{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "de8ed433-5a5d-4287-9fc7-290b5a49a2b3",
   "metadata": {},
   "source": [
    "# **MODELING ONE STORM** #\n",
    "\n",
    "#### Objectives: ####\n",
    "+ Learn how STORM model storm as SHP-polygons and then transform them into RASTER\n",
    "+ Recipes for spatial plotting via [Numpy](https://numpy.org/doc/stable/) (appealing color libraries included)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "822e5f76-ce91-4b5c-86e4-931abdc461bc",
   "metadata": {},
   "source": [
    "Two key concepts are worth considering here (and now):\n",
    "\n",
    "1. STORM uses a bi-variate Gaussian copula to model the dependence between storm rainfall intensity and duration. A copula is a function that links/couples (thus its etymology) a multivariate (joint) distribution function to its univariate marginals, with no prior knowledge of the actual shape (or type) of such marginals (intensity and duration, in this case). \n",
    "2. STORM models individual storms as isotropic circular cells for which maximum intensities $(I_{max})$ are (always) located at their centres, with a quadratic exponential decay $(\\beta^{2})$ as the distance from such centres $(r)$ increases:<a id='eq'></a>\n",
    "    \\begin{equation}\n",
    "    I\\big(r\\big) = I_{max} \\cdot e^{-2 \\cdot \\beta^{2} \\cdot r^{2}},\n",
    "    \\tag{1}\n",
    "    \\end{equation}\n",
    "    where $I\\big(r\\big)$ (in $\\mathrm{mm \\cdot h^{-1}}$) is the rainfall intensity at a distance $r$ (in $\\mathrm{km}$) from the storm centre. $\\beta$ has units of $\\mathrm{km^{-1}}$.\n",
    "\n",
    "<span style=\"color:HotPink\">For a plethora of mathematical background on these and other components of STORM, you're encouraged to visit [this link](https://gmd.copernicus.org/preprints/gmd-2023-98/).</span>\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd61819-5516-4b07-bbeb-d8e1443b4bfc",
   "metadata": {},
   "source": [
    "Rainfall estimation and rasterization is done by STORM through the functions: COPULA_SAMPLING, LAST_RING, LOTR, and RASTERIZE of the [rainfall.py](../rainfall.py) module.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc026969-c5d4-42fe-a6e5-e509007bd830",
   "metadata": {},
   "source": [
    "### STORM PARAMETERS ###\n",
    "<a id='pars'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "0c4e44c8-3244-4da1-b06c-725b965d595f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    ")\n",
    "\n",
    "# spatial resolution\n",
    "# in meters! (pxl.resolution for the 'regular/local' CRS)\n",
    "X_RES = 5000.0\n",
    "# in meters! (pxl.resolution for the 'regular/local' CRS)\n",
    "Y_RES = 5000.0\n",
    "MINRADIUS = max([X_RES, Y_RES]) / 1e3\n",
    "# in meters! -> buffer distance (out of the HAD)\n",
    "BUFFER = 8000.0\n",
    "\n",
    "# some SHP constraints\n",
    "CLOSE_DIS = 0.15  # in km -> small circle emulating the storm centre's point/dot\n",
    "# in km -> distance between (rainfall) rings; heavily dependant on X_Y_RES\n",
    "RINGS_DIS = MINRADIUS * (2) + 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29b50f84-cac6-4cda-abdd-2b65c841ae85",
   "metadata": {},
   "source": [
    "## <u>RASTERIZING ONE STORM AT A TIME</u> ##"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "28ed0905-7d23-4ca6-884d-95a7843a69a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# first get rid of some (potential and) unwanted warnings\n",
    "import warnings\n",
    "\n",
    "# supressing warnings by \"message\"\n",
    "\n",
    "# https://github.com/slundberg/shap/issues/2909\n",
    "warnings.filterwarnings(\"ignore\", message=\".*The 'nopython' keyword.*\")\n",
    "# https://stackoverflow.com/a/9134842/5885810\n",
    "warnings.filterwarnings(\n",
    "    \"ignore\",\n",
    "    message=\"You will likely lose important projection \"\n",
    "    \"information when converting to a PROJ string from another format\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "80f42da0-01de-4e6e-b367-5c3b6eae510e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# loading libraries\n",
    "import cmaps  # -> nice color-palettes\n",
    "import geopandas as gpd\n",
    "import matplotlib.colors as colors\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyproj as pp\n",
    "import xarray as xr\n",
    "from cmcrameri import cm as cmc\n",
    "from matplotlib.patches import Circle\n",
    "from numpy import random as npr\n",
    "from osgeo import gdal\n",
    "from pointpats import random as pran\n",
    "from rasterio import fill\n",
    "from scipy import stats\n",
    "from statsmodels.distributions.copula.api import GaussianCopula"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4340f88a-d01d-4d86-8b6d-81be11e40bf4",
   "metadata": {},
   "source": [
    "### 1. COMPUTE PDFs ###\n",
    "\n",
    "One storm is constructed from 5 PDFs.\\\n",
    "<span style=\"color:RoyalBlue\">(Check out notebook [one_](./one_.ipynb))</span>\n",
    "<a id='pdfs'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a1b3df91-b583-4ca9-a6c4-fd827d7a1b07",
   "metadata": {},
   "outputs": [],
   "source": [
    "COPULA = {\"\": -0.31622, \"Z1\": -0.276457, \"Z2\": -0.312464, \"Z3\": -0.44}\n",
    "MAXINT = {\"\": stats.expon(0.1057, 6.9955)}\n",
    "AVGDUR = {\"\": stats.geninvgauss(-0.089, 0.77, 2.8432, 82.0786)}\n",
    "RADIUS = {\"\": stats.johnsonsb(1.5187, 1.2696, -0.2789, 20.7977)}\n",
    "BETPAR = {\"\": stats.exponnorm(8.2872, 0.0178, 0.01)}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fac8c07-01db-460d-98d3-039d26ef6cac",
   "metadata": {},
   "source": [
    "First, we generate **n_s**-samples of intensity-duration in the [Copula](https://en.wikipedia.org/wiki/Copula_(probability_theory) domain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "166e434b-d28f-4fd4-ac42-6c951a630e56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.61071263 0.87710212]\n",
      " [0.21967047 0.98564441]\n",
      " [0.75474953 0.37494087]]\n"
     ]
    }
   ],
   "source": [
    "n_s = 3\n",
    "BAND = \"\"  # -> one of the key/tag/name's from the COPULA dictionary\n",
    "\n",
    "# sampling from the copula\n",
    "IntDur = GaussianCopula(corr=COPULA[BAND], k_dim=2).rvs(nobs=n_s).reshape(-1, 2)\n",
    "\n",
    "# for reproducibility\n",
    "IntDur = (\n",
    "    GaussianCopula(corr=COPULA[BAND], k_dim=2)\n",
    "    .rvs(nobs=n_s, random_state=npr.RandomState(npr.PCG64(5669876)))\n",
    "    .reshape(-1, 2)\n",
    ")\n",
    "MAX_I = MAXINT[BAND].ppf(IntDur[:, 0])\n",
    "DUR_S = AVGDUR[BAND].ppf(IntDur[:, 1])\n",
    "\n",
    "# how does it look like?\n",
    "print(IntDur)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "addb5977-c65f-4b50-9bb4-ac410a92b6ea",
   "metadata": {},
   "source": [
    "Note that the retrieved samples range from 0 to 1; that's the copula's domain.\n",
    "Now we need to transform those pairs from the copula's domain into the \"intensity-duration\" domain.\n",
    "For that purpose, we use PDFs for storm intensity (i.e., **MAXINT**) and storm duration (i.e., **AVGDUR**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ee61c4a8-77a9-4e1a-ad62-7d33408d492f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6.70551677 1.84085664 9.93770232]\n",
      "[239.73002819 558.8315374   57.29891514]\n"
     ]
    }
   ],
   "source": [
    "MAX_I = MAXINT[BAND].ppf(IntDur[:, 0])\n",
    "DUR_S = AVGDUR[BAND].ppf(IntDur[:, 1])\n",
    "\n",
    "# how do they look like?\n",
    "# storm intensity in mm/h\n",
    "print(MAX_I)\n",
    "# storm duration in minutes\n",
    "print(DUR_S)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "751224b4-3714-45fd-a4a7-f7aa416aef86",
   "metadata": {},
   "source": [
    "Then, **n_s**-samples of storm radius are generated, and truncated to the [MINRADIUS](#pars) limit, so there can't be storms smaller than the resolution of the model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a412f394-d208-4610-b774-0372ed6d5d98",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[13.11900959  8.92419382  5.01969002]\n"
     ]
    }
   ],
   "source": [
    "# set the limits\n",
    "lim_ts = [1 * MINRADIUS, None]\n",
    "\n",
    "# this transform the limits into the probability domain/space\n",
    "LIMITS = [RADIUS[BAND].cdf(x) if x else None for x in lim_ts]\n",
    "\n",
    "# if None in LIMITS\n",
    "# https://stackoverflow.com/a/50049044/5885810 -> (None to NaN to Zero)\n",
    "LIMITS = (\n",
    "    np.nan_to_num(np.array(LIMITS, dtype=\"f8\")) + np.r_[0, 1]\n",
    "    if None in LIMITS\n",
    "    else LIMITS\n",
    ")\n",
    "\n",
    "# for reproducibility\n",
    "npr.seed(894765)\n",
    "# sample the radii\n",
    "RADII = RADIUS[BAND].ppf(npr.uniform(LIMITS[0], LIMITS[-1], size=n_s))\n",
    "\n",
    "# how do they look like?\n",
    "# storm radius in km\n",
    "print(RADII)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eba2f79d-6a9c-4374-9ef6-4660d95967e3",
   "metadata": {},
   "source": [
    "<span style=\"color:HotPink\">For this particular exercise, we increase the size of the radii, so the storms will be visible in such a vast area as it is the HAD.</span>\n",
    "<a id='radii'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "85025988-a254-4f52-afdd-e63d2ac40b54",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72.15455275 49.08306603 27.60829509]\n"
     ]
    }
   ],
   "source": [
    "RADII = RADII * 5.5\n",
    "\n",
    "print(RADII)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bdb888ce-c6ab-4c89-b906-3996de9554b8",
   "metadata": {},
   "source": [
    "Lastly, sampling of the exponential decay [**BETPAR**](#pdfs) (i.e. $\\beta$ in [Equation (1)](#eq)).\\\n",
    "<span style=\"color:RoyalBlue\">$\\beta$-parameters close to zero (very small) imply \"almost no-decrease\" of the maximum storm intensity the further you move from the centre of the storm (as a function of $r$).</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "95d141ce-e3b6-49b1-8ad7-73b1fc41e9bf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.02841443 0.20982082 0.10673361]\n"
     ]
    }
   ],
   "source": [
    "# set the seed\n",
    "npr.seed(233)\n",
    "# sample the betas\n",
    "BETAS = BETPAR[BAND].rvs(size=n_s)\n",
    "\n",
    "# how do they look like?\n",
    "print(BETAS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12f09ea6-238d-486e-bd9c-c141d7d3d6c6",
   "metadata": {},
   "source": [
    "### 2. DEFINE REGION ###\n",
    "\n",
    "Read the shapefile acting as the catchment/region over/for which the storms will be generated/simulated.\n",
    "Some buffer is also used here, to account also for some additional space in the underlying grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e59d6a6-1b28-46d7-bf85-f9854da656d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            geometry\n",
      "0  POLYGON ((2543226.299 767453.737, 2545070.786 ...\n"
     ]
    }
   ],
   "source": [
    "# read the SHP\n",
    "wtrwgs = gpd.read_file(\"../model_input/HAD_basin.shp\")\n",
    "# re-project it to a local CRS\n",
    "wtrshd = wtrwgs.to_crs(crs=WKT_OGC)  # //epsg.io/42106.wkt\n",
    "# convert it into Pandas\n",
    "CATCHM = gpd.GeoDataFrame(geometry=wtrshd.geometry)  # .to_crs(epsg=4326)\n",
    "# the buffer\n",
    "BUFFRX = gpd.GeoDataFrame(geometry=wtrshd.buffer(BUFFER))  # .to_crs(epsg=4326)\n",
    "\n",
    "# how does the catchment look like?\n",
    "print(CATCHM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da7ebd61-9c53-432c-874a-3caa48c53d5c",
   "metadata": {},
   "source": [
    "Set up the (underlying) spatial grid (up to the desired buffer --with regard to the established resolution--)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7ec695a9-6de9-4acb-9b65-75f20c263f1b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1342500. 1347500. 1352500. 1357500. 1362500. 1367500. 1372500.]\n",
      "[-1147500. -1152500. -1157500. -1162500. -1167500. -1172500. -1177500.]\n"
     ]
    }
   ],
   "source": [
    "# infering (and rounding) the limits of the buffer-zone\n",
    "llim = np.floor(BUFFRX.bounds.minx[0] / X_RES) * X_RES  # +X_RES/2\n",
    "rlim = np.ceil(BUFFRX.bounds.maxx[0] / X_RES) * X_RES  # -X_RES/2\n",
    "blim = np.floor(BUFFRX.bounds.miny[0] / Y_RES) * Y_RES  # +Y_RES/2\n",
    "tlim = np.ceil(BUFFRX.bounds.maxy[0] / Y_RES) * Y_RES  # -Y_RES/2\n",
    "\n",
    "# DEFINE THE COORDINATES OF THE X-Y AXES\n",
    "XS, YS = list(\n",
    "    map(\n",
    "        lambda a, b, c: np.arange(a + c / 2, b + c / 2, c),\n",
    "        [llim, blim],\n",
    "        [rlim, tlim],\n",
    "        [X_RES, Y_RES],\n",
    "    )\n",
    ")\n",
    "\n",
    "# flip YS\n",
    "YS = np.flipud(YS)  # -> important...so rasters are compatible with numpys\n",
    "\n",
    "# how do they look like?\n",
    "print(XS[:7])\n",
    "print(YS[-7:])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "031cbbc1-52db-437f-ad3d-c061d6f853ae",
   "metadata": {},
   "source": [
    "### 3. SAMPLE STORM CENTRES ###\n",
    "\n",
    "<span style=\"color:RoyalBlue\">(Check out notebook [two_](./two_.ipynb))</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e39cde3a-5fd8-4905-9694-1b9eaa7ec5d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2505448.49327476  841520.4188831 ]\n",
      " [2278457.56241648  703196.79461003]\n",
      " [2082039.98979901 -825624.9487317 ]]\n"
     ]
    }
   ],
   "source": [
    "# set the seed (for this notebook's sake)\n",
    "npr.seed(546846)\n",
    "CENTS = pran.poisson(CATCHM.geometry.xs(0), size=n_s)\n",
    "\n",
    "# what do they look like?\n",
    "print(CENTS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67b6127b-2eb7-4d07-ae41-ea1e55c5be2d",
   "metadata": {},
   "source": [
    "### 4. COMPUTE $I(r)$ ###\n",
    "\n",
    "Let's compute rainfall intensities for several distances beyond the storm center.\n",
    "The regular spacing between such distances was previously set up in the [**RINGS_DIS**](#pars) variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "93cfacbb-62bf-4916-a4dc-f4e8c9ddbc63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10.1\n"
     ]
    }
   ],
   "source": [
    "# distances separation (in km)\n",
    "print(RINGS_DIS)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f82be94a-5507-4818-b69f-d3d48bea0d87",
   "metadata": {},
   "source": [
    "For every sampled center, several distances (spaced every **RINGS_DIS**-km) are estimated between storm center and storm radius.\\\n",
    "<span style=\"color:RoyalBlue\">Note that the distances increase from *almost* the storm center to storm (maximum) radius (see variable [**RADII**](#radii) above).</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b3c39127-1c16-4e29-b8de-b10a0a7cef38",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([72.15455275, 62.05455275, 51.95455275, 41.85455275, 31.75455275,\n",
       "        21.65455275, 11.55455275,  1.45455275,  0.15      ]),\n",
       " array([49.08306603, 38.98306603, 28.88306603, 18.78306603,  8.68306603,\n",
       "         0.15      ]),\n",
       " array([27.60829509, 17.50829509,  7.40829509,  0.15      ])]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_radii = list(\n",
    "    map(lambda r: np.r_[np.arange(r, CLOSE_DIS, -RINGS_DIS), CLOSE_DIS], RADII)\n",
    ")\n",
    "\n",
    "# what do those distances look like?\n",
    "all_radii"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cba5e8b-84ce-46ef-8863-826e491e6e04",
   "metadata": {},
   "source": [
    "Then, rain is computed using (via FORMUKA 1) for the previous several distances.\\\n",
    "<span style=\"color:RoyalBlue\">Note now that rainfall intensity decreases from *almost* the storm center to storm (maximum) radius (consistent with what [Equation (1)](#eq) proposes).</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7a021b84-a38a-4448-8fe9-bf2c762e5813",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[0.005983238754889605,\n",
       "  0.053398824773079785,\n",
       "  0.34280853641774955,\n",
       "  1.5830554240756662,\n",
       "  5.2585399246679545,\n",
       "  12.564892990989849,\n",
       "  21.59618187118039,\n",
       "  26.7005201184977,\n",
       "  26.790922020930523],\n",
       " [1.2874081948229739e-91,\n",
       "  1.3257668677168718e-57,\n",
       "  2.1558006804063954e-31,\n",
       "  5.53529280643682e-13,\n",
       "  0.022442082000436195,\n",
       "  17.111545600057266],\n",
       " [2.72344971628483e-07,\n",
       "  0.008791395955391681,\n",
       "  2.7177805325374034,\n",
       "  9.485462130217563]]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_rain = list(\n",
    "    map(\n",
    "        lambda i, d, b, r: list(\n",
    "            map(\n",
    "                lambda r:\n",
    "                # # model: FORCE_BRUTE -> a * np.exp(-2 * b * x**2)\n",
    "                # i * d * 1 / 60 * np.exp(-2 * b * r**2),\n",
    "                # model: BRUTE_FORCE -> a * np.exp(-2 * b**2 * x**2)\n",
    "                i * d * 1 / 60 * np.exp(-2 * b**2 * r**2),\n",
    "                r,\n",
    "            )\n",
    "        ),\n",
    "        MAX_I,\n",
    "        DUR_S,\n",
    "        BETAS,\n",
    "        all_radii,\n",
    "    )\n",
    ")\n",
    "\n",
    "# what does rainfall intensity look like?\n",
    "all_rain"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e6d1343-af5b-43df-a3bd-fa702c2641b4",
   "metadata": {},
   "source": [
    "### 5. RINGS OF RAINFALL ###\n",
    "\n",
    "Knowing how rainfall (intensity) behaves the further it is from the storm center, circular polygons can now be created.\n",
    "The idea is to later rasterize such polygons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7d4ae946-c733-4d7c-b01a-87e58f2d7682",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[        rain                                           geometry\n",
       " 0   0.005983  LINESTRING (2577603.046 841520.419, 2577295.24...\n",
       " 0   0.053399  LINESTRING (2567503.046 841520.419, 2567163.10...\n",
       " 0   0.342809  LINESTRING (2557403.046 841520.419, 2557024.23...\n",
       " 0   1.583055  LINESTRING (2547303.046 841520.419, 2546877.02...\n",
       " 0   5.258540  LINESTRING (2537203.046 841520.419, 2536720.62...\n",
       " 0  12.564893  LINESTRING (2527103.046 841520.419, 2526560.12...\n",
       " 0  21.596182  LINESTRING (2517003.046 841520.419, 2516437.52...\n",
       " 0  26.700520  LINESTRING (2506903.046 841520.419, 2506708.17...\n",
       " 0  26.790922  LINESTRING (2505598.493 841520.419, 2505578.39...,\n",
       "            rain                                           geometry\n",
       " 0  1.287408e-91  LINESTRING (2327540.628 703196.795, 2327120.71...\n",
       " 0  1.325767e-57  LINESTRING (2317440.628 703196.795, 2316960.68...\n",
       " 0  2.155801e-31  LINESTRING (2307340.628 703196.795, 2306785.64...\n",
       " 0  5.535293e-13  LINESTRING (2297240.628 703196.795, 2296600.61...\n",
       " 0  2.244208e-02  LINESTRING (2287140.628 703196.795, 2286479.66...\n",
       " 0  1.711155e+01  LINESTRING (2278607.562 703196.795, 2278587.46...,\n",
       "            rain                                           geometry\n",
       " 0  2.723450e-07  LINESTRING (2109648.285 -825624.949, 2109117.7...\n",
       " 0  8.791396e-03  LINESTRING (2099548.285 -825624.949, 2098951.7...\n",
       " 0  2.717781e+00  LINESTRING (2089448.285 -825624.949, 2088884.3...\n",
       " 0  9.485462e+00  LINESTRING (2082189.990 -825624.949, 2082169.8...]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# BUFFER_STRINGS\n",
    "# https://www.knowledgehut.com/blog/programming/python-map-list-comprehension\n",
    "# https://stackoverflow.com/a/30061049/5885810  (map nest)\n",
    "# r,p are lists (at first instance), and the numbers/atoms (in the second lambda)\n",
    "# .boundary gives the LINESTRING element\n",
    "# *1e3 to go from km to m\n",
    "# np.ceil(r /MINRADIUS) +2 ) is an artifact to lower the resolution of small circles\n",
    "# ...a lower resolution in such circles increases the script.speed in the rasterisation process.\n",
    "RINGS = list(\n",
    "    map(\n",
    "        lambda c, r, p: pd.concat(\n",
    "            list(\n",
    "                map(\n",
    "                    lambda r, p: gpd.GeoDataFrame(\n",
    "                        {\n",
    "                            \"rain\": p,\n",
    "                            \"geometry\": gpd.points_from_xy(x=[c[0]], y=[c[1]])\n",
    "                            # or maybe... \"+1\"??\n",
    "                            .buffer(\n",
    "                                r * 1e3, resolution=np.ceil(r / MINRADIUS) + 2\n",
    "                            ).boundary,\n",
    "                        },\n",
    "                        crs=WKT_OGC,\n",
    "                    ),\n",
    "                    r,\n",
    "                    p,\n",
    "                )\n",
    "            )\n",
    "        ),\n",
    "        CENTS,\n",
    "        all_radii,\n",
    "        all_rain,\n",
    "    )\n",
    ")\n",
    "\n",
    "# what does a ring of rainfall look like?\n",
    "RINGS"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "29f0b7cf-f802-461e-a69c-fe307773bfbf",
   "metadata": {},
   "source": [
    "### 6. ONE STORM RASTERIZATION ###\n",
    "\n",
    "We rasterize all storms, but we visualize only one case.\n",
    "<a id='fall'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b95dbd48-02ff-46eb-91e7-dfbee537c43c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manuel\\AppData\\Local\\Temp\\ipykernel_41720\\590111119.py:8: UserWarning: GeoDataFrame's CRS is not representable in URN OGC format. Resulting JSON will contain no CRS information.\n",
      "  ALL_RINGS.to_json(),\n",
      "C:\\Users\\manuel\\AppData\\Local\\Temp\\ipykernel_41720\\590111119.py:8: UserWarning: GeoDataFrame's CRS is not representable in URN OGC format. Resulting JSON will contain no CRS information.\n",
      "  ALL_RINGS.to_json(),\n",
      "C:\\Users\\manuel\\AppData\\Local\\Temp\\ipykernel_41720\\590111119.py:8: UserWarning: GeoDataFrame's CRS is not representable in URN OGC format. Resulting JSON will contain no CRS information.\n",
      "  ALL_RINGS.to_json(),\n"
     ]
    }
   ],
   "source": [
    "# where the rasters are gonna be stored\n",
    "fall = []\n",
    "\n",
    "for ALL_RINGS in RINGS:\n",
    "    # burn the ALL_RINGS inside a storm\n",
    "    tmp = gdal.Rasterize(\n",
    "        \"\",\n",
    "        ALL_RINGS.to_json(),\n",
    "        xRes=X_RES,\n",
    "        yRes=Y_RES,\n",
    "        allTouched=True,\n",
    "        attribute=\"rain\",\n",
    "        noData=0,\n",
    "        outputType=gdal.GDT_Float64,\n",
    "        targetAlignedPixels=True,\n",
    "        outputBounds=[llim, blim, rlim, tlim],\n",
    "        outputSRS=pp.CRS.from_wkt(WKT_OGC).to_proj4(),\n",
    "        format=\"MEM\",\n",
    "        # width=int(abs(rlim - llim) / X_RES),\n",
    "        # height=int(abs(tlim - blim) / X_RES),\n",
    "    )\n",
    "    fall.append(tmp.ReadAsArray())\n",
    "    tmp = None"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "944ed9fa-0377-4a39-bf5c-8f8667c2e49d",
   "metadata": {},
   "source": [
    "<span style=\"color:MediumSeaGreen\">*Change some output parameters from Numpy.*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a90bde02-896f-4971-b10e-65a03c973c13",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.set_printoptions(suppress=True)\n",
    "np.set_printoptions(edgeitems=20, linewidth=2000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1418a748-957b-4670-8cef-1c9f132c9d88",
   "metadata": {},
   "source": [
    "Now let's check if those rainfall rings where rasterized OK."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0af10b28-7724-4480-a66f-385da752c2af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.     0.006  0.006  0.006  0.006  0.006  0.006  0.006]\n",
      " [ 0.     0.     0.     0.     0.     0.     0.     0.     0.     0.006  0.006  0.006  0.     0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.     0.     0.     0.     0.     0.006  0.006  0.006  0.     0.053  0.053  0.053  0.053  0.053  0.053  0.053]\n",
      " [ 0.     0.     0.     0.     0.     0.     0.006  0.006  0.     0.053  0.053  0.053  0.     0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.     0.     0.     0.006  0.006  0.     0.053  0.053  0.     0.     0.343  0.343  0.343  0.343  0.343  0.343]\n",
      " [ 0.     0.     0.     0.     0.006  0.006  0.     0.053  0.053  0.     0.343  0.343  0.343  0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.     0.006  0.006  0.     0.053  0.053  0.     0.343  0.343  0.     1.583  1.583  1.583  1.583  1.583  1.583]\n",
      " [ 0.     0.     0.     0.006  0.     0.053  0.053  0.     0.343  0.343  0.     1.583  1.583  0.     0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.006  0.006  0.     0.053  0.     0.343  0.343  1.583  1.583  1.583  0.     5.259  5.259  5.259  5.259  5.259]\n",
      " [ 0.     0.     0.006  0.     0.053  0.053  0.343  0.343  0.     1.583  0.     5.259  5.259  5.259  0.     0.     0.     0.   ]\n",
      " [ 0.     0.     0.006  0.     0.053  0.     0.343  0.     1.583  1.583  5.259  5.259  0.    12.565 12.565 12.565 12.565 12.565]\n",
      " [ 0.     0.006  0.006  0.053  0.053  0.     0.343  0.     1.583  0.     5.259  0.    12.565 12.565  0.     0.     0.     0.   ]\n",
      " [ 0.     0.006  0.     0.053  0.     0.343  0.343  1.583  1.583  5.259  5.259  0.    12.565  0.    21.596 21.596 21.596 21.596]]\n"
     ]
    }
   ],
   "source": [
    "posx = 0  # 0, 1 or 2 -> because we're only having n_s==3\n",
    "\n",
    "print(fall[posx][50:64, 217:235].round(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3467b126-177c-44d6-8fc6-73b58b87bed7",
   "metadata": {},
   "source": [
    "It looks quite OK and slick, right?.\n",
    "You might be wondering now what are those zeroes inside the storm.\n",
    "To speed things up, rings of rainfall are generated not that close, and the gaps in between are later filled by interpolation.\n",
    "\n",
    "<span style=\"color:MediumSeaGreen\">*Restore Numpy's output parameters back!.*</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6ffbd326-2521-4e6c-85b2-e1f88d77b098",
   "metadata": {},
   "outputs": [],
   "source": [
    "# restore defaults?\n",
    "# https://ahaldane.github.io/reference/generated/numpy.set_printoptions.html\n",
    "np.set_printoptions(edgeitems=3, linewidth=75)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edd08dd8-ff3f-4257-a226-210bd22f2537",
   "metadata": {},
   "source": [
    "What if instead of rainfall rings all i want is the mask of the storm (or any other shape for that matter)?."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "62fdf2fe-167c-43cb-9f6d-78a25933a6f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f60963d940>"
      ]
     },
     "execution_count": 21,
     "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": [
    "# where the rasters are gonna be stored\n",
    "mask = []\n",
    "\n",
    "for ALL_RINGS in RINGS:\n",
    "    # burn the mask\n",
    "    # convert LINESTRING to POLYGON (in shapely). \".iloc[0]\" for the largest/outter RADII\n",
    "    # https://stackoverflow.com/a/2975194/5885810\n",
    "    OUTER_RING = (ALL_RINGS.geometry.iloc[0]).convex_hull\n",
    "\n",
    "    # create a GEOPANDAS from a SHAPELY so you can JSON.it\n",
    "    # https://stackoverflow.com/a/51520122/5885810\n",
    "    tmp = gdal.Rasterize(\n",
    "        \"\",\n",
    "        gpd.GeoSeries([OUTER_RING]).to_json(),\n",
    "        xRes=X_RES,\n",
    "        yRes=Y_RES,\n",
    "        allTouched=True,\n",
    "        burnValues=1,\n",
    "        noData=0,\n",
    "        outputType=gdal.GDT_Int16,\n",
    "        targetAlignedPixels=True,\n",
    "        outputBounds=[llim, blim, rlim, tlim],\n",
    "        outputSRS=pp.CRS.from_wkt(WKT_OGC).to_proj4(),\n",
    "        format=\"MEM\",\n",
    "    )\n",
    "    mask.append(tmp.ReadAsArray())\n",
    "    tmp = None\n",
    "\n",
    "plt.imshow(\n",
    "    mask[posx][50:82, 217:249],\n",
    "    origin=\"upper\",\n",
    "    vmin=0.0,\n",
    "    # cmap=\"MPL_Dark2_r\",\n",
    "    cmap=\"nipy_spectral_r\",\n",
    "    # interpolation='nearest',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5cfccda8-f981-4da8-9f3e-b11768f8555b",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### *INTERMEZZO: EXPORT CATCHMENT AS NUMPY* ###\n",
    "<span style=\"color:MediumSeaGreen\">*(while we're at it... it will be used later in notebook [for_](./for_.ipynb))*</span>\n",
    "<a id='mask'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "52b85a3c-23c9-449c-b5b7-d3284a44ab8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manuel\\AppData\\Local\\Temp\\ipykernel_41720\\3398665059.py:12: UserWarning: GeoDataFrame's CRS is not representable in URN OGC format. Resulting JSON will contain no CRS information.\n",
      "  wtrshd.to_json(),\n"
     ]
    }
   ],
   "source": [
    "# read the shape\n",
    "# catchment shape-file in WGS84\n",
    "SHP_FILE = \"../model_input/HAD_basin.shp\"\n",
    "wtrwgs = gpd.read_file(SHP_FILE)\n",
    "\n",
    "# re-project it\n",
    "wtrshd = wtrwgs.to_crs(crs=WKT_OGC)  # //epsg.io/42106.wkt\n",
    "\n",
    "# rasterize it\n",
    "tmp = gdal.Rasterize(\n",
    "    \"\",\n",
    "    wtrshd.to_json(),\n",
    "    format=\"MEM\",\n",
    "    add=0,\n",
    "    xRes=X_RES,\n",
    "    yRes=Y_RES,\n",
    "    noData=0,\n",
    "    burnValues=1,\n",
    "    allTouched=True,\n",
    "    outputType=gdal.GDT_Int16,\n",
    "    outputBounds=[llim, blim, rlim, tlim],\n",
    "    # targetAlignedPixels=False, # (check: https://gdal.org/programs/gdal_rasterize.html#cmdoption-gdal_rasterize-tap)\n",
    "    targetAlignedPixels=True,\n",
    "    outputSRS=pp.CRS.from_wkt(WKT_OGC).to_proj4(),\n",
    "    # width=(abs(rlim - llim) / X_RES).astype(\"u2\"),\n",
    "    # height=(abs(tlim - blim) / X_RES).astype(\"u2\"),\n",
    ")\n",
    "CATCHMENT_MASK = tmp.ReadAsArray().astype(\"u1\")\n",
    "# flush it\n",
    "tmp = None\n",
    "\n",
    "# the xporting happens here\n",
    "np.save(\n",
    "    \"tre_catchment-mask\",\n",
    "    CATCHMENT_MASK.astype(\"u1\"),\n",
    "    allow_pickle=True,\n",
    "    fix_imports=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7bcdc18e-75c4-4b49-9d7d-c387441908ea",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "### VISUALIZATION ###\n",
    "\n",
    "Check this awesome [link](https://github.com/hhuangwx/cmaps/blob/master/examples/colormaps.png)... and then load some cool color palettes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d5b7204-aed0-4f19-8334-18236c983a50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>Carbone42</strong> </div><div class=\"cmap\"><img alt=\"Carbone42 colormap\" title=\"Carbone42\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAgAAAABACAYAAABsv8+/AAAAGHRFWHRUaXRsZQBDYXJib25lNDIgY29sb3JtYXCn6qzEAAAAHnRFWHREZXNjcmlwdGlvbgBDYXJib25lNDIgY29sb3JtYXDlcUpPAAAAMHRFWHRBdXRob3IATWF0cGxvdGxpYiB2My43LjIsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcfQk4eAAAAMnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHYzLjcuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZzHk0TkAAAH5SURBVHic7dYxalRhGIXhTyVmxMHMgKMjmICFtY2l+3IzNlFQLN2AjbgBQWtLwUCITmERGddwAmk8z1Ofe+8PF37eGy8P3uwndP78Z7T/9PV2tF/Nw3C/zfabJ9n+xaNoPzOzeJbtvy1eRfsf55+j/Zez19F+v4zmM+l+Zo6WR9H+eHl8vfu72f5keZK9PzzPVb6xPlxH+8fzPdrf+vM+2s/uXbg/yPYzM7s70fzv7jDa/9otrnV/Ee6v8o38TDfD90fz+X2ZXRhPL+9H+/2H02g/M7PdZJf2dvUg2q9Xm3Afzefex7fZAzOT/WUA4L8gAACgkAAAgEICAAAKCQAAKCQAAKCQAACAQgIAAAoJAAAoJAAAoJAAAIBCAgAACgkAACgkAACgkAAAgEICAAAKCQAAKCQAAKCQAACAQgIAAAoJAAAoJAAAoJAAAIBCAgAACgkAACgkAACgkAAAgEICAAAKCQAAKCQAAKCQAACAQgIAAAoJAAAoJAAAoJAAAIBCAgAACgkAACgkAACgkAAAgEICAAAKCQAAKCQAAKCQAACAQgIAAAoJAAAoJAAAoJAAAIBCAgAACgkAACgkAACgkAAAgEICAAAKCQAAKCQAAKCQAACAQgIAAAoJAAAoJAAAoJAAAIBCAgAACgkAACgkAACgkAAAgEL/AEVpLoEOikSjAAAAAElFTkSuQmCC\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#7705a3ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #7705a3ff;\"></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=\"#ff0300ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #ff0300ff;\"></div></div>"
      ],
      "text/plain": [
       "<cmaps.colormap.Colormap at 0x1f6084cc580>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmaps.Carbone42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c06ad754-37a3-4dba-8eff-a0749ea50897",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>MPL_Dark2_r</strong> </div><div class=\"cmap\"><img alt=\"MPL_Dark2_r colormap\" title=\"MPL_Dark2_r\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></div><div style=\"vertical-align: middle; max-width: 514px; display: flex; justify-content: space-between;\"><div style=\"float: left;\"><div title=\"#666666ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #666666ff;\"></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=\"#209c74ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #209c74ff;\"></div></div>"
      ],
      "text/plain": [
       "<cmaps.colormap.Colormap at 0x1f609745be0>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# and/or\n",
    "cmaps.MPL_Dark2_r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78348f2b-9ffc-4ba1-99da-6decee4af42d",
   "metadata": {},
   "source": [
    "Now, let's put some color to that [**fall**](#fall) Numpy, i.e., let's see how those rings of rainfall look like. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "79a5ea6d-5cbf-43f9-8501-116ac603d4b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f60973d520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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IFQAAwAhCBQAAMIJQAQAAjCBUAAAAIwgVAADACEIFAAAwgi6lgBe6+d3t9gb222F/p3a7jfpSZ1Q3agX8iJErFceOHdPatWs1b948DRs2TAEBAerfv3+n2+fl5SkgIKDTfz//+c9NlAUAADzIyJWK1atX64033uj2uJSUFMXFxbVbPnbsWBNlAQAADzISKiZOnKjk5GSNGzdO48aN05AhQ65r3JIlS5SZmWmiBAAA4DIjoSInJ8fEywAAAB/G0x8AAMAIV5/+KCws1PHjx9XY2Kjhw4dr9uzZ3E8BAICPcjVUbNu2rc3Xubm5mj9/vrZs2aLw8PDrfp3ExMQOl5eXlys2NtZRjQAA4Pq48uePuLg45efnq6ysTHV1dfrss8+0fft2DRs2TK+99pp+8IMfuFEWAABwwJUrFQsXLmzzdVhYmB544AHddddduv3227V7924VFRVp0qRJ1/V6ZWVlHS7v7AoGAAAwz6tu1IyJiVFWVpYkaf/+/S5XAwAAusOrQoUkxcfHS5IuXrzociUAAKA7vC5UVFdXS1K3btQEAADu86pQYVmWdu3aJYmP6gYAwNd4/EbNS5cuae/evbrvvvsUHBzcuryurk6PP/64SkpKNGTIEGVkZHi6NMC/2e3gabdjqBN0GwW8kpFQ8eabb2r16tVtljU3N2vChAmtX+fm5mrOnDmqq6vTokWL9JOf/ESjRo3SiBEjVFNTo9LSUl2+fFmRkZHauXOnQkNDTZQGAAA8xEioqKqqUklJSZtllmW1WVZVVSVJioqKUk5Ojo4cOaKPP/5Yx48fV9++fXXrrbcqMzNTjz76qIYNG2aiLAAA4EFGQkVmZuZ1dxsdMGCA1q5da2K3AADAi3jVjZoAAMB3ESoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABghMdbnwNOPPX9NFvjkh3sc9+No22NG3VLou19fjr+/9kad7OTNuR224n7UhtyB8fH7s/ECbtzD3ALVyoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABgBKECAAAYQagAAABGECoAAIARdCntAQ/tf8jWuKdi7e+z5uMH7A+26f2NCbbG5bx61vY+7XYpTf/8uO192u0UefO7223v025HTCedNB11OLXDSXdTm7W60WnUyTw4ZXPuOZnvdt9jTtg9l8D7cKUCAAAYQagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABgBKECAAAYQagAAABG0Pq8E5tmbbI91m7r88i4Hbb3abf1efIj9tuQ0664a3Zbpkv2W1c7ae3t8bbgY+zNWUmSD7UwdzIPfEnOq3+xPdbuucTJ+cvuOdMJu78bfAlXKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABgBKECAAAYQagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBF0KYXHOetuOtTWqKe+n2Z7jzk2O4Y66U5pd6zd7qZOeLy7qex3DHXCjW6jdn+eTuY7uuakm7TK7XUpddI129McX6loaGjQ7t27tXjxYiUlJSkiIkJhYWFKTk7WqlWrVFdX1+nYrVu3avz48QoPD9egQYN09913q6ioyGlJAADABY5DxY4dO5SRkaEXX3xRV69eVXp6ulJTU/XJJ59o5cqVGjdunD7//PN247Kzs7Vo0SJ9+OGHmj59usaPH6+CggJNmTJFu3btcloWAADwMMehIigoSEuXLtXZs2f14Ycf6ve//7327dunM2fO6I477tDp06e1fPnyNmMKCwu1fv16RUVF6f3339fu3bu1b98+HTp0SH379lVWVpaqq6udlgYAADzIcaj44Q9/qOeff17x8fFtlsfExOi5556TJL3++utqbm5uXbdu3TpJ0ooVK9qMmzhxoh5++GF98cUXevHFF52WBgAAPKhHn/5ITk6WJDU1Neny5cuSpMbGRh04cECStGDBgnZjWpbt2bOnJ0sDAACG9WioOHfunCQpMDBQgwYNkiSdPn1aTU1NGjx4sIYPH95uzJgxYyRJJ06c6MnSAACAYT36SOmGDRskSenp6QoODpYkVVRUSFKHgUKSwsLCFBkZqerqatXW1mrAgAHX3E9iYmKHy8vLyxUbG2undAAA0E09dqVi79692rx5swIDA7V69erW5S2PmIaGhnY6NiwsrM22AADA+/XIlYpTp05p4cKFsixLTz/9dOu9FZJkWZYkKSAgoNPxLdtcr7Kysg6Xd3YFAwAAmGf8SkVlZaXS09NVXV2t7OxsLVu2rM36lj9n1NfXd/oaDQ0NkqTw8HDT5QEAgB5iNFRcunRJM2bMUEVFhbKyspSfn99umxEjRkj6Jnx0pL6+XjU1NYqMjLyu+ykAAIB3MBYqamtrNXv2bJ0+fVrz5s3TCy+80OGfOEaOHKng4GBVVVV1GCxKS0slSUlJSaZKAwAAHmAkVDQ1Nemee+7R0aNHNWvWLL3yyivq27dvh9uGhIRo6tSpkqSdO3e2W9+ybO7cuSZKAwAAHuI4VHz99de6//779fbbbys1NVWvv/66goKCuhyTnZ0tSVqzZo0++uij1uXFxcXatGmTIiIitHjxYqelAQAAD3L89MfGjRtbG4BFR0frxz/+cYfb5efnKzo6WpI0ffp0LVu2TBs2bNDo0aM1Y8YMNTc3q6CgQFevXtX27dtbPyzLnzy0315bXEl6yubHcdR8/IDtfSY/ctbWOCetz3Ne/YutcU5aQdsda7dlumS/zbYb7bndcMqHvk8n7ejdaGFu9z3m5H1tl5PzF3qG41Dx7cZfXXUXzcvLaw0VkvTss89q9OjR2rhxowoKChQYGKhp06ZpxYoVmjx5stOyAACAhzkOFXl5ecrLy7M1NjMzU5mZmU5LAAAAXqBHe38AAAD/QagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEY57f6C9TbM22RrnpEtpZNwOW+OcdPlzo0Og3U6IOa/a66gq2e8U6ajD5NRIW8Psdph0wo3OqE46f3qaL3UaldzpNmq347GTc5Ddc6aT87Q/4EoFAAAwglABAACMIFQAAAAjCBUAAMAIQgUAADCCUAEAAIwgVAAAACMIFQAAwAhCBQAAMIJQAQAAjCBUAAAAIwgVAADACEIFAAAwglABAACMCLAsy3K7iJ6SmJgoSSorK3O5kuvjRktdu23aJftth+22HJZ8qy2zE260y/Y0J+25/eH4SPaPka+9T5y0MLcrp3yAx/fp5HzrKU5/b3KlAgAAGEGoAAAARhAqAACAEYQKAABgBKECAAAYQagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGX0l7CjQ6nT8XWenyfdjucutG10Qk3OqPa5UsdQ510RrXLX+aeG51GnXQ8tnvO9IVOo07QpRQAAHgFQgUAADCCUAEAAIwgVAAAACMIFQAAwAhCBQAAMIJQAQAAjCBUAAAAIwgVAADACEIFAAAwglABAACMIFQAAAAjCBUAAMAIQgUAADDCcevzhoYGvfXWW9qzZ4/+9re/6fz58/r6668VFxen+fPnKzs7W+Hh4W3G5OXl6Ze//GWnr5mTk6O1a9c6KUuSf7U+t8tfWqY74S/t1u1yo007x7ZrbrQhd8Lue8yN8xetz7vWz2kBO3bs0I9+9KPWYtLT0/Xll1+qqKhIK1eu1CuvvKKDBw/qxhtvbDc2JSVFcXFx7ZaPHTvWaVkAAMDDHIeKoKAgLV26VI8++qji4+Nbl1+8eFFz5szRe++9p+XLl2vHjvZJdMmSJcrMzHRaAgAA8AKO76n44Q9/qOeff75NoJCkmJgYPffcc5Kk119/Xc3NzU53BQAAvFiP3qiZnJwsSWpqatLly5d7clcAAMBljv/80ZVz585JkgIDAzVo0KB26wsLC3X8+HE1NjZq+PDhmj17NvdTAADgo3o0VGzYsEGSlJ6eruDg4Hbrt23b1ubr3NxczZ8/X1u2bGn3xAgAAPBuPRYq9u7dq82bNyswMFCrV69usy4uLk75+fmaPXu2br75ZlVXV+vQoUP62c9+ptdee01ff/21du3add37ankE5l+Vl5crNjbW0fcBAACuT4+EilOnTmnhwoWyLEtPP/10670VLRYuXNjm67CwMD3wwAO66667dPvtt2v37t0qKirSpEmTeqI8AADQA4yHisrKSqWnp6u6ulrZ2dlatmzZdY+NiYlRVlaW8vPztX///usOFZ19SEdnVzAAAIB5Rp/+uHTpkmbMmKGKiorWcNBdLY+mXrx40WRpAACghxkLFbW1tZo9e7ZOnz6tefPm6YUXXlBAQEC3X6e6ulqSuFETAAAfYyRUNDU16Z577tHRo0c1a9YsvfLKK+rbt2+3X8eyrNYbNHm0FAAA3+I4VHz99de6//779fbbbys1NVWvv/66goKCOt3+0qVL2rp1q5qamtosr6ur09KlS1VSUqIhQ4YoIyPDaWkAAMCDHHcp3bBhg5YvXy5JysjIUERERIfb5efnKzo6WufPn9ett96qiIgIjRo1SiNGjFBNTY1KS0t1+fJlRUZG6o9//KNSUlKclCWJLqU9zZc6BLrRtdFu50W3+EPnTzc6qjrhL/PWl84lvZ3rXUpb7oGQ1OVnS+Tl5Sk6OlpRUVHKycnRkSNH9PHHH+v48ePq27evbr31VmVmZurRRx/VsGHDnJYFAAA8zHGoyMvLU15e3nVvP2DAAK1du9bpbgEAgJfp0YZiAADAfxAqAACAEYQKAABgBKECAAAYQagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGOG49bk3o/W5d/K1NsdutJ/2JW60yuZn0jUnPxM33p920b7cPKe/N7lSAQAAjCBUAAAAIwgVAADACEIFAAAwglABAACMIFQAAAAjCBUAAMAIQgUAADCCUAEAAIwgVAAAACMIFQAAwAhCBQAAMIJQAQAAjKBLKXwKHRR7jj90/nSjo6oTzHd4Gl1KAQCAVyBUAAAAIwgVAADACEIFAAAwglABAACMIFQAAAAjCBUAAMAIQgUAADCCUAEAAIwgVAAAACMIFQAAwAhCBQAAMIJQAQAAjCBUAAAAI2h9DlyDL7WfdoMbLa/5mXSNNuSwi9bnAADAKxAqAACAEYQKAABgBKECAAAYQagAAABGECoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhjrUvrMM8/o8OHD+uCDD/T555+rsbFRQ4YMUVpamn72s5+1dj77V1u3btXGjRt18uRJBQUFacKECVqxYoUmTZrkuCa6lALXzx86f9K9E+ia13Qp/dWvfqU//elPGjRokKZNm6Y5c+aof//+2rp1q8aMGaM//elP7cZkZ2dr0aJF+vDDDzV9+nSNHz9eBQUFmjJlinbt2mWqNAAA4AH9TL3QG2+8obFjx6p///5tlv/P//yPfvzjH2vJkiWqqKhQ3759JUmFhYVav369oqKiVFxcrPj4eElScXGx0tLSlJWVpbS0NN1www2mSgQAAD3I2JWKlJSUdoFCkpYuXaq4uDhduHBBZ86caV2+bt06SdKKFStaA4UkTZw4UQ8//LC++OILvfjii6bKAwAAPcwjN2q2XJ0ICgqSJDU2NurAgQOSpAULFrTbvmXZnj17PFEeAAAwoMdDxdatW3XmzBklJCTotttukySdPn1aTU1NGjx4sIYPH95uzJgxYyRJJ06c6OnyAACAIcbuqWjx9NNPq6ysTPX19Tp16pTKyso0dOhQ7dixQ336fJNhKioqJKnDQCFJYWFhioyMVHV1tWprazVgwADTZQIAAMOMh4r9+/e3/mlDkm666SZt27ZNY8eObV1WV1cnSQoNDe30dcLCwlRTU6O6urprhorOHlctLy9XbGxsd8oHAAA2Gf/zx5///GdZlqXq6modOnRII0eOVFpamp588snWbVo+GiMgIKDT1zH08RkAAMBDjF+paBEZGanU1FTt3btXEydOVG5urmbOnKlx48a1Xnmor6/vdHxDQ4MkKTw8/Jr76uxDOjq7ggEAAMzr8Rs1AwMDdd9998myrNanOUaMGCFJqqys7HBMfX29ampqFBkZyf0UAAD4CI88UhodHS1JqqqqkiSNHDlSwcHBqqqq6jBYlJaWSpKSkpI8UR4AADDAI6Hi4MGDktR602RISIimTp0qSdq5c2e77VuWzZ071xPlAQAAA4yEinfeeUe/+93v9NVXX7VZfuXKFf3mN7/Rtm3bFBISovvuu691XXZ2tiRpzZo1+uijj1qXFxcXa9OmTYqIiNDixYtNlAcAADzAyI2a5eXlysrKUnR0tMaOHauoqChdunRJH3zwgS5evKj+/ftry5Ytuummm1rHTJ8+XcuWLdOGDRs0evRozZgxQ83NzSooKNDVq1e1fft2DRo0yER5AADAA4y0Pv/kk0/029/+VgcPHtS5c+d06dIlBQUF6ZZbbtHUqVP105/+VHFxcR2O3bJlizZu3KhTp04pMDCwtfX55MmTnZZF63MAALrB6e9NI6HCWxEqAAC4fk5/b3rkRk0AAND7ESoAAIARhAoAAGAEoQIAABhBqAAAAEYQKgAAgBGECgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABgRK/u/TFgwABduXJFsbGxbpcCAIDXKy8vV2BgoGpra22N79VXKsLCwhQYGNjhuvLycpWXl3u4It/B8bk2jlHXOD5d4/h0jePTtZ46PoGBgQoLC7M9vldfqegKHUy7xvG5No5R1zg+XeP4dI3j0zVvPT69+koFAADwHEIFAAAwglABAACMIFQAAAAjCBUAAMAIv336AwAAmMWVCgAAYAShAgAAGEGoAAAARhAqAACAEYQKAABgBKECAAAYQagAAABG+F2oaGxs1MqVK5WQkKD+/ftr6NChevDBB1VZWel2aa5LS0tTQEBAp//27dvndokecezYMa1du1bz5s3TsGHDFBAQoP79+19z3NatWzV+/HiFh4dr0KBBuvvuu1VUVOSBij2ru8cnLy+vy3n185//3IPV96yGhgbt3r1bixcvVlJSkiIiIhQWFqbk5GStWrVKdXV1nY71h/lj5/j40/xp8cwzz2jevHmKj4/XwIEDFRwcrJtvvlmLFi3qsiupN8whv/rwq8bGRk2bNk1FRUWKiYlRamqqzp8/r3fffVeDBw9WcXGxYmNj3S7TNWlpaTp48KDmz5+v8PDwdusfe+wx3X777S5U5ln33nuv3njjjTbLgoOD1djY2OmY7OxsrV+/XiEhIZo5c6YaGxt14MABWZalP/zhD8rIyOjpsj2mu8cnLy9Pv/zlL5WSkqK4uLh26+fMmaPvfe97PVKrp/32t7/Vj370I0nftKb+j//4D3355ZcqKipSbW2t/v3f/10HDx7UjTfe2Gacv8wfO8fHn+ZPi+joaNXX1yspKUnDhg2T9E2L87NnzyooKEi7d+/W7Nmz24zxmjlk+ZHc3FxLkjVx4kSrtra2dfm6dessSdaUKVNcrM593/nOdyxJ1ieffOJ2Ka5au3at9cQTT1h79uyx/v73v1uSrODg4E63P3DggCXJioqKss6ePdu6vKioyAoKCrIGDhxo/d///Z8nSveI7h6flStXWpKsl156yXNFuuTll1+2li5d2mYeWJZlXbhwwbrjjjssSdb999/fZp0/zR87x8ef5k+Lw4cPW//4xz/aLX/++ectSdbQoUOtr776qnW5N80hvwkVzc3NVmRkpCXJKi0tbbc+KSnJkmQdPXrUheq8A6GiY9f6pXn33Xdbkqz169e3W/fTn/7UkmTl5+f3YIXuIlRcn6KiotZj1dTU1Lrc3+dPi86OD/Onrbi4OEuSVVZW1rrMm+aQ39xTcfjwYdXU1Cg2NlZ33HFHu/ULFiyQJO3Zs8fTpcGHtVxilP45h76NeYUWycnJkqSmpiZdvnxZEvPn2zo6Pmivb9++kqSgoCBJ3jeH+nlkL17g/ffflySNGTOmw/Uty1u282ebN2/W5cuX1adPHyUkJOjee+/ViBEj3C7LK50+fVpNTU0aPHiwhg8f3m59y7w6ceKEp0vzOoWFhTp+/LgaGxs1fPhwzZ49W2PHjnW7LI85d+6cJCkwMFCDBg2SxPz5to6Oz7f5+/yRvrkR88yZM0pISNBtt90myfvmkN+EioqKCknq8KB/e3nLdv5szZo1bb5+/PHHlZubq9zcXJcq8l7XmldhYWGKjIxUdXW1amtrNWDAAE+W51W2bdvW5uvc3FzNnz9fW7Zs6fDG4N5mw4YNkqT09HQFBwdLYv58W0fH59v8cf48/fTTKisrU319vU6dOqWysjINHTpUO3bsUJ8+3/yhwdvmkN/8+aPlUaXQ0NAO14eFhbXZzh9NmTJF27ZtU3l5uRoaGnTmzBk9+eST6tevn5544onWNz3+6VrzSmJuxcXFKT8/X2VlZaqrq9Nnn32m7du3a9iwYXrttdf0gx/8wO0Se9zevXu1efNmBQYGavXq1a3LmT/f6Oz4SP49f/bv36+XX35ZO3fuVFlZmW666Sbt2LGjzRUar5tDHrlzwwssWbLEkmStWLGiw/Vnz561JFkJCQkersz77d+/35JkDRw40GpoaHC7HI9TFzci/u///q8lyZo8eXKn44cOHWpJsi5cuNBTJbqqq+PTlQsXLlhRUVGWJOuvf/1rD1TmHU6ePGndcMMNliTr2WefbbOO+dP18emKv8wfy7Ks6upq69ChQ9b06dMtSdaaNWta13nbHPKbKxUtl3zq6+s7XN/Q0CBJvfYymhMzZ87UnXfeqS+++EJHjhxxuxyvcq15JTG3OhMTE6OsrCxJ3/yPrDeqrKxUenq6qqurlZ2drWXLlrVZ7+/z51rHpyv+MH9aREZGKjU1VXv37tXYsWOVm5urv/3tb5K8bw75TahoudGws0/ObFnODYkdi4+PlyRdvHjR5Uq8y7XmVX19vWpqahQZGdmr/x5uV2+eV5cuXdKMGTNUUVGhrKws5efnt9vGn+fP9Ryfa+nN86cjgYGBuu+++2RZVuvTHN42h/wmVLQ8rlRaWtrh+pblSUlJHqvJl1RXV0vqnf9bcmLkyJEKDg5WVVVVh29q5lXXeuu8qq2t1ezZs3X69GnNmzdPL7zwggICAtpt56/z53qPz7X01vnTlejoaElSVVWVJO+bQ34TKlJSUjRw4ECVl5frvffea7d+586dkqS5c+d6ujSvV1VVpXfeeUdS54/k+quQkBBNnTpV0j/n0LcxrzpnWZZ27dolSb3q0cCmpibdc889Onr0qGbNmqVXXnml9bMF/pU/zp/uHJ+u9Nb5cy0HDx6UpNaWEl43h3r8rg0v8t///d+WJGvSpElWXV1d6/KWj+nu6kaX3q64uNgqLCy0rl692mb5J598YqWkpFiSrO9+97suVecuXeNGxIKCgk4/Ijc4ONiKiIiwLl++7IlSXdHV8amqqrJefvllq7Gxsc3y2tpa66GHHrIkWUOGDLHq6+s9UWqP++qrr6yMjAxLkpWamnpd35c/zZ/uHh9/mz+WZVmHDh2yXn31VevKlSttljc3N1u//vWvrT59+lghISFWRUVF6zpvmkN+11AsLS1NJSUlrQ3FPv30U5WUlCgqKkpHjhzpsGGNP9iyZYuysrIUExOjhIQEDRkyRJWVlTp27JgaGxuVmJiowsLCdo2QeqM333yzzWNtJSUlCggI0Pjx41uX5ebmas6cOa1fL1++XBs2bFBoaKhmzJih5uZmFRQU6OrVq/r973+v+fPne/R76EndOT7nz5/XrbfeqoiICI0aNUojRoxQTU2NSktLdfnyZUVGRuqPf/yjUlJS3PhWjNuwYYOWL18uScrIyFBERESH2+Xn57dexpb8Z/509/j42/yR/nkujo6O1tixYxUVFaVLly7pgw8+0MWLF9W/f3+9/PLL+q//+q8247xmDnkkuniRhoYGKzc314qNjbWCgoKsf/u3f7MWLVrUJvX5o5MnT1pLly61xowZYw0ePNjq16+fNXDgQGvChAnWunXr/OpR0pdeesmS1OW/jvoQvPTSS9bYsWOt0NBQa+DAgdasWbOsd955x/PfQA/rzvH58ssvrZycHOs73/mONWzYMCs4ONgKDQ21EhMTrccee8yqrKx095sxrKVPxbX+ddRfxx/mT3ePj7/NH8uyrHPnzlm/+MUvrJSUFCsmJsYKDAy0wsLCrMTEROsnP/mJ9dFHH3U61hvmkF9dqQAAAD3Hb27UBAAAPYtQAQAAjCBUAAAAIwgVAADACEIFAAAwglABAACMIFQAAAAjCBUAAMAIQgUAADCCUAEAAIwgVAAAACMIFQAAwAhCBQAAMIJQAQAAjCBUAAAAIwgVAADACEIFAAAwglABAACM+P/WDViTvnnJpgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4, 4), dpi=150)\n",
    "# plt.imshow(fall[50:82,217:249], origin='upper', norm='log', vmin=0.005, cmap='MPL_Dark2_r', interpolation=None)\n",
    "plt.imshow(\n",
    "    fall[posx][50:82, 217:249],\n",
    "    origin=\"upper\",\n",
    "    norm=\"log\",\n",
    "    vmin=0.00003,\n",
    "    # cmap=\"MPL_Dark2_r\",\n",
    "    cmap=\"Carbone42\",\n",
    "    interpolation=\"none\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "504c0917-dcc4-4aa4-90bc-ff13b09acd4a",
   "metadata": {},
   "source": [
    "The voids/zeros in [**fall**](#fall) (within the extension of the storm) are the pixels which need now to be filled in.\n",
    "Let's see how (or where) those pixels look like."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "15881f98-8a3d-482e-b2de-332e0f6d1457",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1f609e475e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# re-touching the mask...to do a proper interpolation\n",
    "mask[posx][np.where(fall[posx] != 0)] = 0\n",
    "\n",
    "# plot the voids (inside the storm\n",
    "plt.figure(figsize=(4, 4), dpi=150)\n",
    "plt.imshow(\n",
    "    mask[posx][50:82, 217:249],\n",
    "    origin=\"upper\",\n",
    "    norm=\"log\",\n",
    "    vmin=0.005,\n",
    "    cmap=\"turbo\",\n",
    "    interpolation=\"none\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c357fa-b28c-4dc6-8ce5-b0162c180996",
   "metadata": {},
   "source": [
    "Finally, here is where the interpolation happens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "2985ff20-ae68-4f4c-b802-a5188c78f94c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[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.]]),\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.]]),\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.]])]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(len(fall)):\n",
    "    # note that this is done \"in real time\"\n",
    "    fill.fillnodata(\n",
    "        np.ma.array(fall[i], mask=mask[i]),\n",
    "        mask=None,\n",
    "        max_search_distance=4.0,\n",
    "        smoothing_iterations=2,\n",
    "    )\n",
    "\n",
    "# how does the filling look like?\n",
    "fall"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb92af1e-577e-4644-9884-f62f7dd235da",
   "metadata": {},
   "source": [
    "### VISUALIZATION (VIA XARRAY) ###\n",
    "\n",
    "<span style=\"color:RoyalBlue\">*STORM always computes the storm for the whole region/catchment of analyis.\n",
    "What we've done here is to cherry-pick the area where the storm was being modeled.*</span>\n",
    "<a id='da'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "5db44fe1-9ba0-4273-a401-20396ce7a26a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1f609857be0>"
      ]
     },
     "execution_count": 28,
     "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": [
    "# convert \"fall\" into a xarray\n",
    "da = xr.DataArray(\n",
    "    data=fall,\n",
    "    dims=[\"s\", \"y\", \"x\"],\n",
    "    coords={\"s\": np.r_[0:n_s], \"x\": XS, \"y\": YS},\n",
    ")\n",
    "# pa = xr.DataArray(data=mask, dims=['t','y','x'], coords={'t':np.r_[0:n_s], 'x':XS, 'y':YS},)\n",
    "\n",
    "# find first the \"right\" slices\n",
    "da.isel(\n",
    "    {\n",
    "        \"s\": posx,\n",
    "        \"x\": slice(217, 249),\n",
    "        \"y\": slice(50, 82),\n",
    "    }\n",
    ").plot(cmap=\"nipy_spectral_r\", levels=13, vmin=0.005, vmax=27)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c55b4fc0-df01-404a-9d79-c34af1168612",
   "metadata": {},
   "source": [
    "Import a much nicer color palette, and apply it to the xarray (overlay the rainfall rings, why not)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "42d9421b-99f6-477a-a7aa-eaf77c417f14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>WhiteBlueGreenYellowRed</strong> </div><div class=\"cmap\"><img alt=\"WhiteBlueGreenYellowRed colormap\" title=\"WhiteBlueGreenYellowRed\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></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=\"#921519ff\" style=\"display: inline-block; width: 1em; height: 1em; margin: 0; vertical-align: middle; border: 1px solid #555; background-color: #921519ff;\"></div></div>"
      ],
      "text/plain": [
       "<cmaps.colormap.Colormap at 0x1f60987cc40>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmaps.WhiteBlueGreenYellowRed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fae2463c-626e-49d5-a3b6-1a92e1a4c790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4), dpi=150)\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# find the right slices\n",
    "da.isel(\n",
    "    {\n",
    "        \"s\": posx,\n",
    "        \"x\": slice(217, 249),\n",
    "        \"y\": slice(50, 82),\n",
    "    }\n",
    ").plot(cmap=\"WhiteBlueGreenYellowRed\", levels=17, vmin=0.005, vmax=27, ax=ax)\n",
    "\n",
    "# plotting of rings happens here\n",
    "for rr in all_radii[posx] * 1e3:\n",
    "    circ = Circle(\n",
    "        (CENTS[posx][0], CENTS[posx][1]),\n",
    "        rr,\n",
    "        alpha=1,\n",
    "        facecolor=\"None\",\n",
    "        lw=0.67,\n",
    "        edgecolor=npr.choice(\n",
    "            [\"xkcd:lime green\", \"xkcd:gold\", \"xkcd:electric pink\", \"xkcd:azure\"]\n",
    "        ),\n",
    "    )\n",
    "    ax.add_patch(circ)\n",
    "\n",
    "# # https://stackoverflow.com/a/64035939/5885810  (add vertical lines)\n",
    "# plt.vlines(\n",
    "#     x=np.arange(1920000, 1990000, 5000),\n",
    "#     ymin=-180000,\n",
    "#     ymax=-100000,\n",
    "#     colors=\"xkcd:off white\",\n",
    "#     ls=\"dotted\",\n",
    "#     lw=0.09,\n",
    "# )\n",
    "# plt.hlines(\n",
    "#     y=np.arange(-110000, -180000, -5000),\n",
    "#     xmin=1910000,\n",
    "#     xmax=1990000,\n",
    "#     colors=\"xkcd:off white\",\n",
    "#     ls=\"dotted\",\n",
    "#     lw=0.09,\n",
    "# )\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# # use these for exporting and cleaning [don't forget to comment out \"plt.show()\"!!]\n",
    "# plt.savefig(\n",
    "#     \"tre_.pdf\", bbox_inches=\"tight\", pad_inches=0.02, facecolor=fig.get_facecolor()\n",
    "# )\n",
    "# plt.close()\n",
    "# plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d0bb349-dc59-487b-b5c2-b65ef79901fb",
   "metadata": {},
   "source": [
    "Print the storm instensities and radii again, so you can check their values against the last plot (above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4ce632e5-6fec-48b1-956f-7b2377f2c5e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[72.15455275 62.05455275 51.95455275 41.85455275 31.75455275 21.65455275\n",
      " 11.55455275  1.45455275  0.15      ]\n",
      "        rain                                           geometry\n",
      "0   0.005983  LINESTRING (2577603.046 841520.419, 2577295.24...\n",
      "0   0.053399  LINESTRING (2567503.046 841520.419, 2567163.10...\n",
      "0   0.342809  LINESTRING (2557403.046 841520.419, 2557024.23...\n",
      "0   1.583055  LINESTRING (2547303.046 841520.419, 2546877.02...\n",
      "0   5.258540  LINESTRING (2537203.046 841520.419, 2536720.62...\n",
      "0  12.564893  LINESTRING (2527103.046 841520.419, 2526560.12...\n",
      "0  21.596182  LINESTRING (2517003.046 841520.419, 2516437.52...\n",
      "0  26.700520  LINESTRING (2506903.046 841520.419, 2506708.17...\n",
      "0  26.790922  LINESTRING (2505598.493 841520.419, 2505578.39...\n"
     ]
    }
   ],
   "source": [
    "print(all_radii[0])\n",
    "print(RINGS[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ceace06-c2d8-40f7-a36a-570539c7d598",
   "metadata": {},
   "source": [
    "<span style=\"color:RoyalBlue\">Alternative plot-tweaking.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "90445bbe-702f-443d-8611-6a650ab4f126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/html": [
       "<div style=\"vertical-align: middle;\"><strong>BkBlAqGrYeOrReViWh200_r</strong> </div><div class=\"cmap\"><img alt=\"BkBlAqGrYeOrReViWh200_r colormap\" title=\"BkBlAqGrYeOrReViWh200_r\" style=\"border: 1px solid #555;\" src=\"data:image/png;base64,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\"></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 0x1f609e55df0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cmaps.BkBlAqGrYeOrReViWh200_r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "65510f85-2fcc-43a4-8b38-074eea388239",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 750x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(5, 4), dpi=150)\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# log.scale (find the right slices)\n",
    "da.isel(\n",
    "    {\n",
    "        \"s\": posx,\n",
    "        \"x\": slice(217, 249),\n",
    "        \"y\": slice(50, 82),\n",
    "    }\n",
    ").plot(\n",
    "    cmap=\"BkBlAqGrYeOrReViWh200_r\",\n",
    "    levels=230,\n",
    "    vmin=0.005,\n",
    "    vmax=30,\n",
    "    ax=ax,\n",
    "    norm=colors.LogNorm(vmin=0.005, vmax=30),\n",
    ")\n",
    "\n",
    "# plotting of rings happens here\n",
    "for rr in all_radii[posx] * 1e3:\n",
    "    circ = Circle(\n",
    "        (CENTS[posx][0], CENTS[posx][1]),\n",
    "        rr,\n",
    "        alpha=1,\n",
    "        facecolor=\"None\",\n",
    "        lw=0.67,\n",
    "        edgecolor=npr.choice(\n",
    "            [\"xkcd:lime green\", \"xkcd:gold\", \"xkcd:electric pink\", \"xkcd:azure\"]\n",
    "        ),\n",
    "    )\n",
    "    ax.add_patch(circ)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d49957d7-5bcc-42b9-8510-f3f78d36b29e",
   "metadata": {},
   "source": [
    "#### ALL STORMS AT ONCE ####\n",
    "\n",
    "Now let's do a proper visulization with the storm into the (spatial) context of the HAD.\n",
    "We've read before the HAD-mask, so we can now plot it here.\n",
    "But first, let's collapse the already rasterized (and filled/interpolated) [**da**](#da) xarray into the **s**-dimension (z-axis)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "24244e41-d716-46ba-9534-5f595eb8c934",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 470, x: 408)&gt;\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.]])\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 1.158e+06 ... -1.172e+06 -1.178e+06</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 470</li><li><span class='xr-has-index'>x</span>: 408</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-4bef4d6f-2041-4cef-a15d-d6461de155b7' class='xr-array-in' type='checkbox' checked><label for='section-4bef4d6f-2041-4cef-a15d-d6461de155b7' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>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.]])</pre></div></div></li><li class='xr-section-item'><input id='section-521fdcd2-4cf8-45ce-9013-56dd1cd5c488' class='xr-section-summary-in' type='checkbox'  checked><label for='section-521fdcd2-4cf8-45ce-9013-56dd1cd5c488' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.342e+06 1.348e+06 ... 3.378e+06</div><input id='attrs-0b262211-f0e3-4dc6-8c86-d5abe40147d3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b262211-f0e3-4dc6-8c86-d5abe40147d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d471d83-6e98-4897-94c0-ddd2b0329f31' class='xr-var-data-in' type='checkbox'><label for='data-0d471d83-6e98-4897-94c0-ddd2b0329f31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1342500., 1347500., 1352500., ..., 3367500., 3372500., 3377500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.168e+06 1.162e+06 ... -1.178e+06</div><input id='attrs-3b278f75-a750-41ff-95a5-f0c12f87c5a4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3b278f75-a750-41ff-95a5-f0c12f87c5a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e039481-5a17-4b16-bd31-a0d1fed38162' class='xr-var-data-in' type='checkbox'><label for='data-0e039481-5a17-4b16-bd31-a0d1fed38162' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1167500.,  1162500.,  1157500., ..., -1167500., -1172500., -1177500.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-81ba5e8b-6196-4dc8-9f87-8554e80d155f' class='xr-section-summary-in' type='checkbox'  ><label for='section-81ba5e8b-6196-4dc8-9f87-8554e80d155f' class='xr-section-summary' >Indexes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c15278a5-b3ed-46ad-bf78-1c3cc1ba9a14' class='xr-index-data-in' type='checkbox'/><label for='index-c15278a5-b3ed-46ad-bf78-1c3cc1ba9a14' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1342500.0, 1347500.0, 1352500.0, 1357500.0, 1362500.0, 1367500.0,\n",
       "       1372500.0, 1377500.0, 1382500.0, 1387500.0,\n",
       "       ...\n",
       "       3332500.0, 3337500.0, 3342500.0, 3347500.0, 3352500.0, 3357500.0,\n",
       "       3362500.0, 3367500.0, 3372500.0, 3377500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=408))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d1854f4-cbf0-4899-8bb3-34c58785809b' class='xr-index-data-in' type='checkbox'/><label for='index-7d1854f4-cbf0-4899-8bb3-34c58785809b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 1167500.0,  1162500.0,  1157500.0,  1152500.0,  1147500.0,  1142500.0,\n",
       "        1137500.0,  1132500.0,  1127500.0,  1122500.0,\n",
       "       ...\n",
       "       -1132500.0, -1137500.0, -1142500.0, -1147500.0, -1152500.0, -1157500.0,\n",
       "       -1162500.0, -1167500.0, -1172500.0, -1177500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=470))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e909ae50-604d-410d-afa0-bf3f0369b5bc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e909ae50-604d-410d-afa0-bf3f0369b5bc' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 470, x: 408)>\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.]])\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 1.158e+06 ... -1.172e+06 -1.178e+06"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the \"sum\" of all storms\n",
    "alls = da.sum(dim=\"s\")\n",
    "\n",
    "# what does it look like?\n",
    "alls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "f101eeb6-db37-4619-bbe8-08d572150193",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 4), dpi=200)\n",
    "ax.set_aspect(\"equal\")\n",
    "\n",
    "# how does my entire array look like?\n",
    "alls.plot(\n",
    "    cmap=\"BkBlAqGrYeOrReViWh200_r\",\n",
    "    levels=199,\n",
    "    vmin=0.001,\n",
    "    vmax=30,\n",
    "    ax=ax,\n",
    "    norm=colors.LogNorm(vmin=0.001, vmax=30),\n",
    ")\n",
    "wtrshd.boundary.plot(ax=ax, color=\"xkcd:cement\", lw=0.27, ls=\"dotted\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5cc4af4-2487-4d34-82fd-27dbc17b2058",
   "metadata": {},
   "source": [
    "<span style=\"color:MediumSeaGreen\">Unfortunately there are small storms difficult to observe here (even with the logarithmic color map)... hence, you're highly encouraged to compute many more and much bigger!!</span>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:prll]",
   "language": "python",
   "name": "conda-env-prll-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}