{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6fd04c86-82b8-466f-81df-0a53926fa420",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec\n",
    "import xarray as xr\n",
    "import hvplot.xarray\n",
    "import hvplot.pandas\n",
    "import hvplot.dask\n",
    "# import intake\n",
    "import os\n",
    "import warnings\n",
    "# import intake_xarray\n",
    "import geopandas as gpd\n",
    "from pynhd import NLDI, WaterData\n",
    "# from dask.distributed import LocalCluster, Client\n",
    "# from gdptools.helpers import get_shp_bounds_w_buffer, build_subset\n",
    "# from helpers import configure_cluster\n",
    "\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf27719-ce97-4d35-a7b0-8065b26874f7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# USGS gage 01482100 Delaware River at Del Mem Bridge at Wilmington De\n",
    "gage_id = '01482100'\n",
    "nldi = NLDI()\n",
    "del_basins = nldi.get_basins(gage_id)\n",
    "huc12_basins = WaterData('huc12').bygeom(del_basins.geometry[0])\n",
    "\n",
    "import pandas as pd\n",
    "param_json = \"https://mikejohnson51.github.io/opendap.catalog/cat_params.json\"\n",
    "grid_json = \"https://mikejohnson51.github.io/opendap.catalog/cat_grids.json\"\n",
    "params = pd.read_json(param_json)\n",
    "grids = pd.read_json(grid_json)\n",
    "\n",
    "_id = \"gridmet\"\n",
    "_varname = \"daily_maximum_temperature\"\n",
    "tc = params.query(\"id == @_id & varname == @_varname\")\n",
    "print(type(tc), len(tc))\n",
    "tc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d819411-bb81-46e2-8305-42f9f2c396d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of parameter dataframes for each variable\n",
    "vars = [\"tmmn\", \"tmmx\"]\n",
    "var_params = []\n",
    "for var in vars:\n",
    "    var_params.append(params.query(\"id == @_id & variable == @var\"))\n",
    "param_dict = dict(zip(vars, var_params))\n",
    "param_dict.get('tmmn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d1876e8-4b97-49eb-8d72-0f97843fef3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dictionary of grid dataframes for each variable\n",
    "var_grid = []\n",
    "for var in vars:\n",
    "    gridid = param_dict.get(var)['grid_id'].values[0]\n",
    "    var_grid.append(grids.query('grid_id == @gridid'))\n",
    "grid_dict = dict(zip(vars, var_grid))\n",
    "grid_dict.get('tmmn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75be1872-a9c1-42b1-84d4-eaf85e9d63d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tempfile\n",
    "from gdptools.helpers import calc_weights_catalog\n",
    "wghtf = calc_weights_catalog(\n",
    "    params_json=param_dict.get(\"tmmn\"),\n",
    "    grid_json=grid_dict.get(\"tmmn\"),\n",
    "    shp_file=huc12_basins,\n",
    "    shp_poly_idx='huc12',\n",
    "    wght_gen_file=tempfile.NamedTemporaryFile().name,\n",
    "    wght_gen_proj=6931,\n",
    "    )\n",
    "\n",
    "tmp = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70139be4-efcd-4d47-9302-0142b7990cb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "from gdptools.run_weights_engine import RunWghtEngine\n",
    "eng = RunWghtEngine()\n",
    "eng.initialize(\n",
    "    param_dict=param_dict,\n",
    "    grid_dict=grid_dict,\n",
    "    wghts=wghtf,\n",
    "    gdf=huc12_basins,\n",
    "    gdf_poly_idx='huc12',\n",
    "    start_date=\"2020-01-01\",\n",
    "    end_date=\"2020-01-07\"\n",
    ")\n",
    "ngdf, nvals = eng.run(numdiv=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "36812590-fb33-4162-bf22-f6455bea4ee8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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