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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hurricane Ike Maximum Water Levels\n",
    "Compute the maximum water level during Hurricane Ike on a 9 million node triangular mesh storm surge model.  Plot the results with Datashader. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from dask.distributed import Client, progress\n",
    "from dask_gateway import Gateway\n",
    "import xarray as xr\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Start a dask cluster to crunch the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "gateway = Gateway()\n",
    "cluster = gateway.new_cluster()\n",
    "cluster.scale(25)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "client = Client(cluster)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read the data using the cloud-friendly zarr data format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pangeo = 'PANGEO-GCS-S3'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if pangeo=='ESIP-AWS-S3':\n",
    "    import s3fs\n",
    "    fs = s3fs.S3FileSystem(anon=True)\n",
    "    map = s3fs.S3Map('rsignell/adcirc_test01', s3=fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if pangeo=='PANGEO-GCS-S3':\n",
    "    import gcsfs\n",
    "    fs = gcsfs.GCSFileSystem(project='pangeo-181919')\n",
    "    map = gcsfs.mapping.GCSMap('pangeo-data/rsignell/adcirc_test01', gcs=fs, check=False, create=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_zarr(map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['zeta']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that unfortunately Xarray does not yet understand that `x` and `y` are coordinate variables, becuase we have a triangular mesh. Hopefully Xarray will gain some capabilities in this area in the future. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many GB of sea surface height data do we have?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['zeta'].nbytes/1.e9"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We want to calculate the maximum water level at every cell over all time steps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds['zeta'].max(dim='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask will read data only when necessary, so nothing has been loaded yet. In the next cell, however, we use `persist` to persist the data to the Dask worker nodes, and watch the progress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_var = ds['zeta'].max(dim='time').persist()\n",
    "progress(max_var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize data on mesh using Datashader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import datashader as dshade\n",
    "import holoviews as hv\n",
    "import geoviews as gv\n",
    "import cartopy.crs as ccrs\n",
    "\n",
    "from holoviews.operation.datashader import datashade, rasterize\n",
    "\n",
    "datashade.precompute = True\n",
    "\n",
    "hv.extension('bokeh')\n",
    "%opts Image RGB VectorField [width=800 height=600]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "v = np.vstack((ds['x'], ds['y'], max_var)).T\n",
    "verts = pd.DataFrame(v, columns=['x','y','vmax'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "points = gv.operation.project_points(gv.Points(verts, vdims=['vmax']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tris = pd.DataFrame(ds['element'].values.astype('int')-1, columns=['v0','v1','v2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tiles = gv.WMTS('https://maps.wikimedia.org/osm-intl/{Z}/{X}/{Y}@2x.png')\n",
    "value = 'max water level'\n",
    "label = '{} (m)'.format(value)\n",
    "trimesh = gv.TriMesh((tris, points), label=label)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%%opts Image [colorbar=True] (cmap='rainbow')\n",
    "\n",
    "meshes = rasterize(trimesh,aggregator=dshade.mean('vmax'))\n",
    "tiles * meshes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extract a time series at a specified lon, lat location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because Xarray does not yet understand that `x` and `y` are coordinate variables on this triangular mesh, we create our own simple function to find the closest point. If we had a lot of these, we could use a more fancy tree algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find the indices of the points in (x,y) closest to the points in (xi,yi)\n",
    "def nearxy(x,y,xi,yi):\n",
    "    ind = np.ones(len(xi),dtype=int)\n",
    "    for i in range(len(xi)):\n",
    "        dist = np.sqrt((x-xi[i])**2+(y-yi[i])**2)\n",
    "        ind[i] = dist.argmin()\n",
    "    return ind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#just offshore of Galveston\n",
    "lat = 29.2329856\n",
    "lon = -95.1535041"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = nearxy(ds['x'].values,ds['y'].values,[lon], [lat])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "ds['zeta'][:,ind].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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