DaymetV4_access_subsetting_working.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b482542e",
   "metadata": {},
   "source": [
    "# Daymet V4 - Programmatic Data Discovery, Access, Subsetting, and Download"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50489f29",
   "metadata": {},
   "source": [
    "*Author:  ORNL DAAC*\n",
    "<br>\n",
    "*Date: August 31, 2021*\n",
    "<br>\n",
    "*Contact for [ORNL DAAC](https://daac.ornl.gov/):  uso@daac.ornl.gov*\n",
    "\n",
    "##### Keywords:  ORNL DAAC, Daymet, THREDDS, OPeNDAP, Earthdata, `pydap`, `xarray`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbc9a790",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In this tutorial, we're interested in obtaining gridded weather data that covers the spatial region of the Great Smokey Mountains National Park.  We'll learn to programmatically search NASA's Data Metadata holdings to discover the data based on our user-defined search parameters.  Using Python libraries and modules, we'll programmatically access and subset the data before downloading it.  Specifically, we will demonstrate how to programatically discover, access, subset, and download  [Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1840).  \n",
    "\n",
    "![title](images/NPS_great-smoky-mountains-national-park-map.jpg)\n",
    "\n",
    "The **Daymet** datset provides long-term, gridded estimates of daily weather data and standard climatologies interpolated and extrapoloated from ground-based surface weather observations.  Daymet weather variables include: \n",
    "\n",
    "DAYMET VARIABLES:\n",
    "\n",
    "| Variable | Description (units)                           \n",
    "|:---------|:---------------------------------------:|\n",
    "| tmax     | Daily maximum 2-meter air temperature (°C)\n",
    "| tmin     | Daily minimum 2-meter air temperature (°C)\n",
    "| prcp     | Daily total precipitation (mm/day)\n",
    "| srad     | Incident shortwave radiation flux density (W/m2)\n",
    "| vp       | Water vapor pressure (Pa)\n",
    "| swe      | Snow water equivalent (kg/m2)\n",
    "| dayl     | Duration of the daylight period (seconds/day)\n",
    "\n",
    "Daily files are produced on a **1km x 1km** spatial grid and distributed in a multidimensional, netCDF, file format. Daymet data is available for continenal North America (na), with Hawaii (hi), and Puerto Rico (pr) as separate data files.\n",
    "\n",
    "![title](images/Daymet_V4_Annual_Climatology_Fig1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bffead84",
   "metadata": {},
   "source": [
    "## Source Data\n",
    "The [Daymet Website](https://daymet.ornl.gov) provides information about and access methods for the Daymet datasets under the [Get Data](https://daymet.ornl.gov/getdata) tab.\n",
    "\n",
    "At the time of this webinar (Aug, 2021) the Daymet V4 data files are available from both on-prem resouces at the ORNL DAAC and NASA Earthdata Cloud.  This tutorial covers accessing data from on-prem THREDDS Data Server/OPeNDAP servers.  Cloud-based storage and access will eventually replace on-prem access.  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad88a1e5",
   "metadata": {},
   "source": [
    "OPeNDAP\n",
    "* Support for [OPeNDAP](https://www.opendap.org/) (via the netCDF4 library or Pydap), lets us access large datasets over HTTP."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28640b33",
   "metadata": {},
   "source": [
    "### Requirements\n",
    "* Python 3.7 or higher\n",
    "* geopandas and dependencies\n",
    "* pydap\n",
    "* xarray\n",
    "* rasterio\n",
    "* shapely"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "24350611",
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "import pyproj\n",
    "import datetime as dt \n",
    "from rasterio.features import Affine\n",
    "from pydap.client import open_url\n",
    "from pydap.cas.urs import setup_session\n",
    "import xarray as xr\n",
    "import rioxarray\n",
    "import shapely\n",
    "import rasterio\n",
    "from shapely.geometry import mapping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e6d496fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This Notebook was produced with the following versions\n",
      "geopanda version   :  0.9.0\n",
      "pyproj version     :  3.1.0\n",
      "rioxarray version  :  0.5.0\n",
      "xarray version     :  0.18.2\n",
      "shapely            :  1.7.1\n",
      "rasterio           :  1.2.6\n"
     ]
    }
   ],
   "source": [
    "print(\"This Notebook was produced with the following versions\")\n",
    "print(\"geopanda version   : \", gpd.__version__)\n",
    "print(\"pyproj version     : \", pyproj.__version__)\n",
    "print(\"rioxarray version  : \", rioxarray.__version__)\n",
    "print(\"xarray version     : \", xr.__version__)\n",
    "print(\"shapely            : \", shapely.__version__)\n",
    "print(\"rasterio           : \", rasterio.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "460917a8",
   "metadata": {},
   "source": [
    "***\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48b262b8",
   "metadata": {},
   "source": [
    "## Tutorial\n",
    "\n",
    "Steps we'll explore in this tutorial are: \n",
    "1.  Using a georeferenced polygon file, we'll establish a boundary parameters to use as input for subsetting Daymet Data\n",
    "1. We'll leverage the Daymet DOI and NASA's Common Metadata Repository (CMR) API to search for Daymet data within our spatial and temporal region of interest\n",
    "1. Programmatically download subsets of data via OPeNDAP \n",
    "1. Clip the Daymet subset to our polygon region of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95a1706a",
   "metadata": {},
   "source": [
    "### Step 1.  Setting Search and Subset Parameters: Spatial Area of Interest and Time Range of Interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c1dbba4",
   "metadata": {},
   "source": [
    "We will use a boundary file of the [Great Smokey Mountain National Park (GRSM)](https://www.nps.gov/grsm/planyourvisit/maps.htm) to obtain boundary parameters to use in demonstrating a programmatic spatial subsetting process. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fe0b9e",
   "metadata": {},
   "source": [
    "We'll use Python's [GeoPandas](https://geopandas.org/index.html) to provide a spatial extent from a the polygon file.\n",
    "\n",
    "* **`Geopandas`** is a Pythhon library that enables the use and manipulation of geospatial data. It extends the common datatype used in pandas to allow for the many and unique geometric operations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1cd02e0c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Projected CRS: EPSG:26917>\n",
       "Name: NAD83 / UTM zone 17N\n",
       "Axis Info [cartesian]:\n",
       "- E[east]: Easting (metre)\n",
       "- N[north]: Northing (metre)\n",
       "Area of Use:\n",
       "- name: North America - between 84°W and 78°W - onshore and offshore. Canada - Nunavut; Ontario; Quebec. United States (USA) - Florida; Georgia; Kentucky; Maryland; Michigan; New York; North Carolina; Ohio; Pennsylvania; South Carolina; Tennessee; Virginia; West Virginia.\n",
       "- bounds: (-84.0, 23.81, -78.0, 84.0)\n",
       "Coordinate Operation:\n",
       "- name: UTM zone 17N\n",
       "- method: Transverse Mercator\n",
       "Datum: North American Datum 1983\n",
       "- Ellipsoid: GRS 1980\n",
       "- Prime Meridian: Greenwich"
      ]
     },
     "execution_count": 3,
     "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": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "grsm_poly = gpd.read_file('bnds/GRSM_BOUNDARY_POLYGON_fid17.shp')\n",
    "ax = grsm_poly.plot() # boundary file of the Great Smokey Mountains National Park\n",
    "\n",
    "grsm_poly.crs  # Lists the coordinate reference system (crs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16907818",
   "metadata": {},
   "source": [
    "As we see above, the boundary file is in the `UTM 17N` projection. Daymet is distributed in the `lambert_conformal_conic (LCC)` system. \n",
    "\n",
    "Data can be re-projected using the **`GeoDataFrame.to_crs`** command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "52ae71b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minx</th>\n",
       "      <th>miny</th>\n",
       "      <th>maxx</th>\n",
       "      <th>maxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-84.000404</td>\n",
       "      <td>35.428962</td>\n",
       "      <td>-82.998041</td>\n",
       "      <td>35.789109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        minx       miny       maxx       maxy\n",
       "0 -84.000404  35.428962 -82.998041  35.789109"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's determine and store the geographic bounding box of the Park boundary for the OPeNDAP searching inputs below\n",
    "# data can be re-projected using the GeoDataFrame.to_crs() command:\n",
    "grsm_poly_4326 = grsm_poly.to_crs(epsg=4326)\n",
    "xy = grsm_poly_4326.bounds # bound of polygon in lat, lon \n",
    "xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7a0a4a4d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-84.00040431754826, 35.42896172322691, -82.99804055694513, 35.7891093036265]\n"
     ]
    }
   ],
   "source": [
    "xy = grsm_poly_4326.bounds.values.tolist()[0] # We'll need the bounding box as a Python list \n",
    "                                              # to server as a subsetting parameter\n",
    "print(xy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c2e371",
   "metadata": {},
   "source": [
    "We'll also reproject the geopandas object (`grsm_poly`) to Daymet's Lambert Conformal Conic projection using the **`geopandas`** **`to_crs`** ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4c6898c0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Projected bounding box of the Great Smokey Mountain polygon file in LCC projection\n",
      "----------------------------------------------------------------------------------\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>minx</th>\n",
       "      <th>miny</th>\n",
       "      <th>maxx</th>\n",
       "      <th>maxy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.386957e+06</td>\n",
       "      <td>-612937.065386</td>\n",
       "      <td>1.470587e+06</td>\n",
       "      <td>-565105.068335</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           minx           miny          maxx           maxy\n",
       "0  1.386957e+06 -612937.065386  1.470587e+06 -565105.068335"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#defining Daymet proj - we'll use this in a later step\n",
    "daymet_proj = \"+proj=lcc +ellps=WGS84 +a=6378137 +b=6356752.314245 +lat_1=25 +lat_2=60 +lon_0=-100 +lat_0=42.5 +x_0=0 +y_0=0 +units=m +no_defs\"\n",
    "grsm_poly_lcc = grsm_poly.to_crs(daymet_proj) # to_crs re-projects from UTM 17N to LCC\n",
    "lccbounds = grsm_poly_lcc.bounds # Bounds in LCC projection\n",
    "print('Projected bounding box of the Great Smokey Mountain polygon file in LCC projection')\n",
    "print('----------------------------------------------------------------------------------')\n",
    "lccbounds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf5a80ac",
   "metadata": {},
   "source": [
    "These are the bounding minx, miny, maxx, and maxy in the LCC Projection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "311e0720",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=(12, 8))\n",
    "#grsm_poly.plot(ax=ax1, facecolor='blue');\n",
    "grsm_poly_4326.plot(ax=ax1, facecolor='blue');\n",
    "ax1.set_title(\"GRSM Geographic Coordinate System\");\n",
    "grsm_poly_lcc.plot(ax=ax2, facecolor='grey');\n",
    "ax2.set_title(\"GRSM Daymet LCC Projection\");\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c67bed4f",
   "metadata": {},
   "source": [
    "For our search, we'll use the Bounding Box of the Great Smokey Mountains National Park that we defined above.\n",
    "\n",
    "Let's also define a `time range` in a format that the API recognizes, and the Daymet `variable` of interest :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "15e14b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-01 00:00:00\n",
      "2011-01-01 00:00:00\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt \n",
    "\n",
    "start_date = dt.datetime(2010, 1, 1) # specify your own start date\n",
    "end_date = dt.datetime(2011, 1, 1)  # specify your end start date\n",
    "\n",
    "print(start_date,end_date,sep='\\n')\n",
    "\n",
    "dt_format = '%Y-%m-%dT%H:%M:%SZ' # format requirement for datetime search\n",
    "temporal_str = start_date.strftime(dt_format) + ',' + end_date.strftime(dt_format)\n",
    "\n",
    "var = 'prcp' # select a Daymet variable of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3388dc7",
   "metadata": {},
   "source": [
    "##### Now that we have our spatial area-of-interest, we'll see how we can programatically use that as a search parameter to discover, access, and subset Daymet V4 data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f69c02",
   "metadata": {},
   "source": [
    "### Step 2. Leveraging NASA's Common Metadata Repository (CMR) API to search NASA Earthdata for Daymet data within a spatial and temporal region of interest"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "588ff659",
   "metadata": {},
   "source": [
    "#### A little background ...\n",
    "\n",
    "- Searching [NASA's Earthdata Holdings](https://search.earthdata.nasa.gov/search) can lead to quite a few files for a user to consider. \n",
    "\n",
    "- NASA's [Common Metadata Repository (CMR)](https://earthdata.nasa.gov/eosdis/science-system-description/eosdis-components/cmr) is a metadata system that catalogs all data and metadata records for NASA's Earth Observing System Data and Information System (EOSDIS) system.\n",
    "\n",
    "- These metadata records can be used to discovered and access data files through programmatic interfaces leveraging standard protocols and APIs.\n",
    "\n",
    "- All NASA Earth Science archived data is assigneed a permanent Digital Object Identify [DOI](https://www.doi.org/) that is searchable through the metadata collection (CMR).  \n",
    "\n",
    "- The [Daymet V4 Daily](https://daac.ornl.gov/cgi-bin/dsviewer.pl?ds_id=1840) data DOI, **doi.org/10.3334/ORNLDAAC/1840**.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "Thornton, M.M., R. Shrestha, Y. Wei, P.E. Thornton, S. Kao, and B.E. Wilson. 2020. Daymet: Daily Surface Weather Data on a 1-km Grid for North America, Version 4. ORNL DAAC, Oak Ridge, Tennessee, USA. https://doi.org/10.3334/ORNLDAAC/1840\n",
    "</div>\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00c5edf7",
   "metadata": {},
   "source": [
    "##### To start, let's look at ORNL DAAC holdings of the Daymet V4 Daily Data available through a Thredds Data Server and OPeNDAP Protocols\n",
    "\n",
    "[Daymet V4 Daily Data in the ORNL DAAC THREDDS Data Server](https://thredds.daac.ornl.gov/thredds/catalog/ornldaac/1840/catalog.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a382f912",
   "metadata": {},
   "source": [
    "#### Daymet Version 4 in NASA's EarthData Collections \n",
    "\n",
    "We'll demonstrate how to use the Daymet dataset DOI and the NASA EarthData's unique ID (called the **`Concept ID`**) for the **Daymet V4 Daily** data to search using NASA's Metadata Collections [CMR API](https://cmr.earthdata.nasa.gov/search).  With this information, we can search all NASA Earthdata holdings for relevant data files (granules) based on our spatial and temporal region of interest.\n",
    "\n",
    "1. Search NASA's Daymet metadata `collections` (CMR) to obtain the ConceptID for the Daymet V4 Daily Data\n",
    "1. Define more search parameters (bounding box, temporal range)\n",
    "2. Search for Daymet `granules` (data files) that match our search parameters\n",
    "\n",
    "Let's see how we can search, discover, and subset data from NASA Earthdata Holdings based on:  \n",
    "* A dataset's DOI\n",
    "* A dataset's ConceptID\n",
    "* And Area of Interest\n",
    "* A Time Range of Interest\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0839dc75",
   "metadata": {},
   "source": [
    "##### Obtaining the Daymet V4 Daily data *Concept ID*\n",
    "Using Python's **`requests`** library, we will build and provide an http request to search **NASA's Metadata Collection** for Daymet V4 Daily Dataset Collection to acquire the **concept_id** unique to the Daymet V4 Daily data in NASA's Earthdadta holdings.  \n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "https://cmr.earthdata.nasa.gov/search/collections\n",
    "</div>\n",
    "\n",
    "We'll use the **concept_id** in a next step when we search **NASA Earthdata Granules** of the Daymet V4 Daily Collection for our spatial and temporal area of interest.\n",
    "\n",
    "<div class=\"alert alert-block alert-info\">\n",
    "https://cmr.earthdata.nasa.gov/search/granules\n",
    "</div>\n",
    "\n",
    "* **`requests`** is a simple HTTP libray for Python.  It allows you to easily send HTTP requests."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f5e5eb7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Earthdata Metadata for DaymetV4 Daily -->  https://cmr.earthdata.nasa.gov/search/collections.json?doi=10.3334/ORNLDAAC/1840\n"
     ]
    }
   ],
   "source": [
    "import requests\n",
    "daymet_doi = '10.3334/ORNLDAAC/1840' # define the Daymet V4 Daily Data DOI as the variable `daymet_doi`\n",
    "cmrurl='https://cmr.earthdata.nasa.gov/search/' # define the base url of NASA's CMR API as the variable `cmrurl`\n",
    "doisearch = cmrurl + 'collections.json?doi=' + daymet_doi # Create the Earthdata Collections URL\n",
    "print('Earthdata Metadata for DaymetV4 Daily --> ' , doisearch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c42b5e5c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NASA Earthdata Concept_ID -->  C2031536952-ORNL_CLOUD\n"
     ]
    }
   ],
   "source": [
    "# From the doisearch, we can obtain the ConceptID for the Daymet V4 Daily data\n",
    "# We'll search the json response of the Daymet metadata for \"id\" within the 'entry' dictionary key\n",
    "response = requests.get(doisearch)\n",
    "collection = response.json()['feed']['entry'][0] \n",
    "#print(collection)\n",
    "concept_id = collection['id']\n",
    "print('NASA Earthdata Concept_ID --> ' , concept_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2086317c",
   "metadata": {},
   "source": [
    "`C2031536952-ORNL_CLOUD` is the unique NASA-given concept ID for the Daymet dataset. We can use this to search relevant granules and their names. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe9d6cab",
   "metadata": {},
   "source": [
    "##### Searching for Daymet Files \n",
    "Daymet V4 Daily Data files (or granules) are natively in netCDF4 format, and each file has one year's worth of data. Data files are organized by variables (dayl, prcp, tmin, tmax, srad, swe, vp) and regions (each for na, pr, hi). \n",
    "\n",
    "We'll first search for all the granules in our area of interest (i.e., GRSM) and time period of interest (defined above).  Daymet filenames can be used to identify the files from continental North America (`*_na_*.nc`).  We set the Daymet variable to  download as precipitation data (`prcp`) for the year 2010-2020."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "50d50640",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Variables we have defined to pass to the CMR API\n",
      "-----------------------------------------------\n",
      "time range :      2010-01-01T00:00:00Z,2011-01-01T00:00:00Z\n",
      "Daymet variable : prcp\n",
      "bounding box :    [-84.00040431754826, 35.42896172322691, -82.99804055694513, 35.7891093036265]\n",
      "concept ID :      C2031536952-ORNL_CLOUD\n",
      "cmr url :         https://cmr.earthdata.nasa.gov/search/\n"
     ]
    }
   ],
   "source": [
    "print('Variables we have defined to pass to the CMR API')\n",
    "print('-----------------------------------------------')\n",
    "print('time range :     ', temporal_str)\n",
    "print('Daymet variable :', var)\n",
    "print('bounding box :   ', xy)\n",
    "print('concept ID :     ', concept_id)\n",
    "print('cmr url :        ', cmrurl)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b18731a4",
   "metadata": {},
   "source": [
    "##### Searching NASA's Earthdata Holdings\n",
    "#### Now that we have all our parameters defined, let's search NASA's Earthdata Holdings for all the data that are within those search criteria.\n",
    "\n",
    "We'll first define our **granule search URL**,  based on all our parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "44f2206f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "https://cmr.earthdata.nasa.gov/search/granules.json?collection_concept_id=C2031536952-ORNL_CLOUD&page_size=1000&temporal=2010-01-01T00:00:00Z,2011-01-01T00:00:00Z&bounding_box[]=-84.00040431754826,35.42896172322691,-82.99804055694513,35.7891093036265\n"
     ]
    }
   ],
   "source": [
    "granulesearch = cmrurl + 'granules.json?collection_concept_id=' + concept_id + \\\n",
    "                '&page_size=1000' + '&temporal=' + temporal_str + \\\n",
    "                '&bounding_box[]=' + ','.join(map(str, xy))\n",
    "print(granulesearch)                "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a02f914",
   "metadata": {},
   "source": [
    "Using Python's **`requests`** library, we can provide the URL `granulesearch` created above to create a listing of all the granules (files) in **NASA's Earthdata** holdings that fit the search criteria we defined in the search granule URL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "cb30dcca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Daymet_Daily_V4.daymet_v4_daily_na_prcp_2010.nc\n",
      "Daymet_Daily_V4.daymet_v4_daily_na_prcp_2011.nc\n"
     ]
    }
   ],
   "source": [
    "response = requests.get(granulesearch)\n",
    "granules = response.json()['feed']['entry']  # Michele, look at url feed, 'entry' key in the granulesearch URL {}\n",
    "granule_names = []  # create an empty array\n",
    "for g in granules:\n",
    "    granule_name = g['title'] # fill the array with granule names that match our search parameters\n",
    "    if var in granule_name:\n",
    "        granule_names.append(granule_name)\n",
    "        print(granule_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6599ba19",
   "metadata": {},
   "source": [
    "##### Now the array `granule_names` contains the names of all the granules we need.\n",
    "\n",
    "**Note:** A Daymet V4 Daily Data file has the name **daymet_v4_daily_area_var_year.nc**.  The beginning of the filename (**\"Daymet_Daily_V4.\"**) is specific to the Earthdata naming convention and because we are searching CMR, the Earthdata files will be discovered.  We'll strip out (**\"Daymet_Daily_V4.\"**) below when we access the files from the on-prem ORNL DAAC's OPeNDAP client through THREDDS."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9f08124e",
   "metadata": {},
   "source": [
    "### Step 3. Downloading subset data through ORNL DAAC THREDDS Data Server\n",
    "Daymet data is available through ORNL DAAC's THREDDS Data Server. We will use this to subset and download Daymet V4 Daily Data the based on the **`granual_names`** array we saved above, our polygonal region of interest, time range of interest, and Daymet variable."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa74962e",
   "metadata": {},
   "source": [
    "We'll use Pythons **`pydap`** library and the bounding box of the polygon file.\n",
    "\n",
    "* **`pydap`** can be used to inspect data from any scientific dataset available on the internet from OPeNDAP servers. This allows inspection and manipulation of scientific data as if it were stored locally.  Data can be downloaded on-the-fly as needed.\n",
    "\n",
    "We'll use Python's **`Xarray`** Library to \n",
    "* open the data files we are searching for, \n",
    "* subset the North American file based on the Lambert Conformal Conic xy list we created earlier, \n",
    "* concatenate the time range into one file, and \n",
    "* save concatened file to a netCDF file.\n",
    "\n",
    "Xarray\n",
    "* **[xarray open_dataset](http://xarray.pydata.org/en/stable/generated/xarray.open_dataset.html)** \n",
    "* **xarray slice**\n",
    "* **xarray concat**\n",
    "* **xarray to_netcdf**\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a15b752b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " ***GRANULE_NAME*** ----> Daymet_Daily_V4.daymet_v4_daily_na_prcp_2010.nc\n",
      "https://thredds.daac.ornl.gov/thredds/dodsC/ornldaac/1840/daymet_v4_daily_na_prcp_2010.nc\n",
      " ***GRANULE_NAME*** ----> Daymet_Daily_V4.daymet_v4_daily_na_prcp_2011.nc\n",
      "https://thredds.daac.ornl.gov/thredds/dodsC/ornldaac/1840/daymet_v4_daily_na_prcp_2011.nc\n",
      "Processing Time:  81.66478967666626 seconds\n"
     ]
    }
   ],
   "source": [
    "from pydap.client import open_url\n",
    "from pydap.cas.urs import setup_session # setup_session \n",
    "import xarray as xr\n",
    "import time\n",
    "\n",
    "#username = \"XXXX\" # Not required here.  Earthdata URS Login (only needed for NGAP Hyrax) Get account here https://urs.earthdata.nasa.gov/\n",
    "#password= \"XXXX\"\n",
    "\n",
    "thredds_url = 'https://thredds.daac.ornl.gov/thredds/dodsC/ornldaac/1840/' # ORNL DAAC TDS OPeNDAP URL \n",
    "                                                                           # for Daymet V4 Daily Files\n",
    "#session = setup_session(username, password, check_url=thredds_url)  ### Session is NOT needed for ORNLDAAC THREDDS\n",
    "before = time.time()\n",
    "cnt = 0 \n",
    "for g_name in granule_names:\n",
    "    print(' ***GRANULE_NAME*** ---->', g_name)\n",
    "    granule_dap = thredds_url + g_name.replace('Daymet_Daily_V4.','')\n",
    "    print(granule_dap)\n",
    "    \n",
    "    # Using pydap's open_url \n",
    "    thredds_ds = open_url(granule_dap) #, session=session)  ### session would be needed here only with Earthdata access\n",
    "    \n",
    "    # Xarray DataSet - opening dataset via remote OPeNDAP \n",
    "    ds = xr.open_dataset(xr.backends.PydapDataStore(thredds_ds), decode_coords=\"all\")\n",
    "        \n",
    "    temp=ds['prcp'].sel(x=slice(lccbounds.minx[0],lccbounds.maxx[0]), y=slice(lccbounds.maxy[0],lccbounds.miny[0]))\n",
    "    \n",
    "    if cnt==0:\n",
    "        prcp = temp\n",
    "    else:\n",
    "        prcp = xr.concat([prcp, temp], dim=\"time\")\n",
    "    \n",
    "    cnt += 1\n",
    "    \n",
    "\n",
    "# save to netcdf\n",
    "prcp.to_netcdf(var + '_tdssubset.nc')\n",
    "print(\"Processing Time: \", time.time() - before, 'seconds')\n",
    "#Processing Time:  50.4509379863739 seconds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf62c55c",
   "metadata": {},
   "source": [
    "##### Let's take a look at the Daymet Precipitation Subset\n",
    "* notice the time dimension and the subset of x,y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "57c5f29e",
   "metadata": {},
   "outputs": [
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       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;prcp&#x27; (time: 730, y: 47, x: 83)&gt;\n",
       "array([[[0.  , 0.  , 0.  , ..., 3.25, 2.99, 2.74],\n",
       "        [0.  , 0.  , 0.  , ..., 3.27, 3.  , 2.73],\n",
       "        [0.  , 0.  , 0.  , ..., 3.28, 2.98, 2.71],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.61, 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[1.58, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.62, 1.68, 1.7 , ..., 0.  , 0.  , 0.  ],\n",
       "        [1.63, 1.7 , 1.76, ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [1.66, 1.67, 1.67, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.68, 1.69, ..., 0.  , 0.  , 0.  ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 1.388e+06 1.389e+06 ... 1.47e+06\n",
       "  * y                        (y) float32 -5.66e+05 -5.67e+05 ... -6.12e+05\n",
       "    lat                      (y, x) float32 35.9 35.9 35.9 ... 35.34 35.33 35.33\n",
       "    lon                      (y, x) float32 -83.91 -83.9 -83.89 ... -83.1 -83.09\n",
       "  * time                     (time) datetime64[ns] 2010-01-01T12:00:00 ... 20...\n",
       "    lambert_conformal_conic  int16 -32767\n",
       "Attributes:\n",
       "    long_name:     daily total precipitation\n",
       "    units:         mm/day\n",
       "    cell_methods:  area: mean time: sum\n",
       "    _ChunkSizes:   [1, 1000, 1000]</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'prcp'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 730</li><li><span class='xr-has-index'>y</span>: 47</li><li><span class='xr-has-index'>x</span>: 83</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-ff87df87-4522-404f-8f6f-5946008e0068' class='xr-array-in' type='checkbox' checked><label for='section-ff87df87-4522-404f-8f6f-5946008e0068' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0</span></div><div class='xr-array-data'><pre>array([[[0.  , 0.  , 0.  , ..., 3.25, 2.99, 2.74],\n",
       "        [0.  , 0.  , 0.  , ..., 3.27, 3.  , 2.73],\n",
       "        [0.  , 0.  , 0.  , ..., 3.28, 2.98, 2.71],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.61, 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[1.58, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.62, 1.68, 1.7 , ..., 0.  , 0.  , 0.  ],\n",
       "        [1.63, 1.7 , 1.76, ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [1.66, 1.67, 1.67, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.68, 1.69, ..., 0.  , 0.  , 0.  ]]], dtype=float32)</pre></div></div></li><li class='xr-section-item'><input id='section-e46911d3-6cce-475c-ac4f-bde0c0dec385' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e46911d3-6cce-475c-ac4f-bde0c0dec385' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.388e+06 1.389e+06 ... 1.47e+06</div><input id='attrs-ec645f3a-3f99-4765-826c-241ea6c908e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec645f3a-3f99-4765-826c-241ea6c908e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5152cc6f-e18c-4cdc-98af-bb2bc806657d' class='xr-var-data-in' type='checkbox'><label for='data-5152cc6f-e18c-4cdc-98af-bb2bc806657d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>_ChunkSizes :</span></dt><dd>7814</dd></dl></div><div class='xr-var-data'><pre>array([1387750., 1388750., 1389750., 1390750., 1391750., 1392750., 1393750.,\n",
       "       1394750., 1395750., 1396750., 1397750., 1398750., 1399750., 1400750.,\n",
       "       1401750., 1402750., 1403750., 1404750., 1405750., 1406750., 1407750.,\n",
       "       1408750., 1409750., 1410750., 1411750., 1412750., 1413750., 1414750.,\n",
       "       1415750., 1416750., 1417750., 1418750., 1419750., 1420750., 1421750.,\n",
       "       1422750., 1423750., 1424750., 1425750., 1426750., 1427750., 1428750.,\n",
       "       1429750., 1430750., 1431750., 1432750., 1433750., 1434750., 1435750.,\n",
       "       1436750., 1437750., 1438750., 1439750., 1440750., 1441750., 1442750.,\n",
       "       1443750., 1444750., 1445750., 1446750., 1447750., 1448750., 1449750.,\n",
       "       1450750., 1451750., 1452750., 1453750., 1454750., 1455750., 1456750.,\n",
       "       1457750., 1458750., 1459750., 1460750., 1461750., 1462750., 1463750.,\n",
       "       1464750., 1465750., 1466750., 1467750., 1468750., 1469750.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-5.66e+05 -5.67e+05 ... -6.12e+05</div><input id='attrs-a41c5abd-2f74-4eca-9978-c7d36794563b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a41c5abd-2f74-4eca-9978-c7d36794563b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74727bcd-8404-4c06-8623-b5ed4e85105f' class='xr-var-data-in' type='checkbox'><label for='data-74727bcd-8404-4c06-8623-b5ed4e85105f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>_ChunkSizes :</span></dt><dd>8075</dd></dl></div><div class='xr-var-data'><pre>array([-566000., -567000., -568000., -569000., -570000., -571000., -572000.,\n",
       "       -573000., -574000., -575000., -576000., -577000., -578000., -579000.,\n",
       "       -580000., -581000., -582000., -583000., -584000., -585000., -586000.,\n",
       "       -587000., -588000., -589000., -590000., -591000., -592000., -593000.,\n",
       "       -594000., -595000., -596000., -597000., -598000., -599000., -600000.,\n",
       "       -601000., -602000., -603000., -604000., -605000., -606000., -607000.,\n",
       "       -608000., -609000., -610000., -611000., -612000.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>35.9 35.9 35.9 ... 35.33 35.33</div><input id='attrs-bafe8b7a-723b-4030-878d-67e9e7f66a01' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bafe8b7a-723b-4030-878d-67e9e7f66a01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69c5fd60-c43f-42b2-8ff0-a166753c21ca' class='xr-var-data-in' type='checkbox'><label for='data-69c5fd60-c43f-42b2-8ff0-a166753c21ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude coordinate</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>_ChunkSizes :</span></dt><dd>[1010, 977]</dd></dl></div><div class='xr-var-data'><pre>array([[35.904938, 35.903137, 35.90134 , ..., 35.75718 , 35.75528 ,\n",
       "        35.753384],\n",
       "       [35.895737, 35.893936, 35.89214 , ..., 35.747997, 35.7461  ,\n",
       "        35.744205],\n",
       "       [35.886536, 35.884735, 35.88294 , ..., 35.73882 , 35.736923,\n",
       "        35.735027],\n",
       "       ...,\n",
       "       [35.50019 , 35.4984  , 35.496616, ..., 35.353428, 35.351543,\n",
       "        35.349655],\n",
       "       [35.490993, 35.48921 , 35.48742 , ..., 35.344254, 35.34237 ,\n",
       "        35.340485],\n",
       "       [35.481796, 35.48001 , 35.478226, ..., 35.33508 , 35.333195,\n",
       "        35.33131 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-83.91 -83.9 ... -83.1 -83.09</div><input id='attrs-9b058c40-de95-45f8-a118-873558c9bf13' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b058c40-de95-45f8-a118-873558c9bf13' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6797434-fdf0-457a-a771-76beb450efea' class='xr-var-data-in' type='checkbox'><label for='data-c6797434-fdf0-457a-a771-76beb450efea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude coordinate</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>_ChunkSizes :</span></dt><dd>[1010, 977]</dd></dl></div><div class='xr-var-data'><pre>array([[-83.90819 , -83.89688 , -83.885574, ..., -83.00533 , -82.99407 ,\n",
       "        -82.98281 ],\n",
       "       [-83.9104  , -83.89909 , -83.88779 , ..., -83.00766 , -82.9964  ,\n",
       "        -82.985146],\n",
       "       [-83.912605, -83.9013  , -83.88999 , ..., -83.00999 , -82.998726,\n",
       "        -82.98747 ],\n",
       "       ...,\n",
       "       [-84.00482 , -83.99358 , -83.98234 , ..., -83.1071  , -83.0959  ,\n",
       "        -83.08471 ],\n",
       "       [-84.00701 , -83.995766, -83.98452 , ..., -83.1094  , -83.098206,\n",
       "        -83.087006],\n",
       "       [-84.00919 , -83.99795 , -83.98671 , ..., -83.111694, -83.1005  ,\n",
       "        -83.08931 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-01-01T12:00:00 ... 2011-12-...</div><input id='attrs-cad47bae-e628-4780-a8aa-dc979dc35eb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cad47bae-e628-4780-a8aa-dc979dc35eb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-255b7fb9-0bf3-40ad-aa8f-fd0c8c0d317a' class='xr-var-data-in' type='checkbox'><label for='data-255b7fb9-0bf3-40ad-aa8f-fd0c8c0d317a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>24-hour day based on local time</dd><dt><span>_ChunkSizes :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-01-01T12:00:00.000000000&#x27;, &#x27;2010-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2010-01-03T12:00:00.000000000&#x27;, ..., &#x27;2011-12-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2011-12-30T12:00:00.000000000&#x27;, &#x27;2011-12-31T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lambert_conformal_conic</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>-32767</div><input id='attrs-6a3d3207-b877-4606-88fa-2ec2aa064957' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a3d3207-b877-4606-88fa-2ec2aa064957' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5eb2550f-065a-442d-a3d7-dce98ea24da6' class='xr-var-data-in' type='checkbox'><label for='data-5eb2550f-065a-442d-a3d7-dce98ea24da6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>grid_mapping_name :</span></dt><dd>lambert_conformal_conic</dd><dt><span>longitude_of_central_meridian :</span></dt><dd>-100.0</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>42.5</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>standard_parallel :</span></dt><dd>[25.0, 60.0]</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd></dl></div><div class='xr-var-data'><pre>array(-32767, dtype=int16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9e88b9ea-2a60-4180-8b7b-f295194c5195' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9e88b9ea-2a60-4180-8b7b-f295194c5195' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>daily total precipitation</dd><dt><span>units :</span></dt><dd>mm/day</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: sum</dd><dt><span>_ChunkSizes :</span></dt><dd>[1, 1000, 1000]</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'prcp' (time: 730, y: 47, x: 83)>\n",
       "array([[[0.  , 0.  , 0.  , ..., 3.25, 2.99, 2.74],\n",
       "        [0.  , 0.  , 0.  , ..., 3.27, 3.  , 2.73],\n",
       "        [0.  , 0.  , 0.  , ..., 3.28, 2.98, 2.71],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.61, 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ],\n",
       "        [0.  , 0.  , 0.  , ..., 0.  , 0.  , 0.  ]],\n",
       "\n",
       "       [[1.58, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.62, 1.68, 1.7 , ..., 0.  , 0.  , 0.  ],\n",
       "        [1.63, 1.7 , 1.76, ..., 0.  , 0.  , 0.  ],\n",
       "        ...,\n",
       "        [1.66, 1.67, 1.67, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.67, 1.68, ..., 0.  , 0.  , 0.  ],\n",
       "        [1.67, 1.68, 1.69, ..., 0.  , 0.  , 0.  ]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * x                        (x) float32 1.388e+06 1.389e+06 ... 1.47e+06\n",
       "  * y                        (y) float32 -5.66e+05 -5.67e+05 ... -6.12e+05\n",
       "    lat                      (y, x) float32 35.9 35.9 35.9 ... 35.34 35.33 35.33\n",
       "    lon                      (y, x) float32 -83.91 -83.9 -83.89 ... -83.1 -83.09\n",
       "  * time                     (time) datetime64[ns] 2010-01-01T12:00:00 ... 20...\n",
       "    lambert_conformal_conic  int16 -32767\n",
       "Attributes:\n",
       "    long_name:     daily total precipitation\n",
       "    units:         mm/day\n",
       "    cell_methods:  area: mean time: sum\n",
       "    _ChunkSizes:   [1, 1000, 1000]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prcp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb437875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2010-01-07T12:00:00, lambert_conformal_c...'}, xlabel='x coordinate of projection [m]', ylabel='y coordinate of projection [m]'>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "prcp_grsm = prcp.isel(time=6) # isel = xarray index selection (python index start from 0, so day 7, Jan 7, 2010)\n",
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "prcp_grsm.plot(ax=ax, robust=True, cbar_kwargs={'label': 'mm/day'})\n",
    "grsm_poly_lcc.plot(ax = ax, color = 'none', edgecolor = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e94e398",
   "metadata": {},
   "source": [
    "### Step 4. Clipping the subset area by polygon\n",
    "In this step, we demonstrate how to clip the subset to our polygon of interest.  We'll use Python's *rasterio* library.\n",
    "\n",
    "* **`rasterio`** provides many capabilities found in GDAL\n",
    "\n",
    "First, we'll map the subset precipitation data matrix to real world coordinates using the **'translation'** and **'scale'** methods of **'Affine'**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "34ec5a09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Affine(1000.0, 0.0, 1387250.0,\n",
       "       0.0, 1000.0, -612500.0)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from rasterio.features import Affine\n",
    "res = 1000 # daymet spatial resolution is 1km x 1km\n",
    "transform = Affine.translation(min(prcp.x).values - res / 2, min(prcp.y).values - res / 2) * Affine.scale(res, res)\n",
    "transform"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e33d30e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from rasterio.features import geometry_mask\n",
    "grsm_mask = geometry_mask(grsm_poly_lcc.geometry, out_shape=(len(prcp.y), len(prcp.x)), transform=transform, invert=True)\n",
    "polymask = xr.DataArray(grsm_mask , dims=(\"y\", \"x\"))\n",
    "polymask = polymask.sel(y=slice(None, None, -1)) \n",
    "prcp_masked = prcp.where(polymask == True)\n",
    "prcp_masked.to_netcdf(var + '_subset_poly.nc')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2b4df5a",
   "metadata": {},
   "source": [
    "Now let's plot the subset for a day, this time using Xarray's **sel** method for time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9725d5ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'time = 2010-01-07T12:00:00, lambert_conformal_c...'}, xlabel='x coordinate of projection [m]', ylabel='y coordinate of projection [m]'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "prcp_1 = prcp_masked.sel(time=\"2010-01-07\")\n",
    "fig, ax = plt.subplots(figsize = (20,10))\n",
    "prcp_1.plot(ax=ax, robust=True, cbar_kwargs={'label': 'mm/day'})\n",
    "grsm_poly_lcc.plot(ax = ax, color = 'none', edgecolor = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b346cee5",
   "metadata": {},
   "source": [
    "#### Saving to a GeoTIFF\n",
    "The subset data can also be clipped by a polygon of interest and converted to GeoTiff using the rioxarray and shapely libraries.\n",
    "\n",
    "* **`rioxarray`** is an extention of xarray.  It can be used to clip, merge, and reproject rasters\n",
    "* **`shapely`** is a Python package for manipulation and analysis of planar geometery objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "080269cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import rioxarray\n",
    "from shapely.geometry import mapping\n",
    "#prcp_sub = rioxarray.open_rasterio(var + '_subset_poly.nc', decode_coords=\"all\")\n",
    "prcp_sub = rioxarray.open_rasterio(var + '_tdssubset.nc', decode_coords=\"all\")\n",
    "prcp_clip = prcp_sub.prcp.rio.clip(grsm_poly_lcc.geometry.apply(mapping), grsm_poly_lcc.crs, invert=False, from_disk=True)\n",
    "\n",
    "# saving one time step to geotiff\n",
    "prcp_clip_day6 = prcp_clip.isel(time=6)\n",
    "prcp_clip_day6.rio.to_raster(\"prcp_clip_day6.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55b6bcf0",
   "metadata": {},
   "source": [
    "END"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd52b288",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d8a39d4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e0cf751",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "prcp_1 = prcp_masked.isel(time=6)\n",
    "ax = plt.subplot()\n",
    "prcp_1.plot(ax=ax, cbar_kwargs={'label': 'mm/day'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e454c1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "prcp_1 = prcp_masked.sel(time=\"2010-01-07\")\n",
    "ax = plt.subplot()\n",
    "prcp_1.plot(ax=ax, cbar_kwargs={'label': 'mm/day'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20d641ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69f56f18",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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