{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c4b1d4b3-ad69-4e94-9185-856521456a5d",
   "metadata": {},
   "source": [
    "### Make GloFAS data usable on CESM:\n",
    "\n",
    "run after concatenating the single yearly files into one file.\n",
    "\n",
    "In this code we take the glofas data (after concatenating, i.e. running glofas_concat.ipynb)\n",
    "and prepare the dataset to run the cime mapping codes to map the runoff on cesm.\n",
    "The code will generate the following files:\n",
    "- data file formatted for use in CESM (may need additional edits via nco commands...see instructions).\n",
    "- generate a glofas grid and land mask file for mapping purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "1d457fc6-f809-4823-a1a7-ca8950d4d4bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:    (longitude: 3600, latitude: 1500, time: 7671)\n",
      "Coordinates:\n",
      "  * longitude  (longitude) float32 -179.9 -179.9 -179.8 ... 179.8 179.9 179.9\n",
      "  * latitude   (latitude) float32 89.95 89.85 89.75 ... -59.75 -59.85 -59.95\n",
      "  * time       (time) datetime64[ns] 2000-01-02 2000-01-03 ... 2021-01-01\n",
      "Data variables:\n",
      "    dis24      (time, latitude, longitude) float32 dask.array<chunksize=(12, 1500, 3600), meta=np.ndarray>\n",
      "Attributes:\n",
      "    Conventions:  CF-1.6\n",
      "    history:      2021-06-14 15:50:21 GMT by grib_to_netcdf-2.8.0: grib_to_ne...\n"
     ]
    }
   ],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "import xesmf\n",
    "\n",
    "glofas = xr.open_dataset('/glade/work/gseijo/GloFAS/2000_2020/GloFAS_2000_2020_raw.nc', chunks = {\"time\":12})\n",
    "\n",
    "print(glofas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "df740f44-963e-4c9e-8ade-5ba9edd90b56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#subset to a region slightly larger than Carib domain for efficiency and subset time:\n",
    "glofas= glofas.sortby(glofas.latitude, ascending=True)\n",
    "glofas = glofas.rename({'latitude':'lat','longitude':'lon'})\n",
    "\n",
    "#note the raw time axis if displaced forward by 24 hrs, so for the 2000-01-01 to 2019-12-31 range, the dates will be 2000-01-02 to 2020-01-02 (add an extra time step at end for time interp)\n",
    "glofas = glofas.sel(time=slice('2000-01-02T00:00:00.000000000','2020-01-02T00:00:00.000000000'))\n",
    "\n",
    "#subset = dict(lat=slice(0, 15), lon=slice(-60, -45))\n",
    "#glofas = glofas.sel(**subset)\n",
    "glofas['lon'] = glofas['lon'] % 360\n",
    "glofas= glofas.sortby(glofas.lon,ascending=True)\n",
    "\n",
    "#upload the upArea.nc files from GloFAS (contains upstream area weights of the GloFAS model) which we will use to identify river to ocean endpoints:\n",
    "uparea = xr.open_dataarray('/glade/work/gseijo/python_codes/rivers/upArea.nc')\n",
    "uparea = uparea.sortby(uparea.lat, ascending=True)#.sel(**subset)\n",
    "uparea['lon'] = uparea['lon'] % 360\n",
    "uparea = uparea.sortby(uparea.lon,ascending=True)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "20094352-e458-4d19-b0af-b6b022f71147",
   "metadata": {},
   "outputs": [],
   "source": [
    "# change runoff units:\n",
    "# Convert m3/s to kg/m2/s\n",
    "# Borrowed from https://xgcm.readthedocs.io/en/latest/xgcm-examples/05_autogenerate.html\n",
    "distance_1deg_equator = 111000.0\n",
    "dlon = dlat = 0.1  # GloFAS grid spacing\n",
    "dx = dlon * np.cos(np.deg2rad(glofas.lat)) * distance_1deg_equator\n",
    "dy = ((glofas.lon * 0) + 1) * dlat * distance_1deg_equator\n",
    "glofas_area = dx * dy\n",
    "glofas_rof = glofas.dis24 * 1000.0 / glofas_area\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "31acddd1-889e-4b3f-8e33-98ab24dbd3e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f6093f56210>"
      ]
     },
     "execution_count": 4,
     "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": [
    "#check data so far:\n",
    "glofas_rof[0,:,:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "663117e1-14c3-4d08-bb37-db2c1d0d5f85",
   "metadata": {},
   "outputs": [],
   "source": [
    "#generate land/ocean mask (runoff = nan in ocean, values in land). We want 0 for land, 1 for ocean.\n",
    "land_ocean_mask = np.zeros(np.shape(glofas_rof[0,:,:]))\n",
    "rof_slice = glofas_rof.isel(time=0) if 'time' in glofas_rof.dims else glofas_rof\n",
    "\n",
    "# xx, yy = np.shape(land_ocean_mask)\n",
    "# for x in range(xx):\n",
    "#     print(x)\n",
    "#     for y in range(yy):\n",
    "#         if glofas_rof[0,x,y]>=0:\n",
    "#             land_ocean_mask[x,y]=0\n",
    "#         elif np.isnan(glofas_rof[0,x,y]):\n",
    "#             land_ocean_mask[x,y]=1\n",
    "\n",
    "land_ocean_mask = xr.where(np.isnan(rof_slice), 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3a6d7546-2505-477d-bc2d-561cf6a3eb69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f6093f57b90>"
      ]
     },
     "execution_count": 6,
     "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": [
    "#check land/ocean mask:\n",
    "import matplotlib.pyplot as plt\n",
    "plt.pcolormesh(glofas.lon,glofas.lat,land_ocean_mask)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "82de302f-48de-4f80-9991-50a630a9ff8b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Find river end points by looking for local maxima in upstream area (method by J. Simikins and adapted from Alistair Adcroft?)\n",
    "uparea = uparea.fillna(0).values\n",
    "points = np.zeros_like(uparea)\n",
    "window = 2  # look with +- this number of grid points\n",
    "ni, nj = uparea.shape\n",
    "for i in range(window, ni-window):\n",
    "    for j in range(window, nj-window):\n",
    "        sub = uparea[i-window:i+window+1, j-window:j+window+1]\n",
    "        point = uparea[i, j]\n",
    "        # A river end point has a reasonably large upstream area\n",
    "        # and is a local maximum\n",
    "        if point > 1e6 and sub.max() == point:\n",
    "            points[i, j] = 1\n",
    "            \n",
    "glofas_mask = points\n",
    "glofas_rof = glofas_rof.where(glofas_mask > 0).fillna(0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a343bfc4",
   "metadata": {},
   "source": [
    "# Step2\n",
    "\n",
    "This section is where we collapse any remaining land cells onto the coast using a combination of ESMF and loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3a482017-7fdc-47ff-9af6-8b286d99710a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xx = glofas.lon\n",
    "yy = glofas.lat\n",
    "[x,y] = np.meshgrid(xx,yy)                 \n",
    "lon = x\n",
    "lat = y\n",
    "\n",
    "#flatten mask:\n",
    "flat_mask = land_ocean_mask.values.ravel().astype('bool')\n",
    "coast_lon = lon.ravel()[flat_mask] # Ocean Points \n",
    "coast_lat = lat.ravel()[flat_mask]\n",
    "glofas_id = np.arange(np.prod(land_ocean_mask.shape))\n",
    "coast_id = glofas_id[flat_mask]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ba8a2d4-6b53-4ca1-b4fb-801f9ff64d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Long Running Code to find nearest coast cells\n",
    "# coast_to_gfas = xesmf.Regridder(\n",
    "#     {'lat':coast_lat,'lon':coast_lon},\n",
    "#     {'lat':lat,'lon':lon}, # All POints\n",
    "#     method='nearest_s2d',\n",
    "#     locstream_in=True,\n",
    "#     reuse_weights=False,\n",
    "#     filename = \"coast_glofas_weights.nc\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef5da183-40b2-4ad1-9cf9-19062374f46d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# w = xr.open_dataset(\"coast_glofas_weights.nc\")\n",
    "# source_index = w['col'].values-1\n",
    "# target_index = w['row'].values-1\n",
    "\n",
    "# # Map each target cell to nearest coastal ID, go through every point on the grid, find closest ocean point and set it to the value so nearest_coast[point] = nearest_ocean_point_id\n",
    "# nearest_coast = np.empty(land_ocean_mask.size, dtype=coast_id.dtype)\n",
    "# nearest_coast[target_index] = coast_id[source_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "cd860d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# nearest_coast = coast_to_gfas(coast_id).ravel() #nearest_coast is an array with one entry per GloFAS grid point, and each entry tells you the ID of the nearest coastal point for that grid cell"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f497fe3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# glofas_flat = glofas_rof.stack(space=(\"lat\", \"lon\")) # [0,172100].compute()\n",
    "\n",
    "# filled = np.zeros(glofas_flat.shape, dtype=glofas_flat.dtype)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35f32c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# from multiprocessing import Process, Pool\n",
    "\n",
    "# def find_nearest_coast(coast_id):\n",
    "#     idx = np.where(nearest_coast == coast_id)[0]\n",
    "#     if len(idx) > 1:\n",
    "#         return (coast_id, idx)\n",
    "# with Pool() as pool:\n",
    "#       result = pool.map(find_nearest_coast, coast_id)\n",
    "# result_clean = [r for r in result if r is not None]\n",
    "# indexes_dict_2 = dict(result_clean)\n",
    "\n",
    "# import json\n",
    "# indexes_dict_serializable = {int(k): v.tolist() if hasattr(v, \"tolist\") else v\n",
    "#                              for k, v in indexes_dict.items()}\n",
    "# with open(\"indexes_dict.json\", \"w\") as f:\n",
    "#     json.dump(indexes_dict_serializable, f)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "83981f6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "from pathlib import Path\n",
    "\n",
    "# Path to your JSON file\n",
    "json_path = Path(\"/glade/u/home/manishrv/documents/croc/dev/gustavo_glorys/stuff_for_final_run/runoff_mapping/indexes_dict.json\")\n",
    "\n",
    "# Open and read the file\n",
    "with json_path.open(\"r\") as f:\n",
    "    indexes_dict = json.load(f)\n",
    "indexes_dict = {int(k): v for k, v in indexes_dict.items()}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1b98bc2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2000-01-02T00:00:00.000000000', '2000-01-03T00:00:00.000000000',\n",
       "       '2000-01-04T00:00:00.000000000', ...,\n",
       "       '2019-12-31T00:00:00.000000000', '2020-01-01T00:00:00.000000000',\n",
       "       '2020-01-02T00:00:00.000000000'], dtype='datetime64[ns]')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glofas_rof.time.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b14be49-0908-4ee0-bdd8-faf62a959fbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import pandas as pd\n",
    "\n",
    "# Example: glofas_rof is your DataArray or Dataset with a \"time\" coordinate\n",
    "start_year = 2000\n",
    "num_years = 20\n",
    "\n",
    "yearly_slices = {}\n",
    "\n",
    "for y in range(start_year, start_year + num_years):\n",
    "    start = pd.Timestamp(f\"{y}-01-02\")\n",
    "    end = pd.Timestamp(f\"{y+1}-01-01\")  # exclusive end\n",
    "    yearly_slices[y] = glofas_rof.sel(time=slice(start, end))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "520f7ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{2000: <xarray.DataArray (time: 366, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(366, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2000-01-02 2000-01-03 ... 2001-01-01,\n",
       " 2001: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2001-01-02 2001-01-03 ... 2002-01-01,\n",
       " 2002: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2002-01-02 2002-01-03 ... 2003-01-01,\n",
       " 2003: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2003-01-02 2003-01-03 ... 2004-01-01,\n",
       " 2004: <xarray.DataArray (time: 366, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(366, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2004-01-02 2004-01-03 ... 2005-01-01,\n",
       " 2005: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2005-01-02 2005-01-03 ... 2006-01-01,\n",
       " 2006: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2006-01-02 2006-01-03 ... 2007-01-01,\n",
       " 2007: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2007-01-02 2007-01-03 ... 2008-01-01,\n",
       " 2008: <xarray.DataArray (time: 366, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(366, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2008-01-02 2008-01-03 ... 2009-01-01,\n",
       " 2009: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2009-01-02 2009-01-03 ... 2010-01-01,\n",
       " 2010: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2010-01-02 2010-01-03 ... 2011-01-01,\n",
       " 2011: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2011-01-02 2011-01-03 ... 2012-01-01,\n",
       " 2012: <xarray.DataArray (time: 366, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(366, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2012-01-02 2012-01-03 ... 2013-01-01,\n",
       " 2013: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2013-01-02 2013-01-03 ... 2014-01-01,\n",
       " 2014: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2014-01-02 2014-01-03 ... 2015-01-01,\n",
       " 2015: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2015-01-02 2015-01-03 ... 2016-01-01,\n",
       " 2016: <xarray.DataArray (time: 366, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(366, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2016-01-02 2016-01-03 ... 2017-01-01,\n",
       " 2017: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2017-01-02 2017-01-03 ... 2018-01-01,\n",
       " 2018: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2018-01-02 2018-01-03 ... 2019-01-01,\n",
       " 2019: <xarray.DataArray (time: 365, lat: 1500, lon: 3600)>\n",
       " dask.array<getitem, shape=(365, 1500, 3600), dtype=float32, chunksize=(12, 1500, 3600), chunktype=numpy.ndarray>\n",
       " Coordinates:\n",
       "   * lon      (lon) float32 0.05 0.15 0.25 0.35 0.45 ... 359.6 359.8 359.9 360.0\n",
       "   * lat      (lat) float32 -59.95 -59.85 -59.75 -59.65 ... 89.75 89.85 89.95\n",
       "   * time     (time) datetime64[ns] 2019-01-02 2019-01-03 ... 2020-01-01}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yearly_slices"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "26d9391b-8331-48c2-8e11-2d0dc7cf1c94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2000\n",
      "Year 2000-01-02T00:00:00.000000000 to 2001-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 366, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 362.0 363.0 364.0 365.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2001\n",
      "Year 2001-01-02T00:00:00.000000000 to 2002-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2002\n",
      "Year 2002-01-02T00:00:00.000000000 to 2003-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2003\n",
      "Year 2003-01-02T00:00:00.000000000 to 2004-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2004\n",
      "Year 2004-01-02T00:00:00.000000000 to 2005-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 366, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 362.0 363.0 364.0 365.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2005\n",
      "Year 2005-01-02T00:00:00.000000000 to 2006-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2006\n",
      "Year 2006-01-02T00:00:00.000000000 to 2007-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2007\n",
      "Year 2007-01-02T00:00:00.000000000 to 2008-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2008\n",
      "Year 2008-01-02T00:00:00.000000000 to 2009-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 366, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 362.0 363.0 364.0 365.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2009\n",
      "Year 2009-01-02T00:00:00.000000000 to 2010-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2010\n",
      "Year 2010-01-02T00:00:00.000000000 to 2011-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2011\n",
      "Year 2011-01-02T00:00:00.000000000 to 2012-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2012\n",
      "Year 2012-01-02T00:00:00.000000000 to 2013-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 366, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 362.0 363.0 364.0 365.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2013\n",
      "Year 2013-01-02T00:00:00.000000000 to 2014-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2014\n",
      "Year 2014-01-02T00:00:00.000000000 to 2015-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2015\n",
      "Year 2015-01-02T00:00:00.000000000 to 2016-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2016\n",
      "Year 2016-01-02T00:00:00.000000000 to 2017-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 366, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 362.0 363.0 364.0 365.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2017\n",
      "Year 2017-01-02T00:00:00.000000000 to 2018-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2018\n",
      "Year 2018-01-02T00:00:00.000000000 to 2019-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n",
      "2019\n",
      "Year 2019-01-02T00:00:00.000000000 to 2020-01-01T00:00:00.000000000\n",
      "Subset the time\n",
      "Load Memory\n",
      "Sum the data at non coast points\n",
      "Fill the rest of the coast points\n",
      "Reshape data and set runoff, time\n",
      "Get Rid of negative runoff values\n",
      "Create dataset with correct structure and save\n",
      "<xarray.Dataset>\n",
      "Dimensions:    (time: 365, latitude: 1500, longitude: 3600)\n",
      "Coordinates:\n",
      "  * time       (time) float64 0.0 1.0 2.0 3.0 4.0 ... 361.0 362.0 363.0 364.0\n",
      "  * latitude   (latitude) float32 -59.95 -59.85 -59.75 ... 89.75 89.85 89.95\n",
      "  * longitude  (longitude) float32 0.05 0.15 0.25 0.35 ... 359.8 359.9 360.0\n",
      "Data variables:\n",
      "    runoff     (time, latitude, longitude) float32 0.0 0.0 0.0 ... 0.0 0.0 0.0\n"
     ]
    }
   ],
   "source": [
    "for y in range(start_year, start_year + num_years):\n",
    "    print(y)\n",
    "    yearly_slice = yearly_slices[y]\n",
    "    print(f\"Year {yearly_slice.time[0].values} to {yearly_slice.time[-1].values}\")\n",
    "    print(\"Subset the time\")\n",
    "    glofas_rof_time_0 =yearly_slice# This includes the last date, but because it;s 24 hrs of discargae 2001-01-01 actually is 2000-12-31\n",
    "\n",
    "    print(\"Load Memory\")\n",
    "\n",
    "    gfas_np=glofas_rof_time_0.values\n",
    "    raw = gfas_np.reshape([gfas_np.shape[0],-1])\n",
    "    filled = np.zeros_like(raw)\n",
    "\n",
    "    print(\"Sum the data at non coast points\")\n",
    "\n",
    "    # Sum all points that are not on the coast\n",
    "    for i in indexes_dict:\n",
    "        filled[:,i] = raw[:,indexes_dict[i]].sum(axis=1)\n",
    "\n",
    "    print(\"Fill the rest of the coast points\")\n",
    "    # Then fill in all the coast items that don't need to be summed\n",
    "    for i in coast_id:\n",
    "        if i not in indexes_dict:\n",
    "            filled[:,i] = raw[:,i]\n",
    "\n",
    "    print(\"Reshape data and set runoff, time\")\n",
    "    # set new DS vars:\n",
    "    filled_reshape = filled.reshape(gfas_np.shape)\n",
    "    new_runoff = filled_reshape\n",
    "    X=filled_reshape.shape[0]\n",
    "    new_time = np.linspace(0,X-1,X)\n",
    "\n",
    "    print(\"Get Rid of negative runoff values\")\n",
    "\n",
    "    #get rid of negative runoff values (looks to be an error in the data as downloaded!!)\n",
    "    new_runoff = np.where(new_runoff<0,0,new_runoff)\n",
    "\n",
    "    print(\"Create dataset with correct structure and save\")\n",
    "    #create dataset with correct structure and save\n",
    "    ds_new = xr.Dataset({\n",
    "        'runoff':(['time','latitude','longitude'],new_runoff),\n",
    "    # 'land_ocean_mask':(['latitude','longitude'],land_ocean_mask.data)\n",
    "    },\n",
    "        coords={'time': new_time,'latitude':glofas['lat'].data,'longitude':glofas['lon'].data})\n",
    "\n",
    "    #make sure latitudes are ordered correctly (it seems the latitudes are in decreasing order in the original Glofas data)\n",
    "    ds_new = ds_new.sortby('latitude', ascending=True)\n",
    "\n",
    "    # Drop '_FillValue' from all variables when writing out\n",
    "    all_vars = list(ds_new.data_vars.keys()) + list(ds_new.coords.keys())\n",
    "    encodings = {v: {'_FillValue': 100} for v in all_vars}\n",
    "\n",
    "    # Make sure time has the right units and datatype\n",
    "    # otherwise it will become an int and MOM will fail. \n",
    "    attrsT = { 'standard_name': 'time',\n",
    "                'long_name': 'time',\n",
    "                'axis': \"T\",\n",
    "                'units' : f\"days since {y}-01-01 00:00:00\",\n",
    "                'calendar' : \"gregorian\"}\n",
    "    ds_new.time.attrs = attrsT\n",
    "    ds_new['latitude'].attrs = {'units': 'degrees_north'}\n",
    "    ds_new['longitude'].attrs = {'units': 'degrees_east'}\n",
    "    ds_new['runoff'].attrs = {'units': 'kg m-2 s-1'}\n",
    "\n",
    "    print(ds_new)\n",
    "    ds_new.to_netcdf(f'GloFAS_2000_2020_twostep_nc4_{y}.nc',encoding=encodings,unlimited_dims='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "80042ad5-4a3f-448c-a0ba-f179fce4e079",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "  186521,\n",
       "  186522,\n",
       "  186523,\n",
       "  190118,\n",
       "  190119,\n",
       "  190120,\n",
       "  190121,\n",
       "  190122,\n",
       "  190123,\n",
       "  193718,\n",
       "  193719,\n",
       "  193720,\n",
       "  193721,\n",
       "  193722,\n",
       "  193723,\n",
       "  197318,\n",
       "  197319,\n",
       "  197320,\n",
       "  197321,\n",
       "  197322,\n",
       "  197323,\n",
       "  197324,\n",
       "  200918,\n",
       "  200919,\n",
       "  200920,\n",
       "  200921,\n",
       "  200922,\n",
       "  204518,\n",
       "  204519],\n",
       " '186547': [186546, 186547, 190145, 190146],\n",
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       " '190148': [190147, 190148],\n",
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       " '190155': [190155, 190156],\n",
       " '190162': [190161, 190162],\n",
       " '192389': [192388, 192389],\n",
       " '193678': [193678, 193679],\n",
       " '193686': [193685, 193686, 193687, 197284, 197285, 197286, 197287, 197288],\n",
       " '193743': [190141, 190142, 190143, 190144, 193742, 193743, 193744],\n",
       " '193748': [193747, 193748],\n",
       " '195989': [195988, 195989],\n",
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       "  190137,\n",
       "  190138,\n",
       "  190139,\n",
       "  193737,\n",
       "  193738,\n",
       "  193739,\n",
       "  197338,\n",
       "  197339],\n",
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       "  186507,\n",
       "  186508,\n",
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       "  190106,\n",
       "  190107,\n",
       "  190108,\n",
       "  190109,\n",
       "  190110,\n",
       "  193705,\n",
       "  193706,\n",
       "  193707,\n",
       "  193708,\n",
       "  193709,\n",
       "  193710,\n",
       "  193711,\n",
       "  197305,\n",
       "  197306,\n",
       "  197307,\n",
       "  197308,\n",
       "  197309,\n",
       "  197310,\n",
       "  197311,\n",
       "  197312,\n",
       "  197313,\n",
       "  200907,\n",
       "  200908,\n",
       "  200909,\n",
       "  200910,\n",
       "  200911,\n",
       "  200912,\n",
       "  200913,\n",
       "  200914,\n",
       "  204508,\n",
       "  204509,\n",
       "  204510,\n",
       "  204511,\n",
       "  204512,\n",
       "  204513,\n",
       "  204514,\n",
       "  204515,\n",
       "  204516,\n",
       "  208110,\n",
       "  208111,\n",
       "  208112,\n",
       "  208113,\n",
       "  208114,\n",
       "  208115,\n",
       "  208116,\n",
       "  211711,\n",
       "  211712,\n",
       "  211713,\n",
       "  211714,\n",
       "  211715,\n",
       "  211716,\n",
       "  215313,\n",
       "  215314],\n",
       " '200935': [190135,\n",
       "  193732,\n",
       "  193733,\n",
       "  193734,\n",
       "  193735,\n",
       "  197331,\n",
       "  197332,\n",
       "  197333,\n",
       "  197334,\n",
       "  197335,\n",
       "  200934,\n",
       "  200935],\n",
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       "  204487,\n",
       "  208084,\n",
       "  208085,\n",
       "  208086,\n",
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       "  204489,\n",
       "  204490,\n",
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       "  204495,\n",
       "  208093,\n",
       "  208094,\n",
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       "  197301,\n",
       "  197302,\n",
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       "  200900,\n",
       "  200901,\n",
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       "  204501,\n",
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       "  197303,\n",
       "  197304,\n",
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       "  204503,\n",
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       "  204505,\n",
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       "  218908,\n",
       "  218909,\n",
       "  218910,\n",
       "  218911,\n",
       "  218912],\n",
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       "  208079,\n",
       "  211677,\n",
       "  211678,\n",
       "  211679,\n",
       "  211680,\n",
       "  215278,\n",
       "  215279],\n",
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       "  215305,\n",
       "  215306,\n",
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       "  218905,\n",
       "  218906,\n",
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       "  200928,\n",
       "  200929,\n",
       "  200930,\n",
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       "  204530,\n",
       "  204531,\n",
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       "  208131,\n",
       "  208132],\n",
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       "  218873,\n",
       "  218874,\n",
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       "  208130,\n",
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       "  211730],\n",
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       "  218870,\n",
       "  218871,\n",
       "  222470,\n",
       "  222471,\n",
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       "  208125,\n",
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       "  208127,\n",
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       "  211725,\n",
       "  211726,\n",
       "  211727,\n",
       "  215326,\n",
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       "  244109,\n",
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       "  233318,\n",
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       "  236917,\n",
       "  236918,\n",
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       "  236882,\n",
       "  236883,\n",
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       "  240480,\n",
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       "  287270,\n",
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       "  287273,\n",
       "  287274,\n",
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       "  319693,\n",
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       "  319695,\n",
       "  319696,\n",
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       "  319703,\n",
       "  323289,\n",
       "  323290,\n",
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       "  427715,\n",
       "  431304,\n",
       "  431305,\n",
       "  431306,\n",
       "  431307,\n",
       "  431308,\n",
       "  431309,\n",
       "  431310,\n",
       "  431311,\n",
       "  431312,\n",
       "  431313,\n",
       "  431314,\n",
       "  434902,\n",
       "  434903,\n",
       "  434904,\n",
       "  434905,\n",
       "  434906,\n",
       "  434907,\n",
       "  434908,\n",
       "  434909,\n",
       "  434910,\n",
       "  434911,\n",
       "  434912,\n",
       "  434913,\n",
       "  438500,\n",
       "  438501,\n",
       "  438502,\n",
       "  438503,\n",
       "  438504,\n",
       "  438505,\n",
       "  438506,\n",
       "  438507,\n",
       "  438508,\n",
       "  438509,\n",
       "  438510,\n",
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       "  442097,\n",
       "  442098,\n",
       "  442099,\n",
       "  442100,\n",
       "  442101,\n",
       "  442102,\n",
       "  442103,\n",
       "  442104,\n",
       "  442105,\n",
       "  442106,\n",
       "  442107,\n",
       "  442108,\n",
       "  442109,\n",
       "  442110],\n",
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       "  398847,\n",
       "  398848,\n",
       "  398849,\n",
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       "  402447,\n",
       "  402448,\n",
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       "  431278,\n",
       "  431279,\n",
       "  431280,\n",
       "  431281,\n",
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       "  509279,\n",
       "  509280,\n",
       "  509281,\n",
       "  509282,\n",
       "  512876,\n",
       "  512877,\n",
       "  512878,\n",
       "  512879,\n",
       "  512880,\n",
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       "  520083,\n",
       "  520084,\n",
       "  520085,\n",
       "  523683,\n",
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       "  635334,\n",
       "  635335,\n",
       "  638931,\n",
       "  638932,\n",
       "  638933,\n",
       "  642530,\n",
       "  642531,\n",
       "  646128,\n",
       "  646129,\n",
       "  646130],\n",
       " '632857': [632857, 632858, 632859, 632860],\n",
       " '632865': [632864, 632865, 636462, 636463, 636464, 640062],\n",
       " '632872': [614898,\n",
       "  614899,\n",
       "  614900,\n",
       "  614901,\n",
       "  614902,\n",
       "  614903,\n",
       "  614904,\n",
       "  614905,\n",
       "  614906,\n",
       "  614907,\n",
       "  614908,\n",
       "  614909,\n",
       "  618488,\n",
       "  618489,\n",
       "  618490,\n",
       "  618491,\n",
       "  618492,\n",
       "  618493,\n",
       "  618494,\n",
       "  618495,\n",
       "  618496,\n",
       "  618497,\n",
       "  618498,\n",
       "  618499,\n",
       "  618500,\n",
       "  618501,\n",
       "  618502,\n",
       "  618503,\n",
       "  618504,\n",
       "  618505,\n",
       "  622080,\n",
       "  622081,\n",
       "  622082,\n",
       "  622083,\n",
       "  622084,\n",
       "  622085,\n",
       "  622086,\n",
       "  622087,\n",
       "  622088,\n",
       "  622089,\n",
       "  622090,\n",
       "  622091,\n",
       "  622092,\n",
       "  622093,\n",
       "  622094,\n",
       "  622095,\n",
       "  622096,\n",
       "  622097,\n",
       "  622098,\n",
       "  622099,\n",
       "  625676,\n",
       "  625677,\n",
       "  625678,\n",
       "  625679,\n",
       "  625680,\n",
       "  625681,\n",
       "  625682,\n",
       "  625683,\n",
       "  625684,\n",
       "  625685,\n",
       "  625686,\n",
       "  625687,\n",
       "  625688,\n",
       "  625689,\n",
       "  625690,\n",
       "  625691,\n",
       "  625692,\n",
       "  629273,\n",
       "  629274,\n",
       "  629275,\n",
       "  629276,\n",
       "  629277,\n",
       "  629278,\n",
       "  629279,\n",
       "  629280,\n",
       "  629281,\n",
       "  629282,\n",
       "  629283,\n",
       "  629284,\n",
       "  629285,\n",
       "  629286,\n",
       "  632872,\n",
       "  632873,\n",
       "  632874,\n",
       "  632875,\n",
       "  632876,\n",
       "  632877,\n",
       "  632878,\n",
       "  632879,\n",
       "  632880,\n",
       "  636473,\n",
       "  636474,\n",
       "  636475],\n",
       " '632964': [632961, 632962, 632963, 632964],\n",
       " '635051': [635051, 635052],\n",
       " '635054': [627855,\n",
       "  627856,\n",
       "  627857,\n",
       "  627858,\n",
       "  627859,\n",
       "  627860,\n",
       "  627861,\n",
       "  627862,\n",
       "  627863,\n",
       "  627864,\n",
       "  631454,\n",
       "  631455,\n",
       "  631456,\n",
       "  631457,\n",
       "  631458,\n",
       "  631459,\n",
       "  631460,\n",
       "  631461,\n",
       "  631462,\n",
       "  631463,\n",
       "  631464,\n",
       "  631465,\n",
       "  635053,\n",
       "  635054,\n",
       "  635055,\n",
       "  635056,\n",
       "  635057,\n",
       "  635058,\n",
       "  635059,\n",
       "  635060,\n",
       "  635061,\n",
       "  635062,\n",
       "  635063,\n",
       "  635064,\n",
       "  635065,\n",
       "  638655,\n",
       "  638656,\n",
       "  638657,\n",
       "  638658,\n",
       "  638659,\n",
       "  638660,\n",
       "  638661,\n",
       "  638662,\n",
       "  638663,\n",
       "  638664,\n",
       "  638665,\n",
       "  638666,\n",
       "  642257,\n",
       "  642258,\n",
       "  642259,\n",
       "  642260,\n",
       "  642261,\n",
       "  642262,\n",
       "  642263,\n",
       "  642264,\n",
       "  642265,\n",
       "  642266,\n",
       "  645859,\n",
       "  645860,\n",
       "  645861,\n",
       "  645862,\n",
       "  645863,\n",
       "  645864,\n",
       "  645865,\n",
       "  649460,\n",
       "  649461,\n",
       "  649462,\n",
       "  649463],\n",
       " '635080': [635073, 635074, 635075, 635076, 635077, 635078, 635079, 635080],\n",
       " '635311': [635311, 635312],\n",
       " '635337': [635336,\n",
       "  635337,\n",
       "  638934,\n",
       "  638935,\n",
       "  638936,\n",
       "  642532,\n",
       "  642533,\n",
       "  642534,\n",
       "  646131,\n",
       "  646132,\n",
       "  649729,\n",
       "  649730,\n",
       "  649731],\n",
       " '636457': [636457, 636458, 636459, 636460, 636461],\n",
       " '636466': [636465, 636466, 640063, 640064, 640065],\n",
       " '636471': [636471, 636472],\n",
       " '636554': [636552, 636553, 636554],\n",
       " ...}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indexes_dict"
   ]
  }
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