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   "source": [
    "# xmitgcm llcreader demo\n",
    "\n",
    "This notebooks contains a demonstration of reading and visulizing data from the\n",
    "[NAS ECCO Data Portal](https://data.nas.nasa.gov/ecco/).\n",
    "It makes use of the following software libraries:\n",
    "\n",
    "- [xmitgcm](https://xmitgcm.readthedocs.io/en/latest/llcreader.html): provides the `llcreader` module which makes all of this work\n",
    "- [xarray](http://xarray.pydata.org/en/latest/): the basic data structures and computational library for the datasets \n",
    "- [dask](https://dask.org/): the parallel computing library which enables lazy representations of huge arrays\n",
    "- [holoviews](http://holoviews.org/): interactive visualizations\n",
    "\n",
    "We start by importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xmitgcm.llcreader as llcreader\n",
    "%matplotlib inline\n",
    "import holoviews as hv\n",
    "from holoviews.operation.datashader import regrid\n",
    "hv.extension('bokeh')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize Model Object\n",
    "\n",
    "[xmitgcm.llcreader]() contains pre-configured references to known LLC model configurations.\n",
    "We create one of these references as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = llcreader.ECCOPortalLLC2160Model()\n",
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create Xarray Dataset\n",
    "\n",
    "The `model` object can generate xarray datasets for us.\n",
    "In this example, we generate a dataset which contains the SST for the full model integration.\n",
    "The `type='latlon'` keyword tells xmitgcm to just show us the \"Lat Lon\" (LL) part of the LLC grid."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_sst = model.get_dataset(varnames=['Theta'], k_levels=[0], type='latlon')\n",
    "ds_sst"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset is \"lazy\"; it doesn't actually load any data from the server until required for computation or plotting.\n",
    "That's a good thing, because it represents over 4 TB of data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_sst.nbytes / 1e12"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dynamic Interactive Visualization\n",
    "\n",
    "Here we create an interactive map of SST which automatically resamples the fields at a resolution appropriate for our screen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset = hv.Dataset(ds_sst.Theta.isel(k=0).astype('f4'))\n",
    "hv_im = (dataset.to(hv.Image, ['i', 'j'], dynamic=True)\n",
    "                .options(cmap='Magma', width=950, height=600, colorbar=True))\n",
    "\n",
    "%output holomap='scrubber' fps=3\n",
    "regrid(hv_im, precompute=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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