using_extra_features.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# About this notebook\n",
    "\n",
    "This one is the same as main.ipynb but we use extra x features to give the neural process an advantage, and see how well it can do when given more features.\n",
    "\n",
    "Modified from https://raw.githubusercontent.com/GeoStat-Framework/PyKrige/master/examples/kriging_meuse.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:15.858343Z",
     "start_time": "2020-03-02T06:03:13.912228Z"
    }
   },
   "outputs": [],
   "source": [
    "# This example requires some extra packages compared to the PyKrige package. \n",
    "# At the time of the creation, I used the conda package manager\n",
    "# and installed the following (with the versions at the time):\n",
    "# pandas 0.18.1, geopandas 0.2.1, seaborn 0.7.1, folium 0.2.1, shapely 1.5.16\n",
    "# If you use pip, \"pip install geopandas folium seaborn\" should work to.\n",
    "import os\n",
    "import requests\n",
    "import zipfile\n",
    "import geopandas as gpd\n",
    "import shapely\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import folium\n",
    "from folium import plugins\n",
    "%matplotlib inline\n",
    "from pykrige.ok import OrdinaryKriging\n",
    "from pykrige.rk import RegressionKriging\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import pykrige.kriging_tools as kt\n",
    "import seaborn as sb\n",
    "from IPython.core.display import HTML\n",
    "# HTML(\"<style>.container { width:70% !important; }</style>\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:49:53.203702Z",
     "start_time": "2020-03-02T05:49:53.190990Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:15.865727Z",
     "start_time": "2020-03-02T06:03:15.861508Z"
    }
   },
   "outputs": [],
   "source": [
    "allow_columns = ['lead', 'elev', 'dist', 'x', 'y', 'ffreq', 'soil']\n",
    "label ='lead'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download and import the shapefile\n",
    "\n",
    "- Download the zipfile from http://spatial-analyst.net/book/meusegrids\n",
    "- Extract the zipfile\n",
    "- Delete the zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:14:11.416419Z",
     "start_time": "2020-03-02T05:14:11.411518Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:15.879950Z",
     "start_time": "2020-03-02T06:03:15.869277Z"
    }
   },
   "outputs": [],
   "source": [
    "if not os.path.isfile('./data/meuse.zip'):\n",
    "    print('downloading')\n",
    "    url = 'http://spatial-analyst.net/book/system/files/meuse.zip'\n",
    "    results = requests.get(url)\n",
    "    print('Status code download: {}'.format(results.status_code))\n",
    "    with open('./data/meuse.zip', 'wb') as f:\n",
    "        f.write(results.content)\n",
    "    zip_ref = zipfile.ZipFile('./data/meuse.zip', 'r')\n",
    "    zip_ref.extractall('./data/meuse_example_data/')\n",
    "    zip_ref.close()\n",
    "# os.remove('./data/meuse.zip')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:16.194169Z",
     "start_time": "2020-03-02T06:03:15.883829Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "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>cadmium</th>\n",
       "      <th>copper</th>\n",
       "      <th>lead</th>\n",
       "      <th>zinc</th>\n",
       "      <th>elev</th>\n",
       "      <th>dist</th>\n",
       "      <th>om</th>\n",
       "      <th>ffreq</th>\n",
       "      <th>soil</th>\n",
       "      <th>lime</th>\n",
       "      <th>landuse</th>\n",
       "      <th>dist.m</th>\n",
       "      <th>geometry</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>12.9</td>\n",
       "      <td>95.0</td>\n",
       "      <td>284.0</td>\n",
       "      <td>1052.0</td>\n",
       "      <td>6.86</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>14.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>None</td>\n",
       "      <td>10.0</td>\n",
       "      <td>POINT (180555.000 332707.000)</td>\n",
       "      <td>180555.0</td>\n",
       "      <td>332707.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>2.2</td>\n",
       "      <td>27.0</td>\n",
       "      <td>131.0</td>\n",
       "      <td>317.0</td>\n",
       "      <td>7.82</td>\n",
       "      <td>0.12481</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>W</td>\n",
       "      <td>160.0</td>\n",
       "      <td>POINT (179559.000 331423.000)</td>\n",
       "      <td>179559.0</td>\n",
       "      <td>331423.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    cadmium  copper   lead    zinc  elev     dist    om ffreq soil lime  \\\n",
       "19     12.9    95.0  284.0  1052.0  6.86  0.00000  14.8     1    1    1   \n",
       "85      2.2    27.0  131.0   317.0  7.82  0.12481   4.5     2    1    0   \n",
       "\n",
       "   landuse  dist.m                       geometry         x         y  \n",
       "19    None    10.0  POINT (180555.000 332707.000)  180555.0  332707.0  \n",
       "85       W   160.0  POINT (179559.000 331423.000)  179559.0  331423.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meuse = gpd.read_file('data/meuse_example_data/meuse.shp')\n",
    "meuse.crs = {'init':'epsg:28992'}\n",
    "meuse['x'] = meuse['geometry'].apply(lambda x: x.x)\n",
    "meuse['y'] = meuse['geometry'].apply(lambda x: x.y)\n",
    "meuse.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:16.215879Z",
     "start_time": "2020-03-02T06:03:16.196491Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 155 entries, 0 to 154\n",
      "Data columns (total 7 columns):\n",
      "lead     155 non-null float64\n",
      "elev     155 non-null float64\n",
      "dist     155 non-null float64\n",
      "x        155 non-null float64\n",
      "y        155 non-null float64\n",
      "ffreq    155 non-null float64\n",
      "soil     155 non-null float64\n",
      "dtypes: float64(7)\n",
      "memory usage: 8.6 KB\n"
     ]
    }
   ],
   "source": [
    "for col in ['ffreq', 'soil']:\n",
    "    meuse[col] = pd.to_numeric(meuse[col]) * 1.0\n",
    "meuse[allow_columns].info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:16.546159Z",
     "start_time": "2020-03-02T06:03:16.218192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7fc32bcbc940>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "meuse.plot(label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:18.713513Z",
     "start_time": "2020-03-02T06:03:16.549025Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
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    color_map_3e9879dc6d794dc1a580ab4d03de6d98.x = d3.scale.linear()
              .domain([0.0, 654.0])
              .range([0, 400]);

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.legend = L.control({position: 'topright'});
    color_map_3e9879dc6d794dc1a580ab4d03de6d98.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
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        .orient("top")
        .tickSize(1)
        .tickValues([0.0, 109.0, 218.0, 327.0, 436.0, 545.0, 654.0]);

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        .attr("width", 450)
        .attr("height", 40);

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        .attr("class", "key")
        .attr("transform", "translate(25,16)");

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        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

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        .attr("class", "caption")
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        .text('');
        
        
        </script>
        \" style=\"position:absolute;width:100%;height:100%;left:0;top:0;\"></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fc32bcb09e8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_to_plot = label\n",
    "\n",
    "meuse_lat_long = meuse.to_crs({'init': 'epsg:4326'})\n",
    "meuse_lat_long['long'] = meuse_lat_long.geometry.apply(lambda x: x.x)\n",
    "meuse_lat_long['lat'] = meuse_lat_long.geometry.apply(lambda x: x.y)\n",
    "mean_long = np.mean(meuse_lat_long['long'])\n",
    "mean_lat = np.mean(meuse_lat_long['lat'])\n",
    "m = folium.Map([mean_lat, mean_long], zoom_start=13, tiles='Stamen Toner')\n",
    "scale = folium.colormap.linear.YlOrRd.scale(vmin=0, vmax=meuse_lat_long[feature_to_plot].max())\n",
    "for row in meuse_lat_long.iterrows():\n",
    "    folium.CircleMarker(location=[row[1]['lat'], row[1]['long']], radius=50, color=None, fill_opacity=1,\n",
    "                    fill_color=scale(row[1][feature_to_plot])).add_to(m)\n",
    "m.add_children(scale)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Split the data into test and train\n",
    "\n",
    "Control the random split with setting a seed so the results are reproducable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:18.732655Z",
     "start_time": "2020-03-02T06:03:18.718330Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of observations in training: 122, in test: 39\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(0)\n",
    "test_indexes = np.random.choice(a=meuse.index, size=int(np.round(len(meuse.index.values)/4)))\n",
    "train_indexes = [index for index in meuse.index if index not in test_indexes]\n",
    "meuse_test = meuse.loc[test_indexes,:].copy()\n",
    "meuse_train = meuse.loc[train_indexes,:].copy()\n",
    "print('Number of observations in training: {}, in test: {}'.format(len(meuse_train), len(meuse_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Neural Process"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T06:23:20.801398Z",
     "start_time": "2020-03-01T06:23:20.738602Z"
    }
   },
   "source": [
    "## Helpers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:19.163777Z",
     "start_time": "2020-03-02T06:03:18.735486Z"
    }
   },
   "outputs": [],
   "source": [
    "# From https://github.com/3springs/attentive-neural-processes\n",
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "import math\n",
    "import numpy as np\n",
    "# from .attention import Attention as PtAttention\n",
    "\n",
    "\n",
    "class LSTMBlock(nn.Module):\n",
    "    def __init__(\n",
    "        self, in_channels, out_channels, dropout=0, batchnorm=False, bias=False, num_layers=1\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._lstm = nn.LSTM(\n",
    "                input_size=in_channels,\n",
    "                hidden_size=out_channels,\n",
    "                num_layers=num_layers,\n",
    "                dropout=dropout,\n",
    "                batch_first=True,\n",
    "                bias=bias\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        return self._lstm(x)[0]\n",
    "\n",
    "\n",
    "class NPBlockRelu2d(nn.Module):\n",
    "    \"\"\"Block for Neural Processes.\"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self, in_channels, out_channels, dropout=0, batchnorm=False, bias=False\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(in_channels, out_channels, bias=bias)\n",
    "        self.act = nn.ReLU()\n",
    "        self.dropout = nn.Dropout2d(dropout)\n",
    "        self.norm = nn.BatchNorm2d(out_channels) if batchnorm else False\n",
    "\n",
    "    def forward(self, x):\n",
    "        # x.shape is (Batch, Sequence, Channels)\n",
    "        # We pass a linear over it which operates on the Channels\n",
    "        x = self.act(self.linear(x))\n",
    "\n",
    "        # Now we want to apply batchnorm and dropout to the channels. So we put it in shape\n",
    "        # (Batch, Channels, Sequence, None) so we can use Dropout2d & BatchNorm2d\n",
    "        x = x.permute(0, 2, 1)[:, :, :, None]\n",
    "\n",
    "        if self.norm:\n",
    "            x = self.norm(x)\n",
    "\n",
    "        x = self.dropout(x)\n",
    "        return x[:, :, :, 0].permute(0, 2, 1)\n",
    "\n",
    "\n",
    "class BatchMLP(nn.Module):\n",
    "    \"\"\"Apply MLP to the final axis of a 3D tensor (reusing already defined MLPs).\n",
    "\n",
    "    Args:\n",
    "        input: input tensor of shape [B,n,d_in].\n",
    "        output_sizes: An iterable containing the output sizes of the MLP as defined \n",
    "            in `basic.Linear`.\n",
    "    Returns:\n",
    "        tensor of shape [B,n,d_out] where d_out=output_size\n",
    "    \"\"\"\n",
    "\n",
    "    def __init__(\n",
    "        self, input_size, output_size, num_layers=2, dropout=0, batchnorm=False\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self.input_size = input_size\n",
    "        self.output_size = output_size\n",
    "        self.num_layers = num_layers\n",
    "\n",
    "        self.initial = NPBlockRelu2d(\n",
    "            input_size, output_size, dropout=dropout, batchnorm=batchnorm\n",
    "        )\n",
    "        self.encoder = nn.Sequential(\n",
    "            *[\n",
    "                NPBlockRelu2d(\n",
    "                    output_size, output_size, dropout=dropout, batchnorm=batchnorm\n",
    "                )\n",
    "                for _ in range(num_layers - 2)\n",
    "            ]\n",
    "        )\n",
    "        self.final = nn.Linear(output_size, output_size)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.initial(x)\n",
    "        x = self.encoder(x)\n",
    "        return self.final(x)\n",
    "\n",
    "\n",
    "class AttnLinear(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels):\n",
    "        super().__init__()\n",
    "        self.linear = nn.Linear(in_channels, out_channels, bias=False)\n",
    "        torch.nn.init.normal_(self.linear.weight, std=in_channels ** -0.5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.linear(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "class Attention(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        hidden_dim,\n",
    "        attention_type,\n",
    "        attention_layers=2,\n",
    "        n_heads=8,\n",
    "        x_dim=1,\n",
    "        rep=\"mlp\",\n",
    "        dropout=0,\n",
    "        batchnorm=False,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._rep = rep\n",
    "\n",
    "        if self._rep == \"mlp\":\n",
    "            self.batch_mlp_k = BatchMLP(\n",
    "                x_dim,\n",
    "                hidden_dim,\n",
    "                attention_layers,\n",
    "                dropout=dropout,\n",
    "                batchnorm=batchnorm,\n",
    "            )\n",
    "            self.batch_mlp_q = BatchMLP(\n",
    "                x_dim,\n",
    "                hidden_dim,\n",
    "                attention_layers,\n",
    "                dropout=dropout,\n",
    "                batchnorm=batchnorm,\n",
    "            )\n",
    "\n",
    "        if attention_type == \"uniform\":\n",
    "            self._attention_func = self._uniform_attention\n",
    "        elif attention_type == \"laplace\":\n",
    "            self._attention_func = self._laplace_attention\n",
    "        elif attention_type == \"dot\":\n",
    "            self._attention_func = self._dot_attention\n",
    "        elif attention_type == \"multihead\":\n",
    "            self._W_k = nn.ModuleList(\n",
    "                [AttnLinear(hidden_dim, hidden_dim) for _ in range(n_heads)]\n",
    "            )\n",
    "            self._W_v = nn.ModuleList(\n",
    "                [AttnLinear(hidden_dim, hidden_dim) for _ in range(n_heads)]\n",
    "            )\n",
    "            self._W_q = nn.ModuleList(\n",
    "                [AttnLinear(hidden_dim, hidden_dim) for _ in range(n_heads)]\n",
    "            )\n",
    "            self._W = AttnLinear(n_heads * hidden_dim, hidden_dim)\n",
    "            self._attention_func = self._multihead_attention\n",
    "            self.n_heads = n_heads\n",
    "        elif attention_type == \"ptmultihead\":\n",
    "            self._W = torch.nn.MultiheadAttention(\n",
    "                hidden_dim, n_heads, bias=False, dropout=dropout\n",
    "            )\n",
    "            self._attention_func = self._pytorch_multihead_attention\n",
    "        else:\n",
    "            raise NotImplementedError\n",
    "\n",
    "    def forward(self, k, v, q):\n",
    "        if self._rep == \"mlp\":\n",
    "            k = self.batch_mlp_k(k)\n",
    "            q = self.batch_mlp_q(q)\n",
    "        rep = self._attention_func(k, v, q)\n",
    "        return rep\n",
    "\n",
    "    def _uniform_attention(self, k, v, q):\n",
    "        total_points = q.shape[1]\n",
    "        rep = torch.mean(v, dim=1, keepdim=True)\n",
    "        rep = rep.repeat(1, total_points, 1)\n",
    "        return rep\n",
    "\n",
    "    def _laplace_attention(self, k, v, q, scale=0.5):\n",
    "        k_ = k.unsqueeze(1)\n",
    "        v_ = v.unsqueeze(2)\n",
    "        unnorm_weights = torch.abs((k_ - v_) * scale)\n",
    "        unnorm_weights = unnorm_weights.sum(dim=-1)\n",
    "        weights = torch.softmax(unnorm_weights, dim=-1)\n",
    "        rep = torch.einsum(\"bik,bkj->bij\", weights, v)\n",
    "        return rep\n",
    "\n",
    "    def _dot_attention(self, k, v, q):\n",
    "        scale = q.shape[-1] ** 0.5\n",
    "        unnorm_weights = torch.einsum(\"bjk,bik->bij\", k, q) / scale\n",
    "        weights = torch.softmax(unnorm_weights, dim=-1)\n",
    "\n",
    "        rep = torch.einsum(\"bik,bkj->bij\", weights, v)\n",
    "        return rep\n",
    "\n",
    "    def _multihead_attention(self, k, v, q):\n",
    "        outs = []\n",
    "        for i in range(self.n_heads):\n",
    "            k_ = self._W_k[i](k)\n",
    "            v_ = self._W_v[i](v)\n",
    "            q_ = self._W_q[i](q)\n",
    "            out = self._dot_attention(k_, v_, q_)\n",
    "            outs.append(out)\n",
    "        outs = torch.stack(outs, dim=-1)\n",
    "        outs = outs.view(outs.shape[0], outs.shape[1], -1)\n",
    "        rep = self._W(outs)\n",
    "        return rep\n",
    "\n",
    "    def _pytorch_multihead_attention(self, k, v, q):\n",
    "        # Pytorch multiheaded attention takes inputs if diff order and permutation\n",
    "        q = q.permute(1, 0, 2)\n",
    "        k = k.permute(1, 0, 2)\n",
    "        v = v.permute(1, 0, 2)\n",
    "        o = self._W(q, k, v)[0]\n",
    "        return o.permute(1, 0, 2)\n",
    "\n",
    "\n",
    "class LatentEncoder(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        input_dim,\n",
    "        hidden_dim=32,\n",
    "        latent_dim=32,\n",
    "        self_attention_type=\"dot\",\n",
    "        n_encoder_layers=3,\n",
    "        min_std=0.01,\n",
    "        batchnorm=False,\n",
    "        dropout=0,\n",
    "        attention_dropout=0,\n",
    "        use_lvar=False,\n",
    "        use_self_attn=False,\n",
    "        attention_layers=2,\n",
    "        use_lstm=False\n",
    "    ):\n",
    "        super().__init__()\n",
    "        # self._input_layer = nn.Linear(input_dim, hidden_dim)\n",
    "        if use_lstm:\n",
    "            self._encoder = LSTMBlock(input_dim, hidden_dim, batchnorm=batchnorm, dropout=dropout, num_layers=n_encoder_layers)\n",
    "        else:\n",
    "            self._encoder = BatchMLP(input_dim, hidden_dim, batchnorm=batchnorm, dropout=dropout, num_layers=n_encoder_layers)\n",
    "        if use_self_attn:\n",
    "            self._self_attention = Attention(\n",
    "                hidden_dim,\n",
    "                self_attention_type,\n",
    "                attention_layers,\n",
    "                rep=\"identity\",\n",
    "                dropout=attention_dropout,\n",
    "            )\n",
    "        self._penultimate_layer = nn.Linear(hidden_dim, hidden_dim)\n",
    "        self._mean = nn.Linear(hidden_dim, latent_dim)\n",
    "        self._log_var = nn.Linear(hidden_dim, latent_dim)\n",
    "        self._min_std = min_std\n",
    "        self._use_lvar = use_lvar\n",
    "        self._use_lstm = use_lstm\n",
    "        self._use_self_attn = use_self_attn\n",
    "\n",
    "    def forward(self, x, y):\n",
    "        encoder_input = torch.cat([x, y], dim=-1)\n",
    "\n",
    "        # Pass final axis through MLP\n",
    "        encoded = self._encoder(encoder_input)\n",
    "\n",
    "        # Aggregator: take the mean over all points\n",
    "        if self._use_self_attn:\n",
    "            attention_output = self._self_attention(encoded, encoded, encoded)\n",
    "            mean_repr = attention_output.mean(dim=1)\n",
    "        else:\n",
    "            mean_repr = encoded.mean(dim=1)\n",
    "\n",
    "        # Have further MLP layers that map to the parameters of the Gaussian latent\n",
    "        mean_repr = torch.relu(self._penultimate_layer(mean_repr))\n",
    "\n",
    "        # Then apply further linear layers to output latent mu and log sigma\n",
    "        mean = self._mean(mean_repr)\n",
    "        log_var = self._log_var(mean_repr)\n",
    "\n",
    "        if self._use_lvar:\n",
    "            # Clip it in the log domain, so it can only approach self.min_std, this helps avoid mode collapase\n",
    "            # 2 ways, a better but untested way using the more stable log domain, and the way from the deepmind repo\n",
    "            log_var = F.logsigmoid(log_var)\n",
    "            log_var = torch.clamp(log_var, np.log(self._min_std), -np.log(self._min_std))\n",
    "            sigma = torch.exp(0.5 * log_var)\n",
    "        else:\n",
    "            sigma = self._min_std + (1 - self._min_std) * torch.sigmoid(log_var * 0.5)\n",
    "        dist = torch.distributions.Normal(mean, sigma)\n",
    "        return dist, log_var\n",
    "\n",
    "\n",
    "class DeterministicEncoder(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        input_dim,\n",
    "        x_dim,\n",
    "        hidden_dim=32,\n",
    "        n_d_encoder_layers=3,\n",
    "        self_attention_type=\"dot\",\n",
    "        cross_attention_type=\"dot\",\n",
    "        use_self_attn=False,\n",
    "        attention_layers=2,\n",
    "        batchnorm=False,\n",
    "        dropout=0,\n",
    "        attention_dropout=0,\n",
    "        use_lstm=False,\n",
    "    ):\n",
    "        super().__init__()\n",
    "        self._use_self_attn = use_self_attn\n",
    "        # self._input_layer = nn.Linear(input_dim, hidden_dim)\n",
    "        if use_lstm:\n",
    "            self._d_encoder = LSTMBlock(input_dim, hidden_dim, batchnorm=batchnorm, dropout=dropout, num_layers=n_d_encoder_layers)\n",
    "        else:\n",
    "            self._d_encoder = BatchMLP(input_dim, hidden_dim, batchnorm=batchnorm, dropout=dropout, num_layers=n_d_encoder_layers)\n",
    "        if use_self_attn:\n",
    "            self._self_attention = Attention(\n",
    "                hidden_dim,\n",
    "                self_attention_type,\n",
    "                attention_layers,\n",
    "                rep=\"identity\",\n",
    "                dropout=attention_dropout,\n",
    "            )\n",
    "        self._cross_attention = Attention(\n",
    "            hidden_dim,\n",
    "            cross_attention_type,\n",
    "            x_dim=x_dim,\n",
    "            attention_layers=attention_layers,\n",
    "        )\n",
    "\n",
    "    def forward(self, context_x, context_y, target_x):\n",
    "        # Concatenate x and y along the filter axes\n",
    "        d_encoder_input = torch.cat([context_x, context_y], dim=-1)\n",
    "\n",
    "        # Pass final axis through MLP\n",
    "        d_encoded = self._d_encoder(d_encoder_input)\n",
    "\n",
    "        if self._use_self_attn:\n",
    "            d_encoded = self._self_attention(d_encoded, d_encoded, d_encoded)\n",
    "\n",
    "        # Apply attention as mean aggregation\n",
    "        h = self._cross_attention(context_x, d_encoded, target_x)\n",
    "\n",
    "        return h\n",
    "\n",
    "\n",
    "class Decoder(nn.Module):\n",
    "    def __init__(\n",
    "        self,\n",
    "        x_dim,\n",
    "        y_dim,\n",
    "        hidden_dim=32,\n",
    "        latent_dim=32,\n",
    "        n_decoder_layers=3,\n",
    "        use_deterministic_path=True,\n",
    "        min_std=0.01,\n",
    "        use_lvar=False,\n",
    "        batchnorm=False,\n",
    "        dropout=0,\n",
    "        use_lstm=False,\n",
    "    ):\n",
    "        super(Decoder, self).__init__()\n",
    "        self._target_transform = nn.Linear(x_dim, hidden_dim)\n",
    "        if use_deterministic_path:\n",
    "            hidden_dim_2 = 2 * hidden_dim + latent_dim\n",
    "        else:\n",
    "            hidden_dim_2 = hidden_dim + latent_dim\n",
    "            \n",
    "        if use_lstm:\n",
    "            self._decoder = LSTMBlock(hidden_dim_2, hidden_dim_2, batchnorm=batchnorm, dropout=dropout, num_layers=n_decoder_layers)\n",
    "        else:\n",
    "            self._decoder = BatchMLP(hidden_dim_2, hidden_dim_2, batchnorm=batchnorm, dropout=dropout, num_layers=n_decoder_layers)\n",
    "        self._mean = nn.Linear(hidden_dim_2, y_dim)\n",
    "        self._std = nn.Linear(hidden_dim_2, y_dim)\n",
    "        self._use_deterministic_path = use_deterministic_path\n",
    "        self._min_std = min_std\n",
    "        self._use_lvar = use_lvar\n",
    "\n",
    "    def forward(self, r, z, target_x):\n",
    "        # concatenate target_x and representation\n",
    "        x = self._target_transform(target_x)\n",
    "\n",
    "        if self._use_deterministic_path:\n",
    "            z = torch.cat([r, z], dim=-1)\n",
    "\n",
    "        r = torch.cat([z, x], dim=-1)\n",
    "\n",
    "        r = self._decoder(r)\n",
    "\n",
    "        # Get the mean and the variance\n",
    "        mean = self._mean(r)\n",
    "        log_sigma = self._std(r)\n",
    "\n",
    "        # Bound or clamp the variance\n",
    "        if self._use_lvar:\n",
    "            log_sigma = torch.clamp(log_sigma, math.log(self._min_std), -math.log(self._min_std))\n",
    "            sigma = torch.exp(log_sigma)\n",
    "        else:\n",
    "            sigma = self._min_std + (1 - self._min_std) * F.softplus(log_sigma)\n",
    "\n",
    "        dist = torch.distributions.Normal(mean, sigma)\n",
    "        return dist, log_sigma\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:19.282238Z",
     "start_time": "2020-03-02T06:03:19.166430Z"
    }
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "import torch.nn.functional as F\n",
    "from torch.utils.data import TensorDataset, DataLoader\n",
    "import math\n",
    "\n",
    "# from src.models.modules import LatentEncoder, DeterministicEncoder, Decoder\n",
    "\n",
    "\n",
    "def log_prob_sigma(value, loc, log_scale):\n",
    "    \"\"\"A slightly more stable (not confirmed yet) log prob taking in log_var instead of scale.\n",
    "    modified from https://github.com/pytorch/pytorch/blob/2431eac7c011afe42d4c22b8b3f46dedae65e7c0/torch/distributions/normal.py#L65\n",
    "    \"\"\"\n",
    "    var = torch.exp(log_scale * 2)\n",
    "    return (\n",
    "        -((value - loc) ** 2) / (2 * var) - log_scale - math.log(math.sqrt(2 * math.pi))\n",
    "    )\n",
    "\n",
    "\n",
    "def kl_loss_var(prior_mu, log_var_prior, post_mu, log_var_post):\n",
    "    \"\"\"\n",
    "    Analytical KLD for two gaussians, taking in log_variance instead of scale ( given variance=scale**2) for more stable gradients\n",
    "    \n",
    "    For version using scale see https://github.com/pytorch/pytorch/blob/master/torch/distributions/kl.py#L398\n",
    "    \"\"\"\n",
    "\n",
    "    var_ratio_log = log_var_post - log_var_prior\n",
    "    kl_div = (\n",
    "        (var_ratio_log.exp() + (post_mu - prior_mu) ** 2) / log_var_prior.exp()\n",
    "        - 1.0\n",
    "        - var_ratio_log\n",
    "    )\n",
    "    kl_div = 0.5 * kl_div\n",
    "    return kl_div\n",
    "\n",
    "class LatentModel(nn.Module):\n",
    "    def __init__(self,\n",
    "                 x_dim, # features in input\n",
    "                 y_dim, # number of features in output\n",
    "                 hidden_dim=32, # size of hidden space\n",
    "                 latent_dim=32, # size of latent space\n",
    "                 latent_enc_self_attn_type=\"ptmultihead\", # type of attention: \"uniform\", \"dot\", \"multihead\" \"ptmultihead\": see attentive neural processes paper\n",
    "                 det_enc_self_attn_type=\"ptmultihead\",\n",
    "                 det_enc_cross_attn_type=\"ptmultihead\",\n",
    "                 n_latent_encoder_layers=2,\n",
    "                 n_det_encoder_layers=2, # number of deterministic encoder layers\n",
    "                 n_decoder_layers=2,\n",
    "                 use_deterministic_path=False,\n",
    "                 min_std=0.01, # To avoid collapse use a minimum standard deviation, should be much smaller than variation in labels\n",
    "                 dropout=0,\n",
    "                 use_self_attn=False,\n",
    "                 attention_dropout=0,\n",
    "                 batchnorm=False,\n",
    "                 use_lvar=False, # Alternative loss calculation, may be more stable\n",
    "                 attention_layers=2, \n",
    "                 use_rnn=True, # use RNN/LSTM?\n",
    "                 use_lstm_le=False, # use another LSTM in latent encoder instead of MLP\n",
    "                 use_lstm_de=False, # use another LSTM in determinstic encoder instead of MLP\n",
    "                 use_lstm_d=False,  # use another lstm in decoder instead of MLP\n",
    "                 context_in_target=True,\n",
    "                 **kwargs,\n",
    "                ):\n",
    "\n",
    "        super(LatentModel, self).__init__()\n",
    "\n",
    "        self._use_rnn = use_rnn\n",
    "\n",
    "        if self._use_rnn:\n",
    "            self._lstm = nn.LSTM(\n",
    "                input_size=x_dim,\n",
    "                hidden_size=hidden_dim,\n",
    "                num_layers=attention_layers,\n",
    "                dropout=dropout,\n",
    "                batch_first=True\n",
    "            )\n",
    "            x_dim = hidden_dim\n",
    "\n",
    "        self._latent_encoder = LatentEncoder(\n",
    "            x_dim + y_dim,\n",
    "            hidden_dim=hidden_dim,\n",
    "            latent_dim=latent_dim,\n",
    "            self_attention_type=latent_enc_self_attn_type,\n",
    "            n_encoder_layers=n_latent_encoder_layers,\n",
    "            attention_layers=attention_layers,\n",
    "            dropout=dropout,\n",
    "            use_self_attn=use_self_attn,\n",
    "            attention_dropout=attention_dropout,\n",
    "            batchnorm=batchnorm,\n",
    "            min_std=min_std,\n",
    "            use_lvar=use_lvar,\n",
    "            use_lstm=use_lstm_le,\n",
    "        )\n",
    "\n",
    "        self._deterministic_encoder = DeterministicEncoder(\n",
    "            input_dim=x_dim + y_dim,\n",
    "            x_dim=x_dim,\n",
    "            hidden_dim=hidden_dim,\n",
    "            self_attention_type=det_enc_self_attn_type,\n",
    "            cross_attention_type=det_enc_cross_attn_type,\n",
    "            n_d_encoder_layers=n_det_encoder_layers,\n",
    "            attention_layers=attention_layers,\n",
    "            use_self_attn=use_self_attn,\n",
    "            dropout=dropout,\n",
    "            batchnorm=batchnorm,\n",
    "            attention_dropout=attention_dropout,\n",
    "            use_lstm=use_lstm_de,\n",
    "        )\n",
    "\n",
    "        self._decoder = Decoder(\n",
    "            x_dim,\n",
    "            y_dim,\n",
    "            hidden_dim=hidden_dim,\n",
    "            latent_dim=latent_dim,\n",
    "            dropout=dropout,\n",
    "            batchnorm=batchnorm,\n",
    "            min_std=min_std,\n",
    "            use_lvar=use_lvar,\n",
    "            n_decoder_layers=n_decoder_layers,\n",
    "            use_deterministic_path=use_deterministic_path,\n",
    "            use_lstm=use_lstm_d,\n",
    "            \n",
    "        )\n",
    "        self._use_deterministic_path = use_deterministic_path\n",
    "        self._use_lvar = use_lvar\n",
    "        self.context_in_target = context_in_target\n",
    "\n",
    "    def forward(self, context_x, context_y, target_x, target_y=None):\n",
    "\n",
    "        if self._use_rnn:\n",
    "            # see https://arxiv.org/abs/1910.09323 where x is substituted with h = RNN(x)\n",
    "            # x need to be provided as [B, T, H]\n",
    "            target_x, _ = self._lstm(target_x)\n",
    "            context_x, _ = self._lstm(context_x)\n",
    "\n",
    "        dist_prior, log_var_prior = self._latent_encoder(context_x, context_y)\n",
    "\n",
    "        if target_y is not None:\n",
    "            dist_post, log_var_post = self._latent_encoder(target_x, target_y)\n",
    "            z = dist_post.loc\n",
    "        else:\n",
    "            z = dist_prior.loc\n",
    "\n",
    "        num_targets = target_x.size(1)\n",
    "        z = z.unsqueeze(1).repeat(1, num_targets, 1)  # [B, T_target, H]\n",
    "\n",
    "        if self._use_deterministic_path:\n",
    "            r = self._deterministic_encoder(context_x, context_y,\n",
    "                                            target_x)  # [B, T_target, H]  \n",
    "        else:\n",
    "            r = None\n",
    "\n",
    "        dist, log_sigma = self._decoder(r, z, target_x)\n",
    "        if target_y is not None:\n",
    "\n",
    "            if self._use_lvar:\n",
    "                log_p = log_prob_sigma(target_y, dist.loc, log_sigma).mean(-1)  # [B, T_target, Y].mean(-1)\n",
    "                if self.context_in_target:\n",
    "                    log_p[:, :context_x.size(1)] /= 100\n",
    "                kl_loss = kl_loss_var(dist_prior.loc, log_var_prior,\n",
    "                                      dist_post.loc, log_var_post).mean(-1)  # [B, R].mean(-1)\n",
    "            else:\n",
    "                log_p = dist.log_prob(target_y).mean(-1)\n",
    "                if self.context_in_target:\n",
    "                    log_p[:, :context_x.size(1)] /= 100 # There's the temptation for it to fit only on context, where it knows the answer, and learn very low uncertainty. \n",
    "                kl_loss = torch.distributions.kl_divergence(\n",
    "                    dist_post, dist_prior).mean(-1)  # [B, R].mean(-1)\n",
    "            kl_loss = kl_loss[:, None].expand(log_p.shape)\n",
    "            mse_loss = F.mse_loss(dist.loc, target_y, reduction='none')[:, :context_x.size(1)].mean()\n",
    "            loss = (kl_loss - log_p).mean()\n",
    "\n",
    "        else:\n",
    "            log_p = None\n",
    "            mse_loss = None\n",
    "            kl_loss = None\n",
    "            loss = None\n",
    "\n",
    "        y_pred = dist.rsample() if self.training else dist.loc\n",
    "        return y_pred, dict(loss=loss, loss_p=-log_p.mean(), loss_kl=kl_loss, loss_mse=mse_loss.mean()), dict(log_sigma=log_sigma, dist=dist)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:19.319720Z",
     "start_time": "2020-03-02T06:03:19.284706Z"
    }
   },
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "def npsample_batch(x, y, size=None, sort=False):\n",
    "    \"\"\"Sample from numpy arrays along 2nd dim.\"\"\"\n",
    "    inds = np.random.choice(range(x.shape[1]), size=size, replace=False)\n",
    "    return x[:, inds], y[:, inds]\n",
    "\n",
    "def collate_fns(max_num_context, max_num_extra_target, sample, sort=True, context_in_target=True):\n",
    "    def collate_fn(batch, sample=sample):\n",
    "        # Collate\n",
    "        x = np.stack([x for x, y in batch], 0)\n",
    "        y = np.stack([y for x, y in batch], 0)\n",
    "\n",
    "        # Sample a subset of random size\n",
    "        num_context = np.random.randint(4, max_num_context)\n",
    "        num_extra_target = np.random.randint(4, max_num_extra_target)\n",
    "\n",
    "        x = torch.from_numpy(x).float()\n",
    "        y = torch.from_numpy(y).float()\n",
    "\n",
    "        \n",
    "        x_context = x[:, :max_num_context]\n",
    "        y_context = y[:, :max_num_context]\n",
    "    \n",
    "        x_target_extra = x[:, max_num_context:]\n",
    "        y_target_extra = y[:, max_num_context:]\n",
    "        \n",
    "        if sample:\n",
    "\n",
    "            x_context, y_context = npsample_batch(\n",
    "                x_context, y_context, size=num_context\n",
    "            )\n",
    "\n",
    "            x_target_extra, y_target_extra = npsample_batch(\n",
    "                x_target_extra, y_target_extra, size=num_extra_target, sort=sort\n",
    "            )\n",
    "\n",
    "        # do we want to compute loss over context+target_extra, or focus in on only target_extra?\n",
    "        if context_in_target:\n",
    "            x_target = torch.cat([x_context, x_target_extra], 1)\n",
    "            y_target = torch.cat([y_context, y_target_extra], 1)\n",
    "        else:\n",
    "            x_target = x_target_extra\n",
    "            y_target = y_target_extra\n",
    "\n",
    "        \n",
    "        return x_context, y_context, x_target, y_target\n",
    "\n",
    "    return collate_fn\n",
    "\n",
    "\n",
    "class DataSet(torch.utils.data.Dataset):\n",
    "    def __init__(self, df, num_context=40, num_extra_target=10, label_names=['lead']):\n",
    "        self.df = df\n",
    "        self.num_context = num_context\n",
    "        self.num_extra_target = num_extra_target\n",
    "        self.label_names = label_names\n",
    "\n",
    "    def get_rows(self, i):\n",
    "        rows = self.df.iloc[i : i + (self.num_context + self.num_extra_target)].copy()\n",
    "\n",
    "        # make sure tstp, which is our x axis, is the first value\n",
    "\n",
    "        # This will be the last row, and will change it upon sample to let the model know some points are in the future\n",
    "\n",
    "        x = rows.drop(columns=self.label_names).copy()\n",
    "        y = rows[self.label_names].copy()\n",
    "        return x, y\n",
    "\n",
    "\n",
    "    def __getitem__(self, i):\n",
    "        x, y = self.get_rows(i)\n",
    "        return x.values, y.values\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df) - (self.num_context + self.num_extra_target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:03:19.341023Z",
     "start_time": "2020-03-02T06:03:19.322378Z"
    }
   },
   "outputs": [],
   "source": [
    "# from https://github.com/Bjarten/early-stopping-pytorch/blob/master/MNIST_Early_Stopping_example.ipynb\n",
    "import numpy as np\n",
    "import torch\n",
    "\n",
    "class EarlyStopping:\n",
    "    \"\"\"Early stops the training if validation loss doesn't improve after a given patience.\"\"\"\n",
    "    def __init__(self, patience=7, verbose=False, delta=0):\n",
    "        \"\"\"\n",
    "        Args:\n",
    "            patience (int): How long to wait after last time validation loss improved.\n",
    "                            Default: 7\n",
    "            verbose (bool): If True, prints a message for each validation loss improvement. \n",
    "                            Default: False\n",
    "            delta (float): Minimum change in the monitored quantity to qualify as an improvement.\n",
    "                            Default: 0\n",
    "        \"\"\"\n",
    "        self.patience = patience\n",
    "        self.verbose = verbose\n",
    "        self.counter = 0\n",
    "        self.best_score = None\n",
    "        self.early_stop = False\n",
    "        self.val_loss_min = np.Inf\n",
    "        self.delta = delta\n",
    "\n",
    "    def __call__(self, val_loss, model):\n",
    "\n",
    "        score = -val_loss\n",
    "\n",
    "        if self.best_score is None:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "        elif score < self.best_score + self.delta:\n",
    "            self.counter += 1\n",
    "            print(f'EarlyStopping counter: {self.counter} out of {self.patience}')\n",
    "            if self.counter >= self.patience:\n",
    "                self.early_stop = True\n",
    "        else:\n",
    "            self.best_score = score\n",
    "            self.save_checkpoint(val_loss, model)\n",
    "            self.counter = 0\n",
    "\n",
    "    def save_checkpoint(self, val_loss, model):\n",
    "        '''Saves model when validation loss decrease.'''\n",
    "        if self.verbose:\n",
    "            print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}).  Saving model ...')\n",
    "        torch.save(model.state_dict(), 'outputs/checkpoint2.pt')\n",
    "        self.val_loss_min = val_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:16.645720Z",
     "start_time": "2020-03-02T06:09:16.630341Z"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "df_train = meuse_train[allow_columns].copy()\n",
    "norm_mean = meuse[allow_columns].mean()\n",
    "norm_std = meuse[allow_columns].std()\n",
    "\n",
    "hparams = dict(\n",
    "    num_context=15,\n",
    "    num_extra_target=16,\n",
    "    batch_size=40,\n",
    "    context_in_target=False,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:16:41.659181Z",
     "start_time": "2020-03-02T05:16:41.647335Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:16.984371Z",
     "start_time": "2020-03-02T06:09:16.968427Z"
    }
   },
   "outputs": [],
   "source": [
    "df_train = meuse_train[allow_columns].copy()\n",
    "df_train -= norm_mean\n",
    "df_train /= norm_std\n",
    "\n",
    "data_train = DataSet(\n",
    "    df_train, hparams[\"num_context\"], hparams[\"num_extra_target\"]\n",
    ")\n",
    "loader_train = torch.utils.data.DataLoader(\n",
    "    data_train,\n",
    "    batch_size=hparams[\"batch_size\"],\n",
    "    shuffle=True,\n",
    "    collate_fn=collate_fns(\n",
    "        hparams[\"num_context\"], hparams[\"num_extra_target\"], sample=True, context_in_target=hparams[\"context_in_target\"]\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T04:25:22.556918Z",
     "start_time": "2020-03-02T04:25:22.540648Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:17.268382Z",
     "start_time": "2020-03-02T06:09:17.251121Z"
    }
   },
   "outputs": [],
   "source": [
    "df_test = meuse_test[allow_columns].copy()\n",
    "df_test -= norm_mean\n",
    "df_test /= norm_std\n",
    "\n",
    "data_test = DataSet(\n",
    "    df_test, hparams[\"num_context\"], hparams[\"num_extra_target\"]\n",
    ")\n",
    "loader_test = torch.utils.data.DataLoader(\n",
    "    data_test,\n",
    "    batch_size=hparams[\"batch_size\"],\n",
    "    shuffle=False,\n",
    "    collate_fn=collate_fns(\n",
    "        hparams[\"num_context\"], hparams[\"num_extra_target\"], sample=False, context_in_target=hparams[\"context_in_target\"]\n",
    "    ),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:17.498547Z",
     "start_time": "2020-03-02T06:09:17.481432Z"
    },
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "def test(do_eval=True):\n",
    "    \"\"\"Run model on test/val data\"\"\"\n",
    "    if do_eval:\n",
    "        model_np.eval()\n",
    "    with torch.no_grad():\n",
    "        target_x, target_y = df_test.drop(columns=['lead']), df_test['lead']\n",
    "        context_x, context_y = df_train.drop(columns=['lead']), df_train['lead']\n",
    "\n",
    "        context_x = torch.from_numpy(context_x.values).float()[None, :]\n",
    "        context_y = torch.from_numpy(context_y.values).float()[None, :, None]\n",
    "        target_x = torch.from_numpy(target_x.values).float()[None, :]\n",
    "        target_y = torch.from_numpy(target_y.values).float()[None, :, None]\n",
    "\n",
    "        y_pred, losses, extra = model_np.forward(context_x, context_y, target_x, target_y)\n",
    "    yr=(target_y-y_pred)[0].detach().cpu().numpy() * norm_std['lead']\n",
    "    \n",
    "    return yr, y_pred, losses, extra "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:17.686133Z",
     "start_time": "2020-03-02T06:09:17.665861Z"
    }
   },
   "outputs": [],
   "source": [
    "model_np = LatentModel(len(allow_columns)-1, 1,\n",
    "                    hidden_dim=64, # size of hidden space\n",
    "                 latent_dim=16, # size of latent space\n",
    "                 latent_enc_self_attn_type=\"ptmultihead\", # type of attention: \"uniform\", \"dot\", \"multihead\" \"ptmultihead\": see attentive neural processes paper\n",
    "                 det_enc_self_attn_type=\"ptmultihead\",\n",
    "                 det_enc_cross_attn_type=\"ptmultihead\",\n",
    "                 n_latent_encoder_layers=2,\n",
    "                 n_det_encoder_layers=2, # number of deterministic encoder layers\n",
    "                 n_decoder_layers=3,\n",
    "                 use_deterministic_path=False,\n",
    "                 min_std=0.01, # To avoid collapse use a minimum standard deviation, should be much smaller than variation in labels\n",
    "                 dropout=0.3,\n",
    "                 use_self_attn=False,\n",
    "                 attention_dropout=0.2,\n",
    "                 batchnorm=True,\n",
    "                 use_lvar=True, # Alternative loss calculation, may be more stable\n",
    "                 attention_layers=2, \n",
    "                 use_rnn=False, # use RNN/LSTM?\n",
    "                 use_lstm_le=False, # use another LSTM in latent encoder instead of MLP\n",
    "                 use_lstm_de=False, # use another LSTM in determinstic encoder instead of MLP\n",
    "                 use_lstm_d=False,  # use another lstm in decoder instead of MLP\n",
    "                 context_in_target=False,\n",
    "                   )\n",
    "\n",
    "# torch.optim.Adam?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:17.789106Z",
     "start_time": "2020-03-02T06:09:17.782257Z"
    }
   },
   "outputs": [],
   "source": [
    "opt = torch.optim.Adam(model_np.parameters(), lr=3e-4)\n",
    "early_stopping = EarlyStopping(patience=9, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T01:48:14.953204Z",
     "start_time": "2020-03-02T01:48:14.946276Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T01:48:41.982958Z",
     "start_time": "2020-03-02T01:48:41.976326Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:30.252493Z",
     "start_time": "2020-03-02T06:09:18.408417Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebe09de6c283431aa4ba3b14614a3ed1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "0\n",
      "train_loss 1.905900200208028\n",
      "val_loss 91.01156\n",
      "Validation loss decreased (inf --> 91.011559).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7ac3bcd9e5bd45528c2a0a803a62fa5f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "1\n",
      "train_loss 1.8203574021657307\n",
      "val_loss 90.077965\n",
      "Validation loss decreased (91.011559 --> 90.077965).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b1ea922d5f684b478c2dd6a4179a6ab2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "2\n",
      "train_loss 1.7613677183787029\n",
      "val_loss 88.92264\n",
      "Validation loss decreased (90.077965 --> 88.922638).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "304c06f0bb3d4218bc8c993d068b9963",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "3\n",
      "train_loss 1.685694416364034\n",
      "val_loss 87.42866\n",
      "Validation loss decreased (88.922638 --> 87.428658).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d7392464634d4377a54d61b2a8bf3bc0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "4\n",
      "train_loss 1.6513565381368\n",
      "val_loss 85.389656\n",
      "Validation loss decreased (87.428658 --> 85.389656).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "70b3517927f0494a927d89f7ccabec6c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "5\n",
      "train_loss 1.623356858889262\n",
      "val_loss 82.93993\n",
      "Validation loss decreased (85.389656 --> 82.939934).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da0f8e545f9742f0923ef7473957e5e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "6\n",
      "train_loss 1.5690519014994304\n",
      "val_loss 80.21474\n",
      "Validation loss decreased (82.939934 --> 80.214737).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b98c6795ac1e4c4f9f2449a94e9fdf78",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "7\n",
      "train_loss 1.566269834836324\n",
      "val_loss 77.0155\n",
      "Validation loss decreased (80.214737 --> 77.015503).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2313a096e68e4b0b8c0f2efc99a9e35b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "8\n",
      "train_loss 1.5784167051315308\n",
      "val_loss 74.03796\n",
      "Validation loss decreased (77.015503 --> 74.037956).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "260bde6d492f4fd7b4bd35edb918273c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "9\n",
      "train_loss 1.504947026570638\n",
      "val_loss 71.60636\n",
      "Validation loss decreased (74.037956 --> 71.606361).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b0830b6397134c63afaf1d76af95b428",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "10\n",
      "train_loss 1.5234577258427937\n",
      "val_loss 69.42651\n",
      "Validation loss decreased (71.606361 --> 69.426514).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "48904684f8b1460fbfc320e8880e05ef",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "11\n",
      "train_loss 1.5316803057988484\n",
      "val_loss 67.364204\n",
      "Validation loss decreased (69.426514 --> 67.364204).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5af0fc8cf29847b9a1afe8142323700f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "12\n",
      "train_loss 1.476468841234843\n",
      "val_loss 64.99409\n",
      "Validation loss decreased (67.364204 --> 64.994087).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ee86813acb5f42bca0d7d65b412cf121",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "13\n",
      "train_loss 1.4857571125030518\n",
      "val_loss 62.5033\n",
      "Validation loss decreased (64.994087 --> 62.503300).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e36f359816354e1186e93bec6a7a3795",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "14\n",
      "train_loss 1.4260983069737752\n",
      "val_loss 60.79292\n",
      "Validation loss decreased (62.503300 --> 60.792919).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a9ac6b3d64dd47d5b78ec616e965cc2b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "15\n",
      "train_loss 1.4118024110794067\n",
      "val_loss 59.126183\n",
      "Validation loss decreased (60.792919 --> 59.126183).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "664d7d2dc22143b3a7405cbfa1949e25",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "16\n",
      "train_loss 1.4022262891133626\n",
      "val_loss 56.734287\n",
      "Validation loss decreased (59.126183 --> 56.734287).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5185420e846b48f5a26c582bd962c606",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "17\n",
      "train_loss 1.3722738027572632\n",
      "val_loss 55.833946\n",
      "Validation loss decreased (56.734287 --> 55.833946).  Saving model ...\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b07c68c61b2f4ac299b5b8d2980798e7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "18\n",
      "train_loss 1.3646957874298096\n",
      "val_loss 56.029648\n",
      "EarlyStopping counter: 1 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1dd44cd6aa804c94968d01df5f112c12",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "19\n",
      "train_loss 1.345403830210368\n",
      "val_loss 56.33831\n",
      "EarlyStopping counter: 2 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d85a762996694369871ee5658cc1d588",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "20\n",
      "train_loss 1.3498059908548992\n",
      "val_loss 56.630264\n",
      "EarlyStopping counter: 3 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fe84285ae22b45b6b652e91a210b5622",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "21\n",
      "train_loss 1.2791765928268433\n",
      "val_loss 56.71887\n",
      "EarlyStopping counter: 4 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "08f3f320a5a042b9a6079f21462986ce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "22\n",
      "train_loss 1.2972668011983235\n",
      "val_loss 56.54997\n",
      "EarlyStopping counter: 5 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c30318f22e934257892c1aef9c664f4e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "23\n",
      "train_loss 1.2786013682683308\n",
      "val_loss 56.59252\n",
      "EarlyStopping counter: 6 out of 9\n",
      "reducing lr 3.00e-04 to 3.00e-05\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7942b2499552415d9078f41eedbcf697",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "24\n",
      "train_loss 1.2542187770207722\n",
      "val_loss 56.523396\n",
      "EarlyStopping counter: 7 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "715e3975f1164904a7dd2b25fb49a4e3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "25\n",
      "train_loss 1.3027362426122029\n",
      "val_loss 56.618027\n",
      "EarlyStopping counter: 8 out of 9\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ab7cc95370f14cf5abc41b2e15c0d49b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(FloatProgress(value=0.0, max=3.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "26\n",
      "train_loss 1.2702527443567913\n",
      "val_loss 56.455036\n",
      "EarlyStopping counter: 9 out of 9\n",
      "Early stopping\n"
     ]
    }
   ],
   "source": [
    "from tqdm.auto import tqdm \n",
    "\n",
    "for epoch in range(60):\n",
    "    loss = 0 \n",
    "    model_np.train()\n",
    "    for batch in tqdm(loader_train):\n",
    "        context_x, context_y, target_x, target_y = batch\n",
    "        model_np.zero_grad()\n",
    "        y_pred, losses, extra = model_np.forward(context_x, context_y, target_x, target_y)\n",
    "\n",
    "        losses['loss'].backward()\n",
    "        loss += losses['loss'].cpu().detach().numpy()\n",
    "        opt.step()\n",
    "    loss /= len(loader_train)\n",
    "    \n",
    "    print(epoch)\n",
    "    print('train_loss', loss)\n",
    "    val_loss = test()[0]\n",
    "    val_loss = np.mean(np.abs(val_loss))\n",
    "    print('val_loss', val_loss)\n",
    "    \n",
    "    early_stopping(val_loss, model_np)\n",
    "    \n",
    "    if early_stopping.counter==6:\n",
    "        \n",
    "        lr=opt.state_dict()['param_groups'][0]['lr']\n",
    "        print('reducing lr {:2.2e} to {:2.2e}'.format(lr, lr/10))\n",
    "        opt = torch.optim.Adam(model_np.parameters(), lr=lr/10)\n",
    "\n",
    "    if early_stopping.early_stop:\n",
    "        print(\"Early stopping\")\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T01:53:13.439084Z",
     "start_time": "2020-03-02T01:53:13.433316Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:34.068098Z",
     "start_time": "2020-03-02T06:09:34.056007Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<All keys matched successfully>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_np.load_state_dict(torch.load('outputs/checkpoint2.pt'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:34.212991Z",
     "start_time": "2020-03-02T06:09:34.208034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294.76589392407357"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loss * norm_std['lead'] + norm_mean['lead']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NP Model performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:34.518360Z",
     "start_time": "2020-03-02T06:09:34.511099Z"
    }
   },
   "outputs": [],
   "source": [
    "# # Make predictions\n",
    "# def test(do_eval=True):\n",
    "#     \"\"\"Run model on test/val data\"\"\"\n",
    "#     if do_eval:\n",
    "#         model_np.eval()\n",
    "#     with torch.no_grad():\n",
    "#         target_x, target_y = df_test.drop(columns=['lead']), df_test['lead']\n",
    "#         context_x, context_y = df_train.drop(columns=['lead']), df_train['lead']\n",
    "\n",
    "#         context_x = torch.from_numpy(context_x.values).float()[None, :]\n",
    "#         context_y = torch.from_numpy(context_y.values).float()[None, :, None]\n",
    "#         target_x = torch.from_numpy(target_x.values).float()[None, :]\n",
    "#         target_y = torch.from_numpy(target_y.values).float()[None, :, None]\n",
    "\n",
    "#         y_pred, losses, extra = model_np.forward(context_x, context_y, target_x, target_y)\n",
    "#     yr=(target_y-y_pred)[0].detach().cpu().numpy() * norm_std['lead']\n",
    "    \n",
    "#     return yr, y_pred, losses, extra "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:34.681780Z",
     "start_time": "2020-03-02T06:09:34.670708Z"
    }
   },
   "outputs": [],
   "source": [
    "# Make predictions\n",
    "def train_eval(do_eval=True):\n",
    "    \"\"\"Run model on train data\"\"\"\n",
    "    if do_eval:\n",
    "        model_np.eval()\n",
    "    with torch.no_grad():\n",
    "        target_x, target_y = df_train.drop(columns=['lead']), df_train['lead']\n",
    "        context_x, context_y = df_train.drop(columns=['lead']), df_train['lead']\n",
    "\n",
    "        context_x = torch.from_numpy(context_x.values).float()[None, :]\n",
    "        context_y = torch.from_numpy(context_y.values).float()[None, :, None]\n",
    "        target_x = torch.from_numpy(target_x.values).float()[None, :]\n",
    "        target_y = torch.from_numpy(target_y.values).float()[None, :, None]\n",
    "\n",
    "        y_pred, losses, extra = model_np.forward(context_x, context_y, target_x, target_y)\n",
    "    yr=(target_y-y_pred)[0].detach().cpu().numpy() * norm_std['lead']\n",
    "    \n",
    "    return yr, y_pred, losses, extra "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:34.836095Z",
     "start_time": "2020-03-02T06:09:34.805475Z"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "yr, y_pred, losses, extra  = train_eval()\n",
    "\n",
    "y_pred2 = y_pred[0].detach().cpu().numpy() * norm_std['lead'] + norm_mean['lead']\n",
    "meuse_train['np_residual'] = yr * 1\n",
    "meuse_train['np_prediction'] = y_pred2 * 1\n",
    "\n",
    "yr, y_pred, losses, extra  = test()\n",
    "\n",
    "y_pred2 = y_pred[0].detach().cpu().numpy() * norm_std['lead'] + norm_mean['lead']\n",
    "meuse_test['np_residual'] = yr * 1\n",
    "meuse_test['np_prediction'] = y_pred2 * 1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T04:31:50.408990Z",
     "start_time": "2020-03-02T04:31:50.398077Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T04:30:43.036324Z",
     "start_time": "2020-03-02T04:30:43.024376Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:37.120347Z",
     "start_time": "2020-03-02T06:09:35.316044Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "plt.figure(figsize=(6,6))\n",
    "plt.subplot(221)\n",
    "plt.plot(meuse_train['np_prediction'], meuse_train['lead'], '.')\n",
    "plt.title('Training: pred vs obs')\n",
    "plt.xlabel('Predictions')\n",
    "plt.ylabel('True value')\n",
    "plt.plot([0,700], [0,700], 'g--')\n",
    "plt.ylim(0,700)\n",
    "plt.xlim(0,700)\n",
    "plt.subplot(222)\n",
    "meuse_train['np_residual'].hist()\n",
    "plt.title('Hist training res\\nMedian absolute error: {:.1f}'.format(np.median(np.abs(meuse_train['np_residual']))))\n",
    "plt.subplot(223)\n",
    "plt.plot(meuse_test['np_prediction'], meuse_test['lead'], '.')\n",
    "plt.plot([0,700], [0,700], 'g--')\n",
    "plt.title('Test: pred vs obs')\n",
    "plt.xlabel('Predictions')\n",
    "plt.ylabel('True value')\n",
    "plt.ylim(0,700)\n",
    "plt.xlim(0,700)\n",
    "plt.subplot(224)\n",
    "meuse_test['np_residual'].hist()\n",
    "plt.title('Hist test res\\nMedian absolute error: {:.1f}'.format(np.median(np.abs(meuse_test['np_residual']))))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ordinary kriging\n",
    "\n",
    "Variogram parameters are set with trail and error."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:37.126914Z",
     "start_time": "2020-03-02T06:09:37.123177Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pykrige.ok.OrdinaryKriging"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "OrdinaryKriging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:37.573292Z",
     "start_time": "2020-03-02T06:09:37.129941Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Plotting Enabled\n",
      "\n",
      "Adjusting data for anisotropy...\n",
      "Initializing variogram model...\n",
      "Coordinates type: 'euclidean' \n",
      "\n",
      "Using 'spherical' Variogram Model\n",
      "Partial Sill: 9500\n",
      "Full Sill: 13500\n",
      "Range: 900\n",
      "Nugget: 4000 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Calculating statistics on variogram model fit...\n",
      "Executing Ordinary Kriging...\n",
      "\n",
      "Executing Ordinary Kriging...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = OrdinaryKriging(x=meuse_train['x'], y=meuse_train['y'], z=meuse_train['lead'], verbose=True,\n",
    "                variogram_parameters=[13500, 900, 4000],\n",
    "                enable_plotting=True, nlags=30, weight=True, variogram_model='spherical')\n",
    "meuse_train['prediction'] = model.execute(style='points',xpoints=meuse_train['x'], ypoints=meuse_train['y'] )[0].data\n",
    "meuse_train['kriging_residual'] = meuse_train['lead'] - meuse_train['prediction']\n",
    "meuse_test['prediction'] = model.execute(style='points', xpoints=meuse_test['x'], ypoints=meuse_test['y'] )[0].data\n",
    "meuse_test['kriging_residual'] = meuse_test['lead'] - meuse_test['prediction']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Krig Model performance\n",
    "\n",
    "- Performance on training data (kriging is an exact interpollator, so perfect prediction)\n",
    "- Performance on test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:39.346028Z",
     "start_time": "2020-03-02T06:09:37.575847Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,6))\n",
    "plt.subplot(221)\n",
    "plt.plot(meuse_train['prediction'], meuse_train['lead'], '.')\n",
    "plt.title('Training: pred vs obs')\n",
    "plt.xlabel('Predictions')\n",
    "plt.ylabel('True value')\n",
    "plt.plot([0,700], [0,700], 'g--')\n",
    "plt.ylim(0,700)\n",
    "plt.xlim(0,700)\n",
    "plt.subplot(222)\n",
    "meuse_train['kriging_residual'].hist()\n",
    "plt.title('Hist training res\\nMedian absolute error: {:.1f}'.format(np.median(np.abs(meuse_train['kriging_residual']))))\n",
    "plt.subplot(223)\n",
    "plt.plot(meuse_test['prediction'], meuse_test['lead'], '.')\n",
    "plt.plot([0,700], [0,700], 'g--')\n",
    "plt.title('Test: pred vs obs')\n",
    "plt.xlabel('Predictions')\n",
    "plt.ylabel('True value')\n",
    "plt.ylim(0,700)\n",
    "plt.xlim(0,700)\n",
    "plt.subplot(224)\n",
    "meuse_test['kriging_residual'].hist()\n",
    "plt.title('Hist test res\\nMedian absolute error: {:.1f}'.format(np.median(np.abs(meuse_test['kriging_residual']))))\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark with Nearest Neighbours regression\n",
    "\n",
    "Optimise number of neighbours with sklearn gridsearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:39.529962Z",
     "start_time": "2020-03-02T06:09:39.348143Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Optimal number of neighbours {'n_neighbors': 4}\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_split.py:1978: FutureWarning: The default value of cv will change from 3 to 5 in version 0.22. Specify it explicitly to silence this warning.\n",
      "  warnings.warn(CV_WARNING, FutureWarning)\n",
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/sklearn/model_selection/_search.py:813: DeprecationWarning: The default of the `iid` parameter will change from True to False in version 0.22 and will be removed in 0.24. This will change numeric results when test-set sizes are unequal.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "parameters = {'n_neighbors':np.arange(1,10)}\n",
    "nn_model = KNeighborsRegressor()\n",
    "nn_model_cv = GridSearchCV(nn_model, parameters)\n",
    "nn_model_cv = nn_model_cv.fit(meuse_train[['x', 'y']], meuse_train['lead']) \n",
    "print('Optimal number of neighbours {}'.format(nn_model_cv.best_params_))\n",
    "nn_model = nn_model_cv.best_estimator_\n",
    "meuse_test['nn_prediction'] = nn_model.predict(meuse_test[['x', 'y']])\n",
    "meuse_test['nn_residual'] = meuse_test['lead'] - meuse_test['nn_prediction']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T06:06:37.015399Z",
     "start_time": "2020-03-01T06:06:36.920824Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T06:06:53.203456Z",
     "start_time": "2020-03-01T06:06:53.153655Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:39.893252Z",
     "start_time": "2020-03-02T06:09:39.532295Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Compairing residuals\\nmedian abs res NN: 38.8, Kriging 33.7, NP: 19.7\\nmean abs res NN: 66.1, Kriging: 62.5, NP: 55.8')"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sb.set_style(\"whitegrid\")\n",
    "plt.figure(figsize=(4,4))\n",
    "sb.boxplot(data=meuse_test[[\"nn_residual\",\"kriging_residual\",\"np_residual\"]] )\n",
    "plt.title('''Compairing residuals\n",
    "median abs res NN: {:.1f}, Kriging {:.1f}, NP: {:.1f}\n",
    "mean abs res NN: {:.1f}, Kriging: {:.1f}, NP: {:.1f}'''\\\n",
    "          .format(\n",
    "    np.median(np.abs(meuse_test['nn_residual'])), \n",
    "    np.median(np.abs(meuse_test['kriging_residual'])),\n",
    "    np.median(np.abs(meuse_test['np_residual'])),\n",
    "    np.mean(np.abs(meuse_test['nn_residual'])), \n",
    "    np.mean(np.abs(meuse_test['kriging_residual'])),\n",
    "    np.mean(np.abs(meuse_test['np_residual']))\n",
    "))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Help functions to sample a grid from a polygon\n",
    "\n",
    "As found on http://portolan.leaffan.net/creating-sample-points-with-ogr-and-shapely-pt-2-regular-grid-sampling/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:46.302067Z",
     "start_time": "2020-03-02T06:09:46.275683Z"
    }
   },
   "outputs": [],
   "source": [
    "class PolygonPointSampler(object):\n",
    " \n",
    "    def __init__(self, polygon=''):\n",
    "        u\"\"\"\n",
    "        Initialize a new PolygonPointSampler object using the specified polygon\n",
    "        object (as allocated by Shapely). If no polygon is given a new empty\n",
    "        one is created and set as the base polygon.\n",
    "        \"\"\"\n",
    "        if polygon:\n",
    "            self.polygon = polygon\n",
    "        else:\n",
    "            self.polygon = Polygon()\n",
    "        self.samples = list()\n",
    "        self.sample_count = 0\n",
    "        self.prepared = False\n",
    " \n",
    "    def add_polygon(self, polygon):\n",
    "        u\"\"\"\n",
    "        Add another polygon entity to the base polygon by geometrically unifying\n",
    "        it with the current one.\n",
    "        \"\"\"\n",
    "        self.polygon = self.polygon.union(polygon)\n",
    "        self.prepared = False\n",
    "    \n",
    "    def get_spatial_df(self):\n",
    "        geo_df = pd.DataFrame(self.samples, columns=['geometry']).set_geometry('geometry')\n",
    "        geo_df['x'] = geo_df['geometry'].apply(lambda x: x.coords[0][0])\n",
    "        geo_df['y'] = geo_df['geometry'].apply(lambda x: x.coords[0][1])\n",
    "        return geo_df\n",
    " \n",
    "    def print_samples(self):\n",
    "        u\"\"\"\n",
    "        Print all sample points using their WKT representation.\n",
    "        \"\"\"\n",
    "        for sample_pt in self.samples:\n",
    "            print(sample_pt)\n",
    " \n",
    "    def prepare_sampling(self):\n",
    "        u\"\"\"\n",
    "        Prepare the actual sampling procedure by splitting up the specified base\n",
    "        polygon (that may consist of multiple simple polygons) and appending its\n",
    "        compartments to a dedicated list.\n",
    "        \"\"\"\n",
    "        self.src = list()\n",
    "        if hasattr(self.polygon, 'geoms'):\n",
    "            for py in self.polygon:\n",
    "                self.src.append(py)\n",
    "        else:\n",
    "            self.src.append(self.polygon)\n",
    "        self.prepared = True\n",
    " \n",
    "    def perform_sampling(self):\n",
    "        u\"\"\"\n",
    "        Create a stub for the actual sampling procedure.\n",
    "        \"\"\"\n",
    "        raise NotImplementedError\n",
    "    \n",
    "class RegularGridSampler(PolygonPointSampler):\n",
    "    def __init__(self, polygon = '', x_interval = 100, y_interval = 100):\n",
    "        super(self.__class__, self).__init__(polygon)\n",
    "        self.x_interval = x_interval\n",
    "        self.y_interval = y_interval\n",
    "    \n",
    "    def perform_sampling(self):\n",
    "        u\"\"\"\n",
    "        Perform sampling by substituting the polygon with a regular grid of\n",
    "        sample points within it. The distance between the sample points is\n",
    "        given by x_interval and y_interval.\n",
    "        \"\"\"\n",
    "        if not self.prepared:\n",
    "            self.prepare_sampling()\n",
    "        ll = self.polygon.bounds[:2]\n",
    "        ur = self.polygon.bounds[2:]\n",
    "        low_x = int(ll[0]) / self.x_interval * self.x_interval\n",
    "        upp_x = int(ur[0]) / self.x_interval * self.x_interval + self.x_interval\n",
    "        low_y = int(ll[1]) / self.y_interval * self.y_interval\n",
    "        upp_y = int(ur[1]) / self.y_interval * self.y_interval + self.y_interval\n",
    "        \n",
    "        for x in floatrange(low_x, upp_x, self.x_interval):\n",
    "            for y in floatrange(low_y, upp_y, self.y_interval):\n",
    "                p = shapely.geometry.Point(x, y)\n",
    "                if p.within(self.polygon):\n",
    "                    self.samples.append(p)\n",
    "\n",
    "def floatrange(start, stop, step):\n",
    "    while start < stop:\n",
    "        yield start\n",
    "        start += step\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sample a grid \n",
    "\n",
    "- Find the convex hull surrounding the measurements\n",
    "- Sample a grid as found with the help functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:49.226175Z",
     "start_time": "2020-03-02T06:09:46.305074Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Sampled grid')"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "convex_hull = shapely.geometry.MultiPoint(list(meuse.geometry)).convex_hull.buffer(1050)\n",
    "sampler = RegularGridSampler(convex_hull, x_interval=50, y_interval=50)\n",
    "sampler.perform_sampling()\n",
    "grid_points = sampler.get_spatial_df()\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.plot(grid_points['x'], grid_points['y'], '.')\n",
    "plt.plot(meuse['x'], meuse['y'], 'r.')\n",
    "plt.title('Sampled grid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:50.458744Z",
     "start_time": "2020-03-02T06:09:49.229802Z"
    }
   },
   "outputs": [],
   "source": [
    "# Add spatial info to grid\n",
    "# dist, elev\n",
    "allow_columns\n",
    "grid_points\n",
    "\n",
    "\n",
    "def read_asc(f):\n",
    "    \"\"\"Read asc file\"\"\"\n",
    "    \n",
    "    header = open(f).readlines()[:6]\n",
    "    header = [line.strip().split(' ', 1) for line in header]\n",
    "    header = {k.lower():float(v) for k,v in header}\n",
    "    data = np.loadtxt(f, skiprows=6)\n",
    "    data[data==header['nodata_value']] = np.nan\n",
    "    \n",
    "    xs = np.arange(0, header['ncols']) * header['cellsize'] + header['xllcorner']\n",
    "    ys = np.arange(0, header['nrows'])[::-1] * header['cellsize']+ header['yllcorner']\n",
    "    x, y = np.meshgrid(xs, ys)\n",
    "    \n",
    "    return header, x, y, data\n",
    "\n",
    "def asc2gdf(f, name=None):\n",
    "    header, x, y, data = read_asc(f)\n",
    "    if name is None:\n",
    "        name = Path(f).stem\n",
    "    gdf = gpd.GeoDataFrame(data.flatten(), geometry=gpd.points_from_xy(x.flatten(),y.flatten()), columns=[name])\n",
    "#     gdf = gdf.reset_index()\n",
    "    return gdf#.dropna()\n",
    "\n",
    "\n",
    "df_grid = pd.concat([\n",
    "    (asc2gdf('./data/meuse_example_data/ahn.asc', 'elev')[['elev']]-3656.6709159754228)*1.058657336960814/230.6805791511078,\n",
    "    asc2gdf('./data/meuse_example_data/dist.asc')[['dist']],\n",
    "    asc2gdf('./data/meuse_example_data/ffreq.asc')[['ffreq']],\n",
    "    asc2gdf('./data/meuse_example_data/soil.asc')[['soil']],\n",
    "], axis=1)\n",
    "geom = asc2gdf('./data/meuse_example_data/dist.asc')['geometry']\n",
    "grid_points = gpd.GeoDataFrame(df_grid, geometry=geom).dropna()\n",
    "grid_points['x'] = grid_points.geometry.x\n",
    "grid_points['y'] = grid_points.geometry.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:12:12.645510Z",
     "start_time": "2020-03-02T06:12:09.366514Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for col in ['elev', 'dist', 'ffreq', 'soil']:\n",
    "    grid_points.plot(col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:11:55.820887Z",
     "start_time": "2020-03-02T06:11:55.816381Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lead', 'elev', 'dist', 'x', 'y', 'ffreq', 'soil']"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:22:27.641084Z",
     "start_time": "2020-03-02T05:22:23.049748Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Krige a value for each grid point"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:50.529917Z",
     "start_time": "2020-03-02T06:09:50.461795Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing Ordinary Kriging...\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "68      223.093595\n",
       "145     236.456538\n",
       "146     224.329995\n",
       "147     211.938674\n",
       "222     250.988243\n",
       "           ...    \n",
       "8047    184.041339\n",
       "8048    185.344854\n",
       "8049    186.009049\n",
       "8050    185.963453\n",
       "8051    185.333395\n",
       "Name: prediction, Length: 3024, dtype: float64"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_points['prediction'] = model.execute(style='points', xpoints=grid_points['x'], ypoints=grid_points['y'])[0].data\n",
    "grid_points['prediction']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transform data for a map\n",
    "\n",
    "- Transform to lat long projection\n",
    "- From long to wide format\n",
    "- Fill in missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:50.544956Z",
     "start_time": "2020-03-02T06:09:50.532509Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_grid(grid_points, prediction, title):\n",
    "    \n",
    "    grid_points['prediction'] = prediction\n",
    "    grid_points_gpd = grid_points.set_geometry('geometry')    \n",
    "    grid_points_gpd.crs = {'init':'epsg:28992'}\n",
    "    grid_points_gpd = grid_points_gpd.to_crs({'init': 'epsg:4326'})\n",
    "    grid_points_gpd['long'] = grid_points_gpd.geometry.apply(lambda x: x.x)\n",
    "    grid_points_gpd['lat'] = grid_points_gpd.geometry.apply(lambda x: x.y)\n",
    "    \n",
    "    grid_points_pivot = grid_points_gpd.pivot(values='prediction', columns='x', index='y').fillna(0)\n",
    "    grid_points_pivot = grid_points_pivot.loc[:,grid_points_pivot.columns.sort_values(ascending=True)]\n",
    "    grid_points_pivot = grid_points_pivot.loc[grid_points_pivot.index.sort_values(ascending=True),:]\n",
    "    plt.contourf(np.unique(grid_points_pivot.columns.values), np.unique(grid_points_pivot.index.values), \n",
    "                 grid_points_pivot.values,20,cmap='GnBu', vmin=0, vmax=400)\n",
    "    plt.scatter(meuse['x'], meuse['y'], c=meuse['lead'], cmap='GnBu', vmin=0, vmax=400)\n",
    "    plt.plot(meuse['x'], meuse['y'], '.', ms=1)\n",
    "    plt.title(title)\n",
    "    plt.colorbar()\n",
    "    \n",
    "    return grid_points_pivot.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T04:51:01.162990Z",
     "start_time": "2020-03-02T04:51:01.153814Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:51.886439Z",
     "start_time": "2020-03-02T06:09:50.547390Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing Ordinary Kriging...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pred = model.execute(style='points', xpoints=grid_points['x'], ypoints=grid_points['y'])[0].data\n",
    "z_krig = plot_grid(grid_points, pred, title=\"Kriged grid values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T04:49:30.403607Z",
     "start_time": "2020-03-02T04:49:30.366741Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:43:49.293075Z",
     "start_time": "2020-03-02T05:43:49.286699Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NP Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T05:45:14.200465Z",
     "start_time": "2020-03-02T05:45:14.194165Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:51.953882Z",
     "start_time": "2020-03-02T06:09:51.888901Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3024, 6)\n",
      "ffreq    0.639244\n",
      "dist     0.304400\n",
      "x       -0.042637\n",
      "elev    -7.677574\n",
      "y       -0.285939\n",
      "soil     0.209548\n",
      "dtype: float64\n",
      "ffreq    1.021410\n",
      "dist     1.100555\n",
      "x        0.988839\n",
      "elev     1.000000\n",
      "y        0.978393\n",
      "soil     1.084424\n",
      "dtype: float64\n",
      "(155, 6)\n"
     ]
    }
   ],
   "source": [
    "def eval_grid(grid_points, meuse, model_np):\n",
    "    \n",
    "    cols = list(set(allow_columns)-set(['lead']))\n",
    "    target_x = grid_points.copy()[cols]\n",
    "    target_x-=norm_mean#[['x', 'y']]\n",
    "    target_x/=norm_std#[['x', 'y']]\n",
    "\n",
    "    df = meuse[allow_columns].copy()\n",
    "    df-=norm_mean\n",
    "    df/=norm_std\n",
    "    context_x = df.drop(columns=['lead'])\n",
    "    context_y = df['lead']\n",
    "    \n",
    "    print(target_x.shape)\n",
    "    print(target_x.mean(0))\n",
    "    print(target_x.std(0))\n",
    "    print(context_x.shape)\n",
    "\n",
    "    context_x = torch.from_numpy(context_x.values).float()[None, :]\n",
    "    context_y = torch.from_numpy(context_y.values).float()[None, :, None]\n",
    "    target_x = torch.from_numpy(target_x.values).float()[None, :]\n",
    "\n",
    "    # Make predictions\n",
    "    model_np.eval()\n",
    "    with torch.no_grad():\n",
    "\n",
    "        y_pred, losses, extra = model_np.forward(context_x, context_y, target_x, target_x[:, :, :1])\n",
    "    return yr, y_pred, losses, extra \n",
    "\n",
    "# for col in allow_columns:\n",
    "#     if col not in grid_points:\n",
    "#         grid_points[col]=norm_mean[col]\n",
    "\n",
    "yr, y_pred, losses, extra  = eval_grid(grid_points, meuse, model_np)\n",
    "std = extra['dist'].scale[0, :, 0] * 1\n",
    "loc = extra['dist'].loc[0, :, 0] * 1\n",
    "std *= norm_std['lead']\n",
    "loc *= norm_std['lead']\n",
    "loc += norm_mean['lead']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:53.354430Z",
     "start_time": "2020-03-02T06:09:51.958560Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "z_np =plot_grid(grid_points, (loc)*1.0, title=\"NP grid values \")\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:54.678631Z",
     "start_time": "2020-03-02T06:09:53.357105Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grid(grid_points, (loc-0.5*std)*1.0, title=\"NP grid values (-0.5 std)\")\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:56.000851Z",
     "start_time": "2020-03-02T06:09:54.681248Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1bAajpmk8//zzFBQU8MMf/hCArKwsbr/9dp577jnuuOMO7r//fhoaGtpURXm9XhoaGtpsj5bD2tjYfDPsry9i0ec7uGe5wp+XHqHa30S130pTSfe19miprwtSXtaAYZhEIibBoEFnDbwNEz7bC4YhMSUxQS+thKoW6yU+vTEvJYwe59kfqDAs796Ebf59lDeVH5fX/pUQSs8eJ4geX3nBggW89dZbzJkzh8bGRvr06cPbb79NYWEhv/rVr2L7/ehHP2Lt2rVs2rSJjz/+mGHDhnHllVcC1rT58vLyTktf47dHy2RtbGy+eZqrarp8PjXNbQm12f04hrJqWPMZfHkIdh+GDTth+/7E+9Y2Wh8EAAcrTZ75WzMHK1s2IGmMBBIf+E0RTWnsyeME0a2or1ixgv/6r/8CwO12I4Tgrrvuig3o9Xq9KIrCvn37uPvuu5FSous6DocDRVFYuHBhbNDxF198QW5uLvn5+Rw4cICamhpCoRCbN2/m/PPPZ8SIEaxZswaADz74gJEjRx6nl21jY9MVroy0Tp+Lj6x7SjAM+4/A3hKoC7Sep/25Pi3ViX5O9M9UmH6di34ZCoaEyEkxz0eAovbscYLo1lP//ve/zwMPPMA//dM/EYlEmDVrFj6fj5kzZ6LrOm63m/nz55Odnc2QIUMoKChACMFll13G6NGjY4OH16xZg6qqPPbYY+i6zsyZM5k2bRpSSiZPnkxOTg6FhYXMmDGDwsJCdF3nN7/5zTfxHtjYfKsJhAPsrTvQ4qMH2zwXb7vEC7AqJL3TLR+9uFqN5a13Rv90k5EDIrg02F/vZneVg0RBfllA5a09Tq4+I4iJINen0mQIPi7XcesA1QBkubJPXEbMSe6pn/Lj7LZ/vs1uvWtjc5Tsry/ibwcbWb5OxV/V2Ol+bXzvzAgTvtO6rwT++qmHQ/6OMaIvw8PI3k1c1K8JVbFciZABZQ0ay3ckI0kskL0yFHp5IoRNQVmjikSQk9RMjreZbHeQwSkuenkyv1JLgeI9FZx99tk93j8RS17/gJ8+90WP9v3kocu+9vWOBrv4yMbmW0ogHODVz+t5c6cLIUzS0t1UVQRQtc5dWY/D5PrzG2OLmVEmjmjkv95PxpvaNnr26CYX929qk83iUCEnKcIZGWF2V3UcOJGRrmKYEl2R9E0Kk+lW2F1j7VcWsKYEZTmbASt9xqt5v8Go/TQoPrKxsTk9eXd3A29/7sAwreEMmibIykki0BBqM6koPkofkhuO/V1KSWkV5GYACM7PF3zZLk2xT0oYwwRNsfY/XCnpmylwqIIzfKEOop6RrqIrkhsG1ZPsMHGoEDZhdE4zK/cloWrWfVUEASxhT3EEgG/QjjnJ7ZeT+yPHxsbmuBAIB/jzp37Cpmgp948gpURRBB6vHtuv/UKmS5exqLu0Cp5/x/pTVcChdXRyw0arAB6ulPz3m2EOV0oME5ojicXxvKxmUp2WoAPoihXdX9mvkbIGF2UBF+VNTiqC4A82UxdqIhAJEAh/Q5kxQvTscYKwI3Ubm28Z0eKi2iaBENbsztqKRlKzPOgt1aKQOMtlf6XGqLwQDs2K0H90lfVnxISS2o5WysFavWUR1YrQ//kanb6ZgogJ28udbfbNSLdU/My0cOyDQ0rJwUqT/pkKyQ4Tr25S1uCKOyoINLdE6xz/aF2IYzIk43hiR+o2Nt8S4ouL/v5BPeGQZWVoDpXULE+sOtQ0ZRtB97ok+bkmedkmFfUKRZUaoYjVobF3piBsCHaVavib9A7XNKVg+efJNIYEYVMhO10hYgrWFHmoCLTGlFFBt45pPb59rropQVNMernC5CeHSNUhEIHaoLVw+41E63akbmNjcyKJ7+Oyd28TeQO8CJEEQH1DGL8/hO60pCB++DHAsP4Gg/vKludg5Bnw4ecuvjxiMLR3GBPYVaphmgppop46kjqkN5oOJ/+9WaVPSgSHKjlcpxEyWuPJeEEH2Ol3MCqnGV1pzVXvm6FQ1azSHBFcnecnxRGJRfO6AtWhIFBBbkugflwjdnuh1MbG5kRR3lTOmiOHeXd/Dlu2lDF8eBZaXCpKSrKOlBK/P4RhyJj1ApCZIhncV6K1cxsuPdvkrxs0dh3ROb9vmMnnRTCklbNuSj8rv0ihIahyZkYEr0fh00MmEoXDdW0j+fZiHmVblZO+SRF6eSOIllz1ZkPw7iEPfZKCJOtte8Moworgq0NBUp2tkfpxEXZ7SIaNjc2JJqvFus7PT4sJupSSxoYwniSd1BQHe3bXkO5r66HnZZsocd52ZbVJZrqCRNArXRIOScafG0HXIF6uC85twKVoRF2UMb3hjS+dbCu3PPfOxDyKKQWv7/eS7TbI9hg0hAUH6y1vflBqI7oa/eYgKa6CPi3ZNwB1odaB18cnWj/5G3rZom5j8y3B5Wr9dW9sCPPl9irOGpaBy6O3id6jKEprUFpZbfLm2iDXXOYkLUVFCDi/XyTWWjyartgvU5CkqR10b/xZQWpxEggnsi6i8h9/kKC8SaO8qa1ENYRVwqZluRRXwf+8GeGn12jk+qA+AlaaI3g0L4Fw4PgI+zFoAZBoTkUkEuGOO+6IzZ4oLCxk/PjxLFy4kPfffx9N05g1axbDhw/v8ty2qNvYfEuorQ2SlWX1b/Ik6Zw1LANPko5hSJJTXB32P1gh6Jsp0VXITFe45jIn2T5Bmm5wWZ5JmseMibqVrhjhZz9wkJ9jbYvPXAHBoJQQ26par+PWJFf3lwxJt+R8T63kzQOC+nDnkfCBOhfnZTUgpaRPBjFBN4HDAQdZ7hDx2TBwjCP2Y2S/JJpTMXbsWG699VZuu+222H47duxg48aNLF26lNLSUqZPn86yZcu6PLct6jY2pzkVQWuy0J7d1fh8LlTVylzxJjswTUmVP5jwuCPVguJKQZ9MiaoIcnwKZ6YaIKFPkiRiWBonoSVdUaNvZqvgRTNXrIVOlfiJdgLJj4dI0pzE/PEz06C3V7JoK0Q66SUTNhXePZjOJb1rSXYY9M6wovftVW5SXGHKm5xAEJ+zKeHxX59jU1GaaE7F9u3bKSoqYtWqVQwYMIBZs2axZcsWxowZY2Ua9e6NYRj4/f4uJ8LZom5jcxrSfnIRVFJfH2bjhlLOPieDpCQdw4BQYxNpehjDodAYai9Wgg1fKmQegT4+kwv6Gqi0ug+aCrIlB10IQZ9MQdCU6IpAFa2ZK/0zrS6LRS0LpW5NMtQnSXZINMUqftpXHmFQtoZDFZzjg61VkJPU3Onr21KRhEMxkVhCD9AUUMnxWsf4g82kOKxmZIFYu95j5IX30FP3+/1MmjQp9nNBQQEFBQWxn6NzKt555x2efvppysrKuOmmmxg2bBi/+93vWLRoEcnJyaSltXbMjM6ZsEXdxuZbxP76It44FGDPAQf57hLShkpWb4vEOi7u3l2Lpkp+MlYwMNMg0lLGv71Y5+0dznZNtgSVdVBZp3Ld2eE2C6fRkn8Q7K1SCRkK28tdnJsr+U5WEFUI+mWqRCR8VuGkIawwYWCEIemWh64KMCTsLzN4dEU1syb6yM/R6Z8SoSyYeJJSPCGzY8RcFnCR7U78zeNYIXoo6j6fj+XLl3e5z4IFC/j5z3/OlClTeOmll8jJsbyrq666innz5nHllVcmnD3RFSd3wqWNjc1XpjbYyJkpYcafG+biIRrXjNB4dKqLoX1b97nxUpW8TANNBZduRd3n9A5zwcBQp+cNx+ns4UrJH/5ulfxLCW98mcLbe5IpqdPZXO5mxd4kPqtw8o8KJyv2JrG53M2V/QwGp1ltBjSlNeDNy1aZNdHHoGyNkCGpCR1949hopH7c6GHdUXe6n2hOxd13383WrVsBWL9+PUOHDmXEiBF8+OGHmKZJSUkJpml2GaWDHanb2Jx2CKHgUFvX8zRVoKkwdYxk1ssAknP7hFvsE0lJFfTOAIcmGJUXZmORM+F5Nx9QuTTfwKFZHvrtP9Dp5RPs9esdCo6qmjWqmlvlRRGS4ZkSvaWxV1F5hIHZmhX1CsjP0TGlJGTCvjqz/aV7RHtBrws14dGObfaLAFT169s4ieZU5ObmMm/ePHRdJzMzk3nz5pGUlMSoUaMoKCjANE3mzp3b7bltUbexOU0obyrn8+rD1IY0fC1JJvE2iapA73QIqR4U0QBASRX839uS//d9QZ9McCZoyhVlzW6N3FTJoEwTUwpyfAJ/o8q7e1qFs7McdE202gJF5REeW1HLAzekMihHR0owpORgg8m6MgMjwS10FoFHW/Ee9wg9huix/dIVHo+H3/72tx22v/TSSx22TZ8+nenTp/f43Lb9YmNzGrC16nMWbq/nnv+D6trWtrnRVMPDlRJFAXeSi4gp8DdawtQ7A/7f9wW9M6yqzEP+xKLsy/CQmu7l73vTeHFrKp+WeKhv8pDtdnHtmQY+V9eWSciE+pauvQOzNR64IZWB2ZpVQBSQ/HFXmHeLDRojXZ6mAzne5g6CnuUEn9MVWyg9phwj++V4You6jc0pTnlTObvqmilrcFHtb+Lvm0M0t/jS0VTD3j6oaVSoCli/8m9tdxGOWNWbfTIFhhSEI7D6i47WS/tujZluwfgzBEN8gl5JMDLX5F8vDJHtNamq7myBU/DWAZWwARLBoBxrFmlEwqaKru2Wnkbh2e5gp4uk3mNowwghevQ4UdiibmNzmvHxLoMPPo8QjkiCYchKFdQ1CV7Y1Noa93C1xp/XedhRrFFSo/BFuZtlOzKRrtbMikSDoUFyw5Cw5dm3qIfa0u98/BmWoHcm7PvqFJbsUtldI6hogh1+hb8WmVR3kqwSjcKjFktXRMU8UZR+TAWdk1/Uu/XUE5WzKorCnDlzkFKSl5fH/Pnz0TSNJUuWsHz5coQQ3HbbbYwfP57m5mbuv/9+qqqq8Hq9LFiwAJ/Px3vvvceiRYvQNI3JkyczZcqUTve1sbFJTGngCBsrD1PdoJMVKSErFSpq4f/eC/PRHo2BWVDfDFUhd4d5oFUBlb9vjx8srbb82bGPehSXBiktwbyUkv0VBnlZKooQDEjrfoGztFFh+d7WWDInqdVv6SoiLwu4uo3Yj6vtEsdJ3vql+0g9vpz1nnvu4amnnuLJJ5/k3nvvjZn6q1evxu/38+KLL/LSSy/xpz/9iQULFiCl5MUXX+Sss87ihRde4IYbbmDx4sWEw2Eee+wx/vjHP/Lcc8/x8ssvU1lZmXBfGxubjgTCAVYXb2FDRTGKlAxID3HxWTD3Fp3BfQXpPjf+BthSBJUhT6cDnr8qYcNqwQuwv8Lg8b82sL/CiswbO8+GTEh8cVF3C6EA9UE3OW6d5HYDUrPdwQ6C7tG8eDRvbH7pMWsVIEBRRY8eJ4puRX3cuHHMmzcPaC1nfeaZZ7jgggsIhUJUVFSQlJSEz+djxYoV6LpOZWUlTqcTIQRbtmzhsssuA+Dyyy9n/fr17N27l/79+5OamorD4WDkyJFs2rQp4b42NjYd2Vt3gAMNOrowcbR839ZUcOqC276vE22S1VXUfTQYUvBJqULIgLwslf+4Pom8LJVQBD442HmjqxSHxOeUsfvqqlo0EZfnOpl6lpcLs1K4snc6V+SmkusJxQQ9npiYH6eB1Ke8/QIdy1lVVaW4uJhbb72VpKQkhgwZEtvv+eef55lnnmHq1KkANDQ0xCqgoiWu8dui2xsaGhLua2NjkxifKxTrdR6fb+51CVI9oLqPraBHeW23hkuLcE6WSa90jYgJ64sVNhRbNxOf1pjulEzON0h3WRF+UwTWHolQFteapStbJcfbTKYzmSHpOppiVa8C+Bw6Z6WkU9nst35OYLscF0HnNLBfoixYsIC33nqLOXPm0NjYSJ8+fXj77bcpLCzkV7/6VWy/H/3oR6xdu5ZNmzbx8ccfk5SUFCtzDQQCpKSktNkW3Z6cnJxwXxsbm7bsry9ibZmkPk4Yo/nmJVXWAqY35fgIOkDEFLy4Q2fBOgfPfqrzyw8d/H2PTka61kbQFSGZOtgg0906PDrVCVf3U3GridMRE3FehgO9pZJKSsmesiCKgAyXG4H4Rnz0VgSK6NnjRNGtqCcqZ73rrrvYv38/YEXUiqKwb98+7r77bqSU6LqOw+FAURRGjBjBmjVrAPjgg8giKhAAACAASURBVA8YOXIk+fn5HDhwgJqaGkKhEJs3b+b8889PuK+NjY1FeVM5S4s+4efvqPz3yiBrd0BzGIIGpKYIbhqrkJYiKKlWaO6ifW0i4m2avskwNg8u6QvejmNHYzSEBIfrFbzJWsKio/wUq22vIiwx3lcWtsblAWem9vz+4trAs7c8xC+WlbO33DLwM7rw0Y8L4jSwXxKVs/p8PmbOnImu67jdbubPn092djZDhgyhoKAAIQSXXXYZo0eP5txzz2XGjBkUFhai6zq/+c1v0HWdmTNnMm3aNKSUTJ48mZycHAoLCzvsa2NjA++XfMKLu7JZ9U4z9WXbAHjTn8aoMxykp1jVLr1aCojcLnDqkmAPhT1e0G8eCuf3soTYkHD9YHj2U/jS3/N7jfrluckCRVi91IvKIzy6ooZZN6QxKEcny21Q1sO+W0caQ/RPcqIIQX62g7mTs8nPdmBKkySHtZB63MW8BQFtRv6djAgp5dF3zzkJ2P75NvqfmXuib8PG5riwv76I1w818tFOjbxccDoEkbDJlk/8lPphxs0OdK2tyIQN2LxP55MDjk7O2paoqA/Lgh+dC85252sKS+a8T8Ly/UQRelTU053wwwFqrL1utN+LIeEzf5DSpp6Vj7pVhXF90tEUUIWC2SJZYbMZU5oxQYeuffTiPRWcffbZPbpmZ7z87sf8fE11j/Z9+5a8r329o8EuPrKxOUnZWvU5f/zC5I0NBmf2BY9LQVUETqfKBRdkcOl3nBiy1Ws+UiUt+1OF3B7kjEPbKH1U71ZBl1Kyt8UuAchPb3tcRrrapaADfFHl4mC9AihoisYZvZxIKQhETI70UNABmgyTT6qOcLihnkA4SHOkmZDRhCmPrvHX10KAUESPHicKW9RtbE5yhuY70PXWgdHVNRE0VdC7tzvWibHMD395z6DMDxETqgPdi0r7dMf4I/aVR3j0VT/7yi3xjVrEXYl5+zTFXh7IS1FQoj4zAk1V2V0Xpif2QLTsP9sdJGyaNEbqaQjXI4SJbDlDfBfG4229RLF7v9jY2HwtPK7WX9OaWoN1GxqoqTWIoFFeCxEDcnwwZaxKjg+kCdsOd7HC2QmbSyEYscRyULYW63EuBOxp8dQFkJcM52ZAWkt+eGc559/vDw61NRNEtGSFDE//6pkq7XPRodVHz3Znf2OCDqfBQqmNjc2JpS5g4ku1ouO0VJVLLkwiLVWlOSR5YR1MulRlYJZBVroVob+300l9c9fxWqKipO3lsK0chudYgyz6ZWqETfjzZ5af7nPBv34H3BogrdTJ3bUGH5QmvkafJEk0/rdG1oUZlK3j1hQuyPKwqaKxR68/KujtK0a/iYXR9pwKeeq2qNvYnORs2x3kku+40HUFIQTpaRoRQ7LtyyDeFC9vbQNVkagKhCJf3XaJ4tQkqmJF6qYUBCPwl8/hiyrrnD89XyVZBzXOLz4zVeVIo8mXtR2beAUNKz8dYF95mPmvVjF7Ygb5OQ5yXDp5SQ72N3TsLRDfaTGRoJ9QhEBRTm6D4+S+OxsbGyr8Bn97vZjyqjDhsEltg8Gmbc0cLI3gr2rEX9WIrkKmF9x61251V20DbjtPck6W1XVRCInHIbnlXEmWR+JzQbbXEnRrETVkLcoqgmGdDMb4rDJC2IzaOToP3uBDSmsxV1MEeckds3N6IujHeprRV0URPXucKOxI3cbmJKKkLsSKnTUcqg2RlSyILilWNjv56zvVnDfUy5n9NIYOUnA7VfYdNrjpQoXLB4cxpTXd6JODghVb1Q4j5roS9ByvpG+KNTs0Pv1QFYIx/SXrDwla9Jl95WHmvVrJnImZ5Oc40DppXrW92sDnEgxJU2M+c3y0rvSgyZjP2bbt7omyXaIIOKGZLT3BFnUbm5OErUcaefCdYsKGbMkJVzHNSKzYZdzFHgYPbM1Lz0xT+MFwhcEZEkdcbvn5/SQNIZO3d3beYKs96W4wTEDtOG6ubzJc0ldFE5YMD8rWmTMxk0HZOhFTsjeB9RLlg9II/ZNUvLp13OyJGQzK1jFMyeFAW+slUZQOnHAfvQ0nOLOlJ9j2i43NSYCUkifWltIckW2KfIQQeLwOfKkqQwY62xQa6ZpgSJbVmdHKBofDFSa6Krl0UMccbn9V5wuTJfVWlA5tx81FTMhLU/nuABWnpiCwqkTzcxxEJNSHJVv9iXPOo31d3j0cImxarys/x0HYhPqwwd76xCWlJ52P3o5jkf1iGAYPPPAAN998M4WFhXz55ZccOHCAwsJCbrnlFh566CFM0/o3XLhwITfeeCM333wzW7du7fb+7EjdxuYkYHtVEeWBCGB51pGQgeawbAvdoTI43x1LJJdS4q8x8aUpaAo4FAUFwf5yg0VvBPm3a93kZIBAduij7q9qTGjD1AUFW0olI3KtNMRBOTqGaQ2Mdip6TKSEEJgmVDQbbK+OsKc28aDoeI40SV7cHWRwmopXFzQaQUobE+eqn6w+ejzHIlKPn1OxYcMGnnrqKaSU3HPPPVx44YXMnTuXVatW0bt3bzZu3MjSpUspLS1l+vTpLFu2rMtz26JuY3MCKW8qZ82Rw7z0SQaGEUYoEAkZ1JYFSM3xojs1TFMSDIHZYo/4a0ze+6iRsZd6IMcSdCEEedkq91znoX+mIBCm08EYnQn7sp2CIw2SMf2tJlol9XBWujUXQUrJ3vIQ+dkOVEUQMQW7ajq3XaJRenQcXZMB/6gyWraF2+zbfq5oIkE/4bZLC4Jjk/0ybtw4vvvd7wKtcyrWrVvH6NGjAWuexEcffcTAgQMZM2YMQgh69+6NYRj4/f4uJ8LZom5jcwI50ljJtspU9u6uwu3R8XgdaA6V1BwvmkPFNCU1/iYaVLjoXKugyJemMPZSD740BadCh6/6Qgg8OlgDKXoeVvrSNT48ZPDhIevnAamCwRdYx+8tD/GL5RXMnZTFGTlOwseoQr+9jx6/MHoyiXmMr+Cp+/1+Jk2aFPu5oKCAgoKC2M/t51R89NFHsX/L+NkTaWlpsWOi221Rt7E5BThSUk+f/qk4nRqKpiAl1Nc1I4S1iPn2x0HGXuBEUyE5SSEcaR0tB3CgwuQ//9ZoRetZKgJ6VI4PrY25MtLV2ODog7WSpjA4VGl1R5yURX62g7Ap2VEd7up0X5l4QT8ZffR4epr94vP5WL58eZf7LFiwgJ///OdMmTKFYLD1A6672RNdYS+U2th8w5QEgvzvF0d4eNM+XtztYv2nAar9TZim5ND+Gg7ur+ZIST379/oJBVstjqpaydJ3m3lnQ5BVG0O89HYznx00MVpyDQdkKdxznYd+GQq7yzu3X3qKBJbsNmiMWIVE/TKsxdGtVWEONHRuvYA1W7Sy0YVH8TDUp5Ll6vxeTiVBh2PT+yXRnIphw4axYcMGwJonMWrUKEaMGMGHH36IaZqUlJRgmmaXUTrYkbqNzTdKSSDI4u0lRGSrNfKdc7yEgxGKDlmjjEJBo42Yt6eypnXW53PrwpyZ48SpS1y6INen0hSBlz89+nuMb9hV3gS//tQgP0Xg1iBgNNMQ6T7+T3UIfpino7YU4khUSgImn/kbYt8e2s8XPVl99HgEHJOpRonmVOTn5zNnzhyefPJJBg0axNVXX42qqowaNYqCggJM02Tu3Lnd36PdT93G5puhvKmc/9lZQ21Q7xDJhcMm/uogDofCoeJmvtzbSLhFPNN9XUevuTluLugPfdOgpBY2HYSmLgZkxC+SJuq42J6vOiQa4MZBOmnOtmPdIqbk85pmApHW2cPto/TjKebHop/6sjUb+eXWnr0fS8ZlnZB+6nakbmPzDbC16nNe2eekoVnF0dJAUUpJfV2E5BQNTRPk5rgQAtJSNPLzPLzxbiWR7vIFgWBE8OG+7u+hq4rSeI5GxKE14yUQcpHisAQ9vpGXpggGJDn4vMba/1SzXaKc7BWltqduY3McCYQDvF/yCYu2pvLnpUeor22toqyvi7Dlk1rq6yJtIndNU/C4FfLzjp3QdSXoAshwQfJX79YbI36AdLYnFLNY9pWH+eWrfvaVWwurekvDsPaZLqcKgp4VHp3UrXcNw2D27NkUFRUhhOCRRx5BURTmzJmDlJK8vDzmz5+Ppmn86U9/4vXXXwfgiiuuiA2ivvzyy8nLywPgvPPO47777uO9995j0aJFaJrG5MmTmTJlCs3Nzdx///1UVVXh9XpZsGBBt4sCNjYnG7UNQV5bu5fqumZGnOND+lqj7c8+reCy7/ZB1xWSUzRGjkglKTlqgUhqaw1SUlQ0TaFProtde7tuT9uT6LurffJTBZPyVVyqtbhXGpCsKhYEeuCbx1MWcLVG6hGTsGk1+xqUrfPgRJ/VGkBKKpubO43QT1YfvQ2nQJuAbkU9UeWTEIJ7772XCy64gJkzZ7J69WqGDBnCypUrWbp0KYqiUFhYyLhx43C73QwdOpTf//73sXOGw2Eee+wxXnnlFdxuN4WFhYwdO5bXXnuNs846i+nTp/P666+zePFiZs+effxevY3NMWbjjlJunvU3pJSEwgaapjBkaCpJozIBKKtX+cf2Or4zNAVFAW+ShkCiCKirM9i8uZZRo1JJTlZpau58sbQ7Me/qeUVAkgPcmuCWs1QccQ25+nhhwgAvL+1tSHhsWUPb6DreqokX9s0VjVyc40XBag1gmCZNhkHYaP2QOql6unwFTmQU3hO6FfVElU+PPvooqqoSCoWoqKggKSmJXr168T//8z+oqhV1RCIRnE4nO3bsoKysjKlTp+JyuXjggQcIhUL079+f1NRUAEaOHMmmTZvYsmULP/3pTwGromrx4sXH6WXb2Bx7DMPk1kfeJNDUmsMdMQw++0cNWuU2VJ816HNPUSN79zfidikEQ5Jrx2WS5FVJSVEZNSqVlBQVw5Ds2hNos0g6fCBccCa4HAoHKkN8sl+nud2CaHdi/90BcHW+1c1REQKEgZTW72y0YjTZAU7cHGw4+hyK6pDBu8X19PXqZDhN6sJhNGFZTyd9gVE3nBaeerTyad68eUyYMAFVVSkuLua6666jurqaIUOGoOs6Pp/VL3nBggWcc845DBw4kKysLG6//Xaee+457rjjDu6//34aGhraJNB7vV4aGhrabI9WTtnYnOwEwgEC4QAbdh6kKdgq6JrHEmRpmkT8VW2OkRIam0wMQ7JqrZ/augiGIXF7FMIRycdbavHXtDbK+v75MO48yEoVJLslZ/cxmDy6GYfWc+G9qA/84AyBWxc4VNHSwMvEkAZ7y0M8sqycveUhpISUjq3OO9B+QTXeVwcImZJ99SEOBAIdBP1UWhiNRwhQFNGjx4mix9kv8ZVPr7/+On369OHtt99m6dKl/OpXv2LBggUEg0FmzZqF1+vloYceAmDYsGGx6H3UqFGUl5d3WiUVvz1aUWVjczKztepz1pZZwnr4YF1sKITmcZN65iBqd+8j0tjUZWlnoNHg9XcrSUlWcegK/powptmaypjstqL0+L7lqgIODYbkRth6qHWFs7O+LgDfzxc4tY7j5UxhMihL56HJ2eRnWwVGhwNWLny/JMhyQ3UQiuogmlvfnaAn4lQX9CjHIk/9eNKtqK9YsYKysjLuuOOOWOXTXXfdxUMPPUReXh5erxdFUZBS8rOf/YwLL7yQ22+/PXb8woULSUtL45//+Z/54osvyM3NJT8/nwMHDlBTU4PH42Hz5s1MmzaNkpIS1qxZw/Dhw/nggw8YOXLkcX3xNjZHy/76Il4/1Miyj1R2fLzX2igltATXkcamVkFXFLSM7hf86+oNwPLR422XnDRruLSmWmJc7pdk+wS6KujtM9l6qGf3nBJX6BPNSnlwoo/8HAdCCKuniyH5sjZMIAz/PNTA5xQgrJcWiEj+diBCe6u/M0GP9nU5aXujHyUnuaZ3L+qJKp98Ph8zZ85E13Xcbjfz58/n3XffZePGjYRCIdauXQvAvffey+23387999/PmjVrUFWVxx57DF3XmTlzJtOmTUNKyeTJk8nJyaGwsJAZM2ZQWFiIruv85je/Oe5vgI3NVyHaVfHlTzPZ9HFN2yeFwDEwj9C+IpAyJuhKkhclPb3H12hfbFRvnca6vl+ybHWEyd/TyEoX1DX2XGFKG6Bfy5ff+KyU5gjUm2BKg61VIdYc1riqn4nPpcRmjAKoAsb0Unm3uOsWAdBR0E91H70VcdJ76nZFqY1NF+ytq2NDRQUN4Qhu1WDzkSRWvHaI5qoaXBlpCY+RkQhGdQ0YEZSkJITX22XGRHcVowC3joPMVCtzJRqpR0zB8k1OahvbLo11Zr+c4YPbR4g22S5hA5bvhS9r2t7ffeebuFtCPsuqiTAoW8ME/veLtr3QE0Xq8S0A2tsuJ0rUj0VF6YoPN/Pk3p61qPyv0cl2RamNzcnENr+ftUfKWvq0QFME+ifXk5WucqiKToVdaBpaVmaX51YETLokmbFDNBQB7++K8O7OiDVSLgEvr4UfXgj9sgS+NEFjEN7/wkFto4IQcEme4LJBAlXA1ooIHxerGO1mlO7xw+82S8afCblJUB0UrCmGorqOHzjRYFRiee+PvlrNrIk+BmZr3XZ/jG+neyoWGHXHKW+/2Nicblg55CYOXek0gjal5MOyVkGHaPc9ySXnuXh5DwkF3SoqctLZ999qfxPpPjczrnEytI+KS7eu3yddYfRAlXl/6zjiLRp5v70DnLpEVyUNzYLoouXtFwmG5bYuguamGgzLNvnDJ3qHTo1FNbBok+y258vuGjg7XaIo1ni7WRPTyctSKW+UdBWnxtsuJ0uEfqxR1JO7EN8WdZtvFa+u3s0j/72OsqoAKUlO7ikcyb9M/g4HGvazqbIasAQpZELIsHqxSCnZX2Kwu0RQ3SBQhEbumbnUVDfFxDspyUFu35SW3G8IBiPU1TR3EPd0n5uzcpQ2gg7g0gVnZKsM66OwvdiSzUQ2SjAsCMblpvdPo42gAzhU6J0sOSvDZFdVYvGO9kzvTNx3+OGclrVdIazxdlKC0YWkd7UwmuXKPuXFHDg9KkptbE4X/r6uiH9/cjVNQStFpaY+yII/b+TTysP4s4ez6WOr9W26z42qwC1XS3RNUFJusHpdM65UJ6quYkpwe3Q0TaGiPEBWthdfhqfNAprTqZGW7qba39ThPs7OVWNDnqWU7K8wyMtScenWc9uLzR433/pOngNFGB3O5dQEg9I7F/XuOC+z9dNISklReYSB2RrZboFHg8aWLJ/2fnp7H70u1HTKpzDGY/V+OdF30TUn9/cIG5tuME3Jc6/vYOydL3PxrUt47H83UBdotTC2Vn3O+yWf8H7JJzz87AcxQQcrl7wpGGHlymI2rj9I864vaa6qoXR3KYd3lbJtd5BwWFLVIPCkOVumEUmMsIEQ4HCqZGR68HgdbYZCh1uuoTtUVLWjAtQ0ScItCST7Kwx+vbKR/RUGwQjUNHaft+DL8MQe9SERG/y8v8Lg8b82sL/CIBSR1AePXn3SnK0RaVF5hEdfraaoPIIpwaN1fd7T0UeP55Rv6GVjczIz/YlVvP7hvphY/+6Vf/C3tXv54xOjebcsyLKPVMCKeg+WtkaVbYqDmpqtqc6Z2UCrV75mk1U0VB92IDQr4jXCBk21QdyplsgLAarW+kscCRlUlzaQnpuEqqsoqoJhtE0B3LAvwk8usUo287JUfv5DD3lZKs1h2Ol34MvoupwzWmDkr2rkoxq4/kwFUxXkZan8x/VJ5GWphAzBp0eOLkoHOFAPPhdocZ76wGwNQ0JtyPoUadOZsSXbJV7Qj3d/9BPFSZ7RaIu6zcmDlJJXVn3Jf7+6lfrGENeOGcRdN51PeoqLrVWfd9j/cGkjKz/YQyhuCnIwbHCooo5/e76CncUmzVVxueRCJVrc06Y4SFVBUTosfJoSVm9qIiXVJDnFiRACRVNigg7WN4VQ0EDXVYQQaA6V9NwkNEdLD6QEE5qbw/CL15r5jx+48DohN12lpgn+sB4aOxn9GRXxKNG/hw34zfsmd16qkOIW9PZp1Ifgxe06gS4GZXTH+lKFYRkmirAqKAfl6IQM2FpldBg63X6CEViCftr46HEIYS+U2tj0mFmL1vLS21/Q2GxF3f+17DOWr97Fvz84lL/uzGkjagA1++uJmK3CpXncRBqbCAZNPltXhGPAAFwZabHUw3AkhFFaGts/0mj53WpOdqdfl+NzyKWUCCFQddWyYQzT6uHSGMbj0UGxvprrTg3TlDQ1hkhUBuLL8FAr4cE3oHdLMVBpXWua4BmZOhf2cxI2YW1RE6V1Ruy1J/LaG4HfbJBke61uj2UNAonosAgaXRztCfVhwbM7FC7rYzIwBQJhWH9E4A9Zih6N0jvLR/dop5eYx3Oye+q2qNt8o+yvL0q4vayqief//nmbqDsUMTlSHWTm48VElPKOBzU3WbYJHXutCEerheHKSLNEuKKi4zmEiJ0DOi8EamoM43CqqC1RWjhsEg61lPSnuzEM2bJAJTFNSWMghMfjwJfR+iuWnaTw3XwnqS7BZyVhNh8OU1LX9jrTRqdw5ZluHKrANGHisCT+tKmOt77suq86CMoDltp0ltHSk9F18dSGBH8raj2mfb+XRIL+beCUb71rY3O0NIQMXt5ezdoDAYQwSE8NccDvTvhLUbmvHgMFaBXpSGMTRthAqW+A1LZl9q6MNJorpVU/bxgJeq1ktNlfBoNtxDt6fqTErK6BXr26rOyUEoLNVm8WRRHouoLbreF0abEI3owbPefxtPXFR/XVuXtMEooAXRVcnOfkYHWE+e/WE2m5rcFZOlee4cbVYu0oqvULeusFKWw42Izi7Zlwdpeu2FM6G2vXPuPl2+CjxxDHpk1AOBxm1qxZFBcXEwqFuPPOO8nNzeWOO+6IDRQqLCxk/PjxLFy4kPfffx9N05g1axbDhw/v8ty2qNscc8qbyqkNNvCrNVDZKGOidaRepe5IGXs+3BFblIwRDBItp9Q8bjKGDmbAGakkpbqoKKnmwJEIoVBb+8CVmY6Z7CW8fz+yuZlIcxB0Hb1//zaROkBLwnns/G06KCpKp4Le3u6or2vG5dJaThkVcomqdV7IpClw5yXeNrnkbl0wIF3j8kFO3ttjZetcPMAVK+GXUrKnLMgZOU5M4LIhyXx0qBPDvRO+rriXNbg67caYyEfv5ck8fcU8jmMRqK9cuZK0tDSeeOIJampquOGGG7jrrru49dZbue2222L77dixg40bN7J06VJKS0uZPn06y5Yt6/LctqjbHDPim11FIgrhcFsRVhSF5Jx03Hn9aGpoWznpzM0mVFuNDAY56+IzyRiUQ3KaCyEEfZNc5Aw02LKxhKx0lUhEUloRRkpQnA6cg89ChkJIUyKcjoTiqjidCKfTEv+4qF4oCikDEvcOai/oUkrc7sSDPE1TJkxfBMiPs2CklLFhFC5dcOlAR0zUDbPVV99TFmT20mLm39SHfpnO+C8ZbejMN/+6UXoiOvPRo9F5tju7i6NPDwTHxn655ppruPrqqwHr/4Sqqmzfvp2ioiJWrVrFgAEDmDVrFlu2bGHMmDEIIejduzeGYeD3+7sc82mLus0x4f2ST3hxVw5v/r2J5l2r6D/yDLLzewPR3G0D3WkJjTfFjXR2jIwd+fmIilL6f2cAqtYqSooicOgqPxiXQ4ojDAIMQ/LG+zVUVVuLqsLhoLtftczzhlDz2eeMHZ3J9y/tRTBs8OaWer6UWR32TbQgGb/mKaXEiJixCL2rvnhhU8aEYG95iF8sK2fu5GzOyHESipsF+plfMt60eqWfkeNk/k19rH1M+Ky8Y5SeSLiPVsw7s1ra017Qvy3ReTxqD+0Xv9/PpEmTYj8XFBRQUFAAWEOAABoaGvjXf/1X7rnnHkKhEDfddBPDhg3jd7/7HYsWLSI5OZm0tNasrOjwIFvUbY4b++uL+OMXJitXQenuf8S2BxuaMSMGiqYSDhpUFteR2ScFVVfA4YBgqM15LPvDTcrgrFi/EiklTYEwbq+OUAQhQ0XXWwp7NMH476bx/IrKTvustJ7XQgBPzb+Us3NV3A7Lt75oRDZv7wjx53Ude660J/5X2YiYNFY340l3oelqlx8oRVUGjSETp6aQn+1gbsswiuaw5N3d1nV9GR4O15u8truZH57lQkrol2llwDz7aWOHVMejFe/hOa0pnmWBni9u5nibOzTq+jYKOi39f3qCz+dj+fLlnT5fWlrKXXfdxS233MKECROoq6uLDQa66qqrmDdvHldeeWXCgUJdYYu6zVERCAfYVLGLF77I5s1Xd+IwQrgJ0VRVC5nZVNUZ9G7ZV3eqMUE3DImuq5162OGQEfMsmwJh9uyo5IyhmXiSdDRFIqWkptYgLVVFUaBPjoPDR0I9al97Xn+Ns3M13I62PVeuGebgze0hyuq6+WWNU25VU/Cku1BbFjWF0nnusgQeX93A7HHJqIqgr08nbMD6YoOiJh1fRqul8+beEBtLwgzP1jFMyadHIjSEW+/r60TiY/vUkpfcuuBcF2piV10z2ypTuz42bmE0WmB0OpX+fxUs++Xrn6eyspLbbruNuXPncvHFFwMwbdo05syZw/Dhw1m/fj1Dhw5lxIgRPPHEE0ybNo0jR45gmmaXUTrYom7zNQhGYOfOajL6+Kx2rKYkbWAfyg/7iYQN9u+rpm//NHSHiu5UCYUMqqsSp+aleMChg78uQnOzgdstcHt1zhiaaUXqQKo7TE2twboNDVxyYRJer4JDFz0SdICRA9SYoEsp2V9ukJdt9XIZ3lfjnc+tcLizvitCCFRNwYiYVqGRbgms6GImZfRcB6sauXN5Def11snxudjtN6lqSvwh4m+SvH8g1GH714nOh6YFOSe9b8z33l9fhD/Yc0FPtDD6bUXpYaTeFb///e+pq6tj8eLFLF68GICZM2fy6KOPous6mZmZzJs3j6SkJEaNGkVBQQGmaTJ37txuz22Lx7lprQAAIABJREFUus1R8+5WaGqMoCjRaBWkVPD1SqO+0aCpKcLuXZVkZHlAWouJ7Ulyw/iLVNKTBFK2VHH+o5r6SCrJyQ5cbh3TMMlOCeFQJXqqyiUXJpGWqmKa0Pj/2Xvz+LbqM9//fTbtlm1Jlh07cRxnJZCULGwFAoS9nZZCKPm5/DodCu38pgwzDFMmIROglFxChgK/20JaLr20vXRYGramTdkTkrBlDwkJWb1kl2zLm3bpnHP/OJYs27ItJw5xgt596UV9dHS05OhzHj3f5/k8CYls7t7ZhDkhCCRVHVkSqPerLPlzB/NuKKDMJSGYTN0i5r4wxFxEEiGRNKI2tyd7+uEsj8h3z1Ioswt0xC28tT/BqgaVuhwmB/UkU9CvqvIB8F596aCP44/42dlyiB2tZnyh/gU9xVe1Hr0vhqJKfeHChSxcuLDX9pdeeqnXtrvuuou77ror52PnRT3PoNnWvJNXas28v6Prp6ieUmRRwGQ1IUQiFBUbEXRm/XZPbrhYptBOt0j3qukiyz5oob5WQJQEkgmNwhlWrLKELAsUFUqoKuysjRGLdx17IGfDT+p1rptk/P8qr8S8Gwqo8kpEErDtyMAXhkBzmNEjRCpKROjsWWpsl2gJ9n6u8S6R/2+6KV2i6DTDDRMULLLAm/uTvR+Qhb4ic3/EzKWlAhOntrHWp7PNV9TvQqcvZMEXstAYOwQwYHSeIpVHzwt6FwI6kji8h8XlRT1PzvgjfpbVNXYOW97FiLGlXVUqmk4ykkS2yiAKaUHvjwlVFuyWJKJoVI80teh4igUkEaZUi6z+TEPtvCCs3RRhZKnMqDKZRBJqDyXQJXNO0XWKphD873Uat50voulQ5pIIxuCpD1XiAwTPgeYwlWUiI71Sel6oJEJZkUZ7WxxV6v5+vzVe7lZznipjvKZa5p3aJP1c53AXS1xV5cMfMbPN13sQxzZfEb5QlCmeNi4ttXBpaVu3+/vKk+cq5tB/g9FXcoE0xZngp66qKgsXLqSurg5BEHjooYcQRZH7778fXdepqqpi0aJFyLLM73//e1asWAHAZZddxj//8z8TjUa59957aW5uxm63s2TJElwuFytXruTpp59GlmXmzJnDLbfc0ue+eU4tqUXRpz71sHZFvbGxyU+4xIm9yGakX0QB2SqjC7n/PE3EYugYF4WmFp0Va2J8c5aZEpeIw9r9KLoOB48lCSU6m4qk40vwbj6ks/2oyjiPgKrp7GsyfmD0JFvUX+EVEUVDpNvaVQqdEqIoUDlCYtMX4W6PKbN3LZzu98f52as+fjanlEqPCYcJ2vootrliUgeXlgqU2UYSToY4u8jHysOF+ILdo2Rf0MIUT1tW46wym58ScyrFMvjouq88+lepHr0/hiKnfjIZUNRXrVoFGLmedevW8eSTTyIIAvfccw/nnXce8+fPZ9WqVUyaNInly5ezbNkyRFGkpqaGq666ik8++YQJEyZw1113sWLFCpYuXcq8efNYvHgxr7zyClarlZqaGmbPns1f/vKXXvtmyzvl+fJIpVqMksXtXXd4vLQ3d2B12gAdURQoLhC47WILM0YraDp8sj/B7z6KEox1T5GkzKl8AZ2U4Z2nWOCbs8x4igUSKjQFZVzuk7Myl1DhC1/2L2ZfKRyPx4ooGmmTtnaVdevbueB8J0WFMmaT0Otxx0IaBZ11+WO9Jn42p5SxXhNxFS4dpWBTBD5vVPmiScVVLHWrTqkqGAP07ZMzEF6rF6/VS1VBHfUdLaw8PPgIPZ92yU7XEMHhy4CiftVVV3H55ZcDcOTIEZxOJ4888giSJBGPx2lsbMThcFBWVsZvf/tbJKnTcjSZxGw2s2nTJu644w4AZs2axdKlS9m/fz+VlZUUFhon24wZM9iwYUPWffOcOkKJEIFYlEC4gIumKHhnjycW19i+vZVNm5oxFxfS2hql2GVFFnUeuamAQpuA3Jkfv3i8QnWJxD0vBynO5i4Yg221GlPGiCiyQLlHJJrQicShzndy7U2zibdNgUonBONwqKP7fZpudHyKEhQ6JS4430mh0zjXo/HeX/O/7E3yzzNFTJLhtT6u1ExCBUUSuW6shCTCxaMUGjo0nt8Vp9QexWW2UGLJPQre3lSIPxLm0tIGxjpH90qH2GQ7LnOEUns0a8SezQYA+hd0+xnsvpgrp32kDiDLMvPmzePdd9/ll7/8JZIkcfjwYW677TYcDgeTJk1CURRcLhe6rvNf//VfTJ48mTFjxhAMBtPF8qluqMxtqe3BYDDrvnlOLeGkiNseQ3QYOWOrVWTaNBdOt42PN3b9+1w0VsFm6hJ0MIyrSgpELp5sZ6cv+4Lmx5+HGee2csUkBUUU0HR4f28SVR98hUhf5DIa7uoxcHU1JDVjCEJLFJ7ZBK3pNImAr0Wk3KUhigJFhcZXR9PA19r7ArQ3oPGbzXFunqQwwiHQEQe7InX7fCwyVDlFpngkPvMVsc0HV1Xt5jyPEa1XFYyhxOLFZW5gd3tbr2qXVErmlVqY4jEel7ooNEb9bGhq6TePns3XpT8r3a9sHr0Hwz2nnnM4tGTJEt5++23uv/9+wuEwFRUVvPPOO9TU1PDoo48CEIvF+OlPf0ooFOLBBx8EwOFwpDuiQqEQTqez27bU9oKCgqz75jm1HAj2br9XFJFxVVYs5q4a8dHu7jXgdb4kuq4jS1Be2Pe34IopDr45yYRFFpFEAUUSuGKczM1TT3wNPzXybSAme+DKMcZFyKoYQ5xLbHDHtO77tYUlDjZJRGKQVCEYFaj3S0Ti2b9GflVg6Y4kP3kryu8+S6SNzXRdZ9+xGLquY5YEzvV0VblsbypkxcEwHxzZjD/ix67YmeqezGVlI6mZ6OvWEZpJ6nEbGnezoXE3Kw6GB70w2rNjFIxF0RKLkc7JC7qRepEEPafbqWJAUX/jjTd45plnALBaDdvUO++8k/r6esCIqEXRmN34k5/8hIkTJ/Lzn/88nYaZPn06q1evBmDNmjXMmDGDsWPH0tDQQGtrK/F4nI0bNzJt2rSs++Y5tQQTUreyxdZWQ6w1VafA0SW8B5pVIp3lhUYNeDv1fpWkBkf76dS84Rw57V6o6zq1vgQmCWaP7xrOnCuZsztzHdwMcGkl3V7Dfl8CUYASO5T0OEwwKlLrk9lzWKbBL/cSdHexlL6BYbDlLpaw28X057jfF+eBV4+w32c0GCV7mHX5QhZWHi5k9bFDbGveSSgRwmv1cnn5dL45ykbNRB+ljmivSDv1uJWHCwdtAZAis2O0zObJi3lPOm0CcrmdKgYMh6655hruu+8+br31VpLJJAsWLMDlcjF//nwURcFqtbJo0SLee+891q9fTzweZ+3atQDcc8891NTUMG/ePGpqalAUhccffxxFUZg/fz633347uq4zZ84cSktLs+6b59SQqnhpDBdjkY2yw7Y2lU/Xd3Dh+QU4CiREWYHOFvaP9if43oUWTHKqBtxJhVukOQw7j/V9gnvsXVF8nT/J4jfauO87hYx0y9hM0N6Pz1Quwp1LF2ahtUtVa/1JHnk9wIIbXZS7ZDJNGQV0zhsZYdqIKCZJpzUqsrrOzoE204DPdaBDS5cxji018fM55YwtNRFTdTb6s9etb28qpMTchjPqx64Yi6dVBWM6pwodf3VLNjJnjH6lSxZzYLjPKBX0/uzlTgM+37mdyvHZrVPzHB9dJl1tFNhFLr/AjiwZteRtbSp2u8ARv8rmnd0V1+0Q+NGlVqaPltF1o3zwhc0qod4d72n+/XKFs0pFxE6nwzp/kjFemVAc7n4jlrXcMEV/oj6YlvrLyjW+PsKoO9d1nVp/kmqvTFwTeGKLgKob3+KznO1MKY2iZBw6ocLrO50klIHFdZRD4LazjIS12Fn6ucGf5K/1hqhnW7TMrIgJJULsb2/Iya8lV3rm0asKis/YssXD+xo566yzTugYb63fyGuR3Pb9N6/9hJ/veMg3H+XpRn1HHSsOhvlwq8TRvUc5CkTCJVw0owB3kYzdIVF7IM4Xtb0LrZuDOo++GR5U6uPVbUnmzTZhlo0W/OpShVhS57VtyX4FfagwSzpTPF3RlyAIVHuNksw3G0gLuiToTC2LIncK/+FmnQq3sTB8yZgIqw4NLOoHgzqLN0W5vEJhplehQBE4xy3iD0NDOEtbKrCj1Qy0AGSUJw5NqWdfefQ8/TPcI/W8qOcZkMbmBMvfCeRknDUYQQeoD+g8tjLOnK/JVBaLtIR1lu9IsvFgH1MhcmAwUfpMr45d7l7RkBqStKela6NNVtMWv4ebdZ59K8mPrpMZ6REoNOdeqVNhF5lV3mUfUGwW+PYYMx/7kmxq6h2pp1r8S+3hnL1aciFT0FNplxT5ssX+EbJ4DQ0n8qKep0+s7iLK3Ib4RPWBo8PBCnqK2oDOY6sGN6ot23Mdj4vhuELS6ZTM1EtSExhhh4bOqs1wsmvBuMIt8KPrZCrcRglmSzT3r9G1lea0oKdQJIELS21saYrS16VsqHLnkPd0OREEgbz3S57Tk/GjFL5/kRlrZ1d+LAl/+lDncKDv356B5sGlXo6Xns9xIqPbgom0D1m3RdJRHoVQxnVG1QW+CNiY5AqjiAIjPcbnoGqwvWngyDaVLx9h73rtmXNIRUHAKguEkidXMPpLuaTr0fMLpP1yxtSp5/nqIKAze3wbRQ4Bs8m4OW3w/SvArPQvOoHmMFYFnOaTc+YPpaADrPcJ6ZLCaq/MghtdjC6RCUShKdr9PWxrsrPV7yCcEFF1aIrIrDxYRCCW3VQsVXaYuQDaEutK1ezzxVj4ymH2+Yya9Uh/Ll9DQF8pl1T5YqoePU//iIKe0+1UkY/U8/TCKqkInQlkXdc5FoAyF4DA2aNgc232xxVZRe652snXRprQgcaQxv/6JMr+5uPPj2eSKegDiXmZDaa4jQh8ZwAOZl+H5GBQ4J0DOtdUgqYLVHoU/BH4016BUke0h5GWwJ5WG3ta+/810p8N7se+MDdUOVFEwzpg0c0VVHoUNjZGTurCcE9BT5EvXxwcZ4T3S56vHqIAnb1jHAvAy++rzL1SorQY7P2kXx+5sZiKIgm5M2dc7pSYN9vGvL+GaOljyk8upMQ816j8khEwq5y0Wdj0EvisCf7WkH3/rU0inzfrlNognASTEsPeKXwpgfYFzZzlijO9JI5V1jkckvjkqIXWmPGaXGaBqR6ZQnOS/e0SjdHsi6cHggnePtjBZSPsOBSRUR4T6xojbGzMsU7uOOiruehMLV082Qz39Ete1PP0Iq6JJFUwyUaEPvdKiTKXMemnoTH7YyaPUCgpENP17LX+BNVeBUmEK8YpvLa9n2L1fhisoBeaYFYFKBl1ZyYJvubR2doER7rcKXpF1EmMkXrZmD0qxPhCNb2oOsaZZJQjyEt7CphUbOJbY0yIgCAozPRa2doU4cNjvYX6/BIrF5Ta0HQdVYdDoQRbm06OoGeKeWZzUV7QTwBh+A/JyOfU8/QirgkcaRWJxY1ZnCPcAvFOQT/Qh6iXOrtEt9afYNHrzdT6E5gkgRHOwZ9mqVb/zJb7XJhQRLr00Gj5jxseNCJM6py5nK3Fvj9kQWdCkdqt6UgUjF8CF5XF+fYYE4ooIIkCoiCgiALneqyUWru/7omFJs732lBEAbMkIosCVQUmrhnZ/3T4EyXbfNF82eLxIQAiek63U0U+Us+TBYEVWy0URo9y8blWBEVm3Rcqe3wKfWUU9/oTSJ2/S6u9CgtvdFPtVYgmdHb5B+e4ONjoPJPM4pFaf4KHX2/i/hs9VJUoWOQkpY7cRsllkkgqqHocGeNC0dCoMbpERBIEJhRLaLoOCN2qWSQBJhSa8UW6Bm2f57V1m4aU2ndcoQnTYYH4ECbV+/NFt+UF/YQYivRLIpFgwYIFHD58mHg8zj/90z8xbtw45s+fjyAIjB8/ngcffBBRFHnqqaf44IMPkGWZBQsWMHXq1H6PnY/U82Rl8ogkt1zhYMpohTIHxOM6mt732XyoRWVjQ4xowojux5aaUDUIxXU+qhtcDfqJsKul67JT7VW4/0YP1V4FHdjXfnx2vsGESKq0vKFR43/+NUJDo4auQzTZ9Zns88W4/5Uj7PMZC5Jaj2jNJnffN135ApiloUvU9iXoeZOuoWEoDL2WL19OUVERL7zwAr/97W95+OGHWbx4MXfffTcvvPACuq7z/vvvs2PHDtavX8+yZct44okneOihhwZ8fXlRz9MLp5Lk+nPieIqMcW3eIpE7rlGYUtn/ifroW23897oQ/qBGa0RjdW2CB94KEx18cHzcRJLw6n5IaDoJDUZ5FFQdPjqWoC1+fJFwOClS3y6T1GB0ici//p2V0SVGWeP6xgRiZ+g2rtTMwzeXM67UjKrD7tbu6wgHg4nOqJ505cu4UjMJTSeYOPEKoVJ7tE9Bz5csDg0CRuotl1t/XHfddfzrv/4rYPxqkySJHTt2cP755wPGkKCPP/6YTZs2cckllyAIAuXl5aiqSiAQ6PfY+fRLnl6UWJIoPS73ZkXguqka2w/0nRLRdHhtS5gPDvR//KFyV+yL1mSU5/fAaIeEKEBDUKWPYpQs6LjMcVyWOAIQiCkI6HwWEJFEkUqHxqgSiYgKmxtljoUF3j4Y5JpRDtB1qryGoK/zhWnq8aQf+8KMKTChiCB1ljQmdVh5OHjCGdiei6IpUhF6niFCyN0mIBAIcNNNN6X/njt3LnPnzgUMy3KAYDDIv/zLv3D33XezZMkShM4AIXOgUFFR1/Dx1Pb+ZjfnRT1PGmMeqYlZnaaXuq5zpBnK3WCRFcaVSJxTofH54cGF3rl2mZ5oI1EmcQ32DjrdolNVEMJhSqbTLWZJpdCUoLbdwcZGhc1NOrJgHD+V6NnTFudQqIVxThOSIFDbEact3jvybotrPL+3lfNKrIx0KLTFVDY0RjgSHvjzTIl2yi6grxF12WrR8wwdqSEZueByuXnttdf6vP/o0aPceeedfO973+Nb3/oWjz32WPq+gQYK9Ude1POknRmfXR6jJdDC1+4wU2QXONIMv3tbZd53nIwcYUXXdRZ+Exo7NBa83k5HdOCT+1QIel+zN3tSZILJxeA0gS8C9R1qN0EHo8LFJqs4lCTBhIKmC2TL4oSTOtsCvZ0re9KR0FiZWVeZIz0F3DD56v4eewp6KkovseRz6EPJUOSsm5qa+OEPf8gDDzzARRddBMDkyZNZt24dF1xwAWvWrOHCCy+ksrKSxx57jNtvv51jx46haVq/UTrkRf0rjT/iZ/WxQ7y8xcPaFfXp7S+/Y+UH3yyg3C3wb9+yM7HMYuSNBQGbCcqLBP75CjuL3+zdpvlleL/kQqoTtC9xH2GD2RXGF1QUwWOBSUUSe9oEkrqOruv4AlDqAlEQ0qI+3OlZi57vFh1qhmaq0W9+8xva29tZunQpS5cuBeA///M/WbRoEU888QTV1dVce+21SJLEzJkzmTt3Lpqm8cADDwx47PyQjK8oHxzZzIu7S3njj1uz3n/lJS5uvEhhZrkbSTRik8whFqoG330mgJqRZehL0E9FtN6TnuI+pxocPTRa03VaYgkOhsIca9b500qVW2ZLlBQLHA1baY4Or5xGtuYiyEfofTEUQzLe37Sej8SBf5EBfNfiOSVDMvLVL19BtjXvZOXhQt55q54LR8f4lxst/Og6hXGjDJWzuIv46AuNnz4XI5pRjVjnT/LI663U+ZMIPVb4+xPuQHO4z/syaW45vpLDXPAFLeno3SKBNcNuNzUkWxQEnCbjx2upC26ZLVHqAgRo7cO061TRl6DnObkY1S+nuaGXqqosXLiQuro6BEHgoYceQhRF7r//fnRdp6qqikWLFiHLxqECgQA1NTUsX74cs9mMruvMmjWLqqoqAM4991z+/d//nZUrV/L0008jyzJz5szhlltuIRqNcu+999Lc3IzdbmfJkiUD5o/yHC86d39HptpbgcUioWk6F8zUeW1ViJW7uvb6eE+EKybbkCWBMV6ZBTcWMdojsceXJDEIDc4U9v4uAClhP1lRuy9oQRF1dIxoKzUke94NTsaUykSTAnFVQBKhpBiSmkB9hx1VP/H4xy4LTCo2Y5NEGoIJDgRPvH4/s9IF8lH6l8Ewt34ZWNRXrVoFwEsvvcS6det48sknEQSBe+65h/POO4/58+ezatUqrr76atauXcvjjz9OY2NXL/mBAwc4++yz+c1vfpPelkgkWLx4Ma+88gpWq5Wamhpmz57NX/7yFyZMmMBdd93FihUrWLp0KQsXLjwJb/urS31HHWt9OnV72zjvQgGL2RArURSwmARuvtLBhoY4HZ12JE+/28a5o804rBJWRaDcJRFJwK9WDn6xL0UuvusnU9wTmsC+VpFxRVp6SHaVVyKhwecBgT1tTsyShgBEVZGh+BqPdih8q6qgs3oCprrNHAkn+XNdR5+DMXLlqzBfdLhwRgzJuOqqq7j88ssBOHLkCE6nk0ceeQRJkojH4zQ2NuJwOAAQRZHf/e53zJkzJ/34HTt24PP5+P73v4/FYuG+++4jHo9TWVlJYaExnmvGjBls2LCBTZs2cccddwBG8X1qASHPiRNKhPjbod3pRdGf3OzEYjbqlzNLF1UVJo0U2bDXkJrmoMadL7Zx2QQzE0plDjSrvL8rRjDW/cQeSKirXTJzz7VTVSzj61D507YQR3JYeGxuUU+KsL/VoHCjHKfcARUeGVWHfW2wpw1AIKYO3XOKwDcqHT1MxgTKbQqTis3sbMktR5uirwaj1MJonpPLaR+pA8iyzLx583j33Xf55S9/iSRJHD58mNtuuw2Hw8GkSZMAuPjii3s9tqSkhB//+Mdcf/31bNy4kXvvvZf77ruvW62l3W4nGAwSDAbT21NF9nlOnJSg//o9Gzv++j5EIoRaxqKqZiRJ5Egz/OEdjXk3FDOt3EHpdUl+Lbew6gujuuWoL8LbCXh7R4xRbrjtcoFKt0B7BN78TGdjp796X8I+3iPz4NXFKJJRSeKyScy/QuG5z6Js9Q2cwzkZwh7XBD44qlOgGAumLTEG0aA0OMpsctovJNPzxSQJTB6kqGcT9DxfLqcyX54LOScKlyxZwttvv839999POBymoqKCd955h5qaGh599NE+H3fOOedw5ZVXAjBz5kz8fn+fBfWZ21PF93mGjkzBXf1xgGSniFWVyPx8zgimVjgxSTJVHgs/u6mUmou6OtlaAhFGuuDfrheYMAJsZoGyIoHvfV3gisn9P+/3Zzgwy0K6nR7ALAvMnfzlq1LPaUQdCTgaPnmCDlBkiaUXlXv6w6iDKD7rS9BTNgD58sUvByHH26liQFF/4403eOaZZwCwWq0IgsCdd95JfX09YETUotj3YZ566in+8Ic/ALBr1y5GjBjB2LFjaWhooLW1lXg8zsaNG5k2bRrTp09n9erVAKxZs4YZM2ac6PvLAzRG/fgjZvbtaQKPF6xW6g9GeeHtIPGEjtvsZHyZOV26CGA1ifzjFW5MGSZU15ytpaPtFGZF4JvThPRAimyMKe5Ks+i6zr5jxvi2QrOAKYcA/ESj9Gxj5brdb5X4f8Y5uXuKizvPdnHpCOuA3h2DIRBLkNS6PF9S/jBxVefzHBqWMumZcqkqKGasc3TepOtL4oyofrnmmmu47777uPXWW0kmkyxYsACXy8X8+fNRFAWr1cqiRYv6fPyPf/xj7r33XlavXo0kSSxevBhFUZg/fz633347uq4zZ84cSktLqampYd68edTU1KAoCo8//viQvtmvGv6In1VHD7HtkAlvQZJbZwt8UljAx2/70d1e3t8Q4ZPtUZY/UJ72nNB1nf3+OGO9JnRdZ2SxQm2jYUxVXSYiil37HWrSGekREAUotEGgj5FxrVGVMsU41fb74jzw6hF+PqeckR7TgBU0QyHo/VFoErm5ujBtiWuS4GtuKwWKxN8O9PGGBokOrDnWzBUjPACM8RqeL3vaYuxty214SM/u0XyVy6ni1Ap2LuSbj85QPjiymWV7S5jgbMZiFpE6RSue0NixO8wrfzOc3izuIp76QQXnVxupmX2+GD9/1c8Dc7yMcin83eN1tEWMRdMH51qYONIQ2YONGv/rzQQ/vl6htFhg3os68WT2csXLqy3cfr4DiyKmB1eMdCusbEjw5z19l/UNRR59IFG/stzO2W4zktDdD13V4Xe7W0/YPTHTs0UWYFyhCYskcjCYoDmWW86nPyvdvKjnzlA0H63evI7PzLlVfl0tjsg3H+U5ceo76nh65w7u/j/QvLcekyKkBR3ApIicM9FGkVPC4jZy5r9fEyDSaUA11mvigTleKopl1uwOpQUd4NVP48QSRgww0iPw4+sVSgrh4z0Q78eT6oPaKK9sCxFJaMSSOpUlJtYeTLK8H0GH7M1IE4tkfjjZxj9PtXNZuQnTCZ7BJVYpPdwjM9+d1HWKTvTgGGKe8m1J6rCrNc7W5uhxCXpP8sMuTg1D4ad+Msl7v5wh9CxZpMnPtXeMR1G6WvybWnQ8xQKqqjOqupi9Bw0l3lgX4dG/+vm360owywKVboUjLXESyTiXTbKydrcx6f5wh5n//khnzvlgM4O3WGDtLvjzxoFP4D/vjLBiV4Qx5XbaY4bX+WC5cpSJ2SMt6YES5XaJ88pM/M+tweM6HoAvouK1yUiC0MsPvSV24h7ng6VnmgXyEfpwY7hHwnlRP4Px+6KMGetElgSaWnRWrInxzVlmCgtEWoPdBevNzzp4Z3sH37+4kP/3kgJKiwTKXQ6+PtHG7mNJfvaXNjQdNtXB5jodqwmiCcNDPRdSaZnmyPFFMDZZ4KpRll613sVmkZleE58c652bzsWpcXNThMnFZkRRN8aIlVlIaDr72mKEkl+eqPcl5kCvYRf5KpdTiMApjcJzYbhfdPLkSCAWJaEKjPVEKSwwctGffqGiqcYJ6CkW+OYsM0UFcNQfo7Glt2CVl9r4/qVPJUvFAAAgAElEQVROLIqI1CmeNpPIpDKZi6pN6f10IBzvLeiZ+fTU4OjU7UQZXSClK0h0XWfPsQi6rmOWBCa7escmuQ6Wbotr/Gl/G4dDSTRdJ5rU2NwY4Z2Dx98xOxh6TitK3c4uMqLzlOtiftjF8MAYPJ3b7VSRj9RPQ1KWuQC6brS9JzWjnX1iSYh5Py7lxRcibD+a5PfLGrnpehdFhTLFToFd+yO8/lYzOIxu3mJXl1BMqVBIqjpm2Vg0rPUnqfbKWE0il4wz80Vr9tOlp2FXfyI+rljiLI9MOKGzL6wRTOjdcudOk8AEt0wkqbOrqStRH07qCJ3Vv3t9Uf7zTwf5H7eMYpzXQkcWg/NcPdUBGqMqy2rbc9p3KMk2rSgl4nmGLzlXv5yigD4v6qcZ25p38vS2Qt560zBnGe2M8Pc3ujGbAATEzrrymu9VsedXR2k4HOPJ3x7FahFJJnUSST29QNqTaLLrLKz1J3nk9QALbnQxpkQmKUpA9sW9nhF6NgTgH6dbOcerYJIgoRo+Gv9nVzQt6t8YZ+LvxlvSdr4JTee5nVGOhjUaOlRCCQ2TKDK+1ML/uGUU40stJDT46Gj2Wu+UKyPkHrl/WQxkm9uTvDf68CBVp54TeVHP0x/1HXU8t0tj+ftRju6tS2+fPt2K2dS1GOpv0fEWC6gajBttZsdeQzwiUUMp+xJ0gO2HEnRma6j2yiy40UW1Vyauwpr9fZe35JJemTlC4RyvgqXzomPuPPNunWihtk2lWIBvjrMY9eKdlYxmHf7hLAuPbgqjA898HuL2s+0UmUVGeczENXh9f4TDoYFz34OJ3E82PStaMgdb9CQv5sMP4VS2i+ZAXtSHOf6In2V1jbz6kcSOT/f3ul8zWdA0HVEU8Lfo/PmDBDdcrlBoN1IzADT5jU7SLLQEIukUTFKDny1v46FvFyIKMKLYcC58bXuc/c3ZhTPXfPlFI7sEPbMeXMDIl09zSSgZHuep+82SwGinSH27RiCm89jmIGU2EYsscCiokuta5kiHiMvi4HBQRZYjuT3oJNBzQbQv/5b2uPEa82WLww0d8VSF4DmSF/VhTNd0onpjw8EGAOQCG1de7uHCC0tQFAEJFR0Jb7HADZcreIsF4gmdvZsOQqL7CRhtbu03Wt/rT/L3zzVz7igTJS4zO30qHYPrZM9K5qvY54uxcNlhFn23glEeMzo6lgxvmMz7K9wmYuHu7+FYOPeqFJsMPzrbitsqouuG7W1tu8zfDnQM+NUUGNpf0Nly6NA7Sk8Jep7hST5SzzNoulItcHRvxrg5qxVBgH+7s5pRo2yYFOPsSqo6OiqhuEiRAxIJjT8+X0ciU9AzovWewp4ZrYMRsdcGZWqDx+dyZZXhvBEKTrPInkCSPQGVjw7GmeiWschGPfii71YwrtRMJAkNHRomNcFEt4y5x/1xFfYGkjicx9dd+t3xFrw2ETmjFLLaqTCjxMrGxt7iKQtwRYWTKW4bsgDHwgneOdjOsUj2RqmziixcXObAoYj4I0lWH+ngcLj3vrksiqai8szoPJ96GV6carOuXMiL+jDDH/GzoamFD7faOPrJZ113dAryWeePpKLCmhZ0AFkSiCU0Nq5v5tCRGNu2tRAO9y/I2YQdjGoYXdPxHWjCVVKAYh2ck+KYIol7LrAjYPioxFUzewNJntoYZvPRBDNGKEgiVJYYOfHnd0XRdFh3OMGllSZGOSUsskC117j/hc8jxFRwDOpVGJhEmFAkIYvdLQAUSeRrHktWUb9hTDGjC8zpevgRdhM14108t6uJtnj3z3Sa28rl5QWYOt3MRjlMzB1XzEv7WjiSRdghu6CnovRwMpQW9LyYD1MEkIZ5nXpe1IcpWXPVHi/VI02YTV256WMBKHOBJAi0t8X59NOmE3rextqjJA8eQkCnSddRChxUz/oassU04GMF4CczbFgznB0tMkxwyVw8ysRzn0V4ry7G5BIZQRHY3pQk0qmTqg6/+CTE9BEK08pkgnGdNQ1xDnV0Dus4Dk91OaNYODOlM76sexNTiiKTlBZ0XdfZ64syvtSCKAjMLLHx/uEuf38BmDWiS9BTKKLIrBEOXtrfkt6WbWE0RUrQM1MutrygD1sGVf2SA5999hm/+MUveP7559m5cyf/+I//mB79WVNTwze+8Q2eeuopPvjgA2RZZsGCBUydOrXfY+ZFfRgRSoTY2XKos9V/e9cdGYucbR0q8YSO2SRwLAAvv68y90qJYrtGa9vxz7yMNrdCPA7+Y6Dr6Vxyoq2D/as2M/H6Cwc8RkWBmBb0boudssAlIxXWHIhzoF0jJGX/FaHqsOFIgg1HTnx2J0A4CYGohtcmdUvpqJrO/izuiMVmGVXXURDY64ty358OsPiWSiaUWSmxdp/SZJO7GrQyLwCCIKT3zdYlmiIzj54thx5KhPLCPkwZqvTLs88+y/Lly7FajfNgx44d3Hbbbfzwhz9M77Njxw7Wr1/PsmXLOHr0KHfddRevvvpqv8fNd5QOE7Y17+SeD1v5wf8f6RJ0j7dX1crGHdF0HXeZC+ZeKeEt0kmqOls+axvUc0abW9M3i7sIOtozSmZAthknmxqJEm0d3BSqfb4YC185nB4GkeJkDZQGI90yyiFRbO762i3bGyOm6qg6jC+zkNR0IqrGx8dC+IKWbrXszdEkcucq2PhSC4tvqWR8qfGYo6HuF5qIqpFaRk1dAPb6DBHvmaaB7GmXlJg7Tdb0LV/COPzJ1dArEAhw0003pW8vv/xyt+NUVlbyq1/9Kv33559/zgcffMCtt97KggULCAaDbNq0iUsuuQRBECgvL0dVVQKBQL+vLx+pDwNSDUVv/HGrUeFizahX7iHqkZjOk88H+NGcIooKRNwF4G+M8+zvG7ovjA6SaHMrgthV7SHbrBSOr6Ztby1aPE4iGidbn2PmCLvDHRrhhN61GHqzERnHkjofHowft6Dn8rhLy01cP9qCqhuLnQ0dKn/YFeZAUOOJzWEuGKHgtQjUtWts9CeIql05kLSwO6LsbY0yrsiMIopMKLOi6TqqrrO5qbttgKbDF63tnFXk7HYBSGgaHx4LDrpbNC/mpw+5RsIul4vXXnutz/uvvfZaDh06lP576tSpfPe73+Wcc87h17/+NU8//TQFBQUUFXWtfaXGfLpcrj6Pmxf14UwfteUHjyV54OkmPHoruq7THMghXdHHsVJY3EUkkjHUUBh0nWQ4QtveWpLhCIgC1qKCfh8PxgXh15vCxkKpANVeMzEV9gSSfHgoQXHR4EU9F0GfVCxz3WhLetAFQJVT4u8nWnlmR5jWuM7bDf0Po0g1Jv21oZWvxxxMK7FhEkUOBGOsPNRBRw8byFJ7lC3NUXQdJhc7GVtqIaKqvHcoSG17jNJObR5I0NvjkbxJ12mEkVM/OfUvV199dXqE59VXX83DDz/MlVdemXX0Z3/kRX0YYXEXEW3yD7xj5z4DLokOIOQ9kd0e1KZmSCbTwi6IIraRZTktlALUtancu7I9XdK4uznJviy+6LmQa2R/WYUpbcebmd+uKpRxmgTas3jD9IUGfHgsyIfHBp56pANbAq1sDbQiiwKJTsOxbILeH+FkCHu+yei0QThJon777bdz//33M3XqVD755BPOPvtspk+fzmOPPcbtt9/OsWPH0DSt3ygd8qI+LEiqOg317YiigHVcNcn2DhJNzb13zEXwYdBinkKQJcwTJ5D0+dDa25HNCvZRI7B43d3SLAMRTcLag71/PQymgmUwqRpnxjCLTLOvSo8FuzKwqB+PfUBq8AUYUXuiDw/iTE+XrtfbtUCazqPno/TThpNVp/6zn/2Mhx9+GEVR8Hg8PPzwwzgcDmbOnMncuXPRNI0HHnhg4Nc30Dg7VVVZuHAhdXV1CILAQw89hCiK3H///ei6TlVVFYsWLUKWjetDIBCgpqaG5cuXYzabiUaj3HvvvTQ3N2O321myZAkul4uVK1fy9NNPI8syc+bM4ZZbbulz3/443cfZhRIhvv+7z9nWoKOlrGU1DV3TiAQynANzEfTjFPMUPTtNMxuSUmQT9sFY6/Yn1sdTtgjw7TEWvj7ClK5HT0XqMRUeXNee9rPpi6HyhMk1j54S9Xwe/ctlKMbZfbz1U3xFuRUkTIpUDs9xdqtWrQLgpZde4u677+bJJ5/kiSee4J577uGll17qts/atWv54Q9/SGNjY/rxL774IhMmTOCFF17gO9/5DkuXLiWRSLB48WKee+45nn/+eV5++WWampqy7nsms615J/M+OMiWWjUt6ACCKCKIIpLZhCAK2J0WTLYBfsMPsaD3RaA5nL5l29bXLReOdyF11aEYkaROUjMGXYzrdG/8c21kQEGH7m6O/SECE4tMfKOygCvK7Xgs3V9vZvSeSSAWJRDrfuHIC/rpiSDkfjtVDJh+ueqqq7j88ssBOHLkCE6nk0ceeQRJkojH4zQ2NuJwGP1+oijyu9/9jjlz5qQfv2nTJu644w4AZs2axdKlS9m/fz+VlZUUFhqe3jNmzGDDhg1Z9z0TyTTp2r3qM0yjRqbPAhEjryuIIo5iOxPPKUXXQRSq6GhqZ8+az0n2NxB0EOQq5H2REuv+IvVAc7izu1QgruqADXex1M1D/UTLHDsSOo9vCXJZhYkJRQptcY3Vh2Psb8s9lz+Qi6MowM3VTrxWGZMkouk657gtrDwUZEdLV9mmL2Sh1B7FHzH3miuaKex5o67TFQFxmBsF5JRTl2WZefPm8e677/LLX/4SSZI4fPgwt912Gw6Hg0mTJgFw8cUX93psMBhMr9amynEyt6W2B4PBrPue7tR31PGX+hDrNrXT0uCjoNBMyDWW9avrAaMTNFUbLgIWSSCq6mi6jsvrQJK7BK+gpJAJs85h53tbuz9JDpUtx0NPT5i+6EvcRQFu/7qLm6cXY5EFmsIqL38RZauvK99eVSgw0SvQEoPaNv24DbSCCZ0V9TFWcPzuY/0J+6QiM16rkq6wEQUBUYDZIx3saYv3mVPPRir9ko/ST09OVvXLUJHzQumSJUv46U9/yi233MKKFSuoqKjgnXfeYdmyZTz66KMsWbIk6+McDke6JCcUCuF0OrttS20vKCjIuu/pSmo60dNvm/ni9bXdmnoQGsBTAhYrajCErmkgimhCp6ADgijgLjG+9LquE48mMVlkHCWFmEZWEI/m1nV5otF4rsIO3Scgudw2fjDDybUT7Fg6h1+XOmR+PM3GoveaadRkfjJDYXShgCAYlSTBOPzvL1SCGW/NLOlcMVJjsktHAL4ICKw8JBJVT84XK5WK6SnuE4pMaUHP7JZNamAXbbRqA4+/y88YPf0RGP4ujQPm1N944w2eeeYZAKxWK4IgcOedd1JfXw8YEbUo9n2Y6dOns3r1agDWrFnDjBkzGDt2LA0NDbS2thKPx9m4cSPTpk3Luu/pQH1HHU/v3NHtdv+nCX766whfvP9Zd0EH4+9Gv/Ffj5doWwgtoaJ3Nrpo8TjlZTaUToPxeDSJr6GNeDSJrukopoHTFRZ30QkLeoqU2ddgCLVFuG6iA4siGouXx6Louo5FFpl7rpPrx0pUFQmYZQGTJGCWBIrMcGN117kkoPODSSpf8+hYZcNHZopH5wdnqQgn2dM61W2aEvm4aog5dPnI7PPFEICYpvebl3eZLVQVFDPWORqv1ZsX9NMcIcf/nSoGjNSvueYa7rvvPm699VaSySQLFizA5XIxf/58FEXBarWyaNGiPh9fU1PDvHnzqKmpQVEUHn/8cRRFYf78+dx+++3ous6cOXMoLS3Nuu9wJpQI8bdDu/n1ezZ2/HW9sbFnKiTcPYKTbVZGjS1k2mVjsTqt+PwRNq1vJJKIo6sC8WgcNaES9doRRZBlCUkWKa0qxGSW0TQdzWzF4rYYfi09GKyQF7usxyXaA+GySZ2LvwL7fDH+c9lB/sd3RzG+zEK5U6akINkt8t3vjzPWa6LaKaCIkNBgbKGO09zdmEsWocAEE4p0drd+OV8cX9DC6kMa1U7DeTLTR6YjodPQ3tvfvWc+Pc+Zw3CP1AcsaRzunKqSxm6t/U1+iHQKo7VHqiIaTUfqss1KyZSJXHhREW6PEdlpmo6mwepPWwlFNAQEQq1BzplRid1pGESl/onCoRgHG9po8p/YpPv+0il9CXxK/HNNxSiSwF/+qRqbSeyWrtB12HIkxqjiJAVmQ633+WL8/LVGHriphDElJhZvUolr8PURGpdVaIiCIfx1/iRjvDIgsPaIyNojX6510eyRCrNHmdLeO0ld57c7ohztHKdX6oj2O6quxJKP0k8lQ1HS+Oln62h1D9yYBjC6o/yUlDTmm48GSbdZoZ+s67qjp5inIvZQEFoChvNhPMaFFxbhcneVJ4qiMV9n4jgbW3cYOWmHqwBEId25lvqvrMhDLuipxc1UPrwv0U6JfTbRz/aYhKrz/LoAf3+BC6tJZHyZcRGLJTVe2NLO7PEKs6qN2vKxXlOnoCscC+vEO0WzJWpE7GYJ6vxJHnm9lQU3FlHhUQicgnGjKw8lWO9LUl0oEU3q7GtT6W99dKBO0jynHwKdxQ3DmLyoD4JQIkR9Rws7D3qMARbZovOe6RebHaIRiEQoKLZR4JTT0Xd7u4rTKSGKAsVOCV3TQTBEvL0tiqPAgq7rRMMJLDYFWRYxW2Ri0YFLGlNCmzn8Ihu51pD3R18R/H9vaCEhStw8tYBiq0htIMHvN7azP5DAvzXJlDKZAosxb3SUx0RMhddru8oQdwfgqlGGQdcYr8yCG4uo9MhEVNjVcmJfLJMIxWZoi0N0EC4GwYTOtqahKSnNc3oyvCU9L+rHj8c7oKMiYCTg3CUQjxMWkoidQxXa21U2bmxj5sxCCgtlmpoSEFONsFQArTNajYYTHNjVROUkDyaLzEDZMgG4YYaDWy5w4rRJbGyI8cdPQzSHcp/rOVj6S8m8ty/Me/uMC0e3C4jbxr//tZ2LRps4p8KMLwTrjqiEE0aqJdQe5uDBIDu2CNx9rZNplSaqvAr17bCiXkLVj/+rdU2lyIVlIqoGkghbGjVW1GkM1SeUSr00xvLR+hmHIJw075ehIi/qg2BD425e3F3Khk/rjQ0pQU+JeSwGbS3GsAlJgoJC6GzMwmRCdHtpOBRl9EgLTqfEzJmFOJ0SSVXncGMiLeigU1BgGGhZbEpa0CPhBPFY/2HlPde7+Ma5DqydfiizJ1o5v8rCP/13E+3R3JZPRrskFBlqG/tPL/RHZs16tl8DKS+ZNbVx1tR2d1BUkxp+Xwhd1/G36yxY1opJFhg/oRCPt3dOWhJgsktgXKFAexw2N2q09LFOeVGpyAWlIopoLMgCnOsRiSbh3YNDI+vZGo/ynDkMb0nPi3pO1HfUseJgmGeX9xgEnRmZx2PQ6OsqX0wmoTVghNxOZ7oqZd3mNhKRGOMmFGF3SMTiOjv2hGluSabPFo/bjNctI8sq8SSoZgk1qbFrh6/f1+l2SPzd9ALMGePkZEnAquhcOU7h9c/7t58d5ZJY8A0HRTZjcVPV4Ml3Q2w+kOgWjffMq/dM8Yyb4MkprZOtaUnXdfy+oPEx6kZtug7Ek7BnXweCYunWgSoLcNskCa9dwCILJDWdr48QeXZLgh2NWnrfVAfr18+Vs1bdXFAmDpmop0hF6flF0jMHo059eMt6XtT7IdVAZIyXq+8y1cqWZmlr7VaPLorw7W+O4JqrS7HaZGqP6ry4Osm+3S1sDJnYsqUFk9NOJKohigImk4QgwFlnFePxWJA76/jMgo6WTLL2oyPplExfjCs1kUhqmGUJXdep9Seo9iqYFZHpVRaeW9PWpzjLIvz8H8ootImdHXPGifsf1zm468U2GjtyE7wLLiilZqKPxhi8vMUQ94EWV3uKe+pjFABZh2Rnc1IyYQhzpsXA7CqJMoeQFmq5c8TcP0xVmLcy1m1fAHvGVLr9/jgPvernwTleqr0mJIGcvGL6I2UTAF3NRl7rifny5BleDG9Jz4t6n3xwZDMv7i7ljT8eBY4aG/trx493j4LnzB3LtbOKsHSaPo2vgPm3KLy0aRxRrCBAPK6yb3cj4VCCaDRJQYHSTdDBqI4xmyW8XhvHjoUZV6pQaBPZdSROKNZdgXxtybSo1foTLHq9mYU3uhntUTjQ3HtxL1NYz6syxF/sXMRNlQ9KAlw5ycRLGwyh6qvk8bwLRzF3WhPneeJUFUwHYKJzJ6/Umgk0976QZFtcDTSHKXZZESUBTTUsA1KCDiDJvUsYzyuXskbeoggVBQIH27t/Rg1tOuNcxv5jvSYenONlrNdEUzR3Qc/Ft6bEbEToeZ/0M4989ctpRlfJYmeqJdPytj9RlxUjBQMUljj4vLGImUGRis5GQ1EQUCQ4vzrJRwcMcbJYBM46p4ytGw+hqjpFRV2rarquEwomsDuMqpdxI63813eclBXJqJrRBPPsqjZe+rTLH6e+KcGeY3EmlZup9iosvNFNtVchmtD507oMG98sFFoFBMFoFqrzJ1n8Rhv3faeQ6lIFl73vevBil5VvX1nIzdVBprqnd7tvqnsyZTY/syvaeHF3KevWdU8fZRP2lkAEUQRNBYTufaOFhZZevu7xDCXOjLxHuU0ksvy4eHVXgrvPN6GIxgWz2mvs98L2BM0tg0+/pATeXSwNaAqW5/TH+A07vEU9P3i6E8M5cTM/fVfimd/s4ujeo913GMjatrAw3WpWWgRzL9MpdxvifLhJR9d1JBFK7InOMkUjshcEcLmNaC4WU9Oph1Awwa5tAULBBKqq8ZOLLFS6FWwmkQKLiFkRuf3yQmZWd29Pv/dFP+v2h0moMNIl42tXue9lP/VN/XvF7DySTBsVjfHK3PedQsZ4ZSJxnS1ZBl5ApqDHmOqe3Ov++o46ltU18vMVNt56sz5rlJ9tmyxLKCYxbWGqmCTcHhtmS6dnf0a+fs0BlVjS+NBSkfeYEoW2mM6xYO/Qu6FN578+ibPZp+IPaWz3azy5Ls7u5hPLp2dG776Qhcb8OukZy2lvvftVIJVqeevNCNHd73fdkRJyj5cRIyxceomHEo+ZcERl48YA2z/PiH4tVih2Q2sL/sYYlaUigiBwuEnn929r/MO1ImUuaA4rxCIJDu8LUDHOhdmqpL1cGhsjqKqGJAnYHQqTprqwOxSKFR2vQ0CWhM5ceZJqr4zVJPLd8x1srO2KDjuiGvNeaqSizIZVEdh30GhWGqgT9EibxurdMS4db8ZqEqguNSL8gy1J1tflZh6WotdaRBYy7QyyReySJCJJIi63jUBzmFAoTigU79Ys5XLb2HRUY7xL5cIKCU2HCpeJYAJ+vanvReGjQZ3ntp6cWvNUtO6PmGmPR7DJofwC6RnGcI/Uv9Kivq3ZyPn+YVmE6O53su/k8eL1mrnxhgqUzho4Z4HIpZeUYLVKrN/Q0rWv3Q42G5FiJ1trNc6tFih3wz9cK1LuhqQG23x2zFaRsWcVU+IWEUUN4jKBRpFoTGPLliamTvVgtohYrDLRqEp9QxvJSUWgQK0/ySOvB1hwo4uxpQrF9u453ZQ4huM64biec1s/wNIPwmw7lOS6c8yYZFizJ87bO2LptvhMos2t0OPYoUQoXfb51psRos3b+3yuaHPrgMIO5DRG76UdSd6vUxlbLNIR1/miSTvuUsyhwheysNYX5eyiQ4SToXz1yxnEME+pfzVFPWXE9fIWD2v/8P6AnaEXXuBClrv/SyqKyIwZLjZtbkXNyOtaPMUAPPeuyk1fh8umSJS74WCTzocNBbSrIsUFOnaLTOeaJuVlFtyuUuoOxqiocKDrRkWkpulsWO8nGUsiiYYAVntlFtzootorE0tqbOhRbniifLgvzof7ske52UoZfcFSArG23msROdBT2PuiL2HP3N4Y1mkMH9+A66EiNY4vM7e+o9VMY6yFic5IvhLmjODUOjDmwldS1LPS07slA4/HnK5N1XWd1jaVokIJdHDYZdrajfREpkAlVfjTWpU/rVVJzcEQhGZGjkow8msF3Yz2RVFAlERGjizo9IIh/VzTpnv5cO1hfvl2C/9ybTFmWWBsqUI0rtEcVPnb9i9nYS5T0FPukNHmVt7441bWjR/BuAkSa1fs/FJeSybHa3MwmLmquT63y23rNmc1s7wxz5mBIAztkIzPPvuMX/ziFzz//PM0NDQwf/58BEFg/PjxPPjgg4iiyFNPPcUHH3yALMssWLCAqVOn9nvMr6yop9u3Pd4BK1xaWxIUOIwC59Y2lQ8/DXHJhXYcdpFQ2MjN9hdxphY/dR1ikRjJpAOTYuTHgx1JHAUymi6lBV3XdWKhBJPLFZIOiR2FJv6yJURdY4Kbzy/AXSDx8Z4If94UJBw3Dj6U0XpPsgl6Jkf3Hu29sDzMyWUU30CP7Qtj0dSI1lNzSyeevvNe8vRgqKpLnn32WZYvX461M6BcvHgxd999NxdccAEPPPAA77//PuXl5axfv55ly5Zx9OhR7rrrLl599dV+j/uVE/WUZW7PRVGny4bgctPW2rts4dP1zZSVWVAUkaJCiUsutGO3CWz/vI1kUh+Uh3kkonamXXTkeJid20KcN6MAXerKt3rFBBv2NvODyW6qvSZuGT+CeS/52NIQ5fNDzSfy9rtx8Vgz159jQ5EEVu6O8P4XEZI98uc9Uy4Wd1FWYT8een5u/Vn7nogIDxUDiXnPNFFmGiYQi1J26l56niFCgJyT6oFAgJtuuin999y5c5k7d27678rKSn71q1/xH//xHwDs2LGD888/HzBmNH/00UeMGTOGSy65BEEQKC8vR1VVAoEALperz+f9yoh6V6t/rJtlbpHHzqwbp2Czm9EFiMc1Plx1kEZ/1xf4yJEof3vrKJddWoLTqWCzCmze0sr6DYFBD6XoCCZJRGJcOSmELGpMcumMcLUTiEZZfcCF0yzx468pXF/lodqrIAhG+/svvlfKt5840Kvh6GlQCvIAACAASURBVHi56wonl0+0YO1c/B3nlbligoUFb7SkFxn7ajRKvecTEffMz21yuQW3Q2bH4QiBHLtPUwyVyA+FW2XqOD3TMHnONHITdZermNdee63P+6+99loOHTqU/lvX9XSaN3Oec1FR13cltT0v6p34QhZaAq3phVGpwM7VNdMxWeT0h6koErOvHc0by/YQy/Bkra8PU1/fYDTGdEazmcIkS0YePRfOcbVhlkQkESpLjOd1mRNUF7RT6ihCEATGlpqMDklfnGqvAjpcfpadFVt7G/QPNvUyqlhi9kQrZqXr5LQqIuO9CjNHm1lff3KLrFOfm9cp85sfVDGiUDEaqhSB5z9q5lfv9e9xk0ku1TEDPX6oyXxNvmBXXj2cPDEv/DzDg5O1TJo5FnSgec79Huckvb5hS7HLaiyKWq2MHOdB7BxGoes6He2J9NWyqjp7BJ4p6AJw1QyZh39o4ZE7LCy41czZVf1/pFYTVJcJSGL3xiRFhrNKomjReHp8W60/wcOvN1HrTyCJ4DAPzT/XlAoTemevZurCoes6VpPI9ErDHXLQI+4y1yX6oOfc1CdrKhntNmEzSxRYJcyyyPcudHHl5DMjAZ1qSErl1fOcCQgIgpjTbbBMnjyZdeuMLMKaNWuYOXMm06dP58MPP0TTNI4cOYKmaf1G6fAVidS7movqu6UMrHYTYqeCBjuSfLalla9NK6LAqWCz9f3RpITpugtkLpkipyNel1Pg1qtMPPdmnH2Hs3coSlKXl8mRZvjDOzo/uEagwgOKDNiS/7e9Mw+Pqjz7/+fMmTWTfSUJCQlhD4awuQJFQHBBKSkQ2YpSkbrgpV70RSxYF1zfalvFWm2LWvUni7bqKy7IUkFBiqmABAkEEgwBsodss8/z+2Myk0wyk0xCEgKeD1cukpnnnLnnnDPf88z93AsWuxa9RqJ/rIbVM11uGItd8J8TvoW2I23mAGrNwnNzct84Vs+MJilKQ1VD99Rdb+mmSgzXMCBOj1r2bncXpJOZf1UU2w43JXZ1Z6OP7lgodSdMud0woOfdPD1TUkqosZpIC+2nxKxfxHTXTH3FihWsXr2aF154gf79+zNt2jRkWWbMmDFkZ2fjdDp55JFH2t1Pu6LucDhYtWoVBQUFSJLEY489hkqlYvXq1QghSElJYc2aNajVajZu3Mj69etRq9XcddddXHvttVRXVzNt2jQGDRoEwJQpU1i0aJHPsZWVlSxfvhyz2UxsbCxPP/20Z2W4M7QZOx0dS2mdCuEEZAgOUTNiZDjBIWpsNgelZ31/VXaLk1qG8ZepPVEsJZUQF+lyIUwbqya/2Hesd50JSqsE8VGQEAWLproSlJwCqq1qKu2CwnM2kkM0BOlUpMVpabA6+exgHQVl/jM7OyLsewvMOIRrNtzyxrH9SOAzdK9FUx9RQ22tNwTrXXXk0bh6lK56r5g1sxIZ2EdPiN63L7orGmT7KiLmD7c4dxS3oLf0r28tjOP7YDOTEvNICYkgJSS1w/tWuPB0ZZx637592bhxIwCpqam8/fbbrcYsW7aMZcuWBbzPdkV9x44dAKxfv569e/fyhz/8AUmSePDBBxk7diwPPfQQO3bsIDMzk7feeov3338fi8XCvHnzuOaaazh8+DDTp09n9erVnn2WlZX5HPvnP/+Z6dOnk5WVxWuvvcaGDRu47bbbAn4zbtxp6q9sDSL34z2tBzQKUGWFmdPFtcQnhqDRqAgJ1WC3OamqNHO6uO3mssZm36hLKmHTdjuzJ6npEwXRYW1/9XplUzW/XRyBVuuaodsdgqIKqFG74iz3Vlv4x/Zqrs8Ixu4UbN5fx+5j7QtaoMJudcDDH1Tyu+kRBGkl+oSrqbcInttSTVldx2bq+qhwkqM0LL8hhtEpBix2Jx/9t4Y/b6/Eave/qHu81OxZkB0Qp2PNrEQGxOmw2Jxs/6Ht4mPnQ3s3hrYWZTuCP2EvqdOTW20mUmei1FSqJCNdjPTylNJ2RX3KlClMnDgRgNOnTxMaGspTTz2FLMtYrVbKysoIDg7m4MGDjBw5Eq1Wi1arJTk5mSNHjnDo0CFyc3NZsGABkZGRrFq1iu+//97n2JycHJYuXQq4QnpeeOGFDou6V8ncAPy8u3YUkTYogoGDIpBUEifyqzl6pLJ5aXQv3FmQdSY8ohQXCbMnqYmLBKcQFJ+1+owMcc9cS2zBPPymjftvDcaoFRSUSmzeaWXWJMHlSSosdsFXdTZWbGjf/uZ0xAVzvMzOotfLGBCrRiNL5JXYvMoBREQaApoZRxpl1t3RF6NOhayS0KhlssaGkRKj5f53/Meu253w+IfFPJHVF60MA/voMVmdVNTZeXt3ecDvo6vpqCurLVoKu8LFT1Ongd5LQD51tVrNihUr+OKLL3jxxReRZZni4mJuv/12goODGTJkCDt37vRalTUajdTV1dG/f3+GDx/O1VdfzUcffcSaNWuYPHmyz7F1dXWex92hO4HiruPy0TY4s8e7jotOryY8Oojac2YadN4uASEgP6+K/LwqAsUt2J/uNHLjhGB0WhV9olzP2WxOPv7St93N0+LrzLD1gJOMIQZkI9w3XUtmohqNrMIpBM/NjeCtr+p4d3fXR2e4EcCx0vMrbDVzTChatYTcLBNWr1ExKsVASrSmzeqQX+TW8GPFCW69IpI+4Rq+PlbHP7+tosHaff1UA6E9H35HaC7sEERUhMzBknCgmvFxrpuXMlu/2Ojdsh7wQumzzz7L8uXLmTNnDps3byYxMZEtW7awadMmnnnmGaZOneoz9CYjI8PjF7/uuut48cUXmTFjhs+x7vAdvV7vCelpD7vTxsuHc3n/a5ncj3e2ev7KKamkj07A4RDIahVFP9azbdtpr3otnWXbN/U0mJxcPz6YUKNMcamNf22tobDYv5C5hX1wX4m0cAthwkmUMYjMaA2aRmFUSRJ6rcQvJ4Tw2QEzVS2aRqfFavnVzyIYHK+joMzKup1VHC62dKkYuffT3mx9WIIefWOse/MmFXaHIDVG227J37yzZh778HSX2NvVNH/vgRzTlsfKvU1LYQc4SDgHS2BKyinGRisFvy4eJFS9W9PbD2n84IMPePXVVwEwGAxIksQ999xDYWEh4JpRq1QqMjIyyMnJwWKxUFtby/Hjxxk0aBCrVq3i888/B2DPnj2kp6f7HTtq1Ci+/PJLwBXSM3r06HbfQK3N1ijoe1zx56amD9awUfEMG5WAWiOj06tRq1UkJRsZNy6uwwfKH3sOmPjd2jIeePYsv3+9guN+ao83Z941Du6drmb8cBVp0Q4GhTo9gi6EoKDEjhCuCJXxw4O9th2WqOPvdyQycaiRvpEarhkYxJ8XJXBFmv8eoh3hmv5B/G1BIh/fncIff9GH0Sltuw3yzlqwNHajOF5q5fF/lnG81IqskjhZ0bGSvYFgrqgO+KcraeuYqjUqas61rvFSVWnybOf2z7v/r6hyUFHlYGthHOuOONlXlkdhbUGX2qzQXUgB/lwg64Tw5z120dDQwMqVKykvL8dut7NkyRIiIyN57rnn0Gg0GAwG1qxZQ2xsLBs3bmTDhg0IIVi6dCnTpk2jqKiIhx9+GKDdseXl5axYsYL6+noiIiJ4/vnnCQpqW1R2fbeXB/bYmkS9WbTM3HvGEhrh+lsIwbkaB2GhMg6H4PV1x3AGUJ+1eQRHnKYeSYKTZ21+fe7tMSBJzYpFEV6JP9E6A2FaPZIkUVBi59kPa1gxI5TYcBVPfXCOIxWue29VpYnXFicwIrn1rPFkuZU5a4vOa5Y+ebCR5dfFeGbeACark2Vvn+TbAt9uoKhgmU33JhOkUyHhEva+EWq+P2Xhnn90bgbe1YLsi+bn1Wk246ytQ5JlVGGhSLLv6JuWxzY4REdiUrinzV5lRT1HD5fh8FGr2L2t27fe3MfuzjzNiKsmPdzCsIi+ikumGyjOL2Po0KHntY//fp+Drm9gMeiqs/rzfr3O0K6o93Y2781hwfpTmPOOtnrutt9cg07n+sBUn7Pzn301XD42lJBgmbf+cQyLpX3frT4qnNRYFQ/O0GNsLAJmtgr+990Kfij034jBH/NujmLq6KZeoKcrICVGJik4zPNYYamDlFiZWrNg9h9LsTmaROGju+PRqptm9e7m0kLALX8+06p2SyC4Z5NbfjOI2FBNq+dzi03M/8sJv9v3j9Wy4qYYRiTpsToEnxyo5Y+fl2O2BX5p9YSQt0IIqKqEhkZXYGNUgzatPypj266QIKOWzDGJyHLTB9zhcFJbY+H773zfzJrfFCKjgnwunkZFyExJKWFsdITikuliukrU9QGKunSBRP2iTz6qLqnxKegAZ043kNwvGJVKIixU5vKxoYSFytTW2gIWdJ0GfjtLj9HQdCINOli9KJql/3uW2gYnkSEy0WEyRWU2TH5qs7hnhla7y62ikl3JR//YIvjlVAdq6okNMmKxCWLDVdSYBCvXV2FrUXqgxuwkOth1ozpRauOxf5bxu6wY4iPUAQm633ouGonIYNfl0DwZSJIk+sfofG7j5kSplaWvF3tKDLdFc/HuyuJgncJscgl68zKagPX4CUjoC5LkN9a+b3KYVzlmc70NvVFDSKgOvUGD2dTa9dR8zaN5uGRLcd9aGMf35a54dmXW3suQuPhDGns9Tv9K9s2nR0i4Y1RjazSJ0BAZu12wa2fgtUUy+1hRqVwfOiEEp8oFfaNdiyWTRgUxIs3AqIF6bHZQq+G9L2t454tzXvtoLgz/Oerk+tEyyK7ko182Jh9VWy08+Ho9owdoabAIvv7B6rPj0MacWhZfHYpeo6J/rIbfZcWQECHz4YG264q052e32AUmq5MQvUx+iYXV753miVkJDOyjp7QmMN+4L0FvS7QvqKAD1NV5Ga0OMmBvMDXWSLaAXu+3kYfeoPGUSjbX2zh5pIx+Q2LQ6GX0erVPUXfjDptsK+SxpE7P9mLIrS5jfFy5koXaS7gYGk/Ljz766KMX2ojz4fsjBXyU4zse2mKyk3+oFFktoVKpOPPjOb7cVUZJSfsLie4PcnqiIHOgHlklcapc8OqnNgYlqogIkYgL1zAoSYdOo0KrcfUQHZiopbzaQcHZpg+13WRGHeTKVqo3Q00DDEuWsDpctWCcAv5vn+DmcQYS+6hITZTJSFOTf8qBqbG2lsHgcovkldgwaCUGxGiw2iHcKLP1hwb+9nUt/ibJHVk4zUgyEBeqIbNfEAPidJhtgt9/epb80sCKfJkrqrGbzJ6fXkt5KVgtHlFXBxkIG9gfW20dTrsdgoJA7Trmzc+fG71BQ3CIDpVKQq1RERymR2/UIAGFxyvaXa8xm+wYDBpMJhuGoKb/g5p9IwzW2qm3qTlcpabGXoxRbUaj0qKVtV17LH4i1FY2EBMTc177OFt6Fk2o3Nj/qO1/1Mnn/Xqd4eKfqbdD3TkLX3923PeTPlLb3bhnkYcLNDicAg0SfaMllt6goW+0hNkq6BujRaN2+cGLyp0kRasw6FTM+lko27+r97k/gK27YP+JcNL7qXA44VSF4M5bgtA2XzwNU7F4uoHfv9vQagb8+u5a3t1XR1yITFmdw9MowxcdEfTXd5WjkuD28dH0i9ZSXW9n7bZSPvu+7QzPHptxt5dM1sb59Lm9LHu+6dkbTJw7dsI1U5ck0LVdhOt00Tn6xAcjSRIqlYQhWIvd7uDs6VpstsAWNpqfG/dMvWXJ3pI6ved/xSXTO5ACdL9cqMXKS0PUffUYDYSWH3IfopBfbOO7o2YyB+ox6FQkxUiYrU5OnrUxJEkGJIrKnaz92MK903Ukx8iEBbdfR7vODHvzXB/+qWO1uKtuCiEorRLERkjoNBL9E2QqTa1nZmab4GSl/8ShzoY1/n1nOa/vKseoU1FncXrdUOJC1Sy8OooRyUHkn6pl3dbTHDvTfclRQEBZwa3GtjyP/vYhy+BweAk7ABoNVJR57avljcsM7P2ihrT0BKL6hGKzOTiZV0Jlbev6yy239eXO8S5J4HbFeN9YSupchcEy4spIDz+liPsFo3e7Xy4NUXdj6oSQNb8R+BH53790hIlXRXLdeFep3u27K9j+dQWvP5eBLkxLUrSKe6frSIpW4XAIDhxv246WH+pQo8t1A65iXx/+28aMiRrCgyVCDBId1efOCrpbfPRR4dSavWebyVFa3l6aik6W0GpUDInXMW1kFHe/eoRv8s752t350REx78y2La8VWXbN0GUZmtW1buvGbzbZyP32ZOftbEHrWjMucW/ZbGPHkRAORoQriUsXiN4t6ZeaqHeGtmb5jR9oJ7B9dyXbtxV7jXv5rZP8Zkl/tBoVyTEyNrsTi9XJP9afgHJL++6ARvJPORia4irhGxshMWOihtgICbsDTpY4XDPHAOmMoLeahfpYHLx/aixBWpWnHIBaVqGW4dG5/bn+0e86/Jptcj6CHii+zneA56sV/r4h+KHl8W2ZCeyvGJhC76C3L5Qqou6m+cyt5Qe++XPNft+z28TK0jpm35xEfJyBQ0eqee+LcsoqG+PXy0sD+qB/f8LOuAwNkWEqtGqJuEgJi02w/5iNqlpBRLOa+IPjdAyK1XG2xkbOjyb8rcepZRiaoMNqF+Sd8R1P315kSnPhGd0vyCPozUMeEyJ0hBhkak0Btn1qi64U85YzcYMBomP5+YJMMuLOfw1gx5EQdm3+3vu1fIm7H8H3deP0F/IIEKLVc1WSibQoB2a7xMGK7ms0ruAfV0SjIuoXH80zU9tx6eTl17LmD4e9H2zp0mlH2B1OePUjE1cO03BZmhqrDf7zg42Dx5t85lpZ4qkZfUhP0CM1blNtcnDfxmIq6h1eM/RrBgXx6MxYJMlVR6bG5GD5u2fJL+l4spRb+GtMdkIbG4fkl1h45P0zPP6LeJKjtJgDXBhsk26enadPv4q7pjTwsz4aYg3p572/m5IKWJc0hI+2neNMkR8XjK/31Ox68Bcu2VLcwwzwy0wrejXIKgjRwgR9HQfKQthsamB83En6BEUr/vUeQVJm6r2CpH7ED4znzPZvOr6twdBxX30nFm5tdth10Maug77jm+eNDWd4s+JZADqNxG8mRbP0zULPY/Hhap6cFYde2zROr5VYuyiBm58vpLa0yW/eXFBaJgW1fOzNbad5YEY/gnQyA+J0PP6LePpGqNn8bRm2Nuqmt0tXi3l0LONvuoxhSd7HcfEQFSkhQ7rsZVJCUnl8LExK/C/bJ0/zes7dk7Sk3vV6b2466/2tqIWwg+/FU7e4z7reiE4WXpUwNSrIjDHx//IiOFiiYkrKKQaHKvHsPUOgon5h4l9+EqKefmUakVFBnNlzHgJ9AamqNHHDsGSvaohu98fo1CCCtCpPudqbMkO8ImncZQQ0KrhqYBBbSn27HvwJvJt3dp4lJdbArGvisNicJEVq2JN3jjUb2ylC5WuhsSuEvEWdH3DNxn9xjYPZqXpiDckA1Nvqu1XkJiaMYmKC/+frbfVcFn2ODd8lNblrIGDXHMDAGIFabjr3ReWCpGgJu1MiXOegzKSi1KQjRmemj717369Cr08o/WmIeu6m7R3fyIdoeGEwEH/ViA7P/ttq8dYW7ugY8G7/1i9aS7PyI0QFy2gbC0ydKLXx+D/LeCQrhoQINeFBcruv78/PLgSs2VTAy58WkdYniNOVFk5XtpOQ5M/9cD74uMnqBw9i0ew+zOpvISPK27VyoQXOqDEyO3UUY6ML2DwkhYMlmcQF+07KOlykYd83RYD3DL60xkm/GBUqlURRueDPn1i5+0Yt8ZESxRWgDXI1t04PDyxBTOF8UWbqFy9tzNLTp1/FuEwNb1aOwLz/QOsBAbheIo0ykgQVde0vMm7LPcfPR0egVas87d/6x2g5VmrxCj/ce9zEtMtCCNK5ygg8khXj6T/6XaH/DM9AE4iq6ux8m99Ou7nu8I/7ORfjF03m3ivLGRvT54ILeFukhKRyzzCoH+i/nEPZkFI2Dwl3lZJudj4+2mdlRD8ZvVYiKVri7hu19ImAY2ednDxtJjJKRVyw390qdDFd5VOfOXMmwcGuE9e3b1+ys7N58sknkWWZcePGce+993Zqv4qod5LcTdvJ3dTx7cwV1STH6HjujgGkxWoRwKlKG4+8X9rmQubL20u5emAIEUYZo06mb6SWBptg9T+LvcbtyqvnWImFQX10GLRNjas/PVBLUWVrf31zMTfqZGZcEUNGagjHzzTw3u4Sqgp7QQMLH5FJ8VeNYMktukZXy6gLZFjHaevGY9SkctvAetLD83h3QCaffVoIwNHTTl58o5BfL+iHRpboEyFx8KSdt3ZLmGyuKJmSiBDKLN2QL6DQiq6IfrFYLAgheOuttzyPzZgxg5deeomkpCTuvPNODh8+zLBhwzq8b0XUexitWuKN+wcTHqz2LHylxmh55bYEfv7HH6n3Uz2yusFB1kvHmHZZGBl9DZyssPJ/+6s5W1RBSqyOYINMXrEJs11w75swfWQo12cEY7EJ/vVtDTt+aLvgV2yYlvceGkGwXiZIJ2Oy2LlzaiLzV9dxtKj7GkG3S8sZemNY4j0Z58iI6vgF39sxaoxMTBjFsIhSMuJcs/bjxyrYta+Kr09qiItQY9MHU9fiS1dFVBCkQIO97fOscH5IXRT9cuTIEUwmE4sXL8Zut7Ns2TKsVivJya61oHHjxrF7925F1LudpH7gL3wtQK7NCEfXmMTTfCFTLUtMHR7Mv3L8C+i5kio2llSxsfHv+AgNGx8aQt8orauiowRPbijis5wq3v28mnc/996+pT+9+Sx9+cx+RAS7eqQCGHRqnE7Bml+PZM5vvzyv99xh/OQMjF80meyR5dzYNxyjJrFnbephYg2x3DMslvFxh3nvRB8+2uZqLVgFmIu9I5XcUTKlprZLJCt0DYGKemVlJVlZWZ6/s7Ozyc7OBkCv1/OrX/2K2bNnU1hYyJIlS7zadxqNRoqKijplnyLqAZI+exJ3TWlga6Hra7FPP3pz/Cy0xoVr0TU2uWheDz0tTktcWOvT0Zav+y/3DCAxSuvVqOGRuckUlJjJO9XaB93Wvq5Nj/AIevPommH9w9FrZczWLkguCgQfvvOL1dXSFWREDSMttJ5Jied4Ny/F45Jx4451b11iQKFb6ECnusjISP75z3/6fC41NZV+/fohSRKpqamEhIRQXd30+Qy0R7MvLnpRl4KCiJ90JUDn4tDbIX7SldwyOYzFQ1TE6JOI0eVRUZXErryjnQp3zP2xHptdoFHjqYfeP1ZDvdnB/iMVmCsC84sO7xdEVKgGWVZ5zfi1MmSPi+bx9R24y5eXYrU7AFc5gub11FOitT7bs3U5PmL79YMHcf0NKZesqyVQmrtkpqQY2PBdUqsx1w6p5aakIFJCUi+AhT8tusL98t5773H06FEeffRRSkpKMJlMBAUF8eOPP5KUlMRXX33VfQulDoeDVatWUVBQgCRJPPbYY6hUKlavXo0QgpSUFNasWYNarWbjxo2sX78etVrNXXfdxbXXXktlZSXLly/HbDYTGxvL008/jcFg6NDYthjYR+YfS1zV7N6bPM2V6NHeLDoQkvrx8wWZzB1cQkqIawZ7vOYk24vDXGFnHRH0ZjHJOfl1HC5qYHi/IPRambQ4LRabk6IyC7tyA1/oigxR42wsoXii1MYT/ypn9cxo0uK0xIYHWCumWZTK+9tP8ssb09Dr1AyI0/HErASSI9XsyDmDzdHzoVlNUS2XvqslUGINscxOjWVsdOvcgJSQ88+SVQiMrhD1WbNmsXLlSubOnYskSTz11FOoVCqWL1+Ow+Fg3LhxjBgxonP2tdejdOvWrWzbto2nn36avXv38sYbbyBJErfffjtjx47loYceYvLkyWRmZrJ48WLef/99LBYL8+bN4/333+e5555j2LBhZGVl8dprr6HVarnpppsCHnvbbbe1+QYOHf6e5IHxABysOMzLB8P44O395+37dpM+e5Ln98qKho59G2h+Q2qWaKJVSyyaFMeMK6NQqSQ276tk3RdnMVkDnxFHBKv59LF0dBrvmbrZ6uRPH51mw67y9nfSTNS1GhV/WXEVmYMicV8Sp0obWPTYV1TXdaC8QGfLIDfS5GqJUdLeFbqUruhReiB3P9GpgeWa1Jw09c4epVOmTGHixIkAnD59mtDQUJ566ilkWcZqtVJWVkZwcDAHDx5k5MiRaLVatFotycnJHDlyhJycHJYuXQrAhAkTeOGFF0hKSgp4bHuibnfaKDW5xCmvxsyxfFWXCTp0MnEJ2hQ1q13w1y1n+euWs520yhUv/sbWEhZOiiVI55rxm60OSqptfLi3ssP7s9qcLF7zNUNTwhjcL4yiknpyjlR4jZFVEtddHs91Y+KorbOy6YsT5Ob7ea32krfcYwAMhhYJRD9dV4tC76arol+6k4B86mq1mhUrVvDFF1/w4osvIssyxcXF3H777QQHBzNkyBB27txJSEiIZxuj0UhdXR11dXWex41GI7W1tV6PtTe2PU5UOHhqp0tYcr850zWCntSv6feuukEE0JCjo/zl07Pk/tjA3J/FEBakZuv+KjbsKsfcgRl/S34oPMcPha3dQLJK4m8PXcFlg6IwGjQ4HE5umZTK82/s553Nx3zvrKWwt5zFN1ZOHH/TZRdFApGCAlxC9dSfffZZli9fzpw5c9i8eTOJiYls2bKFTZs28cwzzzB16lTq65tiZOvr6wkJCSE4OJj6+nr0er1nRdf9WCBj28NUVUPupq87+Lb90OhHn5LS1Jh6a2Fm59w57c1UO1D7oy12fZnPri/zmx7oyD7dY9vLADWZmDou2SPoALKswiCrWH5bJh9/eZJz7blofKxBeNdq+WlFtShcxPTy4i+q9gZ88MEHvPrqqwAYDAYkSeKee+6hsLAQcM2oVSoVGRkZ5OTkYLFYqK2t5fjx4wwaNIhRo0bx5ZeuOOedO3cyevTo3tQs9QAAENxJREFUDo3tKfSZI1j66yGsHGNtrNURQalJx969JZ2frZtMbS+otiGmsgruuD6aLU8O5OsXBvP6gylcltKipK+/2ir+nvNHdGy7N4OpVyd5BF0IwbGzZoQQ2OxOLr/Mz7bNb2oGg+dHP3gQS1dN4I9ZTu4Zlq74zhUuKgJpOn0hXTTtztSnTp3KypUrmT9/Pna7nYcffpjIyEgeeughNBoNBoOBNWvWEBMTw8KFC5k3bx5CCB544AF0Oh133XUXK1asYOPGjURERPD8888TFBQU8Nh20XZ9wsW/T/+Xtd9Es+vN/3RNlUZf+3ALnp8mCst/Ece00aEYdK6ON0OS9Pzp132549HvOHEqwJjk8lK0WhU3ZqUxdngQKhXk5pv5aNs5auo75qKpbbDhcDiRZZVXPfX4UBX1Jrv3e3LTMolIcbUoXAKoerkDpt3ol97O9pz/cMcn1ecdo67PHMGi2X1YPETFvvIqXtkaRO7He7q39K4f90xosJqP/nQFOq2MEIKCUgepsTJOp2Db3nJ+98qRgF/ivmUD6ZsYhKaxbK/DIaittfH0sz9gDY12DQpgVp+RFMQbayZh0Ku9kpOqa61MWPQv7FrvJsktj1vz46vEUitcCLoi+uVg7gH6pEUFNLaioPaCRL+0635RgNA+kcSn9yM8MbCTGTB+bhgJMXpP44mCUgfPflhDQakDWVYxIDnw2W1KipGEeINH0AFkWcJgkBk5MqJDbpqDRQ38acMPWKwO6hps9AlRca7WypKn93gLensuJwWFixhXQulF7n7p7WhUwtUAo4v2t+6Ikzc3mTDv/wZZI5MxazzGyBAkyVVT3FLbwHf/+hq72XeHoq7gdKkZTWMpgdRYmRUzQkmNlXE4nBw7WRfwfhLi9Z6KckIISqsEsRESOp1MUt8g9u5tClnU61T87KoIBqcZKT5jYdtXFVTX2L329+Ynx/lwVxFXpEdTb7LzzaEy7O7EJEXIFX4SXCIhjb2ZCF0Q6241sy6zk9mkjREvccEuX3NzN07qVcMIjg5FVjd1cleFGxk0IYPDW3K65g34iJKpqbfzya4Srh8Xh0En0z/OdZqsdidv/l/g6f9l5RacjZ2pS6sE/9phY+a1GsKNTkpKmkQ4LFTNMysHYgyS0etkrDYnN18Xw+N/OM6JH73FurrWyuffNCvH256YN8vMnZiQFrDtCgoKneOiF3Vo3i/yHGu/mc6uN7cFPHOMHxhPRZWDD/64s9U2fQb39Qi6EAKLyYbOoCE6Lb5r30BLWw0Gfv9mPuXVVuZMSyTYoOZIYS1/eOs4BcUBLpKaTOR/b+JcVSLqGD2xERIzr9UQHQZms5N9X50GkwMMBubO6ENYiBp1Y8ckrUaFVgN3L0pi+ROdq3EDTUXQfgpVFRV+OnRFPfXu5JIQdTeROj1REbIrkiTAMMQz27/hjJ+kUalZo1+Lycap/Er6DohEp9cgSRLdtsZsMuEE1r2bx7oPfuzQds0RAl564RCz56Ux/LIIYsIkCk7UsOGd45hNDs82YzJCPYIuhKC4AhKjICFOh1Gy0tEK3d6Lol3X7FlBoTfQ26NfLilR72oqCkuITotHpVKhM2joOyASjU6m+nT5eQl6aKSe0eOTie0bQumpWv6760fOVfppNXeevur6ejtv/DUPlco1w3D4KM5lbZaBWlwBf/vMzh3Xq+kTDnZ7B95nUj9PyKLialG4JJHo9clHl5SoZ0QN44krS8mIS+GvH8Wfd5hj/le5hCVEIWvVqDVqNBoVTquDozsOdnqfMQnBZN89GlmtQlar6JMUSvrl8Wx4OYey04EvgrqRJMgYHcOoy+NwCkHOnhJyD5TT8p7jdIK/Rrhbd5Qy8+YEdDqZxCi443o1cWFODh6qwRJgyYEmV0sURk1yh9+HgsLFgDv6pTdzSYk6eHeMefmKGedVsdFab+Y/b28jdlBfQmLCqK+ooSTvFHarvf2N/XDtzMFo9U2H3S3uk7MGs35txxdf598xlLTBEegak5RS0sIYelkkm946GvA+PvzkNGn9jWSkh+F0CqKMEmfOWnj5b8fb3VZxtSj81Ojdkn4JirqbjKhhvHotTEkp4ZWtaV6dYaoqTQFHyThsDs7knmw/ZNJgCMhVktAvzPO7EIKqcw4iwmTik8Pa2Mo3KWmhXoIOoNPJpI+I5qsdxZw5FZg33OEQ/O+fjtI30UBqPyOlZWbyjrX+1hA/6Uoio4IAVxniAYOiFVeLwk8MJaTxgjM7dRQ/W1BKg72pnEBhLR2OkmkL92y1pE7f7jcDm8WBzuA67FXnHOza3cD4q4MI1vvdxC8DBod7EouEEJRXCaIjJFSyRNqg8IBF3c2pYhOnin0cj8awxHsyzhHqKcugI0avuFoUfoIoPvULT8uCUSkhqYy9uZ5PRo5wlQPobM30Rsz7D/BqgDP/A3tOMXJcEhqtTESY7BJ0AxzcU9zh122ot+OwO1FpZcqrBB/vtDB9go5Qo8DU0DkXkT7Tu9tK2sAo7prSwOzUREAJS1RQUKJfeilGjdFVjfHWgs4nLnWC3Z+fICzSQFp6NHa7k1CjihOHy/nq0/b91y05+N8ypt6cAkB0hMT0CTqiIySsFieH9gfQ+ag5BgPjF03mkZ95u136BEUSa1B85QoKHpSZeu+mVeLS5u+9B3RhFyUAp0Ow+e1DhITriIgJoqqsgdpqS6f2VVdr4+2/Hmbu4iFIkkSIARrqBe/87TDSkOG05dExVzR1Lk+/Mk2JXFFQCAAJZaH0omFiwijG3lzPviu9s0Xfzetkk4x2qK22dFrMm5OfV82TK/eSnBqCEFBUWMM1d07n2Svbm6k3lSZICdEpkSsKCgHSFQulTqeTRx99lLy8PLRaLWvWrKFfv37tbxgAiqg3w6gxMjHBuwPPxASYO7iEtd9c5lpYbc6FKGLlo2a50ykoPF6DPnMEq14IVzoJKSh0G10T/bJ161asVisbNmxg//79PPPMM7zyyitdYJ8i6gExMWEUw24o5cuR3ouIWwvj+OCPW3pG3Fu02is1tW4OMj5OUpo2Kyh0M11R+yUnJ4fx48cDkJmZyaFDh857n24uelG3Wx0U55f1wCtJXE2S1yNXJ8Ejz0/vgdduhqPRBq2P56qguKonjoWCwsWHxXL+7k6tRkfB0dPtDwTKysr47W9/6/k7Ozub7OxsAOrq6ggODvY8J8sydrsdtfr8JfmiF/XMzMwLbYKCgsJPhIEDBwY8dujQoUyYMMHnc8HBwdTXN+WROJ3OLhF0UDofKSgoKPQ4o0aNYufOnQDs37+fQYMGddm+L/oepQoKCgoXG+7ol6NHjyKE4KmnniItrWvKbSiirqCgoHAJobhfFBQUFC4hFFFXUFBQuIRQRF1BQUHhEqJXifqBAwdYuHAhAA888AALFy5k4cKFTJo0iQceeACAp59+mlmzZjFnzhxyclxNJaqrq7niiis84998800ANm7cSFZWFnPmzGHHjh0AVFZWsnjxYubNm8f999+P6TwShwKxd82aNWRlZbFw4UIOHHAVDDt58iRz585l3rx5/O53v8PpakvE2rVrmTVrFrfeeisHDx5sc2xP2nv48GHGjx/vGf/JJ5/0uL0//PADc+bMYe7cuaxcudKz346c4568Hjpib2+4fv3Z637NadOmeeK8zWYzy5YtY968eSxZsoTKykoAtm/fzi9+8Quys7PZuHFjm2N70l4hhNf1+/zzz/eIvRcM0Ut47bXXxPTp08Xs2bO9Hq+urha33HKLKCkpET/88IOYPXu2cDqdoqCgQMycOVMIIcTXX38tHn/8ca/tSktLxfTp04XFYhE1NTWe35944gnx/vvvCyGEePXVV8Xrr7/ebfZu375dLF68WDgcDlFRUeGxd+nSpeKbb74RQgixevVqsWXLFnHo0CGxcOFC4XQ6RXFxscjKyvI7tqft3bhxo/j73//utV1P23v33XeLf//730IIIR588EGxbdu2Dp3jnr4eOmJvb7h+fdkrhBA7d+4UM2bMECNHjhRms1kIIcS6devEiy++KIQQ4uOPPxZPPPGEsFqtYsqUKaK6ulpYLBaRlZUlysrKfI7taXsLCwvF0qVLvfbX3fZeSHrNTD05OZmXXnqp1eMvvfQSCxYsIDY2ltjYWPR6PVarlbq6Ok+w/qFDh8jNzWXBggXcd999lJaWcvDgQUaOHIlWqyUkJITk5GSOHDnilZ47YcIEdu/e3W325ufnM378eFQqFZGRkciyTFlZGbm5uVx++eVeNuTk5DBu3DgkSSIhIQGHw0FlZaXPsT1t76FDh/j3v//N/Pnzefjhh6mrq+txe4cOHUp1dTVCCOrr61Gr1R06xz19PXTE3t5w/fqyF0ClUvH6668THh7uGdvShj179nD8+HGSk5MJCwtDq9UyevRo9u3b53NsT9ubm5tLSUkJCxcuZMmSJZw4caLb7b2Q9BpRnzZtWquMqoqKCvbs2UNWVhYAarUalUrFDTfcwO23387ixYsB6N+/P/fddx9vv/02U6ZMYc2aNdTV1RESEuLZl9FopK6uzutxo9FIbW1tt9k7dOhQdu3ahc1mo6ioiPz8fEwmE0IIT/0Itw0t04bdj/sa29P2ZmRk8D//8z+88847JCUl8fLLL/e4vSkpKTz55JPccMMNVFRUcMUVV3ToHPf09dARe3vD9evLXoBrrrmGiIgIr2176/H1Z29MTAx33nknb731FkuXLuU3v/lNt9t7Iek1ou6Lzz77jOnTpyPLrh6cH3zwAdHR0XzxxRds27aNtWvXcvbsWa688krPSb3uuus4fPhwqzTc+vp6QkJCvB6vr68nNDS02+wdN24cY8aMYeHChbz22mukp6cTHh6OStV02N02+LPX19ietve6665j+PDhQPvHt7vsffLJJ3nnnXf47LPP+PnPf84zzzzToXPc09dDR+ztDdevL3v90VuPrz+GDx/O5MmTARgzZgylpaUXRB96il4t6nv27PGqnRAaGkpQUBCyLGM0GtFqtTQ0NLBq1So+//xzzzbp6elkZGSQk5ODxWKhtraW48ePM2jQIEaNGsWXX34JwM6dOxk9enS32VtQUEB8fDzr16/n7rvvRpIkQkNDGTZsGHv37vXYMGbMGEaNGsVXX32F0+nk9OnTOJ1OIiMjfY7taXt/9atfeRZC3ce3p+0NCwvzfDOIjY2lpqamQ+e4p6+HjtjbG65fX/b6w5cNaWlpnDx5kurqaqxWK99++y0jR47sFfauXbvWs/h85MgR4uPje9zeHuUC+vNbUVRU5LWQd+ONN4pz5855/rbb7WL16tUiOztbzJo1S6xbt04IIcSPP/4oFixYIBYsWCCWLFkiSkpKhBBCbNiwQWRlZYmZM2eKzz77TAghRFlZmVi8eLHIzs4Wv/71r0V9fX232Ws2m8W9994rZs+eLRYsWCCOHj0qhBDixIkTYv78+WLOnDnioYceEna7XQghxIsvvihmzZolsrKyxL59+9oc25P2Hjp0SGRnZ4sFCxaI+++/X9TW1va4vfv27RPZ2dli/vz54rbbbhNFRUVCiI6d4568Hjpib2+4fv3Z6+baa6/1LDw2NDSIZcuWiVtvvVUsXLhQlJaWCiGE2LZtm8fet99+u82xPWlvdXW1WLJkiZg/f7745S9/KfLz83vE3guFUiZAQUFB4RKiV7tfFBQUFBQ6hiLqCgoKCpcQiqgrKCgoXEIooq6goKBwCaGIuoKCgsIlhCLqCgoKCpcQiqgrKCgoXEL8f9BlFj0aPo41AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_grid(grid_points, (loc+0.25*std)*1.0, title=\"NP grid values (+.5 std)\")\n",
    "1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T00:49:47.568631Z",
     "start_time": "2020-03-02T00:49:47.562057Z"
    }
   },
   "source": [
    "# Profile lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:10:52.989352Z",
     "start_time": "2020-03-02T06:10:52.022896Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0, 400)"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im=z_np[::-1]\n",
    "im[im==0]=np.nan\n",
    "\n",
    "plt.title('NP profile lines')\n",
    "plt.imshow(im, vmin=0, vmax=400, cmap='GnBu', interpolation='none')\n",
    "plt.colorbar()\n",
    "plt.hlines(80, 0, 75, colors='orange')\n",
    "plt.vlines(25, 0, 110, colors='b')\n",
    "plt.show()\n",
    "\n",
    "# Show a couple of steep profiles\n",
    "plt.plot(im[:, 25], c='b')\n",
    "plt.plot(im[80], c='orange')\n",
    "plt.ylim(0, 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:10:59.577037Z",
     "start_time": "2020-03-02T06:10:58.643225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0, 400)"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "im=z_krig[::-1]\n",
    "im[im==0]=np.nan\n",
    "\n",
    "plt.title('Kriging profile lines')\n",
    "plt.imshow(im, vmin=0, vmax=400, cmap='GnBu', interpolation='none' )\n",
    "plt.colorbar()\n",
    "plt.hlines(80, 0, 75, colors='orange')\n",
    "plt.vlines(25, 0, 110, colors='b')\n",
    "plt.show()\n",
    "\n",
    "# Show a couple of steep profiles\n",
    "plt.plot(im[:, 25], c='b')\n",
    "plt.plot(im[80], c='orange')\n",
    "plt.ylim(0, 400)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T00:39:10.405318Z",
     "start_time": "2020-03-02T00:39:10.401238Z"
    }
   },
   "source": [
    "# Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:58.544471Z",
     "start_time": "2020-03-02T06:09:58.018584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Executing Ordinary Kriging...\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/numpy/lib/histograms.py:824: RuntimeWarning: invalid value encountered in greater_equal\n",
      "  keep = (tmp_a >= first_edge)\n",
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/numpy/lib/histograms.py:825: RuntimeWarning: invalid value encountered in less_equal\n",
      "  keep &= (tmp_a <= last_edge)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fc2d32032e8>"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Historgram showing extrapolation\n",
    "lw=3\n",
    "plt.hist(meuse['lead'].values.flatten(), range=[0, 1000], histtype='step', density=True, label='true', lw=lw)\n",
    "\n",
    "im = z_np * 1\n",
    "im[im==0]=np.nan\n",
    "plt.hist(im.flatten(), range=[0, 1000], histtype='step', density=True, label='np', lw=lw)\n",
    "\n",
    "\n",
    "im2 = model.execute(style='points', xpoints=grid_points['x'], ypoints=grid_points['y'])[0].data\n",
    "im2[im2==0]=np.nan\n",
    "plt.hist(im2.flatten(), range=[0, 1000], histtype='step', density=True, label='kriging', lw=lw)\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T00:40:15.029988Z",
     "start_time": "2020-03-02T00:40:15.023318Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T09:14:47.744932Z",
     "start_time": "2020-03-01T09:14:47.059419Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:59.204072Z",
     "start_time": "2020-03-02T06:09:58.547452Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'samples')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(meuse['x'], meuse['y'], c=meuse['lead'], cmap='GnBu', vmin=0, vmax=400)\n",
    "plt.plot(meuse['x'], meuse['y'], '.', ms=1)\n",
    "plt.colorbar()\n",
    "plt.title('samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T06:38:52.016630Z",
     "start_time": "2020-03-01T06:38:51.965367Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-01T06:39:44.623253Z",
     "start_time": "2020-03-01T06:39:43.865906Z"
    }
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Folium plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:59.214067Z",
     "start_time": "2020-03-02T06:09:59.207571Z"
    }
   },
   "outputs": [],
   "source": [
    "def color_function(value):\n",
    "    if (value==0) | (value==np.nan) : return (0,0,0,0)\n",
    "    else: \n",
    "        color = matplotlib.cm.YlOrRd(value) \n",
    "        return color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:09:59.450075Z",
     "start_time": "2020-03-02T06:09:59.216712Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "meuse_lat_long = meuse.to_crs({'init': 'epsg:4326'})\n",
    "meuse_lat_long['long'] = meuse_lat_long.geometry.apply(lambda x: x.x)\n",
    "meuse_lat_long['lat'] = meuse_lat_long.geometry.apply(lambda x: x.y)\n",
    "mean_long = np.mean(meuse_lat_long['long'])\n",
    "mean_lat = np.mean(meuse_lat_long['lat'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:10:00.108365Z",
     "start_time": "2020-03-02T06:09:59.452650Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/wassname/.pyenv/versions/jup3.7.3/lib/python3.7/site-packages/pyproj/crs/crs.py:55: FutureWarning: '+init=<authority>:<code>' syntax is deprecated. '<authority>:<code>' is the preferred initialization method. When making the change, be mindful of axis order changes: https://pyproj4.github.io/pyproj/stable/gotchas.html#axis-order-changes-in-proj-6\n",
      "  return _prepare_from_string(\" \".join(pjargs))\n"
     ]
    }
   ],
   "source": [
    "# grid_points['prediction'] = z_np\n",
    "grid_points_gpd = grid_points.set_geometry('geometry')    \n",
    "grid_points_gpd.crs = {'init':'epsg:28992'}\n",
    "grid_points_gpd = grid_points_gpd.to_crs({'init': 'epsg:4326'})\n",
    "grid_points_gpd['long'] = grid_points_gpd.geometry.apply(lambda x: x.x)\n",
    "grid_points_gpd['lat'] = grid_points_gpd.geometry.apply(lambda x: x.y)\n",
    "\n",
    "grid_points_pivot = grid_points_gpd.pivot(values='prediction', columns='x', index='y').fillna(0)\n",
    "grid_points_pivot = grid_points_pivot.loc[:,grid_points_pivot.columns.sort_values(ascending=True)]\n",
    "grid_points_pivot = grid_points_pivot.loc[grid_points_pivot.index.sort_values(ascending=True),:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-02T06:10:02.701988Z",
     "start_time": "2020-03-02T06:10:00.111225Z"
    }
   },
   "outputs": [
    {
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    color_map_3e9879dc6d794dc1a580ab4d03de6d98.x = d3.scale.linear()
              .domain([0.0, 654.0])
              .range([0, 400]);

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.legend = L.control({position: 'topright'});
    color_map_3e9879dc6d794dc1a580ab4d03de6d98.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};
    color_map_3e9879dc6d794dc1a580ab4d03de6d98.legend.addTo(map_363417d6f68941d192556c87a6d56408);

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.xAxis = d3.svg.axis()
        .scale(color_map_3e9879dc6d794dc1a580ab4d03de6d98.x)
        .orient("top")
        .tickSize(1)
        .tickValues([0.0, 109.0, 218.0, 327.0, 436.0, 545.0, 654.0]);

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.svg = d3.select(".legend.leaflet-control").append("svg")
        .attr("id", 'legend')
        .attr("width", 450)
        .attr("height", 40);

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.g = color_map_3e9879dc6d794dc1a580ab4d03de6d98.svg.append("g")
        .attr("class", "key")
        .attr("transform", "translate(25,16)");

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.g.selectAll("rect")
        .data(color_map_3e9879dc6d794dc1a580ab4d03de6d98.color.range().map(function(d, i) {
          return {
            x0: i ? color_map_3e9879dc6d794dc1a580ab4d03de6d98.x(color_map_3e9879dc6d794dc1a580ab4d03de6d98.color.domain()[i - 1]) : color_map_3e9879dc6d794dc1a580ab4d03de6d98.x.range()[0],
            x1: i < color_map_3e9879dc6d794dc1a580ab4d03de6d98.color.domain().length ? color_map_3e9879dc6d794dc1a580ab4d03de6d98.x(color_map_3e9879dc6d794dc1a580ab4d03de6d98.color.domain()[i]) : color_map_3e9879dc6d794dc1a580ab4d03de6d98.x.range()[1],
            z: d
          };
        }))
      .enter().append("rect")
        .attr("height", 10)
        .attr("x", function(d) { return d.x0; })
        .attr("width", function(d) { return d.x1 - d.x0; })
        .style("fill", function(d) { return d.z; });

    color_map_3e9879dc6d794dc1a580ab4d03de6d98.g.call(color_map_3e9879dc6d794dc1a580ab4d03de6d98.xAxis).append("text")
        .attr("class", "caption")
        .attr("y", 21)
        .text('');
        
        
        </script>
        \" style=\"position:absolute;width:100%;height:100%;left:0;top:0;\"></iframe></div></div>"
      ],
      "text/plain": [
       "<folium.folium.Map at 0x7fc2d8132208>"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = folium.Map([mean_lat, mean_long], zoom_start=13, tiles='Stamen Toner')\n",
    "m.add_children(plugins.ImageOverlay(image = (z_np/400), \n",
    "                                           opacity=0.7,origin='lower',\n",
    "                                           colormap=color_function,\n",
    "                                   bounds = [[np.min(grid_points_gpd['lat']), np.min(grid_points_gpd['long'])],\n",
    "                                            [np.max(grid_points_gpd['lat']), np.max(grid_points_gpd['long'])]]))\n",
    "for row in meuse_lat_long.iterrows():\n",
    "    folium.CircleMarker(location=[row[1]['lat'], row[1]['long']], radius=50, color=None, fill_opacity=1,\n",
    "                    fill_color=scale(row[1][feature_to_plot])).add_to(m)\n",
    "m.add_children(scale)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
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   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {
    "height": "calc(100% - 180px)",
    "left": "10px",
    "top": "150px",
    "width": "272px"
   },
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}