jh-dev/test_HERBIE.ipynb

   1 {
   2  "cells": [
   3   {
   4    "cell_type": "code",
   5    "execution_count": null,
   6    "id": "cd28b796-4661-4a6a-a84e-14fcba89fe83",
   7    "metadata": {},
   8    "outputs": [],
   9    "source": [
  10     "# Setup\n",
  11     "\n",
  12     "from herbie.core import Herbie\n",
  13     "from toolbox.gridded_data import pluck_points\n",
  14     "import pandas as pd\n",
  15     "import numpy as np"
  16    ]
  17   },
  18   {
  19    "cell_type": "markdown",
  20    "id": "6b7f5da2-a58c-48f5-99ee-59cd8f08bd12",
  21    "metadata": {},
  22    "source": [
  23     "## Gather Herbie Example\n",
  24     "\n",
  25     "Get HRRR data vars at one time period for list of coordinates. Coords are from the RAWS stations in WA state with complete fmda vars."
  26    ]
  27   },
  28   {
  29    "cell_type": "code",
  30    "execution_count": null,
  31    "id": "071d8d7e-6164-4934-900a-8cae98c91e38",
  32    "metadata": {
  33     "tags": []
  34    },
  35    "outputs": [],
  36    "source": [
  37     "# Read in list of RAWS Stations\n",
  38     "df = pd.read_csv(\"raws_stations_WA.csv\")\n",
  39     "\n",
  40     "# Filter to those with complete fmda data\n",
  41     "df = df[(df[['air_temp', 'relative_humidity', 'precip_accum',\n",
  42     "       'fuel_moisture', 'wind_speed', 'solar_radiation']]==1).sum(axis=1)==6]\n",
  43     "\n",
  44     "# Get list of coords\n",
  45     "points = list(df[[\"lon\",\"lat\"]].itertuples(index=False,name=None))\n",
  46     "names = np.unique(df['STID'])\n",
  47     "\n",
  48     "print(df.shape)"
  49    ]
  50   },
  51   {
  52    "cell_type": "markdown",
  53    "id": "59ebbf15-a92f-40c9-a181-ddfcd1c5e3ec",
  54    "metadata": {},
  55    "source": [
  56     "Get HRRR data."
  57    ]
  58   },
  59   {
  60    "cell_type": "code",
  61    "execution_count": null,
  62    "id": "1d7063e3-619a-40dd-9048-d79d52b7d244",
  63    "metadata": {
  64     "tags": []
  65    },
  66    "outputs": [],
  67    "source": [
  68     "H = Herbie(\n",
  69     "    \"2022-06-01-08:00\",\n",
  70     "    model=\"hrrr\"\n",
  71     ")"
  72    ]
  73   },
  74   {
  75    "cell_type": "markdown",
  76    "id": "edc502bb-9e86-4dec-b8aa-07468a38bf73",
  77    "metadata": {},
  78    "source": [
  79     "For each variable, pluck points at location.\n",
  80     "\n",
  81     "https://www.nco.ncep.noaa.gov/pmb/products/hrrr/hrrr.t00z.wrfsfcf00.grib2.shtml"
  82    ]
  83   },
  84   {
  85    "cell_type": "markdown",
  86    "id": "a1326a02-605a-45ed-8710-cde55093f15f",
  87    "metadata": {},
  88    "source": [
  89     "HOW TO: xarray all variables below at once? The obvious ways break."
  90    ]
  91   },
  92   {
  93    "cell_type": "code",
  94    "execution_count": null,
  95    "id": "b2dc91a9-afd4-45a7-b14b-5fd06f038c3d",
  96    "metadata": {
  97     "tags": []
  98    },
  99    "outputs": [],
 100    "source": [
 101     "len(points)"
 102    ]
 103   },
 104   {
 105    "cell_type": "code",
 106    "execution_count": null,
 107    "id": "441b87b9-e453-40c6-9621-d708d1da3c1a",
 108    "metadata": {},
 109    "outputs": [],
 110    "source": [
 111     "ds = H.xarray(\"(?:TMP|RH):2 m\", remove_grib=False)\n",
 112     "dsi = ds.herbie.nearest_points(\n",
 113     "    points=points,\n",
 114     "    names=names\n",
 115     ")"
 116    ]
 117   },
 118   {
 119    "cell_type": "code",
 120    "execution_count": null,
 121    "id": "7f381b60-502b-4539-8b6f-5f26222a2ec6",
 122    "metadata": {},
 123    "outputs": [],
 124    "source": [
 125     "dsi"
 126    ]
 127   },
 128   {
 129    "cell_type": "markdown",
 130    "id": "7497648c-9d71-40cc-998d-20ef93704758",
 131    "metadata": {},
 132    "source": [
 133     "## FastHerbie for Multiple Times"
 134    ]
 135   },
 136   {
 137    "cell_type": "code",
 138    "execution_count": null,
 139    "id": "eb96adc9-f33d-48f5-8477-d4bbca0fb278",
 140    "metadata": {},
 141    "outputs": [],
 142    "source": [
 143     "from herbie import FastHerbie\n",
 144     "import pandas as pd"
 145    ]
 146   },
 147   {
 148    "cell_type": "code",
 149    "execution_count": null,
 150    "id": "63da6b04-7f10-40cb-8701-08cc5a777e89",
 151    "metadata": {
 152     "tags": []
 153    },
 154    "outputs": [],
 155    "source": [
 156     "# Create a range of dates, June 1 to June 30\n",
 157     "dates = pd.date_range(\n",
 158     "    start=\"2022-06-01 00:00\",\n",
 159     "    periods=3,\n",
 160     "    freq=\"1H\",\n",
 161     ")"
 162    ]
 163   },
 164   {
 165    "cell_type": "code",
 166    "execution_count": null,
 167    "id": "26b4cd8c-1b16-4134-ba35-15a2eb5018c4",
 168    "metadata": {
 169     "tags": []
 170    },
 171    "outputs": [],
 172    "source": [
 173     "dates"
 174    ]
 175   },
 176   {
 177    "cell_type": "code",
 178    "execution_count": null,
 179    "id": "76b013be-3b41-47a6-acf1-634935cb37f6",
 180    "metadata": {
 181     "tags": []
 182    },
 183    "outputs": [],
 184    "source": [
 185     "# Make FastHerbie Object.\n",
 186     "fh = FastHerbie(dates, model=\"hrrr\")"
 187    ]
 188   },
 189   {
 190    "cell_type": "code",
 191    "execution_count": null,
 192    "id": "89b0dba3-7a1b-4f38-96a3-e590d3ff4603",
 193    "metadata": {
 194     "tags": []
 195    },
 196    "outputs": [],
 197    "source": [
 198     "fh.objects"
 199    ]
 200   },
 201   {
 202    "cell_type": "code",
 203    "execution_count": null,
 204    "id": "8336e98b-fc7c-4ce5-8c87-a203659ebe25",
 205    "metadata": {
 206     "tags": []
 207    },
 208    "outputs": [],
 209    "source": [
 210     "ds = fh.xarray(\"(?:TMP|RH):2 m\", remove_grib=False)\n",
 211     "dsi = ds.herbie.nearest_points(\n",
 212     "    points=points[0],\n",
 213     "    names=names[0]\n",
 214     ")"
 215    ]
 216   },
 217   {
 218    "cell_type": "raw",
 219    "id": "e74374e7-32af-4201-816c-a0c198b2c38b",
 220    "metadata": {
 221     "tags": []
 222    },
 223    "source": [
 224     "dsi"
 225    ]
 226   },
 227   {
 228    "cell_type": "markdown",
 229    "id": "59ddb9ba-d0d9-44fe-9173-ce3b1911e1bc",
 230    "metadata": {},
 231    "source": [
 232     "## Manually with PyDAP"
 233    ]
 234   },
 235   {
 236    "cell_type": "raw",
 237    "id": "f3203cf1-a2bc-4934-b09b-c28ca1236f96",
 238    "metadata": {},
 239    "source": []
 240   },
 241   {
 242    "cell_type": "code",
 243    "execution_count": null,
 244    "id": "593668b5-37c6-46dc-a2ce-78cfe4c878de",
 245    "metadata": {},
 246    "outputs": [],
 247    "source": []
 248   }
 249  ],
 250  "metadata": {
 251   "kernelspec": {
 252    "display_name": "Python 3 (ipykernel)",
 253    "language": "python",
 254    "name": "python3"
 255   },
 256   "language_info": {
 257    "codemirror_mode": {
 258     "name": "ipython",
 259     "version": 3
 260    },
 261    "file_extension": ".py",
 262    "mimetype": "text/x-python",
 263    "name": "python",
 264    "nbconvert_exporter": "python",
 265    "pygments_lexer": "ipython3",
 266    "version": "3.9.12"
 267   }
 268  },
 269  "nbformat": 4,
 270  "nbformat_minor": 5
 271 }