COAWST_Dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COAWST Dashboard\n",
    "The COAWST Forecast is the only coupled ocean, atmosphere, wave and sediment transport model. Here we investigate the gridded COAWST forecast data products from [USGS Woods Hole THREDDS server](https://gamone.whoi.edu/thredds) and visualizing the data using the [pyviz](pyviz.org) tools. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_dataset('http://gamone.whoi.edu/thredds/dodsC/coawst_4/use/fmrc/coawst_4_use_best.ncd')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Find all the data variables that depend on time (and are not time `bounds`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rho_vars = []\n",
    "for var in ds.data_vars:\n",
    "    if len(ds[var].dims) > 0:\n",
    "        if 'time' in ds[var].dims[0] and (not 'offset' in var) and ('xi_rho' in ds[var].dims[-1]):\n",
    "            rho_vars.append(var)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the [pyviz](http://pyviz.org) tools we need"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from cartopy import crs as ccrs\n",
    "import hvplot.xarray\n",
    "import holoviews as hv\n",
    "from geoviews import tile_sources as gvts\n",
    "import panel as pn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create widget for variable selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_select = pn.widgets.Select(name='COAWST Variables:', options=rho_vars, \n",
    "                               value='Hwave')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create widget for basemap selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_map_select = pn.widgets.Select(name='Basemap:', options=gvts.tile_sources, value=gvts.OSM)                                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "The `plot` function below creates the `hvplot` panel layout object.  We specify a basemap, pick the `quadmesh` plot type for the selected variable, and indicate we want to `rasterize` the plot so that we can render massive meshes in the browser. We also specify the `groupby` parameter as the list of dimensions that remains after we remove Y and X: `ds[var].dims[:-2]`, which automatically handles variables with either dimensions [T, Y, X] or [T, Z, Y, X].  We also specify which `bokeh` controls we want to be active by default:  the `wheel_zoom` and `pan` controls.\n",
    "\n",
    "We also change the default slider to a selection widget for the `time` dimension so that specific times are easy to select.  See https://stackoverflow.com/a/54912917/2005869"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "crs = ccrs.PlateCarree()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(var=None, base_map=None):\n",
    "    base_map = base_map or base_map_select.value\n",
    "    var = var or var_select.value\n",
    "    mesh = ds[var][-24:,:,:].hvplot.quadmesh(x='lon_rho', y='lat_rho', rasterize=True, title=var,\n",
    "                                    width=600, height=400, crs=crs,\n",
    "                                    groupby=list(ds[var].dims[:-2]), cmap='jet')\n",
    "    mesh = mesh.redim.default(**{d: ds[d].values.max() for d in ds[var].dims[:-2]})\n",
    "    overlay = (base_map * mesh.opts(alpha=0.7)).opts(active_tools=['wheel_zoom', 'pan'])\n",
    "    widgets = {dim: pn.widgets.Select for dim in ds[var].dims[:-2]}\n",
    "    return pn.pane.HoloViews(overlay, widgets=widgets).layout"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_var_select(event):\n",
    "    var = event.obj.value\n",
    "    dashboard[-1] = plot(var=var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def on_base_map_select(event):\n",
    "    base_map = event.obj.value\n",
    "    dashboard[-1] = plot(base_map=base_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "var_select.param.watch(on_var_select, parameter_names=['value']);\n",
    "base_map_select.param.watch(on_base_map_select, parameter_names=['value']);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard = pn.Column(var_select, base_map_select, plot(var_select.value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use `.servable()` below not only to display the panel object, but to makes the panel servable outside the notebook via:  `panel serve COAWST_Dashboard.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dashboard.servable()"
   ]
  }
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