04_Using_vector_data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using vector data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div>\n",
    "    <img style='width: 220px;height: 100px;float: left;' src=\"./Supplementary_data/EODC-Logo-Final.png\" height='100px' width='220px'/>\n",
    "    <img style='width: 200px;height: 100px;float: left;' src=\"./Supplementary_data/BMNT_DE_Logo_dreizeilig_srgb.svg\" height='100px' width='200px'/> \n",
    "    <img style='width: 200px;height: 100px;float: left;' src=\"./Supplementary_data/bmlv.svg\" height='100px' width='200px'/>\n",
    "    <img style='width: 200px;height: 100px;float: left;' src=\"./Supplementary_data/LOGO_TUW_GEO.png\" height='100px' width='200px'/>\n",
    "    <img style='width: 100px;height: 100px;float: left;' src=\"./Supplementary_data/Boku-wien.svg\" height='100px' width='100px'/>\n",
    "    <img style='width: 150px;height: 100px;float: right;' src=\"./Supplementary_data/ADC_Logo_final_transparent.png\" height='100px' width='150px'/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook shows how to use vector data for querying and for masking the data, limiting only to selected polygons. The shapefile is located in `./Supplementary_data/shapefiles/Betrieb_H.shp`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**NOTE:** It is necessary to install geopandas in the datacube environment and upload the shapefiles to a folder called `shapefiles` which should be located in the same folder where the notebook is uploaded. \n",
    "\n",
    "To install geopandas open a New Launcher (denoted by the + icon in the top left of the app, under the Edit menu) and select Terminal option at the bottom of the launcher window.\n",
    "\n",
    "In the Terminal you need to run two commands:\n",
    "```shell\n",
    "source activate datacube\n",
    "conda install -y geopandas decartes\n",
    "```\n",
    "\n",
    "The installation may take some time, but after finishing the Terminal may be closed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import geopandas\n",
    "import datacube\n",
    "from datacube import helpers\n",
    "from datacube.utils import geometry\n",
    "import fiona\n",
    "import rasterio.features\n",
    "import xarray\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.rc('font', size=15)\n",
    "acube = datacube.Datacube(app='sample_4', env='acube')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Polygons"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function `load_polygon` extracts geopolygon for usage in the `datacube` query and pfeature which is used by `rasterio` for the mask."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_polygon(shape_file):\n",
    "    with fiona.open(shape_file) as shapes:\n",
    "        crs = geometry.CRS(shapes.crs_wkt)\n",
    "        first_geometry = next(iter(shapes))['geometry']\n",
    "        return geometry.Geometry(first_geometry, crs=crs), geopandas.read_file(shape_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "geopolygon_h, pfeature_h = load_polygon('./Supplementary_data/shapefiles/Betrieb_H.shp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define the query for desired polygon"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The example shows polygon H being used. For using different geopolygons modify the code below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "polygon = geopolygon_h\n",
    "masking_feature = pfeature_h['geometry']\n",
    "filename = 'LAI_POLYGON_M'\n",
    "query = {\n",
    "    'time': ('2017-03-01', '2017-10-31'),\n",
    "    'product': 'LAI_Sentinel_2',\n",
    "    'geopolygon': polygon,\n",
    "    'output_crs': 'EPSG:32633',\n",
    "    'resolution': (-10, 10)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "masking_feature.plot(figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following load function uses the defined geopolygon for querying, meaning the shapefile will be used for the query instead of the `lat` and `lon`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  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, 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: 79, x: 21, y: 25)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-03-05 2017-03-09 ... 2017-10-31\n",
       "  * y            (y) float64 5.234e+06 5.234e+06 ... 5.234e+06 5.234e+06\n",
       "  * x            (x) float64 4.881e+05 4.881e+05 ... 4.883e+05 4.883e+05\n",
       "    spatial_ref  int32 32633\n",
       "Data variables:\n",
       "    band_1       (time, y, x) int16 32767 32767 32767 ... 32767 32767 32767\n",
       "Attributes:\n",
       "    crs:           EPSG:32633\n",
       "    grid_mapping:  spatial_ref</pre><div class='xr-wrap' hidden><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-02a17a4b-70f3-4730-b29a-1e9510b32416' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-02a17a4b-70f3-4730-b29a-1e9510b32416' 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>: 79</li><li><span class='xr-has-index'>x</span>: 21</li><li><span class='xr-has-index'>y</span>: 25</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9b5d8668-4c08-4c34-a1ab-0fc859558aa7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b5d8668-4c08-4c34-a1ab-0fc859558aa7' 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'>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'>2017-03-05 ... 2017-10-31</div><input id='attrs-9d79b7d6-36ce-44eb-8281-769ca0c39684' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9d79b7d6-36ce-44eb-8281-769ca0c39684' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b427e4c-3e2d-4f5d-8282-e876708f063c' class='xr-var-data-in' type='checkbox'><label for='data-2b427e4c-3e2d-4f5d-8282-e876708f063c' 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>seconds since 1970-01-01 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2017-03-05T00:00:00.000000000&#x27;, &#x27;2017-03-09T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-12T00:00:00.000000000&#x27;, &#x27;2017-03-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-03-25T00:00:00.000000000&#x27;, &#x27;2017-03-29T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-01T00:00:00.000000000&#x27;, &#x27;2017-04-04T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-14T00:00:00.000000000&#x27;, &#x27;2017-04-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-04-24T00:00:00.000000000&#x27;, &#x27;2017-05-01T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-04T00:00:00.000000000&#x27;, &#x27;2017-05-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-14T00:00:00.000000000&#x27;, &#x27;2017-05-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-05-24T00:00:00.000000000&#x27;, &#x27;2017-05-28T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-03T00:00:00.000000000&#x27;, &#x27;2017-06-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-13T00:00:00.000000000&#x27;, &#x27;2017-06-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-06-23T00:00:00.000000000&#x27;, &#x27;2017-06-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-03T00:00:00.000000000&#x27;, &#x27;2017-07-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-07T00:00:00.000000000&#x27;, &#x27;2017-07-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-10T00:00:00.000000000&#x27;, &#x27;2017-07-12T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-13T00:00:00.000000000&#x27;, &#x27;2017-07-17T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-18T00:00:00.000000000&#x27;, &#x27;2017-07-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-22T00:00:00.000000000&#x27;, &#x27;2017-07-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-07-28T00:00:00.000000000&#x27;, &#x27;2017-07-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-01T00:00:00.000000000&#x27;, &#x27;2017-08-02T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-04T00:00:00.000000000&#x27;, &#x27;2017-08-07T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-09T00:00:00.000000000&#x27;, &#x27;2017-08-11T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-12T00:00:00.000000000&#x27;, &#x27;2017-08-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-17T00:00:00.000000000&#x27;, &#x27;2017-08-21T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-22T00:00:00.000000000&#x27;, &#x27;2017-08-24T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-26T00:00:00.000000000&#x27;, &#x27;2017-08-27T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-08-29T00:00:00.000000000&#x27;, &#x27;2017-08-31T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-01T00:00:00.000000000&#x27;, &#x27;2017-09-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-06T00:00:00.000000000&#x27;, &#x27;2017-09-08T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-11T00:00:00.000000000&#x27;, &#x27;2017-09-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-16T00:00:00.000000000&#x27;, &#x27;2017-09-18T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-21T00:00:00.000000000&#x27;, &#x27;2017-09-23T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-09-28T00:00:00.000000000&#x27;, &#x27;2017-09-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-01T00:00:00.000000000&#x27;, &#x27;2017-10-05T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-06T00:00:00.000000000&#x27;, &#x27;2017-10-10T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-11T00:00:00.000000000&#x27;, &#x27;2017-10-13T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-15T00:00:00.000000000&#x27;, &#x27;2017-10-16T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-18T00:00:00.000000000&#x27;, &#x27;2017-10-20T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-21T00:00:00.000000000&#x27;, &#x27;2017-10-30T00:00:00.000000000&#x27;,\n",
       "       &#x27;2017-10-31T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>float64</div><div class='xr-var-preview xr-preview'>5.234e+06 5.234e+06 ... 5.234e+06</div><input id='attrs-dbd0d4ad-c108-4bc7-b101-a1277d76747f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dbd0d4ad-c108-4bc7-b101-a1277d76747f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55d64009-7230-4775-8cdb-c2fa220d964e' class='xr-var-data-in' type='checkbox'><label for='data-55d64009-7230-4775-8cdb-c2fa220d964e' 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>metre</dd><dt><span>resolution :</span></dt><dd>-10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32633</dd></dl></div><div class='xr-var-data'><pre>array([5233915., 5233905., 5233895., 5233885., 5233875., 5233865., 5233855.,\n",
       "       5233845., 5233835., 5233825., 5233815., 5233805., 5233795., 5233785.,\n",
       "       5233775., 5233765., 5233755., 5233745., 5233735., 5233725., 5233715.,\n",
       "       5233705., 5233695., 5233685., 5233675.])</pre></div></li><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'>float64</div><div class='xr-var-preview xr-preview'>4.881e+05 4.881e+05 ... 4.883e+05</div><input id='attrs-3ed17a2f-dbd1-4712-819f-a16e5c02a942' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ed17a2f-dbd1-4712-819f-a16e5c02a942' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59c24aad-7ac7-4ac1-bf16-c7797a01d2b2' class='xr-var-data-in' type='checkbox'><label for='data-59c24aad-7ac7-4ac1-bf16-c7797a01d2b2' 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>metre</dd><dt><span>resolution :</span></dt><dd>10.0</dd><dt><span>crs :</span></dt><dd>EPSG:32633</dd></dl></div><div class='xr-var-data'><pre>array([488085., 488095., 488105., 488115., 488125., 488135., 488145., 488155.,\n",
       "       488165., 488175., 488185., 488195., 488205., 488215., 488225., 488235.,\n",
       "       488245., 488255., 488265., 488275., 488285.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spatial_ref</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>32633</div><input id='attrs-c87a97a9-610d-452b-8ab2-61d6ce539801' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c87a97a9-610d-452b-8ab2-61d6ce539801' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23fa008-13fb-4e6d-8126-12667fe739e8' class='xr-var-data-in' type='checkbox'><label for='data-a23fa008-13fb-4e6d-8126-12667fe739e8' 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>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / UTM zone 33N&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Transverse_Mercator&quot;],PARAMETER[&quot;latitude_of_origin&quot;,0],PARAMETER[&quot;central_meridian&quot;,15],PARAMETER[&quot;scale_factor&quot;,0.9996],PARAMETER[&quot;false_easting&quot;,500000],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;32633&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>transverse_mercator</dd></dl></div><div class='xr-var-data'><pre>array(32633, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e64f1eb-d5c9-4a8d-8ca1-dbefa0d6702d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6e64f1eb-d5c9-4a8d-8ca1-dbefa0d6702d' class='xr-section-summary' >Data variables: <span>(1)</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>band_1</span></div><div class='xr-var-dims'>(time, y, x)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>32767 32767 32767 ... 32767 32767</div><input id='attrs-1ae0dbad-ef05-4467-bd87-1672f0931a62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ae0dbad-ef05-4467-bd87-1672f0931a62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-14d7d5cd-7efd-4d3d-b628-253a3005b0e1' class='xr-var-data-in' type='checkbox'><label for='data-14d7d5cd-7efd-4d3d-b628-253a3005b0e1' 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>m2/m2</dd><dt><span>nodata :</span></dt><dd>32767</dd><dt><span>crs :</span></dt><dd>EPSG:32633</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><pre>array([[[32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        ...,\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767]],\n",
       "\n",
       "       [[32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        ...,\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767]],\n",
       "\n",
       "       [[  902,   912,  1028, ...,  1393,  1390,   999],\n",
       "        [  902,   918,  1051, ...,  1386,  1387,   997],\n",
       "        [  961,   961,  1162, ...,   879,   916,   819],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767]],\n",
       "\n",
       "       [[32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        ...,\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767]],\n",
       "\n",
       "       [[32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        ...,\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767],\n",
       "        [32767, 32767, 32767, ..., 32767, 32767, 32767]]], dtype=int16)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-55e72d78-6648-4d2e-89f5-9b96fbb09a14' class='xr-section-summary-in' type='checkbox'  checked><label for='section-55e72d78-6648-4d2e-89f5-9b96fbb09a14' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>crs :</span></dt><dd>EPSG:32633</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (time: 79, x: 21, y: 25)\n",
       "Coordinates:\n",
       "  * time         (time) datetime64[ns] 2017-03-05 2017-03-09 ... 2017-10-31\n",
       "  * y            (y) float64 5.234e+06 5.234e+06 ... 5.234e+06 5.234e+06\n",
       "  * x            (x) float64 4.881e+05 4.881e+05 ... 4.883e+05 4.883e+05\n",
       "    spatial_ref  int32 32633\n",
       "Data variables:\n",
       "    band_1       (time, y, x) int16 32767 32767 32767 ... 32767 32767 32767\n",
       "Attributes:\n",
       "    crs:           EPSG:32633\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = acube.load(**query)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualize Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `rasterio` to create a mask to mask out all the data outside of the polygon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fde99628198>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 756x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_plot = data.band_1.where(data.band_1 != data.band_1.attrs['nodata']).dropna(dim='time')\n",
    "mask = rasterio.features.rasterize(masking_feature,\n",
    "                                   out_shape=data.isel(time=0).band_1.shape,\n",
    "                                   transform=data.band_1.affine)\n",
    "mask = xarray.DataArray(mask, coords=(data.y, data.x))\n",
    "mask.plot(size=7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code for plotting below can be modified to display a different scale of values with the parameters `vmax` and `vmin`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7fde9920f9e8>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2952x2880 with 17 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "masked_data = data_plot.where(mask)\n",
    "imshowfig = masked_data.plot.imshow(x='x', \n",
    "                        y='y',\n",
    "                        col='time',\n",
    "                        size=10,\n",
    "                        col_wrap=4,\n",
    "                        cmap='magma',\n",
    "                        add_colorbar=True)#, vmax=3534, vmin=746)\n",
    "imshowfig.set_ticks(max_xticks=4)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Datacube [conda env:datacube]",
   "language": "python",
   "name": "conda-env-datacube-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}