OISST_ice.ipynb

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   "source": [
    "# Plotting Mean Sea Ice Concentration\n",
    "\n",
    "[Video walkthrough](https://mediaspace.itap.purdue.edu/media/Dask%20Demo%201%20-%20Plotting%20Mean%20Sea%20Ice%20Concentration/1_7yr1cf21)\n",
    "\n",
    "This is a simple demo that involves reading multiple netCDF files, calculating means, and creating a simple plot using `matplotlib`.\n",
    "\n",
    "The demo notebook uses the following Python dependencies:\n",
    "\n",
    "```\n",
    "dask==2021.6.0\n",
    "dask-core==2021.6.0\n",
    "distributed==2021.6.0\n",
    "ipywidgets\n",
    "matplotlib\n",
    "netcdf4\n",
    "xarray\n",
    "```\n",
    "\n",
    "**If you're running this notebook on Binder, no setup is needed.** \n",
    "\n",
    "If you're running in another notebook environment, you need to install some dependencies and download data files. Please follow the [setup guide](https://pages.github.itap.purdue.edu/xiao253/dask-demo/#user-guides)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "This demo notebook uses the NOAA OISST (v2.1) dataset.\n",
    "\n",
    "Links:\n",
    "- Learn more about the dataset at: https://www.ncdc.noaa.gov/oisst/optimum-interpolation-sea-surface-temperature-oisst-v20\n",
    "- Browse all available data at: https://www.ncei.noaa.gov/data/sea-surface-temperature-optimum-interpolation/v2.1/access/avhrr/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Start a Local Dask Cluster\n",
    "\n",
    "The \"local\" Dask cluster runs on the notebook server."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Suppress warnings, which are generally safe to ignore\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, LocalCluster, progress\n",
    "import xarray as xr\n",
    "\n",
    "# This will automatically determine the number of workers based on available CPU cores\n",
    "cluster = LocalCluster()\n",
    "# Or, manually specify the desired number of workers\n",
    "# cluster = LocalCluster(n_workers=12)\n",
    "\n",
    "client = Client()\n",
    "client"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preview source data\n",
    "\n",
    "Here we load one NetCDF file that contains data from Feb. 1, 2020, and create a plot of sea ice concentration.\n",
    "\n",
    "Note that Dask is not involved in this section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read netCDF file and load sea ice concentration variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read netcdf file as xarray Dataset\n",
    "ds = xr.open_dataset(\"data/oisst-avhrr-v02r01.20200201.nc\")\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load sea ice concentration variable into xarray DataArray\n",
    "da = ds['ice']\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot sea ice concentration with `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib notebook\n",
    "\n",
    "da.load().plot(figsize=(9, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dask / Xarray with Multiple netCDF Files\n",
    "\n",
    "Here we load all NetCDF files that contains data from Feb 2020 (29 files total), and create a plot of the monthly mean sea ice concentration.\n",
    "\n",
    "Note: The following steps use Dask."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Open All netCDF Files and load sea ice concentration variable "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import glob\n",
    "\n",
    "# build file list\n",
    "netcdf_list = glob.glob('data/oisst-avhrr-v02r01.*.nc')\n",
    "netcdf_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "\n",
    "# read files into xarray Dataset\n",
    "ds = xr.open_mfdataset(paths=netcdf_list)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load sea ice concentration variable data into xarray DataArray\n",
    "da = ds['ice']\n",
    "da"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculate mean monthly sea ice concentration "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_mean = da.groupby('time.month').mean()\n",
    "da_mean = da_mean.persist()  # persist mean DataArray in memory\n",
    "progress(da_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_mean"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot mean monthly sea ice concentration with `matplotlib`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_mean.plot(figsize=(9, 5))"
   ]
  }
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