nidichaogequ
164 lines
4.4 KiB
Plaintext
164 lines
4.4 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6fd04c86-82b8-466f-81df-0a53926fa420",
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"metadata": {},
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"outputs": [],
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"source": [
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"import fsspec\n",
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"import xarray as xr\n",
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"import hvplot.xarray\n",
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"import hvplot.pandas\n",
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"import hvplot.dask\n",
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"# import intake\n",
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"import os\n",
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"import warnings\n",
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"# import intake_xarray\n",
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"import geopandas as gpd\n",
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"from pynhd import NLDI, WaterData\n",
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"# from dask.distributed import LocalCluster, Client\n",
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"# from gdptools.helpers import get_shp_bounds_w_buffer, build_subset\n",
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"# from helpers import configure_cluster\n",
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"\n",
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"warnings.filterwarnings('ignore')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "edf27719-ce97-4d35-a7b0-8065b26874f7",
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"metadata": {},
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"outputs": [],
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"source": [
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"# USGS gage 01482100 Delaware River at Del Mem Bridge at Wilmington De\n",
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"gage_id = '01482100'\n",
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"nldi = NLDI()\n",
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"del_basins = nldi.get_basins(gage_id)\n",
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"huc12_basins = WaterData('huc12').bygeom(del_basins.geometry[0])\n",
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"\n",
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"import pandas as pd\n",
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"param_json = \"https://mikejohnson51.github.io/opendap.catalog/cat_params.json\"\n",
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"grid_json = \"https://mikejohnson51.github.io/opendap.catalog/cat_grids.json\"\n",
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"params = pd.read_json(param_json)\n",
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"grids = pd.read_json(grid_json)\n",
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"\n",
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"_id = \"gridmet\"\n",
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"_varname = \"daily_maximum_temperature\"\n",
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"tc = params.query(\"id == @_id & varname == @_varname\")\n",
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"print(type(tc), len(tc))\n",
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"tc"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7d819411-bb81-46e2-8305-42f9f2c396d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a dictionary of parameter dataframes for each variable\n",
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"vars = [\"tmmn\", \"tmmx\"]\n",
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"var_params = []\n",
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"for var in vars:\n",
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" var_params.append(params.query(\"id == @_id & variable == @var\"))\n",
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"param_dict = dict(zip(vars, var_params))\n",
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"param_dict.get('tmmn')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0d1876e8-4b97-49eb-8d72-0f97843fef3b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Create a dictionary of grid dataframes for each variable\n",
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"var_grid = []\n",
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"for var in vars:\n",
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" gridid = param_dict.get(var)['grid_id'].values[0]\n",
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" var_grid.append(grids.query('grid_id == @gridid'))\n",
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"grid_dict = dict(zip(vars, var_grid))\n",
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"grid_dict.get('tmmn')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "75be1872-a9c1-42b1-84d4-eaf85e9d63d1",
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"metadata": {},
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"outputs": [],
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"source": [
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"import tempfile\n",
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"from gdptools.helpers import calc_weights_catalog\n",
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"wghtf = calc_weights_catalog(\n",
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" params_json=param_dict.get(\"tmmn\"),\n",
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" grid_json=grid_dict.get(\"tmmn\"),\n",
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" shp_file=huc12_basins,\n",
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" shp_poly_idx='huc12',\n",
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" wght_gen_file=tempfile.NamedTemporaryFile().name,\n",
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" wght_gen_proj=6931,\n",
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" )\n",
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"\n",
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"tmp = 0"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "70139be4-efcd-4d47-9302-0142b7990cb4",
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"metadata": {},
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"outputs": [],
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"source": [
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"from gdptools.run_weights_engine import RunWghtEngine\n",
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"eng = RunWghtEngine()\n",
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"eng.initialize(\n",
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" param_dict=param_dict,\n",
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" grid_dict=grid_dict,\n",
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" wghts=wghtf,\n",
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" gdf=huc12_basins,\n",
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" gdf_poly_idx='huc12',\n",
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" start_date=\"2020-01-01\",\n",
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" end_date=\"2020-01-07\"\n",
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")\n",
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"ngdf, nvals = eng.run(numdiv=1) "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36812590-fb33-4162-bf22-f6455bea4ee8",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.13",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.13"
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},
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"vscode": {
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"interpreter": {
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"hash": "3365295dbac4842fc2bf269a7ec44c70d326e0f953d0b638b14de2730fd8846e"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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