Static-Chiclet-Plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "dd135869-8899-4d43-8c3d-4b6c715b9dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "import warnings\n",
    "import matplotlib.pyplot as plt\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b099d537-79b0-461e-b6e4-dbc61a2f0ad8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the file \n",
    "gdf=gpd.read_file(\"s3://veda-data-store-staging/EIS/other/feds-output-conus/latest/perim-large.fgb\", engine=\"pyogrio\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c651c949-34e6-4066-8276-85c32a70995d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Change thr crs if needed\n",
    "gdf=gdf.to_crs(4326)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "293fe175-ea02-4a1f-b2f3-0aa6b0460007",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Choose any row form the geo dataframe\n",
    "gdf=gdf.query(\"fid==34789\")\n",
    "\n",
    "# Find the centroid of each Polygon\n",
    "gdf[\"centroid\"] = gdf[\"geometry\"].to_crs(crs=gdf.crs).centroid\n",
    "\n",
    "# Draw a buffer around the perimeter of the polygon to extend it\n",
    "gdf[\"buf\"]=gdf[\"geometry\"].buffer(0.005)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "7639e459-fbf9-44e6-a6dc-f14906bc6c6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the buffered polygon with centroid\n",
    "ax= gdf.plot(alpha=0.8,color='red')\n",
    "gdf['centroid'].plot(ax=ax, color=\"blue\")\n",
    "gdf['buf'].plot(ax=ax,color='red', alpha=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1820a269-ed8c-4b5e-ab1a-9e77e5459808",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Read the fire data\n",
    "ds= xr.open_zarr(\"s3://veda-data-store-staging/EIS/zarr/FWI.GEOS-5.zarr\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "3800b6a9-f3d2-4f3a-942d-db3ba1633622",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Renaming the axis\n",
    "ds=ds.rename({\"lat\": 'y', \"lon\": 'x'})\n",
    "\n",
    "# Changing crs to match with the geo dataframe\n",
    "ds.rio.write_crs(\"epsg:4326\", inplace=True)\n",
    "\n",
    "# Taking the variable of interest\n",
    "ds=ds[\"GEOS-5_FWI\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "bfe87bac-9f55-4baf-a289-7243ca2e8de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecting data on time range.\n",
    "ds=ds.sel(time=slice(\"2021-07-01\",\"2021-07-12\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f1200f5f-21d0-4366-ae2d-7d308cc52995",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Clipping the fire data over the specified region\n",
    "clipped = ds.rio.clip(gdf.buf, gdf.crs, drop=True,all_touched=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "49e90a13-4a50-4058-bdf5-b5a7ca679b15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Taking mean over the region\n",
    "x= clipped.mean(dim=['x','y'],skipna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "15b3e925-363f-4742-8e27-a5b8e381da7a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Diagonally shiftting the data for chiclet plot\n",
    "tmp = np.array([np.roll(np.array(x.sel(forecast=i).values), i) for i in range(9)])\n",
    "for i in range(len(tmp)):\n",
    "    tmp[i,:i]=np.NaN\n",
    "ys = np.arange(9)\n",
    "xs = xs = x.time.values\n",
    "zs=tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "36bfb227-887f-47fb-bfd9-05c71b23e3a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display the Plot\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.title(\"Chiclet plot for mean FWI index over the area\")\n",
    "plt.pcolormesh(xs,ys,tmp, cmap=\"Oranges\")\n",
    "plt.xlabel(\"Date\")\n",
    "plt.ylabel(\"Forecast\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:nb2]",
   "language": "python",
   "name": "conda-env-nb2-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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}