notebooks/Data_Challenge/Weather.ipynb

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    "## 2022 EY Challenge - Weather Data\n",
    "\n",
    "This notebook is used to create a weather dataset from <b>\"TerraClimate\"</b>. These data parameters, or their variability, can be used as \"predictor variables\" to relate to species samples. TerraClimate is a dataset of monthly climate and climatic water balance for global terrestrial surfaces from 1958-2019. These data provide important inputs for ecological and hydrological studies at global scales that require high spatial resolution and time-varying data. All data have monthly temporal resolution and a 4-km (0.036 degree) spatial resolution. The data can be found in the MS Planetary Computer catalog: https://planetarycomputer.microsoft.com/dataset/terraclimate"
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    "# Supress Warnings \n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Import common GIS tools\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "import rasterio.features\n",
    "import folium\n",
    "import math\n",
    "\n",
    "# Import Planetary Computer tools\n",
    "import pystac_client\n",
    "import planetary_computer"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Define the analysis region and view on a map\n",
    "\n",
    "First, we define our area of interest using latitude and longitude coordinates. Our test region is near Richmond, NSW, Australia. The first line defines the lower-left corner of the bounding box and the second line defines the upper-right corner of the bounding box. GeoJSON format uses a specific order: (longitude, latitude), so be careful when entering the coordinates."
   ]
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   "source": [
    "# Define the bounding box using corners\n",
    "min_lon, min_lat = (150.62, -33.69)  # Lower-left corner (longitude, latitude)\n",
    "max_lon, max_lat = (150.83, -33.48)  # Upper-right corner (longitude, latitude)"
   ]
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   "cell_type": "code",
   "execution_count": 3,
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   "source": [
    "bbox = (min_lon, min_lat, max_lon, max_lat)\n",
    "latitude = (min_lat, max_lat)\n",
    "longitude = (min_lon, max_lon)"
   ]
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    "def _degree_to_zoom_level(l1, l2, margin = 0.0):\n",
    "    \n",
    "    degree = abs(l1 - l2) * (1 + margin)\n",
    "    zoom_level_int = 0\n",
    "    if degree != 0:\n",
    "        zoom_level_float = math.log(360/degree)/math.log(2)\n",
    "        zoom_level_int = int(zoom_level_float)\n",
    "    else:\n",
    "        zoom_level_int = 18\n",
    "    return zoom_level_int\n",
    "\n",
    "def display_map(latitude = None, longitude = None):\n",
    "\n",
    "    margin = -0.5\n",
    "    zoom_bias = 0\n",
    "    lat_zoom_level = _degree_to_zoom_level(margin = margin, *latitude ) + zoom_bias\n",
    "    lon_zoom_level = _degree_to_zoom_level(margin = margin, *longitude) + zoom_bias\n",
    "    zoom_level = min(lat_zoom_level, lon_zoom_level) \n",
    "    center = [np.mean(latitude), np.mean(longitude)]\n",
    "    \n",
    "    map_hybrid = folium.Map(location=center,zoom_start=zoom_level, \n",
    "        tiles=\" http://mt1.google.com/vt/lyrs=y&z={z}&x={x}&y={y}\",attr=\"Google\")\n",
    "    \n",
    "    line_segments = [(latitude[0],longitude[0]),(latitude[0],longitude[1]),\n",
    "                     (latitude[1],longitude[1]),(latitude[1],longitude[0]),\n",
    "                     (latitude[0],longitude[0])]\n",
    "    \n",
    "    map_hybrid.add_child(folium.features.PolyLine(locations=line_segments,color='red',opacity=0.8))\n",
    "    map_hybrid.add_child(folium.features.LatLngPopup())        \n",
    "\n",
    "    return map_hybrid"
   ]
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     "execution_count": 5,
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   ],
   "source": [
    "# Plot bounding box on a map\n",
    "f = folium.Figure(width=600, height=600)\n",
    "m = display_map(latitude,longitude)\n",
    "f.add_child(m)"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "### Discover and load the data for analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using <b>pystac</b>, we will load the collection with links to all of the metadata about this dataset. The collection contains assets, which are links to the root of a Zarr store, which can be opened with xarray. Once loaded and opened, we can select the parameters we desire for further analyses. Overall, there are 18 variables for consideration. You can click the \"Data Variables\" output \"arrow\" for a list of those parameters."
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pystac\n",
    "collection = pystac.read_file(\"https://planetarycomputer.microsoft.com/api/stac/v1/collections/terraclimate\")\n",
    "asset = collection.assets[\"zarr-https\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import fsspec\n",
    "import xarray as xr\n",
    "\n",
    "store = fsspec.get_mapper(asset.href)\n",
    "data = xr.open_zarr(store, **asset.extra_fields[\"xarray:open_kwargs\"])"
   ]
  },
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   "execution_count": 8,
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       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  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.Dataset&gt;\n",
       "Dimensions:                 (time: 744, lat: 4320, lon: 8640, crs: 1)\n",
       "Coordinates:\n",
       "  * crs                     (crs) int16 3\n",
       "  * lat                     (lat) float64 89.98 89.94 89.9 ... -89.94 -89.98\n",
       "  * lon                     (lon) float64 -180.0 -179.9 -179.9 ... 179.9 180.0\n",
       "  * time                    (time) datetime64[ns] 1958-01-01 ... 2019-12-01\n",
       "Data variables: (12/18)\n",
       "    aet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    def                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    pdsi                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    pet                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ppt                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ppt_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ...                      ...\n",
       "    tmin                    (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    tmin_station_influence  (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vap                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vap_station_influence   (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    vpd                     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;\n",
       "    ws                      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-32d451b7-5abc-4d57-a98c-1f4cb2e99d30' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-32d451b7-5abc-4d57-a98c-1f4cb2e99d30' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 744</li><li><span class='xr-has-index'>lat</span>: 4320</li><li><span class='xr-has-index'>lon</span>: 8640</li><li><span class='xr-has-index'>crs</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-90d6b145-f0a0-4cdc-bb5e-bf2214eaef55' class='xr-section-summary-in' type='checkbox'  checked><label for='section-90d6b145-f0a0-4cdc-bb5e-bf2214eaef55' class='xr-section-summary' >Coordinates: <span>(4)</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'>crs</span></div><div class='xr-var-dims'>(crs)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>3</div><input id='attrs-b2f0aafb-da68-4102-93d9-47403e4fb3ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2f0aafb-da68-4102-93d9-47403e4fb3ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c1cb392-a6e7-4bbf-9d73-f085ffdb52b3' class='xr-var-data-in' type='checkbox'><label for='data-1c1cb392-a6e7-4bbf-9d73-f085ffdb52b3' 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>latitude_longitude</dd><dt><span>inverse_flattening :</span></dt><dd>[298.257223563]</dd><dt><span>long_name :</span></dt><dd>crs</dd><dt><span>longitude_of_prime_meridian :</span></dt><dd>[0.0]</dd><dt><span>semi_major_axis :</span></dt><dd>[6378137.0]</dd></dl></div><div class='xr-var-data'><pre>array([3], dtype=int16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>89.98 89.94 89.9 ... -89.94 -89.98</div><input id='attrs-cb3375da-80f7-422e-8a3e-d3151d4727d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb3375da-80f7-422e-8a3e-d3151d4727d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-51ce336c-622d-4f36-98c2-68b1830a8020' class='xr-var-data-in' type='checkbox'><label for='data-51ce336c-622d-4f36-98c2-68b1830a8020' 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>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([ 89.979167,  89.9375  ,  89.895833, ..., -89.895833, -89.9375  ,\n",
       "       -89.979167])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-180.0 -179.9 ... 179.9 180.0</div><input id='attrs-74d6f60e-27e0-4aef-aff7-0d33646a0234' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74d6f60e-27e0-4aef-aff7-0d33646a0234' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b02b64a-9edb-4d7b-a3da-0428034e7d4f' class='xr-var-data-in' type='checkbox'><label for='data-4b02b64a-9edb-4d7b-a3da-0428034e7d4f' 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>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([-179.979167, -179.9375  , -179.895833, ...,  179.895833,  179.9375  ,\n",
       "        179.979167])</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'>1958-01-01 ... 2019-12-01</div><input id='attrs-18335bae-44dd-4063-9493-b447e40b63ce' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18335bae-44dd-4063-9493-b447e40b63ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c457840-0bbf-4376-b91b-1b5235ab28d8' class='xr-var-data-in' type='checkbox'><label for='data-8c457840-0bbf-4376-b91b-1b5235ab28d8' 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>axis :</span></dt><dd>T</dd><dt><span>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-01-01T00:00:00.000000000&#x27;, &#x27;1958-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;1958-03-01T00:00:00.000000000&#x27;, ..., &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c320ab5-7a60-4f8d-b837-9b14e296f0d8' class='xr-section-summary-in' type='checkbox'  ><label for='section-2c320ab5-7a60-4f8d-b837-9b14e296f0d8' class='xr-section-summary' >Data variables: <span>(18)</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>aet</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-ec3f675c-17bc-45b3-9869-c494430c5ced' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec3f675c-17bc-45b3-9869-c494430c5ced' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3da21fc2-1335-4372-ba25-cf6ccfdedf71' class='xr-var-data-in' type='checkbox'><label for='data-3da21fc2-1335-4372-ba25-cf6ccfdedf71' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Actual Evapotranspiration</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 103.45 GiB </td>\n",
       "                        <td> 94.92 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (744, 4320, 8640) </td>\n",
       "                        <td> (12, 1440, 1440) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>def</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-9b8ea5bc-74b4-40f5-94e5-51da95af751c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b8ea5bc-74b4-40f5-94e5-51da95af751c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2eb386a-48a3-4cf7-9e9a-e1003f317182' class='xr-var-data-in' type='checkbox'><label for='data-f2eb386a-48a3-4cf7-9e9a-e1003f317182' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Climatic Water Deficit</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>standard_name :</span></dt><dd>water_potential_evaporation_amount_minus_water_evaporation_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 103.45 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (12, 1440, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <td> 1116 Chunks </td>\n",
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       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pdsi</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-880087bc-3cff-473b-9030-7ae9d370019a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-880087bc-3cff-473b-9030-7ae9d370019a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-766e2cc8-3945-4f08-8b79-5a11e6317a61' class='xr-var-data-in' type='checkbox'><label for='data-766e2cc8-3945-4f08-8b79-5a11e6317a61' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Palmer Drought Severity Index</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>palmer_drought_severity_index</dd><dt><span>standard_name :</span></dt><dd>palmer_drought_severity_index</dd><dt><span>units :</span></dt><dd>unitless</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 103.45 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (744, 4320, 8640) </td>\n",
       "                        <td> (12, 1440, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <td> 1116 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppt_station_influence</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-aa3a34a1-3531-4e02-aeda-4cd219c7b877' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa3a34a1-3531-4e02-aeda-4cd219c7b877' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d2d5917-6e38-4c02-b9de-f45e09ac75f0' class='xr-var-data-in' type='checkbox'><label for='data-6d2d5917-6e38-4c02-b9de-f45e09ac75f0' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>A number between 0 and 8, indicating the number of stations contributing to each grid value directly from CRU and interpolated to the TerraClimate grid. When this value is greater than 1, CRU data is used for the anomalies in the method. When this value is 0, JRA-55/ERA-20C/ERA-INTERIM is used for anomalies in the method.</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>station_influence</dd><dt><span>standard_name :</span></dt><dd>station_influence</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 103.45 GiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-fb49f59d-2788-4e9c-92a0-e2cd000c510a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb49f59d-2788-4e9c-92a0-e2cd000c510a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6d79850-a8e2-4524-a35b-13ec643c8dcf' class='xr-var-data-in' type='checkbox'><label for='data-e6d79850-a8e2-4524-a35b-13ec643c8dcf' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Runoff</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>runoff_amount</dd><dt><span>standard_name :</span></dt><dd>runoff_amount</dd><dt><span>units :</span></dt><dd>mm</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (12, 1440, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmax_station_influence</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-2f5f793d-7fdd-4659-90fb-be8e9b6d4b3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f5f793d-7fdd-4659-90fb-be8e9b6d4b3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3eaa36a8-0212-4e3f-805b-d779e712c142' class='xr-var-data-in' type='checkbox'><label for='data-3eaa36a8-0212-4e3f-805b-d779e712c142' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>A number between 0 and 8, indicating the number of stations contributing to each grid value directly from CRU and interpolated to the TerraClimate grid. When this value is greater than 1, CRU data is used for the anomalies in the method. When this value is 0, JRA-55/ERA-20C/ERA-INTERIM is used for anomalies in the method.</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>station_influence</dd><dt><span>standard_name :</span></dt><dd>station_influence</dd><dt><span>units :</span></dt><dd>none</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 103.45 GiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tmin</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 1440, 1440), meta=np.ndarray&gt;</div><input id='attrs-f520d349-9947-410f-80c7-e8ea3730c45f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f520d349-9947-410f-80c7-e8ea3730c45f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fcc5543-d8a2-4c62-92e0-12b87bf24863' class='xr-var-data-in' type='checkbox'><label for='data-0fcc5543-d8a2-4c62-92e0-12b87bf24863' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Minimum 2-m Temperature</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>deg C</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (12, 1440, 1440) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                        <th> Bytes </th>\n",
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       "                    \n",
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       "                        <th> Shape </th>\n",
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       "<xarray.Dataset>\n",
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       "  * crs                     (crs) int16 3\n",
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       "    def                     (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
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       "    ppt_station_influence   (time, lat, lon) float32 dask.array<chunksize=(12, 1440, 1440), meta=np.ndarray>\n",
       "    ...                      ...\n",
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     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# View the dimensions, coordinates and variables of the dataset\n",
    "# Click on the \"data variables\" arrow to expand the output and see all of the possible variables\n",
    "data"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parsing the dataset"
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  },
  {
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   "metadata": {},
   "source": [
    "Assuming we do not need all 18 variables and global coverage, we will \"clip\" the data to our region and select a time window that is representative of weather over this area (e.g. 5 years). Finally, we will select the variables we think are most likely to be \"predictor variables\" for frog species habitats. These are:<br> \n",
    "<br>maximum air temperature (tmax) - 2 meters above surface\n",
    "<br>minimum air temperature (tmin) - 2 meters above surface\n",
    "<br>accumulated precipitation (ppt) - accumulated monthly in millimeters\n",
    "<br>soil moisture (soil) - soil moisture in millimeters at end of month"
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  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "clipped_data = data.sel(lon=slice(min_lon,max_lon),lat=slice(max_lat,min_lat),time=slice('2015-01-01','2019-12-31'))"
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   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
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    "parsed_data = clipped_data[['tmax', 'tmin', 'ppt', 'soil']]"
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  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  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.Dataset&gt;\n",
       "Dimensions:  (time: 60, lat: 5, lon: 5)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -33.52 -33.56 -33.6 -33.65 -33.69\n",
       "  * lon      (lon) float64 150.6 150.7 150.7 150.8 150.8\n",
       "  * time     (time) datetime64[ns] 2015-01-01 2015-02-01 ... 2019-12-01\n",
       "Data variables:\n",
       "    tmax     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 5, 5), meta=np.ndarray&gt;\n",
       "    tmin     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 5, 5), meta=np.ndarray&gt;\n",
       "    ppt      (time, lat, lon) float32 dask.array&lt;chunksize=(12, 5, 5), meta=np.ndarray&gt;\n",
       "    soil     (time, lat, lon) float32 dask.array&lt;chunksize=(12, 5, 5), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d9eb4962-6e92-4ec4-a7ba-cf7febcec995' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d9eb4962-6e92-4ec4-a7ba-cf7febcec995' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 60</li><li><span class='xr-has-index'>lat</span>: 5</li><li><span class='xr-has-index'>lon</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-069d1c7c-92d9-46ca-a909-1cae68eafa01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-069d1c7c-92d9-46ca-a909-1cae68eafa01' class='xr-section-summary' >Coordinates: <span>(3)</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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-33.52 -33.56 -33.6 -33.65 -33.69</div><input id='attrs-fc8b6ea6-12ee-4a4c-8ec6-f90ec1c2bd89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc8b6ea6-12ee-4a4c-8ec6-f90ec1c2bd89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aee5fafb-0145-44f3-a671-ff6f468aea82' class='xr-var-data-in' type='checkbox'><label for='data-aee5fafb-0145-44f3-a671-ff6f468aea82' 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>axis :</span></dt><dd>Y</dd><dt><span>description :</span></dt><dd>Latitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-33.520833, -33.5625  , -33.604167, -33.645833, -33.6875  ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>150.6 150.7 150.7 150.8 150.8</div><input id='attrs-75913ce3-d6e3-410a-b4ec-0119276315fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75913ce3-d6e3-410a-b4ec-0119276315fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9bf92f7b-b3e9-4a58-b535-57a23ca0b4df' class='xr-var-data-in' type='checkbox'><label for='data-9bf92f7b-b3e9-4a58-b535-57a23ca0b4df' 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>axis :</span></dt><dd>X</dd><dt><span>description :</span></dt><dd>Longitude of the center of the grid cell</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([150.645833, 150.6875  , 150.729167, 150.770833, 150.8125  ])</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'>2015-01-01 ... 2019-12-01</div><input id='attrs-29f55739-cd64-4af6-9f32-cb975bad6096' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29f55739-cd64-4af6-9f32-cb975bad6096' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcfbefd6-e2be-4681-8338-635de53988ad' class='xr-var-data-in' type='checkbox'><label for='data-fcfbefd6-e2be-4681-8338-635de53988ad' 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>axis :</span></dt><dd>T</dd><dt><span>description :</span></dt><dd>days since 1900-01-01</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2015-01-01T00:00:00.000000000&#x27;, &#x27;2015-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-03-01T00:00:00.000000000&#x27;, &#x27;2015-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-05-01T00:00:00.000000000&#x27;, &#x27;2015-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-07-01T00:00:00.000000000&#x27;, &#x27;2015-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-09-01T00:00:00.000000000&#x27;, &#x27;2015-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2015-11-01T00:00:00.000000000&#x27;, &#x27;2015-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-01-01T00:00:00.000000000&#x27;, &#x27;2016-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-03-01T00:00:00.000000000&#x27;, &#x27;2016-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-05-01T00:00:00.000000000&#x27;, &#x27;2016-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-01T00:00:00.000000000&#x27;, &#x27;2016-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-09-01T00:00:00.000000000&#x27;, &#x27;2016-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2016-11-01T00:00:00.000000000&#x27;, &#x27;2016-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-01-01T00:00:00.000000000&#x27;, &#x27;2017-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-01T00:00:00.000000000&#x27;, &#x27;2017-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-01T00:00:00.000000000&#x27;, &#x27;2017-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-01T00:00:00.000000000&#x27;, &#x27;2017-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-11-01T00:00:00.000000000&#x27;, &#x27;2017-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-01-01T00:00:00.000000000&#x27;, &#x27;2018-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-03-01T00:00:00.000000000&#x27;, &#x27;2018-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-05-01T00:00:00.000000000&#x27;, &#x27;2018-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T00:00:00.000000000&#x27;, &#x27;2018-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-09-01T00:00:00.000000000&#x27;, &#x27;2018-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2018-11-01T00:00:00.000000000&#x27;, &#x27;2018-12-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-01-01T00:00:00.000000000&#x27;, &#x27;2019-02-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-03-01T00:00:00.000000000&#x27;, &#x27;2019-04-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-05-01T00:00:00.000000000&#x27;, &#x27;2019-06-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-07-01T00:00:00.000000000&#x27;, &#x27;2019-08-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-09-01T00:00:00.000000000&#x27;, &#x27;2019-10-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2019-11-01T00:00:00.000000000&#x27;, &#x27;2019-12-01T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bb784f85-2a52-4e0d-ad54-771249cf1fd2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bb784f85-2a52-4e0d-ad54-771249cf1fd2' class='xr-section-summary' >Data variables: <span>(4)</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>tmax</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(12, 5, 5), meta=np.ndarray&gt;</div><input id='attrs-fed88024-02cb-4abf-af70-cbb54fe736f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fed88024-02cb-4abf-af70-cbb54fe736f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b804c076-fbd8-4207-bdab-b7baaf55b286' class='xr-var-data-in' type='checkbox'><label for='data-b804c076-fbd8-4207-bdab-b7baaf55b286' 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>coordinate_system :</span></dt><dd>WGS84,EPSG:4326</dd><dt><span>description :</span></dt><dd>Maximum 2-m Temperature</dd><dt><span>dimensions :</span></dt><dd>lon lat time</dd><dt><span>grid_mapping :</span></dt><dd>crs</dd><dt><span>long_name :</span></dt><dd>air_temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>deg C</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> (12, 5, 5) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 1122 Tasks </td>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 60, lat: 5, lon: 5)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -33.52 -33.56 -33.6 -33.65 -33.69\n",
       "  * lon      (lon) float64 150.6 150.7 150.7 150.8 150.8\n",
       "  * time     (time) datetime64[ns] 2015-01-01 2015-02-01 ... 2019-12-01\n",
       "Data variables:\n",
       "    tmax     (time, lat, lon) float32 dask.array<chunksize=(12, 5, 5), meta=np.ndarray>\n",
       "    tmin     (time, lat, lon) float32 dask.array<chunksize=(12, 5, 5), meta=np.ndarray>\n",
       "    ppt      (time, lat, lon) float32 dask.array<chunksize=(12, 5, 5), meta=np.ndarray>\n",
       "    soil     (time, lat, lon) float32 dask.array<chunksize=(12, 5, 5), meta=np.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View the dimensions: time is in months, lat/lon is in pixels\n",
    "# View the coordinates: lat, lon and time\n",
    "# View the data variables we have parsed from the overall dataset\n",
    "parsed_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Analysis\n",
    "Now we will look closer at some of this data using plotting and statistics."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12, 6))\n",
    "parsed_data[\"tmax\"].mean(dim=[\"lat\",\"lon\"]).plot(marker='o',markersize=4,linewidth=1)\n",
    "parsed_data[\"tmin\"].mean(dim=[\"lat\",\"lon\"]).plot(marker='o',markersize=4,linewidth=1)\n",
    "plt.title(\"Mean Monthly Maximum and Minimum Air Temperature\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Precipitation\n",
    "precip = (parsed_data[\"ppt\"]).mean(dim=[\"lat\",\"lon\"])\n",
    "precip.plot(figsize=(12, 6),marker='o',markersize=4,linewidth=1)\n",
    "plt.title(\"Mean Accumulated Precipitation (mm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Soil Moisture\n",
    "moisture = (parsed_data[\"soil\"]).mean(dim=[\"lat\",\"lon\"])\n",
    "moisture.plot(figsize=(12, 6),marker='o',markersize=4,linewidth=1)\n",
    "plt.title(\"Mean Soil Moisture (mm)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How will the participants use this data?\n",
    "The GeoTIFF file will contain the Lat-Lon coordinates of each pixel and will also contain the selected parameters for each pixel as separate data layers. Since the FrogID data is also Lat-Lon position, it is possible to find the closest pixel using code similar to what is demonstrated below. Once this pixel is found, then the corresponding parameter data values can be used for modeling species distribution. One example is to use the long-term mean values of a given parameter for a particular location and use that value for species modeling. Below are some sample results for a random Lat-Lon location."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the mean maximum air temperature for the closest pixel:  24.4\n",
      "This is the overall maximum air temperature for the closest pixel:  32.4\n",
      "This is the mean minimum air temperature for the closest pixel:  11.2\n",
      "This is the overall minimum air temperature for the closest pixel:  2.6\n",
      "This is the mean accumulated precipitation for the closest pixel:  62.5\n",
      "This is the mean soil moisture for the closest pixel:  31.0\n"
     ]
    }
   ],
   "source": [
    "val_maxtemp = parsed_data.tmax.mean(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the mean maximum air temperature for the closest pixel: \", np.round(val_maxtemp,1))\n",
    "val_allmaxtemp = parsed_data.tmax.max(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the overall maximum air temperature for the closest pixel: \", np.round(val_allmaxtemp,1))\n",
    "val_mintemp = parsed_data.tmin.mean(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the mean minimum air temperature for the closest pixel: \", np.round(val_mintemp,1))\n",
    "val_allmintemp = parsed_data.tmin.min(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the overall minimum air temperature for the closest pixel: \", np.round(val_allmintemp,1))\n",
    "val_precip = parsed_data.ppt.mean(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the mean accumulated precipitation for the closest pixel: \", np.round(val_precip,1))\n",
    "val_soil = parsed_data.soil.mean(dim=[\"time\"]).sel(lon=150.71, lat=-33.51, method=\"nearest\").values    \n",
    "print(\"This is the mean soil moisture for the closest pixel: \", np.round(val_soil,1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Saving the time series data in CSV format\n",
    "Now, what if you want to save the time series data for a particular parameter and Lat-Lon location? We can use the example above to specify a Lat-Lon position and then save the time series soil moisture data for that location. Below is the code that will create this CSV file. The file will contain columns for time (month), latitude and longitude (for the nearest pixel) and soil moisture. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"Soil_Moisture_sample.csv\"\n",
    "parsed_data[\"soil\"].sel(lon=150.71, lat=-33.51, method=\"nearest\").to_dataframe().to_csv(filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-rw-rw-r-- 1 jovyan users 3.3K Jan 21 18:34 Soil_Moisture_sample.csv\n"
     ]
    }
   ],
   "source": [
    "# Show the location and size of the new output file\n",
    "!ls *.csv -lah"
   ]
  }
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