random_forest_F2021.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "010fcbe6-f7d9-42e3-9b61-aee92ea0bb01",
   "metadata": {},
   "source": [
    "# Random Forest Model\n",
    "By: Cara Piske F'21\n",
    "\n",
    "This code was written to run a random forest model to determine vegetation controls on snowpack characteristics while accounting for terrain variables. See AGU 2021...\n",
    "\n",
    "\n",
    "Snow depth was converted to a geotiff (from .asc) using the Kost. et al., 2019 processed ASO data. For the first test, we sampled only the open pixels, which were extracted from the Kost. snow classified by veg data converted to a geotiff as well (see convertFormat_calcTerrainVars.ipynb). <br>\n",
    "NCALM data was downloaded, merged, and clipped to the ASO extent. It was then used to calculate slope, aspect, and northness (see convertFormat_calcTerrainVars.ipynb). <br>\n",
    "SnowPalm-derived density was resampled to match the ASO grids. SWE was calculated by multiplying the snow density (g/cm3) / 1 (g/cm3) with depth (m)<br>\n",
    "Kane et al. vegetation classification data (derived from FUSION metrics) was resampled using to a 1m grid and the extent of the ASO data (nearest neighbor resampling method)<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "17ba2edf-cf36-4b4f-9f10-0d2d8017454e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import necessary packages\n",
    "from osgeo import gdal\n",
    "import pandas as pd\n",
    "import os\n",
    "import subprocess\n",
    "import pdal\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn import linear_model\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier\n",
    "from sklearn.model_selection import train_test_split # Import train_test_split function\n",
    "from sklearn import metrics #Import scikit-learn metrics module for accuracy calculati\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn import metrics\n",
    "import joblib\n",
    "from sklearn import preprocessing\n",
    "import multiprocessing as mp\n",
    "import time\n",
    "# Import tools needed for visualization\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "# Import tools needed for visualization\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "from sklearn.datasets import make_hastie_10_2\n",
    "from sklearn.inspection import PartialDependenceDisplay\n",
    "from pdpbox import pdp, get_dataset, info_plots\n",
    "import seaborn as sns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85d475e6-118c-41e6-96e7-49871379ccbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "W:\\\n"
     ]
    }
   ],
   "source": [
    "#make sure we're in the right working directory\n",
    "os.chdir('/')\n",
    "print(os.getcwd())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66ee1cb7-4c4f-40b0-86a7-fbc024d3a673",
   "metadata": {},
   "source": [
    "## Load Data Save to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "af609c10-231f-429f-9033-048beef6891f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# get geotransform and projection data\n",
    "raster = gdal.Open(\"SCB/random_forest_data/calc_swe/SWE_20160326.tif\")\n",
    "gt = raster.GetGeoTransform()\n",
    "proj = raster.GetProjection()\n",
    "raster = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d6beb0cc-e25f-4c76-b1cd-3b8dcf95f805",
   "metadata": {},
   "outputs": [],
   "source": [
    "# input - path = str. i.e. 'SCB/kane_veg_metrics/departure/14_withholes/departure_wh_clip2dem.tif'\n",
    "# input - nd_value = int. no data value\n",
    "# output - raster_arr = 1D array of raster values (maintaining columns)\n",
    "def raster_to_array(path, nd_value):\n",
    "    raster = gdal.Open(path) # open the file \n",
    "    raster_arr = raster.GetRasterBand(1).ReadAsArray() #read the first raster band (in this case we know we are only working with single bands) and read to a 2D array\n",
    "    if raster_arr.dtype == 'int32':\n",
    "        raster_arr = raster_arr.astype(float)\n",
    "    raster_arr[raster_arr == nd_value] = np.nan # where the raster is equal to the provided no data value, set values to Nan\n",
    "    raster_arr_flat = raster_arr.flatten() # flatten the array (row-wise)\n",
    "    raster = None\n",
    "    return raster_arr, raster_arr_flat"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a402ee8d-1a02-4184-864b-f2e50b9c9e01",
   "metadata": {},
   "source": [
    "Snow Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "04bb9052-7834-4ba9-aded-4d3581793ab0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define variable path\n",
    "# fSCA_20160326_path = \"SCB/random_forest_data/ASO_sd/ASO_snow_depth_20160326_30m.tif\"\n",
    "# fSCA_20160417_path = \"SCB/random_forest_data/ASO_fsca/ASO_snow_classified_by_veg_20160417_warp2Dep.tif\"\n",
    "# fSCA_20160518_path = \"SCB/random_forest_data/ASO_fsca/ASO_snow_classified_by_veg_20160518_warp2Dep.tif\"\n",
    "\n",
    "SWE_20160326_path = \"SCB/random_forest_data/calc_swe/SWE_20160326.tif\"\n",
    "SWE_20160417_path = \"SCB/random_forest_data/calc_swe/SWE_20160417.tif\"\n",
    "SWE_20160518_path = \"SCB/random_forest_data/calc_swe/SWE_20160518.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "id": "60ec7bba-d8d1-4068-8a61-2829698003a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open data save to array\n",
    "snowVeg_20160326_path = \"SCB/random_forest_data/ASO_sd/ASO_snow_classified_by_veg_20160326_clp.tif\"; [snowVeg_20160326_arr, snowVeg_20160326_flat] = raster_to_array(snowVeg_20160326_path, -9999)\n",
    "snowVeg_20160417_path = \"SCB/random_forest_data/ASO_sd/ASO_snow_classified_by_veg_20160417_clp.tif\"; [snowVeg_20160417_arr, snowVeg_20160417_flat] = raster_to_array(snowVeg_20160417_path, -9999)\n",
    "snowVeg_20160518_path = \"SCB/random_forest_data/ASO_sd/ASO_snow_classified_by_veg_20160518_clp.tif\"; [snowVeg_20160518_arr, snowVeg_20160518_flat] = raster_to_array(snowVeg_20160518_path, -9999)\n",
    "\n",
    "\n",
    "[SWE_20160326_arr, SWE_20160326_flat] = raster_to_array(SWE_20160326_path, -9999)\n",
    "[SWE_20160417_arr, SWE_20160417_flat] = raster_to_array(SWE_20160417_path, -9999)\n",
    "[SWE_20160518_arr, SWE_20160518_flat] = raster_to_array(SWE_20160518_path, -9999)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "419acc26-a596-4c68-a08f-495a6dd30827",
   "metadata": {},
   "source": [
    "Run below code to set all values not in the open (i.e. tall trees) to nans\n",
    "\n",
    "the snow classified by veg has the following classifications: 0 - BE no snow; 25 - BE snow; 75 - tall tree no snow; 100 - tall tree snow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "3a8ec371-0140-454a-94d9-dab13ba8e639",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # filter out only open pixels\n",
    "# SWE_20160326_arr[snowVeg_20160326_arr >30 ] = np.nan; SWE_20160326_flat[snowVeg_20160326_flat >30 ] = np.nan\n",
    "# SWE_20160417_arr[snowVeg_20160417_arr >30 ] = np.nan; SWE_20160417_flat[snowVeg_20160417_flat >30 ] = np.nan\n",
    "# SWE_20160518_arr[snowVeg_20160518_arr >30 ] = np.nan; SWE_20160518_flat[snowVeg_20160518_flat >30 ] = np.nan\n",
    "\n",
    "# # classify values is no canopy or canopy\n",
    "snowVeg_20160326_flatb = snowVeg_20160326_flat\n",
    "snowVeg_20160326_flatb[snowVeg_20160326_flatb <30 ] = 0\n",
    "snowVeg_20160326_flatb[snowVeg_20160326_flatb >50 ] = 1\n",
    "#snowVeg_20160326_flat = snowVeg_20160326_flat.astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "782d3913-b807-467e-87e6-e99561e2ed99",
   "metadata": {},
   "source": [
    "**Terrain Data**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d2c12a3f-7d8c-4942-906a-d3d8c78b27c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "veg_dep_path = \"SCB/random_forest_data/kane_veg/NCALM_veg_departure_clip2ASO_1m.tif\"\n",
    "veg_coreGap_path = \"SCB/random_forest_data/kane_veg/NCALM_veg_raw_coreGap_clip2ASO_1m.tif\"\n",
    "veg_meanClumpSize_path = \"SCB/random_forest_data/kane_veg/NCALM_veg_raw_meanClumpSize_clip2ASO_1m.tif\"\n",
    "veg_TAOperHa_path = \"SCB/random_forest_data/kane_veg/NCALM_veg_raw_TAOperHa_clip2ASO_1m.tif\"\n",
    "elev_path = \"SCB/random_forest_data/NCALM_terrain/NCALM_2014_be_clip2ASO.tif\"\n",
    "nness_path = \"SCB/random_forest_data/NCALM_terrain/NCALM_2014_northness_clip2ASO.tif\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10388521-c281-4452-b787-00aee23676da",
   "metadata": {},
   "outputs": [],
   "source": [
    "[veg_dep_arr, veg_dep_flat] = raster_to_array(veg_dep_path, -9999)\n",
    "[veg_meanClumpSize_arr, veg_meanClumpSize_flat] = raster_to_array(veg_meanClumpSize_path, -9999)\n",
    "[veg_TAOperHa_arr, veg_TAOperHa_flat] = raster_to_array(veg_TAOperHa_path, -9999)\n",
    "[veg_coreGap_arr, veg_coreGap_flat] = raster_to_array(veg_coreGap_path, -9999)\n",
    "[elev_arr, elev_flat] = raster_to_array(elev_path, -9999)\n",
    "[nness_arr, nness_flat] = raster_to_array(nness_path, -9999)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "69053c6e-1603-4a19-92bd-9b2eb11ebda6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correct the elevation data\n",
    "elev_flat[elev_flat < 100] = np.nan\n",
    "elev_arr[elev_arr < 100 ] = np.nan\n",
    "SWE_20160326_flat[SWE_20160326_flat < 0] =np.nan\n",
    "SWE_20160326_flat[SWE_20160326_flat > 5000] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33e23787-7888-4c08-a0cf-f126d0f8c4f2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# df_0326 = pd.DataFrame({'SWE':SWE_20160326_flat,'northness':nness_flat,'elev':elev_flat, 'veg_dep':veg_dep_flat, 'meanClumpSize':veg_meanClumpSize_flat, 'TAOperHa':veg_TAOperHa_flat, 'coreGap':veg_coreGap_flat})\n",
    "# df_0326 = df_0326.dropna().reset_index(drop=True)\n",
    "# # sklearn requires our \"label\" variable (target) to be a categorical or floating point variable. \n",
    "# # convert SWE values to cm\n",
    "# df_0326[\"SWE_mm\"] = df_0326.SWE * 1000\n",
    "# # convert to integer \n",
    "# df_0326[\"SWE_mm\"] = df_0326[\"SWE_mm\"].astype(\"int\") \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18bf934c-b334-43ba-882b-06f48af06dfa",
   "metadata": {},
   "source": [
    "Because of spatial autocorrelation within the dataset, we want to choose an area of the raster to serve as our training dataset\n",
    "\n",
    "For now we are only testing terrain varialbes (not vegetation)\n",
    "\n",
    "we need to add in an raster with the latitude values of each pixel (center) in order to sort by latitude. We could theoretically do this by simply splitting the arrays 70% and 30% for the training and test arrays respectively however because we have quite a few nans (i.e. independence lake and biased open snow depths) we need to get rid of the nans before we separate the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "41e81d53-6379-449a-bf3a-caa1a71ad780",
   "metadata": {},
   "outputs": [],
   "source": [
    "# gt[3] = coordinate of the upper lefthand corner of the upper lefthand pixel, gt[1]= resolution\n",
    "lat_start = gt[3]-gt[1]/2\n",
    "lat_stop = lat_start-elev_arr.shape[0]+1\n",
    "# create dataframe and restructure                     \n",
    "lat_arr = np.arange(lat_stop,lat_start+1,1); lat_arr = np.flip(lat_arr); lat_arr = np.transpose([lat_arr]*elev_arr.shape[1]); lat_flat = lat_arr.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "ecc56c25-4e67-40d2-942a-c3a6af03219d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # lon\n",
    "# lon_start = gt[0]+gt[1]/2\n",
    "# lon_stop = lon_start+elev_arr.shape[1]-1\n",
    "# lon_arr = np.arange(lon_start,lon_stop+1,1); lon_arr = np.tile(lon_arr,(elev_arr.shape[0],1)); lon_flat = lon_arr.flatten()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97830711-a1e3-4864-96fb-94ff57b1a3c1",
   "metadata": {},
   "source": [
    "### Pre preocess the data\n",
    "Running into memory issues, so let's filter the data as much as possible before putting into a dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "31732a9c-c472-4879-872c-2db7072e036f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# because we know there are the most holes in the SWE data, let's filter for SWE nans\n",
    "SWE_nans_0326 = ~np.isnan(SWE_20160326_flat)\n",
    "SWE_nans_0417 = ~np.isnan(SWE_20160417_flat)\n",
    "SWE_nans_0518 = ~np.isnan(SWE_20160518_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "066032e9-9326-438d-a70a-aaf4655b5900",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # filter all datasets\n",
    "# # for now, just flat data\n",
    "# SWE_20160326_fil = SWE_20160326_flat[SWE_nans]\n",
    "# SWE_20160518_fil = SWE_20160518_flat[~np.isnan(SWE_20160518_flat)]\n",
    "# # veg_dep_fil = veg_dep_flat[SWE_nans]\n",
    "# # veg_meanClumpSize_fil = veg_meanClumpSize_flat[SWE_nans]\n",
    "# # veg_TAOperHa_fil = veg_TAOperHa_flat[SWE_nans]\n",
    "# # veg_coreGap_fil = veg_coreGap_flat[SWE_nans]\n",
    "# elev_fil = elev_flat[SWE_nans]\n",
    "# nness_fil = nness_flat[SWE_nans]\n",
    "# lat_fil = lat_flat[SWE_nans]\n",
    "# lon_fil = lon_flat[SWE_nans]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8f0450b0-8886-4669-a09a-d5a93979c95e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the 2014 density product has a band of values outside of the main study zone that we have to filter out - luckily we know the lat and long of the band. (this will be very inefficient for now)\n",
    "SWE_20160417_arr[lat_arr >= 4372000] = np.nan\n",
    "SWE_20160417_arr[lon_arr >= 738590] = np.nan\n",
    "SWE_20160417_flat = SWE_20160417_arr.flatten()\n",
    "SWE_20160417_fil = SWE_20160417_flat[~np.isnan(SWE_20160417_flat)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52b0bded-7f2a-4ae1-afe9-7b6bccffc0a5",
   "metadata": {},
   "source": [
    "sort by lat ascending, mean = 300, descending = 410"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "807e6b7c-4856-40ba-8dcf-deea68cfa87f",
   "metadata": {},
   "source": [
    "lat 621, ascending T, train<br>\n",
    "lat 327.88, ascending T, test<br>\n",
    "lat 409.80, ascending F, train<br>\n",
    "lat 824.02, ascending F, test<br>\n",
    "\n",
    "lon 636.39, ascending T, train<br>\n",
    "lon 292.449, ascending T, test<br>\n",
    "lon 366.13, ascending F, train<br>\n",
    "lon 926.06, ascending F, test<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 538,
   "id": "57d4ac18-ed09-4604-b920-933d2578d740",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def RF_dataset_creation(var_list, var_list_str, feature_list, label_list):\n",
    "    # create a dataframe from the arrays\n",
    "    df = pd.DataFrame()\n",
    "    i = 0\n",
    "    for files in var_list:\n",
    "        name = var_list_str[i]\n",
    "        df[name] = files\n",
    "        i = i+1\n",
    "    \n",
    "    # data cannot contain nans; sklearn requires our \"label\" variable (target) to be a categorical or floating point variable. \n",
    "    df = df.dropna(subset=feature_list).reset_index(drop=True)\n",
    "    df[\"SWE_mm\"] = df[\"SWE\"] *1000\n",
    "    df[\"SWE_mm\"] = df[\"SWE_mm\"].astype(\"int\")\n",
    "    # sort by lat\n",
    "    #df = df.sort_values(by=['lat'], ascending=False).reset_index(drop = True)\n",
    "    #df = df.sort_values(by=['elev'], ascending=True).reset_index(drop = True)\n",
    "    #df = df[df['elev'] >= 2400]\n",
    "    #df = df[df['elev'] < 2400]\n",
    "    df = df.sort_values(by=['lat'], ascending=False).reset_index(drop = True)\n",
    "    # calculate row cutoff\n",
    "    train_cutoff = round(len(df)*0.7)\n",
    "    # we want our training dataset to be the bottom 70% of the raster, so we will define the training df as the top 70% of the latitudes\n",
    "    train_df = df.iloc[0:train_cutoff]\n",
    "    # wwe want our testing dataset to be the top 30% of the raster, so we will define the testing df as the bottom 30% of the latitudes\n",
    "    test_df = df.iloc[train_cutoff:len(df)+1]\n",
    "    all_df = df\n",
    "\n",
    "    lab_train = train_df[label_list]\n",
    "    lab_enc = preprocessing.LabelEncoder()\n",
    "    lab_train_enc = lab_enc.fit_transform(lab_train)\n",
    "    #lab_train_enc = lab_train_enc.reshape(-1,1)\n",
    "    feat_train = train_df[feature_list]\n",
    "    \n",
    "    lab_test = test_df[label_list]\n",
    "    feat_test = test_df[feature_list]\n",
    "    test_all = test_df\n",
    "    return(lab_train_enc, feat_train, lab_test, feat_test, df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 539,
   "id": "35a15a2e-d4e4-4ce5-8de3-0add6b1b3205",
   "metadata": {},
   "outputs": [],
   "source": [
    "var_list = [SWE_20160326_flat[SWE_nans_0326], elev_flat[SWE_nans_0326], nness_flat[SWE_nans_0326],snowVeg_20160326_flatb[SWE_nans_0326], lat_flat[SWE_nans_0326], veg_meanClumpSize_flat[SWE_nans_0326], veg_TAOperHa_flat[SWE_nans_0326], veg_coreGap_flat[SWE_nans_0326], veg_dep_flat[SWE_nans_0326]]\n",
    "var_list_str = [\"SWE\",\"elev\",\"nness\",\"canopy_class\",\"lat\", \"clump_size\",\"TAOperHa\",\"coreGap\", \"veg_dep\"]\n",
    "feature_list = [\"elev\",\"nness\"]#, \"clump_size\" ,\"TAOperHa\",\"coreGap\"]\n",
    "label_list = \"SWE_mm\"\n",
    "[lab_train_20160326, feat_train_20160326, lab_test_20160326, feat_test_20160326, test_all_20160326] = RF_dataset_creation(var_list, var_list_str, feature_list, label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 546,
   "id": "2e262f14-f2e6-4756-bca1-2267195bf169",
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>elev</th>\n",
       "      <th>nness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4219606</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028004</th>\n",
       "      <td>2282.133789</td>\n",
       "      <td>-0.290327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028005</th>\n",
       "      <td>2273.973145</td>\n",
       "      <td>-0.400422</td>\n",
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       "      <th>6028006</th>\n",
       "      <td>2276.633545</td>\n",
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       "      <th>6028007</th>\n",
       "      <td>2281.091553</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>1808402 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                elev     nness\n",
       "4219606  2193.418457  0.122118\n",
       "4219607  2207.635254  0.047845\n",
       "4219608  2208.509521  0.023012\n",
       "4219609  2209.257080  0.100221\n",
       "4219610  2210.605713  0.073422\n",
       "...              ...       ...\n",
       "6028003  2267.433350 -0.278378\n",
       "6028004  2282.133789 -0.290327\n",
       "6028005  2273.973145 -0.400422\n",
       "6028006  2276.633545 -0.291743\n",
       "6028007  2281.091553 -0.295522\n",
       "\n",
       "[1808402 rows x 2 columns]"
      ]
     },
     "execution_count": 546,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feat_test_20160326[['elev','nness']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "id": "4a9780b2-e609-49a3-8035-299754a6a319",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert canopy class to int\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.compose import make_column_transformer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "e2169783-e9c6-4be4-8576-160aba23b217",
   "metadata": {},
   "outputs": [],
   "source": [
    "column_trans = make_column_transformer((OneHotEncoder(), ['canopy_class']), remainder = \"passthrough\")\n",
    "column_trans = column_trans.fit_transform(feat_train_20160326b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "76323371-f010-4b32-a6b7-3a4eec338e1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# var_list = [SWE_20160417_flat[SWE_nans_0417], elev_flat[SWE_nans_0417], nness_flat[SWE_nans_0417], lat_flat[SWE_nans_0417], veg_meanClumpSize_flat[SWE_nans_0417], veg_TAOperHa_flat[SWE_nans_0417], veg_coreGap_flat[SWE_nans_0417]]\n",
    "# var_list_str = [\"SWE\",\"elev\",\"nness\",\"lat\", \"clump_size\",\"TAOperHa\",\"coreGap\"]\n",
    "# feature_list = [\"elev\",\"nness\"]#, \"clump_size\" ,\"TAOperHa\",\"coreGap\"]\n",
    "# label_list = \"SWE_mm\"\n",
    "# [lab_train_20160417, feat_train_20160417, lab_test_20160417, feat_test_20160417, test_all_20160417] = RF_dataset_creation(var_list, var_list_str, feature_list, label_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "11ce6de2-e4c4-4276-b82b-80b4b001cf7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# var_list = [SWE_20160518_flat[SWE_nans_0518], elev_flat[SWE_nans_0518], nness_flat[SWE_nans_0518], lat_flat[SWE_nans_0518], veg_meanClumpSize_flat[SWE_nans_0518], veg_TAOperHa_flat[SWE_nans_0518], veg_coreGap_flat[SWE_nans_0518]]\n",
    "# var_list_str = [\"SWE\",\"elev\",\"nness\",\"lat\", \"clump_size\",\"TAOperHa\",\"coreGap\"]\n",
    "# feature_list = [\"elev\",\"nness\"]#, \"clump_size\" ,\"TAOperHa\",\"coreGap\"]\n",
    "# label_list = \"SWE_mm\"\n",
    "# [lab_train_20160518, feat_train_20160518, lab_test_20160518, feat_test_20160518, test_all_20160518] = RF_dataset_creation(var_list, var_list_str, feature_list, label_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "001b08b1-e712-4d21-a98c-394bec6bf4ea",
   "metadata": {},
   "source": [
    "## Now time for RFM!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "700fe6f7-e9e5-48b4-a9da-15b6f02c3fc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Number of processors: \", mp.cpu_count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "356743d3-db77-4990-b7b2-a4af961355d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a Gaussian Classifier\n",
    "def rf(feat_train, lab_train):\n",
    "    rf_mod=RandomForestClassifier(n_estimators=10, random_state = 42, max_depth=3)\n",
    "    #Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "    rf_mod.fit(feat_train, lab_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "392dba0b-75fe-4b34-89f8-1ab1d924ba00",
   "metadata": {},
   "source": [
    "dec n_est from 700 to 600 and max depth to 8, time = 464, abs error = 153.86 degrees <br>\n",
    "n_est 800, max depth to 8, time = 620, MAE = 153.75 degrees <br>\n",
    "n_est 800, max depth to 9, time = 687, MAE = 157.02 degrees <br>\n",
    "n_est 800, max depth to 9, time = 567, MAE = 153.05 degrees (elev/northness/tree clump) <br>\n",
    "n_est 800, max depth to 10, time = 749.7, MAE = 153.05 degrees (elev/northness/tree clump) <br>\n",
    "\n",
    "n_est 800, max depth to 10, time = 538.22, MAE = 148.74 degrees (elev/northness) <br>\n",
    "n_est 800, max depth to 10, time = 742.22, MAE = 148.74 degrees (elev/northness/allveg) <br>\n",
    "\n",
    "with only open pixels <br> \n",
    "n_est 800, max depth to 10, time = 617.96, MAE = 148.74 degrees, RMSE = 266.9 (elev/northness/allveg) <br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10e523c-6d74-4f4f-9155-ab4c1b384b44",
   "metadata": {},
   "source": [
    "with all pixels<br>\n",
    "n_est 800, max depth to 10, time = 542.21, MAE = 148.74 degrees (elev/northness) <br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 536,
   "id": "3837d933-6940-48d6-a7c1-2d16d05fd126",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # 21 seconds with n_estimators = 10, n_jobs = 20; 179 with n_jobs not specified; 114 with njobs = 24, n_estimators = 100; 498 with n_estimators = 500\n",
    "start = time.time()\n",
    "#Create a Gaussian Classifier\n",
    "rf_0326=RandomForestRegressor(n_estimators=800, random_state = 42,n_jobs = 24, max_depth = 10)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "rf_0326.fit(feat_train_20160326, lab_train_20160326)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 537,
   "id": "a8bcf2a2-a36c-4cc0-8815-55ab1552a8c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "528.8007369041443"
      ]
     },
     "execution_count": 537,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b7b7cd7-d4f5-4c93-bdd5-b12d0ecfa8e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "806ceb93-499f-4ffe-afbe-098ac31c1a4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SCB/random_forest_data/rf_0326.joblib']"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "joblib.dump(rf_0326, \"SCB/random_forest_data/rf_0326.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c1ff411-590c-4661-badc-d2ceefd1c767",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# # 21 seconds with n_estimators = 10, n_jobs = 20; 179 with n_jobs not specified; 114 with njobs = 24, n_estimators = 100; 498 with n_estimators = 500\n",
    "start = time.time()\n",
    "#Create a Gaussian Classifier\n",
    "rf_0417=RandomForestRegressor(n_estimators=800, random_state = 42,n_jobs = 24, max_depth = 10)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "rf_0417.fit(feat_train_20160417, lab_train_20160417)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4e23e70-a446-4482-b69e-aa1be492b9d7",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "end-start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de021965-0b89-446f-ad0f-cab506e97084",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "joblib.dump(rf_0417, \"SCB/random_forest_data/rf_0417.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d6bb4c63-b3d4-44e5-82be-e4feb04044af",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# time = 345\n",
    "start = time.time()\n",
    "#Create a Gaussian Classifier\n",
    "rf_0518=RandomForestClassifier(n_estimators=800, random_state = 42,n_jobs = 24, max_depth = 3)\n",
    "\n",
    "#Train the model using the training sets y_pred=clf.predict(X_test)\n",
    "rf_0518.fit(feat_train_20160518, lab_train_20160518)\n",
    "end = time.time()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e504463b-f584-460a-bd8c-d184d27288b1",
   "metadata": {
    "jupyter": {
     "source_hidden": true
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "joblib.dump(rf_0518, \"SCB/random_forest_data/rf_0518.joblib\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95eb2d37-508a-482f-a537-e788c4787fe7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ba4f026b-f971-4054-9e26-8d8ef10d39f3",
   "metadata": {},
   "source": [
    "## Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ec9bd5-e185-43ef-b40e-6cdda54d4f35",
   "metadata": {},
   "source": [
    "rf_0326_2600m, rf_0326_2400m, rf_0326_2200m, rf_0326_2000m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 547,
   "id": "cdacd10e-c69e-4453-b500-19c526c4b327",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the forest's predict method on the test data\n",
    "predictions_0326 = rf_0326.predict(test_all_20160326[['elev','nness']])\n",
    "#predictions_0417 = rf_0417.predict(feat_test_20160417[0:1000000])\n",
    "#predictions_0518 = rf_0518.predict(feat_test_20160518)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 548,
   "id": "81f5dffe-82a8-4716-9aeb-ad77cb5df183",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "518.3329855871397"
      ]
     },
     "execution_count": 548,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(predictions_0326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 550,
   "id": "ce4584ad-6dea-4453-8011-68f4302134d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.scatter(predictions_0326, lab_test_20160326.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 551,
   "id": "c0026233-e92f-4eb2-8071-57be47f8207b",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SWE</th>\n",
       "      <th>elev</th>\n",
       "      <th>nness</th>\n",
       "      <th>canopy_class</th>\n",
       "      <th>lat</th>\n",
       "      <th>clump_size</th>\n",
       "      <th>TAOperHa</th>\n",
       "      <th>coreGap</th>\n",
       "      <th>veg_dep</th>\n",
       "      <th>SWE_mm</th>\n",
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       "      <td>-0.400422</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4365309.0</td>\n",
       "      <td>2.944882</td>\n",
       "      <td>156.790123</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.446452</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028006</th>\n",
       "      <td>0.057300</td>\n",
       "      <td>2276.633545</td>\n",
       "      <td>-0.291743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4365309.0</td>\n",
       "      <td>2.944882</td>\n",
       "      <td>156.790123</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.446452</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6028007</th>\n",
       "      <td>0.121650</td>\n",
       "      <td>2281.091553</td>\n",
       "      <td>-0.295522</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4365309.0</td>\n",
       "      <td>2.944882</td>\n",
       "      <td>156.790123</td>\n",
       "      <td>3.333333</td>\n",
       "      <td>1.446452</td>\n",
       "      <td>121</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6028008 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              SWE         elev     nness  canopy_class        lat  clump_size  \\\n",
       "0        0.402499  2152.760742 -0.006218           0.0  4371764.0    5.834646   \n",
       "1        0.434483  2152.741943 -0.008408           0.0  4371763.0    5.834646   \n",
       "2        0.502355  2152.890381 -0.013642           0.0  4371763.0    5.834646   \n",
       "3        0.000000  2152.710449  0.003534           0.0  4371763.0    5.834646   \n",
       "4        0.764976  2152.847412  0.034609           0.0  4371762.0    5.834646   \n",
       "...           ...          ...       ...           ...        ...         ...   \n",
       "6028003  0.000000  2267.433350 -0.278378           0.0  4365310.0    1.588785   \n",
       "6028004  0.073725  2282.133789 -0.290327           0.0  4365310.0    3.929134   \n",
       "6028005  0.076350  2273.973145 -0.400422           0.0  4365309.0    2.944882   \n",
       "6028006  0.057300  2276.633545 -0.291743           0.0  4365309.0    2.944882   \n",
       "6028007  0.121650  2281.091553 -0.295522           0.0  4365309.0    2.944882   \n",
       "\n",
       "           TAOperHa   coreGap   veg_dep  SWE_mm  \n",
       "0        313.580247  1.666667       NaN     402  \n",
       "1        313.580247  1.666667       NaN     434  \n",
       "2        313.580247  1.666667       NaN     502  \n",
       "3        313.580247  1.666667       NaN       0  \n",
       "4        313.580247  1.666667       NaN     764  \n",
       "...             ...       ...       ...     ...  \n",
       "6028003  132.098765  4.666667  1.109271       0  \n",
       "6028004  156.790123  1.555556  1.568687      73  \n",
       "6028005  156.790123  3.333333  1.446452      76  \n",
       "6028006  156.790123  3.333333  1.446452      57  \n",
       "6028007  156.790123  3.333333  1.446452     121  \n",
       "\n",
       "[6028008 rows x 10 columns]"
      ]
     },
     "execution_count": 551,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_all_20160326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 588,
   "id": "2ffc623e-31b0-4267-873b-a1539cca0295",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean Absolute Error: 28.91 degrees.\n",
      "249.18863364355093\n"
     ]
    }
   ],
   "source": [
    "# # Use the forest's predict method on the test data\n",
    "# predictions_0326 = rf_0326.predict(feat_test_20160326)\n",
    "# Calculate the absolute errors\n",
    "errors_0326 = test_all_20160326['SWE_mm'] - predictions_0326\n",
    "# Print out the mean absolute error (mae)\n",
    "print('Mean Absolute Error:', round(np.mean(errors_0326), 2), 'degrees.')\n",
    "rmse_0326 = np.sqrt(sum(np.square(errors_0326))/len(errors_0326))\n",
    "print(rmse_0326)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b166417-bf05-496c-9612-66fe31cccccd",
   "metadata": {},
   "source": [
    "## Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc63cf0c-7251-4cb3-94ba-092446054463",
   "metadata": {},
   "source": [
    "#### save tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "031b6f7c-a447-4ab1-adb0-6e691c239631",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=10, max_features='auto', random_state=669991378)"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_0326.estimators_[10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 335,
   "id": "91bc32f0-564a-4da6-8a18-5df88920a917",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Pull out one tree from the forest\n",
    "tree = rf_0326.estimators_[10]\n",
    "\n",
    "# Pull out one tree from the forest\n",
    "tree = rf_0326.estimators_[5]\n",
    "# Export the image to a dot file\n",
    "export_graphviz(tree, out_file = 'tree.dot', feature_names = feature_list, rounded = True, precision = 1)\n",
    "# Use dot file to create a graph\n",
    "(graph, ) = pydot.graph_from_dot_file('tree.dot')\n",
    "# Write graph to a png file\n",
    "graph.write_png('tree.png')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0762dd-3c9c-4b52-8cf6-5cf1c032340c",
   "metadata": {},
   "source": [
    "#### importance plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 553,
   "id": "3d663582-b23f-4435-b6aa-3cdba7653c37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variable: elev                 Importance: 0.85\n",
      "Variable: nness                Importance: 0.15\n"
     ]
    }
   ],
   "source": [
    "# Get numerical feature importances\n",
    "importances = list(rf_0326.feature_importances_)\n",
    "# List of tuples with variable and importance\n",
    "feature_importances = [(feature, round(importance, 2)) for feature, importance in zip(feature_list, importances)]\n",
    "# Sort the feature importances by most important first\n",
    "feature_importances = sorted(feature_importances, key = lambda x: x[1], reverse = True)\n",
    "# Print out the feature and importances \n",
    "[print('Variable: {:20} Importance: {}'.format(*pair)) for pair in feature_importances];"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 554,
   "id": "4593742d-1fee-44d0-bf67-a164a8e68c3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "# Set the style\n",
    "plt.style.use('fivethirtyeight')\n",
    "# list of x locations for plotting\n",
    "x_values = list(range(len(importances)))\n",
    "# Make a bar chart\n",
    "plt.bar(x_values, importances, orientation = 'vertical')\n",
    "# Tick labels for x axis\n",
    "plt.xticks(x_values, feature_list, rotation='vertical')\n",
    "# Axis labels and title\n",
    "plt.ylabel('Importance'); plt.xlabel('Variable'); plt.title('Variable Importances');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f122f0b-792a-4b27-a6f7-6738738229da",
   "metadata": {},
   "source": [
    "Partial Difference Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "id": "bd59e28e-a74d-4165-81e0-4b0477fdbf56",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'dict' object has no attribute 'xlabel'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_15184/3211626917.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0mpdp_plt\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpdp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpdp_plot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdp_dist\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"elev\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"PDP for Elevation\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 4\u001b[1;33m \u001b[0maxes\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Elevation (m)\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      5\u001b[0m \u001b[1;31m#axes.show()\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;31m#pdp.plt.savefig('AGU/box_figs/rf_results/pdp_elev.png', dpi=200)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'dict' object has no attribute 'xlabel'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_dist = pdp.pdp_isolate(model=rf_0326, dataset=feat_test_20160326, model_features=feature_list, feature=\"elev\")\n",
    "pdp_plt, axes = pdp.pdp_plot(pdp_dist, \"elev\")\n",
    "plt.title(\"PDP for Elevation\")\n",
    "axes.xlabel(\"Elevation (m)\")\n",
    "#axes.show()\n",
    "#pdp.plt.savefig('AGU/box_figs/rf_results/pdp_elev.png', dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "id": "0052119a-7811-4a5e-8ea5-70558958a82f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UCukvf/mL7r33Xq1fv36vX++TTz7RP//5z70vbBi99dZb+vDDD3f6ek9Pj2655ZZhe58XXnhBmzdv3u0xfr9fTz311F6/VzQa1WOPPaZ169YlvhaJRPT3v/9d99xzj+65555h+TwBAAAAmM/rdAF9XC6XXn31VY0bN05FRUVOl7PXmpqa1N3drW9+85vD8noHHHCADjjggGF5reFyxBFH7Jf3+fznPz/oMR0dHWpra9ur99m6datefvlltbW1aebMmYmv/+tf/5LP59PXv/51dXR06C9/+YvKy8uVm5u7V+8HAAAAwGwjpqH2er067LDD9Oyzz+qcc86Rx+MZ8Phzzz2nkpISHXbYYTv9+a677lJNTY1qa2sVCoV02GGHaevWrWpsbJTb7dZpp52mnJwcSdL777+vpqYmxWIxHXbYYZo+fbokacOGDXr77bdlWZa8Xq+OOeYYjRs3Tm+99Zbq6+sVCARUWlqqBQsWDKhr/fr1evvtt2XbttLS0nTssccqLS1NL7zwgrq6unT//ffr3HPPlde7/Vv98MMP69BDD1V1dfVOf77ttts0b948bd68WYFAQIcddphmz56tDz74QOvXr9eXvvQltbS06IUXXlA0GlVRUZE6Ojp01FFHKS8vT/fff7++/e1vS4qP2vb/8+rVq7Vy5UrZtq2MjAwdf/zxu/zlxTvvvKMPPvhAPp9P48eP1yeffKKLLrpIzz33nEKhkNrb2zVlyhR1d3cnPoP169frjTfekM/nU3l5edLPecmSJTr88MO1ceNGRSIRHXXUUZo2bZok6e2339bHH38st9utgoICHX/88crOzk58f8rKyvTYY49p8uTJ2rZtm0KhkI4++mhNnTpV//jHP9TV1aXHHntMp59+ul5++WXV19fL7XYrPz9fn//85xOfS3l5uWbNmrVTbStWrNDRRx+tpUuXDvj6J598kvjc8/LyVFVVpXXr1mnu3LlJzxMAAADA6Dcipnz3mT9/vnw+n9544409fm40GtW5556rz372s3rxxRc1Z84cnX/++crNzR0wLdnr9eq8887TGWecoTfeeEMtLS1qa2vTG2+8odNPP13nnXeeTjrpJD311FOKRCKSpM7OTp133nk7NdOtra166aWXdMopp+j888/XEUccoSeeeELZ2dk66aSTVFBQoPPPP39AMz2YWCymzMxMnXPOOVq4cKFeffVVRaPRAcf8/e9/14wZM3T++edr9uzZ2rZt26Cvu2XLFq1Zs0Znn322zjvvPM2bN09PPvnkTsdt3LhRH374oc4991wtWrQo8T3oE4lEdMEFF+iYY45JfC0QCOj555/XKaecokWLFu125Na2bXm9Xi1atEgnn3yynn/+eXV3d+uDDz7Qxo0b9dWvflXnn3++iouL9fzzz+/0fL/fr0mTJumrX/2qjjrqKL3yyityu92J7/cZZ5yh+vp6bdmyReedd54WLVqkvLw8NTc3S4qPdu+qmZakk08+WZMmTdrp652dnQPOKTc3V52dnUnPEQAAAMDYMGJGqKX4tO8FCxboT3/60y4bm93pG+0tKChQVlaWSktLJUn5+fkKBoOJ4/qm8ubk5KiqqkqbN2+W2+1WIBDQo48+OqCW9vZ2SVJFRYXc7p1/91BbW6uJEycqPz9fkjRx4kRlZWWpsbFxj2rfUd/U7rKyMsVisQFNbU9Pj1paWnTwwQdLksaPH6+SkpJBX/PTTz9Ve3u7/vKXvyS+FgqFFAwGlZGRkfjaxo0bVV1dnfja7NmzB9y/PG7cuJ1ee+vWrSopKVFxcbEkadasWXrzzTeT1jJ79mxJUmlpqUpKSlRXV6eNGzfqkEMOkc/nkyTNmTNH//d//6dYLDbguW63W1OmTEl8f/p/tn2Ki4vldrv15z//WZMmTVJ1dbUqKip2/w3aDdu2d/rzrvIAAAAAYGwZUQ21FB/9O/HEE/X8888nmkYp3uD2b2wsyxrwvP5TxHfX7LhcrsR/9zVGtm1r4sSJWrhwYeKxzs5OZWdna/369UpLS9vla9m2PeD1+r5mWdagNezuXPpGtHd87f6P9dd37rt7Xdu2VVNTkxhZtm1bXV1dSk9PH/BaO9a9Yw3Jvhe7e86O+r9H3/dwx+9l3/dxx2bW4/EM+voZGRk677zztHXrVtXW1uqZZ55JTJ0fitzcXAUCAWVnZ0tSYvo/AAAAgLFtRA6zVVdXa/LkyVq+fHnia5mZmYkVuru6urRly5YhvXbf9O+Ojg7V1taqqqpKEydO1ObNm9Xa2iopPpp7//337zTVekcTJ07Upk2b5Pf7JcVHrDs7OwcdDe1/Li0tLYnpyKnw+XwaN26cVq1aJSm++FlTU5MkKT09XZZlqaWlRZL08ccfJ543adIkrV27VoFAQJK0cuVKPfbYYzu9/pQpU7Ru3TqFQiFJ0gcffDBoAzt+/Hi1tLQk6tjVyt/9rVmzRpLU2NiotrY2TZgwQZMmTdIHH3yQGI1fsWKFxo8fn/J0ebfbnfgFwoYNG/Too49q3LhxOuKII3TwwQfv1eruBxxwQOL73dnZqY0bNyZGyQEAAACMXSNuhLrPcccdp61btyb+fOihh+rZZ5/VPffco9zcXE2cOHFIrxuLxfSnP/1JlmXpuOOOU2FhoSTpxBNP1DPPPCNJiYXMBhuNLS4u1vHHH68nn3wycW/wl770pZ1GfXc0f/58Pf/887rvvvtUWFio8ePH79E5LFiwQP/4xz+0evVq5eXlJUZO09PTdfTRR+uvf/2rsrKyEtPgpXhDPW/ePD322GNyuVxKS0vTKaecslOzPHHiRM2cOVN/+ctf5PV6VVxcPGhTm5WVpQULFujZZ5+V2+3WhAkTdnv81q1btWrVKtm2rZNPPlkZGRmaMWOGurq69OCDD8q2bRUUFOx0z/ruFBcXy+Px6MEHH9Q555yjjRs36r777pPP51NGRoZOPPFESdrtomTJfPazn9VLL72ke++9V7Zt65hjjlFBQUHKzwcAAAAwOrna29vtwQ/DSHbffffpuOOOG/IvGfpraGjQ1q1bNWfOHEnSsmXLVF9fP2A6/N645ZZbdNlllykzM3NYXg8AAAAAnDJiR6jhjIKCAr377rtavXq1pPj9wyeddJLDVQEAAADAyMMINQAAAAAAQzAiFyUDAAAAAGCko6EGAAAAAGAIaKgBAAAAABgCGmoAAAAAAIaAhhoAAAAAgCGgoQYAAAAAYAhoqAEAAAAAGAIaagAAAAAAhoCGGgAAAACAIaChBgAAAABgCGioAQAAAAAYAhpqAAAAAACGgIYaAAAAAIAhoKEGAAAAAGAIaKgBAAAAABgCGmoAAAAAAIaAhhoAAAAAgCGgoQYAAAAAYAhoqAEAAAAAGAIaagAAAAAAhoCGGgAAAACAIaChBgAAAABgCGioAQAAAAAYAhpqAAAAAACGgIYaAAAAAIAhoKEGAAAAAGAIaKgBAAAAABgCGmoAAAAAAIaAhhoAAAAAgCGgoQYAAAAAYAhoqAEAAAAAGAIaagAAAAAAhoCGGgAAAACAIaChBgAAAABgCGioAQAAAAAYAhpqAAAAAACGgIYaAAAAAIAhoKEGAAAAAGAIaKgBAAAAABgCGmoAAAAAAIaAhhoAAAAAgCGgoQYAAAAAYAhoqAEAAAAAGAIaagAAAAAAhoCGGgAAAACAIaChBgAAAABgCGioAQAAAAAYAhpqAAAAAACGgIYaAAAAAIAhoKEGAAAAAGAIaKgBAEjBJ598ovPPP181NTUqLCxUQUGB0yWNeFdccYUKCgr02muvOV0KAAD7BA01AGC/KSgoGPBPUVGRJk2apAULFujuu+9WLBYbcPzChQsHHF9cXKxJkybpsMMO0wUXXKA//vGPam9v3+V7bdq0aaf3Ky4u1gEHHKDTTz9djz32WMp1x2IxnXfeeXr66ad17LHH6qqrrtKPf/zjvflWDMlrr72WOJfzzjtvl8cEg0EVFBRo5syZ+7weGmYAwFjndboAAMDY09eMxmIxffrpp3rqqaf0r3/9S//85z91zz337HT8V7/6VVVVVcm2bXV1dam2tlZvvfWWnnjiCV133XX67//+b5111lm7fK+8vDxdccUVkqRwOKy1a9fq2Wef1T//+U8tX75c119//aD1btq0SR999JFOPPFE/d///d9enPnwefrpp/XGG2/oqKOOcroUAADGLBpqAMB+9x//8R8D/vzBBx/opJNO0t/+9je9+eabOvLIIwc8vmjRIh1zzDEDvhaJRHTPPfdo8eLFuuSSS+T1enX66afv9F75+fk7vd9LL72kM888U7///e91ySWXqKqqarf11tfXS5LKyspSPcV9aurUqdqwYYMWL16sl19+WS6Xy+mSAAAYk5jyDQBw3PTp0xMjre+9915Kz/H5fLr44ot18803y7Zt/cd//IeCwWBKzz3hhBNUXV0ty7K0fPny3R5bUFCghQsXSpIefPDBxJTrG2+8MXFMR0eHrr/+eh1++OEqLy9XVVWVTjnlFD355JM7vV7fVPSFCxdq69atuvzyy3XggQeqqKhITz31VEr1H3roofryl7+sFStW6C9/+UtKz5Ek27b1//7f/9OJJ56oCRMmqLKyUkcffbRuu+02hcPhnY6fOXOmCgoKFAwGdcMNN2jOnDkqLS3VT37yE82cOVMPPvigJOnUU08dMLV+V/72t7/phBNOUGVlpSZPnqxvfvObqqur2+m4vmn+mzZt0t13360jjzxS5eXlqq6u1ne/+92kU/wbGhr0k5/8RHPnzlV5ebkmTZqkL3/5y3rllVd2OjYUCun222/Xscceq8mTJ6uiokIzZszQWWedpSeeeGLAse+//74uuugizZw5U+Xl5Zo6daqOPPJI/ehHP5Lf7x/kOw4AGO0YoQYAGO3888/Xr3/9a23ZskWvvfaaPv/5zw/r6//4xz/W5s2b9eCDD2rGjBmJ5vroo4+WJLW3t2vBggX66KOPNGvWLF1++eXy+/3661//qq997Wu6+uqrdc011+z0um1tbfrCF76gvLw8nX766YpGoyosLEy5rl/84hd65plndP311+tLX/qSMjMzB33OpZdeqocffljjxo3TokWL5PP59Oyzz+pnP/uZ/vGPf+jRRx+V17vzpcEFF1yglStX6sQTT1RhYaEmT56sK664Qg888IBWr16dmJKfzF133aW///3vOvnkk3XUUUfp3Xff1eOPP65Vq1bpjTfeUHp6+i7P76WXXtKCBQt0/PHH67XXXtO9996r9evX65lnnhlw7AcffKAvf/nLampq0gknnKCTTz5Zra2tevrpp3X66afr1ltv1de+9rXE8Zdffrkef/xx1dTU6Ctf+Yqys7NVX1+vZcuW6amnntJpp50mSVq5cqW+8IUvyOVyacGCBZoyZYq6urq0efNmPfDAA/r2t7+t/Pz8Qb/vAIDRi4YaAOC4NWvW6I033pAkzZ07d4+e63a7deSRR+qhhx7Su+++m1JD/corr2jdunVyuVw69NBDd3vsf/zHf+i1117Tgw8+qJkzZ+40ffw///M/9dFHH+m8887T7373u8T066uuukonnHCCbr75Zv3bv/2bDjvssAHP+/DDD3XOOefo97///S6b2MFMnjxZl156qW677Tb97ne/01VXXbXb4x955BE9/PDDmj59uv7+978rLy9PUrxxPeuss/TKK6/o9ttv13e/+92dnrtlyxa98cYbKi4uHvD1VatWafXq1buckt/fSy+9pFdeeUU1NTWJr1188cV65JFH9PTTT+uMM87Y6Tnvvfee3nrrLY0fP16SFI1Gdeqpp+rNN9/Uu+++q3nz5kmK34f/9a9/XX6/X08++WTiFx2StG3bNp144om66qqr9G//9m8qKytL/LJj9uzZevHFF3f63re0tCT++89//rNCoZDuu+8+nXrqqQOO6+zsVFpaWtJzBgCMDUz5BgDsdzfeeKNuvPFG3XDDDbrkkkt0/PHHq6enR6eccsqQFtmqrKyUJDU3N+/0mN/vT7zfddddp/PPP19nnHGGbNvWFVdcoUmTJg35PCKRiB566CFlZWXp2muvHXAv8/jx4/XDH/5Qtm3r3nvv3em5aWlpuuGGG4bUTPf593//dxUXF2vJkiVqaGjY7bH333+/pHgD3ddM99Xxy1/+UpJ2uSCcJF1zzTU7NdN74rLLLhvQTEvS17/+dUnSsmXLdvmcq6++OtFMS5LX69X555+/03Oef/55rV+/XhdddNGAZlqSKioqdOWVVyoYDOpvf/ubpPgvYGzbVnp6ujwez07v2/883e74ZVJWVtZOx+Xm5u5yZB0AMLYwQg0A2O9uuukmSZLL5VJubq5mz56ts88+W9/4xjf26nV3tThXR0dH4v3cbrcKCgp01FFH6fzzz9dXvvKVvXq/tWvXqru7W/PmzVNJSclOjx933HGS4vfh7qiqqkqlpaV79f75+fn68Y9/rKuvvlq//OUvtWTJkqTH9tWwq5HkGTNmqLS0VJ988om6urqUk5Mz4PG+0eCh2tUsgL5mOdk90ak+5+2335YUH0Xvf197nw0bNkiKf1ZSvBE++eST9cwzz+ioo47SKaecoiOOOEKHH374Tud95pln6o477tB5552n0047Tccee6zmz5+vAw88cLfnCwAYO2ioAQD7XbImaqj6VuHe1SjqxIkTtWrVqmF9vz4dHR2Skq/+XV5ePuC4/oZrxfALL7xQ/9//9//p/vvv16WXXqoDDjggaa15eXlJ77UuLy9XU1OTOjo6dmos+85jqPqPiPfpGx3ece/xPX1Oa2urJOmJJ57YaUGx/gKBQOK///jHP+q2227Tww8/rF//+teS4ovcLViwQDfccENi1sKcOXP03HPP6b//+7/11FNP6aGHHpIU/2XI97//fV144YXJTxoAMCYw5RsAYDTLsvTmm29Kkg4//PD9+t59TV9jY+MuH++bhr2r5nC4trryer267rrrFIvF9LOf/SzpcXl5eero6FBPT49jte4LffXee++9am9vT/rP7bffnnhORkaGrrrqKi1dulRr1qzRH//4R5144ol68sknddZZZykSiSSOPeyww/Tggw9q48aN+sc//qFrrrlGPT09+uEPf6g///nP+/18AQAjCw01AMBo999/v7Zs2aJx48btdA/tvnbggQcqKytLH3744YDFrPr0bdk02MJne+uLX/yijjnmGL300kv6xz/+sctjZs+eLUl6/fXXd3rsww8/VFNTk6ZNm7bT6PTu9I0YW5Y1hKqHR98vUd56660hPb+yslJnnHGG/vznP2v+/Plat26dPv74452OS0tL07x583T11VfrD3/4gySlvM0ZAGD0oqEGABgpEonorrvu0lVXXSWXy6Ubb7xRGRkZ+7UGn8+nc845R93d3br22mtl23bisfr6ev32t7+Vy+VKLKa1L91www1yu936xS9+scvH+7aNuu6669TV1ZX4eiQS0eLFiyXFt8faE31T7Gtra4dS8rA4+eSTNXXqVN199907bafV5/33309MDW9ubtY777yz0zGhUCixr3Rfjt58881d3p7QN5q/v/MGABh5uIcaADDiPfDAA4mR1UAgoM2bN+utt95SU1OTCgoK9Lvf/U5f+tKXHKntF7/4hd566y3de++9WrlypY477rjE1kxtbW26+uqr93pRr1TMnj1b5557rh544IFdPn7mmWfq2Wef1cMPP6zPfvazWrhwYWIf6vXr1+tzn/ucrrjiij16zxNOOEFLlizRddddpzVr1qigoECSBt3Cazj5fD7df//9OuOMM7Ro0SLNmzdPs2fPVnZ2turq6rRy5UqtW7dOr776qoqKirR161Z9/vOfV3V1tQ499FCNHz9egUBAL730kj755BOdeuqpmjZtmiTpd7/7nV566SUdffTRmjx5snJzc7V+/Xo999xzyszM3OPvFwBg9KGhBgCMeA8++KCk+CrdOTk5Kikp0Wc+8xmdcMIJOuOMMxKNnBMKCgr03HPPacmSJXriiSd0++23Kz09XbNmzdJll12m0047bb/V8rOf/Ux//etf1d3dvcvH//CHP+jII4/Ufffdp/vuu0+WZemAAw7Qddddp8svv1w+n2+P3u9zn/ucfv3rX+vuu+/WnXfeqVAoJGn/NtSSdMghh+iNN97Q//7v/+qZZ57Rgw8+KNu2VV5erpqaGl155ZWqrq6WFF9Q7JprrtFrr72mN954Q83NzcrPz9fUqVP1ve99T4sWLUq87sUXX6zCwkK99957Wrp0qSKRiCorK3XuuefqO9/5Dqt9AwDkam9vtwc/DAAAAAAA9Mc91AAAAAAADAENNQAAAAAAQ0BDDQAAAADAENBQAwAAAAAwBDTUAAAAAAAMAQ01HLdlyxanSwBSQlZhCrIKU5BVmIKsIhkaajiup6fH6RKAlJBVmIKswhRkFaYgq0iGfajhuGAwqIyMDKfLAAZFVmEKsgpTkFWYgqwiGUao4bja2lqnSwBSQlZhCrIKU5BVmIKsIhkaajguMzPT6RKAlJBVmIKswhRkFaYgq0iGhhqOy87OdroEICVkFaYgqzAFWYUpyCqSoaGG45qbm50uAUgJWYUpyCpMQVZhCrKKZGio4biysjKnSwBSQlZhCrIKU5BVmIKsIhlvKgetXbtW9913n66//nq1t7frf//3fxUIBBSLxfS9731PFRUVeuGFF/T888/L7Xbr7LPP1rx58xQKhbRkyRL5/X5lZmbqyiuvVH5+/r4+JxjG7/eTCxiBrMIUZBWmIKswBVlFMoM21I8//rheeeUVpaenS5Luu+8+HXvssTrqqKO0atUqbdmyRenp6Xr66ad18803KxwOa/HixZo9e7aee+45VVVV6dxzz9Xrr7+uRx55RBdddNE+PymYJRQKOV0CkBKyClOQVZiCrMIUZBXJDNpQV1RU6Oqrr9aSJUskSR999JEmTZqk//zP/1RpaakuuugirVy5UjU1NfL5fPL5fKqoqNDGjRu1Zs0anX766ZKkOXPm6OGHH97le7S2tqqlpUXl5eVqbW1VJBLRxIkTVVtbq9zcXHm9XrW1tamyslJNTU2yLEuVlZWqq6tL/KbI7/dr/Pjxqq+vl9vtVmlpqerr61VYWKhoNKrOzs7Ea/p8PhUVFamhoUHFxcUKBoMKBAKJx9PT05Wfn6/GxkaVlJQoEAiop6cn8XhmZqays7PV3NyssrIy+f1+hUKhxOPZ2dnKyMjgnFI8p1AopObm5lF1TqPxc+KcAiotLdW6detG1TmNxs+Jc8pWKBSS3+8fVec0Gj8nzimS+Lk6ms5pNH5OnFO8oe7u7h5V5zQaP6d9dU7V1dVJ+2VXe3u7vZt+WpLU2Nio3/zmN7rpppt09tln6/LLL9eJJ56ohx56SLFYTOPGjdOmTZt0wQUXSJKWLFmi4447To8//rguvvhiTZgwQZZl6dJLL9Wdd9452NthjFm3bt1uQwqMFGQVpiCrMAVZhSnIKpLZ40XJcnNzNX/+fEnSvHnz9MknnygzM1M9PT2JY3p6epSdnT3g631fA3ZELmAKsgpTkFWYgqzCFGQVyexxQ11TU6P33ntPkvThhx9q4sSJqq6u1po1axQOhxUIBFRXV6eqqqoBxy5fvlyHHHLI8FaPUSEjI8PpEoCUkFWYgqzCFGQVpiCrSCalVb77+8Y3vqHbb79dzz33nLKysvSDH/xAOTk5WrhwoRYvXizbtrVo0SKlpaVpwYIFuvXWW3XNNdfI6/XqBz/4wb44BxiupaVFRUVFTpcBDIqswhRkFaYgqzAFWUUyKd1DDexLHR0dysvLc7oMYFBkFaYgqzAFWYUpyCqS2eMp38Bwa21tdboEICVkFaYgqzAFWYUpyCqSoaGG4yKRiNMlACkhqzAFWYUpyCpMQVaRDFO+4bhgMMhCDzACWYUpyCpMQVZhCrKKZBihhuNqa2udLgFICVmFKcgqTEFWYQqyimRoqOG43Nxcp0sAUkJWYQqyClOQVZiCrCIZGmo4zuvd493bAEeQVZiCrMIUZBWmIKtIhoYajmtra3O6BCAlZBWmIKswBVmFKcgqkqGhhuMqKyudLgFICVmFKcgqTEFWYQqyimRoqOG4pqYmp0sAUkJWYQqyClOQVZiCrCIZGmo4zrIsp0sAUkJWYQqyClOQVZiCrCIZ9qGG47q7u5WVleV0GcCgyCpMQVZhCrIKU5BVJMMINRxXV1fndAlASsgqTEFWYQqyClOQVSRDQw3H5efnO10CkBKyClOQVZiCrMIUZBXJ0FADAAAAADAENNRwnN/vd7oEICVkFaYgqzAFWYUpyCqSYVEyOI5FHmAKsgpTkFWYgqzCFGQVyTBCDcfV19c7XQKQErIKU5BVmIKswhRkFcnQUMNxbjcxhBnIKkxBVmEKsgpTkFUkQzLguNLSUqdLAFJCVmEKsgpTkFWYgqwiGRpqOI4pNDAFWYUpyCpMQVZhCrKKZGio4bjCwkKnSwBSQlZhCrIKU5BVmIKsIhkaajguGo06XQKQErIKU5BVmIKswhRkFcnQUMNxnZ2dTpcApISswhRkFaYgqzAFWUUy7EMNxwWDQWVkZDhdBjAosgpTkFWYgqzCFGQVyTBCDcfV1tY6XQKQErIKU5BVmIKswhRkFcnQUMNxPp/P6RKAlJBVmIKswhRkFaYgq0iGhhqOKyoqcroEICVkFaYgqzAFWYUpyCqSoaGG4xoaGpwuAUgJWYUpyCpMQVZhCrKKZGio4bji4mKnSwBSQlZhCrIKU5BVmIKsIhkaajguGAw6XQKQErIKU5BVmIKswhRkFcnQUMNxgUDA6RKAlJBVmIKswhRkFaYgq0iGfajhOPb1gynIKkxBVmEKsgpTkFUkwwg1HMe+fjAFWYUpyCpMQVZhCrKKZGio4bj09HSnSwBSQlZhCrIKU5BVmIKsIhkaajguPz/f6RKAlJBVmIKswhRkFaYgq0iGhhqOa2xsdLoEICVkFaYgqzAFWYUpyCqSoaGG40pKSpwuAUgJWYUpyCpMQVZhCrKKZGio4Ti2IYApyCpMQVZhCrIKU5BVJENDDcf19PQ4XQKQErIKU5BVmIKswhRkFcmwDzUcx75+MAVZhSnIKkxBVmEKsopkGKGG49jXD6YgqzAFWYUpyCpMQVaRDA01HJeZmel0CUBKyCpMQVZhCrIKU5BVJENDDcdlZ2c7XQKQErIKU5BVmIKswhRkFcmk1FCvXbtWP/vZzwZ87dVXX9VPfvKTxJ9feOEFXXXVVfrxj3+sd999V5IUCoX061//WosXL9YNN9wgv98/jKVjtGhubna6BCAlZBWmIKswBVmFKcgqkvEOdsDjjz+uV155Renp6Ymvffrpp3rxxRdl2/H1zNra2vT000/r5ptvVjgc1uLFizV79mw999xzqqqq0rnnnqvXX39djzzyiC666KJ9dzYwUllZmdMlACkhqzAFWYUpyCpMQVaRzKANdUVFha6++motWbJEktTZ2an77rtPF154oW6//XZJ0rp161RTUyOfzyefz6eKigpt3LhRa9as0emnny5JmjNnjh5++OFdvkdra6taWlpUXl6u1tZWRSIRTZw4UbW1tcrNzZXX61VbW5sqKyvV1NQky7JUWVmpuro65efnS5L8fr/Gjx+v+vp6ud1ulZaWqr6+XoWFhYpGo+rs7Ey8ps/nU1FRkRoaGlRcXKxgMKhAIJB4PD09Xfn5+WpsbFRJSYkCgYB6enoSj2dmZio7O1vNzc0qKyuT3+9XKBRKPJ6dna2MjAzOKcVzWrFihaZPnz6qzmk0fk6cU3z/ycbGxlF1TqPxc+KcsrVy5Uodeuiho+qcRuPnxDlFEj9XR9M5jcbPiXOS1qxZo8MPP3xUndNo/Jz21TlVV1cn7ZdT2jarsbFRv/nNb/TLX/5SN998s84//3ylpaXpN7/5jW666Sa98sor2rRpky644AJJ0pIlS3Tcccfp8ccf18UXX6wJEybIsixdeumluvPOOwd7O4wx69at221IgZGCrMIUZBWmIKswBVlFMoOOUPe3YcMG1dfX6w9/+IPC4bC2bNmiu+66SzNnzhyw2XlPT4+ys7OVmZmZ+Hrf14AdTZw40ekSgJSQVZiCrMIUZBWmIKtIZo9W+a6urtaSJUt0/fXX60c/+pEmTJigiy66SNXV1VqzZo3C4bACgYDq6upUVVWlmpoavffee5Kk5cuX65BDDtknJwGzsa8fTEFWYQqyClOQVZiCrCKZPRqhTqawsFALFy7U4sWLZdu2Fi1apLS0NC1YsEC33nqrrrnmGnm9Xv3gBz8YjrfDKMPMBZiCrMIUZBWmIKswBVlFMindQw3sS62trSoqKnK6DGBQZBWmIKswBVmFKcgqktmjKd/AvtDS0uJ0CUBKyCpMQVZhCrIKU5BVJENDDceVl5c7XQKQErIKU5BVmIKswhRkFcnQUMNxra2tTpcApISswhRkFaYgqzAFWUUyNNRwXCQScboEICVkFaYgqzAFWYUpyCqSYVEyOC4YDCojI8PpMoBBkVWYgqzCFGQVpiCrSIYRajiOff1gCrIKU5BVmIKswhRkFcnQUMNxubm5TpcApISswhRkFaYgqzAFWUUyNNRwnNfrdboEICVkFaYgqzAFWYUpyCqSoaGG49ra2pwuAUgJWYUpyCpMQVZhCrKKZGio4bjKykqnSwBSQlZhCrIKU5BVmIKsIhkaajiuqanJ6RKAlJBVmIKswhRkFaYgq0iGhhqOsyzL6RKAlJBVmIKswhRkFaYgq0iGfajhuO7ubmVlZTldBjAosgpTkFWYgqzCFGQVyTBCDcfV1dU5XQKQErIKU5BVmIKswhRkFcnQUMNx+fn5TpcApISswhRkFaYgqzAFWUUyNNQAAAAAAAwBDTUc5/f7nS4BSAlZhSnIKkxBVmEKsopkWJQMjmORB5iCrMIUZBWmIKswBVlFMoxQw3H19fVOlwCkhKzCFGQVpiCrMAVZRTI01HCc200MYQayClOQVZiCrMIUZBXJkAw4rrS01OkSgJSQVZiCrMIUZBWmIKtIhoYajmMKDUxBVmEKsgpTkFWYgqwiGRpqOK6wsNDpEoCUkFWYgqzCFGQVpiCrSIaGGo6LRqNOlwCkhKzCFGQVpiCrMAVZRTI01HBcZ2en0yUAKSGrMAVZhSnIKkxBVpEM+1DDccFgUBkZGU6XAQyKrMIUZBWmIKswBVlFMoxQw3G1tbVOlwCkhKzCFGQVpiCrMAVZRTI01HCcz+dzugQgJWQVpiCrMAVZhSnIKpKhoYbjioqKnC4BSAlZhSnIKkxBVmEKsopkaKjhuIaGBqdLAFJCVmEKsgpTkFWYgqwiGRpqOK64uNjpEoCUkFWYgqzCFGQVpiCrSIaGGo4LBoNOlwCkhKzCFGQVpiCrMAVZRTI01HBcIBBwugQgJWQVpiCrMAVZhSnIKpJhH2o4jn39YAqyClOQVZiCrMIUZBXJMEINx7GvH0xBVmEKsgpTkFWYgqwiGRpqOC49Pd3pEoCUkFWYgqzCFGQVpiCrSIaGGo7Lz893ugQgJWQVpiCrMAVZhSnIKpKhoYbjGhsbnS4BSAlZhSnIKkxBVmEKsopkaKjhuJKSEqdLAFJCVmEKsgpTkFWYgqwiGRpqOI5tCGAKsgpTkFWYgqzCFGQVydBQw3E9PT1OlwCkhKzCFGQVpiCrMAVZRTLsQw3Hsa8fTEFWYQqyClOQVZiCrCIZbyoHrV27Vvfdd5+uv/56ffrpp7rzzjvldrvl8/n03e9+VwUFBXrhhRf0/PPPy+126+yzz9a8efMUCoW0ZMkS+f1+ZWZm6sorr2SFPOyktrZW1dXVTpcBDIqswhRkFaYgqzAFWUUygzbUjz/+uF555ZXE3mt33XWXLr74Yk2ZMkXPPfecHn/8cZ1++ul6+umndfPNNyscDmvx4sWaPXu2nnvuOVVVVencc8/V66+/rkceeUQXXXTRPj8pmCUzM9PpEoCUkFWYgqzCFGQVpiCrSGbQe6grKip09dVXJ/78wx/+UFOmTJEkWZYln8+ndevWqaamRj6fT9nZ2aqoqNDGjRu1Zs0azZkzR5I0Z84crVy5ch+dBkyWnZ3tdAlASsgqTEFWYQqyClOQVSQz6Aj1EUccMWDftaKiIknSRx99pGeeeUY33HCDVqxYoaysrMQxmZmZ6u7uVk9PTyJ8mZmZSVfHa21tVUtLi8rLy9Xa2qpIJKKJEyeqtrZWubm58nq9amtrU2VlpZqammRZliorK1VXV5eYQu73+zV+/HjV19fL7XartLRU9fX1KiwsVDQaVWdnZ+I1fT6fioqK1NDQoOLiYgWDQQUCgcTj6enpys/PV2Njo0pKShQIBNTT05N4PDMzU9nZ2WpublZZWZn8fr9CoVDi8ezsbGVkZHBOKZ7Tu+++qzlz5oyqcxqNnxPnFFAoFFJzc/OoOqfR+DlxTtl69913NX/+/FF1TqPxc+KcIomfq6PpnEbj58Q5Se+//76OOuqoUXVOo/Fz2lfntLvp/iktStbY2Kjf/OY3uummmyRJr7/+uh599FH9+Mc/VkVFhZYuXarly5frsssukyT96le/0llnnaVHH31UZ5xxhqqrqxUIBHTNNddoyZIlg70dxhi/38+99TACWYUpyCpMQVZhCrKKZPZ426xXXnlFf//733XdddepoqJCklRdXa01a9YoHA4rEAiorq5OVVVVqqmp0XvvvSdJWr58uQ455JDhrR6jgt/vd7oEICVkFaYgqzAFWYUpyCqSSWmV7z6xWEx33XWXSkpK9Otf/1qSNH36dJ177rlauHChFi9eLNu2tWjRIqWlpWnBggW69dZbdc0118jr9eoHP/jBPjkJmC0UCjldApASsgpTkFWYgqzCFGQVybAPNRzHvn4wBVmFKcgqTEFWYQqyimT2eMo3MNxqa2udLgFICVmFKcgqTEFWYQqyimRoqOE4tiGAKcgqTEFWYQqyClOQVSRDQw3HMX0GpiCrMAVZhSnIKkxBVpEMDTUc19LS4nQJQErIKkxBVmEKsgpTkFUkQ0MNx5WXlztdApASsgpTkFWYgqzCFGQVydBQw3Gtra1OlwCkhKzCFGQVpiCrMAVZRTI01HBcJBJxugQgJWQVpiCrMAVZhSnIKpJhH2o4jn39YAqyClOQVZiCrMIUZBXJMEINx7GvH0xBVmEKsgpTkFWYgqwiGRpqOC43N9fpEoCUkFWYgqzCFGQVpiCrSIaGGo7zer1OlwCkhKzCFGQVpiCrMAVZRTI01HBcW1ub0yUAKSGrMAVZhSnIKkxBVpEMDTUcV1lZ6XQJQErIKkxBVmEKsgpTkFUkQ0MNxzU1NTldApASsgpTkFWYgqzCFGQVydBQw3GWZTldApASsgpTkFWYgqzCFGQVybAPNRzX3d2trKwsp8sABkVWYQqyClOQVZiCrCIZRqjhuLq6OqdLAFJCVmEKsgpTkFWYgqwiGRpqOC4/P9/pEoCUkFWYgqzCFGQVpiCrSIaGGgAAAACAIaChhuP8fr/TJQApIaswBVmFKcgqTEFWkQyLksFxLPIAU5BVmIKswhRkFaYgq0iGEWo4rr6+3ukSgJSQVZiCrMIUZBWmIKtIhoYajnO7iSHMQFZhCrIKU5BVmIKsIhmSAceVlpY6XQKQErIKU5BVmIKswhRkFcnQUMNxTKGBKcgqTEFWYQqyClOQVSRDQw3HFRYWOl0CkBKyClOQVZiCrMIUZBXJ0FDDcdFo1OkSgJSQVZiCrMIUZBWmIKtIhoYajuvs7HS6BCAlZBWmIKswBVmFKcgqkmEfajguGAwqIyPD6TKAQZFVmIKswhRkFaYgq0iGEWo4rra21ukSgJSQVZiCrMIUZBWmIKtIhoYajvP5fE6XAKSErMIUZBWmIKswBVlFMjTUcFxRUZHTJQApIaswBVmFKcgqTEFWkQwNNRzX0NDgdAlASsgqTEFWYQqyClOQVSRDQw3HFRcXO10CkBKyClOQVZiCrMIUZBXJ0FDDccFg0OkSgJSQVZiCrMIUZBWmIKtIhoYajgsEAk6XAKSErMIUZBWmIKswBVlFMuxDDcexrx9MQVZhCrIKU5BVmIKsIhlGqOE49vWDKcgqTEFWYQqyClOQVSRDQw3HpaenO10CkBKyClOQVZiCrMIUZBXJ0FDDcfn5+U6XAKSErMIUZBWmIKswBVlFMjTUcFxjY6PTJQApIaswBVmFKcgqTEFWkQwNNRxXUlLidAlASsgqTEFWYQqyClOQVSRDQw3HsQ0BTEFWYQqyClOQVZiCrCIZbyoHrV27Vvfdd5+uv/561dfX67bbbpPL5VJVVZUuueQSud1uvfDCC3r++efldrt19tlna968eQqFQlqyZIn8fr8yMzN15ZVXcv8BdtLT0+N0CUBKyCpMQVZhCrIKU5BVJDPoCPXjjz+u22+/XeFwWJJ09913a9GiRfqv//ov2batpUuXqq2tTU8//bR++ctf6uc//7nuv/9+RSIRPffcc6qqqtJ//dd/6bjjjtMjjzyyz08I5pk4caLTJQApIaswBVmFKcgqTEFWkcygI9QVFRW6+uqrtWTJEknShg0bNH36dEnS3LlztWLFCrndbtXU1Mjn88nn86miokIbN27UmjVrdPrpp0uS5syZo4cffniX79Ha2qqWlhaVl5ertbVVkUhEEydOVG1trXJzc+X1etXW1qbKyko1NTXJsixVVlaqrq4uMeLt9/s1fvx41dfXy+12q7S0VPX19SosLFQ0GlVnZ2fiNX0+n4qKitTQ0KDi4mIFg0EFAoHE4+np6crPz1djY6NKSkoUCATU09OTeDwzM1PZ2dlqbm5WWVmZ/H6/QqFQ4vHs7GxlZGRwTime01tvvaU5c+aMqnMajZ8T5xRQKBRSenr6qDqn0fg5cU7ZevfddzV//vxRdU6j8XPinCKJn6uj6ZxG4+fEOUnvv/++jjrqqFF1TqPxc9pX51RdXZ20X3a1t7fbSR/t1djYqN/85je66aabdNFFF+muu+6SJK1atUovvvii5syZo02bNumCCy6QJC1ZskTHHXecHn/8cV188cWaMGGCLMvSpZdeqjvvvHOwt8MYs2XLFk2YMMHpMoBBkVWYgqzCFGQVpiCrSGaPFyVzuVyJ/+7p6VF2drYyMzMH3Fewq6/3fQ3YEbmAKcgqTEFWYQqyClOQVSSzxw311KlTtXr1aknSsmXLdPDBB6u6ulpr1qxROBxWIBBQXV2dqqqqVFNTo/fee0+StHz5ch1yyCHDWz1GhebmZqdLAFJCVmEKsgpTkFWYgqwimZRW+e7vG9/4hm6//XZFo1FNmDBBRxxxhDwejxYuXKjFixfLtm0tWrRIaWlpWrBggW699VZdc8018nq9+sEPfrAvzgGGKysrc7oEICVkFaYgqzAFWYUpyCqSSekeamBf2rx5s6qqqpwuAxgUWYUpyCpMQVZhCrKKZPZ4yjcw3EKhkNMlACkhqzAFWYUpyCpMQVaRDCPUcFwwGFRGRobTZQCDIqswBVmFKcgqTEFWkQwj1HBcbW2t0yUAKSGrMAVZhSnIKkxBVpEMDTUcxzYEMAVZhSnIKkxBVmEKsopkaKjhOKbPwBRkFaYgqzAFWYUpyCqSoaGG41paWpwuAUgJWYUpyCpMQVZhCrKKZGio4bjy8nKnSwBSQlZhCrIKU5BVmIKsIhkaajiutbXV6RKAlJBVmIKswhRkFaYgq0iGhhqOi0QiTpcApISswhRkFaYgqzAFWUUy7EMNx7GvH0xBVmEKsgpTkFWYgqwiGUao4Tj29YMpyCpMQVZhCrIKU5BVJENDDcfl5uY6XQKQErIKU5BVmIKswhRkFcnQUMNxXq/X6RKAlJBVmIKswhRkFaYgq0iGhhqOa2trc7oEICVkFaYgqzAFWYUpyCqSoaGG4yorK50uAUgJWYUpyCpMQVZhCrKKZGio4bimpianSwBSQlZhCrIKU5BVmIKsIhkaajjOsiynSwBSQlZhCrIKU5BVmIKsIhn2oYbjuru7lZWV5XQZwKDIKkxBVmEKsgpTkFUkwwg1HFdXV+d0CUBKyCpMQVZhCrIKU5BVJENDDcfl5+c7XQKQErIKU5BVmIKswhRkFcnQUAMAAAAAMAQ01HCc3+93ugQgJWQVpiCrMAVZhSnIKpJhUTI4jkUeYAqyClOQVZiCrMIUZBXJMEINx9XX1ztdApASsgpTkFWYgqzCFGQVydBQw3FuNzGEGcgqTEFWYQqyClOQVSRDMuC40tJSp0sAUkJWYQqyClOQVZiCrCIZGmo4jik0MAVZhSnIKkxBVmEKsopkaKjhuMLCQqdLAFJCVmEKsgpTkFWYgqwiGRpqOC4ajTpdApASsgpTkFWYgqzCFGQVydBQw3GdnZ1OlwCkhKzCFGQVpiCrMAVZRTLsQw3HBYNBZWRkOF0GMCiyClOQVZiCrMIUZBXJMEINx9XW1jpdApASsgpTkFWYgqzCFGQVydBQw3E+n8/pEoCUkFWYgqzCFGQVpiCrSIaGGo4rKipyugQgJWQVpiCrMAVZhSnIKpKhoYbjGhoanC4BSAlZhSnIKkxBVmEKsopkaKjhuOLiYqdLAFJCVmEKsgpTkFWYgqwiGRpqOC4YDDpdApASsgpTkFWYgqzCFGQVydBQw3GBQMDpEoCUkFWYgqzCFGQVpiCrSIZ9qOE49vWDKcgqTEFWYQqyClOQVSTDCDUcx75+MAVZhSnIKkxBVmEKsjp0lm0rYtnqicbkD8fUFIxoW09EjcGI06UNC6/TBQDp6elOlwCkhKzCFGQVpiCrMAVZ3TXbthWzJUu2wlb8n4hly7JtRS0pJlu27ZJky5ZLHld8RNflcsmyRsdEaRpqOC4/P9/pEoCUkFWYgqzCFGQVphirWbV6G+aIZSlsSWHLkmVLUdtWzLZlJY6TPHLJ7ZLcLpckl1wuySuX5JJ6/9+oNKSGOhqN6rbbblNjY6PcbreuuOIKeTwe3XbbbXK5XKqqqtIll1wit9utF154Qc8//7zcbrfOPvtszZs3b7jPAYZrbGwcsz+kYBayClOQVZiCrMIUozGrlm3Hm+N+zXLUjn89asdHl3v/T67eZtnj6muMXfK4XPIo8ccxa0gN9bJlyxSLxXTjjTdqxYoVeuCBBxSNRrVo0SLNmDFDd9xxh5YuXaqDDjpITz/9tG6++WaFw2EtXrxYs2fPls/nG+7zgMFKSkqcLgFICVmFKcgqTEFWYQoTsxrrHUXe3VRsW/Fp1+7e5tjVO7rsdknuxOgydmdIDfW4ceMUi8VkWZZ6enrk8Xi0du1aTZ8+XZI0d+5crVixQm63WzU1NfL5fPL5fKqoqNDGjRtVXV09rCcBswUCARUWFjpdBjAosgpTkFWYgqzCFCMtqztOxY5YlmL29iY6JsnuvUXZPUanYu8vQ2qoMzIy1NjYqCuvvFKdnZ265ppr9OGHH/b+RkPKzMxUd3e3enp6lJWVlXhe39d31NraqpaWFpWXl6u1tVWRSEQTJ05UbW2tcnNz5fV61dbWpsrKSjU1NcmyLFVWVqquri4x9cLv92v8+PGqr6+X2+1WaWmp6uvrVVhYqGg0qs7OzsRr+nw+FRUVqaGhQcXFxQoGgwoEAonH09PTlZ+fr8bGRpWUlCgQCKinpyfxeGZmprKzs9Xc3KyysjL5/X6FQqHE49nZ2crIyOCcUjynNWvWKCMjY1Sd02j8nDingEKhkHp6ekbVOY3Gz4lzytaaNWuUm5s7qs5pNH5OnFMk8XN1NJ3TaPycOCdpzZo1Kioq2i/nlJGRqYzsLDU0taiwpEStHZ3qCYdVWl6hrdsalJ6ZKa/Pp46ODhUVFqqrs1OxaFSVFeXa1tCg7KwseTxedXR2qKSkRO1tbbIsSyUlpWpsalROTo4kqaurS2WlZWpubpLb7VZBYaGam5uVl5unWCyqQHe3Ksrjr+n1epWfl6eW1lYV5OcrFA6rp6cn8Xiaz6ecnFy1trWqoKBAwZ4eBUOhxOMZ6enKyMxUe3u7igqL1NXVqVA4orLJ443I3u4GhIe0D/Xdd98tn8+n888/X83NzfrFL36hrq4u3XPPPZKkpUuX6v3339fs2bO1fPlyXXbZZZKkX/3qVzrrrLM0bdq0PX1LjGLs6wdTkFWYgqzCFGQVphjOrMYGjC7Hp2P3jTj3PdZn4FTs0cWybVVlpzldxl4b0j7U2dnZiZHnnJwcRaNRTZkyRatXr5YUv8f64IMPVnV1tdasWaNwOKxAIKC6ujpVVVUNX/UYFdjXD6YgqzAFWYUpyCpMkWpWk+25XNcd1uZASJ8GQtoUCGtLd1iNwZg6I5YilhSz49OxPS630tzb//G6XKOymR5NhjTl+9RTT9Xvf/97LV68WNFoVOedd56mTZum22+/XdFoVBMmTNARRxwhj8ejhQsXavHixbJtW4sWLVJamvm/hcDwyszMdLoEICVkFaYgqzAFWYUpMjMzE3sub1/oK94MJ0aXd7PncnyhL1d8NJP+eFQZ0pRvYDi1tbWNqEUegGTIKkxBVmEKsoqRZHdTsds7OpSdmytp+57LHtfonIq9v4yWKd9DGqEGhlNzczN/mcIIZBWmIKswBVnF/tLXGEd7R5UH23PZ029VbEnq8PtVkNe7DzU9NPqhoYbjysrKnC4BSAlZhSnIKkxBVjEcbNuWJQ2653LyqdiD77lcVFi0f04GxqGhhuP8fn9iqXtgJCOrMAVZhSnIKlKx457LYcuSZUvR3j2Xrd7jbHvf7bnc1dWZ2G4K6I+GGo4LhUJOlwCkhKzCFGQVpiCrsPtNxQ4nmYpt96745Optlj2J+5Zd8vRuK9X7x30mHInsuxeH0ViUDI5jD0qYgqzCFGQVpiCro1+sdxR5x6nYfatlW7ZLtuLtyEjeczkcDiktLd3pMkaV0bIo2ZD2oQaGE3tQwhRkFaYgqzAFWTVb357L3dGY2ne753JEzcGYunax57LP7TJiz+VtDQ1Ol4ARiinfcFx2drbTJQApIaswBVmFKcjqyNV/KnbE1i73XLZ2mIo9mvdcZs90JENDDccx1QumIKswBVmFKciqc3a353LfY33cA/Zc3nGhr7EhPc38qcnYN2io4biWlhYVFbEVAUY+sgpTkFWYgqzuG9YuFvra3ijbikkDFvracc9ljyv+NWzX7vcrL48V6bEzGmo4rry83OkSgJSQVZiCrMIUZHXP2f1GkeMjyztPxd79nsujayr2/lLML36QBA01HNfa2qq8vDynywAGRVZhCrIKU5DVnfWfih3ZzZ7Lli15djsVm455OPk7OpSdzT7U2BkNNRwXYV8/GIKswhRkFaYYa1m17PhCXrvbc7n3/3aYii3tzz2XsbNoNOp0CRih2IcajmMPSpiCrMIUZBWmGG1ZTbbnctTacSr2yN5zGTtjH+rhxz7UwDBhD0qYgqzCFGQVpjApqzvuudzYu+fylu6wNg2y57LL5ZK3d89lnwF7LmNn7EONZJjyDcfl5uY6XQKQErIKU5BVmGKkZNXexarYfVOxk+257NnVVGz641ErOyvL6RIwQtFQw3FeLzGEGcgqTEFWYYr9ldVkU7F3uefygKnYY3PPZezM4+HnKnaNZMBxbW1tKikpcboMYFBkFaYgqzDFcGTV2sWq2Mn2XHb3ji6z5zL2VEdnhwoKCpwuAyMQDTUcV1lZ6XQJQErIKkxBVmGKwbKayp7LVmJ0mT2Xse/wS0okQ0MNxzU1NSknh339MPKRVZiCrMIUDY1NSsvKVsSyEtOxdzUV29Zgey4D+1Z7W5uyMrmPGjujoYbjLMtyugQgJWQVpiCr2BcsO77dk21LlvpGj21Zkiyrb7Q43gT3HWfL7nd8758V/7Mk1UdtubrDO+y5zFRsjDz8XEUyNNRwHFMTYQqyClOQVeyL5te2B76Hrfg9yS5Jrt6B4vh/9++Ct/93/JjeZrn3axUlpUpzs4srRr6SklKnS8AIRUMNx9XV1am6utrpMoBBkVWYgqyOLn33EYd7t3QKWVa8Yd6h+bUUb3Kl7c2vq/drw9H87oup1Y1NjaqaWDX8LwwMM7KKZGio4bj8/HynSwBSQlZhCrJqHqtf0xyybIVj8RHk6IBVquNToD39pkVL25vfAeO8hkyV5l5/mIKsIhkaagAAgP0g3iDHm+Zg1FLUVrxhtm1Ztku27J3uJWaVagAY2Wio4Ti/36+ysjKnywAGRVZhCrLqnJhtKxLbPjU71ts0R+3t2zu5d1itevviW2Ova+7q6lJRYZHTZQCDIqtIxtXe3m4Pfhiw73R3dysri20IMPKRVZiCrO47ffczRyxLoX73M0ctKdq7qJet3hWqteM9ythRMBhURkaG02UAgyKrw8+ybVVlpzldxl5jWUU4rr6+3ukSgJSQVZiCrA6PqGWrKxpTQzCird1hbQ6EtLE7rM3dYdUHY/JHYopYUsx2yeVyyedyK83tVrrbLa/LRTOdgubmJqdLAFJCVpEMU77hODfbZcAQZBWmIKt7LmbbCsUsBaKWIpatcO8U7f7Ts7mfefiRVZiCrCIZGmo4rrSUff1gBrIKU5DV3bNsW+GYpW4r3kSHrb77m13y9jbP3r69pbBPFRQWOl0CkBKyimT4VQscx9REmIKswhRkdTvbthW2bLWHY9rWE1FtIKxNgYjqglF1RSzFbJc8Lrd8brd8bqZp72/Nzc1OlwCkhKwiGUao4bhCfuMHQ5BVmGIsZzVi2eqJWuqx4lO3I3Z8obD4Stp99zpLDD+PDHm5eU6XAKSErCIZGmo4LhqNOl0CkBKyClOMlaxGLVtBy1JPND5tO9y7xHZiyrZc8jF1e0SLxcZGVmE+sopkmPINx3V2djpdApASsgpTjMasRi1b3dGYmntX3N4UCKm2J6ymYEzBmC1b8VW2fb0rbMMMge5up0sAUkJWkQz7UMNx7OsHU5BVmMLkrPYtGBa0pGDMUtS2FbVt2ZJkb180DKNDOBxSWlq602UAgyKrw499qIFhUltb63QJQErIKkxhQlZt21bEstUZiamp36jzxkBYW4NR+SMxRRMrb7t7R59ZNGy02dbQ4HQJQErIKpLhHmo4zufzOV0CkBKyClOMtKz27fEctGyFY/FGOqr4YmH993mOLxzmdLXYn7xeLkVhBrKKZEgGHFdUVOR0CUBKyCpM4VRWrd7p2T3R+GJh0d5Vtvvv8ezuW2lbLBYGKT+PlZNhBrKKZJjyDcc1MIUGhiCrMMX+zGrEstUa3r6/85buiNojMUUsye6drp3mdivN7ZKb6drYQUtrq9MlACkhq0iGEWo4rri42OkSgJSQVZhiX2c1Ytnyh6PqicVHoL2ueLPM/s7YUwX5+U6XAKSErCIZGmo4LhgMOl0CkBKyClPsi6yGYpY6IjH1xGzFbFue3iY6jVFn7IVQOOx0CUBKyOrwicViuueJZ7X843U6dsZBuvLMhfJ6PE6XNWQ01HBcIBBwugQgJWQVphiurIZiMfkjVu/2VZKvt4lm6jaGS09Pj9MlACkhq3svZllq9Xforsef0qvvva9oLKZNW+vlcrn0/bNPdbq8IaOhhuMmTpzodAlASsgqTDHUrNq2rWDMUkc03kTHEk20W2n00NgHKsrLnS4BSAlZHVxfw9zQ0qptLa1qaGlTQ0tr4p/G1nZFY7EBzwmGI/rXhx9LoqEGhqy2tlbV1dVOlwEMiqzCFHuSVdu21ROz1NE7Em0p3kR7XG62sMI+t62hQVUTq5wuAxgUWZWsRMPc1tswt/Y2z21qaGnZZcNclJer8uIiHTipSsfMna3y4iK9//F6vfX+aoWjUWWk+fTZQw5y6IyGx5Ab6kcffVTvvPOOotGoFixYoOnTp+u2226Ty+VSVVWVLrnkErndbr3wwgt6/vnn5Xa7dfbZZ2vevHnDWT9GgfT0dKdLAFJCVmGKwbI6oIm2LNmSvHLJ62bzD+xfaSNsz3QgmbGQVcuy1NbR2a9ZjjfO25rjf25qa1MkOrBhLszLVXlxoaonTUw0zBXFRSovLlJZUYHS09J2ep8vHvUZ3fvks1r20Tp9bmaNvnPGyfvrFPcJV3t7u72nT1q9erWeeOIJ/eQnP1EoFNLf/vY3bdiwQaeddppmzJihO+64Q4ceeqgOOuggXXvttbr55psVDoe1ePFi3XzzzfKNgUAidX6/X/msnAgDkFWYYldZtWxb3TFLnX1NtB1fldvF/dBwUFdXl3JycpwuAxjUaMiqZVlq6+yKjyo3t+ww0tymxtbWnRrmgtyceJNcsr1RLi8u7G2YC5Wxi4Y55XpsW1XZQ3/+SDGkEeoVK1aoqqpKN910k3p6enTBBRfoH//4h6ZPny5Jmjt3rlasWCG3262amhr5fD75fD5VVFRo48aNO01Da21tVUtLi8rLy9Xa2qpIJKKJEyeqtrZWubm58nq9amtrU2VlpZqammRZliorK1VXV5e4YPD7/Ro/frzq6+vldrtVWlqq+vp6FRYWKhqNqrOzM/GaPp9PRUVFamhoUHFxsYLBoAKBQOLx9PR05efnq7GxUSUlJQoEAurp6Uk8npmZqezsbDU3N6usrEx+v1+hUCjxeHZ2tjIyMjinFM9p6dKlmjNnzqg6p9H4OXFOAYVCITU2No6qcxqNnxPnlK13331X8+fPl9/vV08wpNyKcfpk6zZlZmYqKz1N7X6/iouK1NzRoWg0qorycm1raFB2VpY8Hq86OjtUUlKi9rY2WZalkpJSNTY1Ji4mu7q6VFZapubmJrndbhUUFqq5uVl5uXmKxaIKdHcnXtPr9So/L08tra0qyM9XKBxWT09P4vE0n085OblqbWtVQUGBgj09CoZCiccz0tOVkZmp9vZ2FRUWqaurU+FIJPF4Zmam0tO2n5OfczLqnCLhsFrb0kbVOY3Gz4lzktatXavZsw8d0edUXlamjz/5VIFQSC3+Lm3Ztk1dobC2NjSpqb1DrR2dikSjA/qw/JxsFeXlanxJoebPqFFWmldVlRUqzstVZrpPkyZMSHpOjQ3b9uqcQuGIyiaPN+Lv3N3dRjWkEerbb79dTU1Nuuaaa9TY2Kgbb7xR3d3d+uMf/yhJWrVqlV588UXNmTNHmzZt0gUXXCBJWrJkiY477jjNnj17T98So1hbW5sKCwudLgMYFFmFKdra2pRXUKDWUFRdMUtuueRhJBojUEdnh/Jy85wuAxjUSMiqbdtq6+jsd9/ywPuYG1tbFY7s0DDn5qiiqLB3ZLmod0p27whzcaEyHbydbUyPUOfm5mr8+PHy+XyJf3d3dyce7+npUXZ2tjIzMwcsMd/3daC/QCBAkwIjkFWYIGrZqvUHlOvLlscl+VzcF42RK9jT43iTAqRif2TVtm21d3YNWPCr/33Mu2yYc7JVXlykKeMr9dlZh6ii/z3MDjfMY8WQGuqDDz5YTz31lE477TS1tbUpFApp1qxZWr16tWbMmKFly5ZpxowZqq6u1gMPPKBwOKxIJKK6ujpVVY3t1fGwM/b1gynIKkaycMxSSzimnpilzmBIRW5GpDHyBUMhp0sAUjIcWe1rmLc3ygPvY25saVMoEhnwnLzsbJUXF2ryuIpEw5wYbS4qVGYGDbPThjTlW5LuvfderVq1SrZt67zzzlN5ebluv/12RaNRTZgwQVdccYU8Hk9ilW/btnXmmWfqiCOOGO5zgOGCwaAyMjKcLgMYFFnFSBSMxdQajikYs+VzueRyuRQOh5SWxkUWRj6yClOkklXbtuXvCvQu+tXabz/m7XsyJ2uYt6+O3X96dqGyRvF1x2iZ8j3khhoYLuvWrWNvXxiBrGIk6Y7G1BaOKWRtb6T7bK7dPOb3S4UZyCpMsbl2syZOmJhomLc3y20D7mMOhcMDnpebnTXgvuX+9zGXFRUpO3P0NsyDGS0N9ZD3oQaGS2ZmptMlACkhq3CabdsKxCy1hWOKWrZ8brfSdjG1O4N75mAIsoqRxLZtdQQC8WnYzf3uY25tU11Do5r9HQqGdmiYszJVXlykCeVlOuyQGpUXF8bvYy4pGvMN81hBQw3HsVAdTEFW4RTbttUZsdQeiSkmyedyybebe6Qz+OUPDEFWsT/Ztq3OQPdO07C39ZuavWPDnNPbMI8rLdHhMw7ZaT/mbDI85tFQw3HNzc2snAwjkFXsb5ZtqyNiyR+JyrZd8rpdSmXN7vb2dlZOhhHIKoaTbdvq7O7e3ijv4j7mnh0WF8vKyFBFSZHGl5Zobs2B25vlkkKVFxUpJyveMHN7ApKhoYbjysrKnC4BSAlZxf5i2bbaI1F1RCxJLnldbmkPFu0uKizaZ7UBw4msYk/Ytq2u7p5Eg7zjSHNDS6u6g7tumCtLSzSnprrfPczxf/c1zIMhq0iGhhqO8/v9ys/Pd7oMYFBkFftazLbVGoqqK2bJ3ddID0FXV6dycnKGuTpg+JFV9NfXMO9qwa++Rb+6g8EBz8nKSE/svTz7wGmJqdjx+5iLU26YB0NWkQwNNRwXYg9KGIKsYl+JWrZawlF1R215XJJviI10n/AO27IAIxVZHXsGNszbG+WG5pZdNsyZ6emqKImPJs888IDE6HL/Eeb+uxzsK2QVybBtFhzH3r4wBVnFcAvHLLWEYwrGLHl32Ppqr16XvX1hCLI6+gR6eqdkN++84FdDS6sCPTs3zIl9mEsGTscuLy5UblbWfmmYB0NWhx/bZgHDpLa2lr19YQSyiuESjMXUGo4pGLN7V+zeuxHpHW1raGDxHBiBrJon0BPc9YJfrfEGuqu7Z8DxGelpiQZ55rSpiUa5r3HOzR4ZDfNgyCqSoaGG49iKCKYgq9hb3dGY2sIxhS1bXpdLacPcSPdhz3SYgqyOPH0Nc7L7mDt3aJjT09ISW0lNnzp5+6JfJfHGOS8724iGeTBkFcnQUMNxTKGFKcgqhqo7GlNLOKaoZcvndu92D+nhkJ5m/hQ6jA1kdf/rDga337e8433MLa3qDHQPOD49LU0VvSPKB0+dnFjwq69pHi0N82DIKpKhoYbjWlpaVFTEVgQY+cgq9lRPbyMd2U+NdJ92v195eaxIj5GPrA6/nmBowD3LDS1tA+5j3qlh9vkS07BrJlf1ro5dlBhpzs8ZGw3zYMgqkqGhhuPKy8udLgFICVlFqvoa6bBlK20/NtJ9ivnFDwxBVvdcTzCUuF+5717m/vsxdwQCA45P83kT9yv3Ncz972MuyM2hYU4BWUUyNNRwXGtrq/Ly8pwuAxgUWcVg+hYbC8XiI9Jp+7mR7uPv6FB2NvulYuQjqzvrCYXU2DuqvKv7mP1du26Yy4uLdNDkqkSj3NdE0zAPD7KKZGio4bgI+/rBEGQVyYRillrCUYVi8cXGhnvV7j0VjUYdfX8gVWMxq30N8y7vY25tk7+za8DxPm/fCHOhqqsm9GuW441zYV4uDfN+MBazitSwDzUcx96+MAVZxY769pHuiVnyDeM+0nuL/VJhitGY1WA4PGCEuf99zA0trWrfZcNcuNN2Un1Tswtyc+R2+Jd0GJ1ZdRr7UAPDhL19YQqyij59jXTQsuTVvtv+aqjYLxWmMDGroXC43z3MbTvdx7xzw+xRWVG8WT7y0BmJhrlvpLmQhtkIJmYV+wcNNRyXm5vrdAlASsgqIr2NdHfviLTPNTIvgrOzspwuAUjJSMxqKBxWY2t7v/uXBy761dbROeB4r8ejsqJCVZQU6YjZM3qb5e0jzYV5uTTMo8BIzCpGBhpqOM7rJYYwA1kdu6KWreZQNNFIj7QR6R15PGQVZnAiq+FIZPvIcr9GuW96dmuShrm8uEifnXnIgAW/yosLVZSfR8M8BvBzFcmQDDiura1NJSUlTpcBDIqsjj1Ry1ZLOKpA1IxGuk9HZ4cKCgqcLgMY1L7IajgSUWNr204LfvXdx9zq7xhwfLxhLlB5cZHmJxrmwsS07KL8PHkM+d8+9h1+riIZGmo4rrKy0ukSgJSQ1bEjZsdHpANRS16DGuk+/OIHphhKVsORqJpa2waMLvefmr1jw+xxuxMjzIdPr4mPLpcU0TBjj/BzFcnQUMNxTU1NyslhXz+MfGR19IvZtlpDUXVFbXlcMq6R7tPe1qasTO73w8gUi8V0zxPPasXH63TA+Ap9Z9HZ8ng8icfDkaia2toGTMve1tw6YEq2bW/fpMbjdqu0qEAVxcXbG+bEol+FKi7Ip2HGXuPnKpKhoYbjLMtyugQgJWR19OprpDt7R6R97pGx/dVQkVWMVJFoVP/3yBP6++v/Ujga1brNW/RpfaMqSooSDXSLv2NAw+x2uxNTsudNr+m3xVS8cS7OzxvQkAP7Aj9XkQz7UMNx3d3dymLlRBiArI4+lm2rLRxVR9SSRy65R8g+0nuLPdOxP9i2rWAorPauLvk7u9TRFVB7VyD+34GA2ju75O/9s78rIH9XlwI9wV2+Vrw53t4o9/9zSUE+DTMcx8/V4cc+1MAwqaurY29fGIGsjh6Wbas9EpU/Ysmtkbv91VA1NjWyXyr2mGVZ6uzu7m2C4w3w9oa4S+2dAXUE4n9u7wqoo6tL4Uh0l6/l9XiUn5Ot/Nwc5efkqLy4SPm52crPydEH6z/V+2vXKxqLyef16IwTPqeLzjhlP58tsGf4uYpkaKjhuPz8fKdLAFJCVs0Xb6Rj6ojE5BqFjXQf7vWHFL8XuaOvKe4dIe4bNe7o6htB3j6y3NkVkGXveuJiVka68nNylJeTreKCfE2dME55OdkqyMnpbZqzBzTQWRnpciWZ8RGLxXTvk89q+UfrVDN5or7xpS/uy28DMCz4uYpkaKgBAKOeZdvyR2Lyh2NyuVzyjtJGGqNfoKdH//vQX/XBJxs1vqxEM6ZNUWegZ/tocldXYnS5Oxja5Wu4XC7lZWcpPydH+bnZmlhRphk7NsY5OSrIjTfQ+TnZSvP5hu0cPB6Pvnn6Qn1TUmtbK9O5ARiNhhqO8/v9Kisrc7oMYFBk1Ty2bcsfseSPRGXLJe8YWem3q6tLRYVFTpeBvRCJRrWloUkbt27Txq312rh1mzZtrdfWppbEMXWNTVq6eo18Xu+AZriypEQFudnKy4l/ra8xLuhtoHOyskbMqtdkFaYgq0iGhhqOGz9+vNMlACkhq+awbVsdEUttkahkj51Guk9ZKb/4MUXMsrStuaW3cd7ePG/Z1qhY76rCbrdbE8pKNW3iBIUjUTW3+xPPP7Bqom77j+8nnV490pFVmIKsIhkaajiuvr5eBxxwgNNlAIMiqyOfbdvq7G2k7b5G2sw+Y680NzdpwoSJTpeBfmzbVku7X59urdfGuu3N8+b6BoUikcRxFSVFmjyuUkfMmq7J4yo0eVylJpSXKc0Xv2T74+NP6/GXXlUoElG6z6fDDjnQ2GZaIqswB1lFMjTUcJx7jI0cwVxkdeTa3kjHZNmSb4w20n3IqrP8XV29TXN9YuR5U/02dXX3JI4pys/T5HEVWnjskb2Nc4UmVVYoMyN9t6/99dMWyOWSln+0TnNqqnXBqQv29ensU2QVpiCrSIZ9qOG4rq4uVk6EEcjqyNQVjak1FFPMlnzuMdxF99Pd062sTPZM39e6g0Ft2mGq9sat29TW0Zk4JicrU5PHVSZGmyePr9Dkygrl5WQ7WPnIQVZhCrI6/NiHGhgm9fX17O0LI5DVkSXRSEvyuVyil96uubmZ/VKHQSwW0z1PPKtlH63VlPGVmlk9VbXbGuONc129GlrbEsemp6VpcmW55s84WJN6m+cp4ypUlJ9n9JTsfY2swhRkFckwQg3HNTc3q6SkxOkygEGR1ZEhEI2pNRxT1LZH7T7Se6u9vV0FBQVOl2Gs9s4urV6/QX9+9kWt27xFdr+9mb0ejyZWlG0fce79d3lxIVNCh4CswhRkdfgxQg0Mk2g06nQJQErIqrO6ozG1hGOKWrZ8brd8jPolFYuR1VTZtq1tLa1avX6DVq//VKvXb1DttsZdHjtlfKV+f80P5WXf5GFDVmEKsopkaKjhuM7OTlVUVDhdBjAosuqMnmhMzf0baeZ2DyrQ3a3iYqerGJksy9Km+m1ate7T3iZ6Q2IbqpysTE0/YIq+cMThmjFtqt5csVpP/PP1xIran515CM30MCOrMAVZRTI01HDcxIlsQQAzkNX9q6d3anfIspVGI71HKsrLnS5hxIhEo1q7qTYxAv3BJ58mVtsuzs/TjOqpmjltqmZMm6rJ4yoGTNuumVwlr8c9albUHonIKkxBVpEM91DDcevWrWOhJxiBrO4fwVhMLaHtjTT23ObazWN28ZzuYFAffrJRqz/5VKvXbdBHGzcpHIlP1ZxQXqaZ06ZoRnW8ga4oLmLBMIeN5azCLGR1+HEPNTBMfD6f0yUAKSGr+1YoZqklHFUoZsvrctFM7wWvd+z89d7W0ZmYur1q/afaUFsny7bldrs1beJ4nXLsUZoxbYqmHzBFhXm5TpeLHYylrMJsZBXJkAw4rqioyOkSgJSQ1X0jHLPUEo6pJ2bJ53LJRyO91/Lz8pwuYdjFYjH9vyf+rnc++EglBfnKz83Rh59sVF1jkyQpzefVwVMm6atfPEkzpk3VwVMnKSsjw+GqMZjRmFWMTmQVydBQw3ENDQ3K44cUDEBWh9eOjTQj0sOnpbVV2dk5TpcxLAI9PVq2Zq3uf/p5fVpXL0nasGWr0rxezT3kIH3x6M9o5rSpmlY1QT5GkIwzmrKK0Y2sIhn+5oHjilkyEYYgq3svFIupI2KpJ2b37iNNI70vFOTnO13CkNm2rY1bt+md1Wu0dPUaffDJp4pZltw73Os8ZcI4XfetixyqEsPF5KxibCGrSIaGGo4LBoNOlwCkhKzuOdu21ROz1BGxFLIsxWzJ53LJ7XIpjcWg9plQOOx0CXukJxjS8o/WaekHH2rp6o/U3NYuSZo6vlJnff44zZ9xiN5e9YH+9vL2Lazm1rBA4GhgWlYxdpFVJENDDccFAgGnSwBSQlZTY9m2umOWOiOWgpYlSfLKJY/LLQ899H7R09PjdAm7Zdu2arc1aunqNXrngzVatW6DorGYsjLSNffgA3X4wi9o3vQalRYWJJ5zyNRJ8rjZwmq0GelZBfqQVSSzV9tmtbe366qrrtIvfvELeTwe3XbbbXK5XKqqqtIll1wit9utF154Qc8//7zcbrfOPvtszZs3bzjrxygQDAaVwcIxMABZTS5m2+qKWOqKxhSO99DsG+2gcDiktLR0p8sYoCcU0vsfr+9toj9SQ0urJGnSuArNn36w5s84WIccMJn7oMeYkZhVYFfI6p6zbVsxW7IUbzfdLskjl7xuyetyKdPrVo7X43CVe2/If2tFo1HdcccdSkuL7x129913a9GiRZoxY4buuOMOLV26VAcddJCefvpp3XzzzQqHw1q8eLFmz57N1jMYoLa2lr19YQSyOlDUstUZjakraili2XK7XPK6XPJxS7TjtjU0jIj9UusamrR09Rot/WCNVq79RJFoVBnpaZpTc6DO+bcTNH/GwSorKnS6TDhopGQVGAxZ3bVYb9Pski1bkscVb5Y9Lpd8HpfSPW6lud3yurTTWhijxZAb6nvuuUf/9m//pscee0yStGHDBk2fPl2SNHfuXK1YsUJut1s1NTXy+Xzy+XyqqKjQxo0bd7ogbW1tVUtLi8rLy9Xa2qpIJKKJEyeqtrZWubm58nq9amtrU2VlpZqammRZliorK1VXV6f83gUC/H6/xo8fr/r6erndbpWWlqq+vl6FhYWKRqPq7OxMvKbP51NRUZEaGhpUXFysYDCoQCCQeDw9PV35+flqbGxUSUmJAoGAenp6Eo9nZmYqOztbzc3NKisrk9/vVygUSjyenZ2tjIwMzinFc9q6deuoO6fR+DlxTvHp3uvWrRtV57Snn1N9Y7Myi0vU1NGpYDiiyvIyNTU2KjMzU+lpaWr3+1VcVCR/R4ei0agqysu1raFB2VlZ8ni86ujsUElJidrb2mRZlkpKStXY1KicnPjKqV1dXSorLVNzc5PcbrcKCgvV3NysvNw8xWJRBbq7E6/p9XqVn5enltZWFeTnKxQOq6enJ/F4ms+nnJxctba1qqCgQMGeHgVDocTjGenpysjMVHt7u4oKi9TV1alwJJJ43ORzam5q2m/nZEt65q33tGzNWs04YIpqJk3Qex+t05qNm1XfHB+FHl9WouMOm6UjZk3X1PEVivaraVs4OGY/J84pKknaXLt5VJ3TaPycOCepualp1J1TKp9TeVmZtjY0yuP1Ki83T21trSrKz1c0Ela4p1sTx1WqeetWZWekqyg/T01NTcrrvY7o7L2O2DQKro12N6AypCnfL730klpaWnT22WfrZz/7mS677DL94he/0F133SVJWrVqlV588UXNmTNHmzZt0gUXXCBJWrJkiY477jjNnj17T98So5jf70/8jwAYycZiVm3bVjBmqSNqKRiLLyrm7V1UDCNXV1dX4oJpX7v9L4/pqVffUjQWS3wt3efToTXVOnz6wTp8Ro0qS1ghH7u2P7MK7I3RnFWrd5TZli2XXHK7bHl6Z515XVKa2610j0ted3zkGQMNaYT6pZdekiStXLlSn376qW699Vb5/f7E4z09PcrOzlZmZuaAG/j7vg7019jYOOaaFJhprGTV2mFlbqt3ZW4WFTNHa1vrPr3wa2xt0xvLV+n1FSu1at2GAY9NLC/T//70R0rj9i6kYF9nFRguJmfVtm1Zik/P7uuHPXIlpmGned1Kd8e3sfSM4qnZ+8qQGuobbrgh8d99I9T33nuvVq9erRkzZmjZsmWaMWOGqqur9cADDygcDisSiaiurk5VVdx7gIFKSkqcLgFIyWjOasy2FYhY6orFFLJsyXbJ53bJ63JL/L1qnIKCgmF/zS0NjXp9+Sq9vnyl1m6qlSRNGV+p6QdM0dpNtYpEo0r3+XT0nJk000jZvsgqsC+M9KzudgGw3mY53S15e5tmF03zsBm2pTS/8Y1v6Pbbb1c0GtWECRN0xBFHyOPxaOHChVq8eLFs29aiRYsSi5gBfQKBgAoLWZQGI99oy2rfomKBqKWwZcut+HQun8tFE224YE+P8nLz9uo1bNvWhrqtemP5Kr22fKU2bd0mSTpocpUu/vIpOurQmRpfXqpYLKZ7n3yW7awwJMORVWB/GAlZ7VsATL1N81hcAGwk2qtts4DhsG7dOlZOhhFGQ1Yjlq2OSFTdUVsRO36PFPdDjT6bazcPaTVay7L08cbNem35Sr2xfJXqm1vkdrk0s3qqjpozS0fOnsGq3BhWQ80qsL/tj6zuOMrsckne3lFmt8uldLdbaW4lpmYzyjwy0FDDceztC1OYmFXbthXaYVExD4uKjXp7sl9qLBbTqvUb9PqylXpjxSq1+Dvk9Xg0p6ZaR8+Zpc/Omq7CvNx9XDHGKvb2hSmGK6uDLQCW7okvAMYvvM0xbFO+gaFib1+YwpSsWn0rc0csBS1LdmJlbrfc/N08Jgy2X2o4EtXyj9bq9eUr9db7H6gjEFC6z6fDZ9ToqENn6TMzD1FOVuZ+rBhjFXv7whSpZtW2bcUUn57t3mEBME/iXmaXfCwANmrQUMNxmZlctMEMIzmrlm0rELXUGY0vKmbbLvlcYlGxMSojffsoSiwW0z1PPKtla9aqpDBfPp9X76xeo+5gSFkZGfrsrEN09JxZOuyQg5SZzkgh9q8MMgdD9M+qZduydlgAzNs7yuxxb5+a7fO4GWUeA2io4Ti2UoMpRlpWY7atzoilQDSmsBXvm1lUDJKU0e+XP7c+8Iiee+sdWZaltZtrle7z6YT5c3XUnFmaU1Mtn5dLATgnYwT/ohJjW/8FwFwuKT0jU27ZvdtMxUeaWQAMEg01RoDm5uZRtXIyRq+RkNWIZaszGlUgaitibV9UzOd2tCyMMG1tbdqwtUGPvPBPLV29ZsBjk8dX6gdfO8ehyoCB2tvbHV85GWNT/wXAXIovAObptwBYjtujdLcSU7PXb23VuIpip8vGCERDDceVlZU5XQKQEqeyGorF1BGx1BOzFbXt3vuhXUrjhmjsIBaL6dVlK/XQcy/qky1blZ+boxnT4vtEhyPxfaLn1oz8dQAwdhQVFjldAkYxa8A2UzssAOZ27dECYFyvIhkaajjO7/crPz/f6TKAQe2vrNq2rZ7eRcVCVnxlbl9fE820MuxCdzCoZ994W4+/+KoaWttUUVyo7513tk76zGHyejzsE40Rq6urUzk5OU6XAUP1XwBswCjzPlgAjOtVJENDDceFQiGnSwBSsi+zatm2umOWOntX5pbie096XPGLAGBXWtr9+uvLr+np195SV3ePZkybqm+d82VVFuZqctWkxHHfPH2hvulgnUAy4UjE6RIwwvXfZkpybgEwrleRDPtQw3Em7u2LsWm4sxqzbXVFLHX1LiomST6mcSMFG7du0yMvvKyXli6TZVk6as4snfX543TwlHgTzd6+MAVZhdS3AFi8JYmPMmv77U0el9J7R5ud3GaK61Ukwwg1HGfK3r7AcGQ1atnqiMYUiFqKWrZcvb9lZ1ExDMa2ba34eL0eeeFlvfPBR0pPS9PCY47Ql088VuNKSwYcy96+MAVZHRt2twCYx+VSeu8CYN7eptk1Am9v4noVydBQw3EjbSsiIJmhZjUUs9QRiSkYsxWx+6/MPfIuGDDyRGMxvfreCj3ywj+1vrZOBbk5+vppX9Spxx6pvJxdZ3Ik75kO9EdWR4/+C4DZkjy9U7M9Lpd8nvgCYH2jzCbuzcz1KpKhoYbjmD4DU6SaVdu2FYxZ6ohaCsbii4p5WVQMe6g7GNTfX39bj7/0qhpb2zShvEw/OP8rOvEzhynN59vtc9PT0vZTlcDeIavm2NUCYN5+20yluz3xe5l792YeiaPMe4PrVSRDQw3HtbS0qKiIbTMw8u0uq/0XFQtZlqzelblZVAx7qrmtPbHQWKAnqJnVU/Xtc8/QZ2YcLLc7tXsD2v1+5eWxGi1GPrI6svRfAMy1wzZTfQuApXviW06ZOMq8N7heRTI01HBceXm50yUAKdkxqzHbViBiqSsWU8iyJTs+jdvrcktj6zoDw+DTuq165IVX9PI78YXGju5daKxmyqTBn7yDYi76YAiyun/Zti1LvaPMvX9PDVgAzOseEQuAjURcryIZGmo4rrW1VXl5eU6XAQyqtbVVWTm56uxdVCxs2XIr/pt6n8tFE409Fl9obJ0efv5lvfvhx/GFxo49Qmec8DlVlhYP+XX9HR3KzmZvX4x8ZHX49V8ATIpvM9W3AJg3sTfzyF4AbCTiehXJ0FDDcRH2oMQIF7Fs+cNR1YVisrrDiUXF0lhUDEMUjcX0yrsr9Mg//qlPautUmJerb5z2RZ3yuSOVNwwL30Sj0WGoEtj3yOrQxPotACYlXwDMyyjzsOF6FcmwDzUcx75+GGls246vzN1vUTGPy6VoJMx+qRiSWCyme554Vu+tWavszHTVNjSppd2viRVlOuvzx+nE+YMvNLYn2NsXpiCru9Y3ytw3NXvnBcDcSnMrMTWbUeZ9j+tVJMMINRzHvn4YCay+lbkjloKWJTuxMrdbfQPR7JeKoVrywCN64a13FLMsSVJpYYGu+9ZFmr8HC43tCbIKU4zlrO5uATCvOz7KnO5xJWZFwVlcryIZGmo4Ljc31+kSMEZZtq1A1FJnNL6omG275HMp6aJi2VlZ+79IGGtbc6tefneZ/vnOcn1aVz/gseL8PH121vR99t5kFaYYzVnd9QJgLnldksftks/tVkbvv1kAbOTjehXJ0FDDcV4vMcT+E7NtdUYsBaIxha1435zqomIeD1nF7rV1dOq1Ze/rpaXL9OGGjZKkQ6ZO1tyDD9Tq9RsUjkSV7vNpTs2+HeUgqzCF6Vnd1QJg3h22mUrrXQDMy7obRuN6FcmQDDiura1NJSUlTpeBUSxi2eqMRhWI2opYdmL6nG8PZ9p2dHaooKBgn9QIcwV6gnrz/VV6+Z3lWrZmrSzL0uRxFfrm6SfruHlzVFlSrFgspnuffFbLP1qnOTXVuuDUBfu0JrIKU5iQ1f4LgLlc8W2mEotTeuOrZrMA2OjH9SqSYVEyOK6rq0s5OWyZgeEVisXUEbHUE7MVte3EHpt7o7unW1mZo3d6IlIXjkT1zgdr9PLSZfrXqg8UjkRVXlSo4+fP1fGHz9GU8eMcrY+swhQjIav9R5ldksQCYNgFrleRDCPUcFxTUxM/oLDXbNtWT++iYiErvjK3r7eJThumi5/2tjbHL/zgnJhlaeXa9Xr5neV6bdn7CvQElZ+bowVHfkbHz5+rQ6ZOHjEX2mQVpthfWWUBMOwtrleRDA01HGf1rnoL7CnLttUds9TZb2Vun8sljys+ijDs70dWxxzbtrV2U61eWrpMr7y3Qq3+DmWmp+uoOTN1/OFzNKfmQHk9HqfL3AlZhSmGK6u2bSum3gXApN6p2dsXAEtzu5XOAmDYC/xcRTJM+Ybjuru7lTWKV/nE8IrZtroilrp6FxWTJN9+WuiFPSjHjs3bGvTPd5brpaXLtLWpWT6vR4dPP1gnzJ+rz8w8ROlpaU6XuFtkFabYk6z2H2WWdlgAzKXE1Gyfx80oM4Yd16tIhhFqOK6uro59/bBbUctWRzSmQNRS1LLl6r2A2tNFxfZWY1PjmN0vdSxoamvXP99ZrpffWab1tXVyuVw69KBpOmfBCTr60FnKzTbnQoqswhQ7ZnVXC4D1rYGR5o2PMqcxygwHcL2KZGio4bj8/HynS8AIFIpZ6ojEFIzZivRbVGx/jUbvCvdOjT4dXQG9tnylXl66TKvWb5Bt2zpo0kRdfvaX9LnDDlVxgZk/n8gqRqqYbcvqN8qcmZUt27bldcdXzk5ze5TRu80UC4BhJOF6FcnQUAMYEWzbVjBmqSNqKRiLLyrmHeZFxQBJ6gmF9K+VH+ilpcv03ocfKxqLaUJ5mb52yhd0/Ly5Gl9e6nSJgJF2nJLdN8Ls7jct2+f2xKdl9zbMzd1ulWWP7FsoAGB3aKjhOL/fr7KyMqfLgAP6LyoWsixZ+3hRsb3V1dWlosIip8vAEERjMb334cd6aekyvfn+aoXCYZUU5Ov0E47RCYfP1QETx4+qkTCyiuHWt7VUrG9rKUkeV99+zPGmOc3tls/tUpo79dWyuQaAKcgqkmFRMjiORR7GlphtKxCx1BWLKWTZku3sNO49wUJPZrEsS6s/+VQvL12m15atVEcgoNzsLB0zd5ZOOHyuZkybKrd7P9+Iv5+QVeyJvhWyLVtyyZbkkstly6PtzbKvdx/m7c3y8EzH5hoApiCrSIYRajiuvr5eBxxwgNNlYB+KWrY6excVC1u23Irv++lzuSQzemlJUnNzkyZMmOh0GdgN27a1YctWvbR0mV5+d7ma29qVnpamI2dP1/GHz9Vhhxwkn3f0/9VHVtHfjvct999Syt27D3N8dHn7vcv7a8EvrgFgCrKKZEb/VQVGvNE6QjTWRSxb/nBUPb2LivVN/0szZDR6V8jqyFXX2KSX31muf76zXJu3Ncjjdmve9Bpd/OWFOmLWDGVmpDtd4n5FVscOq7dZtpLct+x2SWk73Lc8klbHJqswBVlFMkz5huO6urpYkXYUsG07vjJ3v0XFPL2Lio0W3T3dyspkutdI0erv0CvvrtBL7yzTxxs3S5JmVk/V8YfP1bFzZysvJ9vhCp1DVkcfy7YVteOXbF6X5HNv3/kgbQ/vWx5JuAaAKcgqkmGEGo6rr69nXz9DWX0rc0csBS1LdmJlbrcMHohOqrm5mb19HdbV3aM3VqzSS0uX6f2P18mybR0wcbwuPuMUHTdvjsqKCp0ucUQgq2azbVuR3vuZ4yPMLmV5Xcp0e5TucY+qX1RyDQBTkFUkQ0MNxxUWcgFsEsu2FYha6ozGFxWzbZd8Lsnrcht1P/RQ5OXmOV3CmBQKh/X2qjV6+Z1lWrr6Q0WiMY0rLdZXv3iSjj98rqoqy50uccQhq+awbVvR3vubPa74TgdpHpeKvW6le9zGjTjvKa4BYAqyimRoqOG4aDTqdAkYRMy21RmxFIjGFLbifbOJi4rtrViMrO4vsVhMyz9er5eXLtMbK1aqOxhSYV6uTjn2SB1/+FwdNLlqVG1zNdzI6sjUt/WU1Tfy7HLJ53Epy+tWutst72ic2jMIrgFgCrKKZGio4bjOzk5VVFQ4XQZ2ELFsdUajCkRtRazti4r5xvCaHIHubhUXO13F6GXbttZ8ukkvL12mV95bofbOLmVnZuiYubN1wvy5mnXgNHlYFCYlZHVkiNq2bNuW+jXP2V63MsZo87wrXAPAFGQVybAoGRzHfqkjRygWU0fEUk8svviNd5QtKra3wuGQ0tLG1mrR+0IsFtM9TzyrFR+v06EHVetzh8/Rq++u0MvvLtO25lal+bz6zMzpOv7wOZo/42Cl+XxOl2wcsrr/xWxbMduWq3fats/tUqbbrUyvWz6a56S4BoApyCqSoaGG49atW8ciDw6xbVs9vYuKhaz4ytw+l4uptElsrt3MQk/D4P8efUJ/e/l1RaJRuSTZim9HMremWsfPn6sjZ89UdiYXLXuDrO5bfc2z1K959vQ2zy7xM3QPcA0AU5BVJMOUbzjOx+jTfmXZtrpjljr7rcztc7nkccX3J0VyXi8/MvdEdzCozfUN2lzfoE31Ddq8rUGb6rdpW3Nr4hhbUllRoW77yfdVmJfrXLGjDFkdPvEVt2255EpsV5Xj9SjTE9/XmVk8e4drAJiCrCIZ/saF44qKipwuYdSL2ba6Ipa6ehcVk+IXhb4xsDL3cMrPY+XkXekMdGvztn6Nc/02bapvUFNbe+IYn9ejieVlqpk8SSUF+fro082KxmJK9/l04vy5NNPDjKzunahly5Itn8ulDI9LxV6PMkbZdlUjBdcAMAVZRTI01HBcQ0OD8rj4G3ZRy1ZHNKZA1FLUsuVyueQd44uK7a2W1lZlZ+c4XYZj/F1dvQ1zfKS5r4Fu9Xckjkn3+TSxslyzDjxAVZXlmlRRoUmV5aooKZLH45EUv4f63ief1fKP1mlOTbUuOHWBU6c0ao31rO6pvincXpeU5narIMOjzDGwZdVIwDUATEFWkcyQ7qGORqP6/e9/r8bGRkUiEZ111lmaOHGibrvtNrlcLlVVVemSSy6R2+3WCy+8oOeff15ut1tnn3225s2bty/OAwZrbW3lt37DJBSz1BGJKRiLT1FkUbHh1dHhV15evtNl7FO2bauto3PASPPm+gZt2tYgf2dX4rjM9PR4w1xZ3vvvClVVlqu8qFBuVuJ23FjI6t6w7PjCi26XlO52KdPjUY6XlbedwDUATEFWkcyQRqhfeeUV5eTk6Hvf+546Ozv1ox/9SJMnT9aiRYs0Y8YM3XHHHVq6dKkOOuggPf3007r55psVDoe1ePFizZ49m3sQMEAwGHS6BGPZtq1gzFJH1FIwFl9UrK+JTqORHnahcNjpEoaNbdtqbvfv1Dhvrt+mzu6exHHZmRmaVFmhI2dNV1VlRaKBLi0sYOGlEWw0ZXU4JO6DdknpLpeyfC5le7xKc7MIo9O4BoApyCqSGVJDfeSRR+rII49M/Nnj8WjDhg2aPn26JGnu3LlasWKF3G63ampq5PP55PP5VFFRoY0bN7JCHgYIBAJOl2CU/ouKhSxLFouK7Tc9PT2DHzTCWJalxtb27U3ztu3TtbuDocRxednZmjSuXJ+bN6ffyHOFivJyaTgMZGJWhxv3QZuBawCYgqwimSE11JmZmZLif2HffPPN+upXv6p77rkncdGVmZmp7u5u9fT0KCsra8Dzuru7d3q91tZWtbS0qLy8XK2trYpEIpo4caJqa2uVm5srr9ertrY2VVZWqqmpSZZlqbKyUnV1dcrPj09p8/v9Gj9+vOrr6+V2u1VaWqr6+noVFhYqGo2qs7Mz8Zo+n09FRUVqaGhQcXGxgsGgAoFA4vH09HTl5+ersbFRJSUlCgQC6unpSTyemZmp7OxsNTc3q6ysTH6/X6FQKPF4dna2MjIyOKcUzykUCqm5uXlUndNwf07jJkzQus1bZKdnypOeIb/fr9KiInV1dSociaiivFzbGhqUmZmp9LQ0tfv9Ki4qkr+jQ9FoNPF4dlaWPB6vOjo7VFJSova2NlmWpZKSUjU2NSonJ37PZVdXl8pKy9Tc3CS3262CwkI1NzcrLzdPsVhUge7uxGt6vV7l5+WppbVVBfn5CoXD6unpSTye5vMpJydXrW2tKigoULCnR8FQKPF4Rnq6MjIz1d7erqLCkX1OhYWF2ly7eUSekySFLOmDdevV2hlQ7bZGbd7WqG2tbQqFI4mft/nZWZpYWa6jD52h0oI8zaieFm82CvJ3OqdAR7tyM9ON+5xGY/b29Jwi4bC6urpG1TkN9jm1tvtVUFikQKdfikV1wPhKtWzdqqy8XLm8XtWNgJ/lo/Hvp709p9LSUq1bt25UndNo/Jw4JykUCqm7u3tUndNo/Jz21TntbkB4yPtQNzc366abbtKCBQt04okn6uKLL9add94pSVq6dKnef/99zZ49W8uXL9dll10mSfrVr36ls846S9OmTRvKW2KUYl+/XYtatjp7FxULW7bccnF/n8NGwt6+0VhMW5uad1pRu3ZboyLRaOK4ksKC+ChzRXy0eVJlhSZWlikvO9vB6rG/jISs7mv974NOc7uUxX3QRuIaAKYgq0hmSCPU7e3tuvbaa3XJJZdo1qxZkqSpU6dq9erVmjFjhpYtW6YZM2aourpaDzzwgMLhsCKRiOrq6lRVNbr/gseeS09Pd7qEESNi2fKHo+rpXVTM43LJ43IpjQvEESFtP67/EI5EVdfYNHBF7W0NqmtoUjQWSxxXXlykSZXlmnvwgYmFwaoqypTdO5MIY9P+zOr+FLVs2S5baS6XcrgPelTgGgCmIKtIZkgN9aOPPqpAIKCHH35YDz/8sCTpwgsv1F133aVoNKoJEyboiCOOkMfj0cKFC7V48WLZtq1FixYpLS1tWE8A5uubojEW2bYdX5m736JiHhYVG7FycoZ/r+RQOKwticZ5+4hzXWOzLCu+abjL5VJlSbEmVZbrszMPSTTOE8vLlJnBX/DY2b7IqlP6FhRLc7tUlOZRjo/7oEeTsXwNALOQVSQz5CnfwHAZa1NorL6VuSOWgv0WFWOEZeTbm2m0PaFQ/L7mHfZw3tbcIsuO/xh2u90aX1aSWBCsb0XtCWWlSueXkdgDo2HKd8Sy5XLZyvS4VejzKM3Ddmyj0Vi7BoC5yCqSGdIINTCcSkpKnC5hn7NsW4Gopc5oTCHLlm275HNJXpdboo82RkFBwaDHBHqC2rytYafGuaGlNXGM1+PR+PJSTZs4XifMn5sYcR5fVqo0Hz+WsfdSyepI1HdfdJrbpZJ0j7K9jEaPdmPhGgCjA1lFMly5wXGBQECFhYVOlzHsYratzoilQDSmsBXvm71ul3wuF020oYI9PcrLzZMkdQQCqq1v1KYB21E1qLmtPXG8z+vVxIoyHTJ1khYc9ZnEiPO40hJ5PR6HzgJjQf+smqBvNDrb41ZBmk8+1o0YM0brNQBGH7KKZGio4bjRtF9qxLLVGY0qELUVsbYvKuZjpqKx2ju7Evc1x7ek6tbm+m1q7ehMHJOelqaqijIdeuC0fns4l6uipFgeNx8+9r9gKDT4QQ7rG41Od7tUmuFRtsfNrS9j0Gi6BsDoRlaRDPdQw3HBYFAZGRlOlzFkoVhMHRFLPbH4xaG3d1ExmMO2bbV2dCYa5/7Ttf1dgcRxmenpmjQuvgVV33ZUVZUVKisqkJvGGSNIOBxSWtrIXLAuYtlyyVa2163CNC/bXI1xpl8DYOwgq0iGEWo4rra21qhFHmzbVk/vomIhK74yt4+VuY1g27aa2tp3WlF787YGdXVv/81zTlamJlVW6MhDZw5YIKy7069JVZMcPAMgNdsaGkbUomSMRiMZ064BMHaRVSRDQw3HZRqwX65l2+qOWersXZnb7m2iPS63PFwTjjiWZamhtW2nhcFqtzWoO7h9Kmx+bo4mVZTruHlzBjTOhXm5u7zYb4yM/Gm0gCRljJD9UsOWLY/LVpbHrcI0H6PR2IkJ1wCARFaRHA01HJf9/7d377FtlecfwL/n5ti5J01iO00TUEdvK5SW3kuBtrRd6TQQaNr+KNP+6qRykSZx2bRuRVtZi8qEBpu28cfGkGCsGtM0Vm3dyriUiwbdBhOCX2Gwpk0aO/d77Pic9/39YfvETuIkdWMfn/r7kQDXfu3zJnpIz5P3fZ+nrMzpKUzLkhLDMYHhRFExADBUBQYrcxcMSwiEunrQ2hFGayglce4IIxqL2eNqqyrREvRj56Z1dkXt5oAf1RXll3Q9L/8yJZdwMlaTq9FeTUEDV6NpFoV6D0A0GWOVMmFCTY7r7u4umKqJppAYNC2MmAKmkFAUBTqLijnOtCxc7OyeqKid2K59IdyFmGna4+prqtES9OO6mzbbhcGaA35UlJXOyzz6+/tdVTmZile+Y1VKiZiU0BTYlbq5Gk1zUUj3AEQzYaxSJkyoyXENDQ2OXj9qCQzGLESs+A1hsqgY27bk33jMRHtnZ0phsHji3BbugiWEPS5QV4uWYAA3rFhmJ86LAn6U+XJbLKS2pjann080X/IRq6lJtFdTUadr8HI1mi6R0/cARHPFWKVMmFCT4wYGBlBVVZW360kpEbEEBk2BiDVRVExhUbG8iYyPoy3UmV5ROxTGxa4eiETirCoKgvV1aA76sWnVykQ7qgCa/PXwOXQ+dHh4COXll7ZNnMgJuYrV5HZuXVHg0xQ0GBpK2FOdLkO+7wGIssVYpUyYUJPjonnol5paVCwqBASLiuXFWCSKC+FOuzDYuYvx/4Z6eiFlvGOfqqpY2FCHqxoDuOmG69GSkjh7DMPhryDdeMq5bKJCNp+xakkJS0oYqoJyQ0GFbnAHD82bfNwDEM0Hxiplwj7U5Lhc9fWzpMRITGDYshAVEpDcxp0rI2NjKVu0Jyprh3v77DG6pqHJX28XBUtW1V7YUAdDd8fv9gq5ty9RqsuNVVNKCCnhURWU6SoqdI1noikn2NuX3IKxSpm44y6Wrmjz2dfPFBJDiaJi40JChQJdVWAoCitzz4PBkZGpPZw7wujuH7DHeAwdiwJ+rFh8NfbcuNHeqt1YvwCay7eGFlpvX6JMsonVmJCQiPeKrjE0lBsqNB6DoRxjb19yC8YqZcKEmhx3uW0IxhNFxcasZIEcBZqiwMPVlKxIKdE/NJyeOIfiiXPf4JA9zlviQXPAj+uXXZPWw9m/oBaaemWWRWcPSnKLucRqvKgYoCgSXlVFtVdDqaZCZRJNecRWROQWjFXKhAk1Oe5St89IKeOVuVOKimmJytwsKjZ3Ukr0DgzGezh3hNK2bA+OjNjjSr1etAT9WL9yedp27fqaaqhXaOKcSYnH4/QUiOYkU6wmK3OrCuBVVSwwVPhYmZscxC205BaMVcqECTU5rqenB7W1M7d4EcnK3DGBSEpRMVVRwYXomUkp0dXXP6mHczyJHhmL2OMqSn1oaQzgxjXXoiUwkTgvqK7izXZC/8AAKitZ4ZMK3+RYjQkJVZFsb0UFZy73AESFgLFKmTChJsf5/f5pnxdSYsQUGDLjRcWkVGAogK6oPA89DSEEwj19k1abQzgf6sRYSmXK6opytAT92L7+BruHc0swgOqKct5gz2IB/yIll1hQW2uvRhuqgroSDeU6k2gqPJnuAYgKDWOVMmFCTY7r7e1FZWUlgHhl7qGYwIhpYVzE82YWFUtnWRY6unumVNS+EOpENKVVzoKqSjQHA9i9eb2dNDcHG1DFPspZGxgcRFkZv39U2ISU6BkYRGV5Oeo9GrwuLwZIV7bUewCiQsZYpUyYUJPjxsZj6B2PYcSUiImJomJGcR3PnSJmmrjY2R1PnEMT27Xbwp2ImZY9rqG2Bs1BP1Yt/dxE4hzwo7yUBbTmm2maTk+BKCNTSECRKNNU1IpxBH2F1cedaDqxeeyZTpRLjFXKhH2oyRFRy8JgTGDMkhiNRlFaUlK0lWXHYzG0hbumtKJq7+yCJQQAQFEUBBbUpvVvbgn6sSjQgFIWycgb9qGmQhQTApqioMrQUGHEq3SzXyq5BWOV3IKxSplwhZryQkqJsURRsaiIV+Y2EpW5e7s6UV4EvX0j4+O4EOqcUlG7o6sbQsZ/r6UqCoL1dWgO+rH5+pVoSSTOTYEGeFlh2nHsQ02FInk+2qsp8Ht1lOrp27rZL5XcgrFKbsFYpUyYUFPOCCkxagkMJSpzy0QSrSkqtJTF6LLSUucmmQOjkUhKJe0wzofi55zDPX2QicRZU1UsbKjH1QuDuGXt9faKc5O/Hh6D2zQL1ZUWq+Q+ppSQkCjVVAQ9BvQMbQ4qKiryPDOi7DBWyS0Yq5QJE2qaV5aUGI4JDCeKigGAoSowZqjMrWnuDMPh0TE7WU5dce7s7bPHGLqGJn8Dll7VjF2b1qE5EE+cGxvqYOju/LqLmVtjldwvJgVUAFWGhipDm/WIjM6fL+QSjFVyC8YqZcLIoMtmColB08KIKWAKCUVRoF9CUbHBoUFUV1fndI6XY3B4BK0dkxPnEHoGBu0xHkNHc8CPlZ+7Gi3BTfZZ52DdAmissHvFKPRYpStLclt3iaqgvkRH2SX0ju7r60NdXV2OZ0h0+Rir5BaMVcqERckoK1FLYDBmIWLFb/j0xHnobIyOjaLU5+xWWikl+oeGpyTNrR1h9A8N2+O8JR60BCaKgiWrajcsqIGmFnlZ8iJQCLFKVz5LSggJ+DQFtR4NHu3Sf7YMDw+jnC3yyAUYq+QWjFXKhCvUNCdSSkQsgUFTIGJNFBVTFAWey6zO3d/Xl7ckRUqJnv6BiR7OoYkEemhk1B5X5vOiJRjAxus+b682twQDqKuugsrEuWjlM1ap+MSEhKpIVCS2dWuX8bO1q6uLN37kCoxVcgvGKmXChJoySi0qFhUCIkNRscu+TqI11HwSQqCrr38icU5ZeR6NROxxFWWlaAkGcNOaVfZqc0vQj9qqyjlvraTikYtYpeKW3NZtqArqSjSU63Pf1j0Txiq5BWOV3IKxSplwyzelsaTESExg2LIQFRKQCowMVWTny+X09bOEQLind0oP5/OhMCLRcXtcTWVFPGGetF27uqKciTPNGXtQ0nwRUsKUEj5NRY1HhXeeay2Mjo6ilFXpyQUYq+QWjFXKhCvUBFNIDCWKio0LCRUKdFWBoSgZK3PPp86uzll7+1qWhYtdPelnnENhXAiFMR4z7XF11VVoDvqxZ8uGicQ54EdleVmuvwwqAnOJVaKZmEICkCjXVdSUGJe1rXsm7e3t7JdKrsBYJbdgrFImTKiL1HiiqNhYoqiYpijQFAWeHK9GTyf1PErMNNHe2Z222tzaEUJ7ZxdipmWP89fWoDnox+ql19hnnJuDfpT5fHmfPxUPnp2iS2FJCUtKKFCgKYBHVVDp0VBuqFkXcZyrqqqqnH4+0XxhrJJbMFYpEybURUJKiaglMGAKRBNFxbREZe7LLSqWjfFYDBfCnTjfEcb/fXYOXYlCYe2d3fYZFUVREFhQi+agHxuuXWGfcV7kb4DPW5L3ORMRZWJKCSElFAXQET8qU65r8GmAoeY+gSYiIiJnMKG+golkZe6YQCSlqJiqqMjXQvRYNIoLoc4phcFC3T0QMn58X1EULGyoQ3PAjxuvv85ecW4KNMDr8eRnokRzMDw8jNqaWqenQQ6SUsKUgISEqgC6Ek+eK1UNPl2FrqAg6jIMDAygoaHB6WkQzYqxSm7BWKVMWJTsCiOkxIgpMGTGi4pJqcDIww3eaCRiJ8upiXO4p9ceo6kqmvz1k3o4+1FXVYkKbqUlF2BRsuISr8ANABKaEv+FpKEq8OkqvKoK3YEjMnPF4jnkFoxVcgvGKmXCFeorgCUlhmICI6aFcRGvI5aromJDI6M4HwpPFAZLnHXu6uu3xxi6hkX+Biy/ugW7N6+3ezg3NtRBn6aSbVvbBSbU5Ard3V1oalrk9DQoB0SifZUCQFcAQ1Xg0RT4NBUlmpqz4mG50tHRgcWLFzs9DaJZMVbJLRirlAkTapeKCYkh08SIKRETE0XFDHV+Pn9geHjaHs69A4P2mBLDwKKgH9ctWZxoSRVfeQ7U1UK7hBYwqjpPkybKMcbqlcEuFpZy3rlEV+FTFXi0K+O8M2OV3IKxSm7BWKVMmFC7SNSyMBgTGLPi/Uv1ZFGxLLcdSinRNzg0pYdzayiMgaFhe5yvpATNQT/WrlhqFwZrDvrhr62Zlx8u1TU1l/0ZRPnAWHUXKSWsxHlnJLZs64qCCj1+3jkfx2GcUl9f7/QUiOaEsUpuwVilTJhQFzApJcYSRcWiIl6Z28iiMreUEt2JKtqpifP5jhCGRsfscWU+L1qCAWy+7vNp55zra6pzetPZ3d3N3r7kCozVwjW5WJhHUWBoCryaCq9WOMXC8qWjo4P9UskVGKvkFoxVyoQJdYERUmLUEhhKVOaWiSRaU1Ros9wLCiHQ2ds/kTSHJrZrj0ai9rjKsjK0NPpx89rVKT2cA6itrHDkhrOyojLv1yTKBmO1MAgZ36UDwO7vbKgKSnUVngIvFpYvNdxNQS7BWCW3YKxSJkyoC4AlJYZjAsOJomJAvCCOoajTFhWzhECouyelMFgicQ51Ijo+bo+rraxAczCAnRvXpSXO1RWFVQDMskynp0A0J4zV/LPPO0Oxk2ePrqJUVWC4sFhYvpgmY5XcgbFKbsFYpUyYUDvEFBKDpoURU8AUEkribF9qUTHTsnCxq3tKRe0LoU7EUv6nrqupRkvQj9tu3GhX1F4UbEBlWZkDX9mlGxkdxYIFTs+CaHaM1dwyhYRAvFhY8rxzua7BpwGGemUUC8uXoaEhBAIBp6dBNCvGKrkFY5UyYR/qPIpaAoMxCxEr3p4lWVRsPGaivbMrvaJ2KIz2cBdMy7Lf719Qm9K/OV4YrDnQgDKfz8Gv6vKNj0fh8ZQ4PQ2iWRVLrMrEdurUvxymPJZTX5MpzyTHTM6BlcQ4RQGkBNRk8qwq8KkqfHrxnXfOBfZMJ7dgrJJbMFYpE65Q55CUEhFLYNAUiFgCo9EYwp1dibPNE72cL3Z1Q4j4Xm9FURCsq0VzMICN166wk+dF/gb4vFfmjXwoHGahJ3JMMnkEpksM05PH9lA4rQ91Mu1Mjp+cAiqY+KxkApn2HBT73dMlnpM/U5ny7/T3KZPHp702NUGd/N6Jz1CgpCS19nOTrpt8vzLpHyiJsco08510fW7Zzo0LFy6weA65AmOV3IKxSpkwoZ5nQkp0j4zhwwshfHqxA60dYbQlCoR1dPfaN++qqqKxvg4tQT+2rrnO3qrd5K9Hicfj8FeRX7rOMMyX6ZLH1Mdy0hNTx0z8ey7JY+rzc0keUx8rKc9MlyROHpdN8ggAaobkceLxRPI45tHQ4NXSk8+05HGa+c5y/eRnEM0nwzCcngLRnDBWyS0Yq5RJzjMZIQSefvppnDt3DoZh4MCBAwgGg7m+7LwzLQtPvXgC//joY2xYvgT33bUXkfFxfHoxjE/aOnC2rQOftHfg3MUwOnv77PfpmoaF/np8blETtq+/wd6qvbChHh6DiSQAVFXmpnLy5SeP8UeZksfkuEzJY/Ldc0keJx5PnzwmX58+sZw+GZuceAKpyePEO6dLHpPvT1uRxETyOPH50yePma7v9uRxYV0tynTN6WkQzaq2ttbpKRDNCWOV3IKxSpnkPKN75513EIvFcPToUZw9exbPPPMMvv3tb+f6svPuqRdP4NmTryIai+GDz87juVOvp7WiMnQdiwIN+Pziq7AnURysOehHY30ddC0/N+D5PPeY+lz8sZJ18tjd04vS0rJ537qqJq+ipKwiTkoeJ16fPnmceJw662m+ngxbZ5HyOeR+4XAYlTn6BRDRfGKsklswVsktGKuUSc4T6o8++girV68GACxduhSffvppri+ZE//46GNEYzEA8W3dCwN+PP7g/dDV9HHTJVtCCkxJxuY5eUwmi/k69zj5+vbnZZE8Llq2+JLfQ+QEnp0it2CsklswVsktGKuUSc4T6tHRUZSWltp/VlUVlmVBy9Oq7Xx54dADTk+BiIiIiIiICog6+5DLU1pairGxMfvPQgjXJdNEREREREREk+U8oV62bBn+9a9/AQDOnj2LlpaWXF+SiIiIiIiIKOeU/v5+Ofuw7CWrfLe2tkJKiXvvvRdNTU25vCQRERERERFRzuU8oSaaLBqN4sc//jEGBgbg8/lw3333oaqqaso4IQQeffRRrF+/Hrt373ZgplTs5hKrL730Et544w0AwJo1a/CVr3zFialSkZqtNeW7776L48ePQ9M07NixAzt37nRwtlTMZovV06dP409/+hNUVUVLSwv2798PVc35RkqiKeba8vdnP/sZysvLcffddzswSyok/ElFeXfy5Ek0Nzfj0UcfxS233ILf/e530457/vnnMTw8nOfZEU2YLVZDoRBef/11/PCHP8SRI0fw3nvv4dy5c85MlopSamvKffv24ZlnnrFfM00Tv/rVr3Do0CH84Ac/wF//+lf09fU5N1kqajPFajQaxfPPP4/vf//7OHLkCEZHR3HmzBnnJktFbaZYTTp58iRaW1vzPzkqSEyoKe9SW6mtXr0a//nPf6aMeeutt6Cqqj2OyAmzxWpdXR2++93vQtM0u4OBx+NxYqpUpGZqTdnW1oZAIIDy8nIYhoHly5fjo48+cmqqVORmilXDMHDkyBGUlJQAAH+WkqNma/l79uxZfPzxx9i1a5cT06MClPO2WVTcTp06hZdeeintuerqapSVlQEAfD4fRkZG0l5vbW3F6dOn8eCDD+L48eN5mysVt2xiVdd1VFZWQkqJX//617j66qvR2NiYtzkTzdSacmxsLO216WKYKF9milVVVVFdXQ0AOHHiBCKRCFatWuXQTKnYzRSrvb29+O1vf4uHH34Yb775poOzpELChJpy6tZbb8Wtt96a9txjjz1mt1IbGxuzE5ak1157Db29vTh06BA6Ozuh6zrq6+uxZs2avM2bik82sQoA4+Pj+OlPfwqv14v9+/fnZa5ESTO1pvT5fIhEIvZrmWKYKB9ma6MqhMCzzz6Lixcv4qGHHoKiKE5Mk2jGWH377bcxODiIw4cPo7+/H9FoFAsXLsT27dudmi4VACbUlHfLli3DP//5T1xzzTX497//jRUrVqS9/rWvfc1+/MILL6CmpobJNDlitliVUuLo0aNYuXIl7rzzTodmScVs2bJlOHPmDLZs2TKlNWVTUxM6OjowNDQEr9eLDz/8ELfffruDs6ViNlOsAsDPf/5zGIaBb33rWyxGRo6aKVb37t2LvXv3AgD+/ve/o729nck0sco35V80GsWTTz6Jvr4+6LqOb37zm6ipqcEf//hHBAIBrF+/3h6bTKhZ5ZucMFusCiHwxBNPYMmSJfZ79u3bh6VLlzo4ayom07Wm/OyzzxCJRLBr1y67yreUEjt27MCePXucnjIVqZlidfHixXjooYewfPlye2V679692Lhxo8OzpmI028/VpGRCzSrfxISaiIiIiIiIKAvcU0NERERERESUBSbURERERERERFlgQk1ERERERESUBSbURERERERERFlgQk1ERERERESUBSbUREREedba2orq6mq88sorac9fe+21aG1tndNnnDt3Dvfeey8A4PTp03ZvVCIiIsofJtREREQOMAwD999/P4aGhrJ6/4ULF/C///1vnmdFREREl4IJNRERkQMCgQC2bduGgwcPTnntRz/6ETZs2IDNmzfjO9/5DizLQmtrK9atW4cvfOELuOOOO/Dwww/jvffewwMPPAAA6OnpwZe//GWsXbsWX/3qVxGNRtHa2oqtW7di//792LRpE770pS+hr68PAHDq1Cls374dW7duxb59+9Db2wsAOHjwILZs2YKtW7fi6NGjAIDXXnsNN998M2655Rbccccd6OnpydN3iYiIqLAxoSYiInLI4cOH8fLLL6dt/T516hT+/Oc/45VXXsHrr7+Ozz77DL/85S8BAJ988gl+8Ytf4A9/+AMee+wxXH/99Xj88ccBAG1tbTh27BjeeecddHZ24tVXXwUAfPDBB7jnnnvw9ttvo6qqCsePH0d3dzceeeQR/P73v8fp06exY8cOHDp0COfPn8epU6fw5ptv4uTJkzh79iwikQiOHTuGJ554Aq+++iq2bduG999/P+/fKyIiokKkOz0BIiKiYlVZWYknn3wS999/P9566y0A8dXgu+66C6WlpQCAffv24Te/+Q127dqF+vp6tLS0TPtZK1euxFVXXQUAWLJkib2KXF9fj1WrVgEAli9fjr6+Ppw5cwZtbW344he/CAAQQqC6uhqNjY3wer3YvXs3du/ejUceeQRerxd79uzBvn37sHfvXtx2223Ytm1bLr8tRERErsGEmoiIyEHbt29P2/othEh7XUoJ0zQBAF6vN+PnaJpmP1YUBVJKAEBJScmU5y3LwsaNG/HCCy8AACKRCEZGRqDrOl5++WW88cYb+Nvf/oZdu3bhxIkTuOeee7Bnzx785S9/wfe+9z3cfvvt9lZzIiKiYsYt30RERA5Lbv0Oh8O46aab8OKLL2JsbAymaeK5557D1q1bp7xH13VYlpXV9dauXYt3330X//3vfwEAx44dw8GDB/H+++9j79692LJlCw4fPoylS5fik08+wY4dOzA0NIQDBw7gwIED3PJNRESUwBVqIiIihyW3ft95553YvXs3BgYGsG3bNpimiW3btuEb3/gG2tvb096zdOlSDAwMYP/+/bj77rsv6Xp+vx8/+clP8PWvfx1CCDQ2NuLpp59GbW0t1q1bh02bNsHn82HDhg3YuXMnSktLceDAAei6jrKyMjz11FPz+eUTERG5ltLf3y+dngQRERERERGR23DLNxEREREREVEWmFATERERERERZYEJNREREREREVEWmFATERERERERZYEJNREREREREVEWmFATERERERERZYEJNREREREREVEW/h8x2R4+QgLCqAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x684 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pdp_dist = pdp.pdp_isolate(model=rf_0326, dataset=feat_test_20160326, model_features=feature_list, feature=\"nness\")\n",
    "\n",
    "pdp.pdp_plot(pdp_dist, \"nness\")\n",
    "plt.title(\"PDP for Northness\")\n",
    "plt.xlabel(\"Northness\")\n",
    "plt.show()\n",
    "pdp.plt.savefig('AGU/box_figs/rf_results/pdp_nness.png', dpi=200)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79f55c16-6e99-4aa0-b27d-bb3f42f13f64",
   "metadata": {},
   "source": [
    "## Erros vs veg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 555,
   "id": "9b6c801b-9b81-4357-bd03-700b1af25f2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_all_20160326['errors'] = errors_0326"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 556,
   "id": "22ebc8c3-2f55-4049-87e0-7fbf72b19b20",
   "metadata": {},
   "outputs": [],
   "source": [
    "# elevation, either enter number of bins desired or desired width \n",
    "elev_bin_target = 50\n",
    "elev_bin_val = (max(test_all_20160326['elev']) - min(test_all_20160326['elev']))/elev_bin_target\n",
    "# or create discrete bins using rounded values\n",
    "elev_bins_set = np.arange(round(min(test_all_20160326['elev'])), round(max(test_all_20160326['elev']))+elev_bin_target, elev_bin_target)\n",
    "elev_bins_set = ([2000,2100,2200,2300,2400,2500,2600])\n",
    "# departure\n",
    "departure_bins_set = ([-4, -2, -1, 1, 2, 4 ])\n",
    "nness_bins_set = np.linspace(-round(np.nanmax(test_all_20160326['nness']),1), round(np.nanmax(test_all_20160326['nness']),1),num=5)\n",
    "clump_size_bins = ([0, 3, 6, 9, 12, 15, 50])\n",
    "TOA_size_bins = ([0, 50, 100, 150, 200, 300, 500])\n",
    "coreGap_bins = ([0, 3, 6, 9, 12, 15, 50])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 557,
   "id": "41ee12c7-e8fe-4c59-bde1-493472f3c8c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2000, 2100, 2200, 2300, 2400, 2500, 2600]"
      ]
     },
     "execution_count": 557,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elev_bins_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "id": "744267ee-290d-4b3c-8cc3-ef586c3bfea9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1918"
      ]
     },
     "execution_count": 558,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(min(test_all_20160326['elev']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "id": "9a7b68fe-c42f-4676-b59d-5529ada3eb37",
   "metadata": {},
   "outputs": [],
   "source": [
    "test_all_20160326['nness_bins'] = pd.cut(test_all_20160326['nness'], bins=nness_bins_set, right = False,include_lowest= True)\n",
    "test_all_20160326['elev_bins'] = pd.cut(test_all_20160326['elev'], bins=elev_bins_set, right = False,include_lowest= True)\n",
    "test_all_20160326['clump_size_bins'] = pd.cut(test_all_20160326['clump_size'], bins=clump_size_bins, right = False,include_lowest= True)\n",
    "test_all_20160326['TOA_size_bins'] = pd.cut(test_all_20160326['TAOperHa'], bins=TOA_size_bins, right = False,include_lowest= True)\n",
    "test_all_20160326['coreGap_bins'] = pd.cut(test_all_20160326['coreGap'], bins=coreGap_bins, right = False,include_lowest= True)\n",
    "test_all_20160326['vegDep_bins'] = pd.cut(test_all_20160326['veg_dep'], bins=departure_bins_set, right = False,include_lowest= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 416,
   "id": "eec70c11-2018-460a-9d9e-2fc1a0016b76",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  658267.,  5671924., 12316287., 18904147.,  8047130.,  5797494.,\n",
       "         2942608.,  1856568.,  1650230.,   527146.]),\n",
       " array([1839.8645, 1923.7709, 2007.6772, 2091.5835, 2175.49  , 2259.3962,\n",
       "        2343.3025, 2427.209 , 2511.1152, 2595.0217, 2678.928 ],\n",
       "       dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 416,
     "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": [
    "plt.hist(elev_flat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "id": "438ba59e-ba17-4dc7-ac79-42f344b4b8dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([169375., 311305., 317364., 273256., 246857., 347697., 387746.,\n",
       "        238633., 128157.,  96583.]),\n",
       " array([2000.    , 2019.9999, 2039.9998, 2059.9995, 2079.9995, 2099.9995,\n",
       "        2119.9993, 2139.999 , 2159.999 , 2179.999 , 2199.9988],\n",
       "       dtype=float32),\n",
       " <BarContainer object of 10 artists>)"
      ]
     },
     "execution_count": 410,
     "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": [
    "plt.hist(feat_train_20160326['elev'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "id": "2414b0d1-e1cf-4aef-b01d-11c154026ac9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev_nness_box, axes = plt.subplots(1,1,figsize=(10, 5), sharey=False) # create a figure axis\n",
    "# we want a scatterplot\n",
    "# specify figure axis (ax=axes)\n",
    "# data = dataframe name, x = dataframe column name, y = dataframe column name\n",
    "# hue = color values by variable, marker = specify style, s = size of marker, palette = color palette\n",
    "\n",
    "sns.boxplot(ax=axes, data=test_all_20160326, x='elev_bins', y=\"nness\", palette = 'Blues') #, hue = 'nness_bins'\n",
    "axes.set_title('Elevation vs. Northness')\n",
    "axes.set(xlabel = 'Elevation Range (m)',ylabel = 'Northness')\n",
    "axes.set_xticklabels(axes.get_xticklabels(),rotation = 45);\n",
    "#plt.legend(title= 'northness');\n",
    "\n",
    "#elev_nness_box.savefig('lidar_processing/python_scripts/snow_departure_project/figures/elev_nness_box.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "id": "00e83570-fea4-4cf8-b062-195c235ec171",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev_nness_box, axes = plt.subplots(1,1,figsize=(10, 5), sharey=False) # create a figure axis\n",
    "# we want a scatterplot\n",
    "# specify figure axis (ax=axes)\n",
    "# data = dataframe name, x = dataframe column name, y = dataframe column name\n",
    "# hue = color values by variable, marker = specify style, s = size of marker, palette = color palette\n",
    "\n",
    "sns.boxplot(ax=axes, data=test_all_20160326, x='vegDep_bins', y=\"errors\", palette = 'Blues') #, hue = 'nness_bins'\n",
    "axes.set_title('Vegetation Departure vs. RFM Errors')\n",
    "axes.set(xlabel = 'Vegetation Departure',ylabel = 'Error (mm)')\n",
    "axes.set_xticklabels(axes.get_xticklabels(),rotation = 45);\n",
    "#plt.legend(title= 'northness');\n",
    "\n",
    "#elev_nness_box.savefig('lidar_processing/python_scripts/snow_departure_project/figures/elev_nness_box.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 483,
   "id": "5e7f98f0-d361-48ef-9862-9a7db3dc0914",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev_nness_box, axes = plt.subplots(1,1,figsize=(10, 5), sharey=False) # create a figure axis\n",
    "# we want a scatterplot\n",
    "# specify figure axis (ax=axes)\n",
    "# data = dataframe name, x = dataframe column name, y = dataframe column name\n",
    "# hue = color values by variable, marker = specify style, s = size of marker, palette = color palette\n",
    "\n",
    "sns.boxplot(ax=axes, data=test_all_20160326, x='coreGap_bins', y=\"errors\", palette = 'Blues') #, hue = 'nness_bins'\n",
    "axes.set_title('TOA per Ha vs. RFM Errors')\n",
    "axes.set(xlabel = 'TOA per Ha',ylabel = 'Error (mm)')\n",
    "axes.set_xticklabels(axes.get_xticklabels(),rotation = 45);\n",
    "#plt.legend(title= 'northness');\n",
    "\n",
    "#elev_nness_box.savefig('lidar_processing/python_scripts/snow_departure_project/figures/elev_nness_box.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "id": "e095f8cd-1cd6-4850-91db-eba16560eeb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "elev_nness_box, axes = plt.subplots(1,1,figsize=(10, 5), sharey=False) # create a figure axis\n",
    "# we want a scatterplot\n",
    "# specify figure axis (ax=axes)\n",
    "# data = dataframe name, x = dataframe column name, y = dataframe column name\n",
    "# hue = color values by variable, marker = specify style, s = size of marker, palette = color palette\n",
    "\n",
    "sns.boxplot(ax=axes, data=test_all_20160326, x='TOA_size_bins', y=\"errors\", palette = 'Blues') #, hue = 'nness_bins'\n",
    "axes.set_title('TOA per Ha vs. RFM Errors')\n",
    "axes.set(xlabel = 'TOA per Ha',ylabel = 'Error (mm)')\n",
    "axes.set_xticklabels(axes.get_xticklabels(),rotation = 45);\n",
    "#plt.legend(title= 'northness');\n",
    "\n",
    "#elev_nness_box.savefig('lidar_processing/python_scripts/snow_departure_project/figures/elev_nness_box.png')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "id": "37a9ba3a-2ea4-4eb6-803f-8b08a8d2e12a",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "#sns.set(font_scale = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "id": "e02cfcc2-a5b8-45c1-be67-382404a6e3b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x1800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "errors_veg, axes = plt.subplots(3,1,figsize=(25, 25), sharey=False) # create a figure axis\n",
    "# we want a scatterplot\n",
    "# specify figure axis (ax=axes)\n",
    "# data = dataframe name, x = dataframe column name, y = dataframe column name\n",
    "# hue = color values by variable, marker = specify style, s = size of marker, palette = color palette\n",
    "# sns.boxplot(ax=axes[0], data=test_all_20160326, x='elev_bins', y=\"errors\", hue = 'vegDep_bins', palette = 'Greens')\n",
    "sns.boxplot(ax=axes[0], data=test_all_20160326, x='elev_bins', y=\"errors\", hue = 'TOA_size_bins',showfliers = False, palette = 'Greens')\n",
    "#axes[0].set_title('a')\n",
    "axes[0].set_xlabel('')\n",
    "axes[0].set_ylabel('RFM Residuals (mm)', fontsize = 20)\n",
    "axes[0].set_yticklabels(axes[0].get_yticks(),size = 20);\n",
    "axes[0].set_xticklabels([]);\n",
    "\n",
    "axes[0].legend(bbox_to_anchor=(1.02, 0.55), loc='upper left', borderaxespad=0,title= 'TAO per Ha', fontsize = 20, title_fontsize = 16);\n",
    "\n",
    "sns.boxplot(ax=axes[1], data=test_all_20160326, x='elev_bins', y=\"errors\", hue = 'clump_size_bins',showfliers = False, palette = 'Greens')\n",
    "#axes[1].set_title('b', fontsize = )\n",
    "axes[1].set_xlabel('')\n",
    "axes[1].set_ylabel('RFM Residuals (mm)', fontsize = 20)\n",
    "axes[1].set_xticklabels([]);\n",
    "axes[1].set_yticklabels(axes[0].get_yticks(),size = 20);\n",
    "axes[1].legend(bbox_to_anchor=(1.02, 0.55), loc='upper left', borderaxespad=0,title= 'Clump Size', fontsize = 20, title_fontsize = 16);\n",
    "\n",
    "sns.boxplot(ax=axes[2], data=test_all_20160326, x='elev_bins', y=\"errors\", hue = 'coreGap_bins',showfliers = False, palette = 'Greens_r')\n",
    "#axes[2].set_title('c')\n",
    "axes[2].set_xlabel('Elevation (m)', fontsize = 20)\n",
    "axes[2].set_ylabel('RFM Residuals (mm)', fontsize = 20)\n",
    "axes[2].set_xticklabels(axes[2].get_xticklabels(),rotation = 45, fontsize = 20);\n",
    "axes[2].set_yticklabels(axes[2].get_yticks(),size = 20);\n",
    "axes[2].legend(bbox_to_anchor=(1.02, 0.55), loc='upper left', borderaxespad=0,title= 'Canopy Gap', fontsize = 20, title_fontsize = 16);\n",
    "\n",
    "# sns.boxplot(ax=axes[3], data=test_all_20160326, x='elev_bins', y=\"errors\", hue = 'vegDep_bins',showfliers = False, palette = 'Greens')\n",
    "# axes[3].set_title('d')\n",
    "# axes[3].set(xlabel = '',ylabel = 'RFM Residuals (mm)')\n",
    "# #axes[1].set_xticklabels(axes.get_xticklabels(),rotation = 45);\n",
    "# axes[3].legend(loc = 2,title= 'Vegetation Departure');\n",
    "\n",
    "\n",
    "errors_veg.savefig('AGU/box_figs/rf_results/errors_veg.png', transparent=True,bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8478f65b-431c-4a9b-8f16-01301ab7178c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}