgjPPLIIxce79q1i1OnTgHg9Xr5yle+gtfrZXR0lIceeohdu3YBXLgXpKmp6cL2wEumW3/Pe97De97znpd95mOPPZaCIxJJvdnpaZYXFujUPU0iOeVKa06m286adsstt1xYhgTiX+x++ctfvuT7pqenL/z8yle+kh/84AcAfPazn01PUBFJiYWFJQYHJ1laKqWmpotduzQZTy7Iy8Yyl/j9fn7u536OcDiMtZbPfvazWvRZ8tKZ48cJ+3yEFhfpqaqiQTO+ioiIyA6bm5ucODHO+nqY4uIOurvbNBlPDlFj6Vh1dfWFdStF8lUkEsE3N8edNTV42tspKdY/PSIiIhK3vb1NIBBgYWGBlZVKWlq6KSnRhZZckze/3V1uuQ/5iWRnoBVJtYWFBcr9frzNza6jiMg1KNTaqnopkjkzM3MMDp5jc7OCkpJS6utb1VTmqLxoLL1eL8vLyzQ2NhZkAUyGtZbl5WUtAi0ZFw6HCYfD1GuiHpGcUqi1VfVSJDMCgQAnTgwzNxeiqmo3nZ2NriPJDcqLxrKrq4vp6WkWUzgTbDoFAgEnBcvr9dLV1ZXxz5XCtbS0xOQLL4C13KTGUiSn5FptTYXz9Vn1UiR9IpEI586d4/TpJcLhatrabqO4WMuN5YO8aCxLSkrYs2eP6xhJ6+3t5d5773UdQyRtFubm2N7aYm1tjTaPh04NgRXJOblWW1NB9VkkvdbW1hkeHmdy0tLcfAvNzZrIL5/kRWMpItnlbF8fnbEYHR4PdfX1ruOIiIiIQ9ZaBgZGmJ7eIBRqpLu7i2JN5Jd39P+oiKSUz+cDoKmuTkVDRESkwPn9foaGxhkdDdDWdgdeb7nrSJIm+q1PRFIiFAoxOzXF0sQEDeEwHo/HdSQRERFxaH19g8OHh/H7q+nqulX3UuY5NZYikhJra2usnzjBzY2NGv4qIiJSwKy1DA6OMzW1SijUREfHLteRJAPUWIrIDfH7/QQCAc4ODtJRXU1ddbXrSCIiIuLI9vY2o6MTDA35aWm5jcZGzQpfKNRYisg1WVlaYmNl5cK6dktTU1T5/TSXlNDW1OQ4nYiIiLiyubnF4cODbGxU0N6+n9LSMteRJIPUWIrINZkZGaF6aYnK8vjN9zVFRdR3dDhOJSIiIq5YaxkdnWJiYgmfr56urptcRxIH1FiKSFICgQBLS0sEl5e5tamJ0hLdgC8iIlLorLUMDQ1z+vQm9fV76eqqcR1JHFFjKSJXFI1GOTc5yeLZs3hXV9lTW6umUkRERLDW0t8/yNCQj9ZWLSVS6NRYisgVTU9MsHT0KHsaG6nv6rpwb6WIiIgUrq2tLY4cGWRpCTo6dD+lqLEUkasIbW2xp7GRhtpa11FEREQkC2xubnLo0BDBYBPd3V1au1oANZYichmxWIzpiQk2VldpUcEQERERYH19g8OHhwmHW2hp6XYdR7KIGksRuSASiRCLxQA4NzXFyvHj7GlooKZGN+KLiIgUupWVFQ4fHiccbqGtTU2lvJQaSxEBYHl5mYnjxykNhwGIhELsa22lqqLCcTIRERFxbWFhgePHzwKdtLW1uY4jWUiNpYgQDoeZePFFuktLaWludh1HREREssjCwhJHj56ltHQ3tbWNruNIllJjKVLgrLWEQiGKrKWlrs51HBEREcki587Ncvz4DF7vLjWVckVqLEUK3NjICGtnzlCrCXpERERkh6mpKfr6FqmouImamnrXcSTLqbEUKWDRaJS10VHubGzEW6b1p0RERCQuFApx4sQitbV7qazUJH5ydWosRQqYtRaiUTWVIiIiAsR/NxgZmWR7O0JDwz4qKqpcR5IcUeQ6gIi4MzE2RnEg4DqGiIiIZImpqSlOnVrG4/GqqZRrosZSpECdHR9nfWSEfe3trqOIiIhIFlhdXWNwcInGxlspKlKbINdGQ2FFCsza2hpjR45gNzfZp3srRUREBFhcXOTIkSmgncrKara2XCeSXKPGUqSARCIRZmZmaNzepqujA49mghURESl4s7OznDw5Q3FxD/X1Ws9aro+ucYsUkKWlJUL9/bTU16upFBEREfx+P8eOzVBWdpOaSrkhaixFCkggEKC5uppyr9d1FBEREXHM7/dz6NBpYrFGqqu1TqXcGA2FFSkAm5ubzM7Osjk4yM3V1a7jiIiIiGPRaJQXXjhNMNhEW1uP6ziSB9RYiuSxQCDAzPg4q1NT1Eej3N7QQIWuVoqIiBS0aDTKiRMDrK+X0929y3UcyRNpHQprjHmtMWbQGDNijPnwJV7vMcb8qzHmmDHmpDHm9enMI1JoVldX8Q8McGt5OTe1tqqpFMkDqq0icqOef/444+MR2tv3uo4ieSRtjaUxxgN8GngdcAfwNmPMHRdt9lHgSWvtvcBbgc+kK49IoYnFYswMDdFSW0t1ZaXrOCKSAqqtInKjJiYmmJ+Hnp67KS4ucR1H8kg6r1j+FDBirR2z1oaAbwBvvGgbC9Qkfq4FZtKYR6SgrKysULK8THNdnesoIpI6qq0ict1GRiY4eXKZpqZbKSrSHJ6SWsZam54dG/PLwGutte9KPP414EFr7ft3bNMO/E+gHqgEfsFae+QS+3oUeBSgubn5/ieffDItmTNla2uLqqoq1zFuiI4hO1zqGIKBALFwGAuUWEtJcfbfSr0ViVCVAzmvRMfgXjAS4bVvfOMRa+0rXGdJF9XW9MqHunCjdA7i8vE8RKNRtraCeDzepJrKSGSL4uL8OgfXo9DPQzQa4eGHX51UbU3nbxDmEs9d3MW+DfiCtfZPjDE/DXzZGLPfWht7yZusfQJ4AmDfvn324MGD6cibMb29vegY3MvHY4jFYhzr7WVfZSVlpaUUezwYc6m/itmld36eg62trmPcEB2DeyOLi64jZIJqaxrlQ124UToHcfl2HlZWVjh6dJzKyk7q69uSes/8fC+trQfTGywHFPp5WFlZSHrbdF4Dnwa6dzzu4uXDcd4JPAlgrX0O8AJNacwkktfW19fxrK1RWV5OSXFxTjSVInJNVFtF5JqEw2EOHx7H2q6km0qR65HOxvIQsNcYs8cYU0p8AoHvXLTNFPAQgDHmduLFryC+chZJNb/fz9jx4zRUVKihFMlfqq0ikrRoNMpzz50kEKiioSF3R6RIbkhbY2mtjQDvB74HnCY+Q12/MeZjxpiHE5t9EHi3MeYE8HXgEZuumz5F8tza2hpVfj/dTbowIZKvVFtFJFnWWkZGRlhcLKaj41bXcaQApHWWBmvtU8BTFz33Bzt+HgBelc4MIoViZXKSltJSXa0UyXOqrSKSjJmZWfr7t2hru0O/G0hG5O70fyICwMjAAOszMxAIUKerlSIiIgVvZWWFU6dmqa3djddb7jqOFAg1liI5LBKJsD42xh319ZTW1ODxeFxHEhEREYeCwSD9/eNAB3V1ja7jSAHRyqgiOWp5cZHQ9jadpaWUe71qKkVERIRjx86wuFhJY6NmgJXMUmMpkoOCwSATR49Sai1tjfo2UkRERODUqSFmZyP09OzTfZWScWosRXLQxsYG3kiE4mKNZhcRERGYn59ncnKT9va71FSKE2osRXLMytISU3191KupFBEREeIjmY4enaaiYg8lJaWu40iBUmMpkmOWpqdp3t6mQzPAioiIFLxQKMTzz58iGq2npqbBdRwpYGosRXKItZbtQIDaqirXUURERCQLjI9PsrJSTlvbHtdRpMCpsRTJEdFolDMnTxKZnaWqXGtSiYiIFLqFhSXGxjZobb1J91WKc2osRXJANBpldHAQ/9AQ+9vatLSIiIhIgQsEApw8OUlp6S7Kyryu44ig2T9EcsCZvj4CY2Pc0dFBWaluyhcRESlksViMF17oJxSqo7VVcy5IdlBjKZKlYrEYk8PDBHw+AmfPcndnp5YXEREREc6ePcfycjE9PTe5jiJygX5LFclSkUiElaEhbq6uxtvcrKZSREREWFxcpL9/gYaGm3VfpWQV/aYqkqUCgQBFRUXUVVe7jiIiIiJZYHt7m4GBKUpLe6iurnMdR+Ql1FiKZBlrLZPDwyzPztIQi7mOIyIiIlnAWsvRo2dYW6uhs1P3VUr20aywIlkmHA6zPDzMzdayu73ddRwRERHJArOzs8zPx+jsvEVDYCUrqbEUyTLzMzN4QyHqqqtVOERERISVlRVOnJilvl7rVUr2UmMpkmVsLEZzVZXrGCIiIpIlBgensLaVmpp611FELkuNpUgWicViLJ49S4lmgBURERHg5MkzzM5GaWnpdB1F5IrUWIpkkUAgAFtb1NfUuI4iIiIijq2trTE15aOz84CGwErWU2MpkiWstQyfOkVdJOI6ioiIiDgWCAQ4eXIUY5opLi5xHUfkqtRYimSJWCxGZGmJPR0drqOIiIiIY/39wywtVdDa2u06ikhSdCOXSBZYWVmJD4MFior0fY+IiEgh29jYYG4uRGfnbRoCKzlDjaWIY5ubm4z/+Me0Fhezq6zMdRwRERFxKBgMcuLEMLFYo4bASk5RYyni2NDx47R5PHQ2N7uOIiIiIo6dPj3CwoKXXbt2uY4ick005k7EodXVVfD56GxpcR1FREREHFtcXGR2NkBn514NgZWcoyuWIo5MjY6yeCuiTyUAACAASURBVOYM3Rr+KiIiUvBCoRBHjkxRWrqLkpJS13FErpkaS5EMs9YyMzPDYl8ftzU1UVle7jqSiIiIOHbmzCiBQAXNzU2uo4hcFzWWIhkSiURYWlpibW0N3+AgN9fVqakUERERlpeXmZnx09q633UUkeumxlIkQ0bPnGF7YIDmmhp2NTVR7vW6jiQiIiKOhUIhDh+ewOPpprRUt8dI7lJjKZIhvvl5bu/sVEMpIiIiF4yMTODzedm1SxP5SW7TrLAiGVJcXExxsb7LERERkbiVlRXOnt2kre1m11FEbpgaSxERERGRDAuHwxw+PA50Ulam0UyS+9RYimTA8vIy4fV1irQmlYiIiACjo5NsbpbS1NTmOopISqixFEmzaDTKxKlTdJWX4/F4XMcRERERx0KhEGfPrtPevtd1FJGUUWMpkmZra2uULi/TUl/vOoqIiIg4FolEOHKkH7+/WkNgJa+osRRJs4kzZ6gtL8doGKyIiEjBGx+f4uxZS3u7JuyR/KLGUiSN1tbWYGGBzsZG11FERETEsUgkwuTkKp2dt+v2GMk7aixF0mhpbo6GsjIVDxERkQIXjUY5cuQUW1uVeL3lruOIpJwaS5E0WV1dZX10lLbaWtdRRERExLHJybNMTkbp6NCEPZKf1FiKpMn04CDtJSWUe3VjvoiISCGLRqOMjy/T3r5Po5gkb6mxFEmDYDBIaGODxupq11FERETEsfHxKdbXvVRUVLmOIpI2aixF0mB9fZ3yQICy0lLXUURERMShc+dmOH16hZaW3a6jiKSVGkuRFIvFYpwdHKROQ2BFREQKWjQaZWholpqavZSXV7qOI5JWaixFUmxzc5PipSXaGxpcRxERERGHJienWVkppaqqxnUUkbRTYymSBpXl5RhjXMcQERERR8LhMKOjSzQ373YdRSQj0tpYGmNea4wZNMaMGGM+fJlt3myMGTDG9BtjvpbOPCKZsLW1hbXWdQwRyVOqrSLZLxaLcebMID5fBZWVmshPCkNxunZsjPEAnwZeDUwDh4wx37HWDuzYZi/wEeBV1tpVY0xLuvKIZEIoFGLu9Gl2a9IeEUkD1VaR3DA1dY7Tp4P09NzlOopIxqTziuVPASPW2jFrbQj4BvDGi7Z5N/Bpa+0qgLV2IY15RNJqcXGRvmeeoWZ7m8a6OtdxRCQ/qbaKZLlIJMLo6AKtrbdQUqIvmqVwpO2KJdAJnN3xeBp48KJtbgUwxvwI8ACPWWv/x8U7MsY8CjwK0NzcTG9vbzryZszW1paOIQuk8hgikQghn4/SoiI2PR7Ozc+nZL9XsxWJ0Juhz0qnfDgOHYN7wUjEdYRMUG1No3yobTdK5yDuRs5DKBTC749RWrqJz5faXJkUiWwxP9/rOoZzhX4eotHka2s6G8tLzVxy8Y1nxcBe4CDQBTxjjNlvrV17yZusfQJ4AmDfvn324MGDKQ+bSb29vegY3EvVMYTDYU4+/TTdFRW0ZHgm2N75eQ62tmb0M9MhH45Dx+DeyOKi6wiZoNqaRvlQ226UzkHc9Z6HxcVlDh+eoLFxHxUVVakPlkHz8720th50HcO5Qj8PKyvJD3pJ51DYaaB7x+MuYOYS2/yDtTZsrR0HBokXQ5GcEAwGOfn001T6fBlvKkWkIKm2imSpWCxGX98EJSU9Od9UilyPdDaWh4C9xpg9xphS4K3Ady7a5tvAzwEYY5qID98ZS2MmkZSw1jI3O8vZqSnKQyFu6+x0HUlECoNqq0iWmpqaYm2tlPr6ZtdRRJxIqrE0xuy/1h1bayPA+4HvAaeBJ621/caYjxljHk5s9j1g2RgzAPwr8J+stcvX+lkimRYIBDj34otUjo3RXVnpOo6I5CDVVpH8EQ6HGR5eprFxl+soIs4ke4/lXyW+Gf0C8LWL79O4HGvtU8BTFz33Bzt+tsBvJ/6I5IxwOExZaSntTU2uo4hI7lJtFckTY2OTbGyUsnt3jesoIs4kdcXSWvszwH8kfl/HYWPM14wxr05rMpEs5fP5GOnvpyIadR1FRHKYaqtIflhfX2dqap32dt3KLIUt6XssrbXDwEeB3wH+HfAXxpgzxpg3pSucSLaJRCKcOXyY6rk5drVozXERuTGqrSK5LRKJcOjQCNFoO2VlXtdxRJxK9h7LA8aYTxG/n+PngTdYa29P/PypNOYTyRqBQIATzzxD6coKe3t68Hg8riOJSA5TbRXJfRMTU6yvF9Pc3OE6iohzyd5j+V+BzwG/a63dPv+ktXbGGPPRtCQTySJ+v5/BU6eo3tzk1p4e13FEJD+otorksEgkwsTEKm1tt7uOIpIVkm0sXw9sW2ujAMaYIsBrrfVba7+ctnQiWSAajXL6yBGqFha4ubv76m8QEUmOaqtIDhsZmWBjo5z6+grXUUSyQrL3WP4AKN/xuCLxnEhei8ViHH/mGYoXF9m3e7eGv4pIKqm2iuSos2enGRxcp7V1t+soIlkj2cbSa63dOv8g8bO+npG85/P5wOfj7l1al0pEUk61VSQHRSIRhobmaWjYh9erv7Ii5yXbWPqMMfedf2CMuR/YvsL2IjnPWsvwyZM0xmKuo4hIflJtFclBExNnWV8vo6KiynUUkayS7D2WHwD+zhgzk3jcDrwlPZFEskM4HMaurbGrvd11FBHJT6qtIjkmFAoxNrZCS8ttrqOIZJ2kGktr7SFjzG3APsAAZ6y14bQmE3FsYW6OinAYY4zrKCKSh1RbRXLP+Pgkfn8ljY2VrqOIZJ1kr1gCPADsTrznXmMM1tovpSWViGNrq6vMj47SU6VhLiKSVqqtIjliZmaekZENmpvvcB1FJCsl1VgaY74M3AwcB6KJpy2g4id5x1rL6IkTtAeDNDU1uY4jInlKtVUkd4TDYQYGpqmouAmvt/zqbxApQMlesXwFcIe11qYzjEg2CAQC4PfT0drqOoqI5DfVVpEcMTk5xdZWBd3d9a6jiGStZGeFPQW0pTOISLY4fewY9WHd5iQiaafaKpIDQqEQo6NrNDX1uI4iktWSvWLZBAwYY14EgueftNY+nJZUIo5MT09jZ2fZo3UrRST9VFtFcsDg4Bjb2xU0NWnCHpErSbaxfCydIUSyxfzgIPtaWjQTrIhkwmOuA4jIlQUCAWZmfLS27ncdRSTrJbvcyL8ZY3YBe621PzDGVACe9EYTyayNjQ3w+aiqq3MdRUQKgGqrSHYLhUIcPtxPMFhHaWmZ6zgiWS+peyyNMe8Gvgn8deKpTuDb6Qolkml+v5+5uTkai5K97VhE5Maotopkt9HRSebnS2hv3+M6ikhOSPa36PcBrwI2AKy1w0BLukKJZFIwGOT0c8/B6ChNtbWu44hI4VBtFclS1lqmpjbo6NhHkb50FklKsn9Tgtba0PkHxphi4mttieS8lZUVKtfXubWlhaqKCtdxRKRwqLaKZKlgMMT2dpWGwIpcg2Qby38zxvwuUG6MeTXwd8B30xdLJDNisRgzw8M01NS4jiIihUe1VSQL+Xw+QqEora27XUcRySnJNpYfBhaBPuD/BJ4CPpquUCKZYK1loK+P4sVFmjVhj4hknmqrSJYJh8P09w9hrUdXK0WuUbKzwsaAzyX+iOSFaDRKbGiI27u7tbyIiGScaqtI9hkZmWBiooiqKjWVItcqqcbSGDPOJe77sNbelPJEIhkwPz9PyO+nraGB0pIS13FEpACptopkl+3tbaamNujsvIP19RdcxxHJOUk1lsArdvzsBX4FaEh9HJH0C4fDTPf3U2YMLRoCKyLuqLaKZJHR0UmCwVqamspZX3edRiT3JHWPpbV2ecefc9baPwN+Ps3ZRNJieGCA8rU1PB6tQy4i7qi2imQPv9/P5KSPlpYe11FEclayQ2Hv2/GwiPi3rNVpSSSSRgsLC2xPTnKgs5OF5WXXcUSkgKm2imSHaDRKX98gsVgdJSWlruOI5Kxkh8L+yY6fI8AE8OaUpxFJs9mhIXoqKykpTvY/fRGRtFFtFckCQ0OjnD1r6O7e5TqKSE5LdlbYn0t3EJFM8BhDTUWF6xgiIqqtIllge3ub8fFNOjv3U6wvnUVuSLJDYX/7Sq9ba/80NXFE0sdaSzAYhPJy11FERFRbRbLA4OAYkUiN1qwUSYFrmRX2AeA7icdvAJ4GzqYjlEg6TE1OYlZXKanWLUwikhVUW0Uc2tra4ty5AK2tt7qOIpIXkm0sm4D7rLWbAMaYx4C/s9a+K13BRFIlEokwPT7O8vAwe5uaKCpKajJkEZF0U20VcWhwcJxIpJ7iYq1nLZIKyTaWPUBox+MQsDvlaURSbGVlhfFDh8Dv57bmZio1DFZEsodqq4gjCwsLzM6GaGu73XUUkbyRbGP5ZeBFY8zfAxb4D8CX0pZKJEXmxsZoBzp6tC6ViGQd1VYRB/x+P0ePnqWycrcm7BFJoWRnhf1/jTH/DPxs4ql3WGuPpS+WyI0LBoNsr6ywp6rKdRQRkZdRbRVxY2honGCwlubmRtdRRPLKtdxsVgFsWGv/HJg2xuxJUyaRlNjY2MAbDFLu9bqOIiJyOaqtIhm0sbHBuXMB2tq0ZqVIqiXVWBpj/hD4HeAjiadKgK+kK5TIjYpGo0wNDFBXWuo6iojIJam2imTe0NAk0KgJe0TSINkrlv8BeBjwAVhrZwCt2SBZy+/341ldpaNRw1xEJGuptopk0OrqKufOhWhp0bwLIumQbGMZstZa4pMLYIypTF8kkRsXCAQoLyvDGOM6iojI5ai2imTI1tYWhw+PUV7eqWXHRNIk2b9ZTxpj/hqoM8a8G/gB8Ln0xRK5Mctnz1KpplJEsptqq0iGjIxMEgw20NjY5jqKSN5KdlbYTxpjXg1sAPuAP7DWfj+tyUSuUygUwre8TE+1RpSJSPZSbRXJjK2tLc6eDdDaepvrKCJ57aqNpTHGA3zPWvsLgAqeZL2NjQ3KgkEqGhpcRxERuSTVVpHM6esbpqioCY/H4zqKSF676lBYa20U8BtjajOQR+SGLU9PU6viISJZTLVVJDMmJqZYXIzR3NzlOopI3ktqKCwQAPqMMd8nMXsdgLX2N9OSSuQ6BYNBtpaW6NIwWBHJfqqtImnk8/k4dWqR+vp9ulopkgHJNpb/lPgjktUG+/qo9PmorK93HUVE5GpUW0XSqL9/BGMaqKioch1FpCBcsbE0xvRYa6estV/MVCCR6zU/N0f43DnuaNOMbyKSvVRbRdJvfX2d+fkILS3drqOIFIyr3WP57fM/GGO+da07N8a81hgzaIwZMcZ8+Arb/bIxxhpjXnGtnyECMHfuHNMnTtBTWUlxcbIX4kVEnFBtFUmzgYExioqa9TuBSAZdrbHcuRDgTdey48SMd58GXgfcAbzNGHPHJbarBn4TeOFa9i+y09bqKl3G0KwhsCKS/VRbRdJoZmaGhYUYLS2asEckk67WWNrL/JyMnwJGrLVj1toQ8A3gjZfY7uPAHxOfxEDkmq2vrbG+vExZaanrKCIiyVBtFUmTzc1Njh2bpa7uZoqKrrr4gYik0NXGB9xtjNkg/u1qeeJnEo+ttbbmCu/tBM7ueDwNPLhzA2PMvUC3tfYfjTEfutyOjDGPAo8CNDc309vbe5XY2W1ra0vHkEL+zU2KYzE2S0rA70/6fVuRCL3z82lMln75cAyQH8ehY3AvGIm4jpAs1dYslU21zZVcPwd+/zaRiAefbxOf7+rbX04kssX8fG/KcuUinYO4Qj8P0WjytfWKjaW19kbmZjaXeO7CN7PGmCLgU8AjV9uRtfYJ4AmAffv22YMHD95ALPd6e3vRMaRGIBBg8F/+hbs7Oq75vb3z8xxsbU1DqszJh2OA/DgOHYN7I4uLriMkRbU1e2VLbXMpl8/B6uoqzz47Rnv7PTe8vMj8fC+trQdTEyxH6RzEFfp5WFlZSHrbdI4RmAZ2TsXVBczseFwN7Ad6jTETwCuB72iSAbkWmxsbGHutI8lERHKWaqvIJQQCAfr6xigubtWalSKOpHOqrEPAXmPMHuAc8FbgV8+/aK1dB5rOPzbG9AIfstYeTmMmyRPRaJTTR44QXF2ls6TEdRwRkUxRbRW5hP7+YRYXK9i1q9N1FJGClbYrltbaCPB+4HvAaeBJa22/MeZjxpiH0/W5UhhWVlYIzs9zV20tbU1NV3+DiEgeUG0Vebn19XVmZ0N0de3FmEuNFheRTEjr4j7W2qeApy567g8us+3BdGaR/LG6ssLUiy/SXVFBqa5WikiBUW0VeanBwUmM0ZqVIq5pHmbJKbFYjLG+Pto9Hlrq6lzHEREREYeWl5eZnQ1rzUqRLKDGUnLK6uoqbG3R0dzsOoqIiIg4tLGxwZEjE1RWdmvNSpEsoL+FkjOi0SgThw7RVVbmOoqIiIg4Njw8SSjUSH19i+soIoIaS8khiwsL4PfTWl/vOoqIiIg4tLGxwblzIVpbe1xHEZEENZaSE2KxGOcGBri5ocF1FBEREXEoHA7T1zdMSUmLhsCKZBH9bZScMD0xAX4/ddXVrqOIiIiIQwMDw8zNldHY2OE6iojsoHmZJWtFIhGWl5c5d+oUdmODfVqvUkREpKBtbm4yNbVNd/c9eDwe13FEZAc1lpKVNjc3GTpyBI/PR3dZGQ2dnSogIiIiBSw+BHaIoqIm/U4gkoXUWEpWGnrhBdpiMTrb2lxHERERkSxw5swoMzOl9PRozUqRbKR7LCXrrK+vg89HhybqEREREcDn8zE15aO7+zZdrRTJUmosJessLy/TXFKCMcZ1FBEREckCp0+PAg0UF5e4jiIil6HGUrLK7PQ0q8PDNGj2VxEREQEmJ6eZnQ3T1NTtOoqIXIEaS8kqM6dPc1NpKVUVFa6jiIiIiGN+v5/+/nlqavZSXKypQUSymRpLyRrLy8uwsaG1KkVERASAwcExotE6qqpqXEcRkatQYylZY3lhgeaKCt1bKSIiIkxPz3LuXJCWll2uo4hIEtRYSlbY3Nxkc2SE1ro611FERETEse3tbfr6ZqiuvkVDYEVyhBpLyQorKys0eDyUlZa6jiIiIiKODQ2NEw7XUFVV6zqKiCRJjaU4FwwGWRocpF4T9oiIiBS8mZl5pqe3aW3d7TqKiFwDNZbi3ObmJpXb25q0R0REpMAFAgH6+qapqLhJa1aK5Bg1luKc3+/HqyGwIiIiBS0ajTIwMEQwWEVNTb3rOCJyjdRYilPBYJDFwUFqvV7XUURERMShsbFJxsaitLTscR1FRK6DGktxJhKJcPr4cap8PuprtD6ViIhIoQqFQoyPr9LaupeSEo1iEslFmr9ZMspay9LSEgBTZ87gWVri5o4Ox6lERETElVgsxunTQ/j9FTQ2VrmOIyLXSY2lZNTU+DhLx47RXFlJmzF0dHdjjHEdS0RERByZmJhieDhEV9d+11FE5AaosZSM2lpY4KaGBg19FREREba3txkdXaa5WUNgRXKd7rGUjPH5fASCQco0A6yIiEjBi8VivPjiAJFIPVVV+sJZJNepsZSMmRwYoGZtTUuLiIiICFNT0ywtFdPScpPrKCKSAmosJSN8Ph/bKyt0NzdTVKT/7ERERArZ4uIi/f2LNDXtdh1FRFJEv+FL2llrOXP8OA3RKN6yMtdxRERExCFrLQMDU3i9u6iqqnUdR0RSRJP3SFotLy4yMzEBc3Ps6ulxHUdEREQcstbS13eGlZViursbXccRkRRSYylps729zcSxY7THYjR2dGgIrIiISIGbnZ1leNhPZ+ddWm5MJM+osZS0sNYy2NdHbTBIR0eH6zgiIiLi2MrKCidOzNLQcJOWFhHJQ2osJaVWl5dZnpkhGAwSPXeOPZ2driOJiIiIY9ZazpyZBFqpqal3HUdE0kCNpaTUyvw8nvFxOmtqKG9rw+PxuI4kIiIijp04cYa5uRg9PfrCWSRfqbGUlDLGUFddTV11tesoIiIikgVmZ2eZnvbT2XlA91WK5DHNpiIiIiIiabG9vc3RozNUV99McXGJ6zgikkZqLCVlQqEQqzMzFGv4q4iISMGLxWKcOjVENFpHVVWd6zgikmZqLCVlhvr7qdzaorqy0nUUERERcWxoaJSpqQitrVrHWqQQqLGUlFhcWCA4NcUtbW2uo4iIiIhjc3OLjI5u0Nl5l4bAihQINZaSEqsLC7SVllJcrPmgRERECpnf7+fkySm83t1ar1KkgKixlBs2NzfH5rlzNNTUuI4iIiIiDsViMV588TTRaCN1dY2u44hIBunyklw3ay2jp0+zPjLCnupqyr1e15FERETEkVgsxvHjA6yultHTs9t1HBHJMDWWct3C4TDr4+Pc2dSEt6zMdRwRERFx6OzZacbGgnR3H3AdRUQc0FBYuW7zMzN4QyE1lSIiIgVuYWGBU6cWaW7eq8l6RAqUGku5bstzc7RUV7uOISIiIg75fD5OnTpLcXEnVVWab0GkUKmxlOtirSW6vExTnRY8FhERKVTRaJTjx8+wuVlHU5OWHBMpZGos5bqEgkHKgkGMMa6jiIiIiCOnTg0yN1dER8ce11FExLG0NpbGmNcaYwaNMSPGmA9f4vXfNsYMGGNOGmP+xRizK515JDVisRjRUIhbOzpcRxERKTiqrZItJifPMTm5TVfXXRQV6VqFSKFL278CxhgP8GngdcAdwNuMMXdctNkx4BXW2gPAN4E/TlceSZ1YLAZAaYluzhcRySTVVskWExNnOXFijtravRQXa5EBEUnvFcufAkastWPW2hDwDeCNOzew1v6rtdafePg80JXGPJIiC3NzmERzKSIiGaXaKs5tbGwwMrJAY+PtmqxHRC5I51dMncDZHY+ngQevsP07gX++1AvGmEeBRwGam5vp7e1NUUQ3tra2cvoYtv1+IsbQOz/vOsoN2YpEdAxZIh+OQ8fgXjAScR0hE1Rb0yjX63MqXO0cRKNRfL4gUEIo9CLr6xmLllGRyBbz872uYzilcxBX6OchGk2+tqazsbzUrC72khsa83bgFcC/u9Tr1tongCcA9u3bZw8ePJiiiG709vaSq8ewtLjI5NNPs1lezsHWVtdxbkjv/LyOIUvkw3HoGNwbWVx0HSETVFvTKJfrc6pc6RxEIhF+9KMTlJS00dLSmdlgGTY/30tr60HXMZzSOYgr9POwsrKQ9LbpHAo7DXTveNwFzFy8kTHmF4DfAx621gbTmEdSYHl5mY6qKtcxREQKlWqrOBEMBnnhhROsrnrzvqkUkeuTzsbyELDXGLPHGFMKvBX4zs4NjDH3An9NvPAl3w6LE9ZatiYmaKjR/RQiIo6otooTp04NMzdXQWfn7a6jiEiWSltjaa2NAO8HvgecBp601vYbYz5mjHk4sdnjQBXwd8aY48aY71xmd5IFrI2PtiorLXWcRESkMKm2SqbFYjH6+gaZmgrS3X2rlhURkctK6/zQ1tqngKcueu4Pdvz8C+n8fEmdlZUVZmdnKfb5oLnZdRwRkYKl2iqZNDY2zuDgFl1dd+HxeFzHEZEspoWHJCnjhw7RGovR3NHhOoqIiIhkwPz8EkNDa7S330FJiUYriciVqbGUy7LW4vP5WFtbA7+fzu5ujLnUhIQiIiKST6anpzlxYh6vdxdeb7nrOCKSA9RYymUtLy8z+aMfUVFayi0NDWoqRURECsDk5CSnTi1RVbWXqipN2CciyVFjKZe1ODNDS2kp3S0trqOIiIhIBsRiMU6cWKKu7lYqK6tdxxGRHKLGUl7m+HPPEd3YgO1tbmlvdx1HREREMmBtbQ2fL0Bp6T41lSJyzdRYykusrKwQnZnh3p4eTSkuIiJSINbX1zlyZBQopbm503UcEclBaizlgnMTE8xPT9NUUaGmUkREpECsrKxy6NAY1rbj8Wy6jiMiOUrdg1wwNzZGTzBIV2Oj6ygiIiKSAfPz8xw6NAZ00NysJcVE5PqpsRQgfl8F6+s01NRoAWQREZECMDs7y7Fj05SW7qGpSXMqiMiNUWMpQHwWuAYNgRURESkIs7MLHD06g9d7MzU1Da7jiEge0D2Wgs/nY/zoUVq1TqWIiEjem5iYpK9viaqqm6iurnMdR0TyxP/P3p3H13XXd/5/fbRLlm1Zq23JWzZnX0hCmELb8AulJEOBoZSl0JJCYaaUshRmSgeGpvCjv3ZoS2FKm1/YUiiEUjJASukAnaASAlmceJPlTfIm2bJ2ydLVlXSXz/xxjzzXsmRruzp3eT8fj/vQuWe7n+/3Ll99zvme71FiKUxPT7MhHqeloSHsUERERCSDjh07xoEDw9TU7KSqqjrscEQkjyixFBERESkAR4+e4MCBYerrr6Wyck3Y4YhInlFiWaBisRiH9+1jqr8fgCZdWykiIpKXpqamOHr0BB0d4zQ0XEdFRVXYIYlIHlJiWaA69+/Hu7q4ZdMmSkr0MRAREclHExMTPPvsQUZHy2lqupGysvKwQxKRPKWMogDFYjEiw8Nc19CgpFJERCRPRSIRnn76EGNjNTQ3X4FpkD4RySBlFQUmFovR19dHWSRCVY1GghMREck37s7g4CB7954kGq2jpWV72CGJSAFQYllgjh88yMSxY2xety7sUERERGSFuTtHjhzl0KExqqtb2LSpKeyQRKRAKLEsILFYjLH+fnbW1VFdpQv3RURE8kk0GmXPniOcORNn48brqaioDDskESkgSiwLxNDQEMd372ZNJEK1usCKiIjklaGhIfbuPc74eA0tLVspKSkNOyQRKTBKLPOcu3PyxAkGDx6kpbycpubmsEMSERGRFeLudHYeo719hMrKTWzevDnskESkQCmxzHM9p08z+PzzXF1fz7rq6rDDERERkRUyNTXF3r1HOX16isZG3Z9SRMKlxDKPxeNxBnt62LJunZJKERGRPDI6Osru3R2cO7eWTZuuobS0LOyQRKTAKbHMU+7Owb17iZ86xXp1ixEREckL09PTnDhxkqNHz1Fa2khz85awQxIRAZRY5qXB/n66jxwhz+x6TgAAIABJREFU3tvLrVu2UFxcHHZIIiIisgzuzsDAEO3tJ+jrK2fjxp1UVak3kohkDyWWeSKRSBCJROjr6WG0o4PmkhLqmpuVVIqIiOS4aDTKgQMd9PZOU1LSzPbtjRQVFYUdlojIBZRY5oG+3l7Onj5N/PhxqququL6mhsqKirDDEhERkWVwd86e7aW9/TRTU+toaLheB4xFJGspscxhHe3txCIRJs6cobmigvqWFkpK9JaKiIjkuomJCfbuPcqZM3E2bNhBU1Nt2CGJiFySspAclUwmGT11imurqyltaqKsVDdCFhERyXWRSIS+vj4OHRoikaihpWUrJSVq40Uk+ymxzEED/f30nDlDWSTCmoaGsMMRERGRZUomk3R3n+bgwT6SybVUV2twHhHJLUosc0Aymbzg+cnnn2czUNfcHE5AIiIisiLcnePHu+nq6qevDxobr6K6en3YYYmILJoSyyzX19dH1+7dFLmfn1cUjbJxyxbMLMTIREREZKni8TiRSIQ9ezoYHoYNG65i69Y1GitBRHKWfr2yVDwep7enh7MdHWwrKaG+pibskERERGSZ3J0zZ87S09PHqVNJqqrq2bJlW9hhiYgsmxLLLNR9/Di9p05R2t/Ptg0blFSKiIjkuGQyyenTvZw40UtfX4K1a1tobq7TGUoRyRv6NcsiU1NTTE5O0nviBNuTSWq2btX9qkRERHLYuXPnGB8fp62th8lJWLNmOy0t6zTSq4jkHSWWWaRz/378zBk2lJZSU1enpFJERCQHJZNJzpw5y/j4OEePjlFSso6Kiu3U1dWFHZqISMYoscwC7s7k5CTRoSFuqK+norw87JBERERkEZLJJO7OyZNn6Orqp78f1q1rZsOGZior14QdnohIximxzAKd7e2MnjzJBlBSKSIikkPGx8eJRqPs23eKWKyIqakk69dfyZYt1bp+UkQKin7xMszdOXvmDIl4/Py82PQ03SdPnn8+2t/PzrVrqa6qCiNEERERWYTJyUnOnu0nFovR0TEMrKO0tJmGho1hhyYiEhollitkpguMu9PR1oZPTQGp24ZM9/bSUl19fl2bnKT08OHzz7cWFbEmbbmIiIhkj0QiAcCxY90MDIwzNTXJwEAV69bVsmZNA2vWrA05QhGR8CmxXKJoNMpUkDwCdO7dS1E0irtTFI1yTVPT+WWlzc2UpnWHOdjbS5Mu4BcREclK7s65c+dwd3p6+jh+fIzi4mImJhLU1l5NaWkJ27dXYmZhhyoikjWUWC5SLBbj7OnT9HV2snZ6muKiIgAagK0NDeEGJyIiIksyOTnJ9HSMEye6GBsbo7MzSmVlDclkETU1N1BeXhF2iCIiWU2J5SIdbW8nceQIO+rqqG1sDDscERERWYSZbq3uTltbB1NTDkA0OkEkkuDIkTKgjsbG9UomRUQWQYnlIkSjUaIDA9zY0kJ5WVnY4YiIiMgljI6O4u7nn/f29tPRcY7i4mLcnWjUaGq6BoDSUigvH6Ourmm+3YmIyCUosVyAeDxOT3c3fceOsS4apWz9+rBDEhERkcD09DRnzvReMC8SidDREaG8vOb8PHejpuZ6Kioq59zP6GhGwxQRyWtKLNO4O8lk8vzz0ydOcK63l6npacoGB9leU0Pdpk0hRigiIpLfZrqqzmVycpK2tuO4F82aH2VwsIJ169LHOiinvn4rFRW6lZeIyGrIaGJpZq8APg0UA5939z+dtbwc+DJwOzAIvMHdT1xuvyMjIysfLNB97BhTPT0UFxcDkJic5KoNGygrLaV861aKioouswcREZHMylTbmgnuzugiTgOmd1WdSyrp3EBNzYUHeUtLYevWcrXTIiIhylhiaWbFwGeBXwK6gWfN7DF3b09b7e3AsLtfZWZvBP4MeMOl9uuJBINPPZWRmKvcuba+npISncgVEZHsk6m2NRN6e/vp7e3n2LEoFRU1l9+Ay3dVFRGR7JXJDOqFQIe7HwMws68DrwbSG79XAw8E098E/trMzNOvtJ/D1KUWLocZR4aHM7X38ybjcdr7+zP+OpmkMmSHfCgD5Ec5VIbwRdMuZchjGWlbk8kkzz333IoGOj4OIyME93pcWMttBmNjxxkbW9FQFiwen6S/v/3yK+Yx1UGK6kF1MKPQ62FqKrrgdTOZWDYDXWnPu4G75lvH3eNmNgrUAQPpK5nZO4F3Bk+nbrj33raMRLx66plVxhykMmSHfCgD5Ec5VIbssDPsADIsY23rHXfckett60rIh+/AcqkOUlQPqoMZqocFtq2ZTCxtjnmzj5YuZB3c/SHgIQAz2+Xudyw/vPCoDNlBZcge+VAOlSE7mNmusGPIMLWtGaR6UB3MUD2oDmaoHhbetmbyKvduYEva8xbgzHzrmFkJsB4YymBMIiIiuUxtq4iIZKVMJpbPAleb2Q4zKwPeCDw2a53HgLcG068DHr/c9ZUiIiIFTG2riIhkpYx1hQ2u63g38H1SQ6J/0d0PmNnHgF3u/hjwBeArZtZB6mjqGxew64cyFfMqUhmyg8qQPfKhHCpDdsiHMsxLbWvGqR5UBzNUD6qDGaqHBdaB6SCmiIiIiIiILIfuJCwiIiIiIiLLosRSREREREREliX0xNLMXmFmh82sw8w+NMfycjP7h2D502a2PZi/3cyiZrYneDyYts3tZrY/2OYzlro7czaW4c1p8e8xs6SZ3Rosaw32ObOsMeQy/IKZPW9mcTN73axlbzWzo8HjrWnzs+19mLMMZnarmf3MzA6Y2T4ze0PasofN7Hja+3BrNpYhWJZIi/OxtPk7gs/d0eBzWJaNZTCzl876Pkya2WuCZdn2Pvy+mbUHn5f/bWbb0pblyvdhzjLk2PfhUu9DVnwfcomZfTyoyz1m9gMz2xx2TGEws0+a2aGgLr5lZjVhx7TazOzXgt+ApJkV1G0WLve7UwjM7Itm1mdmBXtvWzPbYmY/MrODwXfhvWHHFAYzqzCzZ8xsb1APf3zJDdw9tAepgQc6gSuAMmAvcP2sdd4FPBhMvxH4h2B6O9A2z36fAf4dqXt5/QtwbzaWYdY6NwHH0p63Andk0fuwHbgZ+DLwurT5tcCx4O+GYHpDlr4P85XhGuDqYHoz0APUBM8fTl83W9+HYNn4PPv9BvDGYPpB4HeytQyzPldDQFWWvg8vTYvtd/i/v0u59H2Yrwy59H2YswzZ8n3ItQewLm36PQTtVqE9gJcDJcH0nwF/FnZMIdTBdaRuiN7KKv0vkg2PhfzuFMID+AXgBczzf3YhPIBNwAuC6bXAkQL9LBhQHUyXAk8DL5pv/bDPWL4Q6HD3Y+4+DXwdePWsdV4N/F0w/U3gnksd6TezTaQax595qha+DLxm5UM/b6XK8CbgkQzGeSmXLYO7n3D3fUBy1ra/DPzQ3YfcfRj4IfCKbHwf5iuDux9x96PB9BmgD2jIYKzzWc77MKfgc/b/kPrcQepzmJXvwyyvA/7F3ScyF+q8FlKGH6XF9hSpewlCbn0f5ixDjn0f5nsf5hTC9yGnuPu5tKdrgIIc3c/df+Du8eDpZT9X+cjdD7r74bDjCMFC/qfLe+7+Ywr83rfu3uPuzwfTY8BBoDncqFafp4wHT0uDx7xtQ9iJZTPQlfa8m4vftPPrBD/0o0BdsGyHme02s38zs59PW7/7MvtcScstw4w3cHFi+aWgS9J/y3C3uYWUYbHbZuP7cFlm9kJSRyk702Z/IugS9SkzK19emJe03DJUmNkuM3tqpgspqc/ZSNo/STnxPpA6sz/7+5Ct78PbSZ2BvNS22f59SC/DeTn2fZhdhmz4PuQcM/uEmXUBbwY+GnY8WeBtzPHdkLy1Um2Y5BFLXcJ2G6mzdQXHzIrNbA+pA80/dPd56yHsxHKuZGl2FjzfOj3AVne/Dfh94Gtmtm6B+1xJyylDaqHZXcCEu6f3ZX+zu98E/Hzw+I3lBnoJy6mz+bbNxvfh0jtInVX6CvBb7j5zNu0PgWuBO0l1b/yD5QR5uRDmmLeYMmx19zuAXwf+ysyuXIF9LtZKvQ83kbpP34ysfB/M7C3AHcAnL7Nt1r4Pc5RhZn7OfB/mKUM2fB+yjpn9q5m1zfF4NYC7f9jdtwBfBd4dbrSZc7l6CNb5MBAnVRd5ZyF1UIAK/jdCLmRm1cCjwPtm9eooGO6ecPdbSfXeeKGZ3TjfuiWrF9acuoEtac9bgDPzrNNtZiXAemAo6E42BeDuz5lZJ6lrg7q5sNvKXPtcSUsuQ9ryi87OuPvp4O+YmX2NVPeML69s6BfFN2MxddYN3D1r21ay832YV3BQ4p+Bj7j7UzPz3b0nmJwysy8BH1yBWOezrDIE3RZx92Nm1krq6NqjQI2ZlQRnabL6fQi8HviWu8dmZmTj+2BmLwM+DPyiu0+lbXv3rG1bydLvwzxlyKnvw3xlyJLvQ9Zx95ctcNWvkfoM/FEGwwnN5erBUgNvvRK4J/h/I+8s4rNQSFaiDZM8YWalpNqNr7r7/ww7nrC5+0jQnr4CmHNgp7DPWD4LXG2pUfrKSCVYj81a5zFgZmTF1wGPu7ubWYOZFQOY2RXA1aQGv+kBxszsRUH30d8EvpONZQhiLwJ+jVQ/foJ5JWZWH0yXkmrcMjky10LKMJ/vAy83sw1mtoHUoAffz9L3YU7B+t8Cvuzu/zhr2abgr5G6Fisr34eg/suD6XrgxUB78Dn7EanPHaQ+h1n5PqS56HrjbHsfzOw24P8HXuXufWmLcub7MF8Zcun7cIkyZMv3IaeY2dVpT18FHAorljCZ2StInY1/VUjXeUt4VqINkzwQtHNfAA66+1+GHU9YgnyrJpiuBF7GpdoGD3+0oftIjbTUCXw4mPcxUj/oABXAPwIdpEZVvCKY/6vAAVIjdj0P/EraPu8g9Q9PJ/DXgGVjGYJldwNPzdrfGuA5YF9Qxk8DxSGX4U5SR/IiwCBwIG3btwVl6yDVbS5b34c5ywC8BYgBe9IetwbLHgf2B+X4e4KRsbKwDD8XxLk3+Pv2tH1eEXzuOoLPYXk2liFYth04DRTN2me2vQ//CvSmfV4ey8Hvw5xlyLHvw3xlyJrvQy49SB2ZbyPV9vwT0Bx2TCHVQwep6+xmPlcFNzou8B+C3+mp4Dv2/bBjWsWyX/S7U2gPUgd3e4K2oDv9N7RQHsBLSHWD3pf2W3Bf2HGFUA83A7uDemgDPnqp9S3YSERERERERGRJwu4KKyIiIiIiIjlOiaWIiIiIiIgsixJLERERERERWRYlliIiIiIiIrIsSixFRERERERkWZRYisxiZgkz22NmbWb2j2ZWlcHX+lhwg3fM7H2LfS1LeTy4oT1m9h4zO2hmX12B2O43s81pzz9vZtcvcV/vNrPfWm5MIiKSm9S2nt+32lbJW7rdiMgsZjbu7tXB9FeB53wBN8cNbqZr7p5c4uueAO5w94FFbPPvgZe5+/uD54eAe939+Kz1Stw9vsh4WoEPuvuuxWw3z76qgCfd/bbl7ktERHKP2tbz27SitlXylM5YilzaE8BVAGb2+8GR1jYze18wb3twFPNvgOeBLWb2JjPbH6z3Z8F6xWb2cDBvv5nNNFYPm9nrzOw9wGbgR2b2IzN7u5l9aiYIM3uHmc3VAL8Z+E6wzoOkbv7+mJm938weMLOHzOwHwJeDWJ8ws+eDx8+l7f+/BHHtNbM/NbPXAXcAXw2OMFeaWauZ3RGsf1EZg/njZvaJYD9PmVkTgLtPACfM7IUr8q6IiEguU9uqtlXykbvroYceaQ9gPPhbQqph+R3gdmA/sAaoBg4AtwHbgSTwomCbzcApoCHY/nHgNcH2P0x7jZrg78PA64LpE0B9ML0G6ARKg+c/BW6aI9aTwNq05+n7eAB4DqgMnlcBFcH01cCuYPreYP9VwfPa4G8rqaO8pD+fr4zBOg78SjD934GPpG3/YeADYb+/euihhx56rP5DbavaVj3y/6EzliIXqzSzPcAuUj/yXwBeAnzL3SPuPg78T+Dng/VPuvtTwfSdQKu793uqe8xXgV8AjgFXmNn/MLNXAOcuFYC7R0g1Kq80s2tJNYL751i11t3HLrGrx9w9GkyXAp8zs/3APwIz13S8DPiSp4584u5Dl4rtEmUEmAa+G0w/R+qfgxl9pBpOEREpPGpbL01tq+S8krADEMlCUXe/NX1GcI3HfCLpq861grsPm9ktwC8Dvwu8HnjbZeL4PPBfgUPAl+ZZJ25mRT7/tSfpsb0f6AVuIdUNfjIt5sVcbH2puoi5+8y+Elz4G1MBRC/eRERECoDa1ktT2yo5T2csRRbmx8BrzKzKzNYA/4HUNSKzPQ38opnVm1kx8Cbg38ysHihy90eB/wa8YI5tx4C1M0/c/WlgC/DrwCPzxHWY1LUfC7Ee6Akayt8AioP5PwDeZsGoeWZWO1c8lyvjAl7/GqBtgbGKiEj+U9t6mTIu4PXVtkrWUGIpsgDu/jypazaeIfXj/3l33z3Hej3AHwI/AvYCz7v7d4BmoDXoBvRwsM5sDwH/YmY/Spv3DVIjvg3PE9o/A3cvsBh/A7zVzJ4i1RBFgpj/F/AYsCuI74PB+g8DD84MMLCAMl7Oi4F/XWCsIiKS59S2qm2V/KLbjYhkMTP7LvApd//f8yzfBHzZ3X9pdSNbHDO7Dfh9d/+NsGMREZHCprZVJDN0xlIkC5lZjZkdIXVNypwNH5w/wvk5C27inMXqSXVTEhERCYXaVpHM0hlLERERERERWRadsRQREREREZFlUWIpIiIiIiIiy6LEUkRERERERJZFiaWIiIiIiIgsixJLERERERERWRYlliIiIiIiIrIsSixFRERERERkWZRYioiIiIiIyLIosRQREREREZFlUWIpIiIiIiIiy6LEUkRERERERJZFiaWIiIiIiIgsixJLERERERERWRYlliIiIiIiIrIsSixFRERERERkWZRYioiIiIiIyLIosRQREREREZFlUWIpIiIiIiIiy6LEUkRERERERJZFiaWIiIiIiIgsixJLERERERERWRYlliIiIiIiIrIsSixFRERERERkWZRYioiIiIiIyLIosRQREREREZFlUWIpIiIiIiIiy6LEUkRERERERJZFiaWIiIiIiIgsixJLERERERERWRYlliIiIiIiIrIsSixFRERERERkWZRYioiIiIiIyLIosRQREREREZFlUWIpIiIiIiIiy6LEUiTPmVmrmf32PMu2mtm4mRXPXtfM3mxmP1jNWEVERAqdmT1gZn9/ieUHzOzu2evObtNFVpsSS5FFMLMTZhYNfrh7zexLZlYddlxL5e6n3L3a3RNzLPuqu7985rmZuZldtboRiohINgjav14zW5M277fNrDXEsC7JzB42s+mgzR4ysx+a2bVhx7Vc7n6Du7fOMf+CNv1SB5ZFMkGJpcji/Yq7VwMvAO4EPrKYjc2sJCNRiYiIZFYJ8N4wXngZbed/D9rsFqAPeHgVX1ukoCixFFkidz8N/Atwo5mtN7MvmFmPmZ02s/83rXvp/Wb2pJl9ysyGgAdmzRsxs2Nm9nPB/C4z6zOzt868lpmVm9mfm9mp4Ijxg2ZWGSzbYGbfNbN+MxsOpltmhXulmT1jZqNm9h0zqw223R6cibyo0Qxi+Ukw/eNg9t7gyO8bzKzNzH4lbf1SMxsws1tXsJpFRCR7fBL4oJnVzF5gKZ8K2q9RM9tnZjcGyyrN7C/M7GSw7Cdpbdirgq6dI8EZtuvS9nnCzP7AzPYBETMrMbPNZvZo0OYdN7P3LCRwd58AvgbMxFRkZh8ys04zGzSzb8zRNr7dzE4Bj6fN+62gnR42s/9kZncGZR0xs7+eVSdvM7ODwbrfN7Ntacs+HeznnJk9Z2Y/PyvkCjP7BzMbM7PnzeyWWfXysjneg/Ntupl9Avh54K+DdvuvzeyzZvYXs7b5JzN730LqUORylFiKLJGZbQHuA3YDfwfEgauA24CXA+ndT+4CjgGNwCfS5u0D6kg1dl8ndQb0KuAtpBqDmW62fwZcA9waLG8GPhosKwK+BGwDtgJR4ILGDfhN4G3A5iDOzyymrO7+C8HkLUE3m38AvhzEOeM+oMfd9yxm3yIikjN2Aa3AB+dY9nLgF0i1VTXAG4DBYNmfA7cDPwfUAv8FSJrZNcAjwPuABuB7wD+ZWVnaft8E/Ptgn0ngn4C9pNrBe4D3mdkvXy7woD19M6k2G+A9wGuAXyTVNg4Dn5212S8C1wHp+78LuDoo318BHwZeBtwAvN7MfjF4vdcA/xV4bVC2J4KyzniWVJteS+p/gH80s4q05a8G/jFt+bfNrPRy5Zzh7h8OXvPdQbv9blL/q7zJzIqCGOtJ1eEj8+9JZOGUWIos3rfNbAT4CfBvwOeBe4H3uXvE3fuATwFvTNvmjLv/D3ePu3s0mHfc3b8UXAvxD8AW4GPuPuXuPwCmgavMzIB3AO939yF3HwP+ZGb/7j7o7o+6+0Sw7BOkGsN0X3H3NnePAP+NVOO33Iv7/x64z8zWBc9/A/jKMvcpIiLZ7aPA75lZw6z5MWAtcC1g7n7Q3XuCJOZtwHvd/bS7J9z9p+4+RSo5+2d3/6G7x0gloJWkEtAZn3H3rqDtvBNocPePufu0ux8DPseF7e1sHwza7A6gGrg/mP8fgQ+7e3cQywPA62b14HkgaNejafM+7u6TQTsdAR5x976gF9MTpA4uz+z//wvqIU6q3b515qylu/990H7H3f0vgHJgZ9rrPOfu3wzq5S+BCuBFlyjnZbn7M8AoqWQSUvXW6u69y9mvyAz1GRdZvNe4+7/OPDGzFwKlQE8qBwRSB2260rZJn56R/kMeBZj14x4l1Qg2AFXAc2n7N2Cmq20VqUT2FcCGYPlaMytOG5Qn/fVPBvHWX66gl+LuZ8zsSeBXzexbpJLrUK69ERGR1eHubWb2XeBDwMG0+Y8HXUE/C2wN2oUPkkqIKoDOOXa3mVSbNLOPpJl1kTobOSO9/doGbA4SxRnFpBK6+fy5u881FsI24FtmlkyblwCa5nntGbPb6bna7Zn9f3pW11MjVbaTZvYBUj2bNgMOrOPCdvn8awf10h2su1x/R6q30Q+Dv59egX2KADpjKbISuoApoN7da4LHOne/IW0dX8b+B0g1Vjek7X99MBgBwAdIHeW8y93XkeqKBKkGbMaWtOmtpI4sDywjphkzDdSvAT8LjtiKiEh++yNSPWnSE0Dc/TPufjupbqHXAP+ZVFszCVw5x37OkErAgNR1mqTaq/S2JL397CLV26cm7bHW3e9bQhm6gHtn7atiVju2nLa7C/iPs/Zf6e4/Da6n/APg9cAGd68hdSZxznY7OOvbQqq+FmOu+P8eeHVwzeZ1wLcXuU+ReSmxFFkmd+8BfgD8hZmtCwYEuHLmOosV2H+SVFefT5lZI4CZNaddU7KWVOI5Egw88Edz7OYtZnZ9cHbzY8A357rFyGX0AlfMmvdtUqPjvpfUNZciIpLn3L2D1CUc5wfOCQaxuSu4DjBCKplMBG3YF4G/DAbeKTazf2dm5cA3gH9vZvcE232A1IHan87z0s8A54IBfSqDfd1oZncuoRgPAp+Y6ZpqZg1m9uol7OdS+/9DM7sh2P96M/u1YNlaUuMd9AMlZvZRUmcs091uZq8Nuua+j1S9PLXIGC5qt929m9T1nV8BHp3VzVdkWZRYiqyM3wTKgHZSAwB8E9i0gvv/A1LXhzxlZueAf+X/XovxV6SuSRkg1ej8rzm2/wqpIdbPkuqStKBR9GZ5APi7YOS71wMEDdKjwA7gfy5hnyIikps+BqxJe76O1EHQYVLdWwdJXTMJqS6x+0klNEOkBqQrcvfDpHq9/A9SbdivkLql1/RcLxgcEP0VUoPeHA+2+Tywfgnxfxp4DPiBmY2Raj/vWsJ+5uTu3yJVzq8H7XYbqUtGAL5PalT5I6TqapKLu91+h9Q1qMOkxjB4bXC95WJ8mtR1o8Nmlj5o398BN6FxEWSFmftyzvKLSKELjrRe4+5vuezKIiIiEioz+wVSXWK3B2eURVaEBu8RkSULut6+ndTRVBEREcliQZfj9wKfV1IpKy1jXWHN7IuWuklu2zzLzcw+Y2YdwY1lX5CpWERk5ZnZO0h13fkXd/9x2PGIFAq1ryKyFGZ2HTBC6lKdvwo5HMlDmbzG8mFStz+Yz72kbjB7NfBO4G8zGIuIrDB3/5y7r3H3/xR2LCIF5mHUvorIIgX31Fzj7j/n7ufCjkfyT8YSy+AMxtAlVnk18GVPeQqoMbOVHOxEREQk76h9FRGRbBTmNZbNXDgCVncwr2f2imb2TlJHXamoqLh969atqxJgvkkmkxQVaSDgxVK9LZ3qbmnyrd4WOkicJ5MX3MRt0a8DdHR2Drh7wzJ2kw8W1L6qbb1Yvn33lkJ1kKJ6UB3MyJV6yNSArO5OR0fHgtrWMBPLuf5/mLNG3P0h4CGAnTt3+uHDhzMZV95qbW3l7rvvDjuMnKN6WzrV3dLkU70d2bOHxPHjrKmsvOy6ddXVC1pvPh39/Vx9770nl7yD/LGg9lVt68Xy6bu3VKqDFNWD6mBGGPUQj8c5evQUyeTCksWBgREGBipYs2b27VCXb2iojze/+Y4Fta1hJpbdwJa05y3AmZBiERFZcfF4nHg8vujt3J3JyckMRASRSIQTe/dSukpHX2MTE1zX2EhVRcWqvJ4Aal9FRLLCfO15NBplz57jQPGc2yUSCcbGyqit3TLn8ovVsW3besyW0+9nbsXF5QteN8zE8jHg3Wb2dVI3pB1194u6wYqIXEoikWB4eDjsMOZ0sq2N8snJRf/QT01N0flv/5aRmOKJBJtLSqhfv5T7iS+eVVZSUqI7W60yta8iIqtscnKSc+cuHBNpYGCQo0cnKC+/sDeOe5Li4k3U1DTNu78NG4oykihmUsZaezN7BLgbqDezbuCPgFIAd38Q+B5wH9ABTAC/lalYRCQgTacxAAAgAElEQVQciXic44cOZfQ1zo2N4SdPsqG6OqOvsxSbi4vZ1LD4y/36e3u5ob4+AxFJPlD7KiKSfY4fP0N7e3RWd9RqGhp2UF5eGL12MpZYuvubLrPcgd9dideKxWJ0d3dnrOtYvqiuriYWi1FaWhp2KJKnYrEYw8PDnD5wgJKSEqYmJig9dozK8oV3o1isdcD6LVt0VkwKxmq1r4XYtq5fv56DBw9SUVFBS0uL2kuRAheNRkkmk0xMTHDo0HGGhqbmHchnaipBQ8M1rFmzdpWjzB558Z9Yd3c3a9euZfv27Tl3yni1uDvd3d10d3ezY8eOsMORHDA5OUkkElnw+lNTU/QcOEBxLEZzaSk1lZUMFBfT0jR/Nw8RyV6F2LaOjY1RXV3N4OCg2kuRApRIJBgcHMTdiUQiHDo0TDw+zRNPnCQWK6K+/kaKiua+LrK6GoqL515WKPIisZycnCyohm8pzIz169dz+vTpsEORLJdIJDh98iT9XV1UjowseNAVd2dbWRn1tbXn5+k7KZK7CrVtNTPq6uro7+8POxQRWQXxeJwjR06STKYOLnV3w9q1dUApa9ZcTSQyRn39dWGHmRPyIrEE/QO7EKqj/DI9Pc3h3buxJYw6eilTU1MUDw+zvaaG2uZmfW5EClihfv8LtdwihSAajeLuHDx4jMHBKQDGx2dGYK1j69bqCy6vWUTnrYKXN4mlSD5zd0ZGRi54fnzfPtacO8f2lR7kpaKC0i1bCr47h4iIiOSHqakpRkZGGB8f59ChEYqLq4jHi2louBmzImprc28E1myUl4nl/qeeYjrtn/DlKqup4aYXveiy63V3d/O7v/u7tLe3k0wmeeUrX8knP/lJysrKLljvzJkzvOc97+Gb3/zmJfd333338bWvfY2amppFx/zAAw9QXV3NBz/4wUVvK9nneGcnw/v2saGq6vy8pqIiWpqbQ4xKRArJU0/tZ2RkesX2V1NTxotedNO8ywcHB7nnnnsAOHv2LMXFxTQEoyw/88wzF7WtIiKz9fYO0Ns7zPDwOQYGKlizZh1r1xb2ADuZlJeJ5fTICLcvYYj/+Ty3gOss3J3Xvva1/M7v/A7f+c53SCQSvPOd7+TDH/4wn/zkJ8+vF4/H2bx582WTSoDvfe97y4pbckMymSSZTF4wb3x8nK4DBygORh6LDg1xbWMjayor59qFiEjGjYxM09Bw+4rtr7//uUsur6urY8+ePcD8B0vdHXefd5RGESksiUQidS/ozi56esaJxaCkpJmKika2bVuns5IZlpeJZRgef/xxKioq+K3fSt0urLi4mE996lPs2LGDHTt28KMf/ej8KJtf/OIXeeUrX0lbWxsTExPcf//9HDp0iOuuu44TJ07w2c9+ljvuuIPt27eza9cuxsfHuffee3nJS17CT3/6U5qbm/nOd75DZWUln/vc53jooYeYnp7mqquu4itf+QpVaWe1JLtFo1Han3mG4mj0gvmJRIKmoiLq1qXuhVRcV0eZhr0XEaGjo4PXvOY1vOQlL+Hpp5/mu9/9Ln/8x3/M888/TzQa5Q1veAMf/ehHAWhpaeG3f/u3zx/w/eY3v8k111zD448/zvvf/37MjKKiIp544gnWrFkTcslEZKmmp6cZHh7mxIkezp51iovLqau7kZKSUh14WkVKLFfIgQMHuP32C4/krlu3jq1btxKPx/nZz37Gvn37qK2t5cSJE+fX+Zu/+Rs2bNjAvn37aGtr49Zbb51z/0ePHuWRRx7hc5/7HK9//et59NFHectb3sJrX/ta3vGOdwDwkY98hC984Qv83u/9XsbKKUvX29NDbPrCbmS9nZ1smJzkCt2SQ0Rkwdrb2/nSl77Egw8+CMCf/umfUltbSzwe56UvfSmve93ruP766wFoampi9+7dfOYzn+Ev//IvefDBB/nkJz/JQw89xF133cX4+DgVCxz9WkSyy/T0NEePnmJoaJSzZ8tZt66BTZs2apyIkCixXCHuPufp9Zn5v/RLv0Rt2m0YZvzkJz/hve99LwA33ngjN99885z737Fjx/mk8/bbbz+fnLa1tfGRj3zk/AXJv/zLv7xCJZKFcHcSiQQdbW349PzXHiWTSSZ7e2mZ1ZV1e3ExdUoqRUQW5corr+TOO+88//yRRx7hC1/4AvF4nDNnztDe3n4+sXzta18LpNrOmUtMXvziF/O+972PX//1X+dXf/VXqa6uXv1CiMiSJJNJotEou3cfYmICJiaq2LDhKrZvX6uzkyFTYrlCbrjhBh599NEL5p07d46uri6Ki4vn7WLj7gvaf3l5+fnp4uJiokHXyfvvv59vf/vb3HLLLTz88MO0trYurQBykYmJCWKxGIlEgtHR0TnXOXnkCLH+fkqnpriqsfGS+ytpalJ3VhGRFZDeph49epRPf/rTPPPMM9TU1PCWt7yFycnJ88tn2s/i4mLiwe2ZPvKRj/CqV72Kf/7nf+bOO++ktbWVq6++enULISKLNjAwwOnTZzl+fIqSkrU0Nl6pEV2ziBLLFXLPPffwoQ99iC9/+cv85m/+JolEgg984APcf//9l7zm8SUveQnf+MY3eOlLX0p7ezv79+9f1OuOjY2xadMmYrEYX/3qV2nWKKGXNTU1xcBlBmRyd3oPHWJdIkF8epq+n/1szvWqkkl21Nery4WISEjOnTvH2rVrWbduHT09PXz/+9/nFa94xSW36ezs5Oabb+bmm2/mySef5PDhw0osRbLY4OAwx46d4fTpSSoqmti0qYmSEh2szzZ5mViW1dQsaCTXxezvcsyMb33rW7zrXe/i4x//OMlkkvvuu48/+ZM/4ZFHHpl3u3e961289a1v5eabb+a2227j5ptvZv369QuO7eMf/zh33XUX27Zt46abbmJsbGzB2+ab2Wd/R0dGOHP06EXdIiKRCJV9fdQGA+PMZ0dpKbW1tZzu7eXqFRxlWEQkF9XUlF12JNfF7m8lvOAFL+D666/nxhtv5IorruDFL37xZbf58z//c5544gmKioq4+eabefnLX74isYjIynF3xsfHef75I0QiYLaRpqZ6ysrKL7+xhMIW2hUzW+zcudMPHz58wbyDBw9y3XXXhRTR8iQSCWKxGBUVFXR2dnLPPfdw5MiRjNyfa2xsjO7u7pytq/lMTk7S399PX3s76R0hPJFgoxnr57h2Zk1l5YK7TbT29nK3roNcEtXd0qjelqajv5+r7733OXe/I+xYck2+ta1LNTY2xtq1qfvbFWL5AVpbW7n77rvDDiN0qodw6+DcuXN0d3dz5EiUioo66uq2hnb9ZG9vK01Nd4fy2tlgaKiPl7+8aUFta16escwlExMTvPSlLyUWi+Hu/O3f/q1u+jyHWCxG/zxnoXsOHaIyGuWq6uo5k0gRERERyX6JRILDh0/Q1TVCLFZLY+MVlJdr1OZcocQyZGvXrmXXrl1hhxGarmPHmBgZ4XLnDscjEUp7es7f1zHd1pISGjZuzEyAIiIiIpJRiUSC3t5e2tp6GBsrprFxJ1VVOlmQa/ImsZzvdh/yf2VTt+dIJMKhZ5+FkRGurKmheAHdG9bs2KFhpEVEVlGhtq3Z1F6K5LvR0VEOHz7O6dMJamp2sG3bhoL83ckHeZFYVlRUMDg4SF1dnT6I83B3RkdHM3YT6ImJCc6dO7fg9U8fPEjD9DRbt2zJSDwiIrI8hdq2ujuDg4MZay9FJMXdOXToOCdODJNMNtDc3KxR9nNcXiSWLS0tdHd3z3sNnqREIhFuueWWjOz7WFsbxV1drJvnfp2zbSkuprG+PiOxiIjI8hVi2zo5OUlFRQUVFRW0tLSEHY5I3hoaGmLPnuMMD0NT07VUVi7s/0fJbnmRWJaWlrJjx46ww8h6ra2tlJYu7p4/ExMTc84/feIE57q6zndNTU5McOuWLTrSJCKSJwqxbW1tbeW2224LOwyRvBWLxTh+/DhHjoxRXr6Jbds2FVSPiHyXF4mlZEZPTw9ndu2iao5k1BMJrq+poTwYwdY2qD+8iIiIiMxtbGyMPXuO0NNTyubNOkuZj5RYynlTU1N0HT16foTWkb4+tlRU0FhbG2pcIiIiIpKbEokEJ092cfToILFYPdu3t6iHW55SYimcOn6c/o4OAKonJmhavx6A+uJi1gfTIiIiIiKLEY1GOXSog+PHp2lo0C1E8p0SywIwM8JdPB6/aBCGqakp+g8fZueaNVSWl1OkLq0iIiIiskzDwyPs3dvJ4GAVLS3XUlKyuHE+JPcosSwAXSdO0L97N8mSEqLPPXfR8u1lZVRXVYUQmYiIiIjkk2QyyYkTpzh0aBCzjWzbtlknLQqEEsscEY/HicfjF80/vGcPRKOX/MLGIhGurqtjLBJha0NDJsMUERERkQIVi8U4fPgIhw9P6jYiBUiJZQ6IRCIc2rWL0kjk/O09ZpRMT3NNU9Mlt7fKSkpKSiASyWSYIiIiIlKgotEozz3XzpkzZWzdehOlpWVhhySrTIlllhseHOTYnj3UTk6yvalJXQlEREREJKsMDAywd+9JIpH17NhxxUUnQqQwKLEMkbsTi8Uumh+PxzmyezfFySTT4+NsMmPzxo0hRCgiIiIiMr/Tp0+zf/9ZSku30NzcGHY4EiIlliFJJBIcbmsjeuoUZaUXjpKVSCapdmdrXR1WW0tpid4mEREREckuR4+eoL19kJqaq6iu1i3qCp0ylhCMnTvH8aNHSRw/zk0tLRclliIiIiIi2SqRSLBv3yFOnpykoeE6Kip0dwFRYpkRiUSCQ7t341NTcy6fmpigZmKCrVu26GykiIiIiOSMWCzGvn0HOXEiQUvLLakBIkVQYpkRe376U4r6+rhu06Y5l1t1NeW1tasclYiIiIjI0sViMZ55Zh+nT5exbdtNSirlAvo0rLC+vj7o7+fWbds0gquIiIiI5IXJyUmefvoAIyNV7NixUyO/ykWUWK6wyPAwGysrlVSKiIiISF6YmJjg2WcPMj5eQ0vLlWGHI1lKhxpW2HB/P2sqK8MOQ0RERERk2SYmJnj66YMMD6+nqemKsMORLKbEcgUNDAzgg4OsrdLIWCIiIiKS28bGxnjqqYNEIrVs2XKVeuTJJakr7AoY7O/nbGcnk5EIm8rLKS4uDjskEREREZElSyaT7Np1hHh8I5s2NYcdjuQAJZbLMDU1xeTkJF2dndQNDrKjpoaqmpqwwxIRERERWbJIJEIkMglcwebNSiplYZRYLtG5c+c4uns3lWNjrCkqYmN9ve5JKSIiIiI5LRKJ8Mwzh0gmS2hp2R52OJJDlAktwdDAAMefe46GeJyt89yrUkREREQkl0SjUXbtOsTUVAOlpWNhhyM5RoP3LFIsFuP4gQNscmfrxo1hhyMiIiIismxTU1M880w7Q0PraGjYGnY4koOUWC5Sx/79VAwPs6m+PuxQRERERESWLRaL8fTTbYyOrmXLlqvCDkdylBLLRYpFIlzR0KDhlkVEREQk58XjcZ57ro2hoUqam6/R/7iyZEosFygajXLi2DFiQ0OUlZaGHY6IiIiIyLK4O88/f4Du7hKam68NOxzJcRq8ZwEmJydp//GPqUom2Vlbq/tUioiIiEhOc3f27TtIV1ecrVtvpahI55tkeZRYXoa703noENXT0+zcvDnscERERERElq2jo5POzigtLTfrpImsiIwemjCzV5jZYTPrMLMPzbF8q5n9yMx2m9k+M7svk/EsxemTJ5ns7OSKxsawQxEREcmLtlVEwtXd3UN7+ygbN95ASYku8ZKVkbHE0syKgc8C9wLXA28ys+tnrfYR4BvufhvwRuBvMhXPUo3293NFbS2lJTq5KyIi4cqXtlVEwtPX18e+fWdYv/5Kyssrwg5H8kgmz1i+EOhw92PuPg18HXj1rHUcWBdMrwfOZDCeRYvFYkwODVFVoS+diIhkhZxvW0UkPJFIhH37uqiquoK1a2vCDkfyTCZPwzUDXWnPu4G7Zq3zAPADM/s9YA3wsrl2ZGbvBN4J0NDQQGtr60rHOqfJaBSmpvhZIrEqr5dp4/E4rb29YYeRc1RvS6e6WxrV29JMxeNhh7Aacr5tzWbj4+MFXw+qg5R8rAd3JxKJkkiUUFo6xsTEpdePx8fp7W1dldiyWaHXQyKx8LY1k4nlXDfB8VnP3wQ87O5/YWb/DviKmd3o7skLNnJ/CHgIYOfOnX733XdnIt7zJicnOXnyJOPt7dzQ1ERFeXlGX2+1tPb2cndTU9hh5BzV29Kp7pZG9bY0Hf39YYewGnK2bc0Fra2tFHo9qA5S8q0eEokEP/vZHhKJTbS0LOy2Ir29rTQ13Z3ZwHJAodfD0FDfgtfNZFfYbmBL2vMWLu6O83bgGwDu/jOgAqjPYEwL0t3ZSbytjesaG/MmqRQRkbyQs22riITD3Tl48DBnz5bQ3Lwz7HAkj2UysXwWuNrMdphZGakBBB6btc4p4B4AM7uOVOMX2iHnsbExnn/8cUZPnWJbY6OurRQRkWyTc22riITr+PEujhyJsmXL9ZjN1elBZGVkrCusu8fN7N3A94Fi4IvufsDMPgbscvfHgA8AnzOz95PqynO/u8/u0rMqhoeH6enpoSYSYWtjIyUaBVZERLJMrrWtIhKuvr4+Dh7sp6Fhp24rIhmX0ezJ3b8HfG/WvI+mTbcDL85kDAsxMTHBsSefpK64mMbaWiWVIiKStXKlbRWRcE1MTLB/fxdVVTuoqqoOOxwpAJnsCpsT4vE4B3ftYq0Z29X9VURERERyXDweZ9eug4yPb2Ddutqww5ECUfCJ5cjICEXDw1zV2Bh2KCIiIiIiy+Lu7N59gL6+Ypqbd4QdjhSQgk8suw4cYGNlJUVFBV8VIiIiIpLjjh8/QVdXnC1bbtRgPbKqCj6bsmSS+vXrww5DRERERGRZRkZGaG8forHxWo0ZIquuoBPLSCRCIhKhWGcrRURERCSHTU5Osnt3J2VlLVRWrgk7HClABZ1RjQwPsx7UDVZEREREctqxY6cZG1tHXV1T2KFIgSrojOpcfz/Vpbqnj4iIiIjkrmPHujh9eoT16zUYpYSnYBPL0dFRJgYGqNX1lSIiIiKSo/r6+mhv76Os7Cqqq/V/rYSnIBPL0dFROn78YxqTScp0xlJEREREctDU1BT793dRVbVdSaWEriATy7NdXdQWFbGlvj7sUEREREREFs3d2bWrjbGxdaxfXxd2OCKFl1i6O+N9fTTV1IQdioiIiIjIknR0HKOnB1pargo7FBGgwBLL0dFR2g8coGh4mIqysrDDERERERFZtMHBIY4eHWHTpusxs7DDEQGgYO6c6u507NpF3cQEV23erFuMiIiIiEjOmZ6eZu/e4xQXb6aiojLscETOK5js6mRnJ4yMsH3zZsp1tlJEREREctDu3e2MjlZQX78p7FBELlAwieVITw/XNDSEHYaIiIiIyJKcOtVFT0+CzZt3hh2KyEUKIrF0dxITE1RVVIQdioiIiIjIokUiEdrb+6ipuZKSkoK5mk1ySN4nlu7O8z/5CUWRCMXFxWGHIyIiIiKyKMlkkt27D5FM1rF2re5sINkp7xPLU6dOUdzby61bt4YdioiIiIjIonV0HKO3t5iGBv0/K9krrxPLZDLJQEcHOxoaNBSziIiIiOSc0dFRjhwZZePGnbqrgWS1vP50xuNxmJhgfXV12KGIiIiIiCxKIpFgz54OSkt1axHJfnmdWI6NjVHuHnYYIiIiIiKLdvjwUQYGynVrEckJeZ1YRicmWKsBe0REREQkxwwNDXH0aISWFt1aRHJD3iaW8XicyOgo5RqOWURERERySDweZ9++46xZs5WSktKwwxFZkLxMLKemptj75JNEjx1jna6vFBEREZEc0t5+hOHhKjZsaAg7FJEFy8vTeac6OqgaGODabds0GqyIiIiI5Iy+vn6OHYuyefMtYYcisih5ecZyYmiILY2NSipFREREJGdMT0/T1naKtWu3U6LLuSTH5F1i6e7Eo1Eqy8vDDkVEREREZMHa248yOrqGmpq6sEMRWbS8SywjkQglU1MUazRYEREREckRfX39nDgxSXPz1WGHIrIkeZdYjo+P696VIiIiIpIzYrHY+S6wOjkiuSrvEsvBnh5q1A1WRERERHLEwYPqAiu5L68Sy1gsxmRvL/U1NWGHIiIiIiJyWQMDA8EosFeFHYrIsuRVYjk8PEzl5KRG0RIRERGRrJdIJGhrO8natdv0/6vkvLxKLMdGRlinbrAiIiIikgMOHTrK4GAFNTX1YYcismx5lViO9/Wxtqoq7DBERERERC5peHiYzs4ILS3XhB2KyIrIm8TS3YlHIlRXVoYdioiIiIjIvJLJJPv2HaOiooWSktKwwxFZEXmTWCYSCYjHNUSziIiIiGS1jo5jDA6WUVvbGHYoIismbxLLaDRKme5fKSIiIiJZbGxsjKNHR2lqugozCzsckRWTN4mlu1Oms5UiIiIikqXcnT17jlBU1EhFhS7fkvySN4llNBrFEomwwxARERERmdOJE6fo6yuivr457FBEVlzeJJbTk5NUFeVNcUREREQkj0xMTHDkyAD19VdSpP9ZJQ/lzac6MT1NWalG1RIRERGR7LNv32Gmp9dTXb0u7FBEMiJvEstkPE6xjv6IiIiISJY5c6aHs2eTNDXtCDsUkYzJm0wsEYupW4GIiIiIZJVYLMbBg2dYu3abbosneS1vMjElliIiIiKSbQ4d6uDcuQpqaurDDkUko/ImE5uMRCgrKQk7DBERERERAIaHRzh5coJNm64OOxSRjMubxDIxNUWpEksRERERyQLuzv79nRQXb6S0tCzscEQyLi8Sy2QyCfE4JUosRURERCQLnDx5kt5eaGzUPSulMGQ0sTSzV5jZYTPrMLMPzbPO682s3cwOmNnXlvI6yWQSksnlBSsiIpIDVqttFZGlm5yc5PDhQTZtujbsUERWTcZO8ZlZMfBZ4JeAbuBZM3vM3dvT1rka+EPgxe4+bGaNS3mt6elpdAdLERHJd6vZtorI0u3de4jJyXXU168JOxSRVZPJM5YvBDrc/Zi7TwNfB149a513AJ9192EAd+9bygu5O6UavllERPLfqrWtIrI0vb29nD2bYNOmK8IORWRVZfKixGagK+15N3DXrHWuATCzJ4Fi4AF3/1+zd2Rm7wTeCdDQ0EBra+sFy5PJJNPT0/T29q5Y8PloPB6nVXW0aKq3pVPdLY3qbWmm4vGwQ1gNq9a2FqLx8fGCrwfVQcpy6mFsbIKiojIGBp5Y2aBWWTw+Tm9va9hhhK7Q6yGRWHjbmsnE0uaY53O8/tXA3UAL8ISZ3ejuIxds5P4Q8BDAzp07/e67775gJ+fOnaPnySfZ2ajePpfS2tvL3U1NYYeRc1RvS6e6WxrV29J09PeHHcJqWLW2tRC1trZS6PWgOkhZaj20tx9lcHCC7dtvWfmgVllvbytNTXeHHUboCr0ehoYW3uklk11hu4Etac9bgDNzrPMdd4+5+3HgMKnGcFEmJycxDd4jIiL5b9XaVhFZnLGxMU6dOsfGjdeEHYpIKBaUWJrZjUvY97PA1Wa2w8zKgDcCj81a59vAS4PXqCfVfefYYl9odGCAKpvrIK6IiEh2yva2VUQWzt1pa+skHq+joqIy7HBEQrHQM5YPmtkzZvYuM6tZyAbuHgfeDXwfOAh8w90PmNnHzOxVwWrfBwbNrB34EfCf3X1wkWXg3Nmz1K5fv9jNREREwpTVbauILFxPz/9p787D5LrrO9+/v90tqSVbsvZ984bBYccBEjKJw5IAN2OHuWSAkBkCBG6eCQ9DuMkEEi7JkId7kzATss4MSwKBYcKwZHESJ2xBkJknGC+SbW2WtVmSJbU2S2qppe6uru/9o45MW26pq1uqPnW63q/n0eM6p06f/tTP1fU73zq/8zuHOXhwhOXL15UdRSpNU9dYZuYPFdOXvw24LyK+C3wqM782zs/dDdx90boPjnqcwHuLf5M3PEzv3LlXtAtJkqZS2/etkpoyMjLC9u0HWbToRsIRdOpgTV9jmZmPAh8AfgX4EeAPImJ7RPyrVoVrxuDgINRqdHW18nJRSZKuvnbtWyU1b8eOXZw8OZu5c5saeCBNW81eY/nciPgojWE3Lwf+ZWY+q3j80RbmG9f58+e5xm+HJEkV0859q6TmnD59mt27+1mx4sayo0ila/Z2I38EfAL41cw8d2FlZh6MiA+0JFmT6vU63RaWkqTqadu+VdL4MpOHHnqUrq6lzJw5q+w4UumaLSxfC5zLzBGAiOgCejNzIDM/27J0TajVanTnxbfwkiSp7bVt3yppfPv3P05fH6xdu6rsKFJbaPbCxK8Do+dOnlOsK93Q0BAzyg4hSdLEtW3fKunyhoaG2LGjj4ULb3CeD6nQ7F9Cb2aeubBQPJ7TmkgTMzI0xIzu7rJjSJI0UW3bt0q6vEce2UV//2zmzVtQdhSpbTRbWJ6NiBdeWIiIFwHnLrP9lBkZGqLbwlKSVD1t27dKurTTp0/z2GMDrFhxU9lRpLbS7DWW7wG+GBEHi+UVwBtaE2liakND9FhYSpKqp237VkmX9tBDjxKxlBkzZpYdRWorTRWWmXlvRDwTuAUIYHtmDrc0WZPq3sNSklRB7dy3ShrbgQMH6OuDdetWlx1FajvNnrEE+H5gffEzL4gIMvMzLUk1AbXz5z1jKUmqqrbsWyU93fDwMNu397Fo0Y2Et7qTnqapwjIiPgvcCGwCRorVCZTe+dWGh+numUh9LElS+dq5b5X0dFu37uT06V7WrZtfdhSpLTVbkd0G3JrZfjeMrJ0/z4z5/oFLkiqnbftWSU918uRJDhwYYOXK55QdRWpbzV6cuBlY3sogk5GZUK97jaUkqYrasm+V9FSZydate5ywRxpHs2csFwNbI+K7wOCFlZl5R0tSNWlkZARGRhznLkmqorbsWyU91eOPH+TQoboT9pyDBJYAACAASURBVEjjaLaw/I1Whpiser1Oj0WlJKmafqPsAJIur1arsWPHYRYvvtkTGdI4mr3dyLciYh1wc2Z+PSLmAKVPxVqv1+nyj1ySVEHt2rdK+p4dO3Zz8uRs1q2bV3YUqe01dXFiRLwD+BLwsWLVKuCvWhWqWZmJZaUkqYratW+V1HD69Gn27OlnxYoby44iVUKzs978AvAy4DRAZj4KLG1VqGaNjIxYWEqSqqot+1ZJDQ8//ChdXUuZOXNW2VGkSmi2sBzMzKELCxHRQ+NeW6UaGRmh21naJUnV1JZ9qyQYHh7m8GFYvHhV2VGkymi2sPxWRPwqMDsiXgV8Efib1sVqzvDwsIWlJKmq2rJvlTrd8PAw588PM3/+em9pJ01As38t7wOOAg8D/xdwN/CBVoVq1tDQEN5NSJJUUW3Zt0qdbteuPYyMdDF//qKyo0iV0uyssHXgE8W/tjF07hwzu51AT5JUPe3at0qd7OzZs+za1c+MGV5XKU1UU4VlROxhjOs+MvOGq55oAkYGB5k1Y0aZESRJmpR27VulTrZp03YyF9HV1V92FKlymiosgdtGPe4FfgpYePXjTMzw0JBj3yVJVdWWfavUqQ4dOsSRI7B69VqOHt1bdhypcpqqyjLz+Kh/j2fm7wEvb3G28TJx9uhR5vT2lhlDkqRJace+VepUtVqNrVsPMm/eOk9aSJPU7FDYF45a7KLxLevcliRqUq1Wg8FBZi30y11JUvW0Y98qdaodO3Zz8mQP69cvLjuKVFnNDoX9z6Me14C9wL++6mkmYHBwkB5vNSJJqq6261ulTnTmzBn27etnxYrvKzuKVGnNzgr7o60OMlHDw8P0OiOsJKmi2rFvlTrRli27GB5eyKxZXl4lXYlmh8K+93LPZ+bvXp04zRseHsb5YCVJVdWOfavUaY4cOcLjj9dYu3Z92VGkypvIrLDfD9xVLP9L4NvA/laEaka9XmdGRFm/XpKkK9V2favUSUZGRti2bT/XXbee8JhSumLNFpaLgRdmZj9ARPwG8MXM/LlWBRvPyMgIfgRIkiqs7fpWqZPs2rWXY8dmsn79orKjSNNCs/MprwWGRi0PAeuvepoJqJ0/T4/TQUuSqqvt+lapU5w5c4Zdu06yfPmNZUeRpo1mz1h+FvhuRPwlkMDrgM+0LFUTcmSEbifvkSRVV9v1rVKn2LJlJ/X6Qnp755QdRZo2mp0V9sMR8ffAvyhWvTUzN7Yu1vhqtRpdjoeXJFVUO/atUifo6zvCwYMjrFq1tuwo0rQykbGkc4DTmfn7wIGIuL5FmZpSO3eOGT3NnnCVJKkttVXfKk13FybsmTt3rSPfpKusqcIyIn4d+BXg/cWqGcB/b1WoZmS9TpfXWEqSKqod+1ZputuzZw/Hj89kwYIlZUeRpp1mK7PXAXcAZwEy8yAwt1WhmlGv1SwsJUlV1nZ9qzSdnTt3jh07TrF8+U1lR5GmpWYrs6HMTBqTCxAR17QuUnOyXvcaS0lSlbVd3ypNZw88sJWRkQX09s4uO4o0LTVbWH4hIj4GzI+IdwBfBz7Ruljjy3rdm9lKkqqs7fpWabo6fPgwx47BsmXryo4iTVvNzgr7nyLiVcBp4Bbgg5n5tZYmG0d9ZISYMaPMCJIkTVo79q3SdFSr1di69XGuucYJe6RWGrewjIhu4CuZ+UqgbTo8z1hKkqqqXftWaTrasWM3J050c/31TtgjtdK4Q2EzcwQYiIjrpiBPUzKTkeFhup28R5JUQe3Yt0rTUX9/P/v29bNy5TPLjiJNe83eCPI88HBEfI1i9jqAzHx3S1KNY3BwkBgaclZYSVKVtVXfKk03mcmWLbsYHl7ErFm9ZceRpr1mC8u/K/61heHhYWZZVEqSqq2t+lZpujl8+DCPPz7CunVO2CNNhcsWlhGxNjP3ZeafTVWgZtRqNWZaWEqSKqhd+1ZpOqnVamzffpBFi250Tg5pioxXnf3VhQcR8eUWZ2laZtLth4QkqZrasm+VppPGhD29zJ07v+woUscYr7AcXb3dMNGdR8SrI+KRiNgZEe+7zHavj4iMiNua2W9mYlkpSaqotuxbpeni1KlT7NnTz8qVN5UdReoo4xWWeYnH4yqmUv9j4DXArcCbIuLWMbabC7wbuKfZfddqNajXJxJHkqR20ZZ9qzQdZCabN++ku3sZM2fOKjuO1FHGKyyfFxGnI6IfeG7x+HRE9EfE6XF+9sXAzszcnZlDwOeBO8fY7jeB36ExO15T6vU6MxwKK0mqprbsW6XpYN++xzl0CBYtWll2FKnjXHbynszsvoJ9rwL2j1o+ALxk9AYR8QJgTWb+bUT80qV2FBHvBN4JsGTJEjZv3gzDw+zs67uCeJ3nTK3GBttswmy3ybPtJsd2m5zBWq3sCE1p1751w4YNVxBrejhz5kzHt0OV2yAzOXPmHL29szh69NtXtK9a7Qx9fRuuTrCKsg0aOr0dRkaa71ubvd3IZIx1SvHJIT8R0QV8FPjZ8XaUmR8HPg5wyy235DNvuYXurVtZuWTJVYraGTb09XH7smVlx6gc223ybLvJsd0mZ+fRo2VHmAot61tvv/32q5OwwjZs2ECnt0OV22Djxs30989i5cqbr3hffX0bWLbs9isPVWG2QUOnt8OJE0ea3raV9+w4AKwZtbwaODhqeS7wbGBDROwFXgrc1cwkA/XhYbq93YgkqfO0rG+Vquz48RPs2zfIsmXXlx1F6litrM7uBW6OiOsjYibwRuCuC09m5qnMXJyZ6zNzPfAd4I7MvG+8HdeHh+nuvpKRRJIkVVLL+lapqur1Ops372H27NX09LRyMJ6ky2lZYZmZNeBdwFeAbcAXMnNLRHwoIu640v13OXmPJKnDtLpvlapoz549HDs2g0WLvIRAKlNLv9bJzLuBuy9a98FLbHv7BPZ7ZcEkSaqoVvWtUhUNDAywfftJli9/2l13JE2xSl6omN7DUpIkqeNt3LgNWERv7+yyo0gdr7KFZZeT90iSJHWsAwcO0NcHS5euLTuKJCpaWNZHRgivsZQkSepIQ0NDbN3ax4IFN3iyQWoTlfxLzHrdyXskSZI61IMPbmNgYA7z5i0oO4qkQiULS0mSJHWmI0eOcOhQjRUrbi47iqRRKllYOhRWkiSp89RqNTZv3k9vr/eslNpNJQvLrNctLCVJkjrMtm07OXGi23tWSm2omoXlyIjXWEqSJHWQJ554gv37z7Jq1bPKjiJpDNUsLDM9YylJktQh6vU6Dz20G1jGzJmzyo4jaQzVLCwdCitJktQxdu7cw5EjsGzZ6rKjSLoEC0tJkiS1rf7+fvbsOcnKlbeWHUXSZVSysBweGvIaS0mSpGkuM3nwwR0MDy+mt3d22XEkXUYlC0uGh51iWpIkaZrbu3cfhw/D8uVry44iaRyVLCy7uyoZW5IkSU0aGBjg0UePsWLFs7wESqqASlZoWa+XHUGSJEktkpls2rSd8+cX0ts7p+w4kppQycKy22+tJEmSpq19+/Zz6FCycuX6sqNIalIlC0sn7pEkSZqezp07xyOPHGXZsmc6BFaqkEoWlmSWnUCSJElXWWayceM2zp1bwOzZ15QdR9IEVLKw9NsrSZKk6eexx/Zx6FCyatX1ZUeRNEGVLCw9YylJkjS9nD17lkceOeYQWKmiKllY+lEjSZI0fTSGwG5ncHChQ2CliqpkYdlTdgBJkiRdNbt37+XwYZwFVqqwShaWnrGUJEmaHk6fPs2jj55gxYpbHQIrVVg1C0s/dCRJkipvZGSEjRsfZWRkCb29s8uOI+kKVLKwlCRJUvVt376Tvj5Yvnxt2VEkXaFKFpaer5QkSaq2o0ePsXfvGdaseXbZUSRdBRaWkiRJmlJDQ0M8/PBj9PSsYubMWWXHkXQVVLKw9D6WkiRJ1bVp01ZOnepl8eLlZUeRdJVUsrD0jKUkSVI17d9/gIMHR1i58payo0i6iipXWGamhaUkSVIFnT17lq1b+5g//0Z6erwzuTSdVK6wBPBjSJIkqVrq9ToPPLCdkZFFzJ07v+w4kq6yShaW3sdSkiSpWnbs2MmRIz0sXbqu7CiSWqCShaWT90iSJFXHsWPHeeSRflatepYnCKRpqpKFpR9IkiRJ1XDu3DkefHAv1167jhkzZpYdR1KLVLKw7LKwlCRJanv1ep1Nm7Zz9ux1zJ+/uOw4klqokoWlJEmS2t+OHXs4cKDOihU3lB1FUotVsrB0KKwkSVJ7O3LkCDt3nmTlymfR1VXJQ05JE1DJv3ILS0mSpPZ1/vx5Hn54P3PmrKe3d07ZcSRNAQtLSZIkXTX1ep37799Cf/91XHfdorLjSJoiFpaSJEm6arZu3cHBg7B69Y1lR5E0hapZWJYdQJIkSU9z4MDj7Np1ljVrnuOJAKnDVLKwlCRJUns5ffo0Dz98mHnzbvB+lVIHqmRh6TdgkiRJ7WN4eJiNGx+lu3sF8+YtKDuOpBJYWEqSJGnS6vU6mzZt5cSJOSxatLLsOJJKUr3CMtPCUpIkqU1s2fIoe/fWWL36lrKjSCpR9QpLPGMpSZLUDvbv38/u3WdYter76Oqq5GGlpKukpZ8AEfHqiHgkInZGxPvGeP69EbE1Ih6KiG9ExLpW5pEkqersW9UuTpx4gi1bjrBgwTOYNau37DiSStaywjIiuoE/Bl4D3Aq8KSJuvWizjcBtmflc4EvA77QqjyRJVWffqnYxMDDAxo27gRVcc83csuNIagOtPGP5YmBnZu7OzCHg88CdozfIzG9m5kCx+B1gdTM7diCsJKlDtaxvlZo1PDzMvfduY3BwIUuWOFmPpIaeFu57FbB/1PIB4CWX2f7twN+P9UREvBN4J8CSJUu4r7+frrNnr1bOjnGmVmNDX1/ZMSrHdps8225ybLfJGazVyo4wFVrWt27YsOEqRayuM2fOdHw7NNMGAwPnGBoKZs7sp6/vsakJNsVqtTP09W0oO0apbIOGTm+HkZHm+9ZWFpZjnVjMMTeM+BngNuBHxno+Mz8OfBzgGTffnLfNncu8a6+9Wjk7xoa+Pm5ftqzsGJVju02ebTc5ttvk7Dx6tOwIU6Elfestt9ySt99++1WKWF0bNmyg09vhcm2QmTz44DaOHj3HunXPp7u7e2rDTaG+vg0sW3Z72TFKZRs0dHo7nDhxpOltW1lYHgDWjFpeDRy8eKOIeCXwa8CPZOZgMzuezh9kkiRdRsv6Vmk8u3btZteuc6xZ8xyPxSQ9TSuvsbwXuDkiro+ImcAbgbtGbxARLwA+BtyRmU2Xw95uRJLUoVrWt0qXs3//42zbdpLly29lxoyZZceR1IZaVlhmZg14F/AVYBvwhczcEhEfiog7is0+AlwLfDEiNkXEXZfY3VNYVkqSOlEr+1bpUo4ePcZDDx1m7twb6e2dXXYcSW2qlUNhycy7gbsvWvfBUY9fOZn9esZSktSpWtW3SmM5ceIEGzc+Rm/vOubOnV92HEltrJVDYVsisbCUJElqtf7+MzzwwB4iVjF//uKy40hqc5UrLMHCUpIkqZXOnj3Lffc9wrlzi1i4cHnZcSRVgIWlJEmSnjQ4OMi9927n3LlFrFy5vuw4kiqimoVl2QEkSZKmoczknns2c+rUPJYtW192HEkV0tLJeyRJklQNg4ODnD17juHhlaxZc1PZcSRVTDXPWDoUVpIk6aoZGhrinns2MzLSxZo1z/BYS9KEVbKwlCRJ0tUxODjId7/7MMePz2HGjF6LSkmTUr3CMtMPPEmSpKtgcHCQe+7ZzJEjc1i9+pay40iqsEpeY2lhKUmSdGXOnz/PPfds4fTpuaxde7PHV5KuSCULS0mSJE3ewMAA9967jZMn57JqlUWlpCtnYSlJktRBTp8+zf33P8qpU9exevWNFpWSrgoLS0mSpA5x4sQJHnhgD7XaUtasWVN2HEnTSCULS79ZkyRJmpgjR46xceNj9PSsYcmSpWXHkTTNVLKwlCRJUnMyk8ceO8CWLUeYNWst8+cvKTuSpGnIwlKSJGmaqtfr7NjxKNu3n2HBgpu59tp5ZUeSNE1ZWEqSJE1DtVqNhx7axu7dw6xYcSu9vbPLjiRpGrOwlCRJmmYu3E7k2LGZrFnzHHp6ZpQdSdI0Z2EpSZI0jRw/fpxNm/YyOLiQtWvX0dXVVXYkSR3AwlKSJGma2L17H9u2HWX27LUsW+YkPZKmjoWlJElSxdVqNe6/fyuHDg2zePEzmT37mrIjSeowFpaSJEkVdvr0aR544FFOnZrFypXPp7u7u+xIkjqQhaUkSVJF7d17gO3b++jqWsHq1SvLjiOpg1WusIyyA0iSJJVscHCQ++/fxpEjIyxc+AyuuWZu2ZEkdbjKFZaSJEmdKjPp6+tj8+bHOXfuWlauvMmhr5LagoWlJElSBQwNDbF160727j3HvHnrWLlycdmRJOlJFpaSJEltrF6v09fXx5YtB+nvn8OyZd/HrFm9ZceSpKewsJQkSWpT/f397NixhwMHhrn22nWsWbOICGeckNR+LCwlSZLaTL1eZ/fu/ezadYzh4QUsX76Wnh4P2yS1Lz+hJEmS2sTIyAjHjx/n4Yf3c/IkLF58M4sXzys7liSNy8JSkiSpDTzxxBPs2PEYhw+PMGfOatauXUJXV1fZsSSpKRaWkiRJJRoYGGDXrgM89lg/PT1LWbFipbcQkVQ5FpaSJEklOHfuHJs37+TUqSHOnLmOJUueRW/vnLJjSdKkWFhKkiRNoVOnTtHX18cjj/STOZulS5/DggUzy44lSVfEwlKSJGkKnDp1mgMHjrJv30kilrBgwSpmz76m7FiSdFVYWEqSJLXI0NAQZ8+eZdOm3QwNwfDwYq677hbmzLm27GiSdFVZWEqSJF1l/f39DAwMsHnzAQYHZ9LTs5z585d5L0pJ05afbpIkSVfJoUNHOHGin8cfP8nw8AJmzVrDsmVLy44lSS1nYSlJkjRJw8PD1Go1duzYy5EjAwwOwsyZq+ntXcqiRXPLjidJU8bCUpIkaQIyk2PHjpGZbNmyn/Pne6nVYPHiZ3PddT3eg1JSR7KwlCRJasLRo8fp63uC8+fP89hjg1xzzVJ6elazdOmysqNJUuksLCVJki4yODjIyMgIAwMDPPjgY0R0c+7cCN3dK+jtXcyqVXOYMcN7T0rSBRaWkiSp42UmtVqNI0eOMDg4yNatR8icTWadnp5VXHfdEq69Foe5StIlWFhKkqSOdPTocQ4ffoKIYGBggDNnhnjggUEAentvYO7cBSUnlKTqsLCUJEnT0vDwMMPDw08uHz9+gm3b+p4863j+/IWhrXOARcyYcYwlS9aUlFaSqs3CUpIkVd6JEyeeUkQCbN9+gIGBWXR1NQrJkZER5sy5kTlzGrcBmTv3qUNbBwZi6gJL0jRjYSlJkipheHiYHTseo15/6vp6vc7+/f309Cx9yvqurlUsXbp8ChNKUudqaWEZEa8Gfh/oBj6Zmb910fOzgM8ALwKOA2/IzL2tzCRJUpVVrW8dGhqiVqs1te2FGVgbL+3pGrO0zmb+/JVPe27BgjX09s6+kqiSpCvQssIyIrqBPwZeBRwA7o2IuzJz66jN3g48kZk3RcQbgd8G3tCqTJIkVVm79a1DQ0M88cQTl91m69YDnD/fS0TXuPur10eYOXM18+YtvuQ2Cxd2EeGQVUlqN608Y/liYGdm7gaIiM8DdwKjO787gd8oHn8J+KOIiMzMS+00gZ1Hj7Yk8HQ3WKvZdpNgu02ebTc5ttvknBoaKjvCVGhJ31qv17n//vsnHCYT9u0bf5trrpnV1P66umZQq/Vz4kT/hLNcDbXaIEeP7izld7cL26DBdrANLuj0djh7tvnP41YWlquA/aOWDwAvudQ2mVmLiFPAIuDY6I0i4p3AO4vFwZtf85rNLUk8/S3morZVU2y3ybPtJsd2m7xbyg7QYi3rW2+77Tb7Vv/2wDa4wHawDS6wHZrsW1tZWI41TuXib0ub2YbM/DjwcYCIuC8zb7vyeJ3Htpsc223ybLvJsd0mLyLuKztDi9m3tpDtYBtcYDvYBhfYDs33reNf8DB5B4DRN4NaDRy81DYR0QNcB5xoYSZJkqrMvlWS1JZaWVjeC9wcEddHxEzgjcBdF21zF/CW4vHrgX+83DUgkiR1OPtWSVJbatlQ2OK6jncBX6Exb/ifZuaWiPgQcF9m3gX8CfDZiNhJ49vUNzax64+3KnMHsO0mx3abPNtucmy3yZvWbWff2nK2g21wge1gG1xgOzTZBuGXmJIkSZKkK9HKobCSJEmSpA5gYSlJkiRJuiKVKiwj4tUR8UhE7IyI95Wdp51ExJqI+GZEbIuILRHx74v1CyPiaxHxaPHfBcX6iIg/KNryoYh4YbmvoFwR0R0RGyPib4vl6yPinqLd/mcxSQYRMatY3lk8v77M3GWLiPkR8aWI2F68937A91xzIuIXi7/VzRHx5xHR6/vu6SLiTyPiSERsHrVuwu+xiHhLsf2jEfGWsX5Xp4uI3yzabVNEfDUiVpadqQwR8ZHiM+2hiPjLiJhfdqapFhE/VXw+1SOio26z4LHm2J+7neZSx9Wdpjg2+W5EPFi0w3+83PaVKSwjohv4Y+A1wK3AmyLi1nJTtZUa8H9n5rOAlwK/ULTP+4BvZObNwDeKZWi0483Fv3cC/3XqI7eVfw9sG7X828BHi3Z7Anh7sf7twBOZeRPw0WK7Tvb7wD9k5jOB59FoQ99z44iIVcC7gdsy89k0JmF5I77vxvJp4NUXrZvQeywiFgK/DrwEeDHw6xeKUT3FRzLzuZn5fOBvgQ+WHagkXwOenZnPBXYA7y85Txk2A/8K+HbZQaaSx5pP+jRP/9ztNJc6ru40g8DLM/N5wPOBV0fESy+1cWUKSxoHAzszc3dmDgGfB+4sOVPbyMxDmflA8bifxgH+Khpt9GfFZn8G/GTx+E7gM9nwHWB+RKyY4thtISJWA/8H8MliOYCXA18qNrm43S6055eAVxTbd5yImAf8MI0ZKMnMocw8ie+5ZvUAs6Nxn8E5wCF83z1NZn6bp9+DcaLvsR8HvpaZJzLzCRqFQ6cfND1NZp4etXgN0JGz+2XmVzOzVix+h8a9QjtKZm7LzEfKzlECjzW55OduR7nMcXVHKfrTM8XijOLfJfuGKhWWq4D9o5YP0IH/g5tRDJN7AXAPsCwzD0HjjwRYWmxme37P7wH/AagXy4uAk6MOLEa3zZPtVjx/qti+E90AHAU+VQwj/mREXIPvuXFl5uPAfwL20SgoTwH34/uuWRN9j/nea1JEfDgi9gNvpnPPWI72NuDvyw6hKeNnhZ7mouPqjlNcLrYJOELjS9pLtkOVCsuxvp3vyG9TLycirgW+DLznom+fn7bpGOs6rj0j4ieAI5l5/+jVY2yaTTzXaXqAFwL/NTNfAJzle0MSx2LbFYphmHcC1wMraZwdes0Ym/q+m5hLtZPtV4iIrxfX9V78706AzPy1zFwDfA54V7lpW2e8dii2+TUaw+E+V17S1mmmDTqQnxV6igkcV09bmTlSXCKxGnhxRDz7Utv2TF2sK3YAWDNqeTVwsKQsbSkiZtB4838uM/+iWN0XESsy81AxJOxIsd72bHgZcEdEvBboBebROIM5PyJ6irNDo9vmQrsdKIYwXkfnDhc5ABwY9c3Vl2gUlr7nxvdKYE9mHgWIiL8AfhDfd82a6HvsAHD7Res3TEHOtpOZr2xy0/8B/B2Na1OnnfHaoZjg6SeAV+Q0veH3BN4LncR+Sk+6xHF1x8rMkxGxgcalJGNO7FSlM5b3AjdHY9bEmTQmurir5Exto7je6k+AbZn5u6Oeugu4MAPiW4C/HrX+3xazKL4UOHVhaFknycz3Z+bqzFxP4z31j5n5ZuCbwOuLzS5utwvt+fpi+2l50DGezDwM7I+IW4pVrwC24nuuGfuAl0bEnOJv90Lb+b5rzkTfY18BfiwiFhRni3+sWKdRIuLmUYt3ANvLylKmiHg18CvAHZk5UHYeTSmPNQVc9ri6o0TEkihmxo6I2TS+GL9k3xBVOjYpzir9Ho0ZFP80Mz9ccqS2ERE/BPwT8DDfu1bwV2mMB/8CsJbGwexPZeaJ4g/mj2h86zAAvDUz75vy4G0kIm4HfikzfyIibqBx0f5CYCPwM5k5GBG9wGdpjLU/AbwxM3eXlblsEfF8GpMezQR2A2+l8YWV77lxFFN2v4HGULuNwM/RuJbH990oEfHnNM42Lgb6aJxB+ysm+B6LiLfR+EwE+HBmfmoqX0cVRMSXgVto9CGPAT9fXA/cUSJiJzALOF6s+k5m/nyJkaZcRLwO+ENgCXAS2JSZP15uqqnhsebYn7uZ+Selhppilzquzsy7y0s19SLiuTQmyeumOL7LzA9dcvsqFZaSJEmSpPZTpaGwkiRJkqQ2ZGEpSZIkSboiFpaSJEmSpCtiYSlJkiRJuiIWlpIkSZKkK2JhKV0kIkYiYlNEbI6IL0bEnBb+rg9FxCuLx++Z6O8q7tf3jxExr1h+d0Rsi4jPXYVsPxsRK0ctfzIibp3kvt4VEW+90kySpOqZyn51IiLizCXWj+6bN0TEbcXjuyNifvHv303i962IiL+d4M98OiL2RMSDEbEjIj4TEatGPf/14v68UuksLKWnO5eZz8/MZwNDQFP3LyuKvAn9TWXmBzPz68Xie4CJdravBR7MzNPF8r8DXpuZb74oW88E9wvws8CThWVm/lxmbp3EfgD+FHj3JH9WklRtk+pXASKiu3WxxnZR3zx6/Wsz8yQwn0Z/O1HvBT4xiZ/75cx8Ho37zG4EvhkRM4vnPjvJLNJVZ2EpXd4/ATcBRMR7i29bN0fEe4p164szhP8FeABYExFvioiHi+1+u9iuu/jWcXPx3C8W6z8dEa+PiHfTKOK+GRHfjIi3R8RHL4SIiHdExO+Oke/NwF8X2/w34Abgroj4xYj4XHeIqwAABjhJREFUjYj4eER8FfhMkfWfIuKB4t8Pjtr/fyhyPRgRvxURrwduAz5XfMs8+6JvbZ/2Gov1ZyLiw8V+vhMRywAycwDYGxEvvir/VyRJVTW6X/2ZiPhu0c987EIRWfQlH4qIe4AfiIi9EfH/RsQ/R8R9EfHCiPhKROyKiCeL1Ij45Yi4NyIeioj/OGr9X0XE/RGxJSLeOTpMRPznok/8RkQsKdZ9uugHuWjbvRGxGPgt4MYi90ci4rMRceeo7T4XEXeM8dr/T+Afim1+tsj1N8UZyXcVxxkbi/5z4cU/nA0fBQ4DrylW3wW8qZmGl1rNwlK6hOIs32uAhyPiRcBbgZcALwXeEREvKDa9BfhMZr4AGAZ+G3g58Hzg+yPiJ4vHqzLz2Zn5HOBTo39XZv4BcBD40cz8UeDzwB0RMaPY5K0X/0zhZcD9xT5+ftQ+LhSlLwLuzMyfBo4Ar8rMFwJvAP6geJ2vAX4SeEnxjejvZOaXgPuANxffMp8b1S4rL/EaAa4BvlPs59vAO0ZlvQ/4F5dobknSNHdRv/osGn3RyzLz+cAIjS9LodGXbM7Ml2Tm/yrW7c/MH6BRmH4aeD2N/vhDxb5/DLgZeDGNvulFEfHDxc++LTNfROML03dHxKJRv+eBol/8FvDrTb6U9wG7iv7xl4FP0uiniYjrgB8E7r7otV8PPJGZg6NWPxv46SLzh4GB4ljin4F/e5nf/wDwTIDMfAKYNeo1SaWxsJSebnZEbKJRCO0D/gT4IeAvM/NsZp4B/oLvFUmPZeZ3isffD2zIzKOZWQM+B/wwsBu4ISL+MCJeDZzmMjLzLPCPwE9ExDOBGZn58BibLszM/svs6q5RReEM4BMR8TDwReDC9ZKvBD5VnFUkM09cLttlXiM0hjhduH7kfmD9qJ87wqihtZKkjjFWv/oKGl9+3ls89woao26gUWR++aJ93FX892Hgnszsz8yjwPmImA/8WPFvI98rvG4ufubdEfEg8B1gzaj1deB/Fo//O42+fsIy81vATRGxlMbZwy8X/eNoK4CjF6375qjXcQr4m1Gvcf1lfmVctGz/qrYwmeuupOnuXPHt6ZMi4uIP8dHOjt50rA0y84mIeB7w48AvAP8aeNs4OT4J/CqwnbHPVgLUIqIrM+tNZPtFoA94Ho0vlc6PypzjZBntcm0xnJkX9jXCUz9jeoFzT/8RSdI0d6l+9c8y8/1jbH8+M0cuWnfhTF991OMLyz00+qb/LzM/dtHvuZ3GF6g/kJkDEbGBRn80lon0hRf7LI0zrm9k7P793Bi/9+LXMfo1Xu4Y/QXAN0Yt27+qLXjGUmrOt4GfjIg5EXEN8Doaw3Eudg/wIxGxuLhW5E3At4prMroy88vA/wO8cIyf7QfmXljIzHtofLP608CfXyLXI3zvG97xXAccKorQfwNcmBDhq8Dbopilb9R1HU/JM95rbOL3PwPY3GRWSdL09g3g9cVZPiJiYUSsu4L9fYVGX3Ztsb9Vxb6vozEEdaAYAfTSUT/TRWNILTT62v9Fc8bqHz9NYxI+MnPLGD+zg8ufhRxXNLybxtnPC9dqBrAc2Hsl+5auBgtLqQmZ+QCNTuO7NAqrT2bmxjG2OwS8H/gm8CCNazf+GlgFbCiG+3y62OZiHwf+PiK+OWrdF4D/XVxDMZa/A25v8mX8F+AtEfEdGkXe2SLzP9AYYnRfke+Xiu0/Dfy3C5P3NPEax/My4Gmz7EmSOk8xy/gHgK9GxEPA12gUTJPd31eB/wH8c3HJx5doFH//APQUv+M3aQyHveAs8H0RcT+NeQM+1OTvOg7872ICu48U6/qAbVxihFFxicuuiLhpEi/vI8VQ3h00Lkf50cwcKp57EY25DS4eeitNufjeqDVJ7SYa97v6aGZ+4xLPr6AxcdCrpjbZxBQTHb03M/9N2VkkSbrailE/DwMvzMxTl9jmdcCLMvMDV/H3/j6N+RTGPE6QppJnLKU2FI2bL++gcV3KJTuL4uzhJyJi3tSlm5TFNIYAS5I0rUTEK2nMh/CHlyoqATLzL7n6Q1Y3W1SqXXjGUpIkSZJ0RTxjKUmSJEm6IhaWkiRJkqQrYmEpSZIkSboiFpaSJEmSpCtiYSlJkiRJuiL/P8x36HXb1MdaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(221)                                        # plot original sand and shale porosity histograms\n",
    "plt.hist(df['Porosity'], facecolor='red',bins=np.linspace(0.0,0.25,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.05,0.25]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(222)  \n",
    "plt.hist(df['NPor'], facecolor='blue',bins=np.linspace(-3.0,3.0,1000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Nscore Porosity')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(223)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['Perm'], facecolor='red',bins=np.linspace(0.0,1000.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label='Original')\n",
    "plt.xlim([0.0,1000.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Porosity (fraction)'); plt.ylabel('Frequency'); plt.title('Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplot(224)                                        # plot nscore transformed sand and shale histograms\n",
    "plt.hist(df['NPerm'], facecolor='blue',bins=np.linspace(-3.0,3.0,100000),histtype=\"stepfilled\",alpha=0.2,density=True,cumulative=True,edgecolor='black',label = 'Trans')\n",
    "plt.xlim([-3.0,3.0]); plt.ylim([0,1.0])\n",
    "plt.xlabel('Permeability (mD)'); plt.ylabel('Frequency'); plt.title('Nscore Permeability')\n",
    "plt.legend(loc='upper left')\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=2.2, wspace=0.2, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The normal score transform has correctly transformed the porosity and permeability to standard normal.\n",
    "\n",
    "#### Inspection of Posted Data\n",
    "\n",
    "Data visualization is very useful to detect patterns. Our brains are very good at pattern detection. I promote quantitative methods and recognize issues with cognitive bias, but it is important to recognize the value is expert intepretation based on data visualization.\n",
    "\n",
    "* This data visualization will also be important to assist with parameter selection for the quantitative methods later.\n",
    "\n",
    "Let's plot the location maps of normal score transforms of porosity and permeability for all facies. We will also include a cross plot of the nscore permeability vs. porosity colored by facies to aid with comparison in spatial features between the porosity and permeability data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cmap = plt.cm.plasma                                    # set the color map\n",
    "plt.subplot(131)                                        # location map of normal score transform of porosity\n",
    "GSLIB.locmap_st(df,'X','Y','NPor',0,1000,0,1000,-3,3,'Nscore Porosity - All Facies','X (m)','Y (m)','Nscore Porosity',cmap)\n",
    "\n",
    "plt.subplot(132)                                        # location map of normal score transform of permeability\n",
    "GSLIB.locmap_st(df,'X','Y','NPerm',0,1000,0,1000,-3,3,'Nscore Permeability - All Facies','X (m)','Y (m)','Nscore Permeability',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "facies = df['Facies'].values +0.01                      # normal score porosity / permeability scatter plot color coded by facies\n",
    "plt.scatter(df['NPor'],df['NPerm'],c = facies,edgecolor = 'black',cmap = plt.cm.inferno)\n",
    "#plt.plot([-3,3],[-3,3],color = 'black')\n",
    "plt.xlabel(r'Nscore Porosity')\n",
    "plt.ylabel(r'Nscore Permeability')\n",
    "plt.title('Nscore Permeability vs. Porosity')\n",
    "plt.xlim([-3,3])\n",
    "plt.ylim([-3,3])\n",
    "plt.grid(True)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=0.8, wspace=0.5, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What do you see?  Here's my observations:\n",
    "\n",
    "* there is a high degree of spatial agreement between porosity and permeability, this is supported by the high correlation evident in the cross plot.\n",
    "* there are no discontinuities that could suggest that facies represent a distinct change, rather the porosity and permeability seem continuous and the assigned facies are a truncation of their continous behavoir, we doing 'ok' with no facies\n",
    "* suspect a 045 azimuth major direction of continuity (up - right)\n",
    "* there may be cycles in the 135 azimuth \n",
    "* there will not likely be a nugget effect, but there is an hint of some short scale discontinuity?\n",
    "\n",
    "**Do you agree?** If you have a different observations, drop me a line at mpyrcz@austin.utexas.edu and I'll add to this lesson with credit.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Experimental Variograms\n",
    "\n",
    "We can use the location maps to help determine good variogram calculation parameters. For example:\n",
    "\n",
    "```p\n",
    "tmin = -9999.; tmax = 9999.; \n",
    "lag_dist = 100.0; lag_tol = 50.0; nlag = 7; bandh = 9999.9; azi = azi; atol = 22.5; isill = 1\n",
    "```\n",
    "* **tmin**, **tmax** are trimming limits - set to have no impact, no need to filter the data\n",
    "* **lag_dist**, **lag_tol** are the lag distance, lag tolerance - set based on the common data spacing (100m) and tolerance as 100% of lag distance for additonal smoothing\n",
    "* **nlag** is number of lags - set to extend just past 50 of the data extent\n",
    "* **bandh** is the horizontal band width - set to have no effect\n",
    "* **azi** is the azimuth -  it has not effect since we set atol, the azimuth tolerance, to 90.0\n",
    "* **isill** is a boolean to standardize the distribution to a variance of 1 - it has no effect since the previous nscore transform sets the variance to 1.0\n",
    "\n",
    "#### Dashboard for Interactive Variogram Calculation\n",
    "\n",
    "Below we make a dashboard with the ipywidgets and matplotlib Python packages for calculating experimental variograms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# interactive calculation of the experimental variogram\n",
    "l = widgets.Text(value='                              Variogram Calculation Interactive Demonstration, Michael Pyrcz, Associate Professor, The University of Texas at Austin',layout=Layout(width='950px', height='30px'))\n",
    "lag = widgets.FloatSlider(min = 20, max = 500, value = 100, step = 10, description = 'lag',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag.style.handle_color = 'gray'\n",
    "\n",
    "lag_tol = widgets.FloatSlider(min = 20, max = 500, value = 50, step = 10, description = 'lag tolerance',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "lag_tol.style.handle_color = 'gray'\n",
    "\n",
    "nlag = widgets.IntSlider(min = 1, max = 100, value = 10, step = 1, description = 'number of lags',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "nlag.style.handle_color = 'gray'\n",
    "\n",
    "azi = widgets.FloatSlider(min = 0, max = 360, value = 0, step = 5, description = 'azimuth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi.style.handle_color = 'gray'\n",
    "\n",
    "azi_tol = widgets.FloatSlider(min = 10, max = 90, value = 20, step = 5, description = 'azimuth tolerance',orientation='vertical',layout=Layout(width='120px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "bandwidth = widgets.FloatSlider(min = 100, max = 2000, value = 2000, step = 100, description = 'bandwidth',orientation='vertical',layout=Layout(width='90px', height='200px'))\n",
    "azi_tol.style.handle_color = 'gray'\n",
    "\n",
    "\n",
    "ui1 = widgets.HBox([lag,lag_tol,nlag,azi,azi_tol,bandwidth],) # basic widget formatting    \n",
    "ui = widgets.VBox([l,ui1],)\n",
    "\n",
    "def f_make(lag,lag_tol,nlag,azi,azi_tol,bandwidth):     # function to take parameters, calculate variogram and plot\n",
    "#    text_trap = io.StringIO()\n",
    "#    sys.stdout = text_trap\n",
    "    tmin = -9999.9; tmax = 9999.9\n",
    "    lags, gammas, npps = geostats.gamv(df,\"X\",\"Y\",\"NPor\",tmin,tmax,lag,lag_tol,nlag,azi,azi_tol,bandwidth,isill=1.0)\n",
    "    \n",
    "    plt.subplot(111)                                    # plot experimental variogram\n",
    "    plt.scatter(lags,gammas,color = 'black',s = npps*0.1,label = 'Azimuth ' +str(azi))\n",
    "    plt.plot([0,2000],[1.0,1.0],color = 'black')\n",
    "    plt.xlabel(r'Lag Distance $\\bf(h)$, (m)')\n",
    "    plt.ylabel(r'$\\gamma \\bf(h)$')\n",
    "    plt.title('Directional NSCORE Porosity Variogram - Azi ' + str(azi))\n",
    "    plt.xlim([0,1000]); plt.ylim([0,1.8])\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.subplots_adjust(left=0.0, bottom=0.0, right=1.5, top=1.0, wspace=0.3, hspace=0.3)\n",
    "    plt.show()\n",
    "    \n",
    "# connect the function to make the samples and plot to the widgets    \n",
    "interactive_plot = widgets.interactive_output(f_make, {'lag':lag,'lag_tol':lag_tol,'nlag':nlag,'azi':azi,'azi_tol':azi_tol,'bandwidth':bandwidth})\n",
    "interactive_plot.clear_output(wait = True)               # reduce flickering by delaying plot updating"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interactive Variogram Calculation Demostration\n",
    "\n",
    "* calculate omnidirectional and direction experimental variograms \n",
    "\n",
    "#### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "##### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1) | [GeostatsPy](https://github.com/GeostatsGuy/GeostatsPy)\n",
    "\n",
    "### The Problem\n",
    "\n",
    "Calculate interpretable experimental variograms for sparse, irregularly-space spatial data.\n",
    "\n",
    "* **azimuth** is the azimuth of the lag vector\n",
    "\n",
    "* **azimuth tolerance** is the maximum allowable departure from the azimuth\n",
    "\n",
    "* **unit lag distance** the size of the bins in lag distance\n",
    "\n",
    "* **lag distance tolerance** - the allowable tolerance in lage distance\n",
    "\n",
    "* **number of lags** - number of lags in the experimental variogram\n",
    "\n",
    "* **bandwidth** - maximum departure from the lag vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f6f4dd59ae38463ab7d1ac39fb6338b4",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "VBox(children=(Text(value='                              Variogram Calculation Interactive Demonstration, Mich…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "22918ebf8c0748b5ac2cc3840eea3d46",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output(outputs=({'output_type': 'display_data', 'data': {'text/plain': '<Figure size 432x288 with 1 Axes>', 'i…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ui, interactive_plot)                           # display the interactive plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comments\n",
    "\n",
    "This was a basic demonstration of vairogram calculation for spatial continuity analysis. Much more could be done, I have other demonstrations on the basics of working with DataFrames, ndarrays, univariate statistics, plotting data, declustering, data transformations and many other workflows available at https://github.com/GeostatsGuy/PythonNumericalDemos and https://github.com/GeostatsGuy/GeostatsPy. \n",
    "  \n",
    "#### The Author:\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "*Novel Data Analytics, Geostatistics and Machine Learning Subsurface Solutions*\n",
    "\n",
    "With over 17 years of experience in subsurface consulting, research and development, Michael has returned to academia driven by his passion for teaching and enthusiasm for enhancing engineers' and geoscientists' impact in subsurface resource development. \n",
    "\n",
    "For more about Michael check out these links:\n",
    "\n",
    "#### [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)\n",
    "\n",
    "#### Want to Work Together?\n",
    "\n",
    "I hope this content is helpful to those that want to learn more about subsurface modeling, data analytics and machine learning. Students and working professionals are welcome to participate.\n",
    "\n",
    "* Want to invite me to visit your company for training, mentoring, project review, workflow design and / or consulting? I'd be happy to drop by and work with you! \n",
    "\n",
    "* Interested in partnering, supporting my graduate student research or my Subsurface Data Analytics and Machine Learning consortium (co-PIs including Profs. Foster, Torres-Verdin and van Oort)? My research combines data analytics, stochastic modeling and machine learning theory with practice to develop novel methods and workflows to add value. We are solving challenging subsurface problems!\n",
    "\n",
    "* I can be reached at mpyrcz@austin.utexas.edu.\n",
    "\n",
    "I'm always happy to discuss,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "Michael Pyrcz, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "\n",
    "#### More Resources Available at: [Twitter](https://twitter.com/geostatsguy) | [GitHub](https://github.com/GeostatsGuy) | [Website](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446) | [YouTube](https://www.youtube.com/channel/UCLqEr-xV-ceHdXXXrTId5ig)  | [LinkedIn](https://www.linkedin.com/in/michael-pyrcz-61a648a1)  \n",
    "  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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