{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3ba21e59-2ca7-497e-a5b6-c94182246b9c",
   "metadata": {},
   "source": [
    "# **REGIONALIZING A REALIZATION** #\n",
    "\n",
    "#### Objectives: ####\n",
    "+ Manipulating and visualizing spatial data\n",
    "+ Recipes for awesome spatial plotting (via [Xarray](https://docs.xarray.dev/en/stable/) and [Numpy](https://numpy.org/doc/stable/))\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7339ea5f-6e0b-4cd2-81f5-a66df98757ca",
   "metadata": {},
   "source": [
    "<span style=\"color:HotPink\">*Caveat: Most likely, future developments on STORM won't demand reading a rainfall field as a seed for regionalization.\n",
    "Nevertheless, the exercise presented herewill still be applicable to some eventual parameterization procedure.*</span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b3cf607b-2959-4949-870d-62b707498c9b",
   "metadata": {},
   "source": [
    "Reading and regionalizing the realization is done by STORM through the functions: EMPTY_MAP, READ_REALIZATION, KREGIONS, MORPHOPEN, and REGIONALISATION of the [realization.py](../realization.py) module.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a5421514-f7be-4dcc-b56a-ff20cdcd0d6f",
   "metadata": {},
   "source": [
    "### STORM PARAMETERS ###\n",
    "<a id='pars'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b92538bc-7cd4-4b42-8680-f5aa64c41005",
   "metadata": {},
   "outputs": [],
   "source": [
    "RAIN_MAP = \"../realisation_MAM_crs-OK.nc\"  # with interpretable CRS\n",
    "SUBGROUP = \"\"\n",
    "CLUSTERS = 4  # number of regions to split the whole.region into\n",
    "\n",
    "# OGC-WKT for HAD [taken from https://epsg.io/42106]\n",
    "WKT_OGC = (\n",
    "    'PROJCS[\"WGS84_/_Lambert_Azim_Mozambique\",'\n",
    "    'GEOGCS[\"unknown\",'\n",
    "    'DATUM[\"unknown\",'\n",
    "    'SPHEROID[\"Normal Sphere (r=6370997)\",6370997,0]],'\n",
    "    'PRIMEM[\"Greenwich\",0,'\n",
    "    'AUTHORITY[\"EPSG\",\"8901\"]],'\n",
    "    'UNIT[\"degree\",0.0174532925199433,'\n",
    "    'AUTHORITY[\"EPSG\",\"9122\"]]],'\n",
    "    'PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],'\n",
    "    'PARAMETER[\"latitude_of_center\",5],'\n",
    "    'PARAMETER[\"longitude_of_center\",20],'\n",
    "    'PARAMETER[\"false_easting\",0],'\n",
    "    'PARAMETER[\"false_northing\",0],'\n",
    "    'UNIT[\"metre\",1,'\n",
    "    'AUTHORITY[\"EPSG\",\"9001\"]],'\n",
    "    'AXIS[\"Easting\",EAST],'\n",
    "    'AXIS[\"Northing\",NORTH],'\n",
    "    'AUTHORITY[\"EPSG\",\"42106\"]]'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31592b77-c96e-4460-a397-912feb7a75aa",
   "metadata": {},
   "source": [
    "## <u>READING SPATIAL DATA</u> ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a2c0236f-5f4c-4621-a2f6-1398f0629421",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading libraries\n",
    "import cmaps  # -> nice color-palettes\n",
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import rioxarray as rio\n",
    "import xarray as xr\n",
    "from cmcrameri import cm as cmc\n",
    "from pandas import RangeIndex\n",
    "from rasterio.enums import Resampling\n",
    "from rasterio.features import shapes\n",
    "from skimage import morphology\n",
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c265c0b-0ebf-4ad4-96f1-e16bd7ff8652",
   "metadata": {},
   "source": [
    "STORM must create a void [Xarray](https://docs.xarray.dev/en/stable/), and append the local CRS (via [rioxarray](https://corteva.github.io/rioxarray/stable/)).\n",
    "But first, here we define firs some local coordinates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8150d96f-c54c-4870-aa80-29819095f0a6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(470,)\n",
      "(408,)\n"
     ]
    }
   ],
   "source": [
    "# this fast-tracks the generation (for this notebook) of coordinates\n",
    "yyss = np.linspace(1167500.0, -1177500.0, 470, endpoint=True)\n",
    "xxss = np.linspace(1342500.0, 3377500.0, 408, endpoint=True)\n",
    "\n",
    "# check out the numpy.shapes\n",
    "print(yyss.shape)\n",
    "print(xxss.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f099ff51-e52d-49e2-8765-63d757d28fe0",
   "metadata": {},
   "source": [
    "Now the \"void\" is created..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9702cf6d-f8d1-4196-a73d-801309ff42be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray (y: 470, x: 408)>\n",
      "array([[nan, nan, nan, ..., nan, nan, nan],\n",
      "       [nan, nan, nan, ..., nan, nan, nan],\n",
      "       [nan, nan, nan, ..., nan, nan, nan],\n",
      "       ...,\n",
      "       [nan, nan, nan, ..., nan, nan, nan],\n",
      "       [nan, nan, nan, ..., nan, nan, nan],\n",
      "       [nan, nan, nan, ..., nan, nan, nan]])\n",
      "Coordinates:\n",
      "  * y            (y) float64 1.168e+06 1.162e+06 ... -1.172e+06 -1.178e+06\n",
      "  * x            (x) float64 1.342e+06 1.348e+06 ... 3.372e+06 3.378e+06\n",
      "    spatial_ref  int32 0\n",
      "Attributes:\n",
      "    _FillValue:  nan\n",
      "    units:       mm\n"
     ]
    }
   ],
   "source": [
    "# create an empty numpy\n",
    "void = np.empty((len(yyss), len(xxss)))\n",
    "void.fill(np.nan)\n",
    "\n",
    "# create an empty xarray\n",
    "void = xr.DataArray(\n",
    "    data=void,\n",
    "    dims=[\"y\", \"x\"],\n",
    "    # name=\"void\",\n",
    "    coords=dict(\n",
    "        y=([\"y\"], yyss),\n",
    "        x=([\"x\"], xxss),\n",
    "    ),\n",
    "    attrs=dict(\n",
    "        _FillValue=np.nan,\n",
    "        units=\"mm\",\n",
    "    ),\n",
    ")\n",
    "\n",
    "# append the CRS\n",
    "void.rio.write_crs(rio.crs.CRS(WKT_OGC), grid_mapping_name=\"spatial_ref\", inplace=True)\n",
    "\n",
    "# how does it look like?\n",
    "print(void)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "db22e438-f0dd-4eb0-8ace-258895c4ce5b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PROJCS[\"WGS84_/_Lambert_Azim_Mozambique\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"Normal Sphere (r=6370997)\",6370997,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433]],PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],PARAMETER[\"latitude_of_center\",5],PARAMETER[\"longitude_of_center\",20],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"42106\"]]\n"
     ]
    }
   ],
   "source": [
    "# check the CRS out\n",
    "print(void.rio.crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15540e09-e99d-4e9b-aa7d-34ab9ae26845",
   "metadata": {},
   "source": [
    "Read the realization (previously computed, and stored as NetCDF)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "07303009-8001-45ca-8396-29ee50575814",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:   (x: 480, y: 450)\n",
      "Coordinates:\n",
      "  * x         (x) float64 28.02 28.07 28.12 28.17 ... 51.82 51.87 51.92 51.97\n",
      "  * y         (y) float64 15.48 15.43 15.38 15.33 ... -6.875 -6.925 -6.975\n",
      "    xomethin  int32 0\n",
      "Data variables:\n",
      "    rain      (y, x) float32 ...\n",
      "    mask      (y, x) uint8 ...\n"
     ]
    }
   ],
   "source": [
    "# read the netcdf file (via rioxarray)\n",
    "xile = rio.open_rasterio(RAIN_MAP, group=SUBGROUP)\n",
    "\n",
    "# REMOVING the annoying BAND dimension (assuming we only have ONE band!)\n",
    "if \"band\" in list(xile.dims):\n",
    "    for x in list(xile.data_vars):\n",
    "        # https://stackoverflow.com/a/41836191/5885810\n",
    "        xile[x] = xile[x].sel(band=1, drop=True)\n",
    "    xile = xile.drop_dims(drop_dims=\"band\")\n",
    "\n",
    "# look up for the CRS?\n",
    "xvar = xile.rio.grid_mapping\n",
    "# actual crs\n",
    "xcrs = xile.rio.crs\n",
    "# # trasform4fun\n",
    "# xtra = xile.rio.transform()\n",
    "xile.close()\n",
    "\n",
    "# how does the realization look like?\n",
    "print(xile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e55817eb-c648-4849-bbfc-d42eb5187b63",
   "metadata": {},
   "source": [
    "Usually, rainfall fields/data comes in the WGS84 GCS.\n",
    "So, before we proceed, we must find out whether we need to re-project (or not) the ingested realization.\n",
    "To find this out, we compare the CRS of our (model) void-xarray to the CRS that comes with the realization (if any at all!).\\\n",
    "<span style=\"color:RoyalBlue\">*\"True\" implies that we indeed must carry out with the re-projection.*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cfd437c0-bd29-4bd6-914c-673ec425b13d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "void.rio.crs.to_string() != xcrs.to_string()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a5818e6-3f6e-405c-afba-6f44686dd52d",
   "metadata": {},
   "source": [
    "First, some dimension-compatibility must be set up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "2145b9fe-c91a-4fb5-99e6-e4955dcb6dc3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:   (x: 480, y: 450)\n",
      "Coordinates:\n",
      "  * x         (x) float64 28.02 28.07 28.12 28.17 ... 51.82 51.87 51.92 51.97\n",
      "  * y         (y) float64 15.48 15.43 15.38 15.33 ... -6.875 -6.925 -6.975\n",
      "    xomethin  int32 0\n",
      "Data variables:\n",
      "    rain      (y, x) float32 ...\n",
      "    mask      (y, x) uint8 ...\n"
     ]
    }
   ],
   "source": [
    "# xcrs.is_geographic\n",
    "# renaming coordinates for 'easy' reprojection?\n",
    "# https://www.geeksforgeeks.org/python-get-dictionary-keys-as-a-list/\n",
    "c_xoid = list(void.coords.dims)\n",
    "# ['y', 'x']\n",
    "# ['lat', 'lon']\n",
    "c_xile = list(xile.coords.dims)\n",
    "# ['lat', 'lon']\n",
    "# ['band', 'x', 'y']\n",
    "\n",
    "# https://stackoverflow.com/a/176921/5885810\n",
    "c_ids = list(map(lambda i: c_xile.index(i), c_xoid))\n",
    "\n",
    "# assuming LAT goes first\n",
    "# https://www.geeksforgeeks.org/python-convert-two-lists-into-a-dictionary/\n",
    "# https://stackoverflow.com/a/56163051/5885810  -> rename coordinates\n",
    "# https://stackoverflow.com/a/51988240/5885810 -> slicing lists\n",
    "xile = xile.set_index(\n",
    "    indexes=dict(zip(list(map(c_xile.__getitem__, c_ids)), c_xoid)),\n",
    ")\n",
    "# # the line below gives WARNING\n",
    "# xile = xile.rename(\n",
    "#     dict(map(lambda i, j: (i, j), list(map(c_xile.__getitem__, c_ids)), c_xoid))\n",
    "# )\n",
    "\n",
    "print(xile)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b50f916-e646-4003-a4c2-cf0eb0a1eabe",
   "metadata": {},
   "source": [
    "Then, the re-projection can be done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5eb69242-0ddc-4d8d-8523-0ef654814912",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:   (x: 408, y: 470)\n",
      "Coordinates:\n",
      "  * x         (x) float64 1.342e+06 1.348e+06 1.352e+06 ... 3.372e+06 3.378e+06\n",
      "  * y         (y) float64 1.168e+06 1.162e+06 ... -1.172e+06 -1.178e+06\n",
      "    xomethin  int32 0\n",
      "Data variables:\n",
      "    rain      (y, x) float32 8.853 12.19 12.01 14.22 ... 527.5 527.5 526.5 526.5\n",
      "    mask      (y, x) uint8 1 1 1 1 1 1 1 1 1 1 1 1 1 ... 0 0 0 0 0 0 0 0 0 0 0 0\n"
     ]
    }
   ],
   "source": [
    "# reprojection happens here\n",
    "pile = xile.rio.reproject_match(void, resampling=Resampling.nearest)\n",
    "\n",
    "# how does it look like?\n",
    "print(pile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5bd895d2-9180-457e-8b4b-fea1571c7ad0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'xomethin'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the CRS-mapping\n",
    "pile.rio.grid_mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fc8d76be-95ff-47fe-a3b3-0b227edc716e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRS.from_wkt('PROJCS[\"WGS84_/_Lambert_Azim_Mozambique\",GEOGCS[\"unknown\",DATUM[\"unknown\",SPHEROID[\"Normal Sphere (r=6370997)\",6370997,0]],PRIMEM[\"Greenwich\",0],UNIT[\"degree\",0.0174532925199433]],PROJECTION[\"Lambert_Azimuthal_Equal_Area\"],PARAMETER[\"latitude_of_center\",5],PARAMETER[\"longitude_of_center\",20],PARAMETER[\"false_easting\",0],PARAMETER[\"false_northing\",0],UNIT[\"metre\",1],AXIS[\"Easting\",EAST],AXIS[\"Northing\",NORTH],AUTHORITY[\"EPSG\",\"42106\"]]')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ...and the actual crs\n",
    "pile.rio.crs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "794e3dc6-a63d-4484-80fc-3c4b48a99ac3",
   "metadata": {},
   "source": [
    "### DATA-EXPORTING: THE **WRONG** WAY! ###\n",
    "<a id='wrong'></a>\n",
    "Please, do yourself a favor, and avoid exporting/storing outputs in this way; especially, if saving space is what you're after.\n",
    "\n",
    "<span style=\"color:HotPink\">*Note: Check notebook [fiv_](./fiv_.ipynb) for a better and very optimal way of storing data into NetCDF format.*</span>\n",
    "\n",
    "<span style=\"color:RoyalBlue\">Do run the following block!. (ouputs will serve later as comparison cases)</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b847b1e-86e3-4290-ad24-8d72a4809640",
   "metadata": {},
   "outputs": [],
   "source": [
    "# please don't do this:\n",
    "pile.to_netcdf(\"for_wrong-1.nc\", mode=\"w\")\n",
    "\n",
    "# or this:\n",
    "pile.to_netcdf(\n",
    "    \"for_wrong-2.nc\",\n",
    "    mode=\"w\",\n",
    "    encoding={\n",
    "        \"rain\": {\"dtype\": \"f4\", \"zlib\": True, \"complevel\": 9},\n",
    "        \"mask\": {\"dtype\": \"u1\"},\n",
    "    },\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91ca983d-6880-4289-96a6-2440ad0a1ad6",
   "metadata": {},
   "source": [
    "### VISUALIZATION ###\n",
    "\n",
    "Haven't you checked out [this awesome link](https://github.com/hhuangwx/cmaps/blob/master/examples/colormaps.png) yet?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a1ce537d-a703-4ad4-8fea-c05a9630790d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>precip2_17lev</strong> </div><div class=\"cmap\"><img alt=\"precip2_17lev colormap\" title=\"precip2_17lev\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAABACAYAAABsv8+/AAAAHHRFWHRUaXRsZQBwcmVjaXAyXzE3bGV2IGNvbG9ybWFwKzD+0QAAACJ0RVh0RGVzY3JpcHRpb24AcHJlY2lwMl8xN2xldiBjb2xvcm1hcLZgEJQAAAAwdEVYdEF1dGhvcgBNYXRwbG90bGliIHYzLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZx9CTh4AAAAydEVYdFNvZnR3YXJlAE1hdHBsb3RsaWIgdjMuNy4yLCBodHRwczovL21hdHBsb3RsaWIub3JnMeTROQAAAZ1JREFUeJzt1rFNFQAYReEfCzagxc5QkdhTswVzWDKJpY1zOIRUNgRDIDHEREL1KNzhf8X5vgVud3NODofDYZY9v+7uPT3v7s3M/Pq9u3f3uLv3cr+7NzPz98/b6t7L0+rczMx8vfy0und1/nF178vZ59W9mZnTi9296/m2Ozgz8/3f6tzDze6J/1xd++/H8t7t8t7MzIcjbAIARyYAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAQJAAAIEgAAECQAACBIAABAkAAAgCABAABBAgAAggQAAAS9A/sNG3+zvLpYAAAAAElFTkSuQmCC\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#ffffffff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ffffffff;\"></div> under</div><div style=\"margin: 0 auto; display: inline-block;\">bad <div title=\"#00000000\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #00000000;\"></div></div><div style=\"float: right;\">over <div title=\"#000000ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #000000ff;\"></div></div>"
      ],
      "text/plain": [
       "<cmaps.colormap.Colormap at 0x21140666ac0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing cool (but maybe color-blindly wrong) color-palettes\n",
    "cmaps.precip2_17lev"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56d3c772-df86-473a-9f13-94d36ac37d9d",
   "metadata": {},
   "source": [
    "Assuming that **rain** is the variable we want to plot... <span style=\"color:RoyalBlue\">[Both plots are done via xarray]</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "308e53e4-5f87-4efb-bb4e-dda85ccc982e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x21140db4370>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# realization in WGS84\n",
    "xile.rain.plot(\n",
    "    cmap=\"precip2_17lev\",\n",
    "    levels=10,\n",
    "    vmin=100,\n",
    "    vmax=1000,\n",
    "    add_colorbar=True,  # robust=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5f515ba8-3c3c-4b17-9626-4f7f1fa63306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x21140ff3370>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# realization in local CRS\n",
    "pile.rain.plot(\n",
    "    cmap=\"precip2_17lev\",\n",
    "    levels=10,\n",
    "    vmin=100,\n",
    "    vmax=1000,\n",
    "    add_colorbar=True,  # robust=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f718d4b0-b26b-4a7a-8ee1-c7206cea2016",
   "metadata": {},
   "source": [
    "## <u>REGIONALIZATION</u> ##\n",
    "\n",
    "The catchment/region is needed here as/in Numpy format/type.\\\n",
    "<span style=\"color:RoyalBlue\">Check [last section](tre_.ipynb#mask) of notebook [tre_](./tre_.ipynb).</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5dfb8fe3-4c7c-41a1-a976-22f7fc3db038",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21140f1fb80>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# reading the previously xported.numpy\n",
    "CATCHMENT_MASK = np.load(\"tre_catchment-mask.npy\")\n",
    "\n",
    "# how does it look like?\n",
    "plt.imshow(\n",
    "    CATCHMENT_MASK, origin=\"upper\", cmap=\"cividis_r\", interpolation=\"none\"\n",
    ")  # .resampled(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1588ee08-8df3-496c-abca-7c32a5981b5d",
   "metadata": {},
   "source": [
    "Let's mask the pixels not representing the catchment/region out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4b40dafb-bcaa-4981-a92d-a6150294b13b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " ...\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]\n",
      " [-- -- -- ... -- -- --]]\n"
     ]
    }
   ],
   "source": [
    "# # FOR A MASK IN THE WHOLE [RECTANGULAR] DOMAIN USE:\n",
    "# mask_regn = np.ma.MaskedArray( pile.rain.data.copy(), False )\n",
    "\n",
    "# FOR A MASK COMING FROM AN IRREGULAR DOMAIN/SHP USE:\n",
    "mask_regn = np.ma.MaskedArray(pile.rain.data.copy(), ~CATCHMENT_MASK.astype(\"bool\"))\n",
    "# # ... in the line below (previous cases) BAND was removed!\n",
    "# mask_regn = np.ma.MaskedArray(\n",
    "#     real.rain[\"band\" == 1, :].data.copy(), ~CATCHMENT_MASK.astype(\"bool\")\n",
    "# )\n",
    "\n",
    "# let's have a look\n",
    "print(mask_regn)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1f503e0-37a2-4d94-9208-52862b07811d",
   "metadata": {},
   "source": [
    "<span style=\"color:HotPink\">*The masking seems to be OK.*</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d40a694-5cf8-485f-ae07-01e2859eb167",
   "metadata": {},
   "source": [
    "### (MORE) STORM PARAMETERS ###\n",
    "<a id='more'></a>\n",
    "<span style=\"color:MediumSeaGreen\">*Having this **CLUSTERS** parameter here is very handy (for some toying later) .*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3934bbe0-c5f3-450e-9c79-774ff247fa10",
   "metadata": {},
   "outputs": [],
   "source": [
    "# number of regions to split the whole.region into\n",
    "CLUSTERS = 4\n",
    "\n",
    "# make a copy of the MASK and CLUSTERS\n",
    "REG = mask_regn.copy()\n",
    "N_C = CLUSTERS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d78e9690-4fd0-4c86-b0e0-0f0dc63da6d1",
   "metadata": {},
   "source": [
    "Here we use some Machine Learning technique (i.e., [K-means](https://scikit-learn.org/stable/modules/clustering.html#k-means)) from the [scikit-learn](https://scikit-learn.org/stable/) library... to divide our realization into more \"uniform\" regions/zones."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "29f3065b-2a37-4d0b-b124-7b84fe8a53cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nans outside mask\n",
    "REG[REG.mask] = np.nan\n",
    "# ravel and indexing\n",
    "ravl = REG.ravel()\n",
    "idrs = np.arange(len(ravl))[~np.isnan(ravl)]\n",
    "# transform the non-void (RGB?) field into 1D.numpy\n",
    "X = ravl[idrs].data.reshape(-1, 1)\n",
    "\n",
    "# clustering happens here\n",
    "kmeans = KMeans(n_clusters=N_C, n_init=11, random_state=None).fit(X)\n",
    "\n",
    "# expand the result into a void-array\n",
    "ravl[idrs] = kmeans.labels_\n",
    "LAB = ravl.reshape(REG.shape).data\n",
    "\n",
    "# region labels\n",
    "KAT = np.char.strip(kmeans.get_feature_names_out().astype(\"U\"), \"kmeans\")\n",
    "# https://stackoverflow.com/a/25715954/5885810  -> np.object to np.string\n",
    "# https://www.w3resource.com/numpy/string-operations/strip.php  -> strip np.string.arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "60f24c6b-bc87-4a0c-a3b3-5ad8a536267f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " ...\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]\n",
      " [nan nan nan ... nan nan nan]]\n",
      "['0' '1' '2' '3']\n"
     ]
    }
   ],
   "source": [
    "# can we see the regionalization in the numpy array?\n",
    "print(LAB)\n",
    "print(KAT)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75586de-3235-4cd9-a367-b2412df5fe1c",
   "metadata": {},
   "source": [
    "### VISUALIZATION ###\n",
    "\n",
    "We can definitely see the regionalization by plotting the **LAB** numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "902d51f1-1647-4cbe-b7d6-6c05c05d5b72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21140ed5bb0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8efrMZz6ja665RiNGjNCbb76pW265RVOnTg3j7QAAAFQllKb0AwcOaMuWLQW3OY5TcNuBAwcK7n/ggQc0duxYPfzww1q3bp3q6+s1depULV68WJMmec95cvXVV2vTpk1aunSpXn75ZXV1dekTn/iEbr75Zi1cuDCMtwIgJlEOZ5lY4Sl+vxOu6+XrM/B6jLutie/TS6X3acv7AMqpc6qdwCnl4lqVGrCRTbOlJ3mS9hOU/AYqP2wIJH7fq21hEcmrtGRSXOf12KdNAABXmKGimC0zsxfvWy2fh1f1ywal9tOW/QfyEagAJCINJ80gYTArV/D5ucoxDd8FIJGr/AAAANKEChWAVIuy+hHGkGUUlSp6kJAVlfqn4kSgApAIm4e8bN73uBHskBUEKgCowLYARYgB4kcPFYDUsy0QAfDnwoHe81YmgUAFINXcMFVrqLI1jLEWIBAvAhUAAEBABCoASBkqU0D8aEoHUJNhI+/S7teXJL0bgRRPL0AQAezCtAkAEJJhI+/K/blSwCMwAYgKgQqA1WyvkgFIB3qoAAAAAqJCBaAmUVSGwljKpVppHAasdmJPFimGbUzqnXJRoQJgDJvCzbCRdxX0b9nKps8cMBkVKgA1ScNVftWwKTyVCkleFSiqUkA4CFQAKupaNzH05yx3IjexarL79SWhhqpyzxVGUCUoAfGqcxzHSXonTDJq1ChJ0o4dOxLeE8AMFw6cFEnAiTNQxVVdqjYI+dmvoOGKYIU0qqaHKq7zOj1UAAAAATHkB6CsUtWianuoSlVKglajSk3smUTPU7WfSfFjq5mkFMgiE6/uczHkV4QhP+CUsPqm/Aw5RTGkaOowXxLc6SgY/oPNag1TcZ3XqVABiETUJ2+brrpLmhtYmW8KtjK5MuUiUAHQhQMnFfy/Df94ucK++q6a17UV1SrYxJZ/j2hKBwAACIgKFZBxxdUpKZp5p7yE0TuV1NCf7Q3kVKmAcBGogCKD978gSdo/eHrCexI9rzAVNOS4J+moJ+dMaqgvTQhVMJ0tw30SgQoo4IYpkwze/4J2v75EDdPaAz9Xz8pTuCfT/JNzXMEKAExAoAJKyA9XJlSrutZN9B2q4hqy88O9ZD8MJlakbBzuy0eVCggHTekAAAABEagAnapGlRvuS3oocNjIuzRs5F25/ay0r0EqObVUKyZc1yuWKsfu15cYVxEysWoGIH4M+QE+Dd7/QuRDf15Baf/g6bEHumqH6YJOGOmGEhrNAUh2NaO7CFTIvGrCSlRXANZSHXNvL67Y7Ja9VRNb9xsAGPIDSig3vFRp2K0apZ7Hff1S+1C8ELAbRr4245daMHSW533Ftqw+EctVeHE3Ped/NpU+RwAIAxUqoAKvE7EbUIIOA/oNZUmEgeIQ5Cd4+Q1nUVei8p8/itcinAEoRoUKAAAgoDrHcZykd8Iko0aNkiTt2LEj4T1BnLwqRX6qEG71o9YqVde6iRo28q5QKx75Dd6l1DIE56f65LeZPY5eqXLvP+jrp7lCxZxUSFrYDelxndcZ8gNKKD7pFvcs5Ut6uZriELVg6Czp+fFlt0n7hI5ewZKmdwBRIVABVah0Qq4mWA3e/4J2h7JXPZuwK4WpqJi+zIz7OdUSrIqrUmFXFk2Q5oANO9g4XYKLQAX4RHUjGNs/v1L7XxysbA5aaa9awmw2hymJpnQAAIDAqFAB+vsQXRhzS5Ub7itecHlw4FerjTs0VzxE51WdMH0Yr5IwK0Zez1M8u7ut1SmJIT8gCAIVEAOvoJb0+oBe8sNTWk6uYTal2z5sCSA6BCogTxjr5rmTfZo8aacftlemikUVhvIrVF5N76Ye33LSGKxhLtt7p1wEKqBIWKHKDxtPtrYxYVZ2WxrVS4VomtWBymhKBwAACIgKFYBIpb3vyE9Tug3VqVLSNvQLs6RluE+iQgUAgdjeLwUkJU1hSqJCBSTC1BOv37X4SnF7hdJelcoiGtURprSFKYlABSAPwzsAopTGIOViyA/wsGDorNCfc/frS3I/YaAKBMAmaQ5TEoEKAAAgMIb8gCJ31NvTHxLn/EZ+K2JZq5yZ2g8XNeamQjXSXp2SCFSAp5V72kJ/zmquBstaKLGZLZN2holeO6AnAhWQx61OLRg6K5JQVQ2vZUyQnOLQlMUgBaC0OsdxnKR3wiSjRo2SJO3YsSPhPUES7qjvlXiQ8sOEE3m5oGdCII2C10LLJhyLJDHsB7+SGvaL67xOUzoAAEBADPkBRYqnTKim0uJOalnN5JZpnAgz/zNzP880VKzyq1JZr0wB1aApHciglXvaAs1DVe2JtpbgZUL/Trkeryjm8UpK0p8zADsQqIAiQSsqJoSdOHn1FaWhGgUA1SBQAZIG739BkrQg4PPkV5CikF8VCqspmsV9EYTXFAo0qiNfFob7JJrSAQAAAiNQAQAABMSQHzLPHe4zXdjDiF7Plz+MWOn1sjI8mLWeuCAY6kOWUaECirgN1QuGzgrtajX3MvswL7ev9rnyA1KlsFRreCN4AMiXlf4piUAF5IKTG56KQ1TQUOUVMsIIHkEqVlEGH0JVtuRXpbasPsE6f8gshvwAH9IwOSVBxz8+KyCYLFWmXFSoAAAAAqJChcxb1n1Cd9Tb2UxbrmG6eH4p5pvyj0b0YCoN+9G8jjQiUCF1isPRsu5gPR22DvN5TQLqquZqvlpf28b1CQlR1aumZ4oglQ1ZHO6TCFRIgUrVJff+aoJVmCEq6vBSK8JDT1TxopUfvghXSBsCFawVZJguiSG+uMNUVFMjZAFhCqhNVqtTEk3pAAAAgVGhglVsq0pFzYZKimlDnX7QlB4thvuQRgQqGC/qIJTfLxXWzOhJqja8RNmYbgub9jUNtqw+QahKoSwP90kEKhgqjdWkMOSf+L2u3gvjed3nDoup1SlCFBCOrAcpF4EKRkkiSBVXpWyZJiGJobRaQoipQ35c0RcvKlJIO5rSAQAAAqJCBWNEWZ1auafNd3/UgqGzjKxSUUUJjs8QCBfDfX9HoEKiogpRxYGo2mZzr8ebGLKiRPhAmGhER9ox5IdMSEMYKrV8TNjPW+tzlHoeE/unAARHdaoQFSokKn85mKiqVWFNhZD/PCYENBObvU1dZgcAokaggjHCDFdRzyeVVJ9VmCGl3JAew30IE0N96UN1qieG/AAAAAKiQgUjxTEUGJRbBStVqTJtiDBsNg3pMQQJIGoEKgAV5fdG5f+/F/cxxbO6Jynp1wfSgqG+0ghUMJaplalitq7/V8sCwLYGE3rCAESNQAVEoHgi0bTOa+XVnG9r6AKAIGhKBwAACIgKFYxjy1CfjcLuaypXZfPTbxUlhvmAcNE/VV6d4zhO0jthklGjRkmSduzYkfCeZFNWw1Rcw3+1hIxSgSj/KsdKVzxWI+w5vkwOVu78TFtWn6jwyHRgPiq72Rqo4jqvU6ECEhZ2mCoXborDUZCwkf/8tb6H4t4y93nCDFXFVyiaJithCkg7AhUQsuKG9Ch5hadqgoifsFFpDqdS79XPfvhp3k8zwhRsYWt1Kk40pQMAAAREhQrGcWdJt7mfKqoqVannDDotg9/G8bDfk+0VqVp7grJamXLfN71USCMCFYySv+SMraoJCX5Dj9tTlN9jFMa+VdunFPbr+31sGubsAkEK6UagAgxVHD5qDWrltqv16rxqKnBhXgFoiqDBoHj7rFSs8t8n4coe9E/5Q6ACIhJWgKgmvIRxtZ3f56i0XyYN54V1hZ9pIcCWaRcmXNdLW1afsGZ/gVoQqGCs/OE/m/upgvITTLxCUJBhOT+hqtrnr3V/ggZTU6dLKMVP9cq0YFfMDVCu/DDl3g+kDVf5AQAABESFCjCU3z6oKHhVqbLeJB5lVSV/KCyqak5x1SgqXvtLRQpZQKACIhB24Kh2GoaoA5hJ/VFpUq7HqFwoKXefVyN4lMHKDYT0SyFrCFQwno39U2EsnRKkEb1cdamW17exIhV27xRXqPlHiEIWEaiAkPkNH5WWdPE751Ol4bkwRF2RsjGwRSnMwJZU+CN0pgNTJvhHUzoAAEBABCogApUqOu5w1O7Xl1QcmqpUvXH7q9wfG0Wx78NG3uV7SR2/8nuDUBqfEbIo8JDfsWPH9NOf/lTPPfecfvWrX2nPnj06ceKEzjnnHF199dVatGiR+vbt67ntqlWr9PDDD+v1119XQ0ODPvWpT2nx4sX69Kc/XfL1Nm/erKVLl+rll19WV1eXRo4cqZtvvlnz588P+laA0FQKQX5O9NXORF7tdiaJYsgvivmn0rIWXan9p/cJqF2d4zhOkCd49NFHdeONN0qSRo0apZEjR+q9997T5s2bdfjwYZ1//vl68cUX9ZGPfKRgu0WLFun+++9X7969dcUVV6izs1MbNmyQ4zh68sknNWfOnB6v9fTTT+uaa67RyZMnNXnyZA0aNEgbNmxQR0eHbrvtNi1fvjzIW8m9B0nasWNH4OdCeGxsTK8UEir1UNkYjGoVZQ9VlBN7RhmskghvYQQq28NmFhUf9wnX9UpV71Rc5/XAgWrVqlV6+eWXddttt+ncc8/N3f7nP/9ZM2fO1CuvvKLPfe5z+sEPfpC7b+PGjZo6daoGDhyo9vb23Hbt7e2aMmWKevfurd27d+vDH/5wbpuDBw9q2LBhOnTokJ566inNnTtXkvTWW29p0qRJevPNN7Vx40Z95jOfCfJ2CFQGsCE8VdsEXm1gyFKYkghUpiBQZUelY90wrT2mPYleXOf1wD1U//zP/6xvf/vbBWFKks4880z993//tyRp7dq16urqyt133333SZIWL15csN3EiRN100036dChQ1qxYkXB8z366KM6dOiQZs+enQtTknTGGWfonnvukaRQKlRIzh31vYwOUyv3tOV+opSlMBXH5xmltA6Rub1ihCPAv0ib0seMGSNJOn78uN59911Jyg3tSdK8efN6bOPe9txzzxXc3tbWVnKbmTNnqqmpSevXr1dnZ2d4bwAAAMCHSOeh+sMf/iBJqq+v14ABAyRJO3fu1PHjx3X66adryJAhPba56KKLJEnbt28vuN39f/f+fA0NDbrgggu0detWvfHGG7kgB4TJ9okuTcXnao5aZmNnwlM7lVuKKE3DfXGKNFA9+OCDkqSrrrpKjY2NkqS9e/dKkmeYkqTm5ma1trbq4MGDOnz4sFpaWvTee++po6Oj7HZDhgzR1q1btXfvXl+Byh1TLbZr1y4NHz684vYIj8nDfHHLynBffnCKKkRF2TtVC1uDh037CiQpskD1/PPP63vf+57q6+v19a9/PXf7kSNHJEl9+vQpuW1zc7M6Ojp05MgRtbS05LYpt11zc3PB8wOmoOqSbpX6qNIeSNL+/rKG6lTtIglUv/3tb/X5z39ejuPom9/8ZkHFyL2osK6uruT2xRce+rkQsdqLFUt1+5eqXCF8plem8oNQtXM9EaLKC2OtQ5u5CwgDJiFMBRN6U/q+fft01VVX6eDBg1q0aJFuvfXWgvtbWlokSUePHi35HMeOHZOk3ISg7jb591XaBgAAIC6hVqjeeecdTZs2TXv37tXChQt177339njM2WefLelU8PJy9OhRdXR0qLW1NRek+vXrp/79++vQoUPat2+fRo4c2WM79/nc54fZTK9OSbX3M2W58uJXHJ+RO2lq0r1UaZ1aAUCh0ALV4cOHNX36dO3cuVNz587Vd7/7Xc9hvREjRqixsVEHDhzQvn37ejSZb9u2TZI0evTogtvHjBmjTZs2adu2bT0CVXd3t1577TU1NjZqxIgRYb0lRMCGIAV7JR2egCTVOpTMUF84QhnyO378uGbPnq2tW7fqyiuv1A9/+EP16uV9UHv37q3LLrtMkrRmzZoe97u3zZpVWB2YOXNmyW3a2trU2dmpqVOnqqmpKdB7QTRMn7TTFGmvbtk+kSeQJkzeGq7AgerEiRP63Oc+p5/97Ge65JJLtHbtWjU0NJTdZtGiRZKkpUuX6ve//33u9vb2dj3yyCPq16+frr/++oJtbrjhBvXr10/PPvus1q5dm7v97bff1u23317wvDBL1oJUVqY+qBZBCjBPmtbsS1rgIb+HH35YTz/9tCRp0KBB+tKXvuT5uHvvvVeDBg2SJF1++eW69dZb9eCDD2rs2LGaNm2aurq6tG7dOp08eVLf//73cxOBugYMGKAVK1bo2muv1bx583TppZdq0KBBWr9+vTo6OnTLLbdo6tSpQd8OAABA1QIHqoMHD+b+7AYrL//5n/+ZC1SS9MADD2js2LF6+OGHtW7dOtXX12vq1KlavHixJk2a5PkcV199tTZt2qSlS5fq5ZdfVldXlz7xiU/o5ptv1sKFC4O+FQAAjOJe1MBM9earc6qdwCnl4lqVOgsG73+h5H1pHxYLOryVxs8njiE/m5rSy50gOTHCVSkwBQlUWRnui+u8HunSM8i2/YOnS/IOVu7JtZrgUHxCNjl0VFqfrtS+p6XPKC3vIwmEKeRzvw9bVp9gCg7DEaiQqKydeMMOUqW2SzJsJnFMbapMAWFxFziuJYRnpToVJwIV4GHlnrbQQon7PF5BI4yhwSyFUjc4DRt5FyEKmeRVsarUZ5WPIBWd0JeeAQAAyBoqVIjc/sHTyzao1yrMKlJSivffrTaVW5jZJElVx6hOIWvKVZ/ouzMDgQqQv+G4MNb2qyUoVRsc4wxgcQw5Ep6AcDDcFy0CFVKnXKN2LVcXBlWuh8pVKZSUC1VJ9VERpIKppu8F2cLVfHYiUCGwKIbzwpZk83ap1612fyqFqlqe0zbDRt5V9n6bQph7hRYQB6pT0aMpHQAAICAqVLCa34pM8ePyqz22VHX8DFNGPZQZ12dVqdK0+/UlBdUq0ypTxcN4XpWo/NsY/oNU+1BfpdnSqU7Fg0CFTMoPHpVmNc+/L4rAEubrJ9EjFiY/Qco0hCAAEoEKAdjQO1VJLX1MUunpDqLkt0JlS8WtmnDkTuTp1UNlYsiqBn1UcIXRV1c8czrVqfgQqBCa/BNbpebhNMgPV3EMH1bz3JUeW+mKR9OGRG0JTaWG7sothIzsivL4E6TiR1M6AABAQFSoEIgtlQMvQSovpSbrjHs//PKqQuVXqbyqVVHvl1vFLK5slhraS5tSjer0ZGVHFFNn8P1JDoEKNSsXpmoJWl4n2PzbUTu/k4omPbxnc0CX/n6CJBjBj3LfkyBBi+G+ZBCoUJOudRMDbV/ukncCVLbUerxtDl/0TkEKv5pEiE8WgQqJ8DoZVjpBhhm0kq7EIJ28mtK9qhCc+BAmruozA4EKgBVsqkj5qUDZPplnqfdo6/sxjdfn6PWZE6bMwVV+AAAAAVGhQk0aprUH7qOqVtbmucqKUpUnk5eWyVepGuU2qqd12I9FnuOT/1mn5fuTJgQqAIlK4+znkv0nvPwTd7nARJgKF4HJXgQq1CTu6pSJTJpJ3HZpCFDFODEiLvROmYFAhaoQpBAWN0QNG3mX5/Ce14SfSXFDkZ9qjFeAqtRgTOiCq9J3ge+KuWhKBwAACIgKFXwxrTKVX92IS/5SLcW3F2MY0JufKpNXNcrEIcFqqlbltjdBGnukslIBZLjPHAQqVGRamJKSu8rPKzwhPCYHp+Lbwjhhe139R+8VYCcCFTyZGKKKhVmlCvJc+dWoUlUs+FOqVyrOHiq/QSaM6QLiCk22VplQHtUpsxCokGNDiMpnylxUhCj/8hfArub4mVK5ompklnIzh3OsEDea0gEAAAIiUCHz8qsf5SohxVWolXvacrctGDqLSlVKlRsum3Bdr9ArIcXPaetw3ZbVJ3I/CB/DfeZhyC+jbBvei4spw4hpZ9PnbMLQkU2hxKvRPqznK27YD3qlpY0IUuYiUKUUgakyrz6eWpvTqUzBBKasq5eVKQviRpgyG4EKVssPReWG60oFpOJtKgUpr+BEmKqNKY3maWJCmAqLn/fi9RimnUBSCFSoqNKyGUnyW0mqNjiZqNor40znJwjHgRNvtEr9W5FfTfO7XE8Wh/hcVKfMR1M6AABAQFSokElJV0VgxjGgOmWmco3o7p+rrVKVm7PKdFSn7ECgSqE4GtKTvqzbHf4Kexis0vO5S8+Y0jeVxJqGaWRi303ah7Xy31+lqSkqPabUNkCcCFSwVlQVjnIhJckgFefyK1HJ/2xNeD9uJYQTcLoUh68kjm9YIZ3qlD0IVCmTxHQJfprWy/2jUutv4qY0NSfNxupU1o9ZOWmvTPkV5ueQxGdKkMoemtIBAAACokKVImFWp8Iskds4nGJi1SdNVZ04qot+e2/Cntkb5opj4lP3+xRk2JHqlJ0IVJbL+ozoUfZRmRaq8vfHtnBV/Hnatv9IpyjCNEN92UWgQg9h/AOTht/480/6YYSrUiGi1slJbZJkOE3DdxHhKDWzehTfkVqekzBlN3qoLBfmicq2q53iDhhJBxrTKmamsuk7jOjk/3vm/rn4uxHWv3lbVp/gYgIQqAAAAIIiUFls8P4XJP19Tp+g+C2rtKDVqSirW0lXzvyIax/zKxI24u+fnYJWul559yWG+1KAHiqgClEEg2oWbi6+Os6mYcCoQlXxFVUmznqO+Pn5HhT3T3ltU+55wljOhiCVHgQqoIgJM3j7ZXKwiusz9LuEicls3W8bxLlkDWEq2whUFts/eHrmp02IQtJhyk9IMj30xb1vVKNQCz8rOFSqgPLdg4tAZbmGae2hhyomOrSDqUvvmLY/QCmlqlfl/v0L699GqlPpQ1M6AABAQHWO4zhJ74RJRo0aJUnasWNHwntSPVOXnqkkyOLIWaiGlBr6CzpRaJSSOi62VlbpoTJX2N8pKlPxi+u8zpBfCkTRR2XDsF8WwpRUehkcE4KTq3hW+ayE3TAQpqIXxxp+fhCm0o0hP8tF2ZQe17xUpWYxriSs+bdsQDhJJxNO8llgwudMmEo/KlQWy/oVfoQMM8V5XNwQ7p4wbaisIjnF80uV+q6Uuqqv1u8XYSobqFABAAAERIXKUnFWp6r9rYwqQTRK9VIBWeWnwlTuvuLH+ZmXCiiFQBUjd+29fPsHT6/qOZIa5vMbkvhHB3HyOiGy9Ay8VDO3lN+lZ/xguC87CFQx8ApS+ff5DVWD97+g3WHtFBAC0/rYWNMvW0r9oucViLzuK/e8QRGksodAFYFyASro48sN+ZRaZDeJNdVs5tV8Wu69+X0cksEQdLYlMWUCYSqbaEoHAAAIiApVlaqtPlUjaNNx1A3LpUrnYf/2V+43Sq/3mMSwkykTBSIaVLXSxe+x9Po7Xe33gOpUdhGofIoySLmyegWXrb0uce9vVr8fYSgVkPxeDebFtu8rokeYyjYClQ9xhCkbhVWhyT8xVTpJRVGN8qo2lbvqJwmEqejEWW209ZcH2+UHairLiAqBqgzbg5RpV2CVUutJJsyme9NPcKbOQZWGNftqPcGW2q6a2bcRj6j/flOZgkSgSrW4r/KLm/u+8oNGLe+1lsuqS+GKP/PEHZa9fkHgu2AHlpVBEFzlBwAAEBAVqhRLa2XKFcUQmMlDf/nH05ThPxu+Y0ldsZdfqeKq0OiUujKveFHjKFCdQj4qVCk2bORdxpx4XVtWnwh0ZVU5Npzc3WMS9NjY8F5NkmSYIUglww2yUSFMoRgVKsSq0jIRpW7zOikRKmALQlW8gnzezEGGWhGokLikLyWP83XDDIGmXvlnAwJOeWm4erNWlf49oDKFUhjyAwAACIgKFSKRXwEIowIU5aR8lPj9yWrFIqvSPu2KK79pvVK1nOoUyiFQwShpCjZxzEPkdbKLYhjQ9pNqmr5XCJ+fX9gIU6iEQIXIVapWVXOy89Osjp6C9FvZHqZQHn14f0dlCkEQqJBaJg1ZJD0PUdpPmqzThrARolAtAlUZ+wdPz/251Lp+C4bOyv155Z62yPcJhfwEFT9XLIXd82WT/OqVCeHTj2qPEX1yAKLGVX4AAAABUaHyKb9aJZWuWMFbGqoDQasccS2W67VotN9tTOVWItPwPYIZyn2XGO5DLQhUNcoFrO68E2N94V/QckOA7lAhw4TBleqfqTUkVLM0Tq0n+DivAExL/1QYYYoeq0J+vhu2DQcHRZhCrQhUIVqWF65Mq2AlMfNx3NWEpBu/axX1ftt+IqQqZYa0zJ5OZQpRIVBFZP/g6bqjTMWKylQ0qg0nppwk4hoOtFnQz4XPtTYm/P0IC9MiIEo0pQMAAAREhSpC7hBgcaXKtWDorFgqVXH30CQ9RFNuTiK/n0Xx49L0W7ptqCxFIy29dX4wzIc4UKGKgRusFgydlfuR0jvsZ8oJcMJ1vXyFu+ITSzUnmi2rTxjzfoFq+P0lIc3BizCFMBGoYrKs+0SPAJU/KWiaJF2hqsWwkXflfsrdD6QJlVcgPAz5xWj/4Om6429/TiJMeQ1jmdKUHaWwrqKL63Oy9WpF2MlrnUfb/03w80sd1SmEjQoVAABAQFSoYlapUT0NTJvROr/aU2myS6/f1vO3K2bS+wRqZXtFqhpUphAVKlQZlpWeIEIPkD5eF51UuhCFMIUoUaFKyLLuE8ZWqdy+qjQvOZFfqSp+f9W8X7f6VW1oK96OnikgWoQpRK3OcRwn6Z0wyahRoyRJO3bsiOX1TFuiplp+w0fSVaKoA0uQQEWYAqpTzd83ghTiOq9ToUIsaq3kmKqWIOT1eMIU4J/ffz8IUUgCPVQAAAABUaFKWPEiytXOnh7X8jVhKa7I2F6x8lOpogoFk6VtLjqqU0gKPVRF4u6hymdrP1Wt/xjHGabCDjV+9r2a1/RzxWWaTnpInukXneRfsFHu79uW1SfUMK09rt2CheihyqD9g6dLsi9Y1TrLcqnAEVbQojIUHbcyV+pY8dmbz9QgVazSvweEKZiCQGWg/YOnWxeqTGLTydzkucD8TOlg2iSusFul71L+d5EgBdPQlA4AABAQFSpD2VylSro3I8hkmfkVI1uGRILyW2GiEpVONjalU52CiQhUBnN7qqS/91UtGDord5vpV/clHayKVTt3VNThKonhvmpCUaWhvvw/E7bsZcrfT6nwu1T8/SNEwXQEKku4FSvTQ1QYwjpBez1H8W3uP9om9zKFpdrPtFylr9Jz5d9vU09bVsX1y08tf68JUrAFgcoixcOA+dWqYiv3tOXuTzqE1TKkEPZ8VV4hrdpqVfF7sKUyE3QfbXiPqJ2pv0wQpGAbmtIBAAACokJlGbevKn929VKSrkzlM73x1d03v7+txzkHk8mfm18sAm2HMP+e1lrZZKZz2Cq0QLV8+XK99NJL+s1vfqO3335bnZ2d+uhHP6opU6bo9ttvz81UWmzVqlV6+OGH9frrr6uhoUGf+tSntHjxYn36058u+VqbN2/W0qVL9fLLL6urq0sjR47UzTffrPnz54f1doy3rPuEZ6jKH+rzI86la4L8Yx10ceUohq2CDvmVC3FhnNRMmomeMGUuEwI7IQppENrSM4MGDdLRo0c1evRoDR48WNKpad5/97vfqaGhQc8884ymT59esM2iRYt0//33q3fv3rriiivU2dmpDRs2yHEcPfnkk5ozZ06P13n66ad1zTXX6OTJk5o8ebIGDRqkDRs2qKOjQ7fddpuWL18e6H0kufRMLYJWquJeCzDoP95hhgSvk3y5CpUJJ558pvQ25X+OXvtU6n5CVnpV890kTCFqcZ3XQwtU//u//6tx48apqamp4PbvfOc7+tKXvqSzzjpLe/fuVa9ep/6ibdy4UVOnTtXAgQPV3t6uc889V5LU3t6uKVOmqHfv3tq9e7c+/OEP557r4MGDGjZsmA4dOqSnnnpKc+fOlSS99dZbmjRpkt58801t3LhRn/nMZ2p+H1kLVHELO5RUWuMrzUwJVH6VutAg7ccpa/x+LwlSiIt1a/ldfPHFnrd/8Ytf1PLly/Xmm2/qjTfe0MiRIyVJ9913nyRp8eLFuTAlSRMnTtRNN92khx56SCtWrNBXvvKV3H2PPvqoDh06pNmzZ+fClCSdccYZuueeezR37lwtX748UKBKo2qGAN3wVbxNWKHMtLmpbOVniQ7TA5cN+4hoEKaQRrFc5edWpRoaGiQpN7QnSfPmzevxePe25557ruD2tra2ktvMnDlTTU1NWr9+vTo7O8PbeQAAgAoiv8pv1apVeuONN3Teeefp4x//uCRp586dOn78uE4//XQNGTKkxzYXXXSRJGn79u0Ft7v/796fr6GhQRdccIG2bt2qN954Q2PGjAn7rRjJqzm92sZ0m5XrzwnSr5PfQG9TX1W+oI38Ye9HtffBDvRLAaeEHqi++c1vaseOHTp69Kh++9vfaseOHTrrrLP0gx/8QKeddqogtnfvXknyDFOS1NzcrNbWVh08eFCHDx9WS0uL3nvvPXV0dJTdbsiQIdq6dav27t1bMVCVuupw165dGj58uJ+3aiy30byWUFVqG1MmCfUjimkLSoWqpIYwKw2XJR2igHwEKWRB6IHqJz/5SW44T5I+9rGP6YknntC4ceNytx05ckSS1KdPn5LP09zcrI6ODh05ckQtLS25bcpt19zcXPD8CF9YwSqKeanKVWSqbYCudt+yuKgy4Ce4E6aQFaEHqvXr10uSOjo69Jvf/EZLlizRlClTtHTpUt1xxx2SJPfCwrq6upLPU3zxoZ+LEau5YLFUt3+pypXJlnWfCgn5Q39RD/mFMd1CVPMveWFoCVmT9FA1QQpZE1lTemtrqy655BI9//zzGjdunO6880796le/kiS1tLRIko4ePVpy+2PHjkmS+vbtW7BN/n2VtgEAAIhD5Ff51dfX67Of/awcx8ldtXf22WdLkvbt2+e5zdGjR9XR0aHW1tZckOrXr5/69+9fdjv3dvf5YR9TF2rd/fqSgp9yho28K5b3QdUNfrnfWT/f3zBQnUIWxbKW36BBgyRJBw4ckCSNGDFCjY2NOnDggPbt29ejyXzbtm2SpNGjRxfcPmbMGG3atEnbtm3LzWfl6u7u1muvvabGxkaNGDEiqreCvzG9Od2EsJHlObdM+Pyzrvh7F+R7yGSdQGWxzEP14osvSlLu6rnevXvrsssukyStWbOmx+Pd22bNKuwDmjlzZslt2tra1NnZqalTp/aYrT0r3F6qKK3c0xZLmApS4eFkDoSv3N+rV959iTCFzAslUP3iF7/Qj370I33wwQcFt3d3d+tb3/qWnnjiCfXu3Vuf/exnc/ctWrRIkrR06VL9/ve/z93e3t6uRx55RP369dP1119f8Hw33HCD+vXrp2effVZr167N3f7222/r9ttvL3he2MvUYT9427L6RI8fpE+pKhVBCjgllCG/Xbt2aeHChRo0aJDGjRungQMH6p133tFvfvMb/fnPf1ZTU5NWrlypj33sY7ltLr/8ct1666168MEHNXbsWE2bNk1dXV1at26dTp48qe9///saMGBAwesMGDBAK1as0LXXXqt58+bp0ksv1aBBg7R+/Xp1dHTolltu0dSpU8N4SwAAAL6FEqguvfRS/cd//IdefPFFbd++Xe+8844aGho0dOhQzZs3T7fccovOOeecHts98MADGjt2rB5++GGtW7dO9fX1mjp1qhYvXqxJkyZ5vtbVV1+tTZs2aenSpXr55ZfV1dWlT3ziE7r55pu1cOHCMN4OUFG5yT6BrKA6BfxdnVPN5E0ZENeq1FEqXoomDEk3obsBJuwG72pCkddr+9k+yqZ0E2ZEZ4gv/by+Z4Qp2CKu83osV/khXsXN6WEELK+JQpMIWSbNSJ71ChVBKrsIU0BPBKoMyA9YUVSvkhJFxarS65nGlAWQkV753y2CFFAagSpjoqhelZK/7p/XUjXF6wKWWyfQxDBjqkoLJwN+FH+HCFNAebHMQwUAAJBmVKgyLo6KVaWFmqNeyNlLnBWvOGZMp58JYaI6BVSPQIUCbsDyG6y8hvLc2/3chmgw7Icg8gN6w7T2BPcEsAdDfggk6ekUkuqtSvoKQz+YtRxBNExrJ0wBVaBCBU/Luk9UrFKVqzh5rSuYpisMbUK1Cn653xOG+IDqUaECAAAIiAoVauZVhar0eD9VqnLTJ4Sp2uVjih9v0/IzVKngB5UpoHYEKlSl2hBVbvvB+18ouK+WIFVrL5O7nd/t47hSzzYTrutFj1ZK0CsFBEegQklBw1Ml+wdPl9QzWNkky1UqwlQ6EKaAcNBDBfhQrjJlU9UqzCv/GEK0H2EKCA+BCgAAICCG/JA6XkNwUVeR8p/fliHAMNBHZS+qU0C4CFRInNtLdcff/t9rfqukJxCtRnF4GzbyrkCBrvjKQtMQquxDmALCR6CC1fxWg4KGmiDCfF0TrzYkTNmDIAVEhx4qZIatQ3H5Uzy4P0ERgrKHMAVEiwoVjBX2MJ9JVZ00yb/aj6BmJsIUED0qVAAAAAFRoYLxFgydZVVTehhsHZ6EeahOAfEgUCFxpdb3y7/aL671/UxQKkyZfrWfxBV/JiFIAfEiUCG1TA0dQdjwnghVALKIQAUYIG1DfMXL0hCw4kV1CogfTekAAAABEaiQWsNG3mVF5ceGfYQ9qE4ByWDID8ZZuaetx/IzaWtGLxWiKi27Y0Njuhd3CJChv2gRpoDkEKiQuGXdp06y7tV+XqEiyFV+Ji7XAoSJIAUkj0AFYyzrPlFyCoUwJF3dKa5KeQVHL6XCZJCgWNw0DnsRpgAz0EMFAAAQEBUqWCVLE3xGIenKFOv+AUgrAhWsYmOQ8mpA9zvc5/f5bekPc0NU0sEuLRjuA8zBkB+M4ad/asHQWaGEkfwpFWyftqCaMLVl9QkqQxaacF2vHiGUMAWYhQoVrGJjhQoIasvqEwWBijAFmIdABWtEFaYqVXjcIbX8obVSw2zlql1hDvPZjipZddwwRZACzMWQHwAAQEAEKsAnP71Ku19fUvJxDFf+HU3p/k24rpe2rD6hV959KeldAVAGQ34wlhtAkhgq8zMJKMN7tWPIrzwa0AH7EKhgnOJKThKVnVL9UUlMT1Dq/ds0Q3pxUzVK43MC7ESgAnwoNcVC8USjcVWmbJl3CtXxmviU6hRgB3qoAAAAAqJChUwrtcBwtZN9hl2ZWrmnrcdz2lyVYhirvOLPhwZ0wD4EKhhjWfepIY5lOjVrepJXxeWHlzhnUs9/z/l/tjlMoTT3Cr58NOwDdiJQwUhxhymTl58hTKUbV/QB6UAPFYy0f/D0pHchJy2BhmE3c7lrLDLUB9iLQAUAABAQQ34w0h31vSRDZhaPYzgwyiFOKlPm8JoWQWKYD0gDAhUyzevqvLj7t6J4PVNDVKlAkUWmHiMAtSFQAUUWDJ1VMeREPYFnWvq2UB49U0B6EKhgrOLQEmflKP+1WdQ4Ol7TBgCAjQhUMNKy7hMavP8FSckvNJz06yM96JsC0our/AAAAAKiQgVjuXNR3ZHwfkSJ4cTsyG9Cp3cKSB8CFYznLkmz8m9DgNXa/foSo2dCj0L+0BJXk5lhy+oTDPMBKUagglXcfqZqKjs2hqn8fQ56xV+ppm+CVrwIU0C6EahglbQMkaXlfQTFFX4A0oKmdAAAgICoUMEKJi1FU61aqlFR930x3BcvhvuA9KNCBePdUZ+9k7+NfV/wRpgCsoEKFYwURYiyYYJOtzLF0jP2I0gB2UKFCsbJUkXKKzhFGaYmXNeL4b4YEKaA7KFCBaNUClO1TJuQBD/75wanOKtR7lV1hKroEKaAbKJCBQAAEBAVKhjLq8oTpDK1ck+bFX1UCF+pvrTi5v/8qyvpYwNQDQIVrFAchGoJVoSpbPMz+7zX1ZUELAB+1DmO4yS9EyYZNWqUJGnHjh0J70k2Da5xvb5qBQ1X+YHO67mq6aFKStJ9VFHNkh7llBOVjhn9U4B54jqvU6GCURYMnWV8w3kaJBWm0rrUDEEKAE3pAAAAAVGhgjEG73/B6OVlSlXO/FbUkh7iy7dl9YnEh/zCEues8l6vtX/w9NheH4C5CFRIXFx9U7AfS/IAMBWBColzf8OPM1i5VSVTr/wrFRxMqnKB6hSAv6OHCvAhrkb5YSPv8lWFoVKTPMIUgHwEKgAAgIAY8kPibOihino6B78VJyaZBAAzUaECDLD79SVVhaSgQ362XeHndyg0Lgz3AShGhQqJS6Ip3VZu6Ko1XNgWpADAFgQqGCPJq/1Mkb84b6XH1cKdqdyGYGVSRcpFZQpAKQQqGGf/4OlUqyJiQ5AyFWEKQDn0UAEAAAREoAJ8chvHo77Cjiv4zEN1CkAlDPkBPgXt6Rk28i7fYam4lyotIcsdcnR7uUwUtPEfQDYRqIAKvMJMrSfbakJSWkJUPj9ByrQgQ3UKgB8EKhjJpMb0aipLJjxvnLasPqEJ1/XyvHowiipUpbUXw7xqkyAFoBr0UAEAAAREhQrGMnnCz/zKUq1DVElUp6KYhyq/EpV0b5Rbwaq1UmV7xRBAcghUsEap4Z6kJ+f0OxknKiv1OVYa6gt7HxjuA1AtAhWMZ0I/VRr6naToKlNBRBFGgyxmTZgCUAt6qGC9BUNnRV7BSEOYkk6FoKSH5apVy7GtNUw1TGuvaTsAIFABAAAExJAfjFdpuC/pHiqb+qdYy88bw3wAgiJQwUiD97+QG+pZEOHr1DqUZ2tPVVhX+YUxbFgpiAYZxg3SQwUAtSBQATWyqTLlCqtClT+ZZzX8fGZR9sN5TcVBdQpAGOihgtXirEKEEaDiWmDZL9sa1P3y+l7kByf3z4QpAGGhQgVjFZ8U45yLKAtMCVNxHFev4ESYAhAmKlQAAAABUaGCUZKewNNVanjPvb3ckJ3JDeteVan829J4FSCVKABxIFDBWmH0TxUHp0pByE9QMjVMlRNFkEqyaZ8QBSBuBCoYw898U0EXv0VtTOm3AgBTEahglaiDVLmqio2Vp6y6o76XlnUTAgHEh6Z0AACAgKhQwRj7B083pik9ayrNoO7e7nfor5r+qfyqY7VTKJSrWC6r6pkAIBgCFRJHiEoPG2ePB4AwMOQHwBh+e+RW7mkr+1iu8gMQNypUSJQt1Ska0pPnFaCKj0vDtPa4dgcAClChAgAACCiSQPXXv/5VH/nIR1RXV6fzzz+/7GNXrVql8ePHq2/fvhowYIBmzJihzZs3l91m8+bNmjFjhgYMGKC+fftq/Pjxevzxx8N8C0ABeoPMk1+dapjWTnUKQKIiGfJbtGiR3nnnHV+Pu//++9W7d29dccUV6uzs1Lp16/TTn/5UTz75pObMmdNjm6efflrXXHONTp48qcmTJ2vQoEHasGGDFixYoFdffVXLly+P4i0hAmkf7qv2yjh4X+XHJK4AbBB6hWrDhg16/PHHdeONN5Z93MaNG3X//fdr4MCBevXVV/XMM8/of/7nf7Rp0yb16tVLCxcu1MGDBwu2OXjwoBYuXKgTJ05ozZo1+vnPf641a9Zo586dOuecc3T//ffrZz/7WdhvCQawrYfJ1jXxtqw+UXK9v6SC4YKhs0pOp0BlCoApQg1U77//vm666SaNHDlS//7v/172sffdd58kafHixTr33HNzt0+cOFE33XSTDh06pBUrVhRs8+ijj+rQoUOaPXu25s6dm7v9jDPO0D333CNJVKgs0bVuYtn7bQtQaTHhul6eYbDU7XEodUUfQQqASUId8vva176mXbt26ec//7nq6+tLPq6zs1MbNmyQJM2bN6/H/fPmzdNDDz2k5557Tl/5yldyt7e1tZXcZubMmWpqatL69evV2dmppqamoG8HEatlIWL3tqh6mghyyfCqQLlLxzBBJwAbhFah2r59u+677z4tXLhQkydPLvvYnTt36vjx4zr99NM1ZMiQHvdfdNFFuecsfo38+/M1NDToggsuUGdnp954441a3wYAAEDVQqlQnTx5UjfeeKNaW1tzQ2/l7N27V5I8w5QkNTc3q7W1VQcPHtThw4fV0tKi9957Tx0dHWW3GzJkiLZu3aq9e/dqzJgxtb0ZxKJhWnvFYT8vXG0Xj/x+qQnX9Yq8fyp/SI9JOQHYKJRA9a1vfUu//OUv9dhjj2ngwIEVH3/kyBFJUp8+fUo+prm5WR0dHTpy5IhaWlpy25Tbrrm5ueD5yxk1apTn7bt27dLw4cMrbo/45Q/HhRWsohri48q+2hCmANgqcKD64x//qMWLF+vSSy/VggULfG3jOI4kqa6uruJjSv2/n22QLlFUp4qfM+keqmEj70p8H4pFHQ4XDJ2V65cCAFsFDlRf+tKX1NXVpe985zu+t2lpaZEkHT16tORjjh07Jknq27dvwTbuff369au4TTk7duzwvL1U5QrpZFp4MW1/AAD+BA5UbW1tam1t1Re/+MWC2zs7OyWd6peaMmVK7rF9+/bV2WefLUnat2+f53MePXpUHR0dam1tzQWpfv36qX///jp06JD27dunkSNH9tjOfT73+QEAAOIQSg9VR0eHXnzxRc/73n///dx9H3zwgSRpxIgRamxs1IEDB7Rv374eTebbtm2TJI0ePbrg9jFjxmjTpk3atm1bj0DV3d2t1157TY2NjRoxYkQYbwsAAMCXwIGqVN/Snj17NGzYMI0YMUI7d+4suK9379667LLL9MILL2jNmjX6t3/7t4L716xZI0maNatwbpqZM2dq06ZNWrNmjT7/+c8X3NfW1qbOzk7NmDGDOagQuVL9XAuGziq5VEoUw3le+8GwIQDEL5K1/PxYtGiRXnjhBS1dulQzZ87MzZbe3t6uRx55RP369dP1119fsM0NN9ygZcuW6dlnn9XatWtzs6W//fbbuv3223PPCzu4M10XT59QLqzkS2KNt+J9c/cp7n0Jq0E/jikRSnE/OxrSAaRBYoHq8ssv16233qoHH3xQY8eO1bRp09TV1aV169bp5MmT+v73v68BAwYUbDNgwACtWLFC1157rebNm6dLL71UgwYN0vr169XR0aFbbrlFU6dOTegdoVb5S4hUmpsqSHDZ/fqSyOaxyg97pdadGxZT5aqaKwVNmN7hjvpehCoA1kssUEnSAw88oLFjx+rhhx/WunXrVF9fr6lTp2rx4sWaNGmS5zZXX321Nm3apKVLl+rll19WV1eXPvGJT+jmm2/WwoULY34HAAAAEQaqoUOH+poXasGCBb7nr3JdfPHFeuGFF2rcM5isYVq7FtSXXoS3uPpz9/PjC/6/XAXK6z73Nj8VnfztS1WhKklqiLCUJIf88t3xt2NOpQqArUJbyw8IS5CTarlgZGqzdi3DkEHf54TremnCdb0K/uz+fxKWdZ8gTAGwWqJDfkAQK/e06W6P8FAcKNzAkvQ6gLVWtUrJ7wlz33Ol95hkaCqFIAUgDQhUMNKy7hO5YaC0C6NyVs3QJQAgfAQqGCu/chF1uPrajF+WvM/tdwq7whQ3E6tTAJAW9FABAAAERIUKVkhyCND0ypTfJnQTFH+W9E8BSAsCFawR5hCg6SHJD69me69wZUqYAoA0I1DBSsu6T2jl/hfKhomkr+pDoTSEWAAohUCFVCkXovYPnv73/0lgCDHoZJ5pC4gM9wFIE5rSAQAAAqJCBWsVL3ac/+eCalRI8itMNgxfmdY7tXJPW+5zozoFIG0IVLBW0CGw/BN8LdtKp4JVkKBlykScUa/nR5ACkHYEKqRGLVWpoH1Nxdv7fT5TglScCFMA0oxABWtFMaxXTvEQYxDDRt4Vaqjyei636lRp6C9odSptzfIAUAua0gEAAAKiQoVMGbz/hUDbV6oqVVOtsX1B47CrbABgMwIVUIXiEJS14a7i91vq/ec356/c00b/FIDUI1AhM7rWTdTuv/25UhAqVXkpvr1chcZv2Apa6am0bbleqqiv7gOArCBQAQYoN/wXVhWs1vAU9PUXDJ0lUaECkHI0pSMTutZNTHoXalauAmVDDxPDfQCygEAFAAAQEEN+yAS3T8n0K+u8+qmy1vgOADYiUCEzbAgmXkHP1PCXz4a1DQEgSgz5ARHZ/foSK8JQUIQpAKBChYzJQsAxBc3oALKEChUAAEBAVKiQCfsHT09s6oT8qpgNfVwAgOoRqJAZDdPaC/7fDVgN09qtnqcqKeV6pxjuA5A1BCpkVn7Acv8cVrDKr0TlV6gqrQFYroJlS/8XYQpAFhGogDy1VqvKBSGvuaXy58Qylen7BwAmIVABRaoNVX6CR1ihymti0lLVsFpU2p9KUyRQnQKQVVzlBwAAEBAVKsAipSpQtVSmvKpmlazc0yapZ6WKyhSArCNQAQEE7TOq1KTu57X8hiKv7avdf2ZFBwBvBCrAQ/4VgFFPqeA31NTaH1VLJSofIQoAKqOHCrCY3zAW5RV7DPcBAIEKAAAgMIb8gArKTaMQ13xScc9bxTAfAFSHChVgASbZBACzUaECEuZ3ks5alquJGv1TAHAKFSogoDDX2Nv9+pLcT9L7UglhCgD+jkAFAAAQEIEKqKDSPFR+htzCrhwtGDrLs3E86PAfzegAUBt6qIASop7Q00Tu0jKVMNwHAIUIVIDFiitKfgNRuecohyAFAN4Y8gNKaJjWXrAETSlxNoJHZeWetprCGADgFCpUQAlhDvkFXU/PL7faFEY4ohoFAP5RoQIAAAiIQAWEoNLcUWkYFgQAlMaQHxAiW4MT0yUAQDAEKiAB+T1VUS8dQ1gCgOgx5AckIK4wBQCIB4EKAAAgIIb8gAQErUyt3NNWcSgvyFAfUyYAQHWoUAEl+J3Y00T0TQFAvKhQASVEsZZfWD1TBCYAMAuBCrBIHEGK4T4AqB5DfgAAAAERqIAYVZpRHQBgJ4b8gJjU2j8VZ78Uw30AUBsqVICHKBrSqUwBQHoRqICY1FqhWrmnTSv3tIW8NwCAMBGoAA+2zj8FAEgGgQoAACAgmtKBiIU1mWf+sF/Yjeo0owNAMFSogBJMXnqGvioAMAsVKiACBUFs/wun/jN4uq9tB//t8QAAe9Q5juMkvRMmGTVqlCRpx44dCe8JTFLNNApxVbXc4BV0+I/hPgBpFtd5nSE/AACAgBjyAyy1f/B0X8ODXr1Wcc6+DgBZQKACQpBU8/r+wdN1R4n78oNUcfhy7yNYAUA4GPIDfCgOTPlXACZ9JWClHii3Gd6rKZ7+KQAIBxUqwCev4JR0mCplWfcJLavwGL9XHQIAKqNCBQAAEBAVKiAF/A7dUZUCgGgwD1WRlpYWdXd3a/jw4UnvCgAACGjXrl2qr6/X4cOHI30dhvyKNDc3q7u7W7t27Up6VxCDXbt2cawzgmOdHRzrbPB7nOvr69Xc3Bz5/lCh8sBs6dnBsc4OjnV2cKyzwbTjTIUKAAAgIAIVAABAQAQqAACAgAhUAAAAARGoAAAAAuIqPwAAgICoUAEAAAREoAIAAAiIQAUAABAQgQoAACAgAhUAAEBABCoAAICACFQAAAABEajydHZ26u6779Z5552npqYmnXXWWfrCF76gffv2Jb1r8PDrX/9a3/jGNzR37lwNHjxYdXV1ampqqrjdqlWrNH78ePXt21cDBgzQjBkztHnz5rLbbN68WTNmzNCAAQPUt29fjR8/Xo8//nhYbwVlHDt2TM8884yuv/56jR49Wv369VNzc7PGjBmjJUuW6MiRIyW35VjbZ/ny5Zo7d67OPfdc9e/fX42NjfqHf/gHzZ8/Xzt27Ci5Hcfafn/961/1kY98RHV1dTr//PPLPtbI4+3AcRzHef/9951Pf/rTjiTnzDPPdK699lpn/PjxjiTn9NNPd958882kdxFFZs+e7Ugq+GlsbCy7zW233eZIcnr37u3Mnj3bufLKK50PfehDTq9evZy1a9d6brN27VqnV69eTl1dnXPppZc6V199tdPa2upIcm677bYo3hryfPe7380d31GjRjnXXHONc+WVVzotLS2OJOf888933nrrrR7bcaztNHDgQKepqckZP368M2fOHGfOnDnOeeed50hyGhoanOeff77HNhzrdJg/f75TV1fnSHJGjBhR8nGmHm8C1d/ceeedjiRn4sSJzuHDh3O333fffY4kZ/LkyQnuHbx84xvfcO666y7nueeec/7yl79UDFQbNmxwJDkDBw50fve73+Vu37x5s9PQ0OD079/f+etf/1qwzV//+lenf//+jiTnqaeeyt3+l7/8xTnnnHMcSc7GjRvDf3PIefzxx50vfvGLBcfMcRznT3/6k3PhhRc6kpzPfe5zBfdxrO310ksvOe+//36P27/97W87kpyzzjrL+eCDD3K3c6zTYf369Y4k51/+5V/KBiqTjzeBynGcrq6uXFLdtm1bj/tHjx7tSHK2bt2awN7Br0qBasaMGY4k5/777+9x3y233OJIcu69996C2++55x5HkjN79uwe26xdu9aR5MyaNSvorqNGmzdvzh3348eP527nWKeTe/LbsWNH7jaOtf2OHTvmnHPOOc7IkSOd3/3ud2UDlcnHm0DlOM7GjRsdSc7w4cM971+yZIkjybn77rvj3TFUpVygev/9953GxkZHkvPHP/6xx/2bNm1yJDmXXnppwe2TJ092JDlPPPFEj22OHz/uNDU1OU1NTZ6/USN6R48ezQ0H/ulPf3Ich2OdZiNGjHAkOb///e8dx+FYp8VXv/pVp66uznnxxRed3bt3lwxUph9vmtIlvfrqq5Kkiy66yPN+93b3cbDPzp07dfz4cZ1++ukaMmRIj/vdY7x9+/aC293/9/puNDQ06IILLlBnZ6feeOONCPYalfzhD3+QJNXX12vAgAGSONZptWrVKr3xxhs677zz9PGPf1wSxzoNtm/frvvuu08LFy7U5MmTyz7W9ONNoJK0d+9eSfI8QPm3u4+DfSod4+bmZrW2turgwYM6fPiwJOm9995TR0dH2e34biTrwQcflCRdddVVamxslMSxTotvfvObWrBgga655hpdcMEFmj9/vs466yz94Ac/0GmnnTp1caztdvLkSd14441qbW3VPffcU/Hxph/vDwXaOiXcy6779OnjeX9zc3PB42CfSsdYOnWcOzo6dOTIEbW0tBQcb74b5nn++ef1ve99T/X19fr617+eu51jnQ4/+clPtGHDhtz/f+xjH9MTTzyhcePG5W7jWNvtW9/6ln75y1/qscce08CBAys+3vTjTYVKkuM4kqS6urqy98NelY5x/mNK/b+fbRCP3/72t/r85z8vx3H0zW9+U2PGjMndx7FOh/Xr18txHB08eFCbNm3SiBEjNGXKFC1btiz3GI61vf74xz9q8eLFuvTSS7VgwQJf25h+vAlUklpaWiRJR48e9bz/2LFjkqS+ffvGtk8IV6VjLPU8zu42+fdV2gbR27dvn6666iodPHhQixYt0q233lpwP8c6XVpbW3XJJZfo+eef17hx43TnnXfqV7/6lSSOtc2+9KUvqaurS9/5znd8b2P68SZQSTr77LMlqeSM6O7t7uNgn0rH+OjRo+ro6FBra2vuL2C/fv3Uv3//stvx3YjXO++8o2nTpmnv3r1auHCh7r333h6P4VinU319vT772c/KcRw999xzkjjWNmtra1OfPn30xS9+UVOmTMn9XHfddZJO9TO5t7lDcaYfbwKVlBsu2LZtm+f97u2jR4+ObZ8QrhEjRqixsVEHDhzw/EtV6hiX+250d3frtddeU2Njo0aMGBHBXiPf4cOHNX36dO3cuVNz587Vd7/7Xc/SP8c6vQYNGiRJOnDggCSOte06Ojr04osvFvxs2bJFkvT+++/nbvvggw8kmX+8CVSSLr74YvXv31+7du3SK6+80uP+NWvWSJJmzZoV964hJL1799Zll10m6e/HM1+pYzxz5syS27S1tamzs1NTp071tYYganf8+HHNnj1bW7du1ZVXXqkf/vCH6tWrl+djOdbp9eKLL0qShg8fLoljbTPn1DyYPX52794t6VR4cm9rbW2VZMHxDjSLVYrccccdjiTn05/+tHPkyJHc7e7SM5MmTUpw7+CHKsyUvm7dupJLFjQ2Njr9+vVz3n333YJt3n33Xadfv349lix46623crM2r1+/Pvw3g5wPPvjAmTNnjiPJueSSS5yjR49W3IZjbadNmzY5q1evdrq7uwtu7+rqch566CHntNNOc3r37u3s3bs3dx/HOl3KTezpOGYfbwLV37z//vvOhAkTChZHdv9/4MCBuZl5YY62tjZnwoQJuR9JTl1dXcFtbW1tBdvceuutjiSnT58+zuzZs53p06c7H/rQh5zTTjvNWbNmjefrrFmzxjnttNOcuro6Z8qUKc68efNySxXdcsstcbzVTHvggQdys6HPmTPHmT9/vufPgQMHCrbjWNvnsccecyQ5gwYNcq688krnn/7pn5wrrrjCOfPMMx1JTlNTk/OjH/2ox3Yc6/SoFKgcx9zjTaDKc+zYMefOO+90hg8f7jQ0NDhnnHGGM3/+/ILfhmAO9x/fcj+PPfaY53bjxo1z+vTp4/Tv39+58sornV/84hdlX+ull15yrrrqKqe1tdXp06ePM27cOGfFihURvTPku/vuuyseZ0nO7t27e2zLsbbLH/7wB+c//uM/nIsvvtg588wznfr6eqe5udkZNWqU86//+q9lf7HlWKeDn0DlOGYe7zrHYcINAACAIGhKBwAACIhABQAAEBCBCgAAICACFQAAQEAEKgAAgIAIVAAAAAERqAAAAAIiUAEAAAREoAIAAAiIQAUAABAQgQoAACAgAhUAAEBABCoAAICACFQAAAABEagAAAACIlABAAAERKACAAAIiEAFAAAQ0P8HWhc/PhU5klYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 750x750 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5), dpi=150)\n",
    "plt.imshow(LAB, origin=\"upper\", cmap=\"turbo\", interpolation=\"none\")  # .resampled(3))\n",
    "\n",
    "# # use these for exporting and cleaning\n",
    "# plt.savefig(\n",
    "#     \"for_.pdf\", bbox_inches=\"tight\", pad_inches=0.02, facecolor=fig.get_facecolor()\n",
    "# )\n",
    "# plt.close()\n",
    "# plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea68fb77-9cd3-441d-bc55-ed608219c691",
   "metadata": {},
   "source": [
    "What are the actual means of the regions/classes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "45496ac1-458f-43fb-8d7d-68649f109c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'0': array([120.833336], dtype=float32),\n",
       " '1': array([367.94604], dtype=float32),\n",
       " '2': array([230.80005], dtype=float32),\n",
       " '3': array([575.4106], dtype=float32)}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cdic = dict(zip(KAT, kmeans.cluster_centers_))\n",
    "cdic"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f477960-43d0-44a4-9bdc-7a8c3007cc4d",
   "metadata": {},
   "source": [
    "Plot the realization (xarray **pile**) in local CRS, but this time only for the catchment/region mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "4e510157-af29-44be-b444-4ebd818a29ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x211439578b0>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# masked realization in local CRS\n",
    "pile.rain.where(~np.isnan(REG.data), np.nan).plot(\n",
    "    cmap=\"precip2_17lev\", levels=10, vmin=100, vmax=1000, add_colorbar=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1237411b-be57-4889-984b-6410c8eb9c92",
   "metadata": {},
   "source": [
    "### MORPHOLOGICAL FILTERING ###\n",
    "\n",
    "Maybe there is not need to have a detailed delimitation among the region/zones.\n",
    "Such a detail might translate in slowing STORM computations by having SHPs with too many points.\n",
    "Hence, decreasing the resolution of the boundaries will speed STORM's performance (by having SHPs with much less vertices/points).\n",
    "\n",
    "<span style=\"color:HotPink\">*Note: Following this approach implies that the actual means won't precisely correspond to the newly delimited regions.*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "44aeddbd-973a-4c20-8d9a-3fb9603e293a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x21143a107c0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x750 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# \"decreasing resolution\"\n",
    "new = morphology.opening(LAB, morphology.ellipse(2, 3))\n",
    "\n",
    "# plots and comparison\n",
    "fig, ax = plt.subplots(1, 2, figsize=(8, 5), dpi=150)\n",
    "ax[0].imshow(LAB, origin=\"upper\", cmap=\"turbo\", interpolation=\"none\")\n",
    "ax[1].imshow(new, origin=\"upper\", cmap=\"turbo\", interpolation=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2300b874-d548-4ff2-90f3-02a59ee3f2c9",
   "metadata": {},
   "source": [
    "### REGIONALIZATION MASKS TO NUMPYs ###\n",
    "\n",
    "<span style=\"color:RoyalBlue\">*Such a conversion is necessary for STORM to simulate storms within such regions/masks.*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "00d4d172-7a11-4050-9d73-ec78436f2fd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# copy the masks\n",
    "mopen = new\n",
    "# mopen = LAB\n",
    "\n",
    "# NUMPY to SHAPE\n",
    "# .rio.transform() IS QUITE OF THE ESSENCE HERE!\n",
    "lopen = list(\n",
    "    shapes(mopen, mask=CATCHMENT_MASK, connectivity=4, transform=pile.rio.transform())\n",
    ")\n",
    "\n",
    "# we didn't read \"BUFFRX_MASK\" though!\n",
    "# lopen = list( shapes(mopen, mask=BUFFRX_MASK, connectivity=4, transform=real.rio.transform()) )\n",
    "\n",
    "# remove NAN.regions??\n",
    "# https://stackoverflow.com/a/25050572/5885810\n",
    "# https://stackoverflow.com/a/3179137/5885810\n",
    "lopen = [x for x, y in zip(lopen, ~np.isnan(list(zip(*lopen))[-1])) if y]\n",
    "lopen = list(\n",
    "    map(\n",
    "        lambda x: dict(\n",
    "            geometry=x[0],\n",
    "            properties={\n",
    "                \"label\": f\"region{int(x[-1])}\",\n",
    "            },\n",
    "        ),\n",
    "        lopen,\n",
    "    )\n",
    ")\n",
    "\n",
    "# into GEOPANDAS\n",
    "feats = gpd.GeoDataFrame.from_features({\"type\": \"FeatureCollection\", \"features\": lopen})\n",
    "# # the line below is an alternative... BUT you mig have troubles grouping it\n",
    "# feats = gpd.GeoDataFrame.from_dict(\n",
    "#     list(\n",
    "#         shapes(mopen, mask=BUFFRX_MASK, connectivity=4, transform=real.rio.transform())\n",
    "#     ),\n",
    "# )\n",
    "\n",
    "# grouping to retrieve just the CLUSTER.masks (the output is a Series)\n",
    "nasks = feats.groupby(by=\"label\").apply(lambda x: x.unary_union)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff86f466-efe9-4cb3-85cb-5db3651d0798",
   "metadata": {},
   "source": [
    "Visualizing the different masks/regions..."
   ]
  },
  {
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      "text/plain": [
       "<MULTIPOLYGON (((1925000 -1155000, 1915000 -1155000, 1915000 -1160000, 19100...>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# do we obtain HAD if merging all masks?\n",
    "feats.unary_union"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8d79641-f381-4a92-bde5-c302e2b5ae71",
   "metadata": {},
   "source": [
    "<span style=\"color:RoyalBlue\">If you want to have them grouped into a [GeoPandas](https://geopandas.org/en/stable/).</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2c607d91-e338-4659-a61c-1e03888cd222",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>label</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>region0</th>\n",
       "      <td>MULTIPOLYGON (((1930000.000 -830000.000, 19300...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region1</th>\n",
       "      <td>MULTIPOLYGON (((1950000.000 -1070000.000, 1955...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region2</th>\n",
       "      <td>MULTIPOLYGON (((1935000.000 -985000.000, 19300...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region3</th>\n",
       "      <td>MULTIPOLYGON (((1880000.000 -915000.000, 18750...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                  geometry\n",
       "label                                                     \n",
       "region0  MULTIPOLYGON (((1930000.000 -830000.000, 19300...\n",
       "region1  MULTIPOLYGON (((1950000.000 -1070000.000, 1955...\n",
       "region2  MULTIPOLYGON (((1935000.000 -985000.000, 19300...\n",
       "region3  MULTIPOLYGON (((1880000.000 -915000.000, 18750..."
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# turn them back into GeoPandas\n",
    "masks = gpd.GeoDataFrame(geometry=nasks)\n",
    "# masks.geometry.iloc[0]\n",
    "# masks.geometry.loc['region0']\n",
    "# masks.loc['region0'].geometry\n",
    "# masks.geometry.xs('region0')\n",
    "\n",
    "masks"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b319905-44db-4672-a2e3-3d29c445567c",
   "metadata": {},
   "source": [
    "<span style=\"color:MediumSeaGreen\">I'd would ask then... what would the above exercise look like if [**CLUSTERS**](#more) is equal to **1**... or **3**... or **7**??</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1fc06b2-bec9-4462-8537-8f5fafdb15b3",
   "metadata": {},
   "source": [
    "#### *A PEEK INTO WHAT IT'S PASSED TO STORM* ####"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7c5c9182-418c-47ce-b9ab-45252766bcb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'mask': [                                            geometry\n",
       "  0  MULTIPOLYGON (((1930000.000 -830000.000, 19300...,\n",
       "                                              geometry\n",
       "  0  MULTIPOLYGON (((1950000.000 -1070000.000, 1955...,\n",
       "                                              geometry\n",
       "  0  MULTIPOLYGON (((1935000.000 -985000.000, 19300...,\n",
       "                                              geometry\n",
       "  0  MULTIPOLYGON (((1880000.000 -915000.000, 18750...],\n",
       " 'npma': [array([[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       "  array([[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       "  array([[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8),\n",
       "  array([[0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         ...,\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0],\n",
       "         [0, 0, 0, ..., 0, 0, 0]], dtype=uint8)],\n",
       " 'rain': [array(120.87397, dtype=float32),\n",
       "  array(368.46494, dtype=float32),\n",
       "  array(231.04538, dtype=float32),\n",
       "  array(575.66113, dtype=float32)],\n",
       " 'kmeans': array([[-1, -1, -1, ..., -1, -1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1],\n",
       "        ...,\n",
       "        [-1, -1, -1, ..., -1, -1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1],\n",
       "        [-1, -1, -1, ..., -1, -1, -1]], dtype=int8)}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "real = pile.copy(deep=True)\n",
    "\n",
    "# https://realpython.com/iterate-through-dictionary-python/\n",
    "for keys, values in dict(\n",
    "    zip(\n",
    "        [\"catchm\", \"cacth\", \"kmeans\", \"region\"],\n",
    "        [CATCHMENT_MASK, CATCHMENT_MASK, LAB, mopen],\n",
    "    )\n",
    ").items():\n",
    "    real[keys] = xr.DataArray(values, coords=void.coords, dims=void.coords.dims)\n",
    "    # real[keys] = xr.DataArray(values, coords=real.coords, dims=real.coords.dims)\n",
    "\n",
    "# trims \"real['region']\"\n",
    "# trimming around the CATCHMENT_MASK is what we want; as we compute PTOT within \"catchm\"\n",
    "real[\"region\"] = xr.where(real.catchm == 1, real.region, -1)\n",
    "# # -trims around the BUFFER -> (this we want NOT!)\n",
    "# real[\"region\"] = xr.where(real.buffer == 1, real.region, -1)\n",
    "\n",
    "# -THESE 3 vars ARE IN THE ORDER OF cdic.keys()\n",
    "# new means (regions inside the HAD)\n",
    "# old_ks = list(\n",
    "#     map(lambda x: real.rain.where(real.kmeans == int(x)).mean().data, cdic.keys())\n",
    "# )\n",
    "new_ks = list(\n",
    "    map(\n",
    "        lambda x: real.rain.where(real.catchm == 1, np.nan)\n",
    "        .where(real.kmeans == int(x))\n",
    "        .mean()\n",
    "        .data,\n",
    "        cdic.keys(),\n",
    "    )\n",
    ")\n",
    "\n",
    "# numpy masks\n",
    "# ... 1st transform K-mean into 1s (because of the 0 K-mean); and the assign 0 everywhere else\n",
    "reg_np = list(\n",
    "    map(\n",
    "        lambda x: real.region.where(real.region != int(x), 1)\n",
    "        .where(real.region == int(x), 0)\n",
    "        .data.astype(\"u1\"),\n",
    "        cdic.keys(),\n",
    "    )\n",
    ")\n",
    "\n",
    "# shapes\n",
    "# # the line below are \"pandas.core.series.Series\"\n",
    "# zhapez = list(map(lambda x: masks.loc[f\"region{int(x)}\"], cdic.keys()))\n",
    "# ...in case \"pandas.core.series.Series\" try converting them into \"geopandas.geodataframe.GeoDataFrame\"\n",
    "zhapez = list(\n",
    "    map(\n",
    "        lambda x: gpd.GeoDataFrame(\n",
    "            geometry=masks.loc[f\"region{int(x)}\"], crs=void.rio.crs\n",
    "        ).set_index(RangeIndex(0, 1, 1)),\n",
    "        cdic.keys(),\n",
    "    )\n",
    ")\n",
    "# zhapez = list(\n",
    "#     map(\n",
    "#         lambda x: gpd.GeoDataFrame(geometry=masks.loc[f\"region{int(x)}\"]).set_index(\n",
    "#             RangeIndex(0, 1, 1)\n",
    "#         ),\n",
    "#         cdic.keys(),\n",
    "#     )\n",
    "# )\n",
    "\n",
    "# grouping the output into a dict\n",
    "output = dict(zip((\"mask\", \"npma\", \"rain\"), (zhapez, reg_np, new_ks)))\n",
    "output[\"kmeans\"] = xr.where(real.catchm == 1, real.kmeans, -1).data.astype(\"i1\")\n",
    "\n",
    "output"
   ]
  }
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