diff --git a/LICENSE b/LICENSE
new file mode 100755
index 0000000000000000000000000000000000000000..2081af10dd67e203d84a4cab3191b03cabb9e629
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,29 @@
+BSD 3-Clause License
+
+Copyright (c) 2020, sustainability-lab
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its
+   contributors may be used to endorse or promote products derived from
+   this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/README.md b/README.md
index 7be5fc7f47d5db027d120b8024982df93db95b74..ec81d72b6fa9383d1894bd8e216e495c4c11359e 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,102 @@
----
-license: mit
----
+![Tests](https://github.com/sustainability-lab/polire/actions/workflows/tests.yml/badge.svg)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+[![Coverage](https://coveralls.io/repos/github/sustainability-lab/polire/badge.svg?branch=master)](https://coveralls.io/github/sustainability-lab/polire?branch=master)
+
+## Polire
+
+```python
+pip install polire
+```
+
+
+The word "interpolation" has a Latin origin and is composed of two words - Inter, meaning between, and Polire, meaning to polish.
+
+
+This repository is a collection of several spatial interpolation algorithms. 
+
+
+## Examples
+Please refer to [the documentation](https://sustainability-lab.github.io/polire/) to check out practical examples on real datasets.
+
+### Minimal example of interpolation
+```python
+import numpy as np
+from polire import Kriging
+
+# Data
+X = np.random.rand(10, 2) # Spatial 2D points
+y = np.random.rand(10) # Observations
+X_new = np.random.rand(100, 2) # New spatial points
+
+# Fit
+model = Kriging()
+model.fit(X, y)
+
+# Predict
+y_new = model.predict(X_new)
+```
+
+### Supported Interpolation Methods
+```python
+from polire import (
+    Kriging, # Best spatial unbiased predictor
+    GP, # Gaussian process interpolator from GPy
+    IDW, # Inverse distance weighting
+    SpatialAverage,
+    Spline,
+    Trend,
+    Random, # Predict uniformly within the observation range, a reasonable baseline
+    NaturalNeighbor,
+    CustomInterpolator # Supports any regressor from Scikit-learn
+)
+```
+
+### Use GP kernels from GPy (temporarily unavailable)
+```python
+from GPy.kern import Matern32 # or any other GPy kernel
+
+# GP model
+model = GP(Matern32(input_dim=2))
+```
+
+### Regressors from sklearn
+```py
+from sklearn.linear_model import LinearRegression # or any Scikit-learn regressor
+from polire import GP, CustomInterpolator
+
+# Sklearn model
+model = CustomInterpolator(LinearRegression())
+```
+
+### Extract spatial features from spatio-temporal dataset
+```python
+# X and X_new are datasets as numpy arrays with first three dimensions as longitude, latitute and time.
+# y is corresponding observations with X
+
+from polire.preprocessing import SpatialFeatures
+spatial = SpatialFeatures(n_closest=10)
+Features = spatial.fit_transform(X, y)
+Features_new = spatial.transform(X_new)
+```
+
+## Citation
+
+If you use this library, please cite the following paper:
+
+```
+@inproceedings{10.1145/3384419.3430407,
+author = {Narayanan, S Deepak and Patel, Zeel B and Agnihotri, Apoorv and Batra, Nipun},
+title = {A Toolkit for Spatial Interpolation and Sensor Placement},
+year = {2020},
+isbn = {9781450375900},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3384419.3430407},
+doi = {10.1145/3384419.3430407},
+booktitle = {Proceedings of the 18th Conference on Embedded Networked Sensor Systems},
+pages = {653–654},
+numpages = {2},
+location = {Virtual Event, Japan},
+series = {SenSys '20}
+}
+```
\ No newline at end of file
diff --git a/_quarto.yml b/_quarto.yml
new file mode 100755
index 0000000000000000000000000000000000000000..211ebe4f5c2c56bb64192bb065f18a4a2b2e1d65
--- /dev/null
+++ b/_quarto.yml
@@ -0,0 +1,22 @@
+project:
+  type: website
+  output-dir: docs
+
+# render only the contents mentioned in the _quarto.yml file
+
+
+website: 
+  title: "Polire"
+  sidebar:
+    style: "docked"
+    search: true
+    contents:
+      - section: "Introduction"
+        path: "index.qmd"
+
+      - section: "Examples"
+        contents:
+          - examples/all_in_one.ipynb
+
+execute:
+  freeze: auto
\ No newline at end of file
diff --git a/examples/air_dat.ipynb.bak b/examples/air_dat.ipynb.bak
new file mode 100755
index 0000000000000000000000000000000000000000..c58c997f63053ab641e4775fb94f89dc0233e2f1
--- /dev/null
+++ b/examples/air_dat.ipynb.bak
@@ -0,0 +1,1144 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from polire.interpolate import *\n",
+    "\n",
+    "from polire.custom import CustomInterpolator\n",
+    "import xgboost\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.gaussian_process import GaussianProcessRegressor\n",
+    "from sklearn.gaussian_process.kernels import Matern\n",
+    "from sklearn.neural_network import MLPRegressor\n",
+    "\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from matplotlib import colors\n",
+    "import seaborn as sns\n",
+    "import sys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"../../daily_44201_2018.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": false
+   },
+   "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>State Code</th>\n",
+       "      <th>County Code</th>\n",
+       "      <th>Site Num</th>\n",
+       "      <th>Parameter Code</th>\n",
+       "      <th>POC</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Datum</th>\n",
+       "      <th>Parameter Name</th>\n",
+       "      <th>Sample Duration</th>\n",
+       "      <th>...</th>\n",
+       "      <th>AQI</th>\n",
+       "      <th>Method Code</th>\n",
+       "      <th>Method Name</th>\n",
+       "      <th>Local Site Name</th>\n",
+       "      <th>Address</th>\n",
+       "      <th>State Name</th>\n",
+       "      <th>County Name</th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>CBSA Name</th>\n",
+       "      <th>Date of Last Change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.497478</td>\n",
+       "      <td>-87.880258</td>\n",
+       "      <td>NAD83</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>21</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>FAIRHOPE, Alabama</td>\n",
+       "      <td>FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>Fairhope</td>\n",
+       "      <td>Daphne-Fairhope-Foley, AL</td>\n",
+       "      <td>2019-02-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.497478</td>\n",
+       "      <td>-87.880258</td>\n",
+       "      <td>NAD83</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>30</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>FAIRHOPE, Alabama</td>\n",
+       "      <td>FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>Fairhope</td>\n",
+       "      <td>Daphne-Fairhope-Foley, AL</td>\n",
+       "      <td>2019-02-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>10</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30.497478</td>\n",
+       "      <td>-87.880258</td>\n",
+       "      <td>NAD83</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>41</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>FAIRHOPE, Alabama</td>\n",
+       "      <td>FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...</td>\n",
+       "      <td>Alabama</td>\n",
+       "      <td>Baldwin</td>\n",
+       "      <td>Fairhope</td>\n",
+       "      <td>Daphne-Fairhope-Foley, AL</td>\n",
+       "      <td>2019-02-12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   State Code  County Code  Site Num  Parameter Code  POC   Latitude  \\\n",
+       "0           1            3        10           44201    1  30.497478   \n",
+       "1           1            3        10           44201    1  30.497478   \n",
+       "2           1            3        10           44201    1  30.497478   \n",
+       "\n",
+       "   Longitude  Datum Parameter Name          Sample Duration  ... AQI  \\\n",
+       "0 -87.880258  NAD83          Ozone  8-HR RUN AVG BEGIN HOUR  ...  21   \n",
+       "1 -87.880258  NAD83          Ozone  8-HR RUN AVG BEGIN HOUR  ...  30   \n",
+       "2 -87.880258  NAD83          Ozone  8-HR RUN AVG BEGIN HOUR  ...  41   \n",
+       "\n",
+       "  Method Code Method Name    Local Site Name  \\\n",
+       "0         NaN          -   FAIRHOPE, Alabama   \n",
+       "1         NaN          -   FAIRHOPE, Alabama   \n",
+       "2         NaN          -   FAIRHOPE, Alabama   \n",
+       "\n",
+       "                                             Address  State Name  County Name  \\\n",
+       "0  FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...     Alabama      Baldwin   \n",
+       "1  FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...     Alabama      Baldwin   \n",
+       "2  FAIRHOPE HIGH SCHOOL, 1 PIRATE DRIVE, FAIRHOPE...     Alabama      Baldwin   \n",
+       "\n",
+       "   City Name                  CBSA Name  Date of Last Change  \n",
+       "0   Fairhope  Daphne-Fairhope-Foley, AL           2019-02-12  \n",
+       "1   Fairhope  Daphne-Fairhope-Foley, AL           2019-02-12  \n",
+       "2   Fairhope  Daphne-Fairhope-Foley, AL           2019-02-12  \n",
+       "\n",
+       "[3 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(1240, 29)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "some_value = 'Ozone'\n",
+    "df = df.loc[df['Parameter Name'] == some_value]\n",
+    "\n",
+    "some_value = '2018-05-07'\n",
+    "df = df.loc[df['Date Local'] == some_value]\n",
+    "\n",
+    "df = df.sample(frac=1)\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "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>State Code</th>\n",
+       "      <th>County Code</th>\n",
+       "      <th>Site Num</th>\n",
+       "      <th>Parameter Code</th>\n",
+       "      <th>POC</th>\n",
+       "      <th>Latitude</th>\n",
+       "      <th>Longitude</th>\n",
+       "      <th>Datum</th>\n",
+       "      <th>Parameter Name</th>\n",
+       "      <th>Sample Duration</th>\n",
+       "      <th>...</th>\n",
+       "      <th>AQI</th>\n",
+       "      <th>Method Code</th>\n",
+       "      <th>Method Name</th>\n",
+       "      <th>Local Site Name</th>\n",
+       "      <th>Address</th>\n",
+       "      <th>State Name</th>\n",
+       "      <th>County Name</th>\n",
+       "      <th>City Name</th>\n",
+       "      <th>CBSA Name</th>\n",
+       "      <th>Date of Last Change</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>292720</th>\n",
+       "      <td>39</td>\n",
+       "      <td>167</td>\n",
+       "      <td>4</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.432117</td>\n",
+       "      <td>-81.460443</td>\n",
+       "      <td>NAD83</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>58</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Marietta WTP</td>\n",
+       "      <td>2000 4TH STREET</td>\n",
+       "      <td>Ohio</td>\n",
+       "      <td>Washington</td>\n",
+       "      <td>Marietta</td>\n",
+       "      <td>Marietta, OH</td>\n",
+       "      <td>2019-01-29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399503</th>\n",
+       "      <td>56</td>\n",
+       "      <td>37</td>\n",
+       "      <td>77</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>41.158000</td>\n",
+       "      <td>-108.619000</td>\n",
+       "      <td>WGS84</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>67</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Hiawatha</td>\n",
+       "      <td>Hiawatha Green Site</td>\n",
+       "      <td>Wyoming</td>\n",
+       "      <td>Sweetwater</td>\n",
+       "      <td>Not in a city</td>\n",
+       "      <td>Rock Springs, WY</td>\n",
+       "      <td>2019-02-25</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>159073</th>\n",
+       "      <td>18</td>\n",
+       "      <td>167</td>\n",
+       "      <td>24</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>39.558525</td>\n",
+       "      <td>-87.312883</td>\n",
+       "      <td>NAD83</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>51</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>Sandcut/  SITE LOCATED BY HOME BEHIND SHED.</td>\n",
+       "      <td>7597 N. STEVENSON RD/ SANDCUT</td>\n",
+       "      <td>Indiana</td>\n",
+       "      <td>Vigo</td>\n",
+       "      <td>Not in a city</td>\n",
+       "      <td>Terre Haute, IN</td>\n",
+       "      <td>2019-01-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249423</th>\n",
+       "      <td>35</td>\n",
+       "      <td>13</td>\n",
+       "      <td>8</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>2</td>\n",
+       "      <td>31.930659</td>\n",
+       "      <td>-106.631103</td>\n",
+       "      <td>WGS84</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>54</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>6O La Union</td>\n",
+       "      <td>7048 McNutt, La Union, NM</td>\n",
+       "      <td>New Mexico</td>\n",
+       "      <td>Dona Ana</td>\n",
+       "      <td>Not in a city</td>\n",
+       "      <td>Las Cruces, NM</td>\n",
+       "      <td>2019-04-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>204274</th>\n",
+       "      <td>26</td>\n",
+       "      <td>81</td>\n",
+       "      <td>22</td>\n",
+       "      <td>44201</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.176672</td>\n",
+       "      <td>-85.416608</td>\n",
+       "      <td>WGS84</td>\n",
+       "      <td>Ozone</td>\n",
+       "      <td>8-HR RUN AVG BEGIN HOUR</td>\n",
+       "      <td>...</td>\n",
+       "      <td>46</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>-</td>\n",
+       "      <td>APPROXIMATELY 1/4 MILE SOUTH OF 14 MILE RD</td>\n",
+       "      <td>10300 14 MILE RD NE #B</td>\n",
+       "      <td>Michigan</td>\n",
+       "      <td>Kent</td>\n",
+       "      <td>Not in a city</td>\n",
+       "      <td>Grand Rapids-Wyoming, MI</td>\n",
+       "      <td>2019-03-05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 29 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        State Code  County Code  Site Num  Parameter Code  POC   Latitude  \\\n",
+       "292720          39          167         4           44201    1  39.432117   \n",
+       "399503          56           37        77           44201    1  41.158000   \n",
+       "159073          18          167        24           44201    1  39.558525   \n",
+       "249423          35           13         8           44201    2  31.930659   \n",
+       "204274          26           81        22           44201    1  43.176672   \n",
+       "\n",
+       "         Longitude  Datum Parameter Name          Sample Duration  ... AQI  \\\n",
+       "292720  -81.460443  NAD83          Ozone  8-HR RUN AVG BEGIN HOUR  ...  58   \n",
+       "399503 -108.619000  WGS84          Ozone  8-HR RUN AVG BEGIN HOUR  ...  67   \n",
+       "159073  -87.312883  NAD83          Ozone  8-HR RUN AVG BEGIN HOUR  ...  51   \n",
+       "249423 -106.631103  WGS84          Ozone  8-HR RUN AVG BEGIN HOUR  ...  54   \n",
+       "204274  -85.416608  WGS84          Ozone  8-HR RUN AVG BEGIN HOUR  ...  46   \n",
+       "\n",
+       "       Method Code Method Name                              Local Site Name  \\\n",
+       "292720         NaN          -                                  Marietta WTP   \n",
+       "399503         NaN          -                                      Hiawatha   \n",
+       "159073         NaN          -   Sandcut/  SITE LOCATED BY HOME BEHIND SHED.   \n",
+       "249423         NaN          -                                   6O La Union   \n",
+       "204274         NaN          -    APPROXIMATELY 1/4 MILE SOUTH OF 14 MILE RD   \n",
+       "\n",
+       "                              Address  State Name  County Name      City Name  \\\n",
+       "292720                2000 4TH STREET        Ohio   Washington       Marietta   \n",
+       "399503            Hiawatha Green Site     Wyoming   Sweetwater  Not in a city   \n",
+       "159073  7597 N. STEVENSON RD/ SANDCUT     Indiana         Vigo  Not in a city   \n",
+       "249423      7048 McNutt, La Union, NM  New Mexico     Dona Ana  Not in a city   \n",
+       "204274         10300 14 MILE RD NE #B    Michigan         Kent  Not in a city   \n",
+       "\n",
+       "                       CBSA Name  Date of Last Change  \n",
+       "292720              Marietta, OH           2019-01-29  \n",
+       "399503          Rock Springs, WY           2019-02-25  \n",
+       "159073           Terre Haute, IN           2019-01-09  \n",
+       "249423            Las Cruces, NM           2019-04-09  \n",
+       "204274  Grand Rapids-Wyoming, MI           2019-03-05  \n",
+       "\n",
+       "[5 rows x 29 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train, df_test = train_test_split(df, test_size=0.2)\n",
+    "df_train.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Random"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "r = Random(resolution='standard')\n",
+    "trainX = df_train[['Longitude', 'Latitude']].values\n",
+    "trainy = df_train['Arithmetic Mean'].values\n",
+    "r.fit(trainX, trainy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "testX = df_test[['Longitude', 'Latitude']].values\n",
+    "testy = df_test['Arithmetic Mean'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-66.126531 64.84568999999999\n"
+     ]
+    }
+   ],
+   "source": [
+    "x1max, x2max = np.max(trainX, axis=0)\n",
+    "print (x1max, x2max)\n",
+    "x1min, x2min = np.min(trainX, axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(-158.08861299999998, -66.126531, 18.440774, 64.84568999999999)"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "x1min, x1max, x2min, x2max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lss = [\n",
+    "    Random(),\n",
+    "    SpatialAverage(),\n",
+    "    BSpline(kx=2, ky=2),\n",
+    "    Trend(order=2),\n",
+    "    Idw(),\n",
+    "    Kriging(),\n",
+    "    CustomInterpolator(xgboost.XGBRegressor),\n",
+    "    CustomInterpolator(RandomForestRegressor),\n",
+    "    CustomInterpolator(LinearRegression, reg_kwargs={\"normalize\": True}),\n",
+    "    CustomInterpolator(\n",
+    "        KNeighborsRegressor, reg_kwargs={\"n_neighbors\": 3, \"weights\": \"distance\"}\n",
+    "    ),\n",
+    "    CustomInterpolator(\n",
+    "        GaussianProcessRegressor, reg_kwargs={\"normalize_y\": True, \"kernel\": Matern()}\n",
+    "    ),\n",
+    "    CustomInterpolator(\n",
+    "        MLPRegressor,\n",
+    "        reg_kwargs={\n",
+    "            'hidden_layer_sizes': 32\n",
+    "        }\n",
+    "    )\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:48:12] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apoorv/Desktop/github/polire/.env/lib/python3.6/site-packages/sklearn/ensemble/forest.py:245: FutureWarning: The default value of n_estimators will change from 10 in version 0.20 to 100 in 0.22.\n",
+      "  \"10 in version 0.20 to 100 in 0.22.\", FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apoorv/Desktop/github/polire/.env/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:469: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.61252415]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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CmnRdTfdc6FmEFm9j3oFn07kw18FIMm9V/RhMrIyEhISEDQftdatPmlJstDROBTBzkgpGPWnGXdVmGJN5nnOvQu6f5f8zAyWFLzXv2ol1aYKskHSMm9l2J8K8y3iL41WtMtKkxbKQDNpdEt20LKiVQptuZB8gNACf67plXQo/n2GeVcBmG1xu5zLjv0587Bi3EPNGk5h3w3ZGLFGIeee3skdlPFZrNTKn+hcYO7fRZOZNimR7HN5XfvBZoYyZddPuHjR47vzkx+z0PaUikTrY8It3QkJCwtDYxArLDbl465RanjiSW8bqHGupYthVCNg2lWcRmXYZs3blgfkg9yXF/hmOH8Xk3EF5ILMm2P7DTe3aMnFm+ZntmOX4zOKb7lnEJj8gYvLjUOZtT2A23SazQXc8O+vrY8yb0fFPIw/MTsxblql+xvQlrdFEBMy2m2RiydfvWHgjkFnTexfZvvL3oW0vm59LRqaGY2fKiXnHISJ3wcRC6AHoqupF45hUQkJCwliQFJZr4ifXigVdFw3Rk2LrPYyce6A+I5YnLEMcZJfhGHdbstzJgV9N3iA65hxYm0RY+AqzcTrXyZfLmKtr1hKWbRZPCGXWVK1uTJZZs5w1IhMPrElcG96F8LGVzWYNtC2L5PcrkNk6x5JIWYAymXfT/BHIvFm+HZN5t+k4BmL2ucy7Q3eQx3LjU5vYbqHsXct3jJmgYZ2XmIUH8u+IfJzvlcSYd8QRr1XGiLPxyryTwnIdkJx06oO90xLWRruxeZnXuNFobsJ7tYnFJqOuAgrg4yLyOesGX4CIXC4it4nIbdcfTvlDExIS1hFZr95nCjEq8/4xVb1XRM4EcJOIfF1Vg5RWdd3jGcOKFaJ9DRHPZNj2ozrk1EWZY05GW1YnjmDzQFZIdvL/NVrflaLYJJAGwTnB0BkRh5tA0hAxGwzFKkXlY8x8kNtlrDiLmFCWm7TVR5S8BQpLq4ULFJZsKmgVlTOksGSxScRUMLiXzjknELtQ/1aEIi2+vsFNBZussGz7uQQilFZMbOKPe50iH+T73oo4TwXv0Lh3Sol5x6Gq99r/98Pk8Lt47RYJCQkJ64gsq/eZQgzNvEVkC4CGqh6zx0+FScQ6VlQ55gzXZ0X9kGzdtQsVOHSC67cXr/fXWp8hZkWyG5SzkjJg4c5UkNqssMMQisyXf+nF1Ze4tzvFFV8JP0pXXqIDpLnGvaAc8eU+A7NJ5/Je414Oo28JTAXdMTvmRE0F56L10aiCZD4o9roD5Vvgam/PDR4Q715d92UKS8u8W+TG3ubj+sy7SlHq5lC6C+olJ526GEVschaAD9kvaQvA+2wm7YSEhIQNAe11qk+aUowSmOpOAN83rokMayo4qks8oy7jrpLJVwWmGsQlvgpZicOOU8GwoVTokGPmwGx7hXpw7TmAUEzmHNyxmClfIIf3iAWuCuTb9r5kEfNBAOhFZOrBzsO7tiCG2HPTElf6KNhUsMJJJ5d1l8m8HbISCmvLpePjeXP/2nYy77IwYUXwhsnJuhvEvFuzxLxn2VSw2FfIvNce1507ZPbFwZGY9+SRTAUTEhLGjimVZ9fBhlm8ExISEsaOU5l5i8g1AJ4BYL+qPs6W7QZwPYALANwF4HmqeqjuoGUhnMeFMvHHcFED155hrD6IhMhzcSIUjpUcUcJViYLKlHBhVEHzfy8iKgG8uIRFJatU7xWW8bk0IyrHQDcrzpOO5hS0D/83Y1F7V8Z6wYhykqMmNule5qaC3P8g3qyR9HSlsOISiSkpAW8q2C7xsIwpLFlE4hSV7bipYO7Z2YrP0ysJaUi+r9Y5pzUTF5U0F6izmNiEHqx/3yk2OV1WlTfr2MUpU2rDXQd1VLvvAXBpX9mrAdysqo8AcLP9OyEhIWFjodet95lCVDJvVf20iFzQV/wsAE+0x9cC+BSAK4eZgPulZj3Hesq/Yyx9VPPE0vaOYBEdZpadRRj1IHciprzk15I5iGPhzLaZhXftcZmpYDM6V39Gy/bL47djmXCoPmbqKByPpCL2CcdJaQ6xpyszpcuZY2mIRduOs+PMRpSTM2Qq2KSvXsxJh+EWlxYrQenYMf+S2CZu58DX1whMAS3zno8nWG7M0e7KKkWVs/p06R22X2Sub2XMwu2OrDfAzmYUbGKxybBGlWep6n32eC+M2WAUyT0+ISHhpCE56ZRDVVXWoKrDuMcHbDTyqzxO88BBMMy4sfmXRR2ULFLP7SyblMAlHPHjiMx7leafmwoSnVxWz82rmHfL/cVe4vSH65/ZNjsJOd4Ymvd5+KiBHt1I2qLAOC7iHV7HSSeGqGNJyXdcGjFTQZJPt2aKZc3IV08C4XDxXJajNyMOQa04F4vNm+N1O1k3y7abW0mmPuevy0UwZJd2XfXvTW5CGrBtkp937XtVwrzHHmJiShfmOhh28d4nImer6n0icjaA/eOcVEJCQsI4oLp5FZbDLt43AngpgDfb/z8y6kSkoZUOLXX6KJRFfsjLrVGK5VXjV2XpbhDryDlACV1047dbPXStmzCP75otSA+LynYaRbixwuzxHo5xrwbWJv64Y497nPmbLtaVNiJsGyCZd4lDU0yMPJC1iS1fEYHz3mYxrGPcy9rEnBS/wFV6jcBCQotlAZysuV2UQ5tyy7ibHIObzs3nTN6A/GK5duwyz/1bWbtjxf3o2RjZvAvkYFNO1t1bBGbOtO/dNnK/n6O5umvtUqYd8fPO3wt+GHSs1rsq6xXl4OY4egnD41Rm3iLyfhjl5OkisgfA62EW7Q+IyGUAvg3geaNOZL0i8k0DuhXxHaoW7lMJ7Yrf9tjCnRCHW7g3FabUkqQO6libvLCk6kljnktCQkLCeLGJrU02jIdlVTyQOu2GGncAUUmViKSqjdtKBq8T17vkrjR+dH7cf8m4XixRVFLy8SrJBJfJ28KLTUjZxI4X1r2G59ImGdWMrQkVkvxXvWcc3Ksgdor5oxeIakraWQQiqMhx2buUdZ15XMkA9iFLq0RhacUegaiEFY62Y8l8++hMOFFxcCzh/32IfZ9as/65t6xZYHOHN2+UbfP+mOOI2zE4JZvSuPmsyHxQu/7cxqqNYMjmhSRCafSSwrIuNszinZCQkDB2JOY9XSg1tRuCpcfY9rBOPGKJFTfvkUjWKZHY8aTZ4GP7IvaqZd6uVaiw9H05U8DAPT7CwjmqYJNyZ6qLAS1xhWUsHvioqFJoBjqyir6COOqR9yKLRBhk56rAScV35I+ZWUcUjjGUWkY49ljGIt241D+vWc4sj3cbgSv8drsz2OFtBWXnNn/Miljbsa549/3AQtPeF+mQ+eAMzcuO2yCFZzO4r2NebDcx864UBIjINSKyX0S+TGVXici9InK7/Tx9stNMSEhIGAJjdNIRkUtF5BsicoeIFEKCiMh/EpHPi0hXRJ7bV/dgEfm4iHxNRL4a8VofGHWY93sAvA3Ae/vK36KqfzDMoNFgTfAMp0z+Pap8O++nhDl72Wf9dkPNiROmsN+JCxVQIvN2p7LsuVHx+xvG+CaZt64t886ZN+8CIjemRZYvc9R/JrHAVoPLM8taO4efwH0+4qTDkJL76nY8Ze9FZq1/sm4Jzc/d4+nrFHuJeJEIvI/sfWfLiNgxb9O4L40wc74v9rg949u3t9A7tGuL+X/3Dt9+O/nH8y7CzoVNFYNn1LVjrPr5yyqZFTrmTXG3ghyZzfF8x3OMydpERJoA/gTAUwDsAXCriNyoql+l0+4G8DIAr4p08V4Ab1LVm0RkK8awKR02tsnYsW7B2RMSEk4djE/mfTGAO2wSGojIdTAxnvLFW1XvsnWhnl3kMQBaqnqTPe/4OCY0imHnFSLyRStW2VV2UoptkpCQcNJQU2zC65T9XN7X07kAeAHbY8vq4JEADovI34jIF0Tk9y2THwnDKizfDuBqmB3T1QD+EMDPx04cJrYJYxCxxCDelA6VHpQVopJhYoSzq2D462mVQU2KjsdxIazCskEmZXx5sakEMb4DU0HTV4eYCYtNVjLjNVcmNmlYUcEMiV16RDjqPrWyBMQxxPpky7IenRCLacLPutkkhZm9r0EkvohUotchxVuXRRg1r5bjfXBuRbe15xjefNxdLZTp6oo/dspDUgJ2V/xcO13zvszN+TFb28m8b6cVkezamZfJFhKbMLr2vaAiYXGOnYvOcLxxEqHYmONCgd45qXGjN+YteE3mzevUBNAC8OMAvh9GtHI9jHjlXaN0OhTzVtV9qtpT1QzAn8NsKRISEhI2FsansLwXwPn093m2rA72ALhdVe9U1S6ADwP4gYGuI4KhmLcLSmX/fA6AL691fr0+/fEg8u9BmG9lVpyKnzLHuKtMEavYfhDLIdJXg+OJELVsWlMwjlXNx42SZMH5WHTcc7FN2DEnKzLvjB5Gu+EZv1NUhuaHHMcl5lxUdObgRxLbRZTdSR/vu1jWf5z3zzseUow128Uvb0a8pmefQRAJrzvAS2qZtbLyjBlhjHmvLvvjpSXz/8oKlXG9Kc9OeGa9ctx/tZc65niuQzu27cSMz9ht/t99mu9zjkIM8uK2vGjO5V3EKu0irFmhtH2ZUrTDGPOWiSosxxYe4VYAjxCRh8As2i8A8KIB2u4UkTNU9X4AlwC4bdQJDRvb5IkiciHMd+suAL846kQSEhISxo4x2XmraldErgDwDzDZ+65R1a+IyBsB3KaqN4rIDwH4EIBdAH5GRN6gqo9V1Z6IvArAzWKyiHwORmIxEoaNbTKSrGbSGKecOybfLo1KWMHM8/OIXWjQl2EoLXZDJ2ruMpK0GszMyfGCWWxkXGbDzu29yzLvzLOl5Z6TbXIbigFt9S1cphVsm2+LuwX8AgZhzvM5e1TJvKOx03lnwk4qLbqHEeYd5lW0poLMvIkkR9ldl05YLlZHTQED5k0s2zJuXVz04y8u5cfZIWO80D3s+zx6dEt+fCIzd/lBc76++SBvFihn2lwqO0/3Y7bIJZ6vxT1vnh9n9XG5NZltN5l5u1ACZKpIL0HWGTPzHqOTjqp+DMDH+speR8e3wohTYm1vAvC9Y5sMNqmHZUJCQgKA5B6/3hjKgmPYsUaMglnFzKv653fLmYc2296yoUUn9DrmeNv8CpZXzaNrk6s8Z09vqsvxyJP1h04+3SFrkdVA5m2tBjhjClGknhpm1ivxNXCMO8i+Q3NxPbGIsxUjXWxNQsWxUTWoNw0PYganw1xLKPNm5l100mHm3bWBqdjapEdsOrc8Yea8Qie4cmbo3RjzJjkxHeey7mWyMDnhmXfviClfOeDnt7jq2XDH3ouv79+Niy80qqrGgx/l+z/97PxQtpPc22GZGL+7lrYfP4hT7oJzUZAu5YBducybdmQl8u+x4BR3jz9fRP7RunR+RUR+1ZbvFpGbROQ/7P+ltt4Jg4EXiRjcwp1QDbdwJyBfuMsQXbinHar1PlOIOryzC+CVqvoYAE8A8ArrMfRqADer6iMA3Gz/TkhISNg46HbrfaYQdRSW9wG4zx4fE5GvwXgWPQvGCgUArgXwKQBXTmSWNRFPgzaIk0/R1C8Wfa66Pj5+VIRCO8os31XHt3otG5uiRZl22tR/OxLbusqNi8UeXRKhdOxWnpWcQhfbjUTAk4hyskllPBeXAYcz4TRiKsmy2CWujI67FeK2wDGnXTzm58Mxpps2BnWPFJadRRILrFjlLkfaO0Ee0M5UkEUhQQhEOxbXc7xsV0796xIdL5v2S0d9PO6ts77+6JIRocxcMOfn9yDvHCi7HmT+n/eRBAOHIFauOhEJxTYRCtCj7iYGgeylcC63wQBixoGxiWXeA90qG+Pk+wHcAuAssvXeC+CskjbJPT4hIeGkQDOt9ZlG1Bae2khYfw3g11T1KLMwVVUpobhV7vGOra5HDsuqqIExhMrHoimgRBw/AuY9AKvw/cfvRcsyv3bbs94Zips82/WPM2e2AfMt9sumfl1SWK5GmHeQgDgiJ2xR/23LC5hZx45nqJ9oUEB6rbKY41GFc1eTzCrLotc12sXn5jK+AEDTmrX1SDnMTjCOBevRY74DcqKJMecoSKEZuN9bt3clNo5lYukWs1t8GX8dz7Q7tebp2/3JZBaYM+4Znz0nAEcVjMUkj9uh4ggAACAASURBVLHsCNsGyGGHFZbB9yU+haFxKissAUBE2jAL91+p6t/Y4n0icratPxvA/slMMSEhIWFIaFbvM4Wo42EpME45X1PV/01VNwJ4KUwm+ZcC+MgoE5GGDsW+xyXn5r4qnWxK2LQX98VNn6JZeSLBkKratOd6WDlhTQVbxMKJrc3a4FVtdvKhC3PdsilgJyuaDTLDnmkWX5cm/f636Ngx/lliy7P0WGbtVnWmRNMf4+NBJh1bXOX8HDgGNYrmgQA/NxTKzHFxjieOe/ny7qNGvi37Dvo2bWLmTiZOcmp2XMkvhrbv/FxcuVLgKc4RmS2b42MHvUx7nlj4zp3WrG+B4nXzs6yKPR6k5YksdFWLX0luTV+/9lRGwpSKROqgjtjkRwG8BMCXROR2W/ZamEX7AyJyGYBvA3jeKBNZD7HJZoFbuBMSEiowpZYkdVDH2uQzQGkKlCeNdzoJCQkJY8SU2nDXwVRSuEFifFfFMRlkLO9BSfUxsUuJ2CN3UCzZJua7T/a6bBS3pBkptuaIWcx1/OOcs9vFWRKbzNDATvkYKJ45jondHmfs4Ulb5oZtN0Mx5ef42IlN6PbP06XMqZufLwxStrl5lTy+jr2uTgmtaNn+201/r9g8MCb6iiXtZfC71On6e9k9bBWax4/6sRZIuWvTgHV9NZpewoHGXEQRm/H8TD1HMuS5rh4xY+2jeCaPPvf+/HjpsPW2ZFNE8prMIxhynJoOeVCy52gkJVtgrVEhpnDvm5QoNDHAd7sWNrHCcioX74SEhIRaOJVl3iJyPkzyzLNgeNA7VPWtInIVgF8A4H7iX2ujbg2FsqTDZefE51pRX2WqVztqYJXC0pdxxDSpYN75MTPvLtdbkzEOBU3mY3Mr/nhh2TCjLZkfYI4y9DhTvlhSYcAz7phJILebE3+BW8kNZ4uNxDdHc52lvuZt/0EERZLOueByPLsgAbGtX4kFRIePc94ixxw2D4zF0CBLSawuFt2bOGrh7tM8c116wNyD7qqf7ewW31lmTQwXj/pIfbPzvr4162La0L2g59a09b2V+LNyc93SIlPPE37+SycM884OelPGxiHPzHWLVWTO0Hagyw5BZALpGDvHbonFKa9aNEtSKCUnnfqow7yde/znRWQbgM+JyE22bugM8gkJCQmTRmAvv8kwinv8xDCITHuo/kvidcfPpeOoqR+1t/1G2TYdhxHV1rayCWSfefQ1khMT65knmeYWG1Vua+Yn4NgwACxY+XSL5NTs3q4Rxt2gmzHbsP2Lj163Q31f2+wU56mbOWJBc2Jd/SnvZZfml2nxZnPURMdMj5O8lJ2A5qwRITs0MbNtRN585XjdtPtzjH1l2V/r6orv4OxHG2Z6/D5ff/yQNyV0MdlXln0bbu9ii8/O++eXUQiEuW2mnHcDXO/c9ucoV+TiMc/y3bX07j+RlzX378uPMWedc+YpbyUzVpaPLy+5C/BllEknXywDBcIA3+dkKlgbo7jHAzUzyCckJCScFJzKTjoOEff4WhnkReRyAJcDwBvOeiyev/P8/lNGQkzOPU4LkxhUJeoK79icZsyyqV9m3DMurjF3QIO4AD7suDFjmf1WIFs0DGeGHGt6xLy3LhuZ5ZYlzwaZhc/bwEIss+Yclc4qgBl4i+rnLePeQcz7NGKu2+n7sGCvYYG+JLNsBWJl+Y3AasEfrmgkd6ed15ZOD/e2zBzmWKZu+z96Yg6n7TSMM3DMibz5LAefWfAs1oXoXVzx13qk55lt+5tmLH4Xu6vEki3zXu36slVytZ+xc+2SBUuQ9ccGJGs0FcuLZg7M4mPgsMKu/f7bZ7H7AsOc2/sf8CfP+l2CbCMXeodIhh/lHJqrnPXeBuHaKOKKTcy8ay3eMfd4Vd1H9X8O4KOxtlWxTaYRVUlSYwtDUD9T5XG2dr1buKcBCxVfnmbEFJKxEhGfMNzCXQa3cG8GuIV7WLiFuwzRhXvasYlNBeskY4i6x7u4JhZjySCfkJCQMFZkWu8zhRjFPf6F05BBvq55oDk3EiclcNLR4nkRs8FSUUmrKCqRGZqgS85aElzFiTCCPhvkLEHKy44Vm+xY9lviHSQ22WFjW2xt+PrZphcFNCJ2i7NNEsE0TbvTlRR35ETinG/aJMpwSkoAmLExWQLmHfFk7pIopkX3elvTiIgO0jXtIlu/hQVT35ohJSU76UTefFZizm4ncdSKNdGkc1foGR04sgAA2LngRQnB7iyyUVrNii9mg5LvtluslJbgfwBYIYcs916WmUU6EUz3hG/f23s4P2627XOlBMdoE8tn9upEc+ykw9ESnbiExX1lMVvc/IOd5pgX0lhy6E2CUdzjh7bpTkhISFgP6CYWm5xSHpZR5hw9j44rnHAk4mwQKwM8y2a2LW1yBpkxjyNUYhLLdtHluE3Ds5525hWW8zaS3fYlb9J1GslMT7MKy53i2fZW8tmetbLkLilEt7Z8vOfdYs49k0zqzhPP3HpWVt2jmznf9vPLlZT0LJg5zloTv84SmcSRk8xs27DsM5Z8n2du8ePPzZnyBjHYwIQzFs2xpD6zO4ots36sZTLba0Tep5lZcphZWftr5kwJe8TGZ6TIGMuc1xyzbpYkWM4VpjTnmf3+vZC2iYYopISUWf9eRGN486IYi0POjHcAsUSKKlgfp9TinZCQcIohLd4bC2Vu8JM2EfQnVrjHlznhOJl3hG0DgMxaZswy8VbRTVu2eIbT5PYNb00wZ823ti56NrVr2bOp03uGRZ/R9mW7Wz6w0f62YdarJEfe3lrIj8+0jP1MknOfdaZ3oz5+1LTvcr5Nij3u2GZGbJKZ99bthhkur/rrO9YlUz0ry3/4+Qd8e5Jvu7C5vU4jLx+E1WUkxs2s2d38vC+M5VlnOXNr1l+rMwHk95PFvC27Cyl7rx3jziI7D8Az/yBHJ82lY80WVxfJSeiQn5+0zHvTpPAKMktLA79jrdj2Mpis+Y9jjzNLX+/FdEptuOugjrXJnIh8VkT+TUS+IiJvsOUPEZFbROQOEblehPbfCRPFhrGhnQLwgp5wCmITW5vU4SIrAC5R1e8DcCGAS0XkCQB+Dya2ycMBHAJw2eSmmZCQkDA4tJvV+kwj6libKIDj9s+2/SiASwC8yJZfC+AqAG8f/xTXHzHzwP5yX0Z/NPr+R59ZYJ58lU0FyavRbU9ZWdRmsYhtx9tQMtNq0F68ZV/IhUVfv2vRi1XOPGpEEOdQOqy9TS822TdjktKe6FIbqv8ua6K3m+QL2x7kj9nDz6FLHoZOlNAlT8Mdu71DzfwuoxycP+qVhOyVuH3eiFW2P4zijR/3x8cPmXM5nkkpXCA8DltNEfxcsuH2HIkaSFG8umLGisUAZ7Bic7ZRNJtkKzolEYnzplzt+OvfutUrHF1slA7Vs8ISNtoj1y8fZYcfc4/by/QuLXTomN7ROatUD5Tma6d0izLb9VovN7G1Sd0ExE1r470fwE0AvgngsKo6IdkelASrEpHLReQ2Ebnt+sP3jGPOCQkJCfWwicUmtRSWqtoDcKGI7ATwIQDfXXeAcbrHD5JYuHafQ0QwLDUFzHU5Eq93SWdZCdlmzxDDuAMzLT625n0Bv+N66rfpMukseyXiNlJenrlk2h3reDZ9aNabAu6fMTGeDzc8Qzu/4c99qI0XvmvWM8Dlw8TsbAS+eWJwzCyd8o4VbwunkSmhDXC3sMWzwQ4x9207zLXIHN3gxSLLYvM6jtcNYtmOBXJUwS4xbzfv2YW4rsEpBLtk6tddKUYAZIVss1E0m2TlLsPtWNiUcH6rb+92Ob1F2tFRaG43/4CZL8fimVOM8WU/19YqKUcXrInnAu0ImYW7XeUA2XUmiildmOtgIGsTVT0sIv8I4IcB7BSRlmXf5wG4dxITTEhISBgWMY/OzYI6mXTOANCxC/c8gKfAKCv/EcBzAVwH4KUAPjLJiQ6CsRv69/Vb5aRTJvPO3d9Zjk1uyDnjnvMu60HEN3cuyxhZptcuBi5qEmuaP+5lyrtOGFn2eQ/4uax0PF072DbMew9FDXxY5o/Pbhk1yLatns0fP+TbH1k0xyy7jQWhWtjmKXDoJGPascyaWborV2KIGQW6c2DZe9aJu2E7a7LeKrH0SGxvtjqTiEMM332O+tdzzJschlpkNukYNZsC8n2LMXIe//jR2cJ5M5TVxzF37r+zWjRB5WtudzhHpb/vbbdNIZl9WbajvL2uzcJ1kux4SpWRdVCHeZ8N4FoRacI8pw+o6kdF5KsArhOR3wbwBZjgVQkJCQkbBhP9YTjJqGNt8kWYBAz95XcCuHgSkxo3YrLykthP0fJBmHwu7w7k3nxctDhhJ4hcBs6Me97KortdYGGLyWbiwnceOeyPFxchu0xODD1yFLJrJ7D3IBq7bf23TqB1uu3/28CW3SvAA9tw2nbjVv7NE3N4pJrjf0ELF3VnsKexiEc3jOUJMuBx1u0ebS/DZUZ37iWm7MCHgYc+/AAeuHdrwLgdY+51Gjnrdllftp69mgdPai4AvcVIrHZyPjGsOkPnoO9/9XifrLmVBXGyO0umfrbdzS1KGu0sd8SJMW7AM92y+lYrw2qvgTkr41860c5lzI2GottroNXKAsbtbNB7yw00Gxk6aAaM253b7TXQambo9hrBveScmE1bz3J1lnE3Gxl6WTNwoWf0Og20273clb8918Pqkjme2dpFx7rWt7f30Fs0MeV11Y4118zN7aTdhPYySLMxtMhCszHunk/lxXvasWkWbsAs3IBfrPuO3cJtjneaIXf7+nzhhl24gXzhBpAv3ABwUdcsDPnCDVq4sfbCDZiFG0B04QZQWLiBvqh3tRZurL1wA9GFGwhNAcexcAPIF24gXDi7udikuHAD/h7FFm7A3+u1Fm4+r398126thRsIY7C4hRtAvnADQM++IvnCjdCbUnvuWoZfuPn/kbF5pSabf/FOSEg4dXFKi01EZA7ApwHM2vNvUNXXi8h7APwEgCP21Jep6u3xXiaHQeKZ1DULHGjLFo0qGGfeTnkZxCtpstmgUXmxEjNPDsvHrZKIb5SuKh91xZvyNY56hWX74FEAwMKir2/PeLb3Q8cNi7xgycczOWuLb++i5m0/g2JYn3Fmfjz/XSbN1vIxmhPdlrntxah/7DgyY83WWOHIZK5rFXLLh+Ns0THuZqQN0OeIZYvLovblCXwjjkeA31FUZVgKYmxzvG5rVrhCbJmPXWJhTjDMyZCdOWW3W9yZAMDsvGm3SuaLfK3eBJGSQdOOpcVKXxuaQbushKSLdIslfwcisb3XbVEd4zgicimAt8J4Pb1TVd/cVz8L4L0AfhDAAQDPV9W7bCaydwL4AZg19L2q+rujzqcO83bu8cftJD4jIn9n635TVW8YdRIJCQkJk0DwIzMCrMHGn8BY2+0BcKuI3KiqX6XTLgNwSFUfLiIvgLHKez6AnwUwq6rfIyILAL4qIu9X1btGmdMo7vGnHvJ44BXnsamgRFg4dRCwcHdMMm/M0PGcZcGzng0HzLtXTEUjzLy3HcmPmwvmkXJS3l7HU6jtOw2jPkaZeBgL2w3bc67jADB3x/35sfvSzG0rmuQB/hZ0yKSOmWHPMj82aYsxYxc9EACWlvyOpGPbt0mO3CPzt4AFRxgzM3PtFmXiLL/P3FyJobbbRYcejjSYdXlHYeNtk/t/l3J3nnbaicI8YzL5xRP++ntUP2NN/QKdDl9fnvzGn9Bg5yaW9bvjkkw5+ZmDMN5JyqXH1/fFAO6whhoQkesAPAsAL97PggkTAgA3AHibTSOpALaISAvAPIyL2NFRJzSUe7yq3mKr3iQiXxSRt9gtQ0JCQsKGgWZa68NhPOzn8r6uzgXA8T1iIUHyc6zz4hGY6ME3ADgB4D4AdwP4A1U9OOq1DeUeLyKPA/AaAHsBzMC4v18J4I39be1NuBwA3nDWY/H8necPNMFJuMSPE6HDTkRmymXuZC5jmbcLEtVkCxR2lfeMW+as5UmbfjMp601+15Yoe/qCl5+7bD49byCBE8d8Xy7O9g7Ky8jxotvz1tqEWNkSvdrtLWYGnL2Gmbc7XiE5NedldI4tZU4+joUy21xcKTopdXrN3LkncIJhFl/BvFtWFxC62pMc2N6DE8f9/dtCgaMcS2/PkWXMYtFahTMFMbY/2Mq0j/n6I/u8Q9SMZfQ8Jt8Xd6+ylXj/Tubdy0JHKIeYFdbI1iBlzHzcLLxmfxzGYwK4GCaT6TkAdgH4JxH5hGPxw2Iga0pVPQzjWXmpqt6nBisA3o0Sm29VfYeqXqSqFw26cCfEkS/cCZWILUYJcWzGe6VZvU8N3AuAF7BYSJD8HCsi2QGjuHwRgL9X1Y6q7gfwzwAuGu3K6iVjOMMybpB7/NdF5GxbJgCeDeDLo04mISEhYZzQbr1PDdwK4BE2Cc0MgBcAuLHvnBthQoUAJnTIJ63O8G6YENoQkS0AngDg66Ne2yju8Z+0cU8EwO0AfmnUyWwaxMQnQX0jeixOhMKmgiQ2ERf1j5i3tMmUMAI94fUiMk+h5uywHP3uBKVJcyxs27ZIwBAAzVmz7W2Rs0aPYodkR613YYX5XKAYZLO+SDyPJhU55V/3qC9cpah7zQHEbW7cIPF0iXLPgU3x3DXy/Ge3UoS+WWtKSMrhRssfrxzXyJyLpoyrJ/yz2nPQO1991xlGET0z78ecoQiBTqzDYiNOPxd1ZAtMKYtzGdoDsiK2ydizlo2pP1XtisgVAP4BxlTwGlX9ioi8EcBtqnojTIiQvxCROwAchFngAWOl8m4R+QrMevlu67k+EkZxj79k1METEhISJolx/hio6scAfKyv7HV0vAxjFtjf7nisfFQkD8v1RMx9PqhvhP8DofKy6bKYsOMOsfAIHdIth+hcz7ydwxArHFlh5rK7rCz5XUAQCc8qv5qzZDJHbtSOzbEpIZvXOTbKJnUrlGzYKSrZ5TuLKPRYocnzdyyWXcIbJVmRysIl9CM01fPHzsRx67aV6LmtLc5xhfqa8fWdJXM8z7HNF7xz06duMUYND5nxyucGiuO35yjuNl2rU0hybHVWaDpFLO92WuSwFdyrip2Uuy9S4qTjy2ocjwGbOP9wWrwTEhI2L9LinbC+KAsY3rQsmMwDZYZYOLF0x3sCyxR2/nHMO8JWAe/+vrxcNL8DgBUbBGp2m2d7bErXtYx+eZFc3meJGVqZLwdomlkhFm7NBrkNB046csCYTS514q9wu1Fk9jxWpWMOXYs7LmOdx62J4I4dXj/AmXhmXOApUozFHnGbdjbbT/d9PSY7DAA4cNSbiu6YpXyhljG7ewKELNrpLzh2OjtELdsdDzv2zIFyWDaLMu/B7NQIeeCpuJx77IttifnlZkDtR2Addb4gIh+1fz9ERG4RkTtE5HqrgU1ISEjYMMi6UuszjRjk9/NXAXyN/v49AG9R1YcDOATj15+QkJCwYTBGO+8Nh1piExE5D8BPA3gTgN+wtt2XwBifA8C1MD79b5/AHDcP3FaxKu5D1dskcYWmULRBdSKCWRKbcMo1q6Xj1FwMlzqLFV8cI9p5GHLSXjafc2nAuA2LJdo2Oh0rMVlE4sQmLCrhZMQPHDTXxTFAWOzjRBDcJ8cW4VvoY0ivzcBYucvH83OdoB/TfzHqHscrZxGMY35svsdml9tPNynrDh/3Cmc3JuAjALLYY3GVFM3Ntd8np1wOvFkrYr8EiCok48rdPKogBYxicdK4F1JNYhP8EYDfgtcFnwbgsPXfB+J+/gAQxAy4/vA9sVMSEhISJoJTmnmLyDMA7FfVz4nIEwcdgGMGfOO7f2pjByoZAqGyxZpJMRPhY3cyJw2OHWdZsQ3gY5eUvW0NYrnWuUdaXkmpRDfVRtijJrl5IOAZM7NdjvHsmCUr5nqctcZGA9Tg8jnBrvk/UFguUILhRVN+eMnP/8wLvMORM2Xbu5eyChHm5w0zZWcZZvlhPOu12VmeSSdy/QAwv8WMxfHCg2iNVve4dCSu/HVZf9hkkZM552PScYPYdMPuAnbsXMrLVu73X22nkAyyFnX9g5+NREBkxx0NYsLY/9krMRLPO/ii83fAMe4S80DeyY0DVbupaUYdscmPAnimiDwdwByA7TAByXeKSMuy75iff0JCQsJJxZDZ2KYCdTwsXwMTQRCWeb9KVf+riHwQxn//Ohh//o9McJ4bCoFsM3ZC2TYsJvNmeWDPMCDpERPiGN3uuLMSr8+KDCpw8iHnn95RwxYXj7GTD7lsO+ZNXbH82DGazjLFoI7EqG6wnJscP1yEPXbyaZLjysKiYfxLq3G2Or/LzH/LYb8zYIY4v9WUz2yN3BMAvRX+S2z76KlRhOZzdsfF10/vQHfJlHPUxpgTTRg1kSIQWrn/lrnVQhkArNpnNUvvAgeZOuxish8jJy0af/cuk5iSzTrDTDoUAdEyY10lmT3pTRota1bJ0SQD+bbdxfhLQY+Ox235wbulzYZRruxKGOXlHTAy8HeNZ0oJCQkJ44Fqvc80YiAnHVX9FIBP2eM7URIGdpxgNnXSY3vncylaEhSO3Zk9lllbhkbMWjveakDcMeWixIp31tDlE/n/stPmi1ymeN2xaEEl8vHMZme/7/jWvGz3rB9r53YjPw1kq8Q2u1amzQyNrU3cs2K2Pr/dX9fMrqLjCjO0uW2WWS+TzJ1YvnOv5/7ZQmJuhynXDGja7YPSbWW4XUQVS2MLkyB1acQaI2aZwtYkAXOPvNe8S3G6BG6zuOjl186apExOnffD2enpXKcL2LJjBSeOGJbO95KDl3lZPr1XHIJgJiLzpuearTprE1+tJVY848CpLvNO2GDIF+6ESjQXqs9JMHAL92bCZjYVTIt3QkLCpsW0mgHWQe3F28bzvg3Avar6DBF5D4CfgMnTBgAvU9Xbxz/F9YOTfQXmfwP8cHsHBN+BUIxpWMcU7ZKzCCsnndiEkgZjedEfz1qF0wmfSJjN/4KcZtYGUFeXqL4YdX6FVK7btlJsDmtK1qP5z1FUOhcbo0cxNAKxQMOJTfz1zZ1BysndRjnW3e/7ZAtJJyJg8cHyMa9Qa1hTQjZPXNhOKdsijDtj3W43ItYo2bI79taj+haJDVo2tnmzxOGJxUn5XCnOyP33G9HV3Iyf4NJxLxZx8WeaLV8/N+vvG99jB1ZY7rD/s0KUzRKPHDTxcbZRbBY2q1xd9MuEMw3l+zcjFLPGKqIbJFjKKOa7E11lpATl+zNuhSW/v5sNgzBv5x7PhrW/qao3jHdKCQkJCePBKS/z7nePn+iMxoxA5uUIAMcnHqSvLPy//1jcccRxBwDEMXJm26tEB1cMLdG2Z0DSKpr6KVNUNhucpQiDlpHr0jE/l0XP4p3jSI8oGLuyO+brTO6AkIG5c8uYTaNRdH9vbKX+t5ldRPdb/vp7q+zEY9kmsUXn+GPGN8w0iJ63SK7wR4osmFldwPac+RvHBg+UaMXY5wxnPsdORgx3i9ls8vhRL192rvyhqaa/145Fs3lgaLZp50yhAoKwAC6BssYVgyeWzL1kU8Y5cr9nuHZsIhrEabcsO6MvQUbJmFwceHbuiu2CxoVptSSpg2Hd4x3eJCJfFJG3iMjm03YkJCRMNTSTWp9pxCju8a8BsBfADIz7+5UA3hhpfzmAywHgDWc9FuPOIM/xqBvrZEoYKEEiLDxk22S+5WSiq8S8m2S/1jJshrOQ8BWJY9wsE5/1LuPKDjluMseJee/dnx8vHjBsq0d7j1nKxHLwkBEa7z/snXh2zPlxHeNl5s3MsaURxxe6F07AvXiI5NiRYEhc1m74+7ZqWTgHXWKzxT137gQAbKWMNCw/ZxNI9+WNsW1zbOv5Wtn8zZnyLcQdghr2uTOb5TjpzpWfwec6tQbvPFjWv2L1E7xL4Uw4eRsOHEbM3mUb4kw7SyVx2GH7CHYBtHtsLVuZN30veku0u7MOSzyXWNiEcSGWfWmzoA7zdu7xd8F4U14iIn+pqvepwQqAd6PE5ltV36GqF6nqReNeuBMSEhLWgqrU+kwjKhdvVX2Nqp6nqhfAZEP+pKq+WETOBgAbHvbZAL480ZkmJCQkDIheJrU+04hR7Lz/SkTOgNH53Q7gl8YzpfUFb9lcjIpgGxeJ+yxBIlpf7xSWYSwHDnJh4z6wkrJF28clq9lhhSSZFebikiaFAuRkxdROF42JoB70YpXVfz+YH+/bfxoAYIYugBVPhzpGhXFCSMlIiqeZpplXsC2l427P3ANWci7f58Uust96i2akKuH76qIORszgAGDWig1YobpISsBv9Yy450FH/fg75/34sySWqCtuY7EQm0jm9fwoWAkX8dxkEY6LqR6IRYhXib0ZQWx0TqYcmf8KiWXctbJ5YnaU5mffd06NxqISFiG5mDecXo5FIB2bhLrZYVPDZuHcMjnzuFnwtLLqOhjFPf6SCcwnISEhYWzYzNYmm9LDMsxoMki7tRPN+hM5vnGRhQsrMVlJZ82olBRvWCZlleuL2fpKUaEZRCIkZq4nPDXuHTDMe/Hbvvm37jzDHzeMqd4jmt58cGWJItVZ5rdCpoTLGcUWsRcbZNrJIko0isHRuc+3f+CYUYg+9vH352XH7vFsscoCwClXZylqoAgpVI+b/49mvs+5VWa79AxKnGvyfu2OrE1KzphJG5s6cr2L880xtIPxLXihWV32z8KZGHJ9s1F8R1m5u9rl8V1sdc6UQ9din2vMVLS/PFBeurFIeelYOitM+b1yc+DdAr9D41YwbmaF5aZcvBMSEhKAJDYZG3JmG2ENtdpHfrUngTD7Cx1LsSzw8ollGWGHIMeg2F04I5MwF4GQ2XijyPJdFhwAyI77wToHfb9H7zPM+p77vUPsl2a8TNiRxIdzdDd60WfsxbQ5qiMZLjqzNGZNqz3P0FadsoAj+VFs7o6t75H3/3LEPI0ZODNAJ+suY7vbe0U2XcbC3DVwvUQcOJssWQAAIABJREFUudiUkt8RZ7bHjithpp6ik0+PdBUtey9dRh4AWDlEpniWpZc5sDizTTalDJx8ZuLOQ/1gl/kVYv58X5aW2oU+Wf7v2rFMnJ+bM+1kZq9Drgd1cMozb2smeAxAD0BXVS8Skd0ArgdwAYC7ADxPVQ9NZpoJCQkJg6N3qi/eFj+pqg/Q368GcLOqvllEXm3/vrJORzELj/VEIE8dwFXeMdMymXpm5dtsAAIOhmTZLOe4lBaN2i3GQg5k6sTYe8dN+cphz2qOPuAdar5zdBsA4D9mPJu9p+nZ0G4lixU3F2LRWxpF5jtHzC6W93CJNgwrjnkHWYfI4cYqBg7e4yNIOVYHeObGuwGWrW7dZeTbnDeSrR52CjnE2GttBBYaNC/brEH3uioYHVtgOOeeLu0cYrtDZps8V7eLYTbP+UQd4w7iqZMFi2OXyyt+/JWsqF9gaxtmxo3Id7BLuygOcnXM5hRlmXuMZR885p/rwox/FrFM9pN00tnMYpNRQm49C8C19vhaGFvvhHWAW7gTqrEc+ZFKOHWQ1fxMI+ou3grg4yLyOevuDgBnqep99ngvgLNiDUXkchG5TURu+8CRu0ecbkJCQkJ9KKTWZxpRV2zyY6p6r4icCeAmEfk6V6qqSokWUVXfARP7BF9/5NMnShnd9rHM6aJK4RlTqMadeLgNdeAUmlX6ocDUj+eSBf/199W1yr2VY/6xHT3oIwnuP+G3qne1zRb6HtqmHoPf3p5pGeksbc/ZcSTf6pLCkbe/8/Or9lLoxV8ihajlBW26QfMch8Qq8Q4f9fNnUYETCzRLzDZdGrQlyv7CYoUts2Z+QkrSdituKujinCjJUmJxpVlEE0OQBo7eIRdvm9+7QHlp7wWLbdgkz4mLAlEJi7PsuUvkmLNC3mXO0aq57PvfyR5XFvy9YVFJcC0tJ87y7QLlrZ3XDInotmzx4pos4tzEGHeQqGwTb1JrLd6qeq/9f7+IfAgmjsk+ETlbVe+zrvL71+wkISEhYZ3RG0kyvLFRJ6rgFgANVT1mj58KEz3wRgAvBfBm+/9HhpnAMOaDgyQlHsZhp0yhmfcVmO8V+8+ITQdDOlPCinkEbHvJn+zcjDnX4KHFufx4X9Mzr72WhR4kjWmHKL2TBHMkOnaPj0W64zKXIJjvVfMIM2sbj5uUlAuU/cW5WXfJsSdkxkVJZJBg11YfO+6vf/fpPhmzm2tg/kj9B3HG7b3iEZmFOmbMlgvBfbPteyXvsGP2PCbft2zJuae3Cm0A2vFFlJSAZ/F8/3TF1ztHq6XM9z9PpoROiVgm++UIge2Ic1GgKLWOSFvn/JZtjkwgXYagMiXl2J10xtrbxkId5n0WgA+Z+FNoAXifqv69iNwK4AMichmAbwN43uSmmZCQkDA4plWeXQeVi7eq3gng+yLlBwA8aVwTOdnmg4BnAGWPOy4T9/VOTtpocRk76VSM7+JGk+MJO3441nKMZMsH4d3PDzR9u0NiGNJxovHtyASY6TTbRfMvls2yHLZtY1dzl637mFna8xrM5j0bEzFsbXnV3yyWuW8908hJlzjeNzsE2V1I4GZNMm/H3Mt2Fnyco8QJpsrsz70PQkG8Yg41gXkitXcy8ZVldl8n5m9vO5vkCbHwboUc2YHzla6Sq767PtpQBrHLmW27sdjkj6PyuffpCO0IOU64C77FO6uQhSfmXRfJPT4hIWHTYjMv3ptXmp+QkHDKY5ymgiJyqYh8Q0TusI6J/fWzInK9rb9FRC6gutfY8m+IyNPGcW2juMdfBeAXALiwcK9V1Y+NY1Kjoio1WjQpMeLxvANETAWDvaa1S2Izs0BSEZEGcV/O1IyTs3JEtmWr0DrR82XHm/7cYzTWojUL7NIFtipe0ph4gLf6LCpoWmmN0BsUKAStuKgVpObyxzNWnMMmb0GMauf1yGOSqKNrY08HMTaC1FqFS+mLge3L63r1BR6ajWJfockcp1FzsU3iIh6XJi0UAbFC1ZpNNrJovYvdzeITVhQ7AQkvUqwobvZc/8V46v1wY/C9CJ677YPncuiwNwd17Vhswvdq3IkRujKe/sTIxP4EwFMA7AFwq4jcqKpfpdMuA3BIVR8uIi8A8HsAni8ij4FJZPNYAOcA+ISIPFJV48Hqa2IQ5v2Tqnqhql5EZW+xZRdulIU7ISEhwUFrfmrgYgB3qOqdqroKkxLyWX3nsNf5DQCeZDONPQvAdaq6oqrfAnAHStJGDoINKfMeRHk5jNngIDG+4/3wrzmN6Vh44BlQNCsMnHxo/r2OVVgSg+xSdhUXKW6ZfnOX6VpWaC69yCspEebdDKIe+nLnEMKOIbEdCV8LK7HmIvG+g3jUlnlym8A8byU216L5XBDvg5xoOHZ2DGHkSAn+74ebV6CkrCB0zCCdki5k0xElKF8/vQPuuDXv27ebHPvEJYMmhSftuGbts5Dg/SgqCZVuCsc7YUWyS4zMJpoLC6yINu2OkPMVv+NZXpYX1Va4DoMxyrzPBXAP/b0HwOPLzlHVrogcAXCaLf/XvrbnjjqhUdzjAeAKEfmiiFwjIrtiDZN7fEJCwslCJlLrw+uU/Vxe3fvJxSju8W8HcDXMwn41gD8E8PP9DUd1j5+0CWFU/s3jxNhYab0tZyeeiFQrMC9kN2nLsDj+MbOSVSsbXCWK3OHps3zaMqsWWA5aZEAMnsvyUlGO2iMWHmPG/HzmpHjhofu4nVM7K5TxuczQqphvzMEjlG0X73XQPuKybvqwzLtig90kOW6TTETd7qUdMMyiqVxZ9hqHNsUT53Md8+Y2bXZ1rxDqu/vGAS5Zjh2YiM6Zcg5rwCaM7nm2F9cW5/JcJ5mqrG7XvE6V4F4A59Pf59my2Dl7RKQFYAeAAzXbDoxazJvd4wF8CMDFqrpPVXuqmgH4c4xBhpOQkJAwTowxquCtAB4hIg8RkRkYBeSNfec4r3MAeC6AT6qRRd0I4AXWGuUhAB4B4LPDX5XB0O7xLq6JPe05AL486mRGRSzwVMzyZNgcl/k4Je7zyGWn3H8xyFXgDswuz84BopXlmbwD5muvZSc6OCjeecWhRf22LeWcpQucpd9qd+pRcvjZtuSDFR1f8c4/Dgssfz/unGR8Pd8X55zD9z+Ww7FMT+Hk/7HM64CXbzODnp0nZmqZf6vVy1le4JJO99U9r1I5uT01dCApzjuWER4Ajh8z97hX4gSUD8NycIpO4Jhve64o8weAns2s1Fohtkwy8Y61fspo58XWKO570e018oBUVQ5NHM+d5+LO5VAKgfze7mgmKedmjMvaxMqwrwDwDzAGPNeo6ldE5I0AblPVGwG8C8BfiMgdAA7CLPCw530AwFdhIvy/YlRLE2A09/i/EJELYd7iuwD84qiTSTBwC3cZYgt3Qhwx8UNCHBxJcLNgnBIZa1H3sb6y19HxMoCfLWn7JgBvGuN0RnKPf8k4J5KQkJAwbozZbHxDYUOaCpZh1ATGg44DICoWKYv3jci8NLJVLouo5rbVGokVUQbeabdpWzxv9/psHjjHxy6OC5unkYhi2abRYiXdKolNGicM+49F3zPjGrCogVmwjwdSJjaxCsuMxS6kvLUOS9w/m+K52NwcI7usL1deluDXTZGjQfJzi+3MOUKgbxNXmOb3gsUPEeemRolyN1cSkiiD45HMVEVFdMmkaXwW+wTJgl287tm48tTNqx1JkwcA2WpRtBma+0abDY3N7B4/VYt3QkJCwiCYLM07uZjKxbvKfLDMcSeWaSfm6l6aaccVl4xflYknVhaLqFbJtumYAgFigR0fLPOepa7mqd/t9rq3UdxlhksQ3CG6udIpvi7MlGKuzcz2WEnVsiy32Yrf69zxhNgwOyx1rHKRGWTsvShTXiNQXlrnqCyuRBMtOukwo5eIww2/A47RMtteFVbeFuN9x46ZbXPkShcNktuw85NTXvL1Ba72dn7NwIlobc7aLlFYxubPO5rMRr7ksAg6brpNiCRE2jSopfIVkZ0icoOIfF1EviYiPywiu0XkJhH5D/t/1EknISEh4WRhjKaCGw51mfdbAfy9qj7X2jguAHgtgJtV9c02wtarAVw5oXmuK6oCVwXnRnNcxn/uY6aCg8QvbloWGeSFZDdsYjANe8zMY5bG3dozfaywYwcxW2fq12EnoR7Fq7bzDhhcZMeSlTDvnguGFMR1LrZjk76gvdvlSJFBA4DkLt/xXQ6z5UyLzyVEUSau7NGCIjNnmbBDh+4v7yK8+z3L74m5t5xMPD47V89sl80WHcvt8W6BnpVj6fwsysiwe8cD56qYHxvPP7JjafT4ZhNLH3M87zF3t6FQybxFZAeA/wRjwwhVXVXVwwiDsFwL4NmTmmRCQkLCMNjMzLuO2OQhMGFf3y0iXxCRd1pnnbPISWcvjD14ASm2SUJCwsnCZl6864hNWgB+AMB/U9VbROStMCKSHKqqUqLlGzW2SRWGUV6WxfuuijqYjxWJ5dw/l2h7LZ4XizvN4Kh/TmzCsSp6QUBw8jZsFOfSpnZtu9Vf7HqHn+7xoicco8PR4Ww9+2E2mkXzsCCiHIswImINvhW58rYXbx/rnz0k3btQFq+k2WZvTHsu4mM5c8mq58bvXyuIPWJOjsVT6R8rH7NE7ECTooGLYhs29cusclFIksNfV6eoLItXHkMsDRz3y9+hWH3wvZPivR4XNrO1SR3mvQfAHlW9xf59A8xivk9EzgYA+//+yUwxISEhYTh0pd5nGlHHw3KviNwjIo9S1W/AJB3+qv28FMCb7f8fmehMa2ASTjxV8cJjSq5hsrQwAgZEx06J2OOwCNR/kwZbiLE5Ptc27HJ2FcrQU8WA3K9+zOTMzCXrn140tnfo5OQPY+Z7GmXuvs/ACcjFsQnaxK/FKd+yEkV1XlS6i4j3m/dvWXCLlIhasvtzCNhqo8hmw3NtP5x1iCMcVsRUceeWsunIuIN8x4JsTJFdQhbcwPGupNMqEqmDutYm/w3AX1lLkzsB/BzMV+0DInIZgG8DeN5kppiQkJAwHDaz2KTW4q2qtwO4KFL1pEEGGzVub11b/jI5+LiiDlbL2evNs79/5wwSuknT/K1s0ucwAZpk/jVTZRdF1atqoxaWSM4cM+fsK002L7OMm6PLMQvP5zzIzqXEeSl6rmvCLueBw1PxuthxJcgX6WTeTW6/drzpmAkiR0BssNmdfYaB+R7pJ7ycOM52qyJfuvrQiYccdqqYd7PIhgfxm6nKYBW7ruAdp+fSG3O0wRTbJCEhIWEKkcQmGwSOAY3qTTuI+3ysXRBUZ4yZfnJ5YLuXy3pbIDdk56TT6uWOF60yl+4oy/VzXeqY+qNZPLys452tiMwd8OFDy9ykWxFrknB+xbI4w402j14fu1wzI3e7lxZnOY/lkGSZcUZsMHKPNeJez9fPLHwYx5bg+iqYbf84/X2545lmL59r1AqrJ9HY3cEYNedSNq98lxHoddaW/4+CzSw2GcU9/ioRuVdEbrefp096sqcKymJsOGSbeS84ZjTHqLyedpRFTXSoWrinEV1orc80YhT3+KcBeIuq/sHEZpeQkJAwAqZzWa6HOmnQnHv8ywDjHg9gVSYYCawK5VvpyLkjijViyssyscswporBNtQ61jTB22seKzI/7UXrGxHHiyDe9pItp6CCXVLyOUUli0o4tZZLUMtKuCrwjqFq+12VTNh9K8ucfGLjlIklcrEGx1YhJ6dmheTUzVUiSkjAs//g+bSKfVaZ54WDFovKlJx+LDa7LConY+aJwyKYf4/LI046PO6YV9vNt5fwGMU9HgCuEJEvisg1ZVEFk3t8QkLCyUIm9T7TiFHc498G4GoYDnQ1gD8E8PP9jdk9/muPGL97fDiW+b9sUxCNtz2A+/y4UBbvO1fmUMBuHr4ZcT8vY5aObZU5DDmzvhlS4nG87ti95OwszuGkGWGQjJhibz3gnlsQXoAdiqIxuBGtd61Ko0U65WwQ43vt+cXe0TKX8nycARJnxxSCAbOuOaf+vmLjx0IFVIV9CObP1zXmVyTbxIKTod3jVXWfqvZUNQPw5wAuntQkExISEoZBr+ZnGjG0e7yInE1RBZ8D4MuTnOggqMovOKwcPBa4KsbcB+m/MgBQCfPPZYds3kbWAnkMaGaYbD5ns6+0lsiZY8W/xpzPMm9DLNvJunnMME657ZPesC7WdnwZBD6YVBzuuTPbrmK2IVuNmLJVPCvesfFjy9+HEvfz2JhDIZDjF99BfqJV97+K2Vcx69J+YzsOln+P3T1+8zLvUdzj/1hELoQRm9wF4BcnMsOEAiadgDkhYbNgM39TRnGPf8n4p5OQkJAwPmxma5Op8rAcBlUilPDcerFPRkVViqmysnz33ioRlVA2YpegVoInTPXtotijSSKU7qrZN7MIKCaiaZQkEHZfm6C9sohFCvX8rPI4M1IUS3H7smfixCUx774yBB6KMVO2Ec0SgncxGHeIvrL4cQy+f5pA5FrGsaOrm9YvjC1D7ce8q0xik4SEhIQpxOZduus56TwKwPVU9FAArwPwXlt+AYzM+3mqemjQCVRlnxkEVcwhZv4WTSBcFQlvAJOtYH5R87CiEovPi8WraM4wg6ZjSmsj9lha8fsrtg9p8fgU+8PFuOakvqzwc3FYqH0s00zA1jkDkBsqMBmj9pE5l2Vf6Z+TOQ7/r4PwvtOxm3dgHjd+s8dBlITVkQbpXuQ+OhEvpw2GkZW2feht0OscByqXHlX9hqpeqKoXAvhBAIsAPgRj632zqj4CwM3oS42WkJCQcLJxquewZDwJwDdV9dsi8iwAT7Tl1wL4FIAr63QyCdZS1u9GtMwoc0OOuSkHzNWx5ebabNuUN2xZ/PfZOZRIYErHTiZ2F1LmWFPBkBrWKpDdwEPzNDOvHhnZsplYvguhRlU7ImbmMZn3IIjJZHtanF8djGoWGe0ztuLUlDeXYRjZ+0ZHknl7vADA++1xrezxCQkJCScLm3fpHmDxtjbezwTwmv66tbLHi8jlAC4HgNef/jg8b/uDh5zqcIjLtHl+xXPLWFVVvO9B5lRXvs0y5RjjZmuSBrFsqWDeyCLMlTPhuHu1yjLteFd5l/RVEXsveefA7uPuuNVSH2OawwbYsmYzy59LVZbx2L3MepI7F40cbCli7bIucJY5Pay94xFdk32LqLeoGoBlRx3RSsZx78ggu+uyIFbjwGZm3oNslH4KwOdVdZ/9u1b2eFV9h6pepKoXrffCvVFR6VUZibsR1M+sWT1VqIp3MqqIrSr2ylShijRU/KCs6w/OBkEPWuszjRhk8X4hvMgEAG6EyRoPbJDs8QkJCQmMU15haUPAPgWhC/ybsc7Z42MxngcRYcSjCvr6eLzs+jGoq5CLSkqYdyyRrATxMOz/9NRkhiZNZoG5uKTFMaaLIQY5Bga/xA3nZMP3qkvNs2KjWOyQWIyNoD6IB8LK22I8EQk0f8WHFcQxcc2HfGbBXJ24jMQ+sS981ViBKWVExDOsCWoV8r6y0cU+k2bv40+DNp2sug7qusefAHBaX9kBDJg9PiEhIWE9Ma2sug42pIdljGEPe+4wv+RDRx10DK1ZZIBliCZnLcmo4hh3yHBpruyQ03Cu/sUyABDLXAMuOxO71qISEwDQLVYLH9sdg3QjbBrsRLO2mzSbKvKzzCJlg2SCGWfi6EkglPUXdzHxNvH2w0YArIuq/idlGlwH2STsNDcINuTinZCQkDAObN6le4Mt3oMw7mH6zIMdlbCuqkw80T6zOEteC+WBqYr9xI61CzTmnFCXOmhEWDaXtSi9S2bN55gVkXw8l4+WsibN5xKba3z+MZk31Ufk3+HOgVh6Hlu96OTE6GUNtEa0OMnN4xDfBYyKPE58RfadMpn4pJl11VzCcruj2yCrZm+dBCcishs1woWIyEsB/A/752+r6rW2/IUAXgvzxfoOgBer6gNrjVm53IjIo0TkdvocFZFfE5GrROReKn96/UstYhIL92ZFvnAnVGLUhTthurGO1iaV4ULsAv96AI+HyTz2ehHZJSItAG8F8JOq+r0AvgjgiqoBR4ltAgBvcXWq+rFal5iQkJCwTsigtT5jwLNgwoTA/v/syDlPA3CTqh60rPwmAJfCmE4JgC1itprbYdj3mhgltsmATeNYT8Yd85AcRHEVi/c9KqKilrL+Y+cGpnisEIyITYJ2tjO+Zi2KXSQQpbDy0v1P9zIi4glFJZHYJSVimVzExfPLiiKUQZLmlqGuQq1KVBGLulg6JouL8jKaR+ABGzFxjSb9Hc1JZ1jxy8lUSFahrqkge4JbvMMmT6+LOuFCzgVwD/29B8C5qtoRkV8G8CUAJwD8B4BXVA04qDUpxzYBgCtE5Isico2I7Io1EJHLReQ2EbntA0fvHnC4hISEhOFRV2zCnuD2U1i4ReQTIvLlyOdZfJ6qKgbQlYpIG8AvA/h+AOfAiE0KYUj6MUpsk7cDuNpO8moAfwjg5/vb2ZvwDgD46sN+WoHB2PaoTgHDsORA2XKSTcqizHyc0d+YDWdF5h0w30A5WuwqplwtY6OxnVssEW3w/Epif/sx2exwg2jMaqAqpo5nxAN8bwZg0cMw7jK27b47oamiFOrXCzrGAVX1yWV1IrLPJWVfI1zIvfCRWAHgPJhorBfa/r9p+/oAaoTYHjq2iaruU9WeqmYA/hxGAJ+QkJCwYdCF1vqMAXXChfwDgKdaJeUuAE+1ZfcCeIyInGHPewqAr1UNOIjMO4ht4n5l7J/PAfDlAfpaE+N0wY25t5flpaxiQLXHZLZeEWSKUTXuusZbjsnKY5lYRpxT6IZO5e4ZUZS5UhYeq6/AILlNYxgu7+Tau7iAATeK7+3QMunI92kwZl4lSx90RuuHdXSPj4YLEZGLAPySqr5cVQ+KyNUAbrVt3qiqB+15bwDwaRHp2PYvqxpwlNgm/0tELoT5Ft/VV5eQkJBw0rFeIWHLwoWo6m0AXk5/XwPgmsh5fwbgzwYZc5TYJi8ZZCCgWtY9CONmh5r+47WYlKoUGNq44nTXwaTl5nmGHBh5nwDecqEBbzHSaPjjSGzv9YA0FKgICQv459L//rj41OO0/llPFpnv9Jpq3ttCPWx9/L2t7H8Ixs27z2EZ90ayPhmnzHujYcN4WA6zcK91XHeM9TRVXK+FG6CXlhdmNvXbCAt3DZT9oOZxYKZ84S4bN4hTMsQ7OsrC3X8c77+6j42AzeyitWEW74SEhIRxY73c408G6sq8fx1GbqMwhuQ/B+BsANfBiFM+B+Alqro6oXkCqM+Mys5z4pRxxuheT+Tb6DonO0Y9kBKPWfr470ss3VWgsMwiz4UUd0E87YrY7rmpYg2HrI2ysy6dnw+hOGS/9cet7Kvud3CDMPDNLDapE9vkXAC/AuAiVX0cgCaMs87vwbjHPxzAIQCXTXKiCQkJCYNiHd3j1x11xSYtAPPWjGUBwH0ALgHwIlt/LYCrYBx3BkKly+4Y72tV1MBccRmYbEXcs/knb5hMPgOYD0bB88tK2HJWdC+Xqh1kFduO1Z+kXemklcsngzlWOYeNGj1wEgy7Tr+BXmqd7+tmzqRTJzDVvQD+AMDdMIv2ERgxyWHVPCDoHhi//YSEhIQNg0y11mcaUUdssgsmYtZDYPzut8BEwqoFjm3ywZqxTVT9J4ZMZahPfCzJP8Ogqv9JIFtlhh3/aKbG+oQ+qmt/gnPz9tRvMIlimWb+MyqkoeYj9Gkg/9TFRsqYzu+1ZlL4lLarqK8ct6p/LX6q+iqb96hzHTe05mcaUUds8mQA31LV+wFARP4GwI8C2CkiLcu+z4Nx8SyAY5t8+aHPmNb7tKHQmNk4X46NjmlSSCeMH91NbG1Sh8PcDeAJIrJgY80+CcBXAfwjgOfac8p8+RMSEhJOGqp2m/mucwpRybxV9RYRuQHA52HSzn4Bhkn/LYDrROS3bdm76g4adSCocf9GFU3kCknaKI0pLDmA0CMuL+MYGpXtiwrNIPVV5sr6xCb5cUQ5WabQdGZn7NgTU3iWjRWZX53ytRCYx1V4XuYp4UpSg1VhPbf2dWPmBHOKpHcb55zHqpCM1A/jMDcJTKslSR3UdY9/PUz6HsadSJEEExISNjA2s7XJhvewjLHtQX61B8lo4rhxYCnIzG5UE7+6CK6vyMIDk79Ss0Ebj7vL2W/KMrXE2hfLtIylx+ayTqhi29PgkFUZzztSXLVjHEYSMJApYUU872H7HTemVSRSBxt+8U5ISEgYFqe82GS9sZZZ36AYJJ53dV9ugsPF6472yWy1uXa9RMpCOTcFFrKMO8hBWfUiczAk128py4+URRAEWBqRmQeu7utoAjiJsariicfY6jhd+sfBssc3l9Har4XeJDs/yail4hGRXxeRr9h8be8XkTkReY+IfEtEbrefCyc92YSEhIRBoDX/TSMqmTfFNnmMqi7Z/GovsNW/qao3THKCCQkJCcNiWr0n62DY2CbfGWawYU0E1+5z7XrekrrxJ624CkQ1gcLRKhHZDIzbZeH/QGh2GBVVsFiiS/22KhSWMUQUlmVKythcxysisQeN4v2bVlSnQatfzxjk3LoYVVRSrtBMsU3GhaFim6jqx231m0TkiyLyFhGZjbVn9/gbjn17bBNPSEhIqMJmjm1SR2zCsU0OA/igiLwYwGsA7AUwA+O0cyWAN/a3Z/f4Lz3kZwa+S6Oy9SrFUKC8zH+l6cRKU8P65w6DKJutZSpop8TXEjjpRAabgMJyI2EQs8FRGeJgY7nzqKxCYRm0H4JlD8vMh2XcJwunusIyj23y/7d3/sF2VdUd/3xfHqSJgZAEhFh+REQmWBloecYMPwolQhUdDD/UaqdGCgYKNqG1PzJDp1BaNNAWtDp0BhBUKJZGBJFKRBIBx9GQSFIS+vSlRn6UYkBIAkWsSd7qH3vf9/Y975x7zrn3nnfvfW9/Z/bcffb5nn32Pvvcc9ZZe62kzlK1AAAPEUlEQVS1zWw38DXgRDN73hz+D7iN6LATERHRZZjUE5YEsU2A13GxTTZImmtmz/t4J4uBLa00pB1R+fJWVxmVGopLRWGdU3IGucadkqLnhnSzwrp1A3NcvkcWF86Thgn03+HruS+dm4qaTnvPWD13Mp9a5q9F/VqN7ZPKauPWbJ2djjaYpt9uxnywKrQ3nnfK1/M4CcS9qhIpglZimzwg6SDck3ATcEmVDY2IiIgoi16Vqougldgmp7ezIX2yXOk77SWad0yzDjmp52/TSjj50nh6vvbBsPdX0FcbuXD/noDq99fdvHUSfZo0lGZtwpiyrP1lrE26TTdaJZqxcsqTwtuJKlzpW4mP307YBNZ5d42H5XguZtDr6OuaUYuI6G5E9/iIiIiIHsREtjYp9PCWtBz4OO7D/WYz+4yk2cBdwDzgKeCDZrajSH2txvptVUpPiyTossXe0nkxurOcbEZMwjLqSosHXl9xbRIwJW43icglNRVKMEmZNvmZhTSzxDS1SNokZWo9EZnmg81EFQxRRYTBzLpaVJeM9/0wkaMKFlnD8u24B/cC4DjgfZKOAlYAa8zsrcAavx0RERHRNZjUTjrAMcA6M/sFgKRHgHNxjjunec6XgIdxjjodQ9pbP5RwRlbSyZCwm1lpJ3fyMcVVPtcNOjim3mzQxpSlRR0E0tyN8rV/eeZ/eZJ33XHlpfB6s8LG3E6j2RV8GtdZ3P297rgKrlWpqINdPF81ka1Nitx2W4BTJM3xtt5nAYcBB5vZ857zM+DgitoYERER0RQm+xqWg5KuBR4EXsPZdO9NcEwZdlCSlgJLAf56zrGcv98RpRrYqn681ZVUyri/j3DLmISF0mbt8LpgTIzJh2Z+WbrrkdjfYWEZfWOOFD6Sz/hKGJHMM2Oz13jd72ST/8UwtqydK/wUXQOzDFoOXFXi+uddvyotzSaytUmhDz4z+4KZnWBmvw3sAIaA7ZLmAvjfFzKOvcnMBsxsoOyDOyIiIqIV7B0eLpR6EUUXY3ij/z0cp+++E7gPWOIpS4CvV9HAiIiIiGYxqdUmHndLmgPsBi4zs52SVgL/JulC4Gngg1U1cqIgz0NzVJUQFPalqSXC43OmJJucWMuL1z1qthjubzxJOZ7R78qdo9o6i6pQiqpPOomi6pJuMRGdyGqTou7xp6SUvYQLUhURERHRlehVqboIesrDstMu9GlRAxvxsrh191NadLkMU8JRaSZD2q6LB107vmFT69tVMFJgWF434RpK2V1iPtYNEmDRsci6ZlWv/JSGpqM1Frze4/Vf7lUb7iJok4VqRERERPdhrw0XSq1C0mxJ35a01f/OyuCtlrRT0v2Jckm6RtKQpEFJy/LO2VOSd7cgzzGnnhtseP21MiT3NJ14qv57OEvnHZSOxAZPr0spscPTG5UuWadS8yTzulAAY+N9txPdIHEn0axjT57zWVNtaaPk243XuoZxVJvUPM5XSlrht9OcFv8etw7wxYnyj+H8Z+ab2XDNSKQRilqbLJe0RdKTki73ZVdJek7SJp/OKlJXRERExHhhHFfSeT/O0xz/uzi1PWZrgFdTdv0RcLX5GLZmlmp6HaLIGpZhbJNfAasDkf8GM/uHvDqKoEg875pbe6d13yHyglSlHpPrUh9sDI/VbxsakdzSVqcPuWlxuwFsb2pxSlvDfLWBp1p1yOoltOpe3+lr0ey4j/d/t6jkHToTetzk198tilY9zt8CfEjSOcCLwDIz29rogFZim7QV3fRA7na0K5ZGRMRER9GHd7hQehYkPQQckrLrikRdmR7nDTAV+KWZDUg6F7gVGGPlV4cCxuvH4Dwq5+B0Nd8HPgdchQsF+4Q/0ayM45cCG3xaCiwtYTjfdm6nz99Lbe30+XuprZ0+fy+1tUydvZKAHwNzfX4u8OMG3NOA+xNlPwLe7PMCduWes2DDLgR+CDwK/DPwGdxnwRSc3vwa4NaCdW0ocUHazu30+XuprZ0+fy+1tdPn76W2lqmzVxJuInKFz68ArmvATXt4rwT+MNi/Pu+cTcc2MbPtZrbXnIL9ZpxOPCIiImIyYiVwhqStwLv8NpIGJN1SI0n6LrAKWCTpvyX9bnD8eZI2A58GLso7YdGVdN5oZi8EsU0WSpprowr6c3ChYyMiIiImHSzD49zMNhA8iC3FW92X7wTeW+acrcQ2+Zyk43FmDU8x1m4xC2VmcKvgdvr8ZbiT/fxluJP9/GW4vXT+iAzI61giIiIiInoI0egsIiIiogcRH94RERERPYj48I6IiIjoQVQemErSfJzf/6/7oueA+8xsMOe4L5vZR1PK9wV+D/gfM3tI0keAE4FBnEvr7rZ2oM2oWe50uh0TDZLm+Bn/dtbZ8bGqol+dxkTsUydQqeQt6S+Bf8V5DD3mk4Cv+MhbNd59ifQN4NzadqLa23AmNcsl3Q58AFgHvAO4hTbCW9iklc+UtFLSjyS9LOklH8ZxpaQDAt7sRJoDPCZplqTZiToHJH1H0h2SDvNhJXdJWi/pNxPcKZIulvS3kk5K7PurIP8JSQf6/FGSHvXhKNdJOjZxXL+vc7WkJ3x6QNIlkvYpcK2GUsqOlHSrpL+TNEPSzT7A2SpJ8xLc/SV9WtLt/oUc7rsxsb0y6NeApG3AOklPSzo1we30WLW9X1WMlS8vNF5VjVVESVTsdTQE7JNSvi+wNdh+HLgD51l0qv993udPTRz7hP/tB7YDU/y2avsS/P1xRu+3Ax9J7Lsx4eF0oM8PANuA/8It8ZZsw7dw4R4PCcoO8WUPBmXDwE8Tabf/3Zao8zHgPcCHgWeB8335IuD7Ce4tuHVEL8d5vl4fXssg/2SQ/3fgnMCD63uJOr+C855dCBzq00JfdleC+yrwik+v+rS3Vh7wHsVFS1uB8wP4JC7s5YXA2kSdd/sxWIxbH/VuYGqyT357c5D/DvAOnz+ahPdeF4xV2/tVxViVGa+qxiqmcqnayp2//hEp5UcQ+P7jvgD+BPg2cLwv25ZR5xbcw3+WvwFn+/JfAwZT+IVutJIPhEZxC8J+fRJYDRwblP0047iNQf6ZrH1++4kg34+zm/0aLrjNxoy2rM+qw28PNejTUGL7n4Av4yKpZfarZJ82JbavAL6Hi6mTfCAMAv0+/4PEvs2J7U6PVdv7VcVYlelXVWMVU7lUtc77cmCNnMvos77scOAo4BM1kjkX+xskrfK/28nWx38B91KYgrtpVvlPsYU4FU0SbzGz83z+XklXAGslnZ3g9UvqN7M9wDQzW+/bNiRpaoL7tKS/AL5kZtsBJB2MC6he6ydm9o+S7vJ9eha4ktRVggH4paQzgZmASVpsZvf6T8tkANd9g3PsAZZKuhJYC8wIeF+V9EXgauAeuVjs9wCnA88k6nxZ0geAu/14IKkPp5baERLNbJmkE3Dqr3uBz2f0a1jS0b5P0yUNmNkGSUfhxi/EVEl9NhrP+BpJz+GkwRkJ7o3AN+UWwV4t6bO4l9fpwKYEt9NjVUW/qhgrGB2vA6gfr7dSP15VjVVEGVT9dsBJ1QuB83xaiFd1NDjmvcCnGux/E/Amnz8AOB9YkMEdBPoSZR8DngSeDsr+GHgQd1NdBXwWp7b5G+D2xPGzgGtxL5EdwMv+PNfivwRS2nE28APgZxn7j8N94j8AzPfn3+nbeWKCewfw7pQ6LgJ2p/R1HfBz3JfKfwKfAmYmePOAu3CxhIeArcALvuzNDcZ2GfBd3ARycv8iXLS1QeBk3FdPrd7FCe51wLtS6ng3gYotKD/Nt20jsBn4Ji5q5T4J3niN1Q4/Vie12K/fyetXMFYv+LEaanWsCozX+9swVo8Hfbo4OVYxlUsdb0DlHSxxozV4IPSnHD8fF4BmRrLeFN4inEQyDXh7Gs+XHVPjNqrTly1gVLXzNuBPgbNyeL+BUw+M4SWOmePTHQWv8VzgpYLc+0m8TDN4J/s+nVmAe4rv1xgu8E78iwoX0vhq34ZrCV5gnrd/wLsOeCjJS6lzWladfv8y4LCC16YQF/fltQQ4w4/T7+Mk3MuSD0TP/WjtPwD8AW4+59IM7pKA26jeI4E/w724rgcuqV2/lPYeCfw5ToVzQyNuTMXTpHaPl3SBmd1Wlie3OOhlOAnleGC5mX3d73vczH6rDC/gXoqTEPO4V+ImzPpx8wTvxOnpzwC+ZWbXZPAWAA8neZ6btOoB9xWyFsDMzi7LLVnnY2a2wOc/7q/bPcCZwDfMbGUG9yLPvTeD+yRwnJntkXQT8BpOolzky88tw2uCu8vv/wluonGVmb2Ycl2S3Ds99+cpvH/Bjek0YBfwBn+tFuFCXixJ4U7HfckV4Tas19+r78OpSc7CCTs7cQHqLjWzh4M6l+O+pHO5ESXR6bdHJxOJSZmiPJxUPsPn5+EWmljutzeW5TXJnYL7Q77CqMQ4jfrJzEI8X1bG4qcQF/dHLVpneN3WAwf5/BsYOwlZhjsYtjuxb1NZXhPcjTiVxZm4+ZoXcROjS4D9muFSwuKqCm7tvvL56cDDPn84GfdqEW5M5dKE97AM7GCTaTPBOnNFeR59Zva/AGb2FO6h9B5J11O/lGVRXlnuHnOx1H8B/MTMXvHHvY4zeSvLA2ce+UPcJPAucxLR62b2iJk90iT3hBJ19snZVM/BSXkv+ra+BuxpgbtF0gU+/x+SBgD8xNzuJnhluWZmw2b2oJldiJuvuRGnttvWJLdPzlltP9wDcaYvnwok7byr4vYH+2b4xj+TwivLjSiKTr89qk44CeJ4nHlimOYRTNwU5XnuWrxJY1DWjzPJ2luW1wR3HTDd5/uC8pnUmz8W4iXqPhQXLP7z5HyZFOUW4eHCCm/D21UzuqTUDMZKs2W4M4Ev4lQR63AP123AIzgVRyleE9xM6bI2NmW5OLPabTgfhGXAGtyCKJuBKxPHtZ0LLMctf3gzTs13gS8/CHg0UWdhbkzlUscbUHkH3efnyRn77izL89uHEjh9JPadVJbXBHdqBu9A6u2UC/EyOA0tfprhlqkzOGY6GRYUZbg4Z63jcF8DBzeooxCvKBc4ukRfy3DLWFy1nYub/D4fmF+grYW5MRVPk3rCMiIiIqJXMeF13hERERETEfHhHREREdGDiA/viIiIiB5EfHhHRERE9CDiwzsiIiKiB/H/NOnZtyK8ntAAAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for r in lss:\n",
+    "    r.fit(trainX, trainy)\n",
+    "    Z = r.predict_grid((x1min, x1max), (x2min, x2max)).reshape(100, 100)\n",
+    "    sns.heatmap(Z)\n",
+    "    plt.title(r)\n",
+    "    plt.show()\n",
+    "    plt.close()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7f958c0cf438>"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1008x576 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(14, 8))\n",
+    "h = plt.scatter(trainX[:, 0], trainX[:, 1], c = trainy)\n",
+    "plt.title(\"The training set used.\")\n",
+    "plt.colorbar(h)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "outputs = []"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[00:48:30] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/apoorv/Desktop/github/polire/.env/lib/python3.6/site-packages/sklearn/gaussian_process/gpr.py:469: ConvergenceWarning: fmin_l_bfgs_b terminated abnormally with the  state: {'grad': array([-0.61252415]), 'task': b'ABNORMAL_TERMINATION_IN_LNSRCH', 'funcalls': 67, 'nit': 5, 'warnflag': 2}\n",
+      "  ConvergenceWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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mZGeE+P4ZJ9ZgZCIiUltUd1bbWuW8m8+kRftmvPqXd8hds43ep/bgut9eRutjW9Z0aCIiIt9IVww+gTaNG/DUxOlszitg0DHt+P4ZQ2jbuGFNhyYiIrVAnSw8AfoPP4H+w0+o6TBEREQkYWiXTgzt0qmmwxARkVqoznW1FRERERERkbpFhaeIiIiIiIiklQpPERERERERSSsVniIiIiIiIpJWKjxFREREREQkrVR4ioiIiIiISFqp8BQREREREZG0UuEpIiIiIiIiaaXCU0RERERERNJKhaeIiIiIiIikVbUKTzNrZGZvmdliM1tkZieZWRMzG2NmyxL/Nk53sCIiIuJRbhYRkbqkui2eDwOjnHPdgT7AIuBXwFjnXBdgbOJ3EREROTKUm0VEpM7Yb+FpZg2B04CnAZxzYedcHvAt4PnEZs8DF6crSBEREdlNuVlEROqa6rR4HgNsBZ41s9lm9l8zywZaOuc2JbbZDLRM9WQzu83MZpjZjK1btx6eqEVERL7ZlJtFRKROqU7hGQD6A4855/oBRezRdcc55wCX6snOuSedcwOdcwObN29+qPGKiIiIcrOIiNQx1Sk81wPrnXPTEr+/hZfscs2sNUDi3y3pCVFERET2oNwsIiJ1yn4LT+fcZmCdmXVLLDoLWAi8D9yYWHYj8F5aIhQREZFKlJtFRKSuCVRzux8DL5tZCFgJ3IxXtL5hZrcAa4Ar0hOiiIiIpKDcLCIidUa1Ck/n3BxgYIpVZx3ecERERKQ6lJtFRKQuqe59PEVEREREREQOigpPERERERERSSsVniIiIiIiIpJWKjxFREREREQkrVR4ioiIiIiISFqp8BQREREREZG0UuEpIiIiIiIiaaXCU0RERERERNJKhaeIiIiIiIiklQpPERERERERSSsVniIiIiIiIpJWKjxFREREREQkrVR4ioiIiIiISFqp8BQREREREZG0UuEpIiIiIiIiaVWtwtPMVpvZ12Y2x8xmJJb9wcw2JJbNMbOR6Q1VREREyik3i4hIXRI4gG3PcM5t22PZg865Bw5nQCIiIlJtys0iIlInqKutiIiIiIiIpFV1C08HjDazmWZ2W9LyH5nZPDN7xswapyE+ERERSU25WURE6ozqFp5DnXP9gRHAD83sNOAxoDPQF9gE/CPVE83sNjObYWYztm7dejhiFhEREeVmERGpQ6pVeDrnNiT+3QK8Cwx2zuU652LOuTjwFDB4L8990jk30Dk3sHnz5ocrbhERkW805WYREalL9lt4mlm2mdUv/xk4B5hvZq2TNrsEmJ+eEEVERCSZcrOIiNQ11ZnVtiXwrpmVb/+Kc26Umb1oZn3xxpisBm5PW5QiIiKSTLlZRETqlP0Wns65lUCfFMuvT0tEIiIisk/KzSIiUtfodioiIiIiIiKSVio8RUREREREJK1UeIqIiIiIiEhaqfAUERERERGRtFLhKSIiIiIiImmlwlNERERERETSSoWniIiIiIiIpJUKTxEREREREUkrFZ4iIiIiIiKSVio8RUREREREJK1UeIqIiIiIiEhaqfAUERERERGRtFLhKSIiIiIiImmlwlNERERERETSSoWniIiIiIiIpFWgOhuZ2WqgAIgBUefcQDNrArwOdAJWA1c453amJ0wRERFJptwsIiJ1yYG0eJ7hnOvrnBuY+P1XwFjnXBdgbOJ3EREROXKUm0VEpE44lK623wKeT/z8PHDxoYcjIiIih0C5WUREaqXqFp4OGG1mM83stsSyls65TYmfNwMtD3t0IiIisjfKzSIiUmdUa4wnMNQ5t8HMWgBjzGxx8krnnDMzl+qJiWR4G0CHDh0OKVgRERGpoNwsIiJ1RrVaPJ1zGxL/bgHeBQYDuWbWGiDx75a9PPdJ59xA59zA5s2bH56oRUREvuGUm0VEpC7Zb+FpZtlmVr/8Z+AcYD7wPnBjYrMbgffSFaSIiIjsptwsIiJ1TXW62rYE3jWz8u1fcc6NMrPpwBtmdguwBrgifWHKN008Hqe0OExmvRA+n243my7RWJwJXy5l3NQlZGeFuOjsPvTq1qamwxKR/VNuliMuHneUloTJqhci8dmTNIjG43y6cBmjFi2lfkYGV/bvTZ92rWs6LJFDtt/C0zm3EuiTYvl24Kx0BCXfXM453ntuEi8//CklRWXUy8nk+p+ex4U3DK3p0I46sVicO+99mwVLN1JSGsEMxk1dwk2Xn8x1lwyu6fBEZB+Um+VIcs7x9itf8sozkygpDpOdk8FN3zuDC749cP9PlgMSjcf5zotvM29jLiWRCAZ8tGAJPzn9ZG4+aUBNhydySNSUJLXKRy9N4bn7P6Iwv4RYNE5BXjFP//UDRr3+ZU2HdtSZPH15RdEJ4ByUlkV55vUp7MgrquHoRESktvjfG1/xwhPjKSwoJRaLsyu/hCceHsPoj+bWdGhHndGLllUUneBNXV0aifLg51PYUVxSs8GJHCIVnlKrvPLvMZSVRCotKyuJ8PLDo2sooqPXhGnLKorOZAG/n5lfr62BiEREpDZ65elJlO6RL8pKI7z45PiaCego9umiZRVFZ7KAz8dXq9fVQEQih48KT6k1nHPs3FqQct2O3PwjHM3RLyc7A1+qMToG2fVCRz4gERGpdWLROPl5xSnXbdtLzpaD1zAzM2VuNjOyQ8rNUrep8JRaw8xo1aFpqhU0btmQshStc3Lglq7M5eX/fUVOvUwCgap/Avw+Y9AJnY58YCIiUuv4Az5atGpYeWHcQWmYpjkZhJWbD4tFq3J54cOvaBHJJJTi63nA5+PEY9rXQGQih091ZrUVOWJuvetC7v/py16RaQZ+P5hRWBjm6hPv4df/uo6Bp3Wr6TDrJOcc9z06irFTlhCNxQkGfMRwBAI+QkHvT0HA7+OBu79NMOiv4WhFRKS2+O4dw3ngnve93FxShi+/EMMoLC3lqr6/4e4nv0P/07rXdJh1UjzuuOfJUXw+YxmRWJyg30dOPI61NvwNgjgHoYCP/15zKUG/crPUbSo8pVY55bwTCIYCPPWX91m/ZmfF8vLWznt/9CIvTLiLBo2zayrEOmvyjBWMm7qUsnAU8Ga1BaiXFeKX3z+X+vUy6N+rPYGAEpuIiOw2bPjxhEIBnnpwNJtmLvdmvMFVzMlwz61P89L0P5LTsF6NxlkXjZ+5jM9nLqd0j9zcdHsGP7/mbOpnZjC4UzsVnXJUUFdbqXUGn9mT8646mWAoxR9Zg8mfzj/yQR0FPv58PqVlqbtENcjJZHDfTio6RUQkpZNO68a5w7unzBNmMHXUvBqIqu77cOKClLnZOUcLfxandO6oolOOGio8pVYqLiolGolVWR6LxikuKq2BiOo+51zK5baPdSIiIuWKC0qJRlPk5lic4kLl5oMR31v+dd5tzkSOJio8pVYaeFo3QpnBKst9fh+DNMbzoJw37HiyUpxTB/Tp0e7IByQiInXKoDN7kplVdWZVM2Pg6T1qIKK67/yhx5OVUTU3m884oUubGohIJH1UeEqt1L1PB4ae15vMpNt6ZGaFOPeygXTs0qoGI6u7ThvchaEDO5OZEcQMQiE/GaEA9/z0AjJCGu5dHVsLili+ZTuRWNUr/iIiR7vjBx3LiWf3qpyb64U4/7pTaNe5ZQ1GVnedNbgrJ/fpVJGbM4IBMkMB/vzD8wlq+Eu1bCkqZNn27UTj8ZoORfbDjmQXu4EDB7oZM2YcsdeTus05x/QJSxj33ix8PmP4pQPod3IXLNW9JxPydhbx9otTmT5lGU2a53DZ9afQf0jnIxh17eacY+GyzUybu4qcehkMP6U7TRppoqb9ySsu4Wevf8zMNRsI+HwEfD5+c8EZXNhXV/hrmpnNdM4NrOk46jLlZjkQzjm+GruAz9+diT/gY/jlg+l7Std95uZtBUU8P2EmU5esoUXDHG4aNoAhXTocwahrN+cc85dvYtr8NTTIzmT4id1o0kATNe3PjpJifvTxh8zevBG/z0fQ5+NPZwzngq6aYbmm7S03q/CUo0beziK+d+WjFOwqqRgfmpEZ5NY7zuaiK4fUcHRSl1335Ot8vWEzkdjuq6mZwQDP3Pxt+nZQV6iapMLz0Ck3SzptKyji2/94iYKS0oq/oZnBAD+/aBhXnHRCDUcnddmlb7zC/C25lVo6MwMBXr30Cvq0al2DkcnecrO62spR4+0Xp1YqOsG7DcvT/x5DaUm4BiOTumzt9jwWbNxSqegEKItEeXbyzBqKSkSkbnj28xmVik6A0kiUf3w4kbJItAYjk7ps5c4dLN62tUr32rJolGfmKDfXVhrYJbXOrrxiHr3vA76asJhgMMCIywZx9ffOICPFxDjJZkxdlnImXJ/Px+oVW+jeSxPoyIHbWlBE0O+jbI/vRw7YmF9QIzGJiBxpO/OLefDJz/hq+kqCWQEuPK8vN377xP3OETBlyeoqF+4ADGNF7nZ6ttPYUDlwuYWFBH1+SqmcnB2wfteumglK9kuFp9QqRYUlXH/23ykrjSRuUB3m9WcmMHf6Kv750m37HEPSpHl9Vi7LrbI8Go3RqInGMcrB6daqWcrJhEJ+Pyd31hglETn6FRaUcPU1/yFSEAaDiIPXn5nM3EXr+c89V+3zuc0aZLMid0eV5dFYjCY5GscoB6dn8xaEY1VbzDP8fk7t0OnIByTVoq62Uqvcf9fbXtEJ3g0mDVwclsxfx6I5a/f53MuuP6VKq2gg4KNLjza0atM4TRHL0S4nM4PbTx9CVnD3dbqA30f9zAxuOLl/DUYmInJk3Hvf+0QLwl5adl56pizG4ulrWbR88z6fe/PpA8kMVm7nCPp9nNCxNa0a1U9bzHJ0a5iZyfcGDCYrsPuzFfT5aJCRyQ19+tZgZLIv1S48zcxvZrPN7MPE78+Z2Sozm5N46F2WQxIOR5k+eUnVFQbxuGPpgo37fH6/wcfy3f87h8ysIPWyMwhlBOjeqx2/f2DfV2NF9ud7pw/hb5ePoF+H1nRo0ogrB53AOz+6jqa6Wi81TLlZ0q0sHGXWtJVVlhtAUYQlK6v2NEp2SrdO/GTkULJCAXIyQmQE/PTp2IZ/3nBBegKWb4w7hpzEA+eMoF+r1nRs2Ijrevflw2uup0mWcnNtdSBdbf8PWAQ0SFr2c+fcW4c3JPkmKiku46fXPUEsuvd7MLVo03C/+7nwisGcc1E/Vi3PpVGT7KOqpTMed7z2wQxe/2gmhUWl9O7eljtuPJ1jOzSv6dDSpiwS5emx0/nftAXE4nHO7deV7517Ig2yMo94LMN7Hsfwnscd8det61xsM674TYitgeAgrN5FmGXVdFhHE+VmSZvi4jDfv/MlYtE4qQa6GNCq+f5bLa89tR+XDunF8s3baZpTjzZNGuz3OXVFLB7n6S9m8uJXsykoCzOwQxvuOnsYnZs3renQ0qY0GuHRhVN5e9U84s5xQcee/Pj4oTQIHdncbGaMOK4rI47rekRf92iwOb+At2bMZ92OPAYf056RJ3QjK7TvuVQOh2q1eJpZO+B84L/pDUe+qd5/5Qs2rt3ujetMcYefQNDPkGHVuy9TRmaQ7r3aHVVFJ8CDT3/G029MYduOQkrLokyfu4bbf/MqG3Pzajq0g1IajrJs4zZ2FpakXO+c4wdPvsuzY2ewOa+ALbuKeHXSXC752wus31Y3j/mbxoXn4LadB0VPQOn7UHAfbtv5uPjOmg7tqKDcLOn25nsz2Jibh/NbldTsAH+9IIP7HFOtfWWFgvTu0OqoKjoBfv/xWP4z6Uu2FBZREokwecUarnj2NTbm180JbkrCEZZu3sbOor3n5us/f5WnFk1jU3EBucWFPP/1LEa8/Qzr8/OPcLRyMGat2cD5Dz/PUxO/4oO5i7nv4/Fc8siL5JeUpv21q9vi+RDwC2DPy1p/NrPfAWOBXznnyg5ncPLNMeHT+YTLot7l0/LiM/Gz+Y3f/+ta/P5v7pDkvF3FfDhuPuE9Zu0tC0d4+b3p/Py2s2sosoPz3NgZPP7JF/jMRyQWY1ivY/nTdedWuto2d/Um5q/NpSwarfjCE43H2bKriAv/+jyXndSbX196xj4nnNqfrQVFfPL1EvKLSzn5uI7079jmgPa3YtsOPlm41GuN7d6F7q2O3tbncs45iKKUmn4AACAASURBVMzFlU3AfDmQORLzV71fmnMOl38nuOKkpSUQy8UVPoI1+O2RC/ropdwsaTVu8mLC4RhkBQgURpJTMz6/cd99l+PzHfzf4LpuW2ER781bRDhpAjqHd7uYZ76Yyd3nnVFzwR0g5xxPTZzO4+On4fd5ufmsHp3586XnVhqjO33rOhbl5VIWj0IU2BYiGjc2U8JZTz/LdX37cPcZpx9Sbs4tLuCDVYspKCvjtLbH0L/FgeXmpTu38fGqJcSdY0SnrvRo2uKgY6krnHN8vWITU79eRU69DM4e1I2WTar2RnDO8cs3R1ESjlQsKwlH2JRfyJPjv+LnI05La5z7LTzN7AJgi3NuppmdnrTqLmAzEAKeBH4J3JPi+bcBtwF06KAZICW1zKyk5v3yvy3OS2x/eeo79Bl8bI3EVVus3biTYDBQpfCMxRwLl20iv7CEt0bPYerslTRvnMNVIwfQt3vtvH3MmNlLeeyTLygN756NbuL8ldzz6mf85cYRFcsWrssltsf9uco/G9F4nPemL6B3h1ZcNKjnQcUxcekqfvLqh8SdIxKN8dyUWZzatSP/vPKCan2ReuaLGTw0/gti8RhxB898MZObhvTnp2eeclDx1AXOOdyuX0HpKHClOIJQ8BCu4d/xZY2ovHF8C8RSjf2KQOloUOF5SJSb5UjIzEjkZr8RrR/EF45BzOEP+njwn9fR6/jamWeOlBXbdpARCFQqPMHLUXM3bGJnSQnPzZnNxNWraFW/Prf0H8DANm1rKNp9+3jeEp4YP43SpHurjlu0gnveH8t93z63Ytm8HZuIlOfmHSGIGZZIzlEX5/Wv59OndWsu6lG9Xmp7+mztcn40/n2cc4TjMZ5cMJ3h7Tvz8LAL8VWj+Hx0zjQenj2VSNx7T56YN53v9RnMT/sf3bn5d//9hM9nraAsEiHg9/PYu1O597YRnNG/S6VtN+YVsK2wuMo+IrEYny5YmvbCszpNSKcAF5nZauA14Ewze8k5t8l5yoBngcGpnuyce9I5N9A5N7B586O/NUAOzoVXnVi5+ATMZ7Tr1OwbX3QCtG7RkEiKG237fEbbVo244Vcv8MJ701i4YjMTZiznp399m/fGzQNgw5Y83p/wNeNnLCNcC27W/fSY6ZWKToCyaIzP5i6jqDRcsax1kwYE/b7d3bv2yDcl4SgvT5p9UDGEo1F+9vrHlEaihKMxHFASiTBp6RpGL1y23+evz8vnofFTKYtGicYdcecojUZ5btosFuduPaiY6oTwRCgZBa4E77p+GCiD/F8Q33kH8dzBxLeeSbzoOa8oZS9jtu3Ij9E9Cik3S9pdckE/r/iMRLFdRcTN4XJCdOja6htfdAK0b9ww5S09/GZ0aNKIES+9wBMzpjM3N5fRy5dzwztv89aC+QCsL97GBxu+YuKWBYTjNZ+bn5o4nZJI1dz88ddLKE5qHWuX3ZCQz++1dkZ2F53lSiIRnp91cLm5NBrh/yZ8QGksSlk8kZujEcauW8GYtcv3+/w1u3by0OyplMaixJwj5hylsSiPz/2KZTu3H1RMdcHEuSsZP3sFpeEIzkEkGqMsEuXuJz/hnjtf4fJT/8xNI//Be698Qcjv83oupZARSP9dNvf7Cs65u/CuoJK4qnqnc+46M2vtnNtkXtv3xcD8tEYqR7Vh5/Xmrecms2Lp7haSYMjPb/5xdQ1GVXs0b5LDKQM6M3XWSsqSirZQ0E92/Ux27iohEt19xbU0HOWhFz5nybotfDBxAT4zfD4fAb/xyC8vp1unmut2sm1XUcrlPp+RX1xKdmYIgKE9OpGTmUFxOEI89d9ICpMK1fdnLeSxsdPYsquQrq2acef5pzGgU+oryzPXbEw5UUZJJMJ7sxdyXq99T1Tw+dKqMzwChGMxxixeTveW3hf53IJC/D6jWfbRcR9ZV/I+kGrcTxmUfQo4iOVBwYMQXQrBvhCZBSS3BmRClmaaPlTKzXIknHN6T9667x3WjJ8PPh/E4wQ7tOD3/76xpkOrFdo0bMCpnTsxacVqypJycCjgJ5jlJ6+khHCiddABpdEo90z4nBUsZdTmmfgwfOYj6PPzrwG3cVz9qsMWjpRthalzs2EUlpZRLzEU5sw2XcgOhCgmiivvd72HgjKvd79zjrdXzOdf86awtaSIHk1a8JsBZzCgReqLFl9uXpeyVbM4GuGd5fM5t2OXFM/abfSa5SmLqkg8zqdrltGlsTfhU25+IjfXPzpy8ydfLKKkLFJlebg0woRFywnmhynIL+GZh0ezdsUWerZtwdfrNxNL+nKVGQxw9ZA+aY/1UAbNvWxmXwNfA82Aew9PSPJN9MnbM1i/do8bTJvx7itf1kxAtdBv7xjJyDOOJxQK4PMZHds24f67LmXRqs2Vis5yzuDDiQsJR2KUhqMUl4bZVVTGTX94mYv+31M8+e5USsNV/1Clw7qtedz94ihG/vFpYvHUsyNmBgO0bJRT8XvQ7+eF/7uSPh1bV2ntLF8//AQvCb00ZTb3vDuWtdvzKI1EmbduM7c9/Q5z1qS+BY/Pqk6UUc5fja48Pp+vylXe8v0GfD4W5W5lxFPPc9bjzzDs0ae5+NmXWb3jKJhQJ76viSOSz2gJlLwP9e8CfzuwbLB6QCZknIZl60trGik3y2Hz/qOfsvnLpRB3EI1B3GG5O3nv/vdqOrRa4x+XjOTiE3qSEfDjM6Nzsyb895pLmL1lc0XRmSyQU8KozbMIx6OUxiMUx8rIjxRz85cPc/H4v/H08s8oix2Z3LymcDu/mvkO5455CLoWYQ2qtrxmZwRplrO7QAv5/bx59g30bdmKVOky5PdzXlcvN/934XR+O200awvzKIlFmLV1A9eNeY052w48N1enm63ffClj8hkEzFi0fgsX/e05Rt73DOfe+zRXPfgK646CyQrzSksJZ0NZfQjnQCypWdElFZdlJRFGvzeb359zOm0aNiA7FKReKEhGIMDp3Y45IoWn7a25NR0GDhzoZsyYccReT+qOmy98iE3rdlRZHgwFeHvyrwmF0t/8X1fEYnEi0VjF2Juf/PUtps1bU3XDgKXs6OgADEIhP907tuSp31x5SJMA7M+aLTu5+oFXKCmLEE/6e5N8oTQzFOB3Vw7n/EE9Uu5j1Owl/Pa10URiMWJxR2YwQJOcerzx/64lOzPE0D89TkFp1flTBh3Tjuduv7zi93jcMXrhMv43ZyFTl64huseXgqxggL9fMZKzenTe5zFtKShk+CPPVLrCDV43lZdvvJwbX3+n4opv+bE2qVePCT+45ZC7ssSdY9G2LcTijuObt8DvOzKTbrnwHNyO6/C611aD1ccaPQihoRD+CuIbIXgCFji8t6Qxs5nOuYGHdaffMMrNsjfXHfMDctdUHT4QygzyXv4LBILKzeVi8TiRWLxiIp5r3nqDL9evr7Jd0047CWVX/TvqHMSdEfIF6dmwHY8Nui2tuXllwVaumvgUJbGk3BwD1mThtns9jzKDAf58yTmMOKFbyn28v3gRd40a4+Vm58gKBGienc17119LVihIv9cfpjBS9VhPbd2JF8/e3fMlFo/z8eolvLNiAZM2rCbq9sjNgSD/Of0izmy/79y8qaiAYW/8l7I9uj9n+AO8O/Iabv3XW5V6SvnMaFq/Hp/efQtBv3+f+96feNyxeMMWALq3bXHEJt2atXIDtz72NpHkccbOEdoZJ2djhEBJDAvH8JdGMAfZORnc/c9r6DP4WL5atY7N+QX0bteKzi0O7+1/9pab9RdDaoVdeVUHOoN3paasJKzCM4nf76s0w+9VIwYyd8kGSsuiSdsYocwgRaV7v2oajsRYtm4rMxatY1DP9E0u8tjHX1QpOgECfh+tGtenbdOG3HL2YLq2bca/P5nC5MWradEwhxuHDWBgZ687znn9utG1TXNemzKHjTsKOKlrBy4efDzZmSG2FhQRjqYeH7M0d1vFz5FojOuefp0FG7cQc86bMRnw+wznHHEHxdEof/xkLKWxCOf32vvECC3q5/DHkWfx+4/HJlo+HQ74f2eewte53ky8ybwuVhHGLV/JiO4Hf7+xOZs3cdtH/6MoEsGAzECAy3v0YtyaleSVlXJa+078dMjJtKnv3a5gR0kx49etwodxRsdjaZhx8GMrXcH9pCo6nSPlFWZcBPxtMfNBxokH/boiUnN2bS9IuTwWjVNWElbhmcTv81W6EHjrgIHM3byZkqR8EPD5yAkFCe/jAl44HmXJro3MzVtN38bVu1XNwfjXonGURCPEk9sY/RA6JkIL15z2TRpx+7DBHNu6MffP/ZxJm1bRul59bu1xIoOatwfgou496Nm8BS/NnsOGXQWcdkwnLj2+J9mhEBuLdlWdIDBh4c4tu483FuWyT15iYV6udzHY+QAjYD7izhGPO8I7o9zz5jjC58T2ORSmdXZ97jnpLH73xWcVvZIcjruHnM6cpRsp22MMa9w5isvCTFy4irN6H/xF0TmrNvLTZz6gOBzBzLt10OXdujFzzFIKCkoZPKQzN958Gs0T97zdsauYqQtW4/f7GNr7GOpnZRz0a//zg0mVi04AMyL1ffjDDvw+XKYRzfATyC8lEonRsk0jfD7jxM5HfmI5/cWQWqFXv458NWkJezbAN26WQ06Do/Nm87nbdjFu6hLKwlFOGdCZLscc3LjLE/t04ruXncyTb04l4PcRi8U5pl1Thp/SnSfenlplIp9k4UiUhas2p7XwnLliQ5WiE7zC8/Effpt2TRuyo7CYSx94kfyiUsKxGAvXb+HLpWv5xbeGcflJJwBwbMsm/PrSM6vsp2FWJj6fj8rjCD3tmzQEIBqLc+VTr7JoU9KVe/OmvomXV6A+r3ty7q4ifvP+GLKCQc7stverq5f0OZ6hnTvx2ZLlxOOOM7t2pnXD+nzvrfeqzHAI3vT6m3al/hJXHe8sWsCdn42q1A2pKBLh8VnTK47n3SULGbt6BZ9efROfr1vJbyd9RsB8YBCb4HjwzJGM6Fz9wjcWj/Pasrm8vGQOrw2YQ06KC8JxoOpiHwR7Y4F9X50Wkdrt+JO7MWP03CrLW3RoRr36R2du3lC4iw9XLCYcizG8Y+eDvhXHmcccyx1DTuThaV8S9PmIxON0b9aMS7r147nVYyiN7/3CcCQeY2H++rQWnrN2rK1cdCb4AvD8979Nq6yGbC0pZMQn/2VXuJRwPMb8nZuZvHk1fxxwDpd19rplHte0KX8YflaV/TTJrLfX1+5U37vPeiQe41ufPMfivK0VeRhfHMoMt8sb0hIs9eOLwKZYAb/636dkhYIM67r383JV9xM4o/2xjF6zjDiOczp2oXV2fX7w2DtEYlUL4bJIjM15B5+b3506nz+8OabSmSwui/DEtFk0WVeKLwajP53H1CnLeOb57zJ23gruf2M8gcRFirhz3HfLCE7vW/3CNxaN89FHs/nw/dnMq1+QcuCk83s1vMXZfXW4Xojje7enTYfD27p5IFR4Sq1wy0/OYd7MVYRLo8RiccyMUEaAH//mwrR2Nakpn05YyN8fH0087ojF47z49jQuHH4C//edg7sv5TXnD+JbZ/Zh6eotNGlYj45tmhCJxpg4awWLV2+pGHS+5wyxGaEArZqm92bezRtmsyW/sMryWNzRqJ7XAvf8+JnkFZVUSgqlkSgPvD+Riwb2JGMfV9VDAT/Xn9KPFyfPqjQjX2YwwI/OPgmATxcuY1luihntLOnB7u6/JeEo//hs8j4LT4DmOdlcPaDymIjZmzZV3OsuWdw5erduuc/97c2MjRv41bjRex37Ui7mHEXhCA99NYW3ly+kLBajLKkg/+m4jxnUpi3NsipPqLC1pIg3ln7N6l07GdyqHRce053MQJAfjH+P8RtWUBaLkR8PkOOv+kUp5nz4bc/b3jTAGj9xUMcqIrXHbfdfz4KpSygrCRMvz81ZQe549LtHZW5+a8l8fjNlDHEXJ+4c/5nzJdf17MvdJx7c/ThvHzSYa07ow6KtW2mWnc2xjRsTjkeZsn0Byws3URLzWj7Lu9mWZ46QL0CrzEaH67BSap5Rn+1lVScUijtoEPQuKjy56EvywyW7b58ClMQi3DN7DBd2Op4M/95zc6Y/wA3dB/DC4pmUJHV9zfQH+EmfoQB8uGYRy3dt352Dyz9SIYe/yIe53cnZBRxFFuEfYyfts/AEaJmdw/U9+1VatnhZLl5Xp8qf21g8Tq8Orfa5v72ZsXQdf359LElvXQUHlDXyk7U9RizmKC4u45mXJ/PmoqWEIzHCSbn5rv9+wid/vZVGOZUv5mzNL+S9Lxawfls+A7q045z+XckIBvjdb99i1sxVhMMxrFcmLpT6/2Kl1GxGTrMcfvvgNQd1rIeLCk+pFToc25xHX/8Brz89kUXz1tOuY1Ou+M5pdOtVO+93dSh2FZTw10c/rTQhUFk4yodj59GwYRYTZyxne14R/Xq257tXnEy7Vo2rtd/srBD9euyeKS4Y8POfuy5n8uyVTJi5nNHTFhOJxnFJf+AzggFO7394x9zt6ZazB/PrFz+p1PKaEfBzVp8u5CS6l0xatCrllUgzY/nm7Rzfft8F24/PPpmg38dzk2ZRGonQNCebn488jVO7eclp9IJlVcZzJisf91pReTpYsX0HzrkD/nK1o6ikYp+7b0nrdcXNCgb39rR9enTGtEqJf1/C8Rjj1q4i5lKdT/h01XKu7bm7WJ67dRNXj3qdaDxGWSzGh6sW8+85XzCgVRs+Xbu0YrvRha24tuFqQr7d5W84bkwuacYZ9bYk5XKDjKGYb/dEUSJSNx3TuyOPzfo7r/3lXRZ/tZz23dty9V2X0KX/0Xebsx2lxdw1eXTF/R8BYrEoLy+aQ7MGfibvmMv2cCEDm3Tm9q7DaVevSbX2Wz8jg8HtdufmkC/AIwNvZ/LWRUzeuoAxm+ZR5mKUZwzDyPQHGdoi9ZwHh8ttXU/l17P/R2nSREYZvgAj2h5PvYA3xnP8xhUpc49zsHLXdno03ndu/kW/YYR8fp5ZNIPSWIQWWTn8duBZnNrGy80fr1lUZTxnxWtkxbHiqv1pluzafsC5OR53FOwowRqC8ycVn85hEch0Bze+8+lRX3nnJ9XTfRAP7I4xEokxacEqoilm3/CZMX7uCi4+pVfFsnmrNvH9f79NNBYnHI0xeuZSnh41jYGBxnz5hXdrGQdk7oxT3NznzaBUccCOjPzKEzn6fMbgk4+jXvbBd+s9HFR4Sq3Rul0TfvL7i2s6jLT7yx5FZ7nSsijPvjmV8vJs7NQlTJ29kuf/dgNtWjQ8qNfy+3wMG3AcwwYcx7XnD+R3j3/M6k3e7KrHtWvGn743kow0j589q89x3LHzFB75aCrgdXs9/YTO/O7q4RXbNG2QzbLNVVsko7EYjbL3PS7ROcdXq9axLi+fYT2P4exeXRjeo3Oi+60nJzOEGVW6ckOi6CzfNKn4dAYz1m5gUMcDu1ddqwY5XpdaS+w7sS98cOVrr/PkJd/ilI4dD2ifa/KrP+uez4wGGSFyS6p2HYo7V2n8qXOOn0z4iKKkyR+KoxHWF+azbnlepSu4k3e2oZ4vxvn1N5BpcWLOmFzcnK9LG3Jm9pakV8nAsm8+oOMTkdqr7XGt+dnTP6jpMNLu56NGeWPl9qhnSqNRHpk3iayGXuvg6E1zmbx1Ma8M/TGts6p3YXhPAZ+f01v24vSWvbiq4zB+P+811hZvAwdd6rfmnj5XEfKlNzef2/Z4NhXn858l4wGIuhjDW/fgd30uqNimWVY2KwpS5OZ4jEYZ++5q7Zzji81r2VC4i+HtujCiYzfOaX9c5dwczKg00eDuJ1PlNtCGQdzhYjB74yb6t21T7WP1+YxmDbPZuquIaBbEA94r+ssgWAy3/ek1/nnnJQzo0b7a+wRYvy0fi3vdWquIQ7Bw90H4fEa9nEzi+VVvSRZ3ccKR3d8LnXP85rlPKE66RUpJOML6rfls3baNDByRHB++KAQLHZl+R1njxPBYBxm7HJnbK5/AYNDPpZelvK3zEaXCUyQN9nY1bmNuHlNnrkjZFRMgFnPg99bEnaOkNMJz73zJr7937iHH1LltM17+0w1szy/CZ0bjBnsff3G4XXt6fy475QQ2bM+nSf16NMqunLBuHDaAOas2UhqpPAlDz3Ytadtk30X33z+ewBvTv67oZjtu8UrG9TyOv1x2bsV7cPmA3nz89ZJK+69ge/yb9PPXGzYfcOHZs1VzNhUVVE2k5n2B+e2YsYy79Tspnxt3joKyMrJDoYrxHwADWrdldd7OlLMU75m0Q34/3+8/hLsmjq40qUW5MzvubqnYWlLE+qKqt0iJOQfmKp2Suevb0zCzlBnFTcj2xyiO+YnhY1hwO5F4gKA/BAShwR+xYO+UxyciUpP2lpvX7Mhjysq1kE2KLpOu0iQ5cRwlsTDPrZjAXb0O/WL5cfVb8fIpP2F7WQF+89EodOTuLXlTl5O5+thBrC/Oo2lGNo1Clb8X3Np9CPO2b6IkqVU0aD76NWtL63r7Hqbzh68+483lX1Mc9Z47Zt0yLujUnb+fMrJim2u69OPTdUsqdcUtZ6VVBy4aBjGYvzn3gApPgGOPa8amxcWAw5eo8QJh7+0uDUf5+3Njef1vN6V8bjzuKCwtIzszVGkCqT7HtmbDjnzicbwL2OWfnbgjoyhOsGj35yYY9HP9hYO5542xVebecA6G9upU8XtuXiFb86t2g445R0ELHzs6e9+hnEH9dTEydsUJFYDzGxbzcnegJIbP5w1bC4UC/L+fjaBLl4PrUnw4qfAUOUycc7zz8Wyef/NLduYX07ZVI35w0zBOG7L7hsfT563BH/ARTTH5DCSuViWJxx1zF284rHE2bVgzN0zOCAY4tlXqAe1Du3fixyNO5t+feBMkRWJxurdpxkM3X7jPfa7Ysp3XvppX6bYmJeEIYxYs46ohJ9C3g5eY+rZvzR1nnszDY6cQ8PkoDke8Im6PMZ57ih7g7aZmbdjI1DVrKd91ufKWTwzW5+dTHI5U3Iy73Kvz5/HA1MkUhsvI8Af47oCB/HDQifjM+NGgIXyyYilF4XBFkWnAaR06EvD7mbRujdfSGcrgz2eczfBjOjMrdyNvLllAadSbATcUCHB7n0F0arj7Cn3I7095s+2KKX+TDmJHcTZjl3aje6vNNMosIb80i8VbWjEq2oC2Z57JwKb1wd8RM6UVEak9nHM8t2Q6/1kwlR1lxXTMaczdA4ZzVtvduXnSytUEIrbXXJCxx+1PYi7O7B2rDmucTTPqH9b9VVeGP0jn+s1TrjurbRfu6DWUh+dPIujzE4nHOL5xS/4z9NJ97nPxzq28vmwepUkFZXE0wgerFnFtt370adYagEEt2nNH76E8NG8SAZ+f4mgYh8Oft+88EotWb+hJuRnL1jNr1cbE+5vU/bWeI7TLW7Jm004i0RjBQOXmy9cnz+U/H0+luCxMKBjgO2cN4pbhgzAzvjvyRD6fu8L7TpFo+fQZnNb9WLLXlDF77Wp8PiOnfiY/u3Mkg4d0ZuaajXw8bbF3H3XzbsP2nZGDadNs90X2UMCfclLGeADCDQyX1K22sI2PzO0x8BnmHMTBF3Nk5GRy98/Pp2PbJrRt16TS3RBqkr4hiBwmr703nWde+4LSRNeIDZvzuOefH3HvLy7ixAFeK1NWxdWyWKVWqoq/hSmuxLZunt7Jf2qLG4YN4LITe7Nk41aa1s+mQ7P9T6wwZdmalBPulEaiTFiyqqLwBLj5lAF8q28Pvlq1nnfnLGDyytVUDCtNFIWVJl9y8PjUabwwczY9WjZnS1ERsbjj4l49uH5g35T343x3wQJK93Jrl3JBv5+MpMQWd46Pli7h3omfV7RQRuJhHp/xFT4zfjjoRDo0bMR7V1zLA19MZtrG9TTLqsf3Bw7hW127Y2bsKiulIBymdU59iiJh7v9qEp+vX0mTrEzaZLegZ7MWXNq1J31atK4US6OMLPo1b8PMLRu8Vs7yw0++cpt0TgpKs5i+5piKdT6MQS1bckKzXtgh3p9URCQdHl/4BY/Mn1LRaremcCc/nvwuT5x2Oae29sYa1gsGCeDHn2/EGrpKg/QzfHGCoaoXi9tUc4xnXXd7z5O4tkt/FudtoXlmDh3r77978cQNK1POM1AWizJ+/YqKwhPge8efxGWdT2Ba7lreWDmHqVtWEW8RwZX5oNSb2dYXjlNvYxR/2FHa2Mej477guckz6dGsOVt3FuFwXNS/J9ec3IdQilz0wbQFqWf4d14x549CRtBPIKk4i8cdH89cxD/fn1jx3EgszFNjphHwGzedOYiOLRrz0i+v4ZH3pzB7+QaaNsjmlvMGc95A776nhQWlFBWX0bx5A0qKy3jm8c9ZPG4R7TJ9NOzaii5dW3H+iT3p2bHyWNkm9evRvX1zFqzJJR7fnZsjmeACVMrP8Uwfu9rHabjWQQzMgfmMbr1aM2TwsSnPR02qXdGI1FGxWJwX3ppWUXSWKwtHefKVyRWF5ykDO/P3J6jSPzIY9NOnd3vmLFpfqZ9/ZijADRcPOQJHUDvUywjR75jqTyhVLxSsKOSTBfw+sjNCVbZvkl2P83p1pVe7llz8+EsUle1uQayYYKicQWFZhIJwhNzCoop1D0/6glFLlvH69VdW6nIDEI6lmpx+t8xAgMt7HY8Dnp09k6dmzWBzUSE+sypXN0uiUZ6YOZ3vDxyCz4xjGzfh0ZEXpdxvg4xMGmRkEo7FuPS9l1m7K+//s3fe4VWU2R//vDNzS3pIIZQQei9SRRAQUaogVrCv2Ouq6zZXt7v607Wua+8NKyBSFAFp0ot0CIEUSihJSM9tM/P+/pibm9zcGwgrJeL9PA8PyWTKOzeTOe95zznfg8cfVT/qqaJJlDPE6azmP8MncPWcqRz1uDClaRlixcRrGiFhW6FAemw8B13l6KaBKWBLcT43ff8pUy++LuTzpCJfiQAAIABJREFUiBAhQoQziW6avLptRVCqKIDb0Hlu85KA4zmycwf+/u0iVI+KUigxHVbWh1PXGDYogc1VldY70Y9TsXFzuwtO672cSWJtDvqnNrz+MdpmRxMKvjoFIpqiEmMLFbdJccZwSeuu9EhuxoR5b1GlezGbelH3OXEU6TRd47JskAnxKuhOH6UdnPyQX4H0C/i89N0KFm7bzft3TkJRgldOfXo9trmWwv/lI3phGCafLP6R91Zt4FBlJSoC05BBWbRur87bC9byqwv7I4SgbbMknr0jfHZWbJyT2DgnXo/Ofbe9y+GDJfj8c7zKAxW08tjpNjm8QNNTt1zCrc9/Tmml25ofSNBjTcuhr7Mw7G2mEZMWS/mecnSfiR6tsK60gPtf+opXH7gy5PM4k0RmCREinASqXN4Qp7OaAwdrhGFiouw89cfLiYmyEx1t/XM4NH5/10j+77cTueDcjthtKk67Rnysk9/fMZK+3U+s2B2slbote/JZs2NvveM6G7i4e8ewqaKKUBjXq3O9x6UnJvDF7ddxcZcOKNWiQ6Kef9X4L+PWdbIKiliYtYfvdmXx+uo1fL8nG8M0Gd+lM9FhlGsFYFMVRnboQPdmafR+7WX+uXQJhyoqLEfPDO+uunz6cSOotfkmJ5MD5WUBpxMsB3bZgVy2Fx0Je0zzmDiWXHU7r42YyF8HXsSXl1xnafCGiXhKoMBTjoGBUKwAvcvQ2Vp8iEX5exo8zggRIkQ4HZR4XUEqtbXJKTsa+DrO6eCVqycQY7cRpzlIMJzEeO08OW4Uzw26jmFNu2FTVJyKjURbNI/1vILeSW1OeDymlKw/coAVB/Nw62evbR7XunNYR08Rggltu9R7XOvYJswYOYWLW3ZCVYAYL6nrXSiG1d5TAIoBtiqDqCM6zmIQunUlt09nZ34BS3ZkM39zFm9/v5alO3IwTJOxA7oQZQ+nKi9wCoURAzrSPrUJY294iddfWYRnfSkx+w0M00TawKwjHlTh8lpCVA1k8cJtFBaUBZxOAI/bx+rlWeTlFIQ9pnlSPLP+fgv/vm08v7tqOO88PAmznmwkU8DRoy6qYgTeRBXTLnB7dbbmHmLljrwGj/N0EIl4RohwEoiOsuN02KjQPSE/a9UiOC2lX48MZr19D+u35OHTTfr1yCDWL2/9919fQmWVh7IKN01T4v6nCFLWvgIe+M8MKlweBFYk7U83XszY806tNPuZIDHayX+un8CDU2ejCIHE6sn1xJWjaJF47BTltslNeGnyBF5YtJy3Vq7DW0+7knCKe5U+Lw/OmYvPsFKmNUWhVUICn183mdGdOvLtrizcPh+qoqAKwe3n9ueGPn3ILjnKlK+m1xL9CT177e+aOJ1EnUCazOqD+wNCDkHnlLDxyEG61dMIfX9lKUvzc8gpP8rUrA346pG3t7LBQ1dOq3Qfyw7mcHF6x9CDIkSIEOEMkWiPwq5qeMI4n+3jg1Nlz2/XmpUP3cXK3L34DJPBbVsR67Bs85N9rqXC56bM5yItKgFVnLht3nb0MFMWfEmlz4sQlhP61OCxTGh79tnmJGc0rwy/jPuWzETx2wzDlDw35BLSoo9dy9o+PoXXhlzNqzlL+WLpGhSfN2QfIcFR4sObaMNWCV5/eWSVx8fDH8xB1/22WVXISE7gg/snc2Gv9izavAeX14dNVVAUwa0Xn8vVQ3uRnVvAH/81HbdHD/h19gqJOGBS0Uq1nE+JFa4zoWls9AmlsG7akIfbFWqbhRDs2HaA1m3D19juKyllUX4uecUlvJe5sd62cIoiqNtOG8Dl8bF6Rx7nd2/T4LGeaiKOZ4QIJwFVVZgyeTBvfrwMt6dWv0q7xp03DA3Z32HXGNyvfdhzxUQ7iIn+3/os6brBPc99SXF5sFz3vz6YT+eMprRrEV7c5+fMkI5tWPbInazasxdDSga1zwibZlsfdw8dyLLduWw5Ej4iCLXbe9aI7nj1mlRU3TTJKS7miUVLeHrcGK7p1ZMFe/YQY7MzoWsXWjex6lV/v2BeGKVZ6+zSb9QCpUUCVE2h2O0iKaphCsTpsfE4VDUo4glWW51mMeH7av6Qn8ut33+JzzRqJUWF113ukJDMQXeoCq5dUUlxnhnRqggRIkSoD01RuLf7+by4ZVlQuq1T1Xj4nOEh+zttGhd2DN+jNNbmJNZ27PZe9eE1DG747jOKPcG2+XfL59I9KY12CWdfveiI9Pasm3Qfyw/mYSIZ0rwNMbaG2+ZbJg1i7Zo97DtUVe8+AlB8fuXhaJCaxPCaaBUCgUA3TLKPFPP87B/416/GsDE7nyVbsolx2hnXvwst/YI+H09fEzR3A8u5tVVK0CXSJjCcNdJ+MlahpMpNYnTDnodmLRKx2dSgiCf427ykhnfEl+7J4e5ps9CNGtsskJaybx06JCVRXFBmzUtqYddUkuJOXweDhhBJtY3QaKmscJOVeZCy0vpfOo2Jq8f35f5bLiQ1ORZFEbROT+Ifv5vAgN5tTtsY1uzcG1QjWo1PN5ixdMtpG8fpJspu48Ku7bm4W4ewTmfe0RIenDaHwc+9zvjXPmDm5h2BFF2HpvH5rdeSFH2cnmRIq0l0dapLmJKJOZm7EELQL70lf7hgGPcNPi/gdALkl5fVfwH/+UStcx+urOA3878J2dVj6OwvLw1Jw72qcw+0OivxihDE2ewMS28bch6voXPL91/iCXI6a+64dvw1WrPx9OCxQW1eqlGF4Mp2kRYqESL8EiivcJO55zBl5aH9CBsjt3cdyJ/6XERaVCyqEHSIT+HVoVcyuFmb0zaGpfk5YVN+ddPks6xNp20cp5tom52RGR0ZndEprNOZW1DMQx/PZti/XueyFz9k9sadNbbZYePNF35FfHyobZYCvIlW6qxhl7hamnhSTLxNJO6mksrWJqZaY7/mbNiJEII+7Vvy4GVDuX3MwIDTCXC4ILxtlgF7LC3jLCzl44Nl5Tw6fV7I/h6vzqEjpXjqCBmNndAbVatjmxVBbHwUffqH2ma3T+euL77GawTbZlOx5iKytm2223j80pFB/VFrX+OSgY0roh6JeEZodJim5M1XFvL19PVoNhWfT+fCi7rz0B/GoWnhuvQ2DoQQXDrqHC4ddU6Dj9m9r4D3Zq5m995COmSkcPPEgXRoFT7loiGUVbrD1jwapqS4/OfhwJ9s9peUcuVbH1Pp9WFKSVFlFX+du4C9xSXcf8EgwIoIvjH5Mm6eOg2Xz4cRpuZS0aw6imNh1JMGU8156a3ILi4Ony4j/EYOABmIfi7el0NOyVHaJiYhpeQ/G1bw+qa1AcNzS8/+PNx/CIoQNI2O5YNLruaBhbMpdFVhIuncJIVXLr40xGFcc3gff1jxTZBgRtBgaskuRWs2Zo+/mXbxSUy96DpuXfIFZV43CgJVUXhh8KW0iPllqC9HiPBLxTQlL73zPV/P2xSI3oy6oBsP3z0qSA20sSGE4PpOfbm+U98GH5NXuY+vDnzD3qr9tIlpxWUtx9EquuHCd3Up8bjCtsfQpUmR+5dpm/cdLWHSy1NxVdvmiir+On0++cVl3HHhuYCVTfb4s9fyyAMf4fboVhsVAb4YFW+ChhRQ1aaO0qvflrqbmkQftOaMx7PNvbu34sChEgwjeD8hrXNLVVj22SSw8LwwM5t9R0tplZSAlJJ3pi7ns6/WBgYx+fL+3HLt+QghSEmN58nnruXJv31FaUkVppS065DGn/95RUibk1V79/Ho3O/wKoZlhk0CUU6hCMzqHtumJMZp56tbbqB1UiKvPXglD70yk0q3F4RAUxSevG0cqYnhs53OFCJ8D7dTQ//+/eW6detO2/Ui/DyZ9tlq3n1zCR53TVqMw6Ex4fJ+3HnfxWdwZCeGlJIDh0uQEtKbJYbUxm3elc+v/+9LPD4j0NTaYVP5zx+volenE2uMvOdAIS9P/4FNu/MprXSH/FxRBP+4dQxjGtnK1+ngL3MWMG3jVvQ6zqRT01j+mzuJrRUhrfB4mbM9k2mbtrLtkJV6G+d08OjI4Tw2fz6VvmOLQXRrmsrXv7qx3p8fqihn7EcfUuZx13Lr/C6k6k9vFXV6aEqItduZN/lmvsvL4uk1S4PSdaM0jV/3HczdvWvUj6WU7C8vxa5qpNVKsdVNkyrdx/y9u3hs9Xdhm3bXYI1QFYJL23Tj+aE1qn2mlGwvPozX0OmZ3BybcuYWhIQQ66WU/c/YAM4CIrY5QkP4ePpq3vtsRUg5ydUT+nLnjT8fhVcpJbmlluhfm4RQ27yzLIsnd76Iz/Qh/amNdsXGo11/Q8e48Gm49ZFbuZ+peV+zpXgvq/ckYsrga6lC8NKwiYxrU78Y3tnKn7/8jpkbtge18gIr3XnZY3cF9bqurPSwaP5WZi/ewrbCIgynSlyMk99efyG/3vRNmBIWLNu5x2rH0qNVGp88eF29YzlcUMaUB9+jvMoTWHOVAirSBJXpitUjUwdRW+xdQlyUg1n33Mji73fyztQfgv42nA4bt91wPpMmDqgZkpQcOliC3a6RnFKTYqubJi6fj28zd/GPhTWt1QITBaPG+ZRIpM16di7v3o2nxowOnMc0JTv3HUE3TLq1TjujC0L12eYGRzyFECqwDjggpRwvhGgLfAokA+uBG6WUoRXAESKcIF98sirI6QTweHRmf7WB2++5qFHJQtdHVu4RHn1+FoVHKwBISozh8Ycm0KVdjWz2cx8uCuorJaXE7dV59oPvef/xGxp8rZz8Im5+4hPcHl9YFTmJJS5T7goVPmpMeHWDH/fmI6Wkb+sWJ6331Pp9+SFOJ1iiAzlFR+nZollgW6zDzuQ+PZncpyceXafC46VJdBS6afLHeaFpNbURwHOXjAvZPjsrk2dW/kB+eRnRNhttmzThSKXGwfLymhYuin98dZ1O/88rfF6eX7ucJQdyQgysS9d5fdOaIMfTZ5oszc9h2u5taIrCpE492VVSyIeZP+IzDAxphraPCcIajyIETs3Gr885P+inihD0SGoW7sAIp5mIbY5wuvhs5rqQOjiPV2f63I3cccOwsMJjjY2tRw5z77ezKKiqBKBpdCyvjJ1At9Qa4bV3cz/Ba9b8yUgkHtPLB3mf8c8ejzT4Wnsr83l0y7N4TA9SQGqiQkFJHKZUqk+MaUKZu3HbZp+ps6UkB4CeiW2xKSfJNuflhzidYGUg5RUW07VFze8kJsbB+Mv6Mf6yfnh8OpUuL4mxUeimgb7hGNFMf4bsE9eOCfnRrG07eH7pCg6VVxBls9FheCpiZylH95dj2KCypYInUSCqzXOdDmNWb2sPry5ZzY/Td4b8bbg9Pj6etibI8fQZJov372Xm5h3YVJUr+3Rna8kRpm7djG6aloquUasOsjr5yC9qJJFI1bLBUTYb954X3G5PUURIT9DGxok8PQ8AO4DqfKqngOellJ8KIV4DbgVePcnji/ALpLwsNGIHloErKakkKalxpQ3Upcrt5d5/fE5FZY0xyT9Syv3/+JwZr9xBrF84aFdueDGbXXlHAhHQhvD61ytxe/WgfpS1pWGkABPJjGVbuXp47//tpk4xq7P38euPZ2FKGRj3s9dcwtBObX7yuTOaJLC7oChke4XHy4Mz5nLroH5c06dXQHmvGoem4dA0pJTcMm06Xr+CbbjfSmpMDC9OGMeKA/u4/9s5eA2dLqmpuHQfS/bmBlKsSj0eNh45hE1RApYlYHZFOP3cGr7J3oVbho+4lnisPl/V/UB/9d0XbCzMDzip6wsOgJChRj7sDVkr/KnOaAY2y+Ch3kNpG3/2CV+cRURsc4TTQnmYbBoAl8tLmbeKBEfjFhgr93q47qvPKffWOJV5ZSVcO+Nzlt98B7F2O6Y02Vu1P+zx2RUn1pZi6r6v8Zg1vaLTPFVoexwUx9vw2lQMm0Aqgk+2b+aaLr3+19s6paw7uou/bvkgUMIjhODvPW+if1Knn3zuVkkJ5BUWh2yv9Hq595tZ3H7uAK7p0TNkLuSwaThslm2+/a1pqEUmvkQJtYMSErQqSIuP5ZmbxrMyM4+H35+N1zDo2DqVcuFl2d69Advs83jY4D6EranA20JDGtJKcZVYtvkYvu3cLZlEl4Wvdy4prdlumCa3fDCNbfmHcfks27ym4ACmo45tVkEaBBzemsmcRFEEyXFRnNcqgwfOH0RGYo2GxM+FBsVghRDpwCXAW/7vBTAC+NK/y/vAZadigBF+eXTpFj7NVErJtZNf5r573ufAgaNh92kMLF6dFVInAFad5YIVmYHvY+tRro2NcpzQyvGWPQcDRkECUsGqD1SF9bUQSCHw+BreD/J0Uupyc88HMyl3e6j0eKnw/3tg6iwKKyp/8vnvOH8AzjDRUwnsKy3lqYVL+du339d7/Pzde1i5d1/A7tSW3IlzOJh2w7WsvOdO3t64gX8tXUxmUQE5JcXMzdrFotycsHU9PtNEE1arFbWBv+oqn48OTYJViatFBlKjo9lUYD0Hy/Jz2VR4sFZkVGJIM+zKcl0EEK3Z+WrcTayZdD8vDZtIu4jT2WiJ2OYIp5PO7cNnOcgkncuX/5N71v2XfFfoIl9jYU5WZtj6fV2afLN7F2D1gI5SwyuVxmgnpg66uzw3UIvv2R5N+Xtp2Fc5SPtOJX0utJojUV3gPmbJw5mjxFvJY5veo1J3U2V4qDI8VOpuHtv0HqW+k2Cbhw/AaQu2zRLQ7ZLc8lIeX7KYfyxeVO/xC9Zlkvd1LqkrdbRKED4JUiJMSHQ4+er6G1j4tzt4e+Ea/m/BEjbrhWR5i5mTlcWS3LxQ2yzAZ0pspkC1CdTaxvkYdrrS5SWjZbCdlIDuEMSnx7It7xAAS7Ny2X7wSMDplEh8tjC2WfjncbVQTEjw2vnqxutYefddPD9+HG2aBLfq+7nQ0OTfF4DfU+PzJwMlUsrqv5b9wP9edR0hQi3uuv9inE5bUEqtBExFoOsmO3ce5Nf3fYjH0zibLxcWV4QomoGVdlFUXBH4ftLoPjjtwS9dp11j0ug+IcdKKVm1LY9H35jLH1+dzZKNezD9BrRFihXoCDid1cprEPhfETDm3PqbNp9J5m3ZRe1IX7Vj5zF0fv/FNxwoDm3fcSL0SW/B05eNISUmGlWprpEg8PZz+XSmb97GkYqKsMc/tXSp9UUdwyOBid26cE7z5mwvOML3OdkB1cJjp7FaeKVB3+bNuaPPABKc1RMdUXPyOiiK4OH+QwJOdEBlV4VSw821337G1XOnsnjfnrC9PI+HJhRu6NSb1VfdyzkpzU/4+AhnhIhtjnDaeOC2ETgdtW2zBE3CBeXo0mBb6V7uWfdfvGbjdKSOVFXiCvNudPt0DlfVvP/HpI3ArgQrsDoUO2ObXRRyrJSSpbm5PDB3DvfPmc3C7OzAQnCKw3JGpAlVs5NAr5ZEt6JZig+St8NlHRqn9sLiIxuD1FNBYld0VLWKf+94jyPunxYA6Nc2nX9dNYqkmChUxd+H2ynx+cVmXbrOp1u3UFgVXnzp7dcWI3wSmwdaLTBous4kcYdJ/C6T65r3pEfzZmzbd4jFB3KpjDFR/I/l8dZ6DY9kUHIr7ujbj3h/H9fqEpgQ0yxB8wluv3kYDv98zlSgKk3FlaJyRPNy2wtfcNsLX7BsVw5V3lrP37E8sFqDtCkKN/XuzZJH7qBby8adRtsQjut4CiHGA0eklOv/lwsIIe4QQqwTQqwrKCj4X04R4RdGx87N+e9bt3DhyO4kpcSiaApSE4E0CiklHo/OsqWZxznTmaFnp5Y4bKERtiinjZ6da6K5Uy4byLih3bDbVGKi7NhtKmOHdOOWy88LOfa5Txfzu/9+zbzVO1mwbhePvTGXx96ci5SSW8cPDHFgg/C/LK+/uOGKfqeTMpcHd2AFkMAL3gRW5+xnwksfsCZn30+6xuiuHVn20B30bNnMcs5Vgl7sDk1lx+HQ95NH19lbUsvxFcH/MhIT+TpzJw9+MzewalmdmXMspF+1dv2RfD7YbjWFtil1cm/rWLjmMXGMbN2Bj8dNYnCLDBw2NdB6xWMYuHQfmwsPsaXoMA614WI/UarN6mnXZyj/PG80cfb/rYdshNNLxDZHON107dicN565gYuGdCEpLRqlow+uKkakW5NpicRt+FhWsPUMjzQ8/Zq1IMpmC9nutGn0b1azPnNlqwkMSRmITdiIUp3YhMYFqYOZ2HJsyLF/XfQ9d8+exazMTObs2sUDc+fw23nfIqXk6lbjsCt2zGINqYcaBQFEHZb8qkfoYnNjoMxbhce0frcCSZzdg1PT0RSdrWU7uX/D42wr3f2TrjGmV2eW/OlO2nVNxd3U73TW+qjsqsrOwtD3k8vlpXB/aY2QrYSYg5KknZImu0zSE+OZs3oHj777LVSYVkRU1gj01IsEewls2nKALxdsRi2VAdts2qlxPv0r5IoH0uPjGTqgA8/+42r69srATLMj7QIUS7vC7dXZknOQ7Nwi7LVtc33pu1KChCibRpSm8fDQITw2ZsQJ9SdvzDSkxvN84FIhxDjAiVVH8iKQKITQ/Cur6cCBcAdLKd8A3gBLOe+kjDrCWYmUkpUrdzNzxjqqqrxceGE30lom8tGHK6i7RuVyeTl8+KdFwk4Vvbu2pHvH5mzZlR+IfDrsGp3aNKV/j9aB/VRF4fdTLuauSUPIP1JK89R4EmJD+1Xl5BcxfcmWoFRZl8fHsk3ZbNqdz3nd2/DIjRfzxAfzcRnhWmNYdRnRzsb50jpSXlGrzjH4f1NKXD6dP06bx8KHbz1uCrLPMHh3zQY+27AFr2EwqnMH7h82iMQoJ4oQdGqawqaDh0JSbHyGScv40HYgslbNaTgW78tlff6BsIp6gYrNOnWUkhoBIUNKKn1WvZFdVZnYvguZRwvZXVKEKU1LuV1YCryPDxtp9Qht1pIPx11N5w+eo26GjscwWHv4AEKVtQYgCOxY52YcqsafB4xgYttuJ9TYO0KjIGKbI5wWpJQsXb+HLxduwuX2MvK8LjQdpzD1wKKQ96Pb8HLIFVq31xgYnJ5Bz9Q0Nh05FOiB7NQ0zmnajIEt0wP7qULl9nY3cm3GFRR4Ckl1pBCrhdav7iws4Mtt24L6KVf5fHyblcWN5/SmX/Me3N5uMq+VfVF/Ly67ILqRvnvzir2YpkBRJA5Vt8xWIClH4ja9vLjrQ17v/7fj2mavYfD2rlV8nv0jXtNgbHpX7u8+lAR7FIoi6Ng0mW1FR0LSTn2mSYu4emzzMXQRFq/dw9acg7i8OjYEqlviSbF6cAosoSBZZwEaCVo5CBMMTCoNEzzg9KqMGdiZXQWF5O47iuKWmKalCejQNP589UUIIejVLZ1//+0qBj/0X2Qdp9KrG2zMOkB5BxNM0CpB9SooLokZFTwOKcARLXhsyHAmdOwapO57NnBcx1NK+QjwCIAQYjjwWynl9UKIL4CrsNTzfgXMPIXjjPAL4M03FjFzxnrcfkXbPbsPk5gYY02aw7zUWrRonPntQgiefeQKpn+3kdmLtiIljBvenatG9w6ryBsf4yS+bfiaEoBV2/LC9uZ0e3z8sDmH3h1bcsmgbqSnJjDl6c8C8u/VSMBua7z9T7/bngUcOz21uMrFgZIy0pskhN/Bz/3TZrMiZ29gIvDpj5tZvDuHOXfchNOm8atz+/D1th2BGguw0li6pqXSITUZwzRZsXcvRyoq6duiBW2TmjAgvSVr9h8IcVYHpLdkw8H8Ok6nZQhDNHvqigSEuU/TlKTGRPPiqF+xv7yU1zeuZcPhfNolNOHOPufSI6UmxcYkjFBQ3Yv5nVs7ClGajUrDg17nmGibjUkdeoX0+IzQ+InY5ginixenLuGrRVtw+ctbsvYW0CTFiTlMoDiD3ymGD1raUs7EMI+LEIL3J17Jh5s38uWObSDg6i7dubFXn7COU6wWE9bhrGZZbl7Y/pBuXWdJbg69mzdnRNNBRHdtzm+jP0OrkIja7VQ0E6URN4KamZUN0XY0u06M5gmbyVPsK6PIW0KK49jzsbuWf86agrxAPevHe9az6GAWc0ffiUPVuLVvP+bs2oVRy57aFYVeac1o16QJummyfN9eCqsq6de8BW0Sm9C1awu2bTsQPD8S0Ktva9blHsJVq+RJMQXCZ0Utqx1P8Duf1fhADaP/LXVJa1s8z90+jv1FpbyzaC1b8g7RrmkSt4wYQOeWNb3XDTPQIC3MifwLwiro8eCoUkjAxlGbG92QAfVaaTeJiXJyVdceqGehbf4pmsh/AD4VQjwO/Ai8fXKGFOGXSEFBOdO/XIvPVxOx83h0CovKA6kNtapKEBBoVdIYsWkqk8f1Y/K4fj/5XNFOu1XkXieopmkKsVE1K6W92rcgymmjyqOHvPY8psHR8iqS4k5MHOF0UO7yBNVchsM0ZYgIQV12Hi4Icjol4DVN9paV0ve5lxnZuQOPXTycV666lD/Nmc/RKquJ89B2bXhq/Gj2lpRw3WdfUObxIKXl2F3SuRNPjBrFpE8+xeXzUenzEWWz0SIujk5NU1hzODSYVL0GG2J6BLRPTKRHWjO+y9tNVZ2eoLo0OVRpPdPpcQn8c2j9PWttikq/pi1Yd/hAneuEGjybprLh2vv58+r5TM/eipQSTVEQQvDWhVdEnM6zj4htjnDSOFhYxrSFm/DWss1ur07B4Srk7mhE10qEf/IudZBlGod3+hptZbFD1bitT39u6/PTPb5Yux1NUfDVcT7tqkpMrZKFc9Nb4xkOtg0+OGyzejYbAnFuFbZzXZT5Kom3NT5F4DKvB12PxhGt08TpBkIzqkxpYleOHZHbevQgawv2BpxO4QXHVoWKfBcXff4SF/bvxEM3Xsir4yfwyIL5FLtcmFIyvG07nh41mtySYq6Z/jkVXg9SWplCl3Xuwu9+O44HHvwIj8eHy+XD6bTRLC2B1C7JuFYeChmHrQw8KbXWgQ3rloQN2qY0oUdCU5b+uCfIYQXQDZNDJeVhK5VtAAAgAElEQVQApCcn8Jer6rfNTrtGt4w0tuYeqlMdK/HVacigJKqsvP0uHln2HTN370Bi2WYFjbfGXH5WOp1wgo6nlHIxsNj/dTZw7skfUoRfIlu37MMMs3Ko66a/z6E/W9D/l2wKmPrJKiZe2hf7seobzwJG9OvAM5+EKrspQmHMwBrBICEEI/t3Yuaq7cH+hwC7prJ40x6uGNLzNIz4xOjTugXL9+ytlRoajCoEPVqmkRJ7bMO85eDhWmlABEUVfabJvJ1Z/Lg/n/l3TWHxvbdypKKSGLuNWL94wPWff8HhioqgyOY3mbs4Nz2dJbffyrys3WQVFeG0aQxv25bFudnYwkw6AgOoLQ6gKtzQszd/HjqcAxVlfJOzK+SQaM3G8Iy2x7zH2vzf+WO4YvZHeAzDb9D9465jqyp1L4aUPDFoNFO69mP5wTwSHE5GteoYSa89S4jY5ginio2Z+9HDqLT7DBNzUxwUObClu1DSPJh5Ufi2xPJm0hquuOAcbFrjzbQ5GYzp2JHHlywO2S6EYELnzoHvFSGYcEErlnZfC8UqVCiQqiOiJHbFzorCrYxpPjDkPGeafk1bsqFiFwgocTlJia0M6liioNAlrh3xtmO3uNtSnF8TBZSQsMyBWikQUqAbJt+vyWRrVj6fPTOF5bfezpHKSqJtNuL8tvmqLz/hSGVF0PTg612ZnNuyFZ98dDdLlmayd38Rtlg7557bjsVb9mBTFXx1nlvFxIp6OgnYaJumcOOA3vxhxDD2F5WyeH1ozWq03cb5Xdo0+HP76/UjmfLc53h1A49PRwqr96YrNXiCU+71gBA8dcEYbunZj5X5+0h0OBnVpkOjTb8+GZzdM/YIPxt0w8QwwsRutFq5ibVDnkBphYvFS3cy6uIep2mUZ4a4aCfP3Hcpv395VmCbYZr8/dYxNEsOrn1ITohFhFFekxB28tAY+N2YYSx/5aN6f56elMBzky857nlaJMSF9OKs7fwZUlLq9vDtziwm9uhKWlyNsdxfWkp2cXFIOq1L13l9zVriohzklZfw5sa1GNLkuTXLsStq2JQagSDKpqIIgaYqPHjuYC7t3JUmUVb9bnpcAtd068XnO7YGFBadqkar+ATGd2i48nCHxGSWXn0Hz/34Ax/v3Gil0dYSNK5GFSIgjtAxMYWOiY0zDS5ChAiND69uBBTUq5ECfNFYL5sCB0aBA2RNmmKh28X8zVmM69s4ldRPFk2ionh1wqXcO3tWIFXXlJLnxowlLTbYGUuLikERIJMMSKqJHEqsqGFj5I/9h3PNUmuRtMQdhdOmE+vwIKVVzNMyJpWHu9x83PO0iE5AFZYNsh1WUNwiKOXYMCSlFS6WrNvNyEFdgj673JJi9pWVhlhal+7jtfVriHJq5BhlvLd2I9I0MRasxG7Twia7CgHOOA2hWjbx1xcMZny3LiRGWaVOrVISmTigO7PWbw9EPR02jYyUREaf0/C+pe1bpPD136bw8oLlfL55E27Am0DIorCmKIE5S+ekVDonpYac62wk4nhGaBRER9sth0nWilZV91CqPZOWltHDatfEytV7znrHE2Bgt9Z898JdrNu5D8MwGdA1gyhHaHrLRX06MHXRBtwh7Vwkw3q2Oz2DPUE6N0sl1mGnwuMNWVxIiHbyzQM3N6iv6aA2GSRFR+PyldVb/1jl87HjSAETCZavd+s6YcpvAcvwPfztN/5azpqootffOsWmKDg063ehKQpvTJiIXVWRUtKjaVrYdJm/DRnBgOYt+WDLRip9XiZ06MJNPXuH7Td6LBIdUVzZoQfT9mylUg9TnAJc2q7bCfWFjRAhQoRqYmMcAX0yISWYYERXK7QEK7MYGpYiuQI/7Mg56x1PgGFt2rD2rrtYuW8fpoTBrVqFVc4d1rQ3s/JX4DGD39MSk3OTu52u4Z4QvZKbEa3ZqTK8gOBQeTy2Kh2nppOgxfHy0EcaZFuGpLUjwR6Fy/ChlSuB+sraVLl97NlXyMhBwdtduo5Sj/jD7pIiHloyG5dPJ6qpIDZHQUXg03WkTWC3Kdj8JTp2TeW5OyegRKlIoEezpmFt82NXjaB/h3Q+/WEjLp/OuD6dmXz+iUfvE2OjGDukC5/pG/Eeln5xqeD7uLJL4/y9n2oijmeERkFGRjKaXcPr0S2HM8So+akjzpKaHHe6hnjCbNtzkGnfb6KorIoL+rZn3PndcP4EdTKHTeP8nsdOxeyWkcbVQ3vxxbLNeHy6P+qmcu+EwTRLaryf1R1DB/DKktWBtioIcNpUxp/ThZyiYtqlJB37BFjpTB/fOImHZ85lw/58QitdrdqbtDApuy3i4vDq4RWBTQgICNVN4RVAjN3OC2MuwaGq9GvRskE1k0IIxnfockIRzvromdyMGJstrOPZOTGFpweHtgCIECFChIbQpnkSNqeKt0pH6IAJpo1Q+ywEUpNWywkJaQnHTr88k2wvzWV2/gpKfBUMSenFxWn9sKv/u212ajYubHvshd2u8a0Z23wg3xxchdf0oaCgKiq3tRtPiuPYonlnCiEEt3QazFu7luM2rOwcn6GhyihGtOxPbvlR2sYnH/c8qqLw6Yib+M2qr9gWd9BqZ1ZnbdxuV2mSEqpB0So23lL0r/O4SSSmKnEZVmsydwuJkBJnIagVgICYWAf/umUMdpvGOe1aoKkNs81j+3RmbJ/Ox933eJyT1AKHqlHepApKbEElst2apvL4hSN/8jV+johwapmniv79+8t169adtutF+Hnxh99/yoYfc61o1TFW0azJv0BRBO+/fRvpLY/vlJxuZizazPNTF+PxGUgpcTo00psm8s6fr8UZJlJ5stm+9zALNmRhUxVG9+9Mu+bHNw6nggq3h+zCYtLiY0mLr38iYpqSFxYu56NVPwICLwamIom22fAZJp3TUnht8kSSYhomjlRUWcXVH3zCwbIK9Do1mJpdcGPf3jwy/ILAau2X27bxlwUL8PiCU6AAUEGKmhYodbEpCmtuu4tEZ2grnJ+ClJKPMzfyyubVFLkr6ZaUxmMDLqRfWqhqx7y8Xdy79CsMTKQEFcHI9A68cuEVoenHZxlCiPVSykasDdn4idjmCMfirqe/YPOO/chK613qTg6T04/fGXAIFCGY/egU0pMbn0P19f4feCP7a7ymjkTiVOykR6fyYp8HfpLz2VAyy/ayrGAzNkXlwqZ9yYhJO/5Bp4AKn5ucykLSnPE0dYa2K6nGlJLnti3g4+y1IMHjA91QiNZs+EyDrolpvDnsapo4GmabC6squPWRqRQWVwTKqyQgHVAyVDKlZz/+2G94wDZ/sWozj89bRFmiHrDB1S3JZJTfthsCTKtPJ6ZA9UDCDoFiU1ny3D3EnORWclJKPluxibcXraO4ooouLZvyu0sv4JzWzUP2/W7nTv703hyUQjDtEk87yYXndeTF8yee9ZlI9dnmiOMZodHgdvu4+973yNtbVO8+ATUyRfCHh8cxetSxxXIKisqZMfdHducV0LVDcy4bcw5NEk+telyV28vo+18L9PCsxmHXuH/SUCaNbJzNok8mUkpeWbSKt5auxaaqeA2DQe0yeGbyuGM2Qfb4dL7avJ0n5y8JanmiKQqtmiQwrntn+rVqwaC2Gcd1qIqqqvjrtwuZl2mJBUiktdIqrMbMT44exfiuVsTxd99+y/Tt2wNytIHIpmL9H9bx9D+M0ZrGxjvvw6aeXCGNp9Yt4Z1t6/CYNc5wlKbx2dhr6ZVSY+DyK8sYO/sdyr0eTP+gHKrGfT0GcX+v80/qmBojEcfzpxOxzRGORZXby5RHP2bfvqMAeOKEFdms/Q42JVqViS9Z5V/Xj+GS/l3Dn8xPwcESZn2ymtzdh+nSM51xkweSmHRqbXOl7mbSir/gNYMVxZ2KnTvbT2R8y8Gn9PqNASklr+xayAfZy7EpKl7TYHBqB57sfTVRWv222W34mJa9mSc3fo/LqPn8NKHQNq4Jl7btRP/UDAYktzuuQ1VUWsnT7yxgyfrdSCSeZElZVxPTCVGajeeGXMLYNlbE8Xcfz+WbTZmYqsQXY0XU9XgTbH6bbGApBAc3y0Yrh2YHnCx5/p6Trg77zKwlfLJ8U1CWlNOm8eF9k+nSsmlg28HCMm585AOq3F6qy6TtdpXbLh/ETRPOfv23+mzz2anVG+FnidNpY/DgjmGDnbXbUwhF8NUX9x/X6dydc4Qb7nuHT79ex8p12Xw0bRXX3/s2+/JPbXPrbdmHwqZ0eLw636/LOqXXbizM3ZzJO8vW4dENKjxevLrByuy9/HnG/GMe57BpzNwS3GcTQDdNsouK+e/SVdwydTpjX32fCo/nmOdKjo7mkYsuwOYQSFVahQX+Z8vl03lv/Y+BfdMTErCrao2zWf2vdmp33efS//3lXbqxOn8/MzK3k1dacswxNQQpJU+sWcSrW1YHOZ1g1aI+/+PyoG2vbV1Fpc8bcDoBPIbOy1tXUuE79mcUIUKECMcj2mlnyDltA69Ae4VEmIDpF1swJYpPEp/vY/kT9xzX6czafoA7Ln2R6R8sZ82STD59cwm3j3+eg37H9lSxvSwXTYQuELpNL8sKN53SazcWZu3fyEc5K/CYOhW6B6+ps6JgN//Ycux2v07VxrTcLUFOJ1htwHaVFvH00rVcN3MaVyx8mUr9OLY5IYZ7bh1GySg4fJFBSR/L6QRLNOjdHTWLYOlJ8dhUFcUQOMoUnIXCakdT/TDWdToBBOixMP6C7qzJ2sfsdTvYX3hybPPT0xfz/pINIaU5Hl3nle9WBW17f+ZqXB4ftbW5vF6Dd2aswuUO/hx/SUQczwiNitEje6LWctqkAFMRSFUgFYEUluPpqJWuumXHAf71/BweeXw68xZtQ/e/EP796ndUubyB3qBen0FFlZeX3v7+lN5DTJQ9RAWwmoRY5ym9dmPhrWVrQ5xHr27w/Y49VLjDG6V9xSW8sXwt2YXHnnxIIOdoMU/OX3LccVR6vWiKGjZFtryW4zqpR49AbaaVxiMDCyD1iQ4BKIpgXt5u7vx2Jo8tnc+oT9/l999/G6KOeyLMytnJ+zt/DPszCWw/eiRo2+oje9HDqCLaFIU9pad2IhchQoRfBuOG9wjYZiHBWWASc1Andr9O3D4fCXs8qIYl4lLNuv0H+O3sb7lz2kxmbtuBz7Bs8Yt//QpXlRe92jZ7dCrK3bz577mn9B5iVGc9SuQQrzW+Htengveyl4U4j15TZ+GhbbjqEajLKy3h1Q1ryC09xqK9EOg+wcZsFy/u+O644yj3ea3nKYx9La1lm68+rxeaX2hS2kzMpjp2l/UMKoZAmOENtCoEc7dl8pt3Z/P4lwu57KkP+OfnC/gpWZ6z1+3gi5Wbw7Z9kxJ25gfb5h937scIMxdUVYV9h09tAKQxE3E8IzQqMjKSuffuiwBLBEwqwpr5C7/gkCIwTMmNd73Nxi17+XTGGn7zl8/5bvF2lq/Zw7OvzOeBRz/D7fGxIyu0gbCUkvWb807pPXRtk0ZSfHRI5NZp17jqot6n9NqNhaOVrrDbFUVQ6gp1PD/bsJlLXvuA/yxeQbHL3aBrfL0187j7tEtOsiKZdbCrKqM7dQh83zwujnevuIKEKIcVGVUARWLTBHZbeMcVrM2FrioqfV4qfT48hsGs3TuZnrmtQfcQjje3rsVj1FUlrnVP8cE1za1iE8Pu5zVN0qIbr8BHhAgRfj60z0jh3uuGWtFNj4HmMVF1f29ECUa0ilcTTHrkfTbuOsDrq9Yw5bPpzNy2g4W7s/nzvIX86rPpVLk8ZO88GHJ+aUo2rNhzSu+hS3wGcVp0yOvcrti5tOWQU3rtxkKJtyrsdoGgXA+1ve9v/pFRn7zHc6uXU1ruDet0WT1hrLNICdN3Hd/+dW6Sghomvc2hqIxrUyPs06JJPK9MuZyYVgreXi70Vl5Emg9nnEGUz25dN8yYhISS4ioqPV6qPD68usHs9TuYu+H484b6+HDRhiAdiLq0TQ22zc1Tw9c4+3SD5FNc8tWYiTieERodEy/tx18em1jjcFbj/1oKOHykjN//5Uve/OgHPB6d6kUst8dHVs4Rlq7IQtPCP96OUyzuI4Tgxd9eQbPkeKKdNmKi7NhtKrdOPI8B3TJO6bUbCwPbtQpbgxltt9E8IVhd90h5Bf+atxiPbuAzQyN3tdOsg/pymvUbgGo0ReHJMaNwalpgPE5NIy02llsHBJceZCQm4Kqu/fH3QvWaJq5jOIFCFSEr6C5d58OtG4O2lXrcPLBoNl3fe4Eu7z3PPQu/ptIXfnW5xBPeaQdwqCoP9AmuQ7qr+3lEqcEC5XZFZXCzDJpFN14l4wgRIvy8mDyhP3+7YyRKXZFR/7vVcCgcKCjlvuen8eIPq3DpNdriVT4fWw8d5vvsHJR61EUdUafWNitC4cled5LiSCRKdRCtOrEpGje3HUvPxPan9NqNhX7JbcO2J4mzOUlxBC9UHqwo54kVS/AYlm02XKo11/L/UqU/01q6/OIJWD/z6cePKtoUlf8bPAanqgUc0ChVo1lMHFO6Bdvm9ObxVLZwBUpgpCLRTRO3V0foIniS4P8/5rDm771Xg8ur89ny4JTq0goXj742h2F3/odhd77Eo6/PweUJnwZb6nJbdxk0KbFwaCp3jzovaNtNEwbgsNexzTaV83q1ITnhl+t4RtqpRGiUtExPqqedir+JJ+AzDIQINWBut4+lq3YxclhX5i/dEUi1BbDbNS4d1eu411+3bS/vz1zN/sMldO/QnAduGE5qk4ZHjzKaNWHGv29l656DlFa66dmhOYmxJ1f1tDHz64sGsyQzB5fXh26aCKz6zcfGj6DM4+GD1RtYnJVDSkw0bZKb1CsUlBjlpMTlDmljAtC/Vai6azhGduzA9Buv48MNG8kvK2No2zZc1bMHsfZgIYX52XtOSAE2KSoKl/QFPV/VVPlqDJdb17nwi7cpctesNM/JyWTZgVzWXX8PjjpO44hW7ZmauTHECRfAs0PGcV6z4MWL/k3TeXrwOP66Zj4uXceQJiPS2/PM4EsafC8RIkSI0BDSW6eGTwARwlIVlRKX3USGSYGs8vlYsHsPw8b0ZNm8Lfi8tWyzQ+OSSQOOe/3tZduYc3A2Be5C2sZ04LrWk0mwhc/6CEdGTBofnfdntpflUuFz0T2hLXG2X0aaLcD9nS9mZUEWbsOHLv22WbXxp+4TKPe5+GzvclYU7CTZEU+KbB4sFCQFZokdxWnQJM5BiduF4VbBqDUPE3Be84YtsI9r04V2Ccl8sGMD+ZVlDE9vx9UdehJjq2ObD4SLUkr/5QR4sXQcFGtzU3sMisegitCF7CpPzYKv2+vjqkfepbS8JtI7f2Umq7bm8e3zd4b07hzSpQ0zVm9Dmqbl0/pvWwh48rqx9G7TImj/vl1b8citI3n+w0V4vDqmlAzt257Hbh/doM/nbCXieEZolKSmxKEoot5aSQDDkKhKmHoNIYiPc3LfrSPIP1zKjqyDaKqCrpv065XBLdceW+lz2vyNPP/hInTDemkdLCxjwapM/nznGC4Z1r3B96Aogl4dWxx/x7OQ9KQEvrrvRt5etpZ1uQdIb5LAbcP60zY1iYmvf0RRZRVef73Pipy9YesuFCG4qncPVuXtY8vBw0E/sykKz1zW8P6UnVJS+Oeoi4+5j2GaIRk7AmtFt+5My6lqXN/jHD7esSnIyQQrKjm+Q02q0JdZW4OczmrKvB5e2bSah/oGP4/39BzI1MzaEVNrVKoq+Nu6+ZzTtHlIeu2ENt0Yl9GF/Moy4h1OEuy/jFriCBEinF7SUuOPaZs1l4k3ViBl6KKwIgQJUVHc+9hFHDlQTNb2fFRVQdcN+p3fkWvvvPCY115weD5f7PsUw98QsfBoAasK1nBj+r1c3Kpvg+9BEQo9Eo7dd/NsJSMmmc+H3se7e5bxY3EeraKTuKX9UDJikrhx5YuUeqvwSh3K89FEFo64GNzFjpoTSIFw27i+W38W7t/DtqradY2SaIfgiYETGzyeLk1SeWLwsR0xQ4axzSpgM8GnIhAIQ4BhZTTd2KsPX+T9SBV1bLNNZUyt/pwzl2wNcjqrKS938+F367hl3MCg7beNOpcZ67aBBEVCddKVEq3wzzmL6J6RRovE4NY0Y87vyshBnTlcWE58rJPYaAe/dCKOZ4RGSWJCNIMHtueHlbuDtlf38ARw2FUUmxqiDma3qVw6ujfRUXZeevwasvMK2JdfTNuMFDKO0/PT7fXxn6lLAk5nbf75+rc0T02gb9f0n3RvvxSaJ8bx2IQRQdteXrqKo7WcTgBf7c+6loNnV1Um9OjCr4cP4rUf1vDJhs34DIPz2rTi8UtG0iT65EaQL2rXnid/OL5gUZRmo0dqU+7tP5D+LVpy57df4TPNQGTXkJJy3UOF10us3c683PqUjCVvbFnDiFbtOCfVao/iNQzuWjIDH4ZVpFKdKqRIdCRHPS7+sOIbpo66FoByn4enNn7P17nb0KXJiBYdebjXBZjSbHBftQgRIkRoKMlJsQzo25bV67KDtksABVQvxPlUTE1F1+vYZlVl0jk9iIl18syHd5CdeYj8vUW07ZRGy9Ypx7yux/Dw5f7PA04ngKKAxOSptZ+QpKbRt0XDsmB+6bSITuTRnhOCtr2x+7sap9OPLg2iksooK0lB1kpbtakq4zt24Z7+A/nvhlV8snMTumkwtHUrnhg8jnjHyV34vLhlJ57dsihku5LgRR5yghBIxWqTdk6zZtzRvz+949N4+L3Z6KaJbpgIYdnmEq+bSo+XGIedxRt2h7ma9Sy/+81azuvRhm4ZVp9Vj65z19yvKU01sJVLNI/A9KvdG5gcrajirzMW8OaUKwCoqPLw0qdL+W5VJqZpMrRPe+6cNBhdmCRG/XKy38IR6eMZodFSWenhkqteDK0b9y+kpqbE8dc/TuCxJ2fi8egIAT7d5J4pF3DFJeFXP3XD5JNZa5k2byOVLi8DerXm3uuH0TLNiiDtyD7E7X/7JKzjCVax+IwXbjtJd/jL4+q3prI5/3DIdoemYUoTRRFIKRFCcNeQgdwzdGCYs5w63t24gaeXL0M3DQwpQ2pLNUXhmu49+ccFFwVSkPaWlnDdrM/JryizWpoIq96jTXwTZl95E39ZvoCpmZvDXM1KDYrSND4Zew19Ulvw5PrFvLltDcHru8F/AaoQbL/uYeyKymXfvUtmSQHeOvWuNlXQJi6Jp88dzznJZ/dkLNLH86cTsc0RToSS0iomXv9fgqaPfvE/CTTpkMhfHhzHPTNnBdpO+EyTxy66gGt7hy91MUyDz/Z9z9cHluM2PPRL6szt7S6lWZS1WLynYjdP7XwSk9DShvJyJ0W5/VkwJWKb/1duXvkfMsvzQ7bbhY3C/XHoHjtSWuv+DwwYzD39Tm8fyrcyV/LCtiWWbTasZtuObA1Hth1fHChRCtcOPIffXzq8xjYXFHPrG9M4XFKBoUikagUm2qUm8fnd1/HE+/OZvTRUCEkChtOqOX77oUl0y0jj8UWLee/HDcHPvATVBZrLup4iBBv/8WsUIbjpLx+Rm38UX3XbFQGmBuXdBW2Sk3h69Gh6NWt2ij+1M0t9tjkS8YzQaImJcdCrVzqbt+6v+WP3OwDNmsbz2vM30iQxhunv3s3m7fuprPLSuUMaKzZk8/h/55LRIonxI3qSVEs97F+vfMviNVl4vNaq3tI1u9mwbR9Tn72ZpMQYEuOij5nee6ioHLfHh/MUCxSdLeQUFbN4VzZ2TWVklw40jYsFQh1PIeCd669k15FCdMPkwk7taJ3U8Lqdk8WU3n0Z3qYtL69ZxaysnXhNMygKq0uT3cVHg+peCt1VFHmrMGulfXsMg7yyEu7/fhYx9nBNuWv2dRs6f1o+j/dHX83Hu34kfMJv7YUQBYFgTcFe9pQdDXE6QeIzJbvLCrlh8cfMH3sXzaLjiRAhQoSTQWJCNN26tmB7Zn4t22y9E1u0TOT1v99AYlwUK+69g7X7DlDl89IjtSk/rM/m729+S5vmSVw6rAdN4muyMv5vx0esLNqGxy/w9kPBZjYWZ/H2uY+QaI8lVosL8260SiE8Xhu5R8vwGkZYFfMIoRxwHWZ98VbsQuO85D4kO+KgPHQ/IeCj8ZPZdLAI3ZSMatuejITTb5tv6zyIES068dra5cxflwkHBGqFFYWwlwFlJvv2BNvmgsoqjupudHvNc+PVDXKKivnN9LlEJ2r+epqa61TrSUgF3F6df34+nxfvuYxPt2whJE7ndyYxra+rJUfWbd/LgSMlNU5n9YkNUI5Ksijihi++YMGUKTSN/eUpz0cczwiNmt/cO5p7f/sRXp+B16vjsGs4nTZeePIamiTGoBsmn89az1fzNuJy+3B5LDGb6j/4Nz9ZzoO3jODKsX04VFjGotW78NYSgzGlxO3x8eW8jdwx+Xyap8aT3iyRvQfD91jSVAVNaxyGzTQlXt3AaW98f8ZSSn474xvmbtuFlBJVUXhq/lJuGdSf5dl5QT0+VSFon5LEua3TObf1mU9jbpvYhCl9+vJNThZeX6jAT7OYYEOxqeAgZh0hICkkLuljXt6uapX5UMl3pUY/a0dxAUO+fA2vDHUiqeXQCqBFTBw2RWFXaSFGGBVgy2pakVfdNJm6ZwO/6Tm8QfceIUKECA3hd/eN5t4/TMXnM/D6DBwOjSiHjf/8YzKJcVH4DIN312zg0w2bcXl19CIvaolBhcNE7hE8u2QFf758BFcP7sVBVyErirbiNWvsgonEbXiZnb+CG9qMIs2ZRrI9lQL3YZRa5aOmKdi3PxW7qgZ6MZ9pDGmimwYOtfEtUEsp+XfmO6w5ugEJaELh/bzpjG46mg1Hs3GbNenRKgod4prRJ7U1fVJbn7lB+2kXl8xNbQaw8su8EOVZRQhSE4Nt8+b9h9CNYJtqqFCp6MzLzEJ4IdpmpYdXIxUwHASM8459BZz/5QNCVcIAACAASURBVGuYvjrPlgmOIlD8xwqgVdN4VEWQfaAInx5qmxXTipCCZZs/27KF+wcNOuHP4edO45uxRohQizYZyXz85u3MnreZPTlH6NyhGeNG9SQ+zsqR//tzs1mxIRuPxzJYAfVT/4TelJLn31lIUmI0docNm00NcjwBvD6DrbtqUkxeeXQSlz/4VvBqFaBqKmPO74pWjxT86cJnGDwzYwkzVm3FZ5i0TI7nkStHcH7XNmd0XLX5w1fzmF2rz6ZumugmvLNyHQ8MH8xLS1ahKv/P3n3HSVHeDxz/PDOz7Xo/jrujd5QOIgiCBcGGXWKvqNEYSxJjSaImpppi1MToz4oaY+8dwQJK773DUa/XbTPz/P7Y5bi924PrBZ7363V6O7M78929Zb/7zPM830dgWjZ90lP594xz2zHaugalZdA1Lp5tJcVYNS5zugyDq4cOj7hvVmw8hq7jD/c8yvBwW0RkP2V1z2mtVYIIPypgWwhRa3kWEa7cV+P+Bf4K3tq6ip7xKRiahr9Ofqtxdde22FpW2MBnrSiK0jC9uqfz6lM38MHnK9m6o4CBfbtw5mnHEx8Xmt93+9sfMW/rDnxmKDcLA7QEGapCCpia5KEPQ7nZk+HFEAYBIpeuCkiTtaXbqm//ov893LnoAXSHFykFQsDmLVlUlsdz8XGDG1WVvDUEbZN/bnifj/cuwpIWXT2p3DXgfEal9GvXuGr647qXWFS8pDqnmNICafHZgc+4sff5PLNlNrrQMKVNn/gu/HnYVe0bcC2De3YhPSmWvAOl2DVys9Ohc2mtddK7JMbh1PXqOhISkA6ql0tzFwJChHpEw0u11E7OltMmKC0MQyDNQ/ucRaFGp6gxJCq/uJKPl26gW5dkHIZW5zuk1EJDeCE0KmpbcfQOjqPdERueQgg38A3gCt//TSnlb4QQLwAnA6Xhu14jpVwe/SiK0nRJiTFcccnYOttnLVjCnIUbiZjyUaPReZCU8Njzc/j7ry/CjHIVytA1euakVt9OS47jy2du5ebfvs66bfvRNYGmaYwclMtdVx6+6l5rk1Jy+V//y4Y9+dXbdhWUcudzH/DsbRdzfPf2nzOQV1LKB6vXRd0nJSTHePj+ZzexYX8BSR43PVKTm3SeveXlLNuzl7TYGEZlZ7folw4hBC+eeyE3fPQu20uK0TUNKSUPTjiFYZlZEfc9pVtvYgwH3mAw6lCw0AEPdzZ5aAk0KRGCcOK3Qjmw1mN9lskz6xbwyVk3kBUTz47yYoLSPnSsGufz6Aaj0nMb8cyVzkLlZqW9JSfFctUldXtsXv58EXM2bKHmiipaMPJLOoQuzP3xw695+sfTsOqM9gBD6HSLPZTTUl0p/GfsP7jxoxdYd+AAPp8H29KZ0L0H9598cos9r6aQUnLVN0+yK5hX3SOb5y3g3hUv8MTIH9M/of1H8+zxFvB94VKiLXEuJeTExfDxpF+xuWIvSc5YcmMOX/Cp3vNUlrG8cDdp7lhGpUdf07uphBA8eddF3Pn4u+QdKAnlZiS/vOI0BnTPjLjvqQN783vHXKqCwdB6ozVXfbFAMw8dU0YJUWoSX3bofWnGWhhlBjoC05To/trvZvAFTV6Yu5jX7ryM1MRY/MEyrJqNXg384VHKHsNgRNdjc9WDhvR4+oFTpJQVQggH8J0Q4pPwvp9LKd9svfAUJbrn1yzhH1/MJRYZdTHk2gpLKumansig3l1YvWlvxJUow9C4eFpkMSKX08Hzv72cA0XlbNtdRHZGIjmZR57XsDu/lH1F5fTOTm2VdTvfnL8qotF5kD9o8sznC/jnjQ0vY95aFm7PQxMi4mrkQZa0saXE43AwLCcryqOPTErJI3O/5pUVK3CEe5+T3R5evuRichMTmxV7TV3jE/h4xlVsKS6izO9jUFoGLqPuR6ZT13lz+o+47csP2FBcgI2NiYzsuYzyHCJycY3fHULn0n5DWJa/m/Vl+yN6XA8qCfjQhOC1067k14s+5fO8jZjSDr3uHGq8xjvcXNhzaHNeBqXjUrlZ6XD++94invzkO+yuEvSaa0BGv/+ekjJyPV3oE5fDxvKdBGs0QA2hc172hIj7ewwnL0+fyd7ycrYUFtE9OalBn/s7y0rYU1nOgOQ0ktwtn5tnrVlCXo1G50F+K8is7V/xuyHt33O4rHhzvd+WLGmFcrPh5Pikpg2rlVLy0JLPeW3LchxaaEpSqjuGV065nOzYlsvNWWkJvPbQVWzfW0SF10/f3HRcjii52TCYNfMS7n7tI7YcKMLSJEFhYxPl7WgDWmiazME5nt5ci2By+J4OiSNVcnH2EJbv2svO/MJQkaNaSip96JrGM7+awR9f+JJvl23FsmysBCjLBvRQkcJEt5vzBw1qsdekMzliw1OGyt5WhG86wj9tVwpXUWrxWyaPLvmWgMsitgGNTgCPy4HTafDne87nz898wdwFm7ClJLdLEr+cOYWcLtEblRkp8WSkxB/x+JW+AL/49wcs27Qbh6ETMC0umTyUOy6aGLkAczO9NGdJvfu27OsYQyoTPG4MTce0zbo7JUzu17z10z7ZuInXVq0iYFnVy7J4gyY3vfseH1/d8sm9d/Lhl+AB6J6QzAcXXMWBqgr8psnZ779Eib/G+mC11gJ1ajqWCDXCI9/CElNa/GzYROKdLsa+/Tj5vsqIc2kIJnTpAUCKK4a/jZsOEmxp8/zGhby6ZSk+y+TUrn256/iTiXeodcOORio3Kx2NP2Dy/GvzsR0WAi3yzRhtnjsQ73Gha4JHhtzIPza8zncFq5BIcmMyuLPfpdVVbWvLio8nK/7Iubks4OfmL95lyf49OHWdgGVy7eCR3DOmZXPzC2sXIpMEovaTFLCtYl+Lnac54g0PtnSi11rf8qCRyQ1fpzya93es5Y1tKwnYVnXRO19lkJu/fYsPpl7XrGNH0yPryLm5R1oyb912BfnllfiDJuc99wplPj/oobmcur/GQDk7lJsLh/kxjbq5OaCb/PzkCcToDiY/+DTFFd6Ic+maYPyAUKM9OSGG3/34LKQMLbfy3JIlvLZqFT7T5PQ+fbhr3Lh6Cg8e/Ro0x1MIoQNLgD7Ak1LKBUKIW4BHhBC/BmYDv5RS+qM8diYwE6Bbt24tFrhy7NpTUYYtJYHk0Hh5USURB8dJ1Kp+C6AZMGpyNqa0iPU4eej2swgETYJBi9gWWsz3ty9+wdKNuwmYFv7wHNI3566kR2YK5088vkXOAVDhq/NPrFqfrNR697WlrglxCC16Qv/JpBNJjW3e+pIvLVuGNxiZOG0p2VFSwrbiYnomH3noblUwyAeb1rO24AD9UtI4r9/AFkkCGTGh4gbPnX4hV332RqiQnW3hsy0cmoYuNISAURk5bCzfz4Gqg43KQ9/K3IbBnqoyBroyeOSEadz+3bsELAsbiUPTiTUc3Dl0ItvKC7lv8YcsLcxDQzA5qy+/HXUmtwwa3+znoXQOKjcrHcme/SUIAY7y0NBaS5PVc+Zsx8E5cYdommDcoO4EbZtYw8P9g68mYJuYtkmM0TJrQf5s7scs3rebgG3ht0IXQ19cu5Q+SSlc1L/lcnNJpU1Mct2WtbShV2zTRve0tExXKgHbIGA7iDGCEaNuLut+HvGO5lVYfXHjIry11m61ZKjCel5FCTlxRx41VuUP8PHyDWzck0+/rumcOaw/Ma7m5+b0+NDqBk9dMp0bX3sHENiZFuyxMNCqC1ON6JvDwuQdHKg4eOH4UG72uAT7veX0SUjj1xedyr2vfIrfNJESHLpGrNvJzVPGsj2vkD/853PWbNqLJgQnjerNPTNP59axdaeMHYsa1PCUUlrAMCFEEvCOEOI44F5gH+AEngbuAR6O8tinw/sZNWqUuhqrNFuqJxZLhpa5ODBWkrpc4CoKXZ3SY3UmD+vH3EUbQ9XMBJjHeZndZTHvvLUav9+BJjSynUnoZQY9E1O4bsQI8neWsWzDbnIyk5h+8nFkJB/5SupBVb4Ac5dvqTOR3BcwmfX5khZteI7t352Pl6yPuu+2M9u3wRG0LO546yO+27IDODRHXw9PVLx3ykSuGDO8/gM0UHkgeuNb1zQqA4Go+2raW1HO9DdeoSIQoMoMEmM4+NvCebx70eXkJrTMcKCRmdksvuxWZu/cQnnAz9isXHZVlLK7oozBqRkMScvitq/f5aPtG8JDcg99NFpSkhMelnR6Tl/emHIlz6xdwI6KYk7I6Mb1A8fg1g1O+eQZygI+JKEKkHP2buLyObP4+Iyb2r3IhtI2VG5WOpLUpFiCpo0AkjfYlPbUCMaFv7RrOqcc15vZ67aElqlC4ouzeW/XOj7/5Xqc5Rq6LkjPjcPb06JnWgq3HTeMYXFLIbgIjB4IzyUIPaPB8ZQF/Mzdta3OklNe0+TpVYtbtOF5YlZ35pfkE5tUVWu4reCG3lNa7DxNEbRN7lsxiyVFWwCdgO3Ga7pIdAbRBNzY60LOzj6p2eepCEbPv7oQVJgNyM3FZcz453+pCgTwBkw8ToPHP53Pa7f/iKzkllkSbFS3bObdcRNzNm2lwh9gbI8cdu8tZW9hGYN6ZDKwWya3fPcmnwc2YJkaSInQJMKQSCHoGl6a7LQhfclKjueFOUvIKyxlTN9uXDlxOE5N4+pfvUhFpb86N3+3eAs79xQz69GrWrSXvbNqVFVbKWWJEGIOMFVK+Wh4s18I8TzwsxaPTlGiSHC6OLvnAD7atgGf2yR/rEQEwSN0/n3meZyU3Z3vZi/BX+EHl0RqUFgci2VpgMCSkp2+YtBh56ZSVny+HZfUCQYtHIbOy58s5vGfX8iQPg2b+F3lD9Y74LesylfPnqa59cxxfLNmK5X+QPWaUkLAHWefRN+uTSsE0FKe+2EJ3205VMUQQgmnf5d0Xr36UtxR5mA0xbS+/dhWVIy/Vpl0XQj6px35NXjo268o8FZVz0GtMoP4LJMHvv6SF8+5sEViBPAYDs7uNaD6ds/EyGFBPxkyntm7t0RcIfboDq7oP5x456Ge+ONSuvDYSZFzd1/atJCAZUYM6jKlzT5vGd8f2Mb4zOYNZ1Y6F5WblY4gId7DyWP78u2CzfgDJikbbWwdXG6dv/zifI4b3JURzz2J12+FKogCieu16iUpLEuyd3sZ9l4oGbOP9F5/wLRNDOEDnMjK/4PkFxHOhs1brwj4670IV+L3Rt3eVD8fexLnvLGN8qBBbEoFmm5j+pzc2vccusdlHvkArejl7XNZUrSleo1UAKRGlrs3T4y8CWcLLfsyNbc/T68rrtPQd2kGfROOnJsfeXcOxZXe6tzsDZj4gxaPvDuHJ65tufoVMU4HZw3uX327e0rkKKnbB0/g231b8WqRufmafqOJMQ71vg7O7cJfrjor4rGvfrCYQNCKzM2Wzd78Upavy2P4IFXs74jrQggh0sNXUxFCeIDTgfVCiKzwNgGcB6xuzUAVpaY/nHQG03sPxKXruHWDxDg3D0+ewqTcXmwo2xOaIB4jQYdAwMC2Q43OagLQwF0sEQEIhofHBk0Lrz/Ig09/ioxS1CWa1ISYqIWENCEYM7Blh7DlpCby9i+vYsZJw+jfNZ1Th/ThpZ/O4JpTR7foeZrif0tWRTQ6IdR7t3F/AaZdt2JhU10zcgQ5iYl4woV+dCFwGwZ/OuMMHA1YPPyrHdvqFD6ypeTbXTsa/DevT0XQz/f7drKxuG4BqNr6J6fz2pTLGJmejVPTyfTE8bPhE7l35JErJ28uK8Br1Z1Da0mb7RXHZon2Y43KzUpH9Mtbz2Dy+H44HToup0FyjId7bjiDkUO6sTp/f6jHRwcEOMpAmJHVbgUCLQh35S4l3VkVbnQCBEBWIUvvaXAsXWLjIy7iHaQJwYTsHs17orX0SErm0xnXMD1nHIklgznOnMB/xvyEKwe0//DK9/IWRDY6AQvJhrI9WC04LfyGASfQNTYBT7ghawgNj27w57FnozdgjdXv1m+Pmpu/W7+92bFV+Pws3LqLzfuPXAtjYHImsyZdzvDUg7k5np8NmcTdDVgPe1teAf5A3dwspWRXPevDH2sa0gWRBbwYnkuiAa9LKT8UQnwlhEgn9BV+OXBzK8apKBFcusGfJ0zjwbGnUuL3kRkTV/3B5tYd2DVWUDQtjajtCQ1cJXVLYgMcKC4nv6SiQUNuhRDcf9Vp3PPUh/iDVnXFUktI5q7ewj3/9xF3XjCRLg0oUtQQmUnx/PLC9l3WJZrajc5qApbu2sP+ykq6JycxOje7WcNN4pxO3r/ict5Zu46vt2+ja3wClw8dSu/UIxcagFC118g1eA5ub94QmP9bs4hHl32DQ9MwpaRHfBLPn3oxXWIj/+5bywr5fNdGpIQzuvXjrWlXNvpcx6dk8d4OB1VWrUW0EQxIbPhQNKVTU7lZ6XBcLgf3334md954GuWVPtKS49DD1cfdhiOiYaH7BKLuCmdgw+k523HqUXZaeUirEKEfuaaBJgS/nzCFn8z+oHqevABsLD7ZuZ7g1xb3jZpU5zO6qbLjE3h44qktcqyWFIhW6C9sdck2CgLF5HjSOS6xV7Nyc4LTzUdTb+Dtbav4Zu9WcmITubzvCHolNKz+hKbVXvw6RK+nZkRDPf/NYh7/Yj4OXce0bXqkJfPUNeeRnhA5p3X7/iJmr9yMEIJTh/ThzdOuafS5Bvbuwuz5G/H5axdwEvTpnt70J3EUEc29wt8Yo0aNkosXL26z8ynHJiklF337F3Z7iwCBz29QVuYJp5wabEie58Tw1/1QcxgaH/39JpLiG152fcPOA/zz7e/4ft2OiPWiEOBxGnz42+tJiW9eYZ2O7Ncffclby9dg2pGZw+XQwQi9xgJBblIiL19+MUmelike0Vi/+Ooz3tmwjmCNXliHpjG1dz8en3LWYR5Zv3l7t3PD7Lfx1mgI6kLQPzmdj8+5tnrbM2sX8NcV32BLGylD81LvGHISNw+uuxbe4VSZAaZ8+m8KfBXVy604NZ1ByV14ffI1x9Q8EiHEEinlqPaOozNTuVlpC1JKxs96mj0V5aEezxKI26Uh7MjPK1uTvHfXy+TGVUQ5igOR8T1Ca/icv9UF+/nT/K+Zt3s7tsc+NKVegxjdybyLbiG5FZZY6Sh+v+YNPt2zFKtWq86tG8QYoSkbQkC2J51Hh95KnKN9Xot7//spn67YQNA6FKdD15g2rD+/nzG1Scf8dsN27nzlA7zByClAA7Mz+N+tl1Vve+6LhTz12QIsO/T+0HXBbWeN56rJIxt1vkpvgBk/fY7isipsO5ybHToDenfh3w9dqnIzDRhqqyidjRCCR0dcRawexCFM4lw+NK3Wyk3hei7+pPC6TTXomuC43lmNanQC9MxKYeWOPaFGp6jxQ2iuwr8/mN+MZ9Xx/XTSONLiYvCE53I6dR1dCALSxhs08QZNqoJBthYW8ZtPZ7dbnA+Mn0Sf5BRiHQ5cuk6sw0GPxGQennhKk4/57NrFEY1OCA0z3lpaxObS0NCeHeXF/HXFN/gtk6BtY0obv2Xy2Mrv2FZWFPW4tpTk+yrqVAqMMZy8fep1TMsZRIzhJNHh5ke9RvDixMuPqcSmKErnIYTgubMuQISqrhCMB1snYs1jiUQa8OruAXit2lMndHCOalSjE6BvYiobFuwPNTotgVZqYBQ40IsMvN4g/1wxr/lPrgO7qc9UUlxxuLXQEFinZqALgUPz4rP9+O0APivAjsp9PL7prXaL85fTJ9EjPZkYpwOXoRPjdNAjPZl7zp3U5GPOmrc0otEJ4Uq7+wvZWVgCwPYDxTz12QL8QRPTsjFtG3/Q4okP55FXWBr1uLYtKSitxFdrWG2sx8lzf7ycU8b2w+N2kBDn5vwpQ/nH/Req3BzWMtU+FKWD6RnXhcdHnsD7u56j0nKRlFXFnLz+rCvOwpYCLSBwVAjMdBuHHUQWOxBC4tQ9pCTE8Nubz2z0ORdu2IW/VmXbmmuXfbF0I/dfdlrzn1wHlRobw8e3XM2js7/lo/UbKfH5Int+w4K2zRcbN2PZdoPmfbS0BJeLjy+9ku9372JTUSG9k1MYl9OtWZVgC71VUbdLJOuLD9AnMZXPd22sM38FQvMyP9u1kZsHR84F+jRvHQ8t/ZTyoB+J5Oxug3loxDTc4fkzGZ54/j72/CbHrCiK0tYGpKbzwIiT+ePcb7F1iTcD3IUC3Rf6bAwm21T2tHh2x/EMTzrAhNTd2EIQYzgRWgYi8S+NPue8DTuoigtCQGAUOUCG55WaEt2v8eH69fzmhKM4N7vieWXcz3huyzssKl5ClVWMJWV4FJjAb+mYUseUFt/kL+eXsn0uYCbGuHnrzitZuGUXWw8U0SsjhTG9c0NDcJuoqCJ6brZtyaY9BXRLTWL2ik2hns5aJPDVys11ej0/X7yBP/9vDhW+UFWsM8cM5J4Zk3GFL7qnp8Tz8B1nNznmo51qeCpHrUHJlyDs3awpfoUgguv7f0eF6aLAiidoOSipjCHW7SfG5ac8P4ZAYS+m97+TkQOa9kHnPVjd9uBDa/2/wh8Iz/88eq96Pb9wKW+tWXvoCqOo0c988GlLSVBa5FdW0qUBC4AfSVNeUyEE43K6MS6nZYo/nZrbh/UlB+pU2vVbFnfN+5B1xQdIdnnqrX5ce/uSgl38fOH7+Gr0on60cy0+M8hjJ7Zc5V1FUZS2dv2Joyk6UMnzi5dhOiSmW+JLswikAXpo7U/Tr3PzwikMSM/njGwfdwy5ApyjEaLxFyu9/mBoubVy/VCjk/D/JVTkm0d9bv5wz2csKpmPXwbQNdAkgMShBUlzmVhSsL8ynjK/gwJfJeme5q3pCU3LzZomGNu3G2P7tkxuPnlgL7bkFxGo1SkQMC3uf/ojNk0Zg9NjUF+NpdpVQBZv2MWDL30e0dP5ycL1+IMmj1w3rUViPtqpobbKUW1g6p2c0/01Tk45lcEuLyM8eSTofhyGSXpiGTEuPzo2mRkV3HLG1Ywe1K3JV9dG9a9RJjvKIaSExdvymvhMOr7iKi//nr+wzrCWakKGsp0O6HDKM8/x29lzmlxJ9ofdu5j26ov0euJvDH36CR5bML/OVUvLtllXmM+W4qJmV6ytrchXxRMr53PjnLcoC/pIccXgqq6qK6t/ArbFc+sWkxUbHzUJa0Jjarf+Edv+te67iEYngN82+XLPRgr9lS36PBRFUdraz6dP4qMbr+SGtMFk7TeI2ynQDqaO8Ee15rTYYSYxsecvEK4TmtToBDihXy5GuYYIanUaEgCWKVmxfW8Tn0nHVxos44M9n+O3D62lKQQIITFEqN7Ayj05bDqQSX5pAid/8CSPLPuyyTlz6bIdXDvzWU4548+ce8FjzHplXvV8x4Ms22bTrnx27Ctu8dxcXFbFs+//wN3/fA+ryCTZ5cZphHNzODXrVRAI2rz0xWJyUxOrC2DVJIBTh/aJ2PZ/nyyoM7zWHzSZvXQTpZUtu3ze0Ur1eCpHPZejD11T/oidcDe+/Al00Xew14xlUyAJn9RJM2yGZ9xPWsywZp0nOc7DT8+fwF/e+jrqfltKZs56h1E9cnjy8nNxGkfXP7+Ve/fhNHQCtXr9qkcb1+gBlkDAsnh95Sr6pqUyY+iQRp1r9YH9XPf+23jDlXTL/H7+s3QRRT4vD50cqio4P28nt3/5IVVmECklXWLjeWbaefRJbliFvcPZVV7COR+9iNcy8VsmTk3HoWmc2KUb3+zZhi2JmOPrs4J8u2c7946YzB+WzgEpqws63DNsEt3jI9cR21XPkihOTWe/t5xUV2yzn4OiKEp76tktjV/8+AyuLh7HmQ89R+xmGzPewupiQoyFs1Lnz2MuYGRa89Y+TIuP5Y7TTuKRH76Jul/acPNf3+SEAd149OZzcBhHXparM9lUvg1DMwjWWYJLYEqd9fu7UOpzI9GwJZhY/HfzMvonpnNRr4atmXrQuvV7uO/Xb+L3h85VXuHjldd+oLzCz49vCtVR+GHNDh545mMCARNbSrqkJPDoT86lR5eGVaY/nF37i7n64VfxB00CQQunoWMYOicMyWb+tl1IS6L7QAt/TQkELZasz+P2s8fx2AeH5voK4K7zJtI1JXI+8Z6CsqjnNXSNgtIKEmPbp2hiZ6J6PJVjhmak4054kGQ9kUEuwfT4Yi5NKOPU1KmkxTWtmmltl50yghunnlBnvqAETCf4TItF2/N4au7CFjlfW1alPpKUGE+dq5pw8OK1jGx8hnlNk+cWL2n0uR5f9EOd5Vu8psn/1qyizO9nX0U513/yNgXeKqqCQbymyfbSYi5997U6DeOm+O3irygL+vGHE3nAtqg0g2wrL8bjdIQ+WWs8VwmYts3V/Ufx5Tk38ovhk/jF8El8ec5Mrh1Ydw3WEWm56NGuzEtJj7jmJ2dFUZSOIjM5np+ffzIxwiCm1EHcejcpKxO4JnMsZw4c2CLnuGbyKG4aM7ruslm2xFEeaoAsXL+TFz5d1CLn60i5OcERX088EtsWFFbFIms1B7xWkOc2NP57yksvzyNQu0fQb/LeB8vwegPsKyzjZ0+8R0m5lyp/EF/AZMf+Im768+uYtWtkNMGjr86lwhsgEF6bPWBaVPkC5O8sI8Fv4Kg81OiEUIdA0LS4YtJI3rnvam4/+yR+es5JvHv/NcyYULcz4vheWVFHxUkJ2WlJzY7/WHB0dbkoyhFosTOQrrFI78eAD+E+DeFoXG/bkdxy9okIIXjxy8WhxpEEywlmuJPKb1q8sWQVt582rsnnWLQ1j4fe+ZJt+cU4DZ2LRh/HvedMbtYk/OY6rksmmfFx7CguqVtE5zBhlfn8jT7XhsKCqFMyHLrOnvIyvtixBatWI1gCPstizs6tnNGzb6PPWdO3e7ZFLRS0q6IUR5S/QYzh4JyeoS9QOXFJXDdwzGGPf8vA8Xyat44qM1D9PD26g5n9xxFjOJsVu6IoSkcz4+RhnDigO58t3UDAtDh1WB8G5ma21dycpwAAIABJREFU6DnuPuUkhC54ftEy/OHGkVElcIWLivuDFm9/t4obzx57mKMc3vydO/nV51+yvaQEl6YzY+gQHjhlUrOK1zVX37ieJDkT2O8riKggDKBVr25aV0nA2+hzbdtREHXddEPXyM8v55NlG7CsWrlZhubh/rBmBycN7dXoc9a0eO3OqI3sXfuLEZ66Pdkep4MzRoemuuSkJnLl5BGHPf7Ms8by9YoteAPB6ufpdhrccOYY3E7VpGoI1eOpHHOE0QMt/sdo8Xe1eKMTQoVrbjn7RD54+FrMRIE/Ccw4IhpfdarfNsLqvH1c/39vsi0/NBwzYFq8+v0Kznz0+Xa9yiqE4IUZF9A3PRW3YeBxhEq2CwGyxrDT2gZnZjT6XAPT0qMeLmhZZCcksL+ynIBd9zW2pE1BVfQqd43hNhxRtxtC45GxU3HrBg5NQwAew8HUbv2ZmNWzwcfvHpfCW6dex2ld+5PsjKFPQhoPj5zGrYNOanbsiqIoHVH3zGRmThvLbeeMb/FGJ4Ry1N2TTuKLG64m/oAgNg88BZGpqfb8vcZYvncv17z+FttLQst0+G2LFxcvZdp/Xmhe4M0khOD+gT8lx9MFp+bEpTnR0IjVg1h2/c2AISldG32uXj0ziNbGtmyb9PR49heVE4wy6si2JYVlzc/Nrnoafw5d597LTsHlMDD0cG52GkwZ1Y/R/Rs+lLt7ZjIv3fMjJh7fm6Q4D727pvKrK07n2qmHv5isHKKa54rSStIT4uiXnca6vfkR23VNMLl/wxshtT3y3pw6vXkAu4pKeX3BCi4d27y5qs3RNTGBD6+/ki2FRZT5/AzKTGdJ3h5ufvd9KoOHeu8OzvsUQO/Uxs+5/MnosczdsS1iuK3HMJgxeAjxThfjsrvx9oa1VNVa/xIJo7Oym/r0qv2o71CeW7cYX405M05N56weA7io9/GMycjl3W1rqAwGOD23LyPTsxtd3a93Qhr/Gn9xs2NVFEVRDslKTqBPaiqbdxdEbNd1jUnDejf5uA9+MhtL2kS0vHTB5tIi3lq+iguHHd/kYzdXhjuNR4f9hryqvXgtHz1ic9hUsZ57Fr1S72N6xzd+WsfVV4xn6bLt1XM8AVwug/POHYHH42TMoO58sWhjqNJwDVLC0L6Nb+jWdu6E43jzq+X4g4cat05DZ+qJAzlv/PGM7JfLJwvX4/MHOXlYb4b0zGp0bu7VNZW///jcZsd6rFI9norSih45fwqxLieucLECj8MgJTaGu6dMaPIx1+3Jr3ffk1/+0OTjtqTeqSkMz87CZRiM69GNn598UvVrAIcqlzsMna6JjV9SZVB6BrOmX8TxGZnoQpDi9nDb6LE8MGESAKf36EOf5BTc+qFrax7DwRm9+tIvJa05T42AZdE7MYVUdwyG0Ig1HHh0B8enduHhMacD0C0+iduHjOfekZMZlZFzVJfpVxRF6WwevHoKMW5HdbVTt9MgNT6GW6ePb/IxN+YXEK27T0h48sP5TT5uS8qJyaJvfE8cmoNBCcdzYc4FuLS6fVAuTadrbGKjj9+/Xxf+9Mgl9O2TiaYJkhJjuPrKk5h5/SQATh3Zl24ZSdVrXkLotZ8ypn+ziwv5TZPcAakkpMag6xoxbgdup8HgXl24+7LQ+XPTk5h51lhuv2ACQ3t1Vbm5HageT0VpRQOzMvj0jmt4c/FqtuQXMTQni/OGDyTO7WryMXVNEKxnpG6FPxB9Rzs7q39//vT1N3WG22pCcHb//tEfdASjumbz/qVXRN3n0HVeP28GL61ezrsb1+LSDS4bPJQL+w9u0rkOKg34uOCTWeytKqPKDOLUdCxs/nzimZzba1Czjq0oiqK0jUHdM3n34Wt559tVbN9fzJBeWZx94iBi3U2fQ+/0gj9W1ml8SgHe4sbXMmgLZ3cfxF9Wzq2zXRMa07o1rbDT0CG5PP2va6Lucxg6z947g/99tZzPFqzH5TS4aNJQpo1tXhGpUq+Pi178LwcqKqlKCOKJ1QkEJX847wzOGN607xhK61ANT0VpZWlxsdw86YQWO95Zw/rz1qI1Uff1yuiYFU9TYjz8+7xzue39D6u3SSSPnX0WabGtszSI23Awc9hoZg6rWzW2qf64dA7bygurCwsF7NBwon+unqcanoqiKJ1IWmJsswoJ1XZOTh/+W7gBWaOGjbAksXmSXn3TW+w8LSnNHccT4y/gp/Pfjbgu/MT4C0hxxbTKOd0uB1dPG83V01ouN//+i7nsKihBWqGhnD7NQrrg8SULVcOzg1ENT0XpZH55zmTeX7o+6gT9+86Z3A4RNcyEHj1Y+OObWZiXh5RwQm4OrlZYy9SybXSt8bMIygJ+8ipKyY5NINFVdy2uQl8V/9u8AlvK6gvaB2s57Swv4UBVBRkxcc0JXVEURemk7r3sdGbftpHCXIkZB1oQ4nbZ6EGNe6ZPau/w6jW5ax8WnX8HCw7sRABjMrrh0lshN1s2ut743Fwe9LLXW0KWJ4l4h6fO/oKKSt5fvg4pawyqskP/21pUTFGVl5SYuo9T2odqeCpKJxPjdDD3vhu58bm3WbvnAADxbie/u2gKI3o0v3BOa3IZBhN69GiVY3++fRO//X4Ou8pLSXK5uXnoGG4aOqbOHI7VBft4af0y8r2VnJbbm/N7D+Yvy77h1Y0rcGgaActiYEo6g5IzqTT9LC/cC0B2bDwSGTGKSohQ49OWEqMJjV1FURTl6BAT4+Kjf9zMT/70BlsXHkAa4MyO4aFrpzKke1Z7h3dYLt1gYlbzljKpzzfzNvDks3PZt7+UhHg3V1xyIpecP6pObl55YB+zVi+jwFvF6T36ML3fQJ7c9Bnv7FqMQ9MJ2ib9E7LoH9eFA8WCxXsOIIQgx0qIbHQS/t0GKW2MdlxmTqlLtOXyC6NGjZKLFy9us/MpytHOHzSp9AdIjvUclZPkd5aW8OjC75iXt5Mkl5sbho5kxqAhEc+1xOflwy3reXj+VwSkXb3dqWv8eOgJ3DHq0BIkb25axQPff0HAtrClxGMYxDqcVJj+iAq1ITKi/JomqLMG2kE5sYl8e/6PW+Q5K40jhFgipRzV3nF0Zio3K0rLOtpz847SEh79/jvm5+0kxe3hxuGjuHjQcXVy86c/rOM//5pLpdsmEC8QNsSXa9xw9glcNePQWuavrV3JQ/O+wm8dys3p6Tampwy/HVkB11fhoLIkhoNNTXehAVGm0EokuV2S+PKm61rlNVAOr77cfMSGpxDCDXwDuAj1kL4ppfyNEKIn8BqQCiwBrpRSHrayiUpuiqI0xJwd2/jdvDlsKSmK2O4xDK4YPIz7x0/CtG1+Pe9L3ty4mqBtHVq0+mDek2BogrXX3olT1/GaQUb+94m6S6xo9X0Ghi+hikO3o39/kDgNnWFpXXlm4sUkOOsO01Vaz7Ha8FS5WVGUtjY7bxO/XzyHrXll4QW6QzyGwTVDR/CLcRMIWha/mjObd9evw/JZOPNBMzlUdMmWxJcLvnnqpxi6RmUwwMgX/hWxPBpASm4JQq+bn20bivckVd82SjSMqrojjqSQkC0Z1jWLZ0+7kDhH04s6Ko1XX25uyNgwP3CKlHIoMAyYKoQYC/wJ+LuUsg9QDFzfkgErinJ0s6Xku507eHnVcn7I28nW4iIKvVXM2b6VWz59ny3FRdTuYPSaJi+tXkaxz8s/lszj7U1rCFhRGp3h300p2VgcWn5mVcE+tGgtx0YM+qh7nS60ISgtlhfu4f6FnzT8YIrSPCo3K4rS4mxps6RoAx/snsfKkk0U+PKoMsv4Im8Tt333LlsPlETNzc8tX0KZ38ffvp/P+xvWE7AshA+ETWSlX01QHi/ZsS90YXnF/n04ok1VqeeicOhQh/ZZcXaokVmDRGI7JX7dZFn+Hu7//vMmvBJKazjiHE8Z6hKtCN90hH8kcApwWXj7i8CDwL9bPkRFUY42Rd4qLn3rf+wpL8NrmdUpREPgNPTQlc+I3sZDnLrOxqICXlyzLPIKaT2jmbaWFnNcWhfinK7qarQNJiJvuHSdRKeLfF8l1YkvnC+DtsXnuzfiM4O4DUfjzqMojaRys6IoLa04UM5dyx4n31eGZlZwfOpuNCExNCioSsWSPcB0Ey3hOnWdTYWFvLxyeXVu1n0gZN37CgRbC4vpnZ1GnNOJFSU3mwEdh6tuEUUrqEWcXxqgpwuSvTEUllchkVgxNmZq6LEB2+KT7Rv4y/gzcep6neMpbatB1TCEELoQYjlwAPgC2AKUSCkPfuvLAzp2VRNFUYDQZHsZWIj0voc0t7RLDA/M+ZLtpSVUmWZEL6KNjGxMRmknBiybrrHxlAdqTeqIcl+npldXmh2YnE5mTFyddOnSDJyajlZzjO7Bg9U6plt38NX0m0hwO0EDoUdeyJVS4rdrzxVVlNahcrOiHD1sabO8eDNf7FvCrqr8donh7xteZ6+3kNIKk2HpebgMC4duI4RNiqeAyT02hHsi6ybcgGWRERdHVfDQdBapR6+N4DB00hNCS6kdn55JqjumTm62SuNxCANRKzdXlbojzi8AZ6zBFz+5Fr0n+HODmOlWRAvHlpKgXc8C6EqbalDDU0ppSSmHATnAGGBAQ08ghJgphFgshFicn98+/5AURQmR1n5kwVRk8Uxk2W+QBedhF9+BlG33gWzZNl9s24JphwsB1Vd3oZ6G5Aldc8hNTGJg6uHXRRNAekwsY7rkhG4LwQunX0R2XCKxDidxDicuXee2ISfy/tlXc2aP/mTHJuDQdNyGA4euIUSoF9al6fRLTOP1KZcT53BxclZv9CjDdrvFJZHoVGXblbahcrOiHB3yfSVc+cMfuW/lc/x9w5vcsPCv/G7Ny1g1Cua1tqBtsrBoLVUBjf5JB9BE5Ll1TZIZW05sYlWdxzp1nfG53clNSKR3yqH1xINx1MnxAshMjmNouNKvEIIXz76QrnEJxDocxIdz863HT2TWuFs4tctgstxJODSdOMNBWmoAh8NGF6Hz9k9J581zLiPG4WRCTg80vW5u7p2YQqzD2ezXSGm+Ri2nIqUsEULMAU4EkoQQRvjKag6wu57HPA08DaECBs2MV1GUZpAld4O1C6jR0PR/hayahYi9pk1isKU84pBXAcjwf5GhDboQTOnVhz9OOgOAh8adxlWfvEHAtLAPFgICPEZoxGFOXCLPnnFBxLzO7gnJfHvRTJYe2EORv4qRGdmkuEOLZD9x8nQA/JbJV3mbKfBWMTozh2SXBwl0iYmvPs49wybz3b5teM0AftvCEBoOTecPJ5zVQq+SojScys2K0rk9tHoW+3zF2DUamvPyV/PB7vmcl3PSYR7ZcqSUoVE7pk6sM0C0VUhsqRHjDlAZZ0KlARJ0TWNq77784ZQpADw8+VSuf+8dfKaJdEIgReAsAo8jdP/c1EQev256RAXcXkkpfHvFjSzdt4din5dRWdkku0MXcf80/EcA+K0g8/LXUxSoYERKL2JFqMc0M/bQ+tn3jZ7M9/t24jOD+G0Lh9AwdJ3fj5vaWi+b0khHbHgKIdKBYDixeYDTCRUvmANcRKh63tXAe60ZqKIozSPtYgguI6LRCYAPql6FNmp4OnSdMV2zWbAnL0qxnhBNCBLdbkr8PuKdTq4bOpLrh44gznmoKt2YrBzenn45Ty77gfVF+QxKzeDmoWMwpU2sw0mfpNSoxxZCMDKz/tGHLt1gWvfDdxxlxybyxVkzmbVpCUvy8+iTmMbV/UbRIz7lsI9TlJaicrOiHB2KAuVsLM+LaHQC+Owg77Vhw9OpOxiY0IMl/jz2VcST5PKi1yrw49BAk6loriAJcTo39B/DNQNGE+s81Js4NieX1y+ewb8WLWBjYQGD+2Uyc+QobJ9NrNtJz4zoeVITglFZh8vNDk7pcvxhn0O3+CS+PP8GXlq/lGUH9tA3KZVrB42iW3zSYR+ntJ2G9HhmAS8KIXRCQ3Nfl1J+KIRYC7wmhPgdsAx4thXjVBSluaSfekfXS1+bhvKHU6Zw3uuvUBrwV/do1nTd0BHcN/5k/JaJSzfqXQdtUGoGT552busHHEWqO5Y7jp/YLudWFFRuVpSjQsAKhkbmRLkQ67MOuxJSi7t7wAxur3yMjfld6J1ciBBmdc+naWuMT7uIXx932RFz8+CMDJ4865w2jPyQdE8sdw+f0C7nVo6sIVVtVwLDo2zfSmhOiaIonYGWCXpGeKhtTQ5wn96mofRISuabq2/k1VUreHn1CnZXlAGhtcB+OuZEZg4fjRBCVYdVlHqo3KwoR4dMdzKJjlgO+EsitjuEzsT0IW0aS25MBrPGPsB7efP576Z4klwb6RpfgiCWKV1mcGrXaQAqNytNJmRjlxdoBrVItaK0LxlYgiy+DqQJBAEPaCmItLcQWvsNEzVtG0vauPRGTTtXlHoXqVYaTuVmRWlfy4u3cO+K/8OUFqa0cGtOUpzx/Hv0HSQ4YtotLpWblaaqLzerd5KiHEOEcySkfYqs+h9YO8AxBuGZjtBaPrFZMkhZYBtOLYFYR5fD3tfQNIyGFdnutIK2yawt3/PWzqWYtsXU7OO5se8E4hzu9g5NURRFaUfDknvzwthf8OHu79njLWJYcm9O7zISt97ylVgDlsXmokKS3G66xicc9r7HQm72mybPL13K66tXY9k25wwYwC1jxkTMW1VajurxVBSlxW0v+4xFBX8OrRmKRbKzHxOy/oTHiF7w52hn2hYXf/0UW8rzq9c0c2o6ubEpvHHyzTg0dQ2wqVSPZ/Op3Kwox4a316/hN19/BRKCtsWQjC7868xzSYtpv17V9hS0LM5+9SW2FhRjh5tDTl2nV3Iy711xBYZ2dDe6W1N9uVm9ooqiNIqUFtL3CXbxrdgldyH98yP2F/rWsiD/9wTtCkxZhSX9FPrXMnfvne0UcfuSUnL9/BfYXH4gYiHtgG2xt6qU2XvX13lMwDLrVDhUFEVRlPpYts2H29dy09dvcce89/l+346I/Uv37uGBOV9SEQhQEQzgtyyW7d/LDR++004Rty8pJZd/MYvNBUXVjU4I9QjvLC1l9pYtdR7jt8wjLgenHJ66zK4oSoNJaSNLboXA9yC9oW2+2ciYK9ASfg7A+pLXsKQ/8nFYlAV2UOLfQpKrd5vH3Z6WF+1iVXFe1H1VVoDFBds5ObMfHsPJ0qJt/GHN+2yvyMehGZyXO4o7+k/FqebXKIqiKPWwpeT6uW+wKH8XVWYQgM93beT6gaO5e+jJADy7fAk+04x4nGnbbCgsYEtxEb2Tj63lwBYV7GD1/gMg9Tr7qoJBFuXlMbFHDzwOB9/v385vlnzGtrIiXLrOpb2Hc8/QU3DqdR+rHJ76NqMoSsMF5kHgh+pGZ4gXql5CxsxAGLlUmfuJVhdeQ8dnFQLHVsNzceF2zHp6LwXw1q6FvLdnIT1j09nlLSBgh74Y+O0g7+5aRJG/nD+GF9BWFEVRlNrm7N4c0egE8FpBnlm7gBl9hpEdm8je8vJoK7bg0DTyqyqPuYbnwoIdmFoQhF7nK4sQMGvhcl75YQW9uyaz1bMffzg3ey2T17Yso9hfxd9OnN4OkXduaqitoigNJn1fgayKskeEGqVAVsxYNFF3Ur5FkBTXgFaOsONJdsXgqmcOp0SCCFUN3FKxr7rReZDfNvn6wHryfWVtEaqiKIrSCc3evTmi0XmQrmnMDw+5ndi9B64oPXQBy2JQWkarx9jRJDtjcMcLEJLIlqdESoltSixbsj6wD78VmZt9lsknu9ZT4Kts05iPBqrhqShKw2kJRB0oIXQQ8QD0S7wQt5aExqF1vnThZlDSVTj1w1fQOxpN6ToYrZ4CBbpmc3D97fpmjTg1nd3e4tYJTlEURen0Ep1uDFE3z2gI4hyhC8FXDxlOktuNo0Y+8hgObh99IgkuV5vF2lGclXscutDQs7zgtAEZaoQaNlpAIAglZ+m0Cf8awakb7K4sbdugjwKq4akoSoMJzwVEH6EvwDUZAKeewNRus+if9CMSHD1Jdw9jXObDDEm9sU1j7SgSHB7+M/Yq0lxxxOhOPJoDl9BxaJLa7dFoNQsCtkn3mLS2CVZRFEXpdC7qNSRqBVYhBJO7hqa3JHs8fPSjq7h26Aj6JKdwQtccHjvjTH48+oS2DrdDSHJ6eGb8ZaTFxZDYzSKuewBPTgDdIZAah4oB+rWoV4aDlkX3uOS2DfoooJZTURSlUWzvB1B6P4iDDVANkfwUwqlWtDgcW9p8s38jD618G58VwC/NWveQ1b2fB7k1B2dmD+P+485rszg7G7WcSvOp3Kwond9bW1bxwKJPqxughtB4dtLFjEjPaefIOjZL2nyVt4mff/Mp3oCFz7RCnZ8mOIp1cEjsHH9EV51Hd3BRryE8OPKM9gu8g6svN6viQoqiNIrmOQfpOgUCC0E4wTkaEWVOpxJJExovbP2actOLHeXyqS50Eh0esjyJbCjfi0szOD93FD8dMK0dolUURVE6kwt7H88Z3fqxYP9OXLrBCZndcGiq6uqR6ELjsSULKPUGQ7lZAAKkA6wYG7fPQVJJDOk9XawrPYBbM7i85wh+PmJye4feKamGp6IojSa0WHCrD93GKA1Usa50T9RGpwCmdR1KhtvDm7u+x6GZSCze3/0D6e5Yruw5qc3jVRRFUTqXOIeLU3P6tncYnUqBt5INRQV1c7MAGQPT+wwg0XDz+sKVOE03UsBra9eQ6UvkmpNGtk/QnZia46koitIGojU4D0pzxXNdnwm8uWs+fjuAxMbGImAHeGbz5+RVFbZhpIqiKIpybLAPM+UwKzGea0eP5H8LVlChB6lKtqlMsSmLC/LXud+yu1gVF2os1fBUFEVpA8nOWHrFZ9QpjufQdKZ2HcLX+9cQlGZolI849GNh8cr2r9sjZEVRFEU5qmXExNEjManOdpeuM733QL5cu5kqh4kZC+hUD8OtjLN5buGSNo+3s1MNT0VRlDbyu6EXEe9w49ZCS83E6E66xaRyY5/JaAiiFRgCWFCwoW0DVRRFUZRjxD8nnU2C04XHCOXmWMNBr8QUbh06FiEgGEPdFpOA2Xnb2jzWzu6IczyFELnAS0AmoYLCT0spHxNCPAjcCOSH73qflPLj1gpUURSls+sdn8mHk37Gp3tXsruqmOOScpiYMQCHpjMmtQ9PbYn+uDKzqm0DVTo8lZsVRVFaxsDUDObNuIn3t6wjr7yU4RldObVbbwxNY2zfbrBiftTHFQe8bRxp59eQ4kImcLeUcqkQIh5YIoT4Irzv71LKR1svPEVRlKNLnMPNRd3G1NneJ6ErTs0gYNdeZgVy1DqeSl0qNyuKorSQBKeLKwYOq7P9uKxMnLpOwLbq7OuZnNIWoR1VjjjUVkq5V0q5NPx7ObAOyG7twBRFUY4lutC4rtdpuMLDcA9yaQ5m9p7STlEpHZXKzYqiKK3PoevcOmYMbj2yr85tGNx14rh2iqrzatQcTyFED2A4sCC86TYhxEohxHNCiOQWjk1RFOWYckWPSczsM4UERwwCyHQn8avBlzAufWB7h6Z0YCo3K4qitJ7bxozlp2NPJMntRgA5CQn8/YxpTOzeo71D63SEPEwZ4Yg7ChEHfA08IqV8WwiRCRQQmlvyWyBLSnldlMfNBGYCdOvWbeSOHTtaKnZFUZSjlmlbGGrx7yMSQiyRUo5q7zjai8rNiqIobce0bQxN1WY9kvpyc4NeOSGEA3gLeEVK+TaAlHK/lNKSUtrAM0DdSUuh+z0tpRwlpRyVnp7e9GegKIpyDFGNTuVIVG5WFEVpW6rR2TxHfPWEEAJ4Flgnpfxbje1ZNe52PrC65cNTFEVRFKU2lZsVRVGUzqYhVW3HA1cCq4QQy8Pb7gN+JIQYRmg4z3bgplaJUFEURVGU2lRuVhRFUTqVIzY8pZTfAVGWNEetC6YoiqIo7UDlZkVRFKWzUQOVFUVRFEVRFEVRlFbV4Kq2LXIyIfKB5pbOSyNUsa8zUTG3jc4YM3TOuFXMbUPFfGTdpZSqOk4zqNzcqaiY205njFvF3DZUzEcWNTe3acOzJQghFne20vkq5rbRGWOGzhm3irltqJiVzqIz/t1VzG2jM8YMnTNuFXPbUDE3nRpqqyiKoiiKoiiKorQq1fBUFEVRFEVRFEVRWlVnbHg+3d4BNIGKuW10xpihc8atYm4bKmals+iMf3cVc9vojDFD54xbxdw2VMxN1OnmeCqKoiiKoiiKoiidS2fs8VQURVEURVEURVE6kQ7b8BRCXCyEWCOEsIUQo2ps7yGE8Aohlod/nqqxb6QQYpUQYrMQ4p9CiGiLa7d5zDX2dxNCVAghflZj21QhxIZwzL9sy3hrxFDfaz2mxuu8QghxfkeJ+zAxny6EWBJ+HywRQpxSY1+HfH8IIVKFEHPC740naj2mQ8Yc3ndvOK4NQogzamxv9/d0TUKIoUKI78Ov4wdCiIQa+6I+h/YmhBgmhPgh/G9vsRBiTHi7CL8PNgshVgohRrR3rAcJIf5X4/NiuxBieY19HfJ1VhpP5ea2o3Jzu8escnMrUrm5bXS43Cyl7JA/wECgPzAXGFVjew9gdT2PWQiMBQTwCTCtI8RcY/+bwBvAz8K3dWAL0AtwAiuAQR3otY4BjPDvWcABwOgIcR8m5uFA1/DvxwG7O/r7A4gFTgJuBp7oDO9pYFD47+4CeobfD3pHeG9EeQ6LgJPDv18H/PZwz6E9Y60R8+cH/9bAmcDcGr9/En4/jAUWtHes9cT/V+DXHf11Vj9N+tuq3Nz+r7XKzW0Ts8rNrfscVG5u+/jbPTd32B5PKeU6KeWGht5fCJEFJEgpf5ChV/Ql4LxWCzCKw8UshDgP2AasqbF5DLBZSrlVShkAXgOmt36kkeqLW0pZJaU0wzfdwMEJwe0e92FiXial3BO+uQbwCCFcHfn9IaWslFJ+B/hqbu/IMRP6e78mpfRLKbcBmwm9L9r9vRFFP+Cb8O95dn0HAAAMLUlEQVRfABeGf6/vOXQEEjh49TcROPieng68JEN+AJLC75MOI3zl/xLgv+FNHfl1VhpJ5ea2o3Jz21C5ud2o3NyGOkpu7rANzyPoKYRYJoT4WggxIbwtG8ircZ+88LZ2J4SIA+4BHqq1KxvYVeN2h4n5ICHECUKINcAq4OZwsuvwcYddCCyVUvrpwO+Pw+jIMdf3HuiI7401HEqwFwO54d87YqwH3QH8RQixC3gUuDe8vSPHfNAEYL+UclP4dmeIWWkZKje3EZWb21VHjlnl5talcnMzGa19gsMRQnwJdImy634p5Xv1PGwv0E1KWSiEGAm8K4QY3GpB1tLEmB8E/i7l/7dztyGTlWUAx//Xs4taBAmpFG26K22omyFprh80w5fFikhTTAm3D32RCCqIkjQxP0j6ISUUKjI0i4oKY0FtPxgUSGy5uqXbq2uCpRS7irKZu9pefZgzz86ceXle5rnPzHn2/4PhmXOf+77Pda45O2evmTMn9zf8M4B5y4ybzNwBbIqIU4F7I+KhUjHWLTfmauwm4FZgS4nYxmx32TFPSxtjrhu3D3Qu4flGRHwF2AYcbDK2URaI+ULg85n5s4i4ErgbuKjJ+IZZ5LFyNYc/UVULeW5ujufmZrTxPNfGmOs8NzejTefmqRaembnkF6v6hOxA9XxnROyh83X9P4F1PV3XVW0rajkxA5uBKyLiNuBY4FBEvArs5PAnPFAoZlh23L3j/xQR+6l+m0EDcS835ohYB9wPbM3MPVXzLB8fo8xyzOOOgUaO6V6L2IctABHxLuDDVVsjx/Eo42KOiO8Bn60WfwJ8p3o+szEDRMRa4GPAmT3NU41ZS+e52XPzAtv03Dy7MXtunpDn5rJad6ltRBwfEWuq5ycDG4GnM/N54OWIOKe6jnkrMBOfCGXmeZm5PjPXA3cAt2TmnXR+WL0xIjZExFHAVXQ+9ZkJVVxrq+cnAacAzzDDcUfEscADwHWZ+Ui3fZaPj1FmPOZtwFXVb3Q20Pl3+Ftm8NiIiBOqv3PADUD3bpuj9mEWPAecXz2/AOheGrMN2Bod5wAvVcfJrLgI+HNm9l6GNst51grx3Nwcz83TNeMxe24uy3PzpHIG7rI07AFcRud64wPAv4DtVfvldK4L3wU8BnykZ8xZwJN07sx0JxCzEHOtz01Ud86rlj8E/LWK+foZy/U1tVxfOitxj4n5BuA/VczdxwmzfnzQ+U/DC8D+qs9pLYj5+iquv9BzR79pHxtD9uGzVTx/Bb7Wm8NR+zDtB507Ke6kc8e5HcCZVXsAd1UxP8GQO3ROOe576PzerN4+k3n2sazX2HPz9HPtubmh4wPPzSX3wXNzc3Hfw4ycm6PasCRJkiRJRbTuUltJkiRJUrtYeEqSJEmSirLwlCRJkiQVZeEpSZIkSSrKwlOSJEmSVJSFpyRJkiSpKAtPSZIkSVJRFp6SJEmSpKIsPCVJkiRJRVl4SpIkSZKKsvCUJEmSJBVl4SlJkiRJKsrCU5IkSZJUlIWnJEmSJKkoC09JkiRJUlEWnpIkSZKkoiw8JUmSJElFWXhKkiRJkoqy8JQkSZIkFWXhKUmSJEkqysJTkiRJklSUhackSZIkqSgLT0mSJElSURaekiRJkqSiLDwlSZIkSUWtnXYAUhscF2/Ngxzsb4w4/LQ+IEauGRg7uG6hsQusHxfQyCEx9Omytj9mfC4Yx1LmGz9XLjKmxW2rt08Mzr/kOYY3D8y5AnkaGudK5qO+nRV5bcf3G5n7pW5/if2zxL4tqX8ufe6JXusRmV7KP+OoL4599ca+NXbjWfzb2+C2Rs1fj2vc29zIvgNjh2x/obkGtjdinxcxdnScOaStO8f4MYuKoz5mRFzLmmuBeBc1Zuz2FznX/DE3OMvoOYaPGfY/ie6znX84sD0zLxkSqtRKFp7SIhzkIJvnLgYg5rpnnMMXDAy0zS9H1TzXt0x9uWdMjJhj5NjeOeb71McuMOeQOOYLrPk5GTt2oN9i+tTbozYOyIG+9bkZOnf25aXbNmqu6u/ciPZFjF1w7r59Weqcw+IYtVzPy+Aco8cusP1xcTC878g5F9F3ReYYEe+4uQb75pLnGDl25LZyYN2ouUaOHfJWMLBuvk9/ewydo96nf86I/sJi3BxRX66NnRto7ymORvSZY/wcc31z9LcdHltrH/G3d8xCfQf7HTo8xwJ91gysP9TXb01PYTawbn5sfUy9X0889LfNx1Hb/uDy4D512+bnqs9N/z72z3GoNkfWxvSvr8/dF+PAmP5cz2+Dwde423Z4XX3uanm+PfraO+u6bVHrE7X1c7X2w7N0161529+OQ1pFvNRWkiRJklSUhackSZIkqSgLT0mSJElSURaekiRJkqSiLDwlSZIkSUVZeEqSJEmSirLwlCRJkiQVZeEpSZIkSSrKwlOSJEmSVJSFpyRJkiSpKAtPSZIkSVJRFp6SJEmSpKIsPCVJkiRJRVl4SpIkSZKKsvCUJEmSJBVl4SlJkiRJKsrCU5IkSZJUlIWnJEmSJKkoC09JkiRJUlEWnpIkSZKkoiw8JUmSJElFWXhKkiRJkoqy8JQkSZIkFWXhKUmSJEkqysJTkiRJklRUZOa0Y5BmXkT8Ajhu2nEs03HA3mkHscqZ42aY5/LMcXnmuBmrIc97M/OSaQchrRQLT2mVi4hHM/OsacexmpnjZpjn8sxxeea4GeZZmj1eaitJkiRJKsrCU5IkSZJUlIWntPp9e9oBHAHMcTPMc3nmuDxz3AzzLM0Yf+MpSZIkSSrKbzwlSZIkSUVZeEotFRGXRMRfIuKpiLhuyPqjI+LH1fodEbG+ar84InZGxBPV3wuajr1NJsjz2RGxq3r8PiIuazr2tlhujnvWnxgR+yPiC03F3EYTHMvrI+K/PcfzN5uOvS0mOZYj4j0R8ZuI2F29Px/TZOxtMcFx/ImeY3hXRByKiDOajl86kll4Si0UEWuAu4APAqcBV0fEabVunwJezMx3ArcDt1bte4GPZObpwCeB+5qJun0mzPOTwFmZeQZwCfCtiFjbTOTtMWGOu74OPFQ61jZbgTzvycwzqse1jQTdMpPkuHpv+D5wbWZuAj4AvNZQ6K0xSY4z8wfdYxi4Bvh7Zu5qLnpJFp5SO50NPJWZT2fmQeBHwEdrfT4K3Fs9/ylwYUREZj6emc9V7buBN0TE0Y1E3T6T5PmVzHy9aj8G8Af1wy07xwARcSnwdzrHskabKM9alElyvAX4Q2b+HiAz92Xm/xqKu01W6ji+uhorqUEWnlI7vR14tmf5H1Xb0D5VAfQS8JZan8uBxzLzQKE4226iPEfE5ojYDTxB55uM11HdsnMcEW8CvgR8tYE4227S94wNEfF4RPwqIs4rHWxLTZLjdwEZEdsj4rGI+GID8bbRSp37Pg78sFCMkkbwsi/pCBURm+hcgrRl2rGsVpm5A9gUEacC90bEQ5n56rTjWkVuAm7PzP1+MVfU88CJmbkvIs4Efh4RmzLz5WkHtoqsBc4F3ge8AjwcETsz8+HphrX6RMRm4JXMfHLasUhHGr/xlNrpn8A7epbXVW1D+1S/H3ozsK9aXgfcD2zNzD3Fo22vifLclZl/AvYD7y4WaXtNkuPNwG0R8QzwOeDLEfGZ0gG31LLznJkHMnMfQGbuBPbQ+YZO/SY5lv8B/Doz92bmK8CDwHuLR9w+K/GefBV+2ylNhYWn1E6/AzZGxIaIOIrOiXRbrc82OjcPArgC+GVmZkQcCzwAXJeZjzQWcTtNkucN3ZsJRcRJwCnAM82E3SrLznFmnpeZ6zNzPXAHcEtm3tlU4C0zybF8fHVTFyLiZGAj8HRDcbfJsnMMbAdOj4g3Vu8b5wN/bCjuNpkkx0TEHHAl/r5TmgovtZVaKDNfr77Z2Q6sAb6bmbsj4mbg0czcBtwN3BcRTwEv0DlBA3wGeCdwY0TcWLVtycx/N7sXs2/CPJ8LXBcRrwGHgE9n5t7m92K2TZhjLdKEeX4/cHPPsXxtZr7Q/F7MtklynJkvRsTX6RRWCTyYmQ9MZUdm2Aq8X7wfeDYz/eBEmoKoPgSSJEmSJKkIL7WVJEmSJBVl4SlJkiRJKsrCU5IkSZJUlIWnJEmSJKkoC09JkiRJUlEWnpIkSZKkoiw8JUmSJElFWXhKkiRJkor6P1HOaXQBYeDrAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1152x360 with 3 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "for r in lss:\n",
+    "    r.fit(trainX, trainy)\n",
+    "    try:\n",
+    "        fig, ax = plt.subplots(ncols=2, sharex=True, figsize=(16, 5))\n",
+    "        pred_y = r.predict(testX)\n",
+    "        outputs.append(pred_y)\n",
+    "        vmin = min(testy)\n",
+    "        vmax = max(testy)\n",
+    "        norm = colors.Normalize(vmin=vmin, vmax=vmax)\n",
+    "        inter = ax[0].scatter(testX[:, 0], testX[:, 1], c = pred_y)\n",
+    "        ax[0].set_title(f\"Interpolation: {r}\")\n",
+    "        gt = ax[1].scatter(testX[:, 0], testX[:, 1], c = testy)\n",
+    "        \n",
+    "        # normalize colorbar\n",
+    "        inter.set_norm(norm)\n",
+    "        gt.set_norm(norm)\n",
+    "        fig.colorbar(gt, ax=ax, orientation='horizontal', fraction=.1)\n",
+    "        \n",
+    "        ax[1].set_title(\"GT\")\n",
+    "        plt.show()\n",
+    "        \n",
+    "    except NotImplementedError as e:\n",
+    "        print (f\"Was not able to. for {r}!\")\n",
+    "        print(str(e))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.metrics import (\n",
+    "    r2_score, mean_absolute_error, \n",
+    "    mean_squared_error\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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h7D8BDYFmwHLgnlJe29fMZpnZrNWrVydSXxGRvUEmieFL4PC06XrhvBLXMbN9gGpAflmvNbN9CZLC0+4+sWgFd1/p7tvcfTvwCEFT1g7cfZS7t3L3VrVr185gM0REJBOZJIaZQCMza2Bm+xF0Jk8qts4koEf4/BJgmrt7OL9reNZSA6ARMCPsfxgNLHT3e9MLMrND0yYvAj7+rhslIiLf307PSnL3QjPrB0wBKgJj3H2+md0JzHL3SQQ7+afMbAmwhiB5EK43HlhAcCbSde6+zczaAt2Bf5nZh2GoW9z9VeBuM2sGOLAUuDrG7RURkZ3YaWIACHfYrxabd1va82+BLqW8djgwvNi89wArZf3umdRJRESyQ1c+i4hIhBKDiIhEKDGIiEiEEoOIiEQoMYiISIQSg4iIRCgxiIhIhBKDiIhEKDGIiEiEEoOIiEQoMYiISIQSg4iIRCgxiIhIhBKDiIhEKDGIiEiEEoOIiEQoMYiISIQSg4iIRCgxiIhIhBKDiIhEKDGIiEiEEoOIiEQoMYiISIQSg4iIRCgxiIhIhBKDiIhEZJQYzKyDmS0ysyVmNqSE5fub2TPh8ulmVj9t2c3h/EVm1j6cd7iZvWVmC8xsvpkNSFu/ppm9YWb/Cf/W2PXNFBGRTO00MZhZReAh4FygCdDNzJoUW603sNbdjwbuA0aEr20CdAWOBzoAD4flFQKD3L0J0Aa4Lq3MIcBUd28ETA2nRUQkIZkcMbQGlrj7p+6+BRgHdCq2TifgifD5s8BZZmbh/HHuvtndPwOWAK3dfbm7zwFw9w3AQqBuCWU9AVz4/TZNRES+j0wSQ11gWdp0Hv/bie+wjrsXAgVArUxeGzY7NQemh7MOcffl4fMVwCEZ1FFERGKS085nM6sCPAdc7+7riy93dwe8lNf2NbNZZjZr9erVWa6piMjeI5PE8CVweNp0vXBeieuY2T5ANSC/rNea2b4ESeFpd5+Yts5KMzs0XOdQYFVJlXL3Ue7eyt1b1a5dO4PNEBGRTGSSGGYCjcysgZntR9CZPKnYOpOAHuHzS4Bp4a/9SUDX8KylBkAjYEbY/zAaWOju95ZRVg/gxe+6USIi8v3ts7MV3L3QzPoBU4CKwBh3n29mdwKz3H0SwU7+KTNbAqwhSB6E640HFhCciXSdu28zs7ZAd+BfZvZhGOoWd38VuAsYb2a9gc+BS+PcYBERKdtOEwNAuMN+tdi829Kefwt0KeW1w4Hhxea9B1gp6+cDZ2VSLxERiZ+ufBYRkQglBhERiVBiEBGRCCUGERGJUGIQEZEIJQYREYlQYhARkQglBhERiVBiEBGRCCUGERGJUGIQEZEIJQYREYlQYhARkQglBhERiVBiEBGRCCUGERGJUGIQEZEIJQYREYlQYhARkQglBhERiVBiEBGRCCUGERGJUGIQEZEIJQYREYlQYhARkQglBhERicgoMZhZBzNbZGZLzGxICcv3N7NnwuXTzax+2rKbw/mLzKx92vwxZrbKzD4uVtbtZvalmX0YPs77/psnIiLf1U4Tg5lVBB4CzgWaAN3MrEmx1XoDa939aOA+YET42iZAV+B4oAPwcFgewOPhvJLc5+7Nwser322TRERkV2RyxNAaWOLun7r7FmAc0KnYOp2AJ8LnzwJnmZmF88e5+2Z3/wxYEpaHu/8dWBPDNoiISIwySQx1gWVp03nhvBLXcfdCoAColeFrS9LPzOaFzU01MlhfRERiUh47n/8ENASaAcuBe0paycz6mtksM5u1evXqJOsnIrJHyyQxfAkcnjZdL5xX4jpmtg9QDcjP8LUR7r7S3be5+3bgEcKmpxLWG+Xurdy9Ve3atTPYDBERyUQmiWEm0MjMGpjZfgSdyZOKrTMJ6BE+vwSY5u4ezu8anrXUAGgEzCgrmJkdmjZ5EfBxaeuKiEj89tnZCu5eaGb9gClARWCMu883szuBWe4+CRgNPGVmSwg6lLuGr51vZuOBBUAhcJ27bwMws78BpwMHm1keMNTdRwN3m1kzwIGlwNVxbrCIiJRtp4kBIDxl9NVi825Le/4t0KWU1w4Hhpcwv1sp63fPpE4iIpId5bHzWUREckiJQUREIpQYREQkQolBREQilBh2Ys6cObmugohIojI6K2lvUTwJuDudOnXipZdewt1p0aJFjmomIpIcJYY0rVq1ok2bNuy///6pefn5+QwcOBAzY9q0aTmsnYhIMpQY0kyYMIGRI0dy0003ce655wLQoEED3nrrrRzXTEQkOepjSNO5c2deeeUVXn/9dbp06cIXX3xBMHq4iMjeQ0cMxVSpUoX77ruPOXPm0KNHDzZs2JDrKomIJEqJoRQtWrRg2rRpSgwistdRYkhTWFjI6NGjef755/nqq68AqFu3Lp06daJ3797su+++Oa6hiEj2KTGk6d69O9WrV+f222+nXr16AOTl5fHEE0/w05/+lGeeeSbHNRQRyT4lhjSzZ89m8eLFkXn16tWjTZs2NG7cOEe1EhFJls5KSlOzZk0mTJjA9u3bU/O2b9/OM888Q40auvW0iOwdlBjSjBs3jmeffZZDDjmExo0b07hxYw455BAmTpzIuHHjcl09EZFEqCkpTf369VP9CPn5+QDUqlUrl1USEUmcjhhKUatWLWrVqkXfvn1zXRURkUQpMezErFmzcl0FEZFEKTHsxA9+8INcV0FEJFFKDDsxefJkILj4TURkb6DEkKZt27ap5927d48sa926ddLVERHJCSWGNF9//XXq+fz58yPL3D3p6oiI5IQSQ5qyhtjW8NsisrfQdQxp1q1bx/PPP8/27dtZt24dEydOBIKjhYKCghzXTkQkGUoMadq1a8ekSZNSz1966aXUstNOOy1X1RIRSZQSQ5rHHnss11UQEcm5jPoYzKyDmS0ysyVmNqSE5fub2TPh8ulmVj9t2c3h/EVm1j5t/hgzW2VmHxcrq6aZvWFm/wn/JjZ63fTp01m/fj0AmzZtYujQoVxwwQUMHjxYTUkistfYaWIws4rAQ8C5QBOgm5k1KbZab2Ctux8N3AeMCF/bBOgKHA90AB4OywN4PJxX3BBgqrs3AqaG04no1asXlSpVAmDAgAEUFBQwePBgKlWqRM+ePZOqhohITmXSlNQaWOLunwKY2TigE7AgbZ1OwO3h82eBBy04jacTMM7dNwOfmdmSsLwP3P3v6UcWxco6PXz+BPA2MDjjLdoF27dvZ599grdk1qxZzJkzBwiub2jWrFkSVRARyblMmpLqAsvSpvPCeSWu4+6FQAFQK8PXFneIuy8Pn68ADilpJTPra2azzGzW6tWrM9iMnTvhhBNS/QxNmzZNjZO0ePFi3dZTRPYa5fo6Bg+uKivxyjJ3H+Xurdy9Ve3atWOJ9+ijj/LOO+/QsGFDFixYwCmnnMJRRx1Fnz59ePTRR2OJISJS3mXSlPQlcHjadL1wXknr5JnZPkA1ID/D1xa30swOdfflZnYosCqDOsaiWrVqPP7446xfv57PPvuMwsJC6tWrxyGHlHjQIiKyR8rkiGEm0MjMGpjZfgSdyZOKrTMJ6BE+vwSYFv7anwR0Dc9aagA0AmbsJF56WT2AFzOoY6yqVq1K06ZNadmypZKCiOx1dpoYwj6DfsAUYCEw3t3nm9mdZtYxXG00UCvsXB5IeCaRu88HxhN0VE8GrnP3bQBm9jfgA+AYM8szs95hWXcBZ5vZf4Afh9OJmDdvHm3atOHwww+nb9++rF27NrVMg+iJyN4iowvc3P1V4NVi825Le/4t0KWU1w4Hhpcwv1sp6+cDZ2VSr7hde+213H777bRp04ZHH32Utm3bMmnSJBo2bMjWrVtzUSURkcTpyuc0GzZsoEOH4NKKG264gZYtW9KhQweeeuopDaInInsNJYZiCgoKqFatGgBnnHEGzz33HJ07d2bNmjU5rpmISDLK9emqSRs8eDALFy6MzDvxxBOZOnUqF198cY5qJSKSLB0xpLn88stLnH/EEUfwyCOPJFwbEZHc0BFDhkaNGpXrKoiIJEKJIUO6taeI7C3UlFSG9957jxkzZnDCCSdw9dVX57o6IiKJ0BFDmvSL2B555BH69evHhg0buOOOO7jrrsSusxMRySklhjTpF7GNGjWKN954g6FDh/L666/z9NNP57BmIiLJUVNSmu3bt7N27Vq2b9+Ou1M0amvlypVT92kQEdnTaW+XpqCggJYtW+LumBnLly/n0EMPZePGjep8FpG9hhJDmqVLl5Y4v0KFCjz//PPJVkZEJEfUx5CBSpUq0aBBg1xXQ0QkEUoMIiISocQgIiIRSgwJmzx5cup5QUEBvXv35sQTT+Tyyy9n5cqVOayZyPezJ32mx4wZk3qel5fHWWedRfXq1Tn11FNZvHhxDmuWLCWGhN1yyy2p54MGDeLQQw/lpZde4qSTTtotr67WF0n2pM/0gw8+mHo+cOBALrvsMtasWcONN97Iz3/+8xzWLFlKDDk0a9Yshg0bxpFHHskvf/nLUs+K+j7mzZuXer5161aGDRtGx44dueWWW/jmm29ii6MvkqTL5mc6aYsXL6Zv375UqFCBiy66aK+6J4tOV03YqlWruPfee3F31q9fn7pmAoIL7OLys5/9jDlz5gAwZMgQ8vPzGTRoEC+88ALXXHMNTz75ZGyxiixevJjx48cDcNFFF3HnnXfGHkPKn6Q+00nIy8ujf//+uDurV69m69at7LvvvgB71e19lRgS1qdPHzZs2ABAjx49+O9//0vt2rVZsWIFzZo1iy1O+gV5U6dOZebMmey7776cdtppNG3aNLY4+iJJUp/pJPz+979PPW/VqhUbN26kRo0arFixgo4dO+awZslSYkjY0KFDS5xfp06dWH/FFxQU8Pzzz7N9+3Y2b96c2lmbWaz3r96Tvkj//e9/Ofjgg1PTY8eOTY2u26dPn9jet3nz5nHiiScCQfIcMWJEKs6vf/1rKlWqFEucpCT1mU7ifevRo0eJ8+vUqcNvf/vbXS5/d2F7wlAPrVq18lmzZuW6GhmbMmUKL7zwAl9++SUAdevWpVOnTnTo0CG2GD179oxM33XXXRxyyCGsWLGCK664gqlTp8YWKwlJ7BRatGiRan4bNmwY7777Lpdffjkvv/wy9erV47777tvlGMXjDBo0iPz8fHr27MkLL7xAfn5+bDvTpBIdJPOZTuJ9KywsZPTo0SVuS+/evVM/sL6v+kNe2eU6Frf0rvO/92vNbLa7t9phvhJDsq6//noWL17MlVdeSb169YCgOebJJ5+kUaNGPPDAAzmu4XeT7S9SkSR2Cs2bN2fu3LmpeO+++y6VK1dm69attGjRgn/961+7HKN4nGbNmqWa+dydpk2bRk4c2BVJJbqkPtNJvG/dunWjevXq9OjRI7ItTzzxBGvWrOGZZ57ZpfJ3l8SgpqSEvfrqqyWexnnZZZfRuHHjWBPDv//9b1588cXIDrtjx44cd9xxscXo3r071atXZ+jQoTt8kX7605/u8hepSBJ9Jps2bWLu3Lls376dbdu2UblyZQD23XdfKlasGEsMSK6ZL/09mzhxYirRXX755bRo0SK2OEl9ppN432bPnr3DttSrV482bdrQuHHjWGLsDpQYEnbAAQcwc+ZMTjrppMj8mTNncsABB8QWZ8SIEfztb3+ja9euqRsQ5eXl0a1bN7p27cqQIUNiiZPUFymJncKhhx7KwIEDAahZs2ZqdN38/PxYh11v164dkyZNAqBNmzasXLky1cyX3vSzq5JKdEl9ppN432rWrMmECRPo3LkzFSoEZ/Nv376dCRMmUKNGjVhi7A7UlJSwOXPm8POf/5wNGzakfmEvW7aMatWq8dBDD9GyZctY4jRu3Jj58+fv0JSzZcsWjj/+eP7zn//EEqdNmzYMGjSoxC/Svffey/Tp02OJk8s+k23btrF58+bdrlP4jDPOiEz/9a9/TSW69u3bE9d3JqnPdBKWLl3K4MGDmTZtWioRrF27ljPPPJO77rprlwfT3F2akjJKDGbWAXgAqAg86u53FVu+P/Ak0BLIBy5z96XhspuB3sA2oL+7TymrTDN7HGgHFITF/8zdPyyrfrtTYiiyYsWKSBNPnTp1Yi3/2GOPZcqUKRx55JGR+YdwhAQAACAASURBVJ9//jnnnHMOixYtiiVOtr9ISSsoKGDy5MmR/0379u2pXr16rHGSaOYrTbYSXbY/05Ds+5afnw9ArVq1Yitzj0kMZlYRWAycDeQBM4Fu7r4gbZ1rgRPd/Roz6wpc5O6XmVkT4G9Aa+Aw4E2gqH2hxDLDxPCyuz+b6cbtSmIoD/+o22+/ndtvvz3WOkyePJl+/frRqFEjDj/8cAC++OILlixZwoMPPhjr2SJFsvFFSpftncKTTz7JHXfcwTnnnEPdunWBoPmt6BavV155ZSxx0pv50vtlxo0bF2szHySX6IrLxmc6yfctXd++fRk1alQsZZWH/U26XUkMpwC3u3v7cPpmAHf/Xdo6U8J1PjCzfYAVQG1gSPq6ReuFLyuxzL0xMaSfPRKn7du3M2PGjMhO4aSTToq1fbkkcX6RiiSxUzjmmGOYPn36DjvNtWvXcvLJJ8c29lNSzXxJJbqSZOMzndT7Vlyc21Ie9jfpduWspLrAsrTpPODk0tZx90IzKwBqhfP/Wey1dcPnZZU53MxuA6YCQ9x9cwkb1BfoC3DEEUdksBnlV7b6eSpUqECbNm1S06NGjYpMZ0s2mvVGjx5d4k5h4MCBHH/88bEkhvShHNJVqFAh1v9RhQoV+Oqrr3Zo5lu+fHmqnyYOw4cPZ/bs2aUmumwmhmx8ppN634r7wQ9+kLWyy6vyeFbSzQRHHPsBo4DBwA6D7rj7qHA5rVq12q170GfPnp1InD//+c/07ds363Gy8UVKYqfwq1/9ihYtWnDOOedEmt/eeOMNbr311lhiANx///2cddZZpTbzxSWpRFeSbHymk3rfiisaVrywsDDWs9PKs0y28kvg8LTpeuG8ktbJC5uSqhF0Qpf12hLnu/vycN5mM3sMuCGDOu42Lr300tRAc4MHD2bEiBGpHds555zD66+/nrXYSZ2Blo0vUhI7hR49etCxY0emTJmSan47/fTT+d3vfhfrqYodOnRg8eLFWW/mSyrRJfWZTuJ9a9u2Le+99x4QXKPz1FNPpZa1bt06K02+5VEm39qZQCMza0Cw8+4KXF5snUlAD+AD4BJgmru7mU0C/mpm9xJ0PjcCZgBWWplmdqi7L7fgp86FwMe7uI3lSno76BtvvMGIESNS06tXr85q7Jdeein2MpP6IiW1M61RowZdu3ZNTb/88stZOX89iWa+pBJdkp/pbL9vX3/9der5/PnzI8v2hFP7M7XTY3B3LwT6AVOAhcB4d59vZneaWdEoaaOBWma2BBjI/zqd5wPjgQXAZOA6d99WWplhWU+b2b+AfwEHA8Pi2dTyoayLseK86vX6669PPS+68rSo0/ZnP/tZbHGS/CIV7RQ6d+5M586dmTdvXtY70m+77basll/kz3/+c1bKLUp0gwYNYtCgQVSpUiX2RJfUZ7okcb9vudyW8iSj43x3fxV4tdi829Kefwt0KeW1w4HhmZQZzj8zkzrtrr755pvU1ahFV6a6O+7Opk2bYovz97//PfX8iSeeYMCAAanpuMbigdzvFLLdZ5LUr8Sk4tx222385Cc/ibXMpD7TJYn7fVu3bl3qCvt169YxceLEVJyCgoKdvHrPsXf0pJQj6cMu1KlTJ/W8aDou6V+YbO50cvlFSmJn+pe//CXrMSA7zXwlycZ7lv45zuZnuiRxv2/pw260a9cuUv5pp50Wa6zyTIkhYW+99VYicbZv387atWvZvn176nnRTmHbtm2xxcnlFynuncLdd9/NTTfdBMCECRPo0qVLapypW265Jbbx+K+//nruv/9+IGjmGzBgQKSZ7/HHH48lTkmykejefvvt2MssSRLv22OPPbbLZewJdM/nHNi4cSPPPvss9913HyNHjmTy5Mmx3wKxoKCAli1b0qpVK9avX0+LFi1o2bIlLVu2TN1tKw6PPfZYmY+4JNFnMm7cuNTz3/3ud5FlRWdaxaF4M1+6OJv57r777tTzCRMmAEQSXVwmTZrE5s07XGoUu6Tet9LsTUlDiSFh48eP58wzz2Ty5Mk8+OCDzJw5k6eeeopmzZrF+uFeunQpn376KZ999tkOj08//TS2ONOnT2f9+vVAMJrn0KFDueCCCxg8eHCsTUlJ7BTKan6LswkmqWa+pBLdZZddRt26denevTuvvvpqrEek6ZJ630pT2p3q9kRKDAkbNmwYb7/9No8++ijTp09n1apVPP3004wdO5Zrrrkmq7Hj/JVYpFevXqnB2AYMGEBBQQGDBw+mUqVKO4yIuiuS2Cmkd5YX7ziPsyO9qGkvPz8/9XzNmjWsWbMm1p1qUonu2GOP5T//+Q+nnXYa99xzD4cddhjXXHMN77zzTmwxIJn37cQTTyzx8cMf/pCVK1fGEmN3oD6GhLk7Bx54IACVK1dm1apVQPCBLPrlHYf+/fvvEPepp55i48aNAIwcOTKWONu3b09dxDZr1qzUdQtt27aN9UbwSfSZfPTRR1StWjV1Nk3VqlWB4L379ttvY4kB/2vmK6p/+k1z4kxASSU6M6NGjRr06dOHPn36sGLFCsaPH8+QIUPIy8tj2bJlOy8kA0m8bytXrmTKlCk7nNLr7px66qmxxNgdKDEk7LzzzqNDhw6cdtppTJ48mS5dgrN816xZE+uvuOeff5527dpxzjnnpModN25c7GPjn3DCCTz22GP07NmTpk2bMmvWLFq1asXixYtju60nJLNTyFYTSHFLly5NJE5Sia7457ZOnTr079+f/v378/nnn8cWJ4n37Sc/+QkbN24s8UfN6aefnvX45cVef6OeXIx2+Oqrr7JgwQKaNm3K2WefDQS/iLdu3cr+++8fSx02bNjArbfeyqpVq/jDH/7AYYcdxlFHHRVr/wIEO+wBAwbw7rvvcvDBBzNnzhwOP/xwDj/8cEaOHBnbbTeT4u47XF3dunXr2K/J+Pvf/84hhxzCMcccw/vvv88HH3xAkyZNOO+882KNk4S33347sZ3m7v6+7Umjq0rMzjvvvMgHec2aNdSsWTO2pABw0EEHcf/99zN79myuuOIKzj///NjPfAKoVq0ajz/+OOvXr+ezzz6jsLCQevXqccghh8QeK9s7hddff51rr72WRo0aRYapXrJkCQ8//DDnnHNOLHGuv/56ZsyYQWFhIe3bt2fq1Kmce+653HvvvUybNo0//OEPscQBUv/zChUqsGXLFj7++GPq169PzZo1Y4uRVFJI8n3b2+mIIeEM/v7773PVVVdRoUIFxowZw69//Ws+/fRTtmzZwvjx4znllFNir4+78/DDD/PBBx8wduzY2MuHYEycvLw8KlasyFFHHUWVKlViLb+0ncI777xDs2bNYtkpHHfccbz22mvUr18/Mv+zzz7jvPPOY+HChbscA+D444/n448/ZtOmTdStW5cvv/ySSpUqsXXrVpo3b87HH8czPNgLL7zA1VdfTYUKFfjzn//Mb3/7W6pUqcKiRYv405/+xAUXXBBLnJo1a3LxxRfTrVs3zjzzzKxd8Z7E+zZv3jz69u3Ll19+ybnnnsuIESNS/Q2tW7dmxowZu1S+jhikRL/85S8ZP348Gzdu5Pzzz+eFF16gbdu2zJkzh1/84he8//77scUqGt3UzOjRowcnn3xy6ugkLgsWLKB///4sXbqUL774gubNm7Nq1SratWvHAw88QLVq1WKJ88Ybb5S4UxgyZAjNmzePJTEUHe0UV7duXbZu3brL5RcxM8wsNQJp0Y60QoUKsR7V3XHHHXz00Uds2rSJpk2bMnPmTI455hg+//xzOnfuHFtiqF27Ns2aNeO2227jyiuv5JJLLqFbt26xDwqYxPt27bXXcvvtt9OmTRseffRR2rZty6RJk2jYsGGsn4HyTokhYVu3buWHP/whEHyh2rZtCwSdqXGOK/P4448zaNAgatWqxQMPPMB1111HgwYNWLx4MXfffTfdunWLJU6vXr144oknOOaYY5gxYwYPPfQQ06dP55FHHqF37948+2zGN+IrUxI7hV69enHSSSfRtWvXyDDVzzzzDL17944lBsD555/Pj370I7799luuuuoqLr30Utq0acM777wT+9XiRUNSHHHEERxzzDEAHHnkkbEmoMqVK9OvXz/69evHF198wbhx47j22mtZt24dXbt2je2K8STetw0bNqRue3vDDTfQsmVLOnTowFNPPaVB9CR70r+QxS862rJlS2xx7rnnHhYtWsSGDRto2rQpc+fOpWHDhqxcuZKzzz47tsSwadOm1A6ndevWqWsx+vTpw7333htLDEhmp3DzzTdz4YUX8uKLL/LBBx8AwdHC008/TZMmTWKJAcFtSj/44APMjDZt2vDJJ5/w/PPPc9VVV3HJJZfEFgeCz1tRs2WRbdu2xfpZS2+OPuKII7jpppu46aab+Pe//80zzzwTW5yk3reCgoLUke4ZZ5zBc889R+fOnVmzZk1sMco7JYaE/eY3v+Gbb76hUqVKXHjhhan5n3zySay3WqxYsSIHH3wwBx98MFWqVKFhw4YAsXcKN2zYkN/85jeceeaZTJw4MXWa39atW2P9VZrUTuG4447juOOOS03PmTMn1qRQJL0vqWHDhtxwQ/z3oxo1ahRbtmzhgAMOSA2FAbBs2bJYboVa5Iwzzihx/rHHHhv71cJF79vatWupXbt27O/b4MGDWbhwYaQZ7MQTT2Tq1Kn85je/iTVWeabO53LWGRSXjh07cvzxx7NhwwYWLFhA8+bNufjii3nzzTf5xz/+wZQpU2KJs27dOn7729+mTr8dMmQIBx10EAUFBTt8wcq7km4q1LFjR1566SXcPXLtxK5YtmwZN954Y6qD88Ybb0xd83HhhRfywgsvxBKnJHPmzIltO5L21VdfMWTIEF588UU2btyYOnOsV69e/OpXv4r1uplsKW/7m9I6nzUkRjkyatSo2MoaO3YsVatWpV69ekyaNIlTTz2V3/3ud6xatSrW0TurV6/O3Xffzcsvv8zw4cM56KCDgOA01jiTQnpTSF5eHmeddRY1atTg1FNPZfHixbHEaNWqFf369Uvd1GbQoEHk5+czcODAWH+Z9urVi9NPP50//vGPLF++nHbt2pGfnw8Q6wVhc+bMiTxmz55Nx44dmTt3bmK3qHz55ZdjK+unP/0pvXr1oqCggAkTJtC5c2cWLlxIYWEh1113XWxxShPn97O8U1NSORLn0VvVqlW5+eabU9NFdz1L0qhRo2K7kc6DDz5Ir169ABg4cCCXXXYZb7zxBi+++CI///nPmTp16i7HmDBhAiNHjuSmm27i3HPPBaBBgwaxD5W+evXqVF/MH//4R8aOHctpp53GpEmTYu3gbNWqFW3atIlcH1OU6MyMadOmxRarNDNnzoztxkD5+fmpayYuvvhihg8fTuXKlRk2bBjHHntsLDHKsie0rmRKiSEHPv30UyZOnMiyZcuoWLEijRs35vLLL+fqq69OJH6cO+yyZOuLtHjx4tTN5y+66CLuvPPOWMrt3Lkz7du359Zbb2XMmDHcc889WTkTZevWrXz77bcccMABQPBLuE6dOrRv3z5yq9RdlVSi27JlC+PGjeOwww7jxz/+MX/961/5xz/+wXHHHcevf/3r2OLUrl2bsWPHcsYZZzBx4sTU9SbuHmt/Vq6/n+WBmpISNnLkSK655hq+/fZbZs6cyebNm1m2bBlt2rRJ7IYnce6wR44cWeogaXF+kfLy8ujfvz+/+MUvWL16deSc8jjPL69SpQr33Xcft9xyCz169EgNOhinq666iunTp0fm/fjHP2bChAmccMIJscXp3Lkzr7zyCq+//jpdunThiy++yEqi69mzJ6+88goPPPAA3bt3Z8KECZx88snMnDmTPn36xBZnzJgxTJo0ifbt2zN9+nQefPBBIBg5oPgZft9Xefh+lgfqfE64M+iHP/whH374IRUrVuSbb77hvPPO4+233+aLL76gU6dOzJ07N5Y6TJ8+neOOO46qVauyadMm7rrrrtQZNrfccktsF55Vq1aNypUr07BhQ7p160aXLl2oXbt2LGWnK34Pho4dO1KjRg1WrFjByJEjYztXPp27s2HDhtTgc7uzuXPnMnDgQObPn58a0TcuJ554IvPmzaOwsJC6devy1VdfUbFiRdydpk2bJnITnbhk+/upzmcpVWFhIQCbN29O/SI94ogjYv3lm9R9Eo466ijy8vK49dZbmT17Nk2aNKFDhw488cQTsd4prkePHpFH0TAFderUyUpSgOAiuqKkEGcnalmyFad58+ZMmzaNJUuWxF729u3b2bJlCxs2bOCbb75J3aBp8+bNiV0tHOf7lsT3s7xTYkjYVVddxUknnUSfPn045ZRTUmdTrF69OtahKorfJ+H++++nbdu2DB06NNYRVouuRj7nnHMYPXo0X331Fddeey2TJ0/mqKOOii1OWZLYac+cOTPrMbIdJ1uJrnfv3hx77LE0a9aM4cOH06VLF/r06ZO6ijwJcb1vSX0/yzs1JeXg0G7+/PksXLiQE044IWtnU3Tp0oXzzjuPnj170rNnT6677rrUfRKuuOKK2L5IzZs3L/XwuuhCvmwbOnQod9xxR1bKvvLKK3nyySezUnYu4hSJ+z376quvADjssMNYt24db775JkcccUTkwro4zJgxAzPjpJNOYsGCBUyePJljjz021mG3s/n93F2aknRWUkJ2/EAcCLM+AT753mWW9YF49NFHGTBgAMOGDePggw/mlFNOSd0n4dFHH/3eMYsra8iDuJNCaTuFuHZwHTt2jEy7O2+99Rbr1q0Dgpve705xILvvWck7uaIfCQfCrNUwMfMd4c52cHfccQevvfYahYWFnH322UyfPp0zzjiDu+66i7lz5/KrX/0q88oXk/T3s7xTYthDZfs+CdEv0n92ubxc7hSK5OXl0aRJE6666irMDHdn1qxZDBo0aJfLzkWcJN6zJD377LN8+OGHbN68mTp16pCXl0fVqlW54YYbOPnkk3e77SnPlBj2MGUfqq74zuWVl189SewUZs2axQMPPMDw4cP5/e9/T7NmzTjwwANp165dDFuQfJw9bUe6zz77ULFiRSpVqkTDhg1T/SUHHnhgatRdiUdGicHMOgAPABWBR939rmLL9weeBFoC+cBl7r40XHYz0BvYBvR39ylllWlmDYBxQC1gNtDd3eMbClJ2S9ncKUSTaWMKG9Xk/7r2o2Ll6nzz343fq124pISaVJwie9qOdL/99kv1W82ePTs1v6CgYLfcnvJsp4nBzCoCDwFnA3nATDOb5O4L0lbrDax196PNrCswArjMzJoAXYHjgcOAN82scfia0socAdzn7uPM7M9h2X+KY2Nl95K+o1y+8huOGPQcFfY9AD/nztSy7Zu/ZuWKzHeqmRwB7VP1YGpfeDPffDIT2y97nefZilP0XiT5nmVLeh39RzfT5M4dr9re9k0B25r33i22Z3eRSZptDSxx90/DX+7jgE7F1ukEFF2B9CxwlgWXWHYCxrn7Znf/DFgSlldimeFrzgzLICzzQmSvV+fyEVTYNxhCwux/H1vfVkit83+ZlZiVGp5EjXY9slJ2EnFy8Z5lk+1T8uipFStVY7/a9ZOtzB5up6ermtklQAd3vyqc7g6c7O790tb5OFwnL5z+BDgZuB34p7uPDeePBl4LX7ZDmWnrHx3OPxx4zd13GCfAzPoCRQP+HAMs+q4b/x0dDPw3yzEUp3zH2ZO2RXHKb4wk4xzp7jsMVbDbdj67+yggsXFwzWxWSef7Ks7eE2dP2hbFKb8xkoxTmkyakr4EDk+brhfOK3EdM9sHqEbQCV3aa0ubnw9UD8soLZaIiGRRJolhJtDIzBqY2X4EncnFr8CZBBQ1kl4CTPOgjWoS0NXM9g/PNmoEzCitzPA1b4VlEJb54vffPBER+a522pTk7oVm1g+YQnBq6Rh3n29mdwKz3H0SMBp4ysyWAGsIdvSE640HFgCFwHXuvg2gpDLDkIOBcWY2jOAyytHxbe4uSarZSnHKb5w9aVsUp/zGSDJOifaIsZJERCQ+uipEREQilBhERCRCiUFERCKUGPZCZhb7OA9mVrWsR9zx9iRmVtHM/rCnxElCuC1Zv3w7wTgVzOzUbMfJlDqfy2Bm1YErgfqkncHl7v2zEKst0MjdHzOz2kCVcBiROGOcCjwaln2EmTUFrnb3a2MoexnggBGMi7UhfF4F+MrdDy/j5d835ifAP4F3gXfTzmyLM8bIEmYXEJyRF9up1Gb2T3dvE1d5uYwTDqrZmR2/N3fGHGeGu8d7J6Dcxpnr7s2zHScTu+2Vzwl5lWDH8y9ge7aCmNlQoBXB0B6PAfsCY4H/iznUfUB7wutQ3P0jMzstjoKLdvzhwIevhqcxY2YXAPHdXiuqCcFQKj8Cfm9mxwDz3P2iGGMcABwLTAinOwOfAU3N7Ax3vz6mOHPNbFIY5+uime4+Mabyk4zzIkHynA1sjrHc4t43sweBZ4huy5zdNM5UM+sMTPQc/2JXYijbAe4+MIE4FwHNgTkA7v6VmR2UjUDuviwYqzBlW8wh/s/dr0mL95KZDY85RpFtwNbw73ZgVfiI04kE21R0/c2fCI5Q2hL8YIjLAQRX/p+ZNs+BuBNDEnHquXuHGMsrTbPwb/qRiBPdtt0pztXAQGCbmW0iOOJ2d0+8KVaJoWxPmVkf4GXSfvm4+5qY42xxdzczBzCzyjGXX2RZ2JzkZrYvMABYGHOM5WY2hOCIB+AKYGXMMYqsJ9g53ws84u75WYhRg6A5rCCcrgzUdPdtZhbbr2F37xlXWeUgzj/M7IfuHmfi3IG7n5HN8nMQJys/Br8PdT6XbQvwe+ADgsPi2cCsLMQZb2Z/IRgnqg/wJvBIFuJcA1wH1CUYg6pZOB2nywnGwXotfBwBdIs5RpFuwN+Bawmulr/DzM6KOcbdwIdm9piZPU5wNf7vw+T9ZlxBzKyemT1vZqvCx3NmVi+u8hOO0xaYbWaLzGyemf3LzObFHAMzq2Zm95rZrPBxj5lV213jhLE6mtkfwsdPshEjo3qo87l0ZvYp0Nrdsz78rZmdDZxDcPg4xd3fyHbMPYWZHQucC1wP/MDdD4y5/EMJ7iECMNPdv4qz/DDGG8BfgafCWT8FrnD3s3e3OGZ2ZEnz3f3zuGKEcZ4DPuZ/94LpDjR194t30zh3AScBT4ezuhGc5HBznHEyqosSQ+nM7HXgQnf/Jtd1iUN4tlMfdjxbpFeMMY4maCctHuOcuGKkxXoOaAp8QnDk8B4w3d2/jTlOXeBIotvz95hjfOjuzXY2bzeK05TgpAAIzhj7KM7ywxh72ns2D2jm7tvD6YrAXHc/Mc44mVAfQ9m+JmhGeItoH0Osp6ua2QaCzqx0BQTNVoPc/dOYQr1I0HH6JvF3Ohd5lmDgw7FZjFHkdwRfnKzFMbMRwGXAfP53ZpoTJKI45ZvZT4G/hdPdCDqJ45b1OGY2gOAHSFGH9lgzG+Xuf4wzDrDJzNq6+3th3P8DNsUcI8k4ANUJBiKF4PYFOaEjhjKYWYn3W3T3J0qavwtxfkNw7+u/EjQldQUaEpyl9HN3Pz2mOLH/yikhxhx3b5HlGGUewsd56qWZLQJOdPdsnnZZ1PzyR+CUcNb7QH93/2J3ixP+8j3F3b8OpysDH8T9y9fMmhE071Qj+N6sAX4W99FJgnG6AXcR3HrAgNOAIe7+TJxxMqqLEkPZLLhfRONwcpG7b81CjI/cvWmxeR+6e7OSlu1CnGHAP9z91TjKKyXGUGA58DzRo6z1McZ4LHz6A+BUYFo4fQbB9sXWaWdmrwFd3H1jXGXu6czsX8BJRU16ZnYAQd/MD7MUryrE+xnLVZywP+ukcHKGu6/IVqyy6KykMpjZ6cB/gIeAh4HFcV0QVsw3ZnapBZfFVzCzS4GidvI4M/cA4GUz22Rm681sg5nF/SG/CriV4Ghnfvj4OM4A7t4zPO1yP6CJu3d2987A8QQXB8bpG4LmxL+Y2ciiR8wxMLO7LRg+ZF8zm2pmq8Mmn90xzmPAdDO73cxuJ7hINPb7qpjZgHBnvQG418zmmFk2+rKSivN/wPrw4tCqwE2ldeRnnbvrUcqD4PTUY9KmGwOzsxDnKOAlgpt/rw6fHw0cCLTN9ftQXh/AwmLTFYrPiyFGj5IeWdiWD8O/FxHsRKsBH+3GcVoA/cNH8yz9/z8K/7YnOEI9HpizG8eZR9CE1JTgh9V1wDvZeO929lDnc9n2dfdFRRPuvtiCC8Ni5UHn8gWlLH5vV8s3s2Pd/d9mVmLbv8d4ab+ZHUhwZHKku/88PEupkbu/FleMNFPNbAr/60i9jBivLYD4+5PKUPRdPB+Y4O4FFr1CvdzHMbOq7r7ezGoCS8NH0bKaHv+FoUUVPw940oM7RmbjTUsqTqG7u5l1Ah5y99Fm1jsLcXZKiaFss8zsUaJX8cZ+gVvYBtub4JfIAUXzPb7TSAcRnCVyTwnL4r60fwzB1chFpyp+RTAuT+yJwd37hR3RRbFGufvzcZRtZuPd/dKwvXyH5jyP/xTCl83s3wRnu/w8PLU41tNuE4jzV+AnBEfa6e+ZhdNHxRSnyOzwlPIGwM0WDCOTjTHNkoqzwcxuJri25DQzq0D8TaMZUedzGSwYJfI6gis5ITjV82GP+QwVM5sA/JvgquE7CRLQQncfEGecJJjZLHdvZWkjRSZxNlTczOxQd19eWhuvx3yxVhizJlDgwXAblYCqnoXOx6TiZFu442wGfOru68LtqufusV5lnWCcOgT7gJnu/q6ZHQGc7u5Pxhkno7ooMeRe0U7UzOa5+4lhc9W7HtPwyAmf3vkPgiOQf7h7CzNrADzjMQ5bbCVf9wE5HHRsV5lZF2Cyu28ws18TtNEPi7OZL6k4YSfqh+7+ddix3QK43+M/9bakOA/EnbQTjFMZ+DZM2I0JRvV9zbNwJuRO66LEsKPSmg+KxN2MYOF4B5d0xAAAIABJREFU72ZWNO7PCoJT1WI59E47vbMkHmOTFWbWARhCMCT2a0A7oLe7T40rRpJKSUKxX3yY9qOgLTCMYIyu29z95DjKTzKOBdcxNCUYmfZxgnuAXOru7eKKsYfGmU3QLFqD4PqSmQQDbF4RZ5xMqI+hZEXnwRcNMJc+rkw2MukoM6sB/JrgXglVCE75jIUnNHJn2CH3EdCF4PoCA25097iHwk7S/ZR+8eEY4PSY4hRdvX0+QV/JKxZcdxK3JOKkd6I+mMVO1D0tjrn7N2HZD7v73WYW+1AiGcnFqVC7y4NguIXi82I9TY3gFMtLE9qeWsBIgp3abOABoFbMMT7O9f8t5u3Z4VRO/nfKZ2yneRIM7f4X4FOCYRH2j7P8JOMA7wA3A4uBOuFn/F9Z2JaiOP/ZQ+LMJbgi/Z/A8eG82ONk8tAFbmWzsH2xaOJUYr4o0IMBs26Ks8wyjCO4TqIzcEn4PO7L7T80s3Jxe8KYJHXx4aXAFKC9u68DagI3xlh+knEuI7jqvbcHndr1CJqs4lYUp9ceEud6ggT0vAenxB5FMDxG4tTHUAYza0nQXFA0Rspagg9H3B2CdxFc3Fb81oGxnvdtZh+7+wnF5v3LYxiqwMz2cfdCM5tPcIvSTwi2pahDOKvjJ2VL+OV8gOCXnBP8mvslwf0sWno4sFpMsbJ+3+8k4iTZiRqeNdbI3d8Mz7Cq6O4bdtc4YaxKnuMRnZUYMmDhTTncvWBn637P8kv6UrrH1PmcFudeYAYwPpx1CcH9Jm6Ioew5HpyF1LCk5e7+ya7G2JNZ2n2/3b2xmR1GcAFarPf9TiLO/7d35mGWVdX5fj8QAogNCkgcQLERCCIINIbpZ0ADYgAVHBBFVIKK5MEWRMSoEYcoqEENRAWFFhEnRAJogiizIqKNLYhCoogjzoZGQZm+3x97365T1beG7t5731t11/s89VSdc+ue71RX9V5n77XXt1olUZWaWr2C1FFvvqTHAx+2XbRZU0OdXUjV6Ova3lTJuvyVto8sqTOje4nAMDm5juE5LN9b4G2TvWeYyTtsHsxYcc5qjM1Q7FXY5tmtW5gLSDrOKfl3Cv0L3Epbry8h9/32WP3HDS6/A666Tuch4Shg7fzvWMwMsqOzhNRA6Rudn6XIDHhAOt8gPaxd2NFZbpbfgtiVNDUXkLYmLqbjFFqaPDU9BtjU9ivyE8mWtr9QUsd1e8puJOmYKbRPrqhdg14v7BqtXPvRqu93Cx3lp98XkSr6oY5h519s36PsTiHpQdTZNdhKB9s/1Xi3jdo9TfoSgWFqHm17nwY6i0jBZ9d8/HOSjUSRwJDXR/+vtxQmaU/g2SQvm/+wfU8BmdVJ22yrGPy0xvZFSh20nlhiqW0GTOz7fRh1+n630GmVRL1S0j8Dayu1xj2SZEA5W3V+mje4WKnIdSFjDyhNiaWkKZB0OnCK7Rsr6/SzkSjZh+EbwAG2f6HUdOQrpO5n2wL32j68gEb1Bj2DQNLXbe8y/XcW0WrS97uhTtUkqpJVxT/S+VmAj7rwoNZQZ0PSRoe/zzqXAAtt1+jkNyUxY5ia3YGX5uTwXxjbYVPaQO0eJVfS3vR+PmWXrtb2WAP7Q4Azbf9b/oNfUkhjTswU+rBE0oWkGVx3x1hJG5HVga/Y3hOoMkg31lmWRAWqJFHzz/LxnNCuMbMahM6LSyfoV5YIDFPzjEY6JwAXA5tIOgfYDXhpwet3B+2nkqb52H5A5dyDi+7QGCLWIvVE7jrQmrF+xqtM3tb5gKT1au18a6lDqhZ/OqmKH9vfUeEGV/lneYykNQsthQ6DzguB99XSWBEiMEyBs0mWpIfTscOuoHNJ3uK3M2kQX2j7twUlLpP0WVLLzYeSW2EqtREs8sdeuuZi0EjaxPZP3cdORFKx1qEd/gjcKOnLjJ+ZFN391EqnURL1VuBreUbX/VlKb3RopfNVSaeyfD1T0bqpmRCBYQokPZPUw+CRwK+Bx5CSQU8orHMRyYvnQucG6oV5Dal68xGkjnC9QqO/Bt5YQW8u8GVJ+9i+rXtS0stInlZFd4yRZiDFZiED1mmVRP1h/lgNqLnjrpVOz5q+ux2+dL+UGRHJ5ylQMrB6Kmlddvu8m+cQ20UNtCT9HWng3pdUDPRp4AvOzdRrkBNdvyudQJsrSPoH0pLIvrb/N597A8kv/xm2fzbI+xtmhimJGqwcMWOYmntt/07ZJ8f25ZLeX1rE9pWkLXGrkwLRy0lWHEX6CkjaGTgR+D3wdpJb7IbAapIOtX1xCZ25hO3/kvQX4L8lPRs4nFTk9BTbfyitp/5W7z1773eUGlRr67RMouaZ9mQ/y2mlHqwa6vSrA7qD1Ge+1CaRGRGBYWr+T9K6wFXAOZJ+TWftryR5V9L+pJnDDkDJXsOnAv9M8ny6jPTEe62krUj9kiMw9MH2pXnp6ArgGuCpFWdx/01ah/9kPn4BsA6pN8fHmLwn+FDpNE6i3gpsxPie33cCW5B2EL14luksyB+9Gon9gBuAIySda/vdhXSmJZaSpiBXhd5NWlt8EWlgPaf0lDgnhp9MGqA/A1zp5Lpa6vrLWmtK+r7tv+m8NqesLEqhsQY9IllT30saUKt0ietXB6Ixa4li9gstdCS9j9SruGoSVdI3be/U75ykm2wXyQU21LkK+Afbf8zH6wJfBPYhzRq2LqEzE2LGMAWdRPADwFl53//BwDmFpc4ADra9bOeGpDVczo2yG2TunvBaPBn0obJ9SD9Wl/Rk29cBSNqJVE0OcN8s02mVRF1X0qbOLUOVeiSvm18rubW0lc7DGV+/dC+wse2787JmMyIw9EHSPFL3tkeR9mJ/OR8fS+pQVjQw2P5S1hXpP88LSdPIjQtJbCdpKelpd+38Nfm42jbcYIU4HDgzPyVCWqo4PM9a3zWbdHIBXQteS9ri+UPS3/JmwJH5Zym5FNtK5xzgG5IuyMf7A5/MOt8rqDMtsZTUh/yL+QPwdVLh1sMZqy8ongTKyeEXkvyLHkYKQhfWSHIGw40qW7y30GmZRFVyQN4qH95SKwfUUGcBqcAV4Gu2W5k4jr+PCAzL011rzbssbic5nxb9Y5D0TlJ/5J+QElvnA9+yvVlJnWD4kbQx8E7gkbafIWlrYBfbZ8w2HUmfpH8S9bGk3g9FkqgacyV+jO2Xq5IrcSudrNWkWdN0RGvP/ixb28/r/j+r9IRwOPAr4EPA2TmpHZF6NPkYyZztkfn4f0iFibNR59HADrZfa/u1wI6kWfdTKGv1soi0xt8zOfw58I6C12+qo9RE6fVkyxpSAv8TpXVmQgSG/mwnaWn+uBPYtvd1Z32+BI8g/YHtD/xQ0tmkHEDkfkaPDW1/lrxRwPZ91LGRaKEzaRKVsuaQ8/Ps414AJyfXGmaOrXQOAJ5J3snlZHzZehMEEMnnvtheffrvKqJzP2mL6sV5DXM/YG3g55Iutf3CFvcRDAV/krQBYw67O5PW5WejTqskam1X4uY6dpNmTdMSOYYhRNJDSP0TPj7oewnaIGkH4BRgG+C7pIKq59n+zizVqZ5EVeor8SZga5Ltxm7AS21fMUt1jgUeD+xF2iF2GPAp2/9eUmdG9xKBYXBMsntjGRXcG4MhJi8hbklaprilYB1Lc51WSdQ8++m5El/rsq7Eg9Bp0kRp2vuIwDA4crJpUmy/tdW9BMNFHiCOs73XbNPJf9cLSDt3tpD0SNJupN2meeuq6m4BvM72y+eIzmqkwtfSBbXTEjmGARIDfyDpqcCHSbuE/hM4ibQLRsC/zjadzAHA9sD1kJKoeXm0CJK2Bd7L2M/yHyQ/sL8l2eTPNp2mBbUzIQLDECBpLVJP2SfQqUS2fdjAbipoxb8BryAVUz4jfz7e9qmzVAfqJ1E/Qtri/XWSj9ASUgXyiwpvK2+lczZjBbWHkwwvBTy7tatqj1hKGgIknQvcTKp+fhvJsO/7thcO9MaC6kw0tZN0i+0tZ6tOvnbVJKo6ppD5+Fbbjytx7QHpNCmoXRFixjAcbG77eZKeZfusXDl69aBvKmjC+pIO7Bw/qHtsu1S3tVY62H5vzl0sJSW5/6VwEnUtSdszVkvwl+5xQRfXVjrjCmol1SqonTExYxgCJF1n+8lKtrtHkrzxr6vxdBIMF5IWTfGySy0nttKZRLtoElXS5VO8bNtFXFwb6txPKmrrBaC1gV4RnV3Y4n1G9xSBYfBIOhw4D9iWlBBcl/SU9eGB3lgw55C02cRto/3OreS1p0yi2n7WqmoEbYjAEAQDRqnn9x9s3yDp+SRPoR8CH7RdtMJ2Yq4hn1tse8cC127tSvw84GLbd0p6E6nz4dttf3s26eTNJ0cAm5PMBs/MViUDI3IMA0TSIbY/MVmhWxS4zX0k/QdppriWpFtIs8WLSdW1Z5I2IpTQ2Yq06229CbmGeZTryfG4ThL1o9RPor7Z9rm5mO7vgfeQtuT+7SzTOYuUZ7ga+AfS72mgG08iMAyW3ja+fnu8Yyo3Guxpe+v81Phz4OE5AXka6emxFFuSvLjWZ3xf5zuBUoVarZOoPfO/fYHTbX9RUg131do6W3cC6hnAdQWvvVJEYBggtk/LX37F9te6r0mqWiUaDA1/BrD9Z0k/zsaK5DqAYlYVti8ALpC0i+2vl7ruBLqdAmGsW2CtJOrPcwDdCzgpG1HWcIyurdMNqPdJNYxbV4zIMQwBk6z7LncumHtI+hlwMmnwPDp/TT5+je1NCuu9m2T1fjdpyWpb4GjbA/H9XxWUGujsA9xo+38lPQJ4ou1LZpNOZ1cSpN/7wHclxYxhgEjaBdgV2GhCnmEeYw3ag7nNRxhbSux+DfDRCnp72z5O0gHAbcCBwFUUagiTC7Rusr3VtN+86jrXd3Vs307Ka5TmEcAXbf9F0h6kYFrM+diNbP5XhAgMg2VNUrLxQYwfEJYCzx3IHQVNGYBf1hr5874kY7s7Si5d5NzCLZI2tf2TYhcekE7mPGCBpM2B04ELgE+SEsVVkfQT25vW1plIBIYBYvtK4EpJH7P940HfT9AeSU8gdQi7MB+/D1gvv3xqweraHhdJupm0lPSqbIldOkH8UOAmSdcxtkSC7WfOUp0H8tr/gcAptk+RVHRL7BQMJOEQgWE4uEvSe1jeRK9IZWUw1JxI8hPq8XTgzcA6wL8Azy4pZvv4nGe4Iz913wWULjx7c+HrDVrnXkkHA4cytqNrjSm+vyQDSQJHYBgOzgE+Q9pOeATwEuA3A72joBWPsH1N53ip7fMAJL2ytFhOpB4JbEpyW30kaSvrF0pp5JlwdWxfKWljYKd86jrbv64g9TLS/8t/tf0jSZuRHFGLMFkdE2m2sG4pnRWhxtauYMXZwPYZwL22r8y+NTFbGA3G1bDY3rlz+PAKeouAe0ibHiDVThTd+y9pZ0nflPRHSfdIuj9vWy1KrhK/Dnge8HxSn+niuTnb3wNez1h/iR/ZPqmgxEMm+VgX+EBBnRkTM4bhoLeP+XZJ+wK/AB42wPsJ2vELSX9r+xvdk5J2Jv0dlGa+7YPy0gi271L5jfOnAi8AziV1cjsU2KKwBsAbgZ16s4ScL/kK8LmSIpL2JzXsWRPYTNKTgLeVymUMYAPCtERgGA7eIWk94LWkRu3zSHvag7nP64HPSPoY+YkU2JG0nHhQBb17JK1NXruWNB8o6scEYPsHklbPBXuLcrL2DYVlVpuwdPQ76qyCnAA8GbgCwPYSScWcjyVN2afC9qtLac2UCAxDgO3e+u4dwJ6DvJegLbavy7ODfwJemk/fBOxs+1cVJN9CKmzbRNI5JE+ml075jhXnLklrAktyovt26gzYF0v6EvCpfHwQ8F8VdO7ts633gYLXPwL4LvBZ0ixx4KXPUfk8BOSnjw8Au5D+4L5Oqka9daA3FlRH0rq2/zjJa/Nt/7CgloBHk6pqdyYNQNfa/m0pjazzGOBXpKWXo0nbbz9o+wcldbLWgcDu+fBq2+dX0DgDuBQ4HngO8GpgDdtHFLr+BqQ8yUHAfaSNKJ+z/X8lrr9S9xSBYfBIupbUaLz35PMC4CjbpV0igyFD0g+BN9j+bOfcWsCbgBfY3ryw3rI2kjXJy1Wb2r6lss5fk1xOHwC+afuXFTTWIeUz9s6nvgS8o4ZBoKRHk/7/HwO83nax3U8rQuxKGg7WsX227fvyxycoZ4UcDDd7Ay+TdImkzSU9C7gR+CvgSVO/daW4XtJO03/bypOTtUtIS1ZIepKkCyvoHE7alXQAySngWknFO9HZvsv2G23vlD/eVCko7ECy2z4E+G9gcWmNGd9LzBgGj6STSA1OPk1KCh5Equp8D4Dt3w/u7oIWSHodqdDtl8DTbd9USedmUkOYHzPWTtK2ty2osZi03foK29vnc8VnKkr9K3a1/bt8vAFwje0tC+t8GXheb2lH0kOBT9t+eqHrv41kUfJ90hhw8aAb9URgGAIk9doq9n4Z3eSTHb2f5yySHgS8DjgcOInkv/MQ4MgayzB5/X85SlqySLrW9s6Svt0JDDeUDD75mtcAe9i+Jx+vSQpGu079zhXWWfZzTHVuFa7/APAjUu4Hxo8DRYP2TIldSQMkT+l/anuzfPwSUnLrNuCEmCmMBEtI2yB3sH0HcLqk/YALJZ1n+59Litn+saTtgP+XT11t+zslNUj+RS8EVpf0eFKy9ppp3jNjOpXCPyAVtV1AGkyfRdnmRj0e6Jr15eBa8ol6s4LXKkLMGAaIpOuBv7f9e0lPIU0jjyKtLf+N7XBYneNI2tH2cmvJOXn7JttvLKy3kNSx7fP51AGkrmSnFNToJmtFSta+vdS6vKS3TPV66YIxSfuQXFWvJP08/w94he0vldQZJiIwDBBJ37G9Xf76P4Df2D4hHy+xXSP5GIwwkm4AdrH9p3z8YODrg1iumE1I2pC0xRcqbPGdQvd0269oodUllpIGy+qSHpQTTU8jmZr1iN/NiFNpUBBjPYzJXxctqJK0BXAs8Fg6f8el3YJb6WR2BZ7SOS5mOjgNp03/LeWJwWewfIrUj+G3JH/8qwGUGoLcMcgbC4aCGoPCItK6fK8Q7NnAGYU1zgU+TOpAd/803zv0OpJOJDm4npNPLZS0a8n8j1JHupNsH9s932+ZsQWxlDRgsh3CI4BLOtP7LYB1KzRpCYaQyQaFino7ML5auGjTGUmLbe9Y8poD1rkBeJLtB/Lx6sC3K+yyunaCu+7AiBnDgLF9bZ9z/zOIewkGQ26Ys/v037nySDrQdi/h/KPKDx0XSToSOJ+OQV+pXXaSes7DVXUmsD7Qu+56U33jKvDtXAh4LuM70n1+8rfUIWYMQTAESPoQ8CgqDQqSrre9w8Sva9Cpy+lSrB4nX9/0z40Ur/tRsig/Ebg8az4FON72ZwrrLOpz2k79WZoSgSEIhoDag8KEYrNixVlznY7x4H2M7xRX3JNpmIjAEAQjQLbCOJjkj/YJ4IV0nrhLLC1Jeqrty7Lj6XKUXhKZROcO4EYXbPHZ0Hjw0aR+LLvlU1cDC23/rLb2RCLHEARDQINB4Xbg5Pz1LztfQ1qWKbHF8++Ay4D9+7xmxorqSvGPJKv6y/PxHiTjuc0kva2gM+n1knay/c1C15uMRcAnSRbckMz0FgF7VdZdjpgxBMEQkI3aPslYk/lDgBfZbj4o1EDSc2yfV/iaXwIOdW5oJGlj4OOkmdFVtrcppHMz8HiSVU0V48Gss1xR66AKXWPGEATDwUa2u3mGj0l6TWmRvNVyX5YvCjt5svcU4n1A0cAAbOLxXe5+nc/9XtK9k71pJSjiojoDfifpEMb6shxMalfanAgMQTActBoULgL+TOr5ULI95XTUaFd5haQvkHZyQTKgvCLbfKxy97PcMOkIkk35jcAZle2wDyMtJ76PtPR2DeXbrs6IWEoKgiEgO3aeQloz7w0KR9n+aWGd4vbXM9T9ie1NC19TpGDQy8t8DTjPhQY1SZ8B7iXle54B/Nj2whLXnkRvN9tfm+5cCyIwBMEQ0GpQyE2hLrV9Scnr5mvfSH87agFb2P6r0po16e5Gyn0zrqtc/7FcfUntmpPJiKWkIBgOTgEmDgD9zq0q1wLnS1qN9DTcS6TOK3Dt/QpcY1okfdX27pLuZHwgKvmzQPr3gXTR+9IEpTySdiGZ9G3U6TUBMA9YvYroNERgCIIBMoBB4WTSctWNpZZcepTsAjeNzu7580MqS20naWn+WsDa+bh0AFoTWJc0Hnd/pqWkXtbNicAQBIOl9aDwU+C7pYPCdNTqK5A9ph5ve1HumfAQ2/0sOVYY202e1m1fSXJZ/lir4DodkWMIgiFA0mNaDAqSPgY8DvhvxhvPVd2uOlmnulW85luABcCWtreQ9EjgXNu7TfPWldFaHdiY8Vt8f1JYYyPgOOAJwFodnRr9JaYkZgxBMBzcJek91B8UfpQ/1swfRWncV+AAYHvg+qzxC0nFl5ckHQW8BfgVY1t8DZTe3XUO8BlSruYI4CXAbwprzIgIDEEwHDQZFJz7IUtaNx//sfD1q1uId7jHtiUZlrUprcFC0qykdrHZBrbPkLSws7xU24ajLxEYgmA4aDIoSNqGZLvxsHz8W5KtxE0FZVr1FfispNOA9SW9nFQg9pHCGpDyMi06KvZ2Qd0uaV/gF+TfU2siMATBcNBqUDgdOMb25QCS9iANprsW1FiLVLXdXQYrbqJn+72S9iIl6rcE/sX2l0tqZG4lVVR/kbp5mXdIWg94LWmr8jzg6MIaMyKSz0EwBEjaj1Rhuwljg8JbbV9YWOc7treb7lwwRk5yL0dvWW4uEoEhCEYISeeTkrVdF9cdbR9QUKOqhXing1s/bHt+CZ1WSDqFyX8ebL+64e0AsZQUBANlAIPCYcBbGVvWuTqfK0ntvgILJhyvBjwfOBb4diGNZTTYRvqtQtcpRswYgmCASHrJVK/bPqvVvZSiVV+BbOvxYuB1wBLgnba/V1Ij61xC2jF2LJ0dY7ZfX1or61XZMbYixIwhCAbIxIG/1qAg6SKmnpk8s6BcVQtxSWuQZjlHA18Fnm37B6Wu34dB7BiTpN9QfsfYjIjAEARDQINB4b3584HAX5P6PkMatH/V9x0rT+2+Aj8C7gPeD/wE2FbSsmKzCtti59KOsRkRS0lBMARIugZ444RB4Z22iw4Kkr5le8F051ZRo6qFeLb1mCr5XDRnMoo7xiIwBMEQ0GpQkPR9YF/bt+bjzYD/sv03BTWq9hWQtJ7tvgVnkhbYHrpk7kxosWNspsRSUhAMB7dKejPjB4VbK+gcTSrWupVkH/0Y4JUlLtzQQvwrkva2/YcJ+nsBZ5Ke7FcZScfZfvdkO8dm6Y6xGRGBIQiGgyaDgu2LJT0e2Cqfutn2X6Z6zwrQykL8dOBySXvZ/g2ApBcC/wrsW1Dn+/lzkxlIDnSvztXPD9i+s4VuP2IpKQiGiBaDgqRdgccy3kL64wWvX91CXNKLSbUFewMHkbaR7mP7tsq6qwHr2l467Tev+LV3Is14ekH1DuCwSs60UxIzhiAYAiYOCpKqDAqSzgbmk/b9359PGygWGGhgIW77bEl/JhW0/QTY3fZvS12/i6RPkgLP/cA3gXmSPmD7PYWlzgCOtH111t2dVBhY2t57WiIwBMFw0GpQWABsXbmDW1ULcUk3koKZgHWADYDLlJoy23bpf7OtbS+V9CJSg6PjgcVA6cBwf+/3D2D7q5LuK6wxIyIwBMFw0GpQ+C6pjuH2CtfuUbsgbL+C15oJa+SiumcDp9q+t9cDojBXZhvxT5EC30GkjQI7ANi+voJmXyIwBMFw0GpQ2BD4nqTrGG8hXbLyuWpB2AD6Ip8G3AZ8B7hK0mNICfXS9LYmT3Rz3Z70N9GsxWckn4NgCJB0+RQvu9T6vKS/m0TgyhLXzxpNCsIm0T7d9isa6DzIdrEZXU5qP9f2Z0tdc1WIwBAEA2bYBoXZjKQdKyTsF5LyPXcCHyU9wR9v+5LCOkUr0FeFCAxBMAS0GhQk7Ux6iv8bUt3B6sCfbM8rcO1mFuKSVgdOsn1sqWtOofUd29tJejqpGPDNwNmlKrk7OicCvyUl7rstUX9fUmcmRI4hCIaDr0g6lvqDwqnAC0j9mBcAhwJbFLp2MysK2/fnnVstUP78D6SAcFPeAVWag/Lnf+qcM/C4ClpTEjOGIBgCcleyidh20UGhNzORdENvW6ekb9vevqROvm7VvgKSPgQ8ihTkusG0qLuqpEVZZzNSgnh14ArbO5bUGSZixhAEQ4DtzRpJ3SVpTWCJpHeTtq2uVlKgYV+BtUh9HrqJeTNmK1KKfwSeBNxq+y5JGwAvK6yBpHWAY4BNbb8iW5dsafsLpbWmvZeYMQTB4Gk1KOStlr8i5ReOBtYDPliy0U0rC/FWSHpKv/O2ryqs8xlS4dyhtrfJfxPXlO58N6N7icAQBINnkINCyV4J+XqtLMQfTUqk75ZPXQ0stP2zwjoXdQ7XAp4MLC5p8ZF1est8y5b2BtWPoegUMgiClWa+7XeTi8Ns38VY0nOVkbS6pIMlHZuXepC0X366P7WUTuZWSW+W9Nj88SbqWIgvAi4EHpk/LsrnimJ7/87HXsA2wB+me99KcI+ktck7uyTNp1OE2JLIMQTBcFB7UDiDVHB2HfDvkn5B2pV0vO3/LKgD7foKbGS7Gwg+Juk1FXQm8jPSdt/SnABcDGwi6RzSTKh4LmMmRGAIguHgBOoOCguAbW0/IGkt4JekWcrvCmoATfsK/E7SISQbEUj9q4v/PBPqM1YjJaKL+xbZvkTSYmBn0mxxYS3H2OmIHEMQDAl5t0tvULi25KCgCa01Jx6XpFVfgZxIPwXYhTRwXwMcZfunhXVe0jm8D7itZE6mo3Op7adNd64FERiCYAioPShIugvo7TwSqSfDD/LXRa2qJd0A/NMEC/EPlrbD7pc0L51Ib0Gewa0DXA7swVhuaR5wse2tJnlrNWJcKDLUAAARVklEQVQpKQgGSGdQ2FDSQxk/KDyqoFSNNfHJaGUhfgowcdbT79wqkbcOvwvYmvGNh0oVH74SeA0pgb6Ysb+BpZTfGDAjIjAEwWBpMig0tqquaiEuaRdgV2AjScd0XppHqkouzSKSFfb7gD1JuZ9iOzptfwD4gKSjbJ9S6rqrQiwlBcEQMMhBobRVdW0L8WwdvgepO9yHOy/dCVxk+39X5fp99Bbb3lHSjbaf2D1XUidft2o/7hnfRwSGIBgOBjUolLSqbmkhLukxLWZCudZjd+BzwGXAz4ETbW9ZWKdvP+6SrrQzvpcIDEEweFoMCq2sqhtaiG8EHAc8gfFr/6UrkncCvg+sD7ydZCPybtvXFtb5PvX7cc+IyDEEwXCwgMqDQkOr6lYW4udkjf1Iy0ovAX5TWAPbvX7Vf6RuwVmLftwzImYMQTAESDoXeLXtqoNCC6vqhhbivbX/roX4N23vVOj6uwOP6y3nSfocY72r32H7shI6Hb3LScVzNftxz4iYMQTBcLAh8D1JtQeF6lbVDS3E782fb5e0L/ALxgbuErwVOKpzvCXwUuDBwD+T8g0lOaHw9VaamDEEwRCQd9osh+0rW9/LqtLQQnw/kg/TJqT6hXnAW21fWOj642Yfkj5v+8D89dds7zb5u2c3ERiCYIRoYVU9TH0FVgVJ/2v78ZO89gPbmxfSuZP+vbJ7Vemr3I97RYmlpCAYIAMYFBYBnwSel48Pyef2Kqgx3/ZBkg6GZCEuleuRPMHUbjkK7uS6WdK+tr84QX8/4JZCGth+yPTf1ZYIDEEwQAYwKLSwqq5tIf6tgteaiqOBL0p6LmNuqjuSqq73qyGYbVE2YXwtS3En1+mIwBAEQ0KjQaGFVfUJVLQQt31W91jSuvn8H0tp5Ov9QNK2wItItRIAVwFH2P5zSS0ASW8nJbdvBR7o3QbjNwo0IXIMQTAETDYoVCjWamVVXc1CvKOxDXA2aSeSSDUMh9q+qbRWCyTdAjzR9j2DvpeYMQTBcPB80tp87UHh0RO3wEraDSgWGDp24V/sc64kpwPH2L48a+wBfIS01FOV0v5Sme+Sqqt/Xfi6K0wEhiAYDloNCtWsqhtaiPd4cC8oANi+QtKDK+j047QK13wX8G1J3yUK3IIgoPKg0MiqunVfgVslvZm0nARph9WtJQUm85cq3Y0ucxZwEnAjY8uJAyECQxAMB7UHhTWBdUn/57s7oZYCzy0hMIC+AoeRqpN7VdtX53PFaOgvBXCX7X9vpDUlkXwOgiGgpMfPNDqtrKqbWYhLWg94wPadla5f3V8q65xMmi1eyPhZY/PtqhEYgmAIaDUotLCqbtVXINthn8nYDOgO4LDSyzySFvU5bdtFZyeTNDgqvjNtRvcSgSEIBk+rQUHSJSSr6mPpWFXbfn1BjSZ9BSTdAPyTc3/pvOTzwZ7TarDyRI4hCIYA23s2ktrA9hmSFmaDvislfXPad60YrfoK3N8LCgC2vyrpvtIiLfylss56pN7ST8mnrgTeZvuOkjozoVhD6yAIVh5J60k6WdK38se/5YGiNOOsqiVtT1mrahizEP+SpAt7H4U1IAW10yTtIenvJH0QuELSDpJWeftth0WkJb5H5o+L8rnSnEnqW/38/LG0ks60xFJSEAwBks4jPWn37B5eDGzXs3kuqFPVqjprNLEQn2T5rSNXZhlO0pKJzrD9zs0WnZkQS0lBMBzMt/2czvFbJS0pLdLpiXAHUGX5qkUPCUmrAR+y/dnaWrTxlwK4W9Lutr8KyyrS766gMy0RGIJgOKg6KLSwqm5pIW77AUnHAS0Cw2Gk2dX7GPOXemkFnSOAj3eWEP9A2hzQnAgMQTAc1B4UqltVD8BC/CuSjiXtsurWF/y+sE51f6nMUtvbSZoHYHuppFZtUscROYYgGAIkbWb7RxMHBds/qqRXxaq6c/3qFuKS+v3b2PbjCutcb3uH6c5V0llse8eSOjMhZgxBMBycB+xge2nn3OdIjWGKMdGqWlJxq+pWfQVsV32abuQvhaStSAWH60nqbjaYR6cIsSURGIJggAxgUGhhVd3EQlypl/QxwKa2XyHp8cCWnQT7qlLdXyqzJakj3PrA/p3zdwIvL6gzY2IpKQgGiKRnAc8GnknaK9/jTuDTtq8prPcd29tNd24VNc4DXmW7qoW4pM+QXFwPtb1NDhTXVNhG2spfahfbX6+tMxNixhAEA8T2BcAFDQeF6lbVtOsrMN/2QZIOzte/S5Kme9NKcJek91DRXypzgKSbSLvRLga2BY62/YnCOtMSlc9BMBwcIGmepDUkXSrpN3nvfGkOAzYiWVV/Pn9d1AyOMQvxE4F/63yU5h5Ja5O3yEqaTycQFeQc4GZgM5LN921AaRsRgL1zjmm/rLE58LoKOtMSM4YgGA72tn2cpANIg8KBpMbzRZ8Wbf8BeHVlq+pWfQVOID1ZbyLpHJKX0csq6LTwlwJYI3/eFzjX9h11JkDTE4EhCIaDJoPCRKtqSTWsqq+W9C4qW4jbvkTSYmBnUhHdQtu/LamRGecvBfyC8v5SABdJupm0lPSqbJH+5wo60xLJ5yAYAiSdSEpC3w08mbRD5Qu2/7awTnWr6oYW4pfaftp05wroVPeX6mg9DLgjd45bB5hn+5eldaa9jwgMQTActBgUJH3b9vYTzhUv1qqJpLWAdYDLgT0Y6y09D7jY9lYDurVVQtKh/c7X6nw3FbGUFARDQHdQmLCEVHpQuFLSaSRDOAMHka2qocxyT4O+Aq8EXkOywF7MWGBYCpxaSKOJv9QEuq1d1wKeBlxP+b+BaYkZQxAMAXkQ6rFsULBdspCqiVV1Qwvxo2yfMv13rvT1p/Sqsn3WVK8X0F+fVMuyT02dvtoRGIJg+KgxKGSr6ufWtqpu2VdA0q7AYxnvyVTlCbu2v1QfvTWAm2xv0UKvSywlBcFw8iegqBlcQ6vqJn0FJJ0NzAeWAPfn06bw0ksLf6mscxFjS1erAVvTxlZ8+XuJGUMQDJ7JBgXbxxfWORH4LRWtqiVtRxqcx1mI276hlEbW+T6wtSsPYpKuAd44wV/qnbaL+EtJ2hzYmPEP6veRcie32/5hCZ0VuqcIDEEwOFoPCi2sqltZiEs6F3i17dtLXrePTlV/KUlfAN5g+8YJ559ICkD7939nPWIpKQgGy/uZfFB4P+PdNleZ2lbVmSYW4sCGwPckXUddT6ba/lIbT/z9A9i+UdJjC+rMmAgMQTBYmg4KNa2qB2AhfkKFa/bjMJJH0ufz8dWU9Zdaf4rX1i6oM2MiMATBYGk9KCwi7f3vrY//HDgXKNHDoGlfgexbVJ0G/lLfkvRy2x/pnpR0OOl31ZzIMQTBAJH0KeCySQaFvWwfVFjvW7YXdCugK/RjqGohLulO+heeiZQvmVdYb5y/FFDUX0rSxsD5wD2MBYIFpEZBBwzCEiNmDEEwWF4DnC/pRfQZFCrotbCqrtpXwPZDpv+uopwBHDnBX2oR6edaZWz/CthV0p7ANvn0F21fVuL6K0PMGIJgCJgwKNxUa1CQtDfwRtJ22EvIVtW9rZiFNJbYflK2EN+PlNO4quSspKP1UJK5XbfAraiL61zwl1pRIjAEwYghaQPGrKqvLW1VLekm20+Q9FHgc7YvLr1clXXeDryUtEPogXy6hovr+0n5nq6/1J/JvTJKB6JhIAJDEIwQLayqG1qI3wI80fY9Ja/bR6e6v9SwETmGIBgBOlbVG+bll65V9aNKatk+XtK7GbMQ/xPwrJIame+Sgs6vK1wbWOYv9aHa/lLDRswYgmAEkLSQMavqnzPeqvojtkvaVTfpKyBpAXABKUBUK3Dr7eQqec1hJwJDEIwQta2qs0YrC/GbgNOAGxnLMRSvb2jhLzVsRGAIghGjpVV11qvSV0DSN23vNP13rrJOdX+pYSMCQxCMEJNZVVfoRtbVrNJXQNLJpCWkCxm/lDTndgm1JpLPQTBaLKCyVXXDvgK92oKdO+cMlN6uWs1faliJwBAEo8V3gb8GiltVdyzE39s5vcxCvLSe7T1LX3MSavpLDSURGIJgtKhpVd3UQjyb2r0FeEo+dSXwNtt3lNQB5ts+SNLBALbvkqTp3jSbicAQBKPFCRWv3bqvwJmkGdDz8/GLSU/3B076jpWjhb/UUBGBIQhGiMpW1a0txOfbfk7n+K2SllTQOYFkBriJpHPI/lIVdIaGCAxBMAI0sqpu3Vfgbkm72/5q1tmNZMNRFNuXSFrMmL/UwtL+UsNGbFcNgqAIrfsKSNoO+DiwXj71B+Altm8orFPdX2rYiBlDEIwYtayqB9BXYKnt7STNy/pLJRXrad3SX2rYiBlDEIwQrayqW9CvJ4KkxbZ3LHT9Zv5Sw0bMGIJgtHg+KWlb1aq6JpK2Ap4ArCepuwNpHsmbqQi2PwB8oIW/1LARgSEIRovqVtUN2JLUGW59xtdG3Am8vLSY7VNa+0sNmlhKCoIRopVVdQsk7WL76w10mvtLDZqYMQTBaHEWcBITrKpnKQdk6+27SXUG2wJH2/5EYZ3q/lLDRgSGIBgt7rL974O+iULsbfs4SQcAt5Eqnq8i92IuSDV/qWElAkMQjBZXS3oXc8Oqeo38eV/gXNt3VLIwqukvNZREYAiC0aKJVXUjLpJ0M2kp6VWSNgL+XEHnhArXHGoi+RwEwaxF0sOAO2zfn/smzCtdYT2KxIwhCEaIhlbV1ZF0aOfr7ktFtpE28pcaSmLGEAQjhKTzSMnUs/KpFwPb2S5tVV0dSd2is7WApwHX237ugG5pzhCBIQhGCElLbD9punOzEUnrA5+2vU+Fa1fxlxpWYikpCEaLJlbVA+JPwONKX3QyfylmZ8J+RkRgCILR4gjg4znXANmqeoD3s9JIuoixHMBqwNbAZytIzXp/qRUlAkMQjBZVrapbIGlzYGPgvZ3T95GSwjWK0OaCv9QKETmGIBghaltVt0DSF4A3TOwvLemJwDtt79//nSutN2f8pWZKzBiCYARoZVXdiI0nBgUA2zdKemwFvbnkLzUjIjAEwWjQ1Kq6MutP8draFfTmkr/UjIilpCAYIVpZVddE0qeAy2x/ZML5w4G9bB9UWO9k0hLSXPCXmhERGIJghJD0buAd1LeqroakjYHzgXuAxfn0AmBN4IDSlhiSLu9zela2Q50pERiCYIToFbNlq+r9gGOAq2xvN+BbW2Ek7Qlskw9vsn3ZIO9nLhE5hiAYLVpZVVfH9uVAv6f5oswlf6mZstqgbyAIgqb0rKp3BC6taFU9lziTlKR/fv5YCiwa6B1VJpaSgmDECKvqFWMu+0tNRiwlBcEIUduqeo4yl/2l+hKBIQhGi506Xy+zqiYCw1TMGX+pmRKBIQhGCNtHdY97VtUDup3Zwqz3l1pRIvkcBKNNFavqOcZ5kAKC7aX53OcGeD/ViRlDEIwQDa2qZz1zzF9qhYjAEAQjwACsqucCc8lfaoWI7apBMAK0tqqeS8wFf6kVJXIMQTAaTGpVDTy2/e3MKg6QNE/SGpIulfQbSYcM+qZqEoEhCEaD1lbVc4m9c9J5P+A2YHPgdQO9o8pEYAiC0eBbkpZbF89W1Yv7fH8wxnL+UoO8mRZE8jkIRoPXAOdLehF9rKoHdlezg56/1N3Aq0bBXyqSz0EwQoRV9coxav5SERiCIAimoOsv1cX2nLURiaWkIAiCqRk5f6mYMQRBEKwAPX8p2/sM+l5qEbuSgiAIVow57y8VS0lBEARTMIr+UrGUFARB0IeOv1T3AXqZv5TtHw7kxhoQgSEIgqAPo+wvFTmGIAiC/oysv1QEhiAIgv6MrL9UBIYgCIL+jKy/VOQYgiAI+iBpY+B84B76+EuFJUYQBMGIMor+UhEYgiAIgnFEjiEIgiAYRwSGIAiCYBwRGIIgCIJxRGAIgiAIxhGBIQiCIBjH/wd0cHhxd+QvqgAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "metrics = [\n",
+    "    r2_score,\n",
+    "    mean_absolute_error, \n",
+    "    mean_squared_error\n",
+    "]\n",
+    "\n",
+    "temp2 = range(len(lss))\n",
+    "for metric in metrics:\n",
+    "    metric_vals = [metric(testy, output) for output in outputs]\n",
+    "    plt.bar(temp2, metric_vals, label=metric.__name__)\n",
+    "    plt.xticks(temp2, [l for l in lss], rotation='vertical')\n",
+    "    plt.title(metric.__name__)\n",
+    "    for i, metric_val in enumerate(metric_vals):\n",
+    "        plt.text(i-0.3, metric_val + .5 * metric_val,\n",
+    "                 \"%.2E\" % metric_val, rotation=\"vertical\")\n",
+    "    plt.show()\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# import itertools\n",
+    "# for i, out in enumerate(outputs):\n",
+    "#     list(zip(range(len(lss)), outputs)), \n",
+    "#     3):\n",
+    "#     ix0 = comb[0][0]\n",
+    "#     ix1 = comb[1][0]\n",
+    "#     if ix0 == 0 or ix1 == 0:\n",
+    "#         continue # we don't care about random\n",
+    "#     plt.scatter(outputs[ix0], outputs[ix1])\n",
+    "#     plt.xlabel(lss[ix0])\n",
+    "#     plt.ylabel(lss[ix1])\n",
+    "#     corr = np.correlate(comb[0][1], comb[1][1]).item()\n",
+    "#     plt.title('Correlation: %1.2f' % corr)\n",
+    "#     plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "lims = 1, 3, 3, 5\n",
+    "resolution = 12\n",
+    "x1min, x1max, x2min, x2max = lims\n",
+    "x1 = np.linspace(x1min, x1max, resolution)\n",
+    "x2 = np.linspace(x2min, x2max, resolution)\n",
+    "X1, X2 = np.meshgrid(x1, x2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/all_in_one.ipynb b/examples/all_in_one.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..c29108111b76e540c621164cddd561643dcf7143
--- /dev/null
+++ b/examples/all_in_one.ipynb
@@ -0,0 +1,1068 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "title: Basic Example\n",
+    "toc: true\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "In this notebook, we will see how to use Polire for spatial interpolation."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<xarray.core.options.set_options at 0x7fc3b0198c40>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import xarray as xr\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import polire\n",
+    "from sklearn.neighbors import KNeighborsRegressor\n",
+    "from sklearn.linear_model import LinearRegression\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "from sklearn.metrics import (\n",
+    "    mean_squared_error,\n",
+    "    mean_absolute_error,\n",
+    "    mean_absolute_percentage_error,\n",
+    "    r2_score,\n",
+    ")\n",
+    "\n",
+    "# set default cmap\n",
+    "plt.rcParams[\"image.cmap\"] = \"RdYlGn_r\"\n",
+    "xr.set_options(cmap_sequential=\"RdYlGn_r\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
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+       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
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+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
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+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
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+       "\n",
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+       "  margin-left: 2px;\n",
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+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
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+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
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+       "\n",
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+       "  font-size: 11px;\n",
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+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
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+       "  content: '▼';\n",
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+       "\n",
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+       "  display: none;\n",
+       "}\n",
+       "\n",
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+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
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+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
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+       "  color: var(--xr-font-color2);\n",
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+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
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+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
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+       "\n",
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+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
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+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
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+       ".xr-attrs dt:hover {\n",
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+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
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+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
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+       "\n",
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+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
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+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
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+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cfc2e349-7f19-4121-a071-0e870e16af64' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cfc2e349-7f19-4121-a071-0e870e16af64' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 25</li><li><span class='xr-has-index'>time</span>: 2920</li><li><span class='xr-has-index'>lon</span>: 53</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-6355f394-ba58-4cab-b415-d270a696c923' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6355f394-ba58-4cab-b415-d270a696c923' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>75.0 72.5 70.0 ... 20.0 17.5 15.0</div><input id='attrs-696a123e-5b8f-4a74-95a0-da82f2e200c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-696a123e-5b8f-4a74-95a0-da82f2e200c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b8f5c8-adbf-4977-9c76-62bd296a82f0' class='xr-var-data-in' type='checkbox'><label for='data-c8b8f5c8-adbf-4977-9c76-62bd296a82f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([75. , 72.5, 70. , 67.5, 65. , 62.5, 60. , 57.5, 55. , 52.5, 50. , 47.5,\n",
+       "       45. , 42.5, 40. , 37.5, 35. , 32.5, 30. , 27.5, 25. , 22.5, 20. , 17.5,\n",
+       "       15. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>200.0 202.5 205.0 ... 327.5 330.0</div><input id='attrs-e80d0179-fa1f-4929-bd39-af2cc2d4202e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e80d0179-fa1f-4929-bd39-af2cc2d4202e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73f515d1-b13c-452b-8f23-23470d0a8b7f' class='xr-var-data-in' type='checkbox'><label for='data-73f515d1-b13c-452b-8f23-23470d0a8b7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([200. , 202.5, 205. , 207.5, 210. , 212.5, 215. , 217.5, 220. , 222.5,\n",
+       "       225. , 227.5, 230. , 232.5, 235. , 237.5, 240. , 242.5, 245. , 247.5,\n",
+       "       250. , 252.5, 255. , 257.5, 260. , 262.5, 265. , 267.5, 270. , 272.5,\n",
+       "       275. , 277.5, 280. , 282.5, 285. , 287.5, 290. , 292.5, 295. , 297.5,\n",
+       "       300. , 302.5, 305. , 307.5, 310. , 312.5, 315. , 317.5, 320. , 322.5,\n",
+       "       325. , 327.5, 330. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2013-01-01 ... 2014-12-31T18:00:00</div><input id='attrs-83f52ba1-b472-4957-a4b2-56219a896e62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83f52ba1-b472-4957-a4b2-56219a896e62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc75263b-7d87-4597-9e16-fe563a462562' class='xr-var-data-in' type='checkbox'><label for='data-cc75263b-7d87-4597-9e16-fe563a462562' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2013-01-01T00:00:00.000000000&#x27;, &#x27;2013-01-01T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2013-01-01T12:00:00.000000000&#x27;, ..., &#x27;2014-12-31T06:00:00.000000000&#x27;,\n",
+       "       &#x27;2014-12-31T12:00:00.000000000&#x27;, &#x27;2014-12-31T18:00:00.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4940db08-9df3-43e2-8f3f-2b2e2570db1d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4940db08-9df3-43e2-8f3f-2b2e2570db1d' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>air</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1fb1c6e5-9dfc-4467-b64e-a3cfb77bbf63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1fb1c6e5-9dfc-4467-b64e-a3cfb77bbf63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d43b876-1fd4-4985-93e2-e0c2d307e46e' class='xr-var-data-in' type='checkbox'><label for='data-2d43b876-1fd4-4985-93e2-e0c2d307e46e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>4xDaily Air temperature at sigma level 995</dd><dt><span>units :</span></dt><dd>degK</dd><dt><span>precision :</span></dt><dd>2</dd><dt><span>GRIB_id :</span></dt><dd>11</dd><dt><span>GRIB_name :</span></dt><dd>TMP</dd><dt><span>var_desc :</span></dt><dd>Air temperature</dd><dt><span>dataset :</span></dt><dd>NMC Reanalysis</dd><dt><span>level_desc :</span></dt><dd>Surface</dd><dt><span>statistic :</span></dt><dd>Individual Obs</dd><dt><span>parent_stat :</span></dt><dd>Other</dd><dt><span>actual_range :</span></dt><dd>[185.16 322.1 ]</dd></dl></div><div class='xr-var-data'><pre>[3869000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46841388-31b9-40ed-bd6b-a2372877f766' class='xr-section-summary-in' type='checkbox'  ><label for='section-46841388-31b9-40ed-bd6b-a2372877f766' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fc442c31-1183-44ff-9f0a-571cc137953c' class='xr-index-data-in' type='checkbox'/><label for='index-fc442c31-1183-44ff-9f0a-571cc137953c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([75.0, 72.5, 70.0, 67.5, 65.0, 62.5, 60.0, 57.5, 55.0, 52.5, 50.0, 47.5,\n",
+       "       45.0, 42.5, 40.0, 37.5, 35.0, 32.5, 30.0, 27.5, 25.0, 22.5, 20.0, 17.5,\n",
+       "       15.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d3e596fd-cf8d-4eee-960b-ac6b607f152d' class='xr-index-data-in' type='checkbox'/><label for='index-d3e596fd-cf8d-4eee-960b-ac6b607f152d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([200.0, 202.5, 205.0, 207.5, 210.0, 212.5, 215.0, 217.5, 220.0, 222.5,\n",
+       "       225.0, 227.5, 230.0, 232.5, 235.0, 237.5, 240.0, 242.5, 245.0, 247.5,\n",
+       "       250.0, 252.5, 255.0, 257.5, 260.0, 262.5, 265.0, 267.5, 270.0, 272.5,\n",
+       "       275.0, 277.5, 280.0, 282.5, 285.0, 287.5, 290.0, 292.5, 295.0, 297.5,\n",
+       "       300.0, 302.5, 305.0, 307.5, 310.0, 312.5, 315.0, 317.5, 320.0, 322.5,\n",
+       "       325.0, 327.5, 330.0],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-21bf2639-f44a-4872-a20c-3c0dc74234cb' class='xr-index-data-in' type='checkbox'/><label for='index-21bf2639-f44a-4872-a20c-3c0dc74234cb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2013-01-01 00:00:00&#x27;, &#x27;2013-01-01 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-01 12:00:00&#x27;, &#x27;2013-01-01 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 00:00:00&#x27;, &#x27;2013-01-02 06:00:00&#x27;,\n",
+       "               &#x27;2013-01-02 12:00:00&#x27;, &#x27;2013-01-02 18:00:00&#x27;,\n",
+       "               &#x27;2013-01-03 00:00:00&#x27;, &#x27;2013-01-03 06:00:00&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2014-12-29 12:00:00&#x27;, &#x27;2014-12-29 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 00:00:00&#x27;, &#x27;2014-12-30 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-30 12:00:00&#x27;, &#x27;2014-12-30 18:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 00:00:00&#x27;, &#x27;2014-12-31 06:00:00&#x27;,\n",
+       "               &#x27;2014-12-31 12:00:00&#x27;, &#x27;2014-12-31 18:00:00&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=2920, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-83479ca4-0457-4ded-a4bc-213c200423b1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-83479ca4-0457-4ded-a4bc-213c200423b1' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>COARDS</dd><dt><span>title :</span></dt><dd>4x daily NMC reanalysis (1948)</dd><dt><span>description :</span></dt><dd>Data is from NMC initialized reanalysis\n",
+       "(4x/day).  These are the 0.9950 sigma level values.</dd><dt><span>platform :</span></dt><dd>Model</dd><dt><span>references :</span></dt><dd>http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanalysis.html</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset>\n",
+       "Dimensions:  (lat: 25, time: 2920, lon: 53)\n",
+       "Coordinates:\n",
+       "  * lat      (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 25.0 22.5 20.0 17.5 15.0\n",
+       "  * lon      (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0\n",
+       "  * time     (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00\n",
+       "Data variables:\n",
+       "    air      (time, lat, lon) float32 ...\n",
+       "Attributes:\n",
+       "    Conventions:  COARDS\n",
+       "    title:        4x daily NMC reanalysis (1948)\n",
+       "    description:  Data is from NMC initialized reanalysis\\n(4x/day).  These a...\n",
+       "    platform:     Model\n",
+       "    references:   http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly..."
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds = xr.tutorial.open_dataset(\"air_temperature\")\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's visualize the spatial data from a time-stamp."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_spatial = ds.isel(time=0)\n",
+    "ds_spatial[\"air\"].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's assume that only 10% of the data is available and try to interpolate the rest of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_spatial_train = ds_spatial.copy()\n",
+    "\n",
+    "np.random.seed(42)\n",
+    "ds_spatial_train[\"air\"].values = np.where(\n",
+    "    np.random.rand(*ds_spatial_train[\"air\"].shape) > 0.1,\n",
+    "    np.nan,\n",
+    "    ds_spatial_train[\"air\"].values,\n",
+    ")\n",
+    "\n",
+    "\n",
+    "ds_spatial_train[\"air\"].plot()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Prepare the data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(146, 2) (146,) (1325, 2) (1325,)\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_df = ds_spatial_train[\"air\"].to_dataframe().reset_index()\n",
+    "train_df = train_df.dropna()\n",
+    "\n",
+    "X_train = train_df[[\"lat\", \"lon\"]].values\n",
+    "y_train = train_df[\"air\"].values\n",
+    "\n",
+    "test_df = ds_spatial[\"air\"].to_dataframe().reset_index()\n",
+    "X_test = test_df[[\"lat\", \"lon\"]].values\n",
+    "y_test = test_df[\"air\"].values\n",
+    "\n",
+    "print(X_train.shape, y_train.shape, X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Useful plotting function"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_predictions(y_pred, model_name):\n",
+    "    fig, axes = plt.subplots(\n",
+    "        1, 3, figsize=(15, 4), gridspec_kw={\"width_ratios\": [1, 1, 0.05]}\n",
+    "    )\n",
+    "\n",
+    "    ax = axes[0]\n",
+    "    ds_spatial[\"air\"].plot(ax=ax, add_colorbar=False)\n",
+    "    ax.set_title(\"True values\")\n",
+    "\n",
+    "    ax = axes[1]\n",
+    "    ds_spatial_pred = ds_spatial_train.copy()\n",
+    "    ds_spatial_pred[\"air\"].values = y_pred\n",
+    "    mappable = ds_spatial_pred[\"air\"].plot(ax=ax, add_colorbar=False)\n",
+    "    ax.set_title(f\"{model_name}\")\n",
+    "\n",
+    "    ax = axes[2]\n",
+    "    fig.colorbar(mappable, cax=ax)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Models"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### IDW"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/patel_zeel/polire/polire/idw/idw.py:82: RuntimeWarning: divide by zero encountered in divide\n",
+      "  weights = 1 / np.power(dist, self.exponent)\n",
+      "/home/patel_zeel/polire/polire/idw/idw.py:83: RuntimeWarning: invalid value encountered in divide\n",
+      "  result = (weights * self.y[:, None]).sum(axis=0) / weights.sum(axis=0)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "idw = polire.IDW(exponent=2)\n",
+    "idw.fit(X_train, y_train)\n",
+    "idw_pred = idw.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(idw_pred, \"IDW\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Kriging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "kriging = polire.Kriging(variogram_model=\"spherical\")\n",
+    "kriging.fit(X_train, y_train)\n",
+    "kriging_pred = kriging.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(kriging_pred, \"Kriging\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Spline"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/patel_zeel/miniconda3/envs/polire/lib/python3.10/site-packages/scipy/interpolate/_fitpack_impl.py:593: RuntimeWarning: The required storage space exceeds the available storage space.\n",
+      "Probable causes: nxest or nyest too small or s is too small. (fp>s)\n",
+      "\tkx,ky=3,3 nx,ny=11,11 m=146 fp=1143.783495 s=128.911993\n",
+      "  warnings.warn(RuntimeWarning(_iermess2[ierm][0] + _mess))\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spline = polire.Spline()\n",
+    "spline.fit(X_train, y_train)\n",
+    "spline_pred = spline.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(spline_pred, \"Spline\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Trend"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trend = polire.Trend(order=2)\n",
+    "trend.fit(X_train, y_train)\n",
+    "trend_pred = trend.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(trend_pred, \"Trend\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### SpatialAverage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "spatial_average = polire.SpatialAverage(radius=15)\n",
+    "spatial_average.fit(X_train, y_train)\n",
+    "spatial_average_pred = spatial_average.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(spatial_average_pred, \"SpatialAverage\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### LinearRegression"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "lr = polire.CustomInterpolator(LinearRegression())\n",
+    "lr.fit(X_train, y_train)\n",
+    "lr_pred = lr.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(lr_pred, \"Linear Regression\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### NearestNeighbors"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "K = 3\n",
+    "knn = polire.CustomInterpolator(KNeighborsRegressor(n_neighbors=K))\n",
+    "knn.fit(X_train, y_train)\n",
+    "knn_pred = knn.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(knn_pred, \"KNN\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Random Forest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x400 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "rf = polire.CustomInterpolator(RandomForestRegressor(random_state=42))\n",
+    "rf.fit(X_train, y_train)\n",
+    "rf_pred = rf.predict(X_test).reshape(ds_spatial[\"air\"].shape)\n",
+    "\n",
+    "plot_predictions(rf_pred, \"Random Forest\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Checking performance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_15362_row0_col0, #T_15362_row0_col1, #T_15362_row0_col2, #T_15362_row0_col3 {\n",
+       "  background-color: green;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_15362\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th id=\"T_15362_level0_col0\" class=\"col_heading level0 col0\" >RMSE</th>\n",
+       "      <th id=\"T_15362_level0_col1\" class=\"col_heading level0 col1\" >MAE</th>\n",
+       "      <th id=\"T_15362_level0_col2\" class=\"col_heading level0 col2\" >MAPE</th>\n",
+       "      <th id=\"T_15362_level0_col3\" class=\"col_heading level0 col3\" >Neg R2</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row0\" class=\"row_heading level0 row0\" >Kriging (spherical)</th>\n",
+       "      <td id=\"T_15362_row0_col0\" class=\"data row0 col0\" >3.10</td>\n",
+       "      <td id=\"T_15362_row0_col1\" class=\"data row0 col1\" >1.95</td>\n",
+       "      <td id=\"T_15362_row0_col2\" class=\"data row0 col2\" >0.01</td>\n",
+       "      <td id=\"T_15362_row0_col3\" class=\"data row0 col3\" >-0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row1\" class=\"row_heading level0 row1\" >Random Forest</th>\n",
+       "      <td id=\"T_15362_row1_col0\" class=\"data row1 col0\" >3.96</td>\n",
+       "      <td id=\"T_15362_row1_col1\" class=\"data row1 col1\" >2.74</td>\n",
+       "      <td id=\"T_15362_row1_col2\" class=\"data row1 col2\" >0.01</td>\n",
+       "      <td id=\"T_15362_row1_col3\" class=\"data row1 col3\" >-0.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row2\" class=\"row_heading level0 row2\" >Spline</th>\n",
+       "      <td id=\"T_15362_row2_col0\" class=\"data row2 col0\" >4.43</td>\n",
+       "      <td id=\"T_15362_row2_col1\" class=\"data row2 col1\" >2.85</td>\n",
+       "      <td id=\"T_15362_row2_col2\" class=\"data row2 col2\" >0.01</td>\n",
+       "      <td id=\"T_15362_row2_col3\" class=\"data row2 col3\" >-0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row3\" class=\"row_heading level0 row3\" >3-Nearest Neighbors</th>\n",
+       "      <td id=\"T_15362_row3_col0\" class=\"data row3 col0\" >4.60</td>\n",
+       "      <td id=\"T_15362_row3_col1\" class=\"data row3 col1\" >3.19</td>\n",
+       "      <td id=\"T_15362_row3_col2\" class=\"data row3 col2\" >0.01</td>\n",
+       "      <td id=\"T_15362_row3_col3\" class=\"data row3 col3\" >-0.94</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row4\" class=\"row_heading level0 row4\" >Spatial Average</th>\n",
+       "      <td id=\"T_15362_row4_col0\" class=\"data row4 col0\" >6.04</td>\n",
+       "      <td id=\"T_15362_row4_col1\" class=\"data row4 col1\" >4.32</td>\n",
+       "      <td id=\"T_15362_row4_col2\" class=\"data row4 col2\" >0.02</td>\n",
+       "      <td id=\"T_15362_row4_col3\" class=\"data row4 col3\" >-0.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row5\" class=\"row_heading level0 row5\" >Inverse Distance Weighting</th>\n",
+       "      <td id=\"T_15362_row5_col0\" class=\"data row5 col0\" >6.54</td>\n",
+       "      <td id=\"T_15362_row5_col1\" class=\"data row5 col1\" >5.04</td>\n",
+       "      <td id=\"T_15362_row5_col2\" class=\"data row5 col2\" >0.02</td>\n",
+       "      <td id=\"T_15362_row5_col3\" class=\"data row5 col3\" >-0.88</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row6\" class=\"row_heading level0 row6\" >Trend</th>\n",
+       "      <td id=\"T_15362_row6_col0\" class=\"data row6 col0\" >8.02</td>\n",
+       "      <td id=\"T_15362_row6_col1\" class=\"data row6 col1\" >6.30</td>\n",
+       "      <td id=\"T_15362_row6_col2\" class=\"data row6 col2\" >0.02</td>\n",
+       "      <td id=\"T_15362_row6_col3\" class=\"data row6 col3\" >-0.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_15362_level0_row7\" class=\"row_heading level0 row7\" >Linear Regression</th>\n",
+       "      <td id=\"T_15362_row7_col0\" class=\"data row7 col0\" >8.46</td>\n",
+       "      <td id=\"T_15362_row7_col1\" class=\"data row7 col1\" >6.98</td>\n",
+       "      <td id=\"T_15362_row7_col2\" class=\"data row7 col2\" >0.03</td>\n",
+       "      <td id=\"T_15362_row7_col3\" class=\"data row7 col3\" >-0.80</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x7fc23fb54580>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "models = {\n",
+    "    \"Inverse Distance Weighting\": idw_pred,\n",
+    "    \"Kriging (spherical)\": kriging_pred,\n",
+    "    \"Spline\": spline_pred,\n",
+    "    \"Trend\": trend_pred,\n",
+    "    \"Linear Regression\": lr_pred,\n",
+    "    f\"{K}-Nearest Neighbors\": knn_pred,\n",
+    "    \"Random Forest\": rf_pred,\n",
+    "    \"Spatial Average\": spatial_average_pred,\n",
+    "}\n",
+    "\n",
+    "result = pd.DataFrame(columns=[\"RMSE\", \"MAE\", \"MAPE\", \"Neg R2\"], index=models.keys())\n",
+    "\n",
+    "for model_name, y_pred in models.items():\n",
+    "    result.loc[model_name, \"RMSE\"] = mean_squared_error(\n",
+    "        y_test.ravel(), y_pred.ravel(), squared=False\n",
+    "    )\n",
+    "    result.loc[model_name, \"MAE\"] = mean_absolute_error(y_test.ravel(), y_pred.ravel())\n",
+    "    result.loc[model_name, \"MAPE\"] = mean_absolute_percentage_error(\n",
+    "        y_test.ravel(), y_pred.ravel()\n",
+    "    )\n",
+    "    result.loc[model_name, \"Neg R2\"] = -r2_score(y_test.ravel(), y_pred.ravel())\n",
+    "\n",
+    "result.sort_values(\"RMSE\").style.highlight_min(axis=0, color=\"green\").format(\"{:.2f}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "polire",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  },
+  "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/index.qmd b/index.qmd
new file mode 100755
index 0000000000000000000000000000000000000000..c043bd113d80edddb8b02af5b37255c8c7ceda5c
--- /dev/null
+++ b/index.qmd
@@ -0,0 +1,11 @@
+## Polire
+
+```python
+pip install polire
+```
+
+
+The word "interpolation" has Latin origin and is composed of two words - Inter meaning between and Polire meaning to polish.
+
+
+Polire is a collection of several spatial interpolation algorithms. 
\ No newline at end of file
diff --git a/polire.egg-info/PKG-INFO b/polire.egg-info/PKG-INFO
new file mode 100755
index 0000000000000000000000000000000000000000..2fb058e9c1565512761ea6ea1059151007d8ab9f
--- /dev/null
+++ b/polire.egg-info/PKG-INFO
@@ -0,0 +1,112 @@
+Metadata-Version: 2.1
+Name: polire
+Version: 0.1.3
+Summary: A collection of interpolation methods.
+Home-page: https://sustainability-lab.github.io/polire
+Download-URL: https://sustainability-lab.github.io/polire
+Maintainer: Zeel B Patel, Apoorv Agnihotri, S Deepak Narayanan
+Maintainer-email: patel_zeel@iitgn.ac.in, apoorv.agnihotri@iitgn.ac.in, deepak.narayanan@iitgn.ac.in
+License: new BSD
+Classifier: Intended Audience :: Science/Research
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved
+Classifier: Programming Language :: Python
+Classifier: Topic :: Software Development
+Classifier: Topic :: Scientific/Engineering
+Classifier: Operating System :: Microsoft :: Windows
+Classifier: Operating System :: POSIX
+Classifier: Operating System :: Unix
+Classifier: Operating System :: MacOS
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Description-Content-Type: text/markdown
+Provides-Extra: tests
+Provides-Extra: docs
+License-File: LICENSE
+
+![Tests](https://github.com/sustainability-lab/polire/actions/workflows/tests.yml/badge.svg)
+[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
+
+
+## Polire
+
+```python
+pip install polire
+```
+
+
+The word "interpolation" has Latin origin and is composed of two words - Inter meaning between and Polire meaning to polish.
+
+
+This repository is a collection of several spatial interpolation algorithms. 
+
+## Examples
+### Minimal example of interpolation
+```python
+import numpy as np
+from polire import Kriging
+
+# Data
+X = np.random.rand(10, 2) # Spatial 2D points
+y = np.random.rand(10) # Observations
+X_new = np.random.rand(100, 2) # New spatial points
+
+# Fit
+model = Kriging()
+model.fit(X, y)
+
+# Predict
+y_new = model.predict(X_new)
+```
+
+### Supported Interpolation Methods
+```python
+from polire import (
+    Kriging, # Best spatial unbiased predictor
+    GP, # Gaussian process interpolator from GPy
+    IDW, # Inverse distance weighting
+    SpatialAverage,
+    Spline,
+    Trend,
+    Random, # Predict uniformly within the observation range, a reasonable baseline
+    NaturalNeighbor,
+    CustomInterpolator # Supports any regressor from Scikit-learn
+)
+```
+
+### Use GP kernels from GPy and regressors from sklearn
+```python
+from sklearn.linear_model import LinearRegression # or any Scikit-learn regressor
+from GPy.kern import Matern32 # or any other GPy kernel
+
+from polire import GP, CustomInterpolator
+
+# GP model
+model = GP(Matern32(input_dim=2))
+
+# Sklearn model
+model = CustomInterpolator(LinearRegression(normalize = True))
+```
+
+### Extract spatial features from spatio-temporal dataset
+```python
+# X and X_new are datasets as numpy arrays with first three dimensions as longitude, latitute and time.
+# y is corresponding observations with X
+
+from polire.preprocessing import SpatialFeatures
+spatial = SpatialFeatures(n_closest=10)
+Features = spatial.fit_transform(X, y)
+Features_new = spatial.transform(X_new)
+```
+
+## More info
+
+Contributors:  [S Deepak Narayanan](https://github.com/sdeepaknarayanan), [Zeel B Patel*](https://github.com/patel-zeel), [Apoorv Agnihotri](https://github.com/apoorvagnihotri), and [Nipun Batra*](https://github.com/nipunbatra) (People with * are currently active contributers).
+
+This project is a part of Sustainability Lab at IIT Gandhinagar.
+
+Acknowledgement to sklearn template for helping to package into a PiPy package.
+
+
diff --git a/polire.egg-info/SOURCES.txt b/polire.egg-info/SOURCES.txt
new file mode 100755
index 0000000000000000000000000000000000000000..962a0adc77f79d26b4f120f649c4f4759c467def
--- /dev/null
+++ b/polire.egg-info/SOURCES.txt
@@ -0,0 +1,41 @@
+LICENSE
+README.md
+setup.py
+polire/__init__.py
+polire/constants.py
+polire.egg-info/PKG-INFO
+polire.egg-info/SOURCES.txt
+polire.egg-info/dependency_links.txt
+polire.egg-info/not-zip-safe
+polire.egg-info/requires.txt
+polire.egg-info/top_level.txt
+polire/base/__init__.py
+polire/base/base.py
+polire/custom/__init__.py
+polire/custom/custom.py
+polire/gp/__init__.py
+polire/gp/gp.py
+polire/idw/__init__.py
+polire/idw/idw.py
+polire/kriging/__init__.py
+polire/kriging/kriging.py
+polire/natural_neighbors/__init__.py
+polire/natural_neighbors/natural_neighbors.py
+polire/nsgp/__init__.py
+polire/nsgp/nsgp.py
+polire/preprocessing/__init__.py
+polire/preprocessing/sptial_features.py
+polire/random/__init__.py
+polire/random/random.py
+polire/spatial/__init__.py
+polire/spatial/spatial.py
+polire/spline/__init__.py
+polire/spline/bspline.py
+polire/trend/__init__.py
+polire/trend/polynomials.py
+polire/trend/trend.py
+polire/utils/__init__.py
+polire/utils/distance.py
+polire/utils/gridding.py
+tests/__init__.py
+tests/test_basic.py
\ No newline at end of file
diff --git a/polire.egg-info/dependency_links.txt b/polire.egg-info/dependency_links.txt
new file mode 100755
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/polire.egg-info/dependency_links.txt
@@ -0,0 +1 @@
+
diff --git a/polire.egg-info/not-zip-safe b/polire.egg-info/not-zip-safe
new file mode 100755
index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc
--- /dev/null
+++ b/polire.egg-info/not-zip-safe
@@ -0,0 +1 @@
+
diff --git a/polire.egg-info/requires.txt b/polire.egg-info/requires.txt
new file mode 100755
index 0000000000000000000000000000000000000000..cc33fac141b0699db7ef596b3136217249e2ee66
--- /dev/null
+++ b/polire.egg-info/requires.txt
@@ -0,0 +1,21 @@
+matplotlib
+numpy
+pandas
+pykrige
+scikit_learn
+scipy
+seaborn
+Shapely
+xgboost
+GPy
+
+[docs]
+sphinx
+sphinx-gallery
+sphinx_rtd_theme
+numpydoc
+matplotlib
+
+[tests]
+pytest
+pytest-cov
diff --git a/polire.egg-info/top_level.txt b/polire.egg-info/top_level.txt
new file mode 100755
index 0000000000000000000000000000000000000000..97ec47ed53938b5dd463b16d87758753228f127e
--- /dev/null
+++ b/polire.egg-info/top_level.txt
@@ -0,0 +1,2 @@
+polire
+tests
diff --git a/polire/__init__.py b/polire/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..9a34a0bdc7eef8e654e83e1fe0899fd77df6f368
--- /dev/null
+++ b/polire/__init__.py
@@ -0,0 +1,12 @@
+from .random.random import Random
+from .idw.idw import IDW
+from .spline.bspline import Spline
+from .trend.trend import Trend
+from .spatial.spatial import SpatialAverage
+from .natural_neighbors.natural_neighbors import NaturalNeighbor
+from .kriging.kriging import Kriging
+
+# from .gp.gp import GP
+from .custom.custom import CustomInterpolator
+
+# from .nsgp.nsgp import NSGP
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diff --git a/polire/base/__init__.py b/polire/base/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..d56e0a779559c57e948ff59fa343c486b9851284
--- /dev/null
+++ b/polire/base/__init__.py
@@ -0,0 +1 @@
+from .base import Base
diff --git a/polire/base/__pycache__/__init__.cpython-310.pyc b/polire/base/__pycache__/__init__.cpython-310.pyc
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diff --git a/polire/base/base.py b/polire/base/base.py
new file mode 100755
index 0000000000000000000000000000000000000000..e664ff1886cd63d39ba9cce431d62b9111f0dc1b
--- /dev/null
+++ b/polire/base/base.py
@@ -0,0 +1,130 @@
+from ..constants import RESOLUTION
+
+
+class Base:
+    """A class that is declared for performing Interpolation.
+    This class should not be called directly, use one of it's
+    children.
+    """
+
+    def __init__(self, resolution="standard", coordinate_types="Euclidean"):
+        self.resolution = RESOLUTION[resolution]
+        self.coordinate_type = coordinate_types
+        self._fit_called = False
+
+    def fit(self, X, y, **kwargs):
+        """The function call to fit the model on the given data.
+
+        Parameters
+        ----------
+
+        X: {array-like, 2D matrix}, shape(n_samples, 2)
+            The set of all coordinates, where we have ground truth
+            values
+        y: array-like, shape(n_samples,)
+            The set of all the ground truth values using which
+            we perform interpolation
+
+        Returns
+        -------
+
+        self : object
+            Returns self
+
+        """
+        assert len(X.shape) == 2, "X must be a 2D array got shape = " + str(
+            X.shape
+        )
+        # assert X.shape[1] == 2, "X can not have more than 2 dimensions"
+        assert len(y.shape) == 1, "y should be a 1d array"
+        assert y.shape[0] == X.shape[0], "X and y must be of the same size"
+
+        # saving that fit was called
+        self._fit_called = True
+
+        # saving boundaries
+        self.x1min_d = min(X[:, 0])
+        self.x1max_d = max(X[:, 0])
+        self.x2min_d = min(X[:, 1])
+        self.x2max_d = max(X[:, 1])
+        return self._fit(X, y, **kwargs)  # calling child specific fit method
+
+    def predict(self, X, **kwargs):
+        """The function call to return interpolated data on specific
+        points.
+
+        Parameters
+        ----------
+
+        X: {array-like, 2D matrix}, shape(n_samples, 2)
+            The set of all coordinates, where we have ground truth
+            values
+
+        Returns
+        -------
+
+        y_pred : array-like, shape(n_samples,)
+            The set of interpolated values for the points used to
+            call the function.
+        """
+
+        assert len(X.shape) == 2, "X must be a 2D array got shape = " + str(
+            X.shape
+        )
+        # assert X.shape[1] == 2, "X can not have more than 2 dimensions"
+
+        # checking if model is fitted or not
+        assert self._fit_called, "First call fit method to fit the model"
+
+        # calling child specific _predict method
+        return self._predict(X, **kwargs)
+
+    def predict_grid(self, x1lim=None, x2lim=None, support_extrapolation=True):
+        """Function to interpolate data on a grid of given size.
+        .
+        Parameters
+        ----------
+        x1lim: tuple(float, float),
+            Upper and lower bound on 1st dimension for the interpolation.
+
+        x2lim: tuple(float, float),
+            Upper and lower bound on 2nd dimension for the interpolation.
+
+        Returns
+        -------
+        y: array-like, shape(n_samples,)
+            Interpolated values on the grid requested.
+        """
+        # checking if model is fitted or not
+        assert self._fit_called, "First call fit method to fit the model"
+
+        # by default we interpolate over the whole grid
+        if x1lim is None:
+            x1lim = (self.x1min_d, self.x1max_d)
+        if x2lim is None:
+            x2lim = (self.x2min_d, self.x2max_d)
+        (x1min, x1max) = x1lim
+        (x2min, x2max) = x2lim
+
+        # extrapolation isn't supported yet
+        if not support_extrapolation:
+            assert self.x1min_d >= x1min, "Extrapolation not supported"
+            assert self.x1max_d <= x1max, "Extrapolation not supported"
+            assert self.x2min_d >= x2min, "Extrapolation not supported"
+            assert self.x2max_d <= x2max, "Extrapolation not supported"
+
+        # calling child specific _predict_grid method
+        pred_y = self._predict_grid(x1lim, x2lim)
+        return pred_y.reshape(self.resolution, self.resolution)
+
+    def __repr__(self):
+        return self.__class__.__name__
+
+    def _fit(self, X, y):
+        raise NotImplementedError
+
+    def _predict_grid(self, x1lim, x2lim):
+        raise NotImplementedError
+
+    def _predict(self, X):
+        raise NotImplementedError
diff --git a/polire/constants.py b/polire/constants.py
new file mode 100755
index 0000000000000000000000000000000000000000..35f7053b1a763bf0e15e3a76b294940426a621ae
--- /dev/null
+++ b/polire/constants.py
@@ -0,0 +1,9 @@
+"""This python script contains all the constants that
+might be needed in the various interpolation pacakages. 
+"""
+
+low_res = 10
+med_res = 100
+high_res = 1000
+
+RESOLUTION = {"low": low_res, "standard": med_res, "high": high_res}
diff --git a/polire/custom/__init__.py b/polire/custom/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..b3f793979566422c231a8613b5578bb473a37594
--- /dev/null
+++ b/polire/custom/__init__.py
@@ -0,0 +1 @@
+from .custom import CustomInterpolator
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diff --git a/polire/custom/custom.py b/polire/custom/custom.py
new file mode 100755
index 0000000000000000000000000000000000000000..3d8bff3cc98d39128d52dcb6886fc0655a1a7854
--- /dev/null
+++ b/polire/custom/custom.py
@@ -0,0 +1,62 @@
+import numpy as np
+
+from ..base import Base
+
+
+class CustomInterpolator(Base):
+    """
+    Class to interpolate by fitting a sklearn type Regressor to
+    the given data.
+
+    Parameters
+    ----------
+    regressor: class definition,
+        This variable is used to pass in the Regressor we would like
+        to use for interpolation. The regressor sould be sklearn type
+        regressor. Example from sklearn.ensemble -> RandomForestRegressor
+
+    reg_kwargs: dict, optional
+        This is a dictionary that is passed into the Regressor initialization.
+        Use this to change the behaviour of the passed regressor. Default = empty dict
+
+    Attributes
+    ----------
+    reg : object
+        Object of the `regressor` class passed.
+    """
+
+    def __init__(
+        self, regressor, resolution="standard", coordinate_type="Euclidean"
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.reg = regressor
+
+    def _fit(self, X, y):
+        """Function for fitting.
+        This function is not supposed to be called directly.
+        """
+        self.reg.fit(X, y)
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Function for grid interpolation.
+        This function is not supposed to be called directly.
+        """
+        # getting the boundaries for interpolation
+        x1min, x1max = x1lim
+        x2min, x2max = x2lim
+
+        # building the grid
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+        X1, X2 = np.meshgrid(x1, x2)
+        return self.reg.predict(np.asarray([X1.ravel(), X2.ravel()]).T)
+
+    def _predict(self, X):
+        """Function for interpolation on specific points.
+        This function is not supposed to be called directly.
+        """
+        return self.reg.predict(X)
+
+    def __repr__(self):
+        return self.__class__.__name__ + "." + self.reg.__class__.__name__
diff --git a/polire/gp/__init__.py b/polire/gp/__init__.py
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diff --git a/polire/gp/__pycache__/__init__.cpython-310.pyc b/polire/gp/__pycache__/__init__.cpython-310.pyc
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diff --git a/polire/gp/__pycache__/gp.cpython-310.pyc b/polire/gp/__pycache__/gp.cpython-310.pyc
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diff --git a/polire/gp/gp.py b/polire/gp/gp.py
new file mode 100755
index 0000000000000000000000000000000000000000..624026c5a2c94ff066997d4749092b6595d3ee34
--- /dev/null
+++ b/polire/gp/gp.py
@@ -0,0 +1,65 @@
+"""
+This is a module for GP Interpolation
+"""
+import numpy as np
+from ..base import Base
+from GPy.models import GPRegression
+from GPy.kern import RBF
+
+
+class GP(Base):
+    """A class that is declared for performing GP interpolation.
+    GP interpolation (usually) works on the principle of finding the
+    best unbiased predictor.
+
+    Parameters
+    ----------
+    type : str, optional
+    This parameter defines the type of Kriging under consideration. This
+    implementation uses PyKrige package  (https://github.com/bsmurphy/PyKrige).
+    The user needs to choose between "Ordinary" and "Universal".
+
+    """
+
+    def __init__(
+        self,
+        kernel=RBF(2, ARD=True),
+    ):
+        super().__init__()
+        self.kernel = kernel
+
+    def _fit(self, X, y, n_restarts=5, verbose=False, random_state=None):
+        """Fit method for GP Interpolation
+        This function shouldn't be called directly.
+        """
+        np.random.seed(random_state)
+        if len(y.shape) == 1:
+            y = y.reshape(-1, 1)
+        self.model = GPRegression(X, y, self.kernel)
+        self.model.optimize_restarts(n_restarts, verbose=verbose)
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """The function that is called to return the interpolated data in Kriging Interpolation
+        in a grid. This method shouldn't be called directly"""
+        lims = (*x1lim, *x2lim)
+        x1min, x1max, x2min, x2max = lims
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+
+        X1, X2 = np.meshgrid(x1, x2)
+        X = np.array([(i, j) for i, j in zip(X1.ravel(), X2.ravel())])
+
+        predictions = self.model.predict(X)[0].reshape(len(x1), len(x2))
+
+        return predictions.ravel()
+
+    def _predict(self, X, return_variance=False):
+        """This function should be called to return the interpolated data in kriging
+        in a pointwise manner. This method shouldn't be called directly."""
+
+        predictions, variance = self.model.predict(X)
+        if return_variance:
+            return predictions.ravel(), variance
+        else:
+            return predictions.ravel()
diff --git a/polire/gp/tests/GP interpolation.ipynb b/polire/gp/tests/GP interpolation.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..5739938bda62c97e490de06ffc2d26fe9fb91a76
--- /dev/null
+++ b/polire/gp/tests/GP interpolation.ipynb	
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pykrige import OrdinaryKriging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ok = OrdinaryKriging(data[:,0],data[:,1],data[:,2])\n",
+    "ok.ex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a,b = ok.execute('grid',x[0],y[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pykrige import OrdinaryKriging\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "def ordinary_kriging(dataset, resolution='standard', coordinate_type='euclidean',verbose='False',method='grid', isvariance = False):\n",
+    "    if coordinate_type == 'latlong_small':\n",
+    "        \"\"\"\n",
+    "            Assume that the Earth is a Sphere, and use polar coordinates\n",
+    "            $| \\vec{r_2}− \\vec{r_1}| ≈ \\text{R }\\times \\sqrt[]{(Lat_2 - Lat_1)^{2} + (Long_2 - Long_1)^{2}}$\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type == 'latlong_large':\n",
+    "        \"\"\"\n",
+    "            Code to be written after understanding all the projections.\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type==\"euclidean\":\n",
+    "        \n",
+    "        ok = OrdinaryKriging(dataset[:,0],dataset[:,1],dataset[:,2])\n",
+    "        X = dataset[:,0]\n",
+    "        y = dataset[:,1]\n",
+    "        \n",
+    "        if resolution=='high':\n",
+    "            xx,yy = make_grid(X,y,1000)\n",
+    "            \n",
+    "        elif resolution=='low':\n",
+    "            xx,yy = make_grid(X,y,10)\n",
+    "            \n",
+    "        elif resolution=='standard':\n",
+    "            xx,yy = make_grid(X,y,100)\n",
+    "            \n",
+    "        else:\n",
+    "            print('Value Error - Resolution can only be one of \\nhigh, low or standard')\n",
+    "        \n",
+    "        values, variances = ok.execute(method, xx[0], yy[:,0])\n",
+    "        \n",
+    "    if isvariance:\n",
+    "        return values, variances\n",
+    "    else:\n",
+    "        del variances\n",
+    "        return np.array(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[129.94984945, 129.7682324 , 129.58820662, ..., 159.34079485,\n",
+       "        159.99175016, 160.63241067],\n",
+       "       [130.22090025, 130.03615966, 129.8529146 , ..., 159.9575165 ,\n",
+       "        160.61228126, 161.25625641],\n",
+       "       [130.50105231, 130.31324536, 130.12683652, ..., 160.59265384,\n",
+       "        161.25084023, 161.8977369 ],\n",
+       "       ...,\n",
+       "       [207.22133238, 207.82739139, 208.44615116, ..., 248.64646661,\n",
+       "        248.3790241 , 248.11033441],\n",
+       "       [207.92838926, 208.53490708, 209.15376273, ..., 248.91678379,\n",
+       "        248.65601627, 248.39371596],\n",
+       "       [208.61942088, 209.22595474, 209.84445913, ..., 249.17442481,\n",
+       "        248.9203453 , 248.66446245]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordinary_kriging(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* What does ok('points') really do?\n",
+    "* Specifically test when points aren't really passed - they are let's say the point of an array\n",
+    "* Returns the diagonal matrix of all these coordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([129.94984945, 130.03615966, 130.12683652, 130.22219703,\n",
+       "       130.32258826, 130.42839089, 130.54002324, 130.65794596,\n",
+       "       130.7826674 , 130.91474976, 131.05481629, 131.20355964,\n",
+       "       131.36175158, 131.53025441, 131.71003442, 131.90217771,\n",
+       "       132.107909  , 132.32861401, 132.56586607, 132.82145795,\n",
+       "       133.0974399 , 133.39616477, 133.72034153, 134.07309736,\n",
+       "       134.45804822, 134.87937482, 135.34189663, 135.85112772,\n",
+       "       136.41328222, 137.03517039, 137.72388496, 138.48612122,\n",
+       "       139.326921  , 140.24763047, 141.24300526, 142.29757046,\n",
+       "       143.37881815, 144.38425962, 144.49187978, 143.1202101 ,\n",
+       "       141.66667134, 140.45686022, 139.66795657, 142.48270308,\n",
+       "       147.03665055, 151.8487008 , 156.90272514, 162.25791164,\n",
+       "       168.04938768, 173.63870768, 180.93567147, 190.3440156 ,\n",
+       "       199.86834472, 208.48375248, 215.75635742, 222.1915652 ,\n",
+       "       228.08641413, 233.15249702, 236.89713686, 239.83524192,\n",
+       "       242.45744315, 244.57483343, 245.52139699, 245.88236757,\n",
+       "       246.12295211, 246.3306567 , 246.52369882, 246.70598807,\n",
+       "       246.87792737, 247.03919426, 247.18952217, 247.3288843 ,\n",
+       "       247.45749059, 247.57573348, 247.68412862, 247.78326467,\n",
+       "       247.87376505, 247.95626051, 248.03137024, 248.09968963,\n",
+       "       248.16178271, 248.21817801, 248.26936683, 248.31580309,\n",
+       "       248.35790422, 248.39605277, 248.43059841, 248.46186013,\n",
+       "       248.49012851, 248.51566797, 248.53871897, 248.55950011,\n",
+       "       248.57821004, 248.59502931, 248.61012204, 248.62363741,\n",
+       "       248.63571111, 248.64646661, 248.65601627, 248.66446245])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordinary_kriging(data,method='points')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_grid(X,y,res):\n",
+    "    y_min = y.min()-0.2\n",
+    "    y_max = y.max()+0.2\n",
+    "    x_min = X.min()-0.2\n",
+    "    x_max = X.max()+0.2\n",
+    "    x_arr = np.linspace(x_min,x_max,res)\n",
+    "    y_arr = np.linspace(y_min,y_max,res)\n",
+    "    xx,yy = np.meshgrid(x_arr,y_arr)  \n",
+    "    return xx,yy\n",
+    "x, y = make_grid(data[:,0],data[:,1],100)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/polire/idw/__init__.py b/polire/idw/__init__.py
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diff --git a/polire/idw/__pycache__/idw.cpython-310.pyc b/polire/idw/__pycache__/idw.cpython-310.pyc
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diff --git a/polire/idw/idw.py b/polire/idw/idw.py
new file mode 100755
index 0000000000000000000000000000000000000000..c6417444ce9f74ef8278a2a299cf90fe75ae3f99
--- /dev/null
+++ b/polire/idw/idw.py
@@ -0,0 +1,91 @@
+"""
+This is a module for inverse distance weighting (IDW) Spatial Interpolation
+"""
+import numpy as np
+from ..utils.distance import haversine, euclidean
+from ..base import Base
+
+
+class IDW(Base):
+    """A class that is declared for performing IDW Interpolation.
+    For more information on how this method works, kindly refer to
+    https://en.wikipedia.org/wiki/Inverse_distance_weighting
+
+    Parameters
+    ----------
+    exponent : positive float, optional
+        The rate of fall of values from source data points.
+        Higher the exponent, lower is the value when we move
+        across space. Default value is 2.
+
+    Attributes
+    ----------
+    Interpolated Values : {array-like, 2D matrix}, shape(resolution, resolution)
+    This contains all the interpolated values when the interpolation is performed
+    over a grid, instead of interpolation over a set of points.
+
+    X : {array-like, 2D matrix}, shape(n_samples, 2)
+    Set of all the coordinates available for interpolation.
+
+    y : array-like, shape(n_samples,)
+    Set of all the available values at the specified X coordinates.
+
+    result : array_like, shape(n_to_predict, )
+    Set of all the interpolated values when interpolating over a given
+    set of data points.
+
+    """
+
+    def __init__(
+        self, exponent=2, resolution="standard", coordinate_type="Euclidean"
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.exponent = exponent
+        self.interpolated_values = None
+        self.X = None
+        self.y = None
+        self.result = None
+        if self.coordinate_type == "Geographic":
+            self.distance = haversine
+        elif self.coordinate_type == "Euclidean":
+            self.distance = euclidean
+        else:
+            raise NotImplementedError(
+                "Only Geographic and Euclidean Coordinates are available"
+            )
+
+    def _fit(self, X, y):
+        """This function is for the IDW Class.
+        This is not expected to be called directly
+        """
+        self.X = X
+        self.y = y
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Gridded interpolation for natural neighbors interpolation. This function should not
+        be called directly.
+        """
+        lims = (*x1lim, *x2lim)
+        x1min, x1max, x2min, x2max = lims
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+        X1, X2 = np.meshgrid(x1, x2)
+        return self._predict(np.array([X1.ravel(), X2.ravel()]).T)
+
+    def _predict(self, X):
+        """The function call to predict using the interpolated data
+        in IDW interpolation. This should not be called directly.
+        """
+
+        dist = self.distance(self.X, X)
+        weights = 1 / np.power(dist, self.exponent)
+        result = (weights * self.y[:, None]).sum(axis=0) / weights.sum(axis=0)
+
+        # if point is from train data, ground truth must not change
+        for i in range(X.shape[0]):
+            mask = np.equal(X[i], self.X).all(axis=1)
+            if mask.any():
+                result[i] = (self.y * mask).sum()
+
+        return result
diff --git a/polire/idw/tests/IDW Initial.ipynb b/polire/idw/tests/IDW Initial.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..5f7adad0923d2bd24ff68f05d330d9af66ffa83b
--- /dev/null
+++ b/polire/idw/tests/IDW Initial.ipynb	
@@ -0,0 +1,313 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Inverse Distance Weighting (IDW) Interpolation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let us suppose we have a data that shows the variation of one quantity of interest across space.\n",
+    "This could be equivalently viewed as { ($\\vec{x_1}, y_1)$,$(\\vec{x_2}, y_2)$,$(\\vec{x_3}, y_3)$, ...}, where the $\\vec{x_i}$'s represent the coordinates of the points where we have data and the $y_i$'s are the actual data at those points. <br><br>\n",
+    "We would like to perform an interpolation using these data points such that a few things are satisifed.\n",
+    "1. The interpolation is exact - the value at the known data points is the same as the estimated value, and \n",
+    "2. We would want far away points from a given source data point to receive less importance than nearby points.\n",
+    "3. Wikipedia has an excellent article on IDW. I am linking it [here](https://en.wikipedia.org/wiki/Inverse_distance_weighting)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We are using the following approximation for coordinate_type being latlong_small<br>\n",
+    "$| \\vec{r_2}− \\vec{r_1}| ≈ \\text{R }\\times \\sqrt[]{(Lat_2 - Lat_1)^{2} + (Long_2 - Long_1)^{2}}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "df = pd.read_csv('../../testdata/30-03-18.csv')\n",
+    "data = np.array(df[['longitude','latitude','value']])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_grid(X,y,res):\n",
+    "    y_min = y.min()-0.2\n",
+    "    y_max = y.max()+0.2\n",
+    "    x_min = X.min()-0.2\n",
+    "    x_max = X.max()+0.2\n",
+    "    x_arr = np.linspace(x_min,x_max,res)\n",
+    "    y_arr = np.linspace(y_min,y_max,res)\n",
+    "    xx,yy = np.meshgrid(x_arr,y_arr)  \n",
+    "    return xx,yy\n",
+    "\n",
+    "def idw(dataset, exponent = 2,  resolution='standard', coordinate_type='euclidean',verbose='False'):\n",
+    "    \"\"\"\n",
+    "        Here X is the set of spatial locations - Usually assumed to be Lat-Long\n",
+    "        To be extended to higher dimenstions y - estimated value , exponenet - how\n",
+    "        much weight to assign to far off locations to be estimated for each data point, \n",
+    "        extent - interpolate over a grid - what is xmax xmin ymax ymin\n",
+    "    \"\"\"\n",
+    "    if coordinate_type == 'latlong_small':\n",
+    "        \"\"\"\n",
+    "            Assume that the Earth is a Sphere, and use polar coordinates\n",
+    "            $| \\vec{r_2}− \\vec{r_1}| ≈ \\text{R }\\times \\sqrt[]{(Lat_2 - Lat_1)^{2} + (Long_2 - Long_1)^{2}}$\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type == 'latlong_large':\n",
+    "        \"\"\"\n",
+    "            Code to be written after understanding all the projections.\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type==\"euclidean\":\n",
+    "        \n",
+    "#         print(dataset)\n",
+    "        X = dataset[:,0]\n",
+    "        y = dataset[:,1]\n",
+    "        if resolution=='high':\n",
+    "            xx,yy = make_grid(X,y,1000)\n",
+    "            \n",
+    "        if resolution=='low':\n",
+    "            xx,yy = make_grid(X,y,10)\n",
+    "            \n",
+    "        if resolution=='standard':\n",
+    "            xx,yy = make_grid(X,y,100)\n",
+    "        \n",
+    "        new = []\n",
+    "        new_arr = dataset\n",
+    "        for points in new_arr:\n",
+    "            mindist = np.inf\n",
+    "            val = 0\n",
+    "            for j in range(len(yy)):\n",
+    "                temp = yy[j][0]\n",
+    "                for i in range(len(xx[0])):\n",
+    "                    dist = np.linalg.norm(np.array([xx[0][i],temp]) - points[:2])\n",
+    "                    if dist<mindist:\n",
+    "                        mindist = dist\n",
+    "                        val = (i,j)\n",
+    "            new.append((points,val))\n",
+    "        print(new)\n",
+    "        new_grid = np.zeros((len(xx),len(yy)))\n",
+    "        for i in range(len(new)):\n",
+    "            x = new[i][1][0]\n",
+    "            y = new[i][1][1]\n",
+    "            new_grid[x][y] = new[i][0][2]\n",
+    "            print(new[i])\n",
+    "        x_nz,y_nz = np.nonzero(new_grid)\n",
+    "        list_nz = []\n",
+    "        for i in range(len(x_nz)):\n",
+    "            list_nz.append((x_nz[i],y_nz[i]))\n",
+    "        \n",
+    "        final = np.copy(new_grid)\n",
+    "        \n",
+    "        for i in range(len(xx[0])):\n",
+    "            for j in range(len(yy)):\n",
+    "                normalise = 0\n",
+    "                if (i,j) in list_nz:\n",
+    "                    continue\n",
+    "                else:\n",
+    "                    \"\"\"\n",
+    "                    Could potentially have a divide by zero error here\n",
+    "                    Use a try except clause\n",
+    "                    \"\"\"\n",
+    "                    for elem in range(len(x_nz)):\n",
+    "                        source = np.array([x_nz[elem],y_nz[elem]])\n",
+    "                        target = np.array([xx[0][i],yy[j][0]])\n",
+    "                        dist = (np.abs(xx[0][source[0]] - target[0])**exponent + np.abs(yy[source[1]][0] - target[1])**exponent)**(1/exponent)\n",
+    "                        final[i][j]+=new_grid[x_nz[elem],y_nz[elem]]/dist\n",
+    "                        normalise+=1/(dist)\n",
+    "                final[i][j]/=normalise\n",
+    "    \n",
+    "    return final\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(array([ 77.234291,  28.581197, 194.      ]), (60, 39)), (array([ 77.245721,  28.739434, 267.      ]), (62, 60)), (array([ 77.101961,  28.822931, 273.      ]), (42, 72)), (array([ 76.991463,  28.620806, 129.      ]), (27, 44)), (array([ 77.0325413,  28.60909  , 176.       ]), (33, 42)), (array([ 77.072196,  28.570859, 172.      ]), (38, 37)), (array([ 77.1670103,  28.5646102, 168.       ]), (51, 36)), (array([ 77.1180053,  28.5627763, 105.       ]), (45, 36)), (array([ 77.272404,  28.530782, 203.      ]), (66, 32)), (array([ 77.26075 ,  28.563827, 192.      ]), (64, 36)), (array([77.0996943, 28.610304 , 95.       ]), (42, 43)), (array([ 77.2273074,  28.5918245, 148.       ]), (59, 40)), (array([ 77.09211 ,  28.732219, 203.      ]), (41, 59)), (array([ 77.317084,  28.668672, 221.      ]), (72, 51)), (array([ 77.1585447,  28.6573814, 141.       ]), (50, 49)), (array([ 77.2011573,  28.6802747, 192.       ]), (56, 52)), (array([ 77.237372,  28.612561, 203.      ]), (61, 43)), (array([ 77.305651,  28.632707, 152.      ]), (70, 46)), (array([ 77.1473105,  28.6514781, 185.       ]), (49, 48)), (array([ 77.16482 ,  28.699254, 290.      ]), (51, 55)), (array([ 77.170221,  28.728722, 273.      ]), (52, 59)), (array([ 77.2005604,  28.6372688, 173.       ]), (56, 46)), (array([ 77.2011573,  28.7256504, 269.       ]), (56, 58)), (array([ 77.136777,  28.669119, 160.      ]), (47, 51)), (array([77.267246, 28.49968 , 78.      ]), (65, 27)), (array([ 77.2494387,  28.6316945, 211.       ]), (62, 45)), (array([ 77.2735737,  28.5512005, 252.       ]), (66, 34)), (array([ 77.2159377,  28.5504249, 133.       ]), (58, 34)), (array([77.1112615, 28.7500499, 77.       ]), (44, 62)), (array([77.22445, 28.63576, 96.     ]), (59, 46))]\n",
+      "(array([ 77.234291,  28.581197, 194.      ]), (60, 39))\n",
+      "(array([ 77.245721,  28.739434, 267.      ]), (62, 60))\n",
+      "(array([ 77.101961,  28.822931, 273.      ]), (42, 72))\n",
+      "(array([ 76.991463,  28.620806, 129.      ]), (27, 44))\n",
+      "(array([ 77.0325413,  28.60909  , 176.       ]), (33, 42))\n",
+      "(array([ 77.072196,  28.570859, 172.      ]), (38, 37))\n",
+      "(array([ 77.1670103,  28.5646102, 168.       ]), (51, 36))\n",
+      "(array([ 77.1180053,  28.5627763, 105.       ]), (45, 36))\n",
+      "(array([ 77.272404,  28.530782, 203.      ]), (66, 32))\n",
+      "(array([ 77.26075 ,  28.563827, 192.      ]), (64, 36))\n",
+      "(array([77.0996943, 28.610304 , 95.       ]), (42, 43))\n",
+      "(array([ 77.2273074,  28.5918245, 148.       ]), (59, 40))\n",
+      "(array([ 77.09211 ,  28.732219, 203.      ]), (41, 59))\n",
+      "(array([ 77.317084,  28.668672, 221.      ]), (72, 51))\n",
+      "(array([ 77.1585447,  28.6573814, 141.       ]), (50, 49))\n",
+      "(array([ 77.2011573,  28.6802747, 192.       ]), (56, 52))\n",
+      "(array([ 77.237372,  28.612561, 203.      ]), (61, 43))\n",
+      "(array([ 77.305651,  28.632707, 152.      ]), (70, 46))\n",
+      "(array([ 77.1473105,  28.6514781, 185.       ]), (49, 48))\n",
+      "(array([ 77.16482 ,  28.699254, 290.      ]), (51, 55))\n",
+      "(array([ 77.170221,  28.728722, 273.      ]), (52, 59))\n",
+      "(array([ 77.2005604,  28.6372688, 173.       ]), (56, 46))\n",
+      "(array([ 77.2011573,  28.7256504, 269.       ]), (56, 58))\n",
+      "(array([ 77.136777,  28.669119, 160.      ]), (47, 51))\n",
+      "(array([77.267246, 28.49968 , 78.      ]), (65, 27))\n",
+      "(array([ 77.2494387,  28.6316945, 211.       ]), (62, 45))\n",
+      "(array([ 77.2735737,  28.5512005, 252.       ]), (66, 34))\n",
+      "(array([ 77.2159377,  28.5504249, 133.       ]), (58, 34))\n",
+      "(array([77.1112615, 28.7500499, 77.       ]), (44, 62))\n",
+      "(array([77.22445, 28.63576, 96.     ]), (59, 46))\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(100, 100)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idw(data).shape\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "temp = data[10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(array([10, 10, 10]), array([0, 1, 2]))"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.where(data==temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "result  = np.nonzero(data==temp)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10"
+      ]
+     },
+     "execution_count": 37,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.unique(result[0])[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "listOfCoordinates= list(zip(result[0], result[1]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[(10, 0), (10, 1), (10, 2)]"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "listOfCoordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/polire/idw/tests/Numpy+IDWTest.ipynb b/polire/idw/tests/Numpy+IDWTest.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..d13ef33474278c9e96c29ad0852e0c31c7b204ef
--- /dev/null
+++ b/polire/idw/tests/Numpy+IDWTest.ipynb
@@ -0,0 +1,411 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a = np.array([[1,2,3],[4,5,6]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "b = np.array([[2,3,4],[5,6,9]])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[2, 3, 4],\n",
+       "       [5, 6, 9]])"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[1, 2, 3],\n",
+       "       [4, 5, 6]])"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1, -1, -1],\n",
+       "       [-1, -1, -3]])"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a - b"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1.7320508075688772"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.argmin([np.linalg.norm(a[i] - b[i]) for i in range(len(a))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.min?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\"\"\"\n",
+    "This is a module for IDW Spatial Interpolation\n",
+    "\"\"\"\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from copy import deepcopy\n",
+    "class idw():\n",
+    "    \"\"\" A class that is declared for performing IDW Interpolation.\n",
+    "    For more information on how this method works, kindly refer to\n",
+    "    https://en.wikipedia.org/wiki/Inverse_distance_weighting\n",
+    "\n",
+    "    Parameters\n",
+    "    ----------\n",
+    "    exponent : positive float, optional\n",
+    "        The rate of fall of values from source data points.\n",
+    "        Higher the exponent, lower is the value when we move\n",
+    "        across space. Default value is 2.\n",
+    "    resolution: str, optional\n",
+    "        Decides the smoothness of the interpolation. Note that\n",
+    "        interpolation is done over a grid. Higher the resolution\n",
+    "        means more grid cells and more time for interpolation.\n",
+    "        Default value is 'standard'\n",
+    "    coordinate_type: str, optional\n",
+    "        Decides the distance metric to be used, while performing\n",
+    "        interpolation. Euclidean by default.     \n",
+    "    \"\"\"\n",
+    "    def __init__(self, exponent = 2, resolution = 'standard', coordinate_type='Euclidean'):\n",
+    "        \n",
+    "        self.exponent = exponent\n",
+    "        self.resolution = resolution\n",
+    "        self.coordinate_type = coordinate_type\n",
+    "        self.interpolated_values = None\n",
+    "        self.x_grid = None\n",
+    "        self.y_grid = None\n",
+    "\n",
+    "    def make_grid(self, x, y, res, offset=0.2):\n",
+    "\n",
+    "        \"\"\" This function returns the grid to perform interpolation on.\n",
+    "           This function is used inside the fit() attribute of the idw class.\n",
+    "        \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        x: array-like, shape(n_samples,)\n",
+    "            The first coordinate values of all points where\n",
+    "            ground truth is available\n",
+    "        y: array-like, shape(n_samples,)\n",
+    "            The second coordinate values of all points where\n",
+    "            ground truth is available\n",
+    "        res: int\n",
+    "            The resolution value\n",
+    "        offset: float, optional\n",
+    "            A value between 0 and 0.5 that specifies the extra interpolation to be done\n",
+    "            Default is 0.2\n",
+    "        \n",
+    "        Returns\n",
+    "        -------\n",
+    "        xx : {array-like, 2D}, shape (n_samples, n_samples)\n",
+    "        yy : {array-like, 2D}, shape (n_samples, n_samples)\n",
+    "        \"\"\"\n",
+    "        y_min = y.min() - offset\n",
+    "        y_max = y.max()+ offset\n",
+    "        x_min = x.min()-offset\n",
+    "        x_max = x.max()+offset\n",
+    "        x_arr = np.linspace(x_min,x_max,res)\n",
+    "        y_arr = np.linspace(y_min,y_max,res)\n",
+    "        xx,yy = np.meshgrid(x_arr,y_arr)  \n",
+    "        return xx,yy\n",
+    "\n",
+    "    \n",
+    "    def fit(self, X, y):\n",
+    "        \"\"\" The function call to fit the model on the given data. \n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        X: {array-like, 2D matrix}, shape(n_samples, 2)\n",
+    "            The set of all coordinates, where we have ground truth\n",
+    "            values\n",
+    "        y: array-like, shape(n_samples,)\n",
+    "            The set of all the ground truth values using which\n",
+    "            we perform interpolation\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        self : object\n",
+    "            Returns self\n",
+    "        \"\"\"\n",
+    "\n",
+    "#          if self.coordinate_type == 'latlong_small':\n",
+    "# \t \t\"\"\"\n",
+    "# \t \t\tUse the conversions and projections for small changes in LatLong\n",
+    "#  \t\t\"\"\"\n",
+    "# \t \t    print (\"To be done later\")\n",
+    "#             return self\n",
+    "\n",
+    "#         if self.coordinate_type == 'latlong_large':\n",
+    "#             \"\"\"\n",
+    "#                 Code to be written after understanding all the projections.\n",
+    "#             \"\"\"\n",
+    "#             print (\"To be done later\")\n",
+    "#             return self\n",
+    "\n",
+    "        if self.coordinate_type==\"Euclidean\":\n",
+    "            \n",
+    "            X = deepcopy(np.c_[X,y])\n",
+    "\n",
+    "            if self.resolution=='high':\n",
+    "                xx,yy = self.make_grid(X,y,1000)\n",
+    "                \n",
+    "            if self.resolution=='low':\n",
+    "                xx,yy = self.make_grid(X,y,10)\n",
+    "                \n",
+    "            if self.resolution=='standard':\n",
+    "                xx,yy = self.make_grid(X,y,100)\n",
+    "\n",
+    "            new = []\n",
+    "            new_arr = deepcopy(X)\n",
+    "            for points in new_arr:\n",
+    "                min_dist = np.inf\n",
+    "                val = 0\n",
+    "                for j in range(len(yy)):\n",
+    "                    temp = yy[j][0]\n",
+    "                    for i in range(len(xx[0])):\n",
+    "                        dist = np.linalg.norm(np.array([xx[0][i],temp]) - points[:2])\n",
+    "                        if dist<min_dist:\n",
+    "                            min_dist = dist\n",
+    "                            val = (i,j)\n",
+    "                new.append((points,val))\n",
+    "            new_grid = np.zeros((len(xx),len(yy)))\n",
+    "            for i in range(len(new)):\n",
+    "                x = new[i][1][0]\n",
+    "                y = new[i][1][1]\n",
+    "                new_grid[x][y] = new[i][0][2]\n",
+    "            x_nz,y_nz = np.nonzero(new_grid)\n",
+    "            list_nz = []\n",
+    "            for i in range(len(x_nz)):\n",
+    "                list_nz.append((x_nz[i],y_nz[i]))\n",
+    "            final = np.copy(new_grid)\n",
+    "            for i in range(len(xx[0])):\n",
+    "                for j in range(len(yy)):\n",
+    "                    normalise = 0\n",
+    "                    if (i,j) in list_nz:\n",
+    "                        continue\n",
+    "                    else:\n",
+    "                        for elem in range(len(x_nz)):\n",
+    "                            source = np.array([x_nz[elem],y_nz[elem]])\n",
+    "                            target = np.array([xx[0][i],yy[j][0]])\n",
+    "                            dist = (np.abs(xx[0][source[0]] - target[0])**self.exponent + np.abs(yy[source[1]][0] - target[1])**self.exponent)**(1/self.exponent)\n",
+    "                            final[i][j]+=new_grid[x_nz[elem],y_nz[elem]]/dist\n",
+    "                            normalise+=1/(dist)\n",
+    "                    final[i][j]/=normalise\n",
+    "            self.interpolated_values = final\n",
+    "            self.x_grid = xx\n",
+    "            self.y_grid = yy\n",
+    "        \n",
+    "        return self\n",
+    "\n",
+    "#     def predict(self, X):\n",
+    "#         \"\"\" The function call to predict using the interpolated data\n",
+    "#         Parameters\n",
+    "#         ----------\n",
+    "#         X: {array-like, 2D matrix}, shape(n_samples, 2)\n",
+    "#             The set of all coordinates, where we have ground truth\n",
+    "#             values\n",
+    "        \n",
+    "\n",
+    "#         Returns\n",
+    "#         -------\n",
+    "#         y: array-like, shape(n_samples,)\n",
+    "#             The set of all the ground truth values using which\n",
+    "#             we perform interpolation \n",
+    "#         \"\"\"\n",
+    "#         if self.coordinate_type == 'Euclidean':\n",
+    "#             for i in range(self.x_grid[0]):\n",
+    "#                 for j in range()\n",
+    "        \n",
+    "#         else:\n",
+    "#             print(\"Will be done later\")\n",
+    "#             return \n",
+    "            \n",
+    "                \n",
+    "# self.x_grid\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<__main__.idw at 0x7f36db6f9c88>"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a = idw()\n",
+    "import pandas as pd\n",
+    "df = pd.read_csv('../../testdata/30-03-18.csv')\n",
+    "data = np.array(df[['longitude','latitude','value']])\n",
+    "a.fit(data[:,:2],data[:,2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[171.89189189, 171.89597641, 171.90813547, ..., 173.89050472,\n",
+       "        173.89261459, 173.89466512],\n",
+       "       [171.77142857, 171.77625338, 171.79060316, ..., 173.89585441,\n",
+       "        173.89787202, 173.89983245],\n",
+       "       [171.63636364, 171.64211895, 171.65921778, ..., 173.9012935 ,\n",
+       "        173.90321551, 173.90508269],\n",
+       "       ...,\n",
+       "       [174.49681529, 174.49676176, 174.49660126, ..., 174.24671184,\n",
+       "        174.24416446, 174.24164382],\n",
+       "       [174.49056604, 174.49051451, 174.49035999, ..., 174.24671343,\n",
+       "        174.24419773, 174.2417078 ],\n",
+       "       [174.48447205, 174.48442242, 174.48427358, ..., 174.2466762 ,\n",
+       "        174.24419219, 174.24173298]])"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "a.interpolated_values"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/polire/kriging/__init__.py b/polire/kriging/__init__.py
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diff --git a/polire/kriging/kriging.py b/polire/kriging/kriging.py
new file mode 100755
index 0000000000000000000000000000000000000000..442c77c5d7202c4ffdb1abd3ea0b72b8e701258b
--- /dev/null
+++ b/polire/kriging/kriging.py
@@ -0,0 +1,146 @@
+"""
+This is a module for Kriging Interpolation
+"""
+import numpy as np
+from ..base import Base
+from pykrige.ok import OrdinaryKriging
+from pykrige.uk import UniversalKriging
+
+
+class Kriging(Base):
+    """A class that is declared for performing Kriging interpolation.
+    Kriging interpolation (usually) works on the principle of finding the
+    best unbiased predictor. Ordinary Kriging, for an example, involves finding out the
+    best unbaised linear predictor.
+
+    Parameters
+    ----------
+    type : str, optional
+    This parameter defines the type of Kriging under consideration. This
+    implementation uses PyKrige package  (https://github.com/bsmurphy/PyKrige).
+    The user needs to choose between "Ordinary" and "Universal".
+
+    plotting: boolean, optional
+    This parameter plots the fit semivariogram. We use PyKrige's inbuilt plotter for the same.s
+
+    variogram_model : str, optional
+    Specifies which variogram model to use; may be one of the following:
+    linear, power, gaussian, spherical, exponential, hole-effect.
+    Default is linear variogram model. To utilize a custom variogram model,
+    specify 'custom'; you must also provide variogram_parameters and
+    variogram_function. Note that the hole-effect model is only technically
+    correct for one-dimensional problems.
+
+    require_variance : Boolean, optional
+    This variable returns the uncertainity in the interpolated values using Kriging
+    interpolation. If this is True, kindly call the attribute return_variance, of this class
+    to retreive the computed variances. False is the default value.d
+
+        nlags: int, optional
+        Number of lags to be considered for semivariogram. As in PyKrige, we set default to be 6.
+    """
+
+    def __init__(
+        self,
+        type="Ordinary",
+        plotting=False,
+        variogram_model="linear",
+        require_variance=False,
+        resolution="standard",
+        coordinate_type="Eucledian",
+        nlags=6,
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.variogram_model = variogram_model
+        self.ok = None
+        self.uk = None
+        self.type = type
+        self.plotting = plotting
+        self.coordinate_type = None
+        self.require_variance = require_variance
+        self.variance = None
+
+        if coordinate_type == "Eucledian":
+            self.coordinate_type = "euclidean"
+        else:
+            self.coordinate_type = "geographic"
+
+        self.nlags = nlags
+
+    def _fit(self, X, y):
+        """This method of the Kriging Class is used to fit Kriging interpolation model to
+        the train data. This function shouldn't be called directly."""
+        if self.type == "Ordinary":
+            self.ok = OrdinaryKriging(
+                X[:, 0],
+                X[:, 1],
+                y,
+                variogram_model=self.variogram_model,
+                enable_plotting=self.plotting,
+                coordinates_type=self.coordinate_type,
+                nlags=self.nlags,
+            )
+
+        elif self.type == "Universal":
+            self.uk = UniversalKriging(
+                X[:, 0],
+                X[:, 1],
+                y,
+                variogram_model=self.variogram_model,
+                enable_plotting=self.plotting,
+            )
+
+        else:
+            raise ValueError(
+                "Choose either Universal or Ordinary - Given argument is neither"
+            )
+
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """The function that is called to return the interpolated data in Kriging Interpolation
+        in a grid. This method shouldn't be called directly"""
+        lims = (*x1lim, *x2lim)
+        x1min, x1max, x2min, x2max = lims
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+
+        if self.ok is not None:
+            predictions, self.variance = self.ok.execute(
+                style="grid", xpoints=x1, ypoints=x2
+            )
+
+        else:
+            predictions, self.variance = self.uk.execute(
+                style="grid", xpoints=x1, ypoints=x2
+            )
+
+        return predictions
+
+    def _predict(self, X):
+        """This function should be called to return the interpolated data in kriging
+        in a pointwise manner. This method shouldn't be called directly."""
+        if self.ok is not None:
+            predictions, self.variance = self.ok.execute(
+                style="points", xpoints=X[:, 0], ypoints=X[:, 1]
+            )
+
+        else:
+            predictions, self.variance = self.uk.execute(
+                style="points", xpoints=X[:, 0], ypoints=X[:, 1]
+            )
+
+        return predictions
+
+    def return_variance(self):
+        """This method of the Kriging class returns the variance at the interpolated
+        points if the user chooses to use this option at the beginning of the interpolation
+        """
+        if self.require_variance:
+            return self.variance
+
+        else:
+            print(
+                "Variance not asked for, while instantiating the object. Returning None"
+            )
+            return None
diff --git a/polire/kriging/tests/Kriging Interpolation.ipynb b/polire/kriging/tests/Kriging Interpolation.ipynb
new file mode 100755
index 0000000000000000000000000000000000000000..5739938bda62c97e490de06ffc2d26fe9fb91a76
--- /dev/null
+++ b/polire/kriging/tests/Kriging Interpolation.ipynb	
@@ -0,0 +1,224 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pykrige import OrdinaryKriging"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ok = OrdinaryKriging(data[:,0],data[:,1],data[:,2])\n",
+    "ok.ex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "a,b = ok.execute('grid',x[0],y[:,0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from pykrige import OrdinaryKriging\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "\n",
+    "def ordinary_kriging(dataset, resolution='standard', coordinate_type='euclidean',verbose='False',method='grid', isvariance = False):\n",
+    "    if coordinate_type == 'latlong_small':\n",
+    "        \"\"\"\n",
+    "            Assume that the Earth is a Sphere, and use polar coordinates\n",
+    "            $| \\vec{r_2}− \\vec{r_1}| ≈ \\text{R }\\times \\sqrt[]{(Lat_2 - Lat_1)^{2} + (Long_2 - Long_1)^{2}}$\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type == 'latlong_large':\n",
+    "        \"\"\"\n",
+    "            Code to be written after understanding all the projections.\n",
+    "        \"\"\"\n",
+    "        return \"To be done later\"\n",
+    "    if coordinate_type==\"euclidean\":\n",
+    "        \n",
+    "        ok = OrdinaryKriging(dataset[:,0],dataset[:,1],dataset[:,2])\n",
+    "        X = dataset[:,0]\n",
+    "        y = dataset[:,1]\n",
+    "        \n",
+    "        if resolution=='high':\n",
+    "            xx,yy = make_grid(X,y,1000)\n",
+    "            \n",
+    "        elif resolution=='low':\n",
+    "            xx,yy = make_grid(X,y,10)\n",
+    "            \n",
+    "        elif resolution=='standard':\n",
+    "            xx,yy = make_grid(X,y,100)\n",
+    "            \n",
+    "        else:\n",
+    "            print('Value Error - Resolution can only be one of \\nhigh, low or standard')\n",
+    "        \n",
+    "        values, variances = ok.execute(method, xx[0], yy[:,0])\n",
+    "        \n",
+    "    if isvariance:\n",
+    "        return values, variances\n",
+    "    else:\n",
+    "        del variances\n",
+    "        return np.array(values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[129.94984945, 129.7682324 , 129.58820662, ..., 159.34079485,\n",
+       "        159.99175016, 160.63241067],\n",
+       "       [130.22090025, 130.03615966, 129.8529146 , ..., 159.9575165 ,\n",
+       "        160.61228126, 161.25625641],\n",
+       "       [130.50105231, 130.31324536, 130.12683652, ..., 160.59265384,\n",
+       "        161.25084023, 161.8977369 ],\n",
+       "       ...,\n",
+       "       [207.22133238, 207.82739139, 208.44615116, ..., 248.64646661,\n",
+       "        248.3790241 , 248.11033441],\n",
+       "       [207.92838926, 208.53490708, 209.15376273, ..., 248.91678379,\n",
+       "        248.65601627, 248.39371596],\n",
+       "       [208.61942088, 209.22595474, 209.84445913, ..., 249.17442481,\n",
+       "        248.9203453 , 248.66446245]])"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordinary_kriging(data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "* What does ok('points') really do?\n",
+    "* Specifically test when points aren't really passed - they are let's say the point of an array\n",
+    "* Returns the diagonal matrix of all these coordinates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([129.94984945, 130.03615966, 130.12683652, 130.22219703,\n",
+       "       130.32258826, 130.42839089, 130.54002324, 130.65794596,\n",
+       "       130.7826674 , 130.91474976, 131.05481629, 131.20355964,\n",
+       "       131.36175158, 131.53025441, 131.71003442, 131.90217771,\n",
+       "       132.107909  , 132.32861401, 132.56586607, 132.82145795,\n",
+       "       133.0974399 , 133.39616477, 133.72034153, 134.07309736,\n",
+       "       134.45804822, 134.87937482, 135.34189663, 135.85112772,\n",
+       "       136.41328222, 137.03517039, 137.72388496, 138.48612122,\n",
+       "       139.326921  , 140.24763047, 141.24300526, 142.29757046,\n",
+       "       143.37881815, 144.38425962, 144.49187978, 143.1202101 ,\n",
+       "       141.66667134, 140.45686022, 139.66795657, 142.48270308,\n",
+       "       147.03665055, 151.8487008 , 156.90272514, 162.25791164,\n",
+       "       168.04938768, 173.63870768, 180.93567147, 190.3440156 ,\n",
+       "       199.86834472, 208.48375248, 215.75635742, 222.1915652 ,\n",
+       "       228.08641413, 233.15249702, 236.89713686, 239.83524192,\n",
+       "       242.45744315, 244.57483343, 245.52139699, 245.88236757,\n",
+       "       246.12295211, 246.3306567 , 246.52369882, 246.70598807,\n",
+       "       246.87792737, 247.03919426, 247.18952217, 247.3288843 ,\n",
+       "       247.45749059, 247.57573348, 247.68412862, 247.78326467,\n",
+       "       247.87376505, 247.95626051, 248.03137024, 248.09968963,\n",
+       "       248.16178271, 248.21817801, 248.26936683, 248.31580309,\n",
+       "       248.35790422, 248.39605277, 248.43059841, 248.46186013,\n",
+       "       248.49012851, 248.51566797, 248.53871897, 248.55950011,\n",
+       "       248.57821004, 248.59502931, 248.61012204, 248.62363741,\n",
+       "       248.63571111, 248.64646661, 248.65601627, 248.66446245])"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ordinary_kriging(data,method='points')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_grid(X,y,res):\n",
+    "    y_min = y.min()-0.2\n",
+    "    y_max = y.max()+0.2\n",
+    "    x_min = X.min()-0.2\n",
+    "    x_max = X.max()+0.2\n",
+    "    x_arr = np.linspace(x_min,x_max,res)\n",
+    "    y_arr = np.linspace(y_min,y_max,res)\n",
+    "    xx,yy = np.meshgrid(x_arr,y_arr)  \n",
+    "    return xx,yy\n",
+    "x, y = make_grid(data[:,0],data[:,1],100)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/polire/natural_neighbors/__init__.py b/polire/natural_neighbors/__init__.py
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new file mode 100755
index 0000000000000000000000000000000000000000..d64c8b6fb57a37f6bc13b77e2011dda8722893fb
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diff --git a/polire/natural_neighbors/natural_neighbors.py b/polire/natural_neighbors/natural_neighbors.py
new file mode 100755
index 0000000000000000000000000000000000000000..a677c40b5bd42bf1f3760759fbba158b843903e7
--- /dev/null
+++ b/polire/natural_neighbors/natural_neighbors.py
@@ -0,0 +1,210 @@
+"""
+This is a module for Natural Neighbors Interpolation
+"""
+
+import numpy as np
+from scipy.spatial import Voronoi, voronoi_plot_2d
+import matplotlib.pyplot as plt
+from ..base import Base
+from shapely.geometry import Point
+from shapely.geometry.polygon import Polygon
+from math import atan2
+from copy import deepcopy
+
+
+def is_row_in_array(row, arr):
+    return list(row) in arr.tolist()
+
+
+def get_index(row, arr):
+    t1 = np.where(arr[:, 0] == row[0])
+    t2 = np.where(arr[:, 1] == row[1])
+    index = np.intersect1d(t1, t2)[0]
+    # If length of index exceeds one!! - Uniqueness Error
+    return index
+
+
+def order_poly(vertices):
+    """This function essentially is used to order the vertices
+    of the Voronoi polygon in a clockwise manner. This ensures
+    that Shapely doesn't produce Polygon objects that are potentially
+    non-convex and non-zero area.
+
+    Arguments
+    ---------
+    vertices : {array-like, 2D matrix}
+    This contains the list of vertices of the Polygon to be sorted
+
+    Returns
+    -------
+    new_vertices : {array-like, 2D matrix}
+    All the vertices reordered in a clockwise manner
+    """
+    mean_x = np.mean(vertices[:, 0])
+    mean_y = np.mean(vertices[:, 1])
+
+    def condition(x):
+        """This is the condition to be used while sorting. We convert the coordinates
+        to Polar and sort the points
+        """
+        return atan2(x[0] - mean_x, x[1] - mean_y) * 180 / np.pi
+
+    return sorted(vertices, key=condition)
+
+
+class NaturalNeighbor(Base):
+    """Class used for natural neighbors interpolation. This method is an implementation first
+    proposed by Sibson et al. [1] in 1981. We use the weights derived using the work in [1]
+    and leave it for future addition, the use of Laplace Weights [2].
+
+    Parameters
+    ----------
+    weights: str, optional
+        This defines the type of weights to be used for natural neighbor interpolation.
+        We use Sibson Weights, and plan to add Laplace weights in the future
+        Default value is "sibson"
+
+    display: Boolean, optional
+        True value displays the voronoi tesselation to the user after fitting the model.
+        Default value is False.
+
+    Notes
+    -----
+    This is for contributors:
+        The way in which part of the code is used is in the assumption that
+        we use the data's ordering to find its voronoi partitions.
+
+    References
+    ----------
+    [1]  Sibson, R. (1981). "A brief description of natural neighbor interpolation (Chapter 2)". In V. Barnett (ed.). Interpolating Multivariate Data. Chichester: John Wiley. pp. 21–36.
+    [2]  V.V. Belikov; V.D. Ivanov; V.K. Kontorovich; S.A. Korytnik; A.Y. Semenov (1997). "The non-Sibsonian interpolation: A new method of interpolation of the values of a function on an arbitrary set of points". Computational mathematics and mathematical physics. 37 (1): 9–15.
+    [3]  N.H. Christ; R. Friedberg, R.; T.D. Lee (1982). "Weights of links and plaquettes in a random lattice". Nuclear Physics B. 210 (3): 337–346.
+    """
+
+    def __init__(
+        self,
+        weights="sibson",
+        display=False,
+        resolution="standard",
+        coordinate_type="Eucledian",
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.weights = weights
+        self.X = None
+        self.y = None
+        self.result = None
+        self.voronoi = None
+        self.vertices = (
+            None  # This variable stored the voronoi partition's vertices
+        )
+        self.vertex_poly_map = (
+            dict()
+        )  # This variable stores the polygon to data point map
+        self.display = display
+
+    def _fit(self, X, y):
+        """This function is for the natural neighbors interpolation method.
+        This is not expected to be called directly.
+        """
+        self.X = X
+        self.y = y
+        self.voronoi = Voronoi(X, incremental=True)
+        self.vertices = self.voronoi.vertices
+
+        self.vertex_poly_map = {i: 0 for i in range(len(X))}
+
+        for i in range(len(self.X)):
+            index = np.where(self.voronoi.point_region == i)[0][0]
+            point = Point(self.X[index])
+            region = self.voronoi.regions[i]
+            if -1 not in region and region != []:
+                # -1 corresponds to unbounded region - we can't have this in interpolation
+                # and the function returns an empty list anyways
+                # at least in the case of non-incremental NN
+                p = Polygon(order_poly(self.vertices[region]))
+                self.vertex_poly_map[index] = p
+        # Remove all the data points that do not contribute to Nearest Neighhbor interpolation
+        for i in range(len(self.vertex_poly_map)):
+            if self.vertex_poly_map[i] == 0:
+                self.vertex_poly_map.pop(i, None)
+
+        if self.display:
+            voronoi_plot_2d(self.voronoi)
+            plt.show()
+            self.display = False
+
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Gridded interpolation for natural neighbors interpolation. This function should not
+        be called directly.
+        """
+        lims = (*x1lim, *x2lim)
+        x1min, x1max, x2min, x2max = lims
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+        X1, X2 = np.meshgrid(x1, x2)
+        return self._predict(np.array([X1.ravel(), X2.ravel()]).T)
+
+    def _predict(self, X):
+        """The function taht is called to predict the interpolated data in Natural Neighbors
+        interpolation. This should not be called directly.
+        If this method returns None, then we cannot interpolate because of the formed Voronoi
+        Tesselation
+        """
+        result = np.zeros(len(X))
+        # Potentially create so many class objects as the
+        # length of the to be predicted array
+        # not a bad idea if memory is not a constraints
+        for index in range(len(X)):
+            if is_row_in_array(X[index], self.X):
+                idx = get_index(X[index], self.X)
+                # Check if query data point already exists
+                result[index] = self.y[idx]
+
+            else:
+                # QHull object can't bgit ae pickled. Deepcopy doesn't work.
+                # So we need to fit the model for each and every query data point.
+                self._fit(self.X, self.y)
+
+                vor = self.voronoi
+                vor.add_points(np.array([X[index]]))
+                vor.close()
+                # We exploit the incremental processing of Scipy's Voronoi.
+                # We create a copy to ensure that the original copy is preserved.
+                new_regions = vor.regions
+                new_vertices = vor.vertices
+                final_regions = []
+
+                for i in new_regions:
+                    if i != [] and -1 not in i:
+                        final_regions.append(i)
+
+                new = []  # this stores the newly created voronoi partitions
+                for i in range(len(new_vertices)):
+                    if new_vertices[i] not in self.vertices:
+                        new.append(new_vertices[i])
+                new = np.array(new)
+                if len(new) < 3:
+                    # We need atleast a traingle to interpolate
+                    # Three new voronoi vertices form a triangle
+                    result[index] = np.nan
+                    continue
+
+                weights = {}  # Weights that we use for interpolation
+                new_polygon = Polygon(order_poly(new))
+                new_polygon_area = new_polygon.area
+
+                for i in self.vertex_poly_map:
+                    if new_polygon.intersects(self.vertex_poly_map[i]):
+                        weights[i] = (
+                            new_polygon.intersection(self.vertex_poly_map[i])
+                        ).area / new_polygon_area
+
+                prediction = np.array(
+                    [self.y[i] * weights[i] for i in weights]
+                ).sum()
+                result[index] = prediction
+                del vor, weights, new_polygon, new_polygon_area
+
+        return result
diff --git a/polire/nsgp/__init__.py b/polire/nsgp/__init__.py
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diff --git a/polire/nsgp/__pycache__/nsgp.cpython-310.pyc b/polire/nsgp/__pycache__/nsgp.cpython-310.pyc
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diff --git a/polire/nsgp/nsgp.py b/polire/nsgp/nsgp.py
new file mode 100755
index 0000000000000000000000000000000000000000..f40860b73ce1fb0c1bca51edc85b0602e2e48820
--- /dev/null
+++ b/polire/nsgp/nsgp.py
@@ -0,0 +1,243 @@
+from ..base import Base
+import numpy as np
+import multiprocessing as mp
+from GPy.kern import Matern32, Matern52, RBF, ExpQuad
+from scipy.optimize import least_squares
+
+
+class NSGP(Base):
+    """
+    A class to learn Nott and Dunsmuir's non-stationary kernel. For more information, refer to
+    https://academic.oup.com/biomet/article-abstract/89/4/819/242307
+
+    Parameters
+    ------------
+
+    N : int, default=10
+        Number of nearby points to learn each kernel locally
+
+    eta : int, default=1
+        A hyperparameter used in weight function
+
+    loc_kernel : str, default='m32', ('m32', 'm52' or 'rbf')
+        type of kernel to be used
+    """
+
+    def __init__(self, N=10, eta=1, kernel_name="m32", verbose=True):
+        super().__init__()
+        self.__N = N + 1  # Number of datapoints for local kernel learning
+        self.__eta = eta  # Eta hyperparameter for weighting function
+        self.__kernel_name = kernel_name
+        self.__param_dict = {
+            "N": self.__N,
+            "eta": self.__eta,
+            "kernel_name": self.__kernel_name,
+        }
+        self._KX_inv = None
+
+    def get_all_params(self):
+        """
+        Returns class parameters
+        """
+        return self.__param_dict
+
+    def get_param(self, param):
+        """
+        Returns the value of a parameter
+        """
+        return self.__param_dict[param]
+
+    def __calculate_dmat(self):
+        self.__dmat = np.zeros((self._X.shape[0], self._X.shape[0]))
+        for i in range(self._X.shape[0]):
+            for j in range(i, self._X.shape[0]):
+                self.__dmat[i, j] = np.linalg.norm(self._X[i] - self._X[j])
+                self.__dmat[j, i] = self.__dmat[i, j]
+
+    def __get_close_locs(self):
+        self.__calculate_dmat()  # Distance matrix
+        return [
+            self.__dmat[i].argsort()[: self.__N]
+            for i in range(self._X.shape[0])
+        ]
+
+    def __weight_func(self, S):
+        return np.exp(-(1 / self.__eta) * ((S - self._X) ** 2).sum(axis=1))
+
+    def _model(self, loc):
+        def __D_z(sj):
+            return self._Gamma[np.ix_(sj, sj)]
+
+        def __obfunc(x):
+            kernel = kern_dict[self.__kernel_name]
+            kernel.variance = x[0]
+            kernel.lengthscale = x[1:]
+            kern_vals = kernel.K(self._X[self.__close_locs[loc]])
+            term = (__D_z(self.__close_locs[loc]) - kern_vals) / kern_vals
+            return np.sum(term**2)
+
+        # ARD can be added
+        kern_dict = {
+            "m32": Matern32(
+                input_dim=self._X.shape[1],
+                active_dims=list(range(self._X.shape[1])),
+                ARD=True,
+            ),
+            "m52": Matern52(
+                input_dim=self._X.shape[1],
+                active_dims=list(range(self._X.shape[1])),
+                ARD=True,
+            ),
+            "rbf": RBF(
+                input_dim=self._X.shape[1],
+                active_dims=list(range(self._X.shape[1])),
+                ARD=True,
+            ),
+            "expqd": ExpQuad(
+                input_dim=self._X.shape[1],
+                active_dims=list(range(self._X.shape[1])),
+                ARD=True,
+            ),
+        }
+
+        kernel = kern_dict[self.__kernel_name]
+        params = least_squares(__obfunc, np.ones((self._X.shape[1] + 1))).x
+        kernel.variance = params[0]
+        kernel.lengthscale = params[1:]
+        return kernel.K
+
+    def _c_inv(self, kern_func):
+        return np.linalg.pinv(kern_func(self._X))
+
+    def __learnLocal(self):
+        # self._verbose_print('Training local kernels. This may take a few moments')
+
+        job = mp.Pool()
+        self.__kernels = job.map(self._model, list(range(self._X.shape[0])))
+        self.__C_inv = job.map(self._c_inv, self.__kernels)
+        job.close()
+
+        # self._verbose_print('Training complete')
+
+    def _Kernel(self, S1, S2=None):
+        """
+        This function is for the NSGP Class.
+        This is not expected to be called directly.
+        """
+        S2exists = True
+        if np.all(S1 == S2) or S2 is None:
+            S2exists = False
+            S2 = S1
+
+        assert S1.shape[1] == self._X.shape[1]
+        assert S2.shape[1] == self._X.shape[1]
+
+        # Calculating Weights & c_mats
+        self.__v_s1 = np.zeros((S1.shape[0], self._X.shape[0]))
+        self.__v_s2 = np.zeros((S2.shape[0], self._X.shape[0]))
+        self.__c_mat_s1 = np.zeros(
+            (self._X.shape[0], S1.shape[0], self._X.shape[0])
+        )
+        self.__c_mat_s2 = np.zeros(
+            (self._X.shape[0], self._X.shape[0], S2.shape[0])
+        )
+        self.__c_mat_s1s2 = np.zeros(
+            (self._X.shape[0], S1.shape[0], S2.shape[0])
+        )
+
+        for s1i, s1 in enumerate(S1):
+            s_vec = self.__weight_func(s1)
+            self.__v_s1[s1i, :] = s_vec / s_vec.sum()
+        if S2exists:
+            for s2i, s2 in enumerate(S2):
+                s_vec = self.__weight_func(s2)
+                self.__v_s2[s2i, :] = s_vec / s_vec.sum()
+            for i in range(self._X.shape[0]):
+                self.__c_mat_s1[i, :, :] = self.__kernels[i](S1, self._X)
+                self.__c_mat_s2[i, :, :] = self.__kernels[i](self._X, S2)
+                self.__c_mat_s1s2[i, :, :] = self.__kernels[i](S1, S2)
+        else:
+            self.__v_s2 = self.__v_s1
+            for i in range(self._X.shape[0]):
+                self.__c_mat_s1[i, :, :] = self.__kernels[i](S1, self._X)
+                self.__c_mat_s2[i, :, :] = self.__c_mat_s1[i, :, :].T
+                self.__c_mat_s1s2[i, :, :] = self.__kernels[i](S1)
+
+        # Calculating main covariance function
+        first_term = np.zeros((S1.shape[0], S2.shape[0]), dtype="float64")
+        for i in range(self._X.shape[0]):
+            for j in range(self._X.shape[0]):
+                first_term += (
+                    self.__c_mat_s1[i, :, :]
+                    .dot(self.__C_inv[i])
+                    .dot(self._Gamma)
+                    .dot(self.__C_inv[j])
+                    .dot(self.__c_mat_s2[j, :, :])
+                ) * (
+                    self.__v_s1[:, i]
+                    .reshape(-1, 1)
+                    .dot(self.__v_s2[:, j].reshape(1, -1))
+                )
+
+        second_term = np.zeros((S1.shape[0], S2.shape[0]))
+        for i in range(self._X.shape[0]):
+            second_term += np.sqrt(
+                self.__v_s1[:, i]
+                .reshape(-1, 1)
+                .dot(self.__v_s2[:, i].reshape(1, -1))
+            ) * (
+                self.__c_mat_s1s2[i, :, :]
+                - self.__c_mat_s1[i, :, :]
+                .dot(self.__C_inv[i])
+                .dot(self.__c_mat_s2[i, :, :])
+            )
+
+        return first_term + second_term
+
+    def _fit(self, X, y, ECM):
+        """
+        This function is for the NSGP Class.
+        This is not expected to be called directly.
+        """
+
+        self._Gamma = ECM  # Empirical Covariance Matrix
+        assert type(self._Gamma) == type(
+            np.zeros((1, 1))
+        ), "ECM must be a numpy array"
+        assert self._Gamma.shape[0] == self._Gamma.shape[1] == X.shape[0], (
+            "ECM must have ("
+            + str(X.shape[0])
+            + ", "
+            + str(X.shape[0])
+            + ") shape"
+        )
+
+        self._X = X  # training fetures
+        self._y = y  # Training values
+        self.__param_dict["X"] = X
+        self.__param_dict["y"] = y
+        self.__param_dict["ECM"] = self._Gamma
+
+        # Get closest N locations for each train location
+        self.__close_locs = self.__get_close_locs()
+        self.__learnLocal()  # Learning local kernels
+        return self
+
+    def _predict(self, X, return_cov=False):
+        """
+        This function is for the NSGP Class.
+        This is not expected to be called directly.
+        """
+        if self._KX_inv is None:
+            self._KX_inv = np.linalg.pinv(self._Kernel(self._X, self._X))
+        KX_test = self._Kernel(X, self._X)
+        pred_mean = (
+            KX_test.dot(self._KX_inv).dot(self._y - self._y.mean())
+            + self._y.mean()
+        )
+        if return_cov:
+            pred_var = self._Kernel(X, X) - KX_test.dot(self._KX_inv).dot(
+                KX_test.T
+            )
+            return (pred_mean, pred_var)
+        return pred_mean
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diff --git a/polire/preprocessing/__init__.py b/polire/preprocessing/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..9114b3aab83e55619e82ea8bf741940b3ae085ec
--- /dev/null
+++ b/polire/preprocessing/__init__.py
@@ -0,0 +1 @@
+from .sptial_features import SpatialFeatures
diff --git a/polire/preprocessing/sptial_features.py b/polire/preprocessing/sptial_features.py
new file mode 100755
index 0000000000000000000000000000000000000000..35f7cb17c737d2c0debd03e4fac0f28a964f2227
--- /dev/null
+++ b/polire/preprocessing/sptial_features.py
@@ -0,0 +1,131 @@
+import numpy as np
+from ..idw.idw import IDW
+from ..utils.distance import haversine, euclidean
+
+
+class SpatialFeatures:
+    """Generate spatial features from N-closest locations
+
+    Args:
+        n_closest : 'N' closest locations
+
+        idw : To use idw output as one of the feature
+
+        idw_exponent : Exponent to be used in idw (if idw is False, ignore)
+
+        coordinate_type : 'Eucleadian' or 'Geographic' (if idw is False, ignore)
+
+        resolution : 'low', 'standard' or 'high' (if idw is False, ignore)
+    """
+
+    def __init__(
+        self,
+        n_closest: int = 5,
+        idw: bool = True,
+        idw_exponent: float = 2,
+        coordinate_type: str = "Euclidean",
+        resolution: str = "standard",
+    ) -> None:
+        self.n_closest = n_closest
+        self.idw = idw
+        self.idw_exponent = idw_exponent
+        self.coordinate_type = coordinate_type
+        self.resolution = resolution
+        if self.coordinate_type == "Eucledian":
+            self.distance = euclidean
+        elif self.coordinate_type == "Geographic":
+            self.distance = haversine
+        else:
+            raise NotImplementedError(
+                '"'
+                + self.coordinate_type
+                + '" is not implemented yet or invalid'
+            )
+
+    def fit(self, X: np.ndarray, y: np.ndarray) -> object:
+        """[summary]
+
+        Args:
+            X : Reference X data (longitude, latitude, time, ...)
+            y : Reference y data
+
+        Returns:
+            self
+        """
+        self.X = X
+        self.y = y
+
+    def transform(self, X: np.ndarray) -> np.ndarray:
+        """Transform features
+
+        Args:
+            X (np.ndarray): (longitude, latitude, time, ...)
+
+        Raises:
+            Exception: If not already fitted
+
+        Returns:
+            np.ndarray: Transformed features
+        """
+        try:
+            self.X
+        except AttributeError:
+            raise Exception("Not fitted yet. first call the 'fit' method")
+
+        Xflag = False
+        if np.all(X == self.X):
+            Xflag = True
+
+        F = (
+            np.empty(
+                (X.shape[0], (X.shape[1] - 3) + self.n_closest * 2 + self.idw)
+            )
+            * np.nan
+        )
+        for t in np.unique(X[:, 2]):  # Iterating over time
+            mask = X[:, 2] == t  # rows with time t
+            trn_mask = self.X[:, 2] == t
+            X_local = X[mask]
+            self_X_local = self.X[trn_mask]
+
+            lonlat = X_local[:, :2]  # locs
+            self_lonlat = self_X_local[:, :2]  # Reference locs
+            dst = self.distance(lonlat, self_lonlat)
+            if Xflag:
+                idx = dst.argsort()[:, 1 : self.n_closest + 1]
+            else:
+                idx = dst.argsort()[:, : self.n_closest]
+
+            # Feature set 1: closest distances
+            f1 = dst[np.arange(lonlat.shape[0])[:, None], idx]
+
+            self_y_local = self.y[trn_mask]  # Train obs
+            ymat = self_y_local[:, None].repeat(lonlat.shape[0], 1).T
+            # Feature set 2: closest observations
+            f2 = ymat[np.arange(lonlat.shape[0])[:, None], idx]
+
+            if self.idw:
+
+                def for_each_row(i):
+                    i = i[0]
+                    model = IDW(exponent=self.idw_exponent)
+                    model.resolution = self.resolution
+                    model.coordinate_type = self.coordinate_type
+                    model.fit(self_lonlat[idx[i]], self_y_local[idx[i]])
+                    return model.predict(lonlat[i][None, :])
+
+                # Feature set 3: IDW observation
+                f3 = np.apply_along_axis(
+                    for_each_row,
+                    axis=1,
+                    arr=np.arange(lonlat.shape[0]).reshape(-1, 1),
+                )
+                F[mask] = np.concatenate([X_local[:, 3:], f1, f2, f3], axis=1)
+            else:
+                F[mask] = np.concatenate([X_local[:, 3:], f1, f2], axis=1)
+
+        return F
+
+    def fit_transform(self, X: np.ndarray, y: np.ndarray):
+        self.fit(X, y)
+        return self.transform(X)
diff --git a/polire/random/__init__.py b/polire/random/__init__.py
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diff --git a/polire/random/random.py b/polire/random/random.py
new file mode 100755
index 0000000000000000000000000000000000000000..11b40c635c427ac6ebad3f3d6847137db4c857e1
--- /dev/null
+++ b/polire/random/random.py
@@ -0,0 +1,42 @@
+import numpy as np
+
+from ..base import Base
+
+
+class Random(Base):
+    """
+    Class to randomly interpolate by picking values between maximum and
+    minimum measurements.
+
+    Note: Even if a point on the requested grid is present in
+    the training set, we return a random value for it.
+    """
+
+    def __init__(self, resolution="standard", coordinate_type="Euclidean"):
+        super().__init__(resolution, coordinate_type)
+
+    def _fit(self, X, y):
+        """Function for fitting random interpolation.
+        This function is not supposed to be called directly.
+        """
+        self.ymax = max(y)
+        self.ymin = min(y)
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Function for random grid interpolation.
+        This function is not supposed to be called directly.
+        """
+        return np.random.uniform(
+            low=self.ymin,
+            high=self.ymax,
+            size=(self.resolution, self.resolution),
+        )
+
+    def _predict(self, X):
+        """Function for random interpolation.
+        This function is not supposed to be called directly.
+        """
+        return np.random.uniform(
+            low=self.ymin, high=self.ymax, size=(X.shape[0])
+        )
diff --git a/polire/spatial/__init__.py b/polire/spatial/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/polire/spatial/__pycache__/__init__.cpython-310.pyc b/polire/spatial/__pycache__/__init__.cpython-310.pyc
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diff --git a/polire/spatial/__pycache__/spatial.cpython-310.pyc b/polire/spatial/__pycache__/spatial.cpython-310.pyc
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diff --git a/polire/spatial/spatial.py b/polire/spatial/spatial.py
new file mode 100755
index 0000000000000000000000000000000000000000..c7ba90cc2639d92d645009f743b8f9adaab21456
--- /dev/null
+++ b/polire/spatial/spatial.py
@@ -0,0 +1,64 @@
+import numpy as np
+from scipy.spatial.distance import cdist
+
+from ..base import Base
+from ..utils.distance import euclidean, haversine
+
+
+class SpatialAverage(Base):
+    """
+    Class to interpolate by fitting a XGBoost Regressor to given
+    data.
+    Note that radius you specify must be in kilometres if you are passing latitude and longitude as inputs
+    """
+
+    def __init__(
+        self,
+        radius=100,
+        resolution="standard",
+        coordinate_type="Euclidean",
+        **kwargs
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.radius = radius
+        if self.coordinate_type == "Geographic":
+            self.distance = haversine
+        elif self.coordinate_type == "Euclidean":
+            self.distance = euclidean
+        else:
+            raise NotImplementedError(
+                "Only Geographic and Euclidean Coordinates are available"
+            )
+
+    def _fit(self, X, y):
+        """Function for fitting.
+        This function is not supposed to be called directly.
+        """
+        self.X = X
+        self.y = y
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Function for grid interpolation.
+        This function is not supposed to be called directly.
+        """
+        # getting the boundaries for interpolation
+        x1min, x1max = x1lim
+        x2min, x2max = x2lim
+
+        # building the grid
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+        X1, X2 = np.meshgrid(x1, x2)
+        return self._predict(np.asarray([X1.ravel(), X2.ravel()]).T)
+
+    def _predict(self, X):
+        """Function for interpolation on specific points.
+        This function is not supposed to be called directly.
+        """
+        return self._average(X)
+
+    def _average(self, X):
+        dist = self.distance(X, self.X)
+        mask = self.radius >= dist
+        return (self.y * mask).sum(axis=1) / mask.sum(axis=1)
diff --git a/polire/spline/__init__.py b/polire/spline/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391
diff --git a/polire/spline/__pycache__/__init__.cpython-310.pyc b/polire/spline/__pycache__/__init__.cpython-310.pyc
new file mode 100755
index 0000000000000000000000000000000000000000..13251a60ad58be767489b2b2dbbf556679726ed4
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diff --git a/polire/spline/__pycache__/bspline.cpython-310.pyc b/polire/spline/__pycache__/bspline.cpython-310.pyc
new file mode 100755
index 0000000000000000000000000000000000000000..0d2985ddc8c68f285c8f8a0dfdbc8d4c22f75337
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diff --git a/polire/spline/bspline.py b/polire/spline/bspline.py
new file mode 100755
index 0000000000000000000000000000000000000000..899271993ed71d1492ddb5804de38b78e1a74fe9
--- /dev/null
+++ b/polire/spline/bspline.py
@@ -0,0 +1,94 @@
+import numpy as np
+from scipy.interpolate import bisplrep, bisplev
+
+
+from ..base import Base
+from ..utils import find_closest
+
+
+class Spline(Base):
+    """
+    Class to use a bivariate B-spline to interpolate values.
+    https://docs.scipy.org/doc/scipy-0.18.1/reference/generated/scipy.interpolate.bisplrep.html#scipy.interpolate.bisplrep
+
+    Parameters
+    ----------
+    kx, ky: int, int, optional
+        The degrees of the spline (1 <= kx, ky <= 5).
+        Third order (kx=ky=3) is recommended.
+
+    s : float, optional
+        A non-negative smoothing factor. If weights correspond
+        to the inverse of the standard-deviation of the errors
+        in z, then a good s-value should be found in the
+        range `(m-sqrt(2*m),m+sqrt(2*m))` where `m=len(x)`.
+    """
+
+    def __init__(
+        self,
+        kx=3,
+        ky=3,
+        s=None,
+        resolution="standard",
+        coordinate_type="Euclidean",
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.kx = kx
+        self.ky = ky
+        self.s = s
+
+    def _fit(self, X, y):
+        """The function call to fit the spline model on the given data.
+        This function is not supposed to be called directly.
+        """
+        # fitting the curve
+        # bisplrep returns details of the fitted curve
+        # read bisplrep docs for more info about it's return values.
+        self.tck = bisplrep(
+            X[:, 0], X[:, 1], y, kx=self.kx, ky=self.ky, s=self.s
+        )
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """The function to predict grid interpolation using the BSpline.
+        This function is not supposed to be called directly.
+        """
+        # getting the boundaries for interpolation
+        x1min, x1max = x1lim
+        x2min, x2max = x2lim
+
+        # interpolating over the grid
+        # TODO Relook here, we might expect the result to be transpose
+        return bisplev(
+            np.linspace(x1min, x1max, self.resolution),
+            np.linspace(x2min, x2max, self.resolution),
+            self.tck,
+        )
+
+    def _predict(self, X):
+        """The function to predict using the BSpline interpolation.
+        This function is not supposed to be called directly.
+        """
+        results = []
+        for ix in range(X.shape[0]):
+            interpolated_y = bisplev(
+                X[ix, 0], X[ix, 1], self.tck
+            ).item()  # one value returned
+            results.append(interpolated_y)
+
+        return np.array(results)
+
+        # # form a grid
+        # x1 = np.linspace(self.x1min_d, self.x1max_d, self.resolution),
+        # x2 = np.linspace(self.x2min_d, self.x2max_d, self.resolution),
+        # X1, X2 = np.meshgrid(x1, x2)
+
+        # # be default run grid interpolation on the whole train data
+        # interpolated_grid = bisplev(
+        #     x1, x2,
+        #     self.tck,
+        # )
+
+        # # find the closest points on the interpolated grid
+        # ix = find_closest(grid=(X1, X2), X)
+        # return interpolated_grid[ix] # TODO this can be wrong, must depend on
diff --git a/polire/trend/__init__.py b/polire/trend/__init__.py
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diff --git a/polire/trend/__pycache__/polynomials.cpython-310.pyc b/polire/trend/__pycache__/polynomials.cpython-310.pyc
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diff --git a/polire/trend/__pycache__/trend.cpython-310.pyc b/polire/trend/__pycache__/trend.cpython-310.pyc
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diff --git a/polire/trend/polynomials.py b/polire/trend/polynomials.py
new file mode 100755
index 0000000000000000000000000000000000000000..7a1b3df754fdae70b16292269beefd847c4269db
--- /dev/null
+++ b/polire/trend/polynomials.py
@@ -0,0 +1,39 @@
+"""File containing polynomials supported by Trend interpolation
+Class.
+"""
+
+
+def _create_polynomial(order):
+    if order is None:  # custom function by the user
+        return None
+
+    elif order == 0:
+
+        def func(X, a):
+            return a
+
+    elif order == 1:
+
+        def func(X, a, b, c):
+            x1, x2 = X
+            return a + b * x1 + c * x2
+
+    elif order == 2:
+
+        def func(X, a, b, c, d, e, f):
+            x1, x2 = X
+            return (
+                a
+                + b * x1
+                + c * x2
+                + d * (x1**2)
+                + e * (x1**2)
+                + f * x1 * x2
+            )
+
+    else:
+        raise NotImplementedError(
+            f"{order} order polynomial needs to be defined manually"
+        )
+
+    return func
diff --git a/polire/trend/trend.py b/polire/trend/trend.py
new file mode 100755
index 0000000000000000000000000000000000000000..da1e84557ffab9f18243c4144423937899ba4e67
--- /dev/null
+++ b/polire/trend/trend.py
@@ -0,0 +1,76 @@
+import numpy as np
+from scipy.optimize import curve_fit
+
+from ..base import Base
+from .polynomials import _create_polynomial
+
+
+class Trend(Base):
+    """Class to interpolate by fitting a curve to the data points
+    available using `scipy`'s `curve_fit`.
+
+    Parameters
+    ----------
+    order: int, default 1
+        Selects the order of the polynomial to best fit.
+        Possible values 0 <= order <= 2.
+
+    custom_poly: functor, default None
+        If you would like to fit to your custom function,
+        _set order to None_ and then pass a functor.
+        See Example functor passing below
+        .. highlight:: python
+        .. code-block:: python
+            def func(X, a, b, c):
+                x1, x2 = X
+                return np.log(a) + b*np.log(x1) + c*np.log(x2)
+            t = Trend(order=None, custom_poly=func)
+            ...
+    """
+
+    def __init__(
+        self,
+        order=1,
+        custom_poly=None,
+        resolution="standard",
+        coordinate_type="Euclidean",
+    ):
+        super().__init__(resolution, coordinate_type)
+        self.order = order
+        # setting the polynomial to fit our data
+        if _create_polynomial(order) is not None:
+            self.func = _create_polynomial(order)
+        else:
+            if custom_poly is not None:
+                self.func = custom_poly
+            else:
+                raise ValueError("Arguments passed are not valid")
+
+    def _fit(self, X, y):
+        """Function for fitting trend interpolation.
+        This function is not supposed to be called directly.
+        """
+        # fitting the curve using scipy
+        self.popt, self.pcov = curve_fit(self.func, (X[:, 0], X[:, 1]), y)
+        return self
+
+    def _predict_grid(self, x1lim, x2lim):
+        """Function for trend interpolation.
+        This function is not supposed to be called directly.
+        """
+        # getting the boundaries for interpolation
+        x1min, x1max = x1lim
+        x2min, x2max = x2lim
+
+        # forming the grid
+        x1 = np.linspace(x1min, x1max, self.resolution)
+        x2 = np.linspace(x2min, x2max, self.resolution)
+        X1, X2 = np.meshgrid(x1, x2)
+        return self.func((X1, X2), *self.popt)
+
+    def _predict(self, X):
+        """Function for random interpolation.
+        This function is not supposed to be called directly.
+        """
+        x1, x2 = X[:, 0], X[:, 1]
+        return self.func((x1, x2), *self.popt)
diff --git a/polire/utils/__init__.py b/polire/utils/__init__.py
new file mode 100755
index 0000000000000000000000000000000000000000..0443481ae1c440128e0a168dafd44d915d137b1e
--- /dev/null
+++ b/polire/utils/__init__.py
@@ -0,0 +1 @@
+from .gridding import *
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diff --git a/polire/utils/distance.py b/polire/utils/distance.py
new file mode 100755
index 0000000000000000000000000000000000000000..23d91900b950f2572f0dda2992f7da38d26df5d2
--- /dev/null
+++ b/polire/utils/distance.py
@@ -0,0 +1,51 @@
+"""
+A module to have different distance metrics for spatial interpolation
+"""
+import numpy as np
+from scipy.spatial.distance import cdist
+
+
+def haversine(X1, X2):
+    """
+    Arguments
+    ---------
+    One test point
+    Multiple Train Points
+
+    Long Lat Order
+    """
+
+    # Non-vectorized version
+    # X1 = X1.reshape(1, 2)
+    # difference = (X1 - X2) * np.pi / 180
+    # test_point_lat = X1[:, 1] * np.pi / 180
+    # training_locations_lat = X2[:, 1] * np.pi / 180
+
+    # a = np.sin(difference[:, 0] / 2)**2 * np.cos(test_point_lat) * np.cos(training_locations_lat) +\
+    #     np.sin(difference[:, 1] / 2)**2
+    # radius = 6371
+    # c = 2 * np.arcsin(np.sqrt(a))
+    # return radius * c
+
+    # Vectorized code
+    lon1, lat1, lon2, lat2 = map(
+        np.radians,
+        [X1[:, 0, None], X1[:, 1, None], X2[:, 0, None], X2[:, 1, None]],
+    )
+
+    dlon = lon2.T - lon1
+    dlat = lat2.T - lat1
+
+    a = (
+        np.sin(dlat / 2.0) ** 2
+        + np.cos(lat1) @ np.cos(lat2.T) * np.sin(dlon / 2.0) ** 2
+    )
+
+    c = 2 * np.arcsin(np.sqrt(a))
+    km = 6371 * c
+    return km
+
+
+def euclidean(X1, X2):
+    # return np.linalg.norm(X1 - X2, 2, axis=1)
+    return cdist(X1, X2)
diff --git a/polire/utils/gridding.py b/polire/utils/gridding.py
new file mode 100755
index 0000000000000000000000000000000000000000..2ff8c7468b04c60f8cf68c9dede9072f7a19c2bf
--- /dev/null
+++ b/polire/utils/gridding.py
@@ -0,0 +1,77 @@
+""" Standard Utility Script for Gridding Data
+    1. Contains all the common functions that 
+        will be employed across various different interpolators
+
+"""
+import numpy as np
+from scipy import spatial
+
+
+def make_grid(self, x, y, res, offset=0.2):
+    """This function returns the grid to perform interpolation on.
+       This function is used inside the fit() attribute of the idw class.
+
+    Parameters
+    ----------
+    x: array-like, shape(n_samples,)
+        The first coordinate values of all points where
+        ground truth is available
+    y: array-like, shape(n_samples,)
+        The second coordinate values of all points where
+        ground truth is available
+    res: int
+        The resolution value
+    offset: float, optional
+        A value between 0 and 0.5 that specifies the extra interpolation to be done
+        Default is 0.2
+
+    Returns
+    -------
+    xx : {array-like, 2D}, shape (n_samples, n_samples)
+    yy : {array-like, 2D}, shape (n_samples, n_samples)
+    """
+    y_min = y.min() - offset
+    y_max = y.max() + offset
+    x_min = x.min() - offset
+    x_max = x.max() + offset
+    x_arr = np.linspace(x_min, x_max, res)
+    y_arr = np.linspace(y_min, y_max, res)
+    xx, yy = np.meshgrid(x_arr, y_arr)
+    return xx, yy
+
+
+def find_closest(grid, X, l=2):
+    """Function used to find the indices of the grid points closest
+    to the passed points in X.
+
+    Parameters
+    ----------
+    grid: {list of 2 arrays}, (shape(res, res), shape(res, res))
+        This is generated by meshgrid.
+
+    X: {array-like, 2D matrix}, shape(n_samples, 2)
+        The set of points to which we need to provide closest points
+        on the grid.
+
+    l: str, optional
+        To decide the `l`th norm to use. `Default = 2`.
+
+    Returns
+    -------
+    ix: array, shape(X.shape[0],)
+        The index of the point closest to points in X.
+
+    ref - https://stackoverflow.com/questions/10818546/finding-index-of-nearest-point-in-numpy-arrays-of-x-and-y-coordinates
+    """
+    points = np.asarray(
+        [grid[0].ravel(), grid[1].ravel()]
+    ).T  # ravel is inplace
+    kdtree = spatial.KDTree(points)
+    ixs = []  # for containing the indices of closest points found on grid
+
+    for point_ix in range(X.shape[0]):
+        point = X[point_ix, :]
+        _, ix = kdtree.query(point)
+        ixs.append(ix)
+
+    return ixs
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100755
index 0000000000000000000000000000000000000000..d89325b0a45e068d65d9b168e28348bb38bd5079
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,12 @@
+[build-system]
+requires = [
+    "setuptools>=50.0",
+    "setuptools_scm[toml]>=6.0",
+    "setuptools_scm_git_archive",
+    "wheel>=0.33",
+    "numpy>=1.16",
+    "cython>=0.29",
+]
+
+[tool.setuptools_scm]
+write_to = "polire/_version.py"
\ No newline at end of file
diff --git a/requirements-dev.txt b/requirements-dev.txt
new file mode 100755
index 0000000000000000000000000000000000000000..a7b86d28b29fac6c1b3414cc1e8c56b1ab27f652
--- /dev/null
+++ b/requirements-dev.txt
@@ -0,0 +1,8 @@
+pytest
+pytest-cov
+coveralls
+scikit-learn
+pre-commit
+xarray
+pooch
+jinja2
\ No newline at end of file
diff --git a/requirements.txt b/requirements.txt
new file mode 100755
index 0000000000000000000000000000000000000000..6765744cac0b0e3a47055570dfb03386c43dff79
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,9 @@
+matplotlib
+numpy
+pandas
+pykrige
+scipy
+seaborn
+Shapely
+xgboost
+# GPy
\ No newline at end of file
diff --git a/setup.cfg b/setup.cfg
new file mode 100755
index 0000000000000000000000000000000000000000..e8e07794ca4117a2d524761fb11cb4d6ec691e38
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,10 @@
+[metadata]
+name = polire
+author = Zeel B Patel, Deepak Narayanan, Apoorv Agnihotri, Nipun Batra
+author_email = patel_zeel@iitgn.ac.in
+description = Spatial Interpolation in Python
+url = https://github.com/sustainability-lab/polire
+license = BSD 3-Clause License
+license_file = LICENCE
+long_description = file: README.md
+long_description_content_type = text/markdown
\ No newline at end of file
diff --git a/setup.py b/setup.py
new file mode 100755
index 0000000000000000000000000000000000000000..c676b16e1c22a1dc3987b65274359e2e18728587
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,11 @@
+from setuptools import find_packages, setup
+
+with open("requirements.txt") as f:
+    requirements = f.read().splitlines()
+
+setup(
+    packages=find_packages(exclude=["docs"]),
+    python_requires=">=3.8",
+    install_requires=requirements,
+    include_package_data=True,
+)
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100755
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diff --git a/tests/__pycache__/test_basic.cpython-310-pytest-7.4.0.pyc b/tests/__pycache__/test_basic.cpython-310-pytest-7.4.0.pyc
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diff --git a/tests/data/30-03-18.csv b/tests/data/30-03-18.csv
new file mode 100755
index 0000000000000000000000000000000000000000..fc3212b265189489a6f9249cdbf6f2c135d62fe2
--- /dev/null
+++ b/tests/data/30-03-18.csv
@@ -0,0 +1,31 @@
+,location,parameter,value,latitude,longitude
+4087,"Jawaharlal Nehru Stadium, Delhi - DPCC",pm25,194.0,28.581197,77.234291
+3930,"Sonia Vihar, Delhi - DPCC",pm25,267.0,28.739434,77.245721
+4020,"Narela, Delhi - DPCC",pm25,273.0,28.822931,77.101961
+4057,"Najafgarh, Delhi - DPCC",pm25,129.0,28.620806,76.991463
+3998,"NSIT Dwarka, New Delhi - CPCB",pm25,176.0,28.609090000000002,77.03254129999999
+4104,"Dwarka-Sector 8, Delhi - DPCC ",pm25,172.0,28.570859,77.072196
+4009,"R K Puram, New Delhi - DPCC",pm25,168.0,28.5646102,77.1670103
+4099,"IGI Airport Terminal - 3, New Delhi - IMD",pm25,105.0,28.562776300000003,77.1180053
+4074,"Okhla Phase-2, Delhi - DPCC",pm25,203.0,28.530782,77.272404
+4068,"Nehru Nagar, Delhi - DPCC",pm25,192.0,28.563827,77.26075
+4039,"Pusa, New Delhi - IMD",pm25,95.0,28.610304,77.0996943
+3947,"Lodhi Road, New Delhi - IMD",pm25,148.0,28.5918245,77.2273074
+4004,"Rohini, Delhi - DPCC",pm25,203.0,28.732219,77.09211
+4112,"Vivek Vihar, Delhi - DPCC",pm25,221.0,28.668672,77.317084
+4103,"North Campus, DU, New Delhi - IMD",pm25,141.0,28.657381400000002,77.15854470000001
+4078,"IHBAS, Dilshad Garden,New Delhi - CPCB",pm25,192.0,28.6802747,77.20115729999999
+4015,"Major Dhyan Chand National Stadium, Delhi - DPCC",pm25,203.0,28.612561,77.237372
+3959,"Patparganj, Delhi - DPCC",pm25,152.0,28.632707,77.305651
+3960,"Shadipur, New Delhi - CPCB",pm25,185.0,28.651478100000002,77.1473105
+3980,"Wazirpur, Delhi - DPCC",pm25,290.0,28.699254,77.16482
+4041,"Jahangirpuri, Delhi - DPCC",pm25,273.0,28.728722,77.170221
+4005,"Mandir Marg, New Delhi - DPCC",pm25,173.0,28.6372688,77.2005604
+3970,"Burari Crossing, New Delhi - IMD",pm25,269.0,28.725650399999996,77.20115729999999
+4089,"Punjabi Bagh, Delhi - DPCC",pm25,160.0,28.669119,77.136777
+396386,"Dr. Karni Singh Shooting Range, Delhi - DPCC",pm25,78.0,28.49968,77.267246
+4117,"ITO, New Delhi - CPCB",pm25,211.0,28.631694500000002,77.2494387
+4072,"CRRI Mathura Road, New Delhi - IMD",pm25,252.0,28.5512005,77.27357370000001
+4033,"Sirifort, New Delhi - CPCB",pm25,133.0,28.5504249,77.2159377
+396397,"DTU, New Delhi - CPCB",pm25,77.0,28.7500499,77.1112615
+4180,US Diplomatic Post: New Delhi,pm25,96.0,28.635759999999998,77.22445
diff --git a/tests/test_basic.py b/tests/test_basic.py
new file mode 100755
index 0000000000000000000000000000000000000000..ce337c28075abe5c94246e299909b64014e8d5e5
--- /dev/null
+++ b/tests/test_basic.py
@@ -0,0 +1,55 @@
+import pytest
+import numpy as np
+from time import time
+from polire import (
+    IDW,
+    Spline,
+    Trend,
+    # GP,
+    Kriging,
+    NaturalNeighbor,
+    SpatialAverage,
+    CustomInterpolator,
+    # NSGP,
+)
+from sklearn.linear_model import LinearRegression
+
+X = np.random.rand(20, 2)
+y = np.random.rand(20)
+
+X_new = np.random.rand(40, 2)
+
+
+@pytest.mark.parametrize(
+    "model",
+    [
+        IDW(),
+        Spline(),
+        Trend(),
+        # GP(),
+        Kriging(),
+        NaturalNeighbor(),
+        SpatialAverage(),
+        CustomInterpolator(LinearRegression()),
+        # NSGP(),
+    ],
+)
+def test_fit_predict(model):
+    init = time()
+    model.fit(X, y)
+    y_new = model.predict(X_new)
+
+    assert y_new.shape == (40,)
+    print("Passed", "Time:", np.round(time() - init, 3), "seconds")
+
+
+@pytest.mark.skip(reason="Temporarily disabled")
+def test_nsgp():
+    model = NSGP()
+    init = time()
+    model.fit(X, y, **{"ECM": X @ X.T})
+    y_new = model.predict(X_new)
+
+    assert y_new.shape == (40,)
+    assert y_new.sum() == y_new.sum()  # No NaN
+    print("Passed", "Time:", np.round(time() - init, 3), "seconds")
diff --git a/usage.py b/usage.py
new file mode 100755
index 0000000000000000000000000000000000000000..fdcec3743ea1992651d5a80cfcabea301ad728c3
--- /dev/null
+++ b/usage.py
@@ -0,0 +1,114 @@
+# imports
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+from GPy.kern import Matern32
+
+from polire import (
+    Random,
+    Trend,
+    Spline,
+    IDW,
+    Kriging,
+    SpatialAverage,
+    NaturalNeighbor,
+    GP,
+)
+
+# sample data
+X = [[0, 0], [0, 3], [3, 0], [3, 3]]
+y = [0, 1.5, 1.5, 3]
+X = np.array(X)
+y = np.array(y)
+regressors = [
+    Random(),
+    SpatialAverage(),
+    Spline(kx=1, ky=1),
+    Trend(),
+    IDW(coordinate_type="Geographic"),
+    Kriging(),
+    GP(Matern32(input_dim=2)),
+]
+
+
+def test_grid():
+    # Gridded interpolation testing
+    print("\nTesting on small dataset")
+    for r in regressors:
+        r.fit(X, y)
+        y_pred = r.predict_grid()
+        Z = y_pred
+        sns.heatmap(Z)
+        plt.title(r)
+        plt.show()
+        plt.close()
+    print("\nTesting completed on a small dataset\n")
+
+    print("\nTesting on a reasonable dataset")
+
+    df = pd.read_csv("tests/data/30-03-18.csv")
+    X1 = np.array(df[["longitude", "latitude"]])
+    y1 = np.array(df["value"])
+
+    for r in regressors:
+        r.fit(X1, y1)
+        y_pred = r.predict_grid()
+        Z = y_pred
+        sns.heatmap(Z)
+        plt.title(r)
+        plt.show()
+        plt.close()
+
+
+def test_point():
+    # Pointwise interpolation testing
+    for r in regressors:
+        r.fit(X, y)
+        test_data = [
+            [0, 0],
+            [0, 3],
+            [3, 0],
+            [3, 3],
+            [1, 1],
+            [1.5, 1.5],
+            [2, 2],
+            [2.5, 2.5],
+            [4, 4],
+        ]
+        y_pred = r.predict(np.array(test_data))
+        print(r)
+        print(y_pred)
+
+
+def test_nn():
+    print("\nNatural Neighbors - Point Wise")
+    nn = NaturalNeighbor()
+    df = pd.read_csv("tests/data/30-03-18.csv")
+    X = np.array(df[["longitude", "latitude"]])
+    y = np.array(df["value"])
+    nn.fit(X, y)
+    test_data = [[77.16, 28.70], X[0]]
+    y_pred = nn.predict(np.array(test_data))
+    print(y_pred)
+    del nn
+    print("\nNatural Neighbors - Entire Grid")
+    # Suggested by Apoorv as a temporary fix
+    # Patience pays
+    nn = NaturalNeighbor()
+    nn.fit(X, y)
+    y_pred = nn.predict_grid()
+    print(y_pred)
+    sns.heatmap(y_pred)
+    plt.title(nn)
+    plt.show()
+    plt.close()
+
+
+if __name__ == "__main__":
+    print("Testing Gridded Interpolation")
+    test_grid()
+    print("\nTesting Pointwise Interpolation")
+    test_point()
+    print("\nTesting Natural Neighbors")
+    test_nn()
diff --git a/usage3.py b/usage3.py
new file mode 100755
index 0000000000000000000000000000000000000000..6546917323d0977c6fb13064c4e803756eb64cb8
--- /dev/null
+++ b/usage3.py
@@ -0,0 +1,34 @@
+# imports
+import seaborn as sns
+import matplotlib.pyplot as plt
+import numpy as np
+
+from polire import CustomInterpolator
+import xgboost
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.linear_model import LinearRegression
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.gaussian_process import GaussianProcessRegressor
+from sklearn.gaussian_process.kernels import Matern
+
+# sample data
+X = [[0, 0], [0, 3], [3, 0], [3, 3]]
+y = [0, 1.5, 1.5, 3]
+X = np.array(X)
+y = np.array(y)
+
+for r in [
+    CustomInterpolator(xgboost.XGBRegressor()),
+    CustomInterpolator(RandomForestRegressor()),
+    CustomInterpolator(LinearRegression(normalize=True)),
+    CustomInterpolator(KNeighborsRegressor(n_neighbors=3, weights="distance")),
+    CustomInterpolator(
+        GaussianProcessRegressor(normalize_y=True, kernel=Matern())
+    ),
+]:
+    r.fit(X, y)
+    Z = r.predict_grid((0, 3), (0, 3)).reshape(100, 100)
+    sns.heatmap(Z)
+    plt.title(r)
+    plt.show()
+    plt.close()