examples/Running_WOfS.ipynb

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   "source": [
    "# Example of running WOfS Classifier on DEA ARD Data"
   ]
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       "Datacube<index=Index<db=PostgresDb<engine=Engine(postgresql://sandbox_reader:***@db-aurora-dea-sandbox-eks.cluster-ro-cos5zfpkso9m.ap-southeast-2.rds.amazonaws.com:5432/odc)>>>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "import datacube\n",
    "from dea_tools.plotting import plot_wo\n",
    "from wofs.wofls import woffles_ard\n",
    "\n",
    "dc = datacube.Datacube()\n",
    "dc\n"
   ]
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   "id": "971dbea5-045b-4c01-b363-498c1d3943a4",
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       "Dimensions:       (time: 3, y: 1632, x: 1398)\n",
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       "  * time          (time) datetime64[ns] 2023-09-21T23:50:21.853344 ... 2023-1...\n",
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       "    spatial_ref   int32 3577\n",
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       "    oa_fmask      (time, y, x) uint8 dask.array&lt;chunksize=(1, 1632, 1398), meta=np.ndarray&gt;\n",
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       "Attributes:\n",
       "    crs:           EPSG:3577\n",
       "    grid_mapping:  spatial_ref</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-5b43e4d2-f039-4367-b497-e8835cae26e5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b43e4d2-f039-4367-b497-e8835cae26e5' 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>: 3</li><li><span class='xr-has-index'>y</span>: 1632</li><li><span class='xr-has-index'>x</span>: 1398</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-cbd997c9-41d9-4a19-82e9-ad613af6a177' class='xr-section-summary-in' type='checkbox'  checked><label for='section-cbd997c9-41d9-4a19-82e9-ad613af6a177' 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'>2023-09-21T23:50:21.853344 ... 2...</div><input id='attrs-3438a079-bee2-4461-a7ad-c237fb1c4c6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3438a079-bee2-4461-a7ad-c237fb1c4c6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0c0505b-ad1b-4246-be2d-61ad72cd6f68' class='xr-var-data-in' type='checkbox'><label for='data-d0c0505b-ad1b-4246-be2d-61ad72cd6f68' 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;2023-09-21T23:50:21.853344000&#x27;, &#x27;2023-09-28T23:56:35.971208000&#x27;,\n",
       "       &#x27;2023-10-07T23:50:26.184438000&#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'>-3.949e+06 ... -3.998e+06</div><input id='attrs-efced4af-0842-459b-bf23-e2d316891503' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efced4af-0842-459b-bf23-e2d316891503' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7311003-f7d7-4e43-9996-7e0251ada914' class='xr-var-data-in' type='checkbox'><label for='data-f7311003-f7d7-4e43-9996-7e0251ada914' 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>-30.0</dd><dt><span>crs :</span></dt><dd>EPSG:3577</dd></dl></div><div class='xr-var-data'><pre>array([-3949395., -3949425., -3949455., ..., -3998265., -3998295., -3998325.])</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'>1.54e+06 1.54e+06 ... 1.582e+06</div><input id='attrs-05f4c8b0-79d7-42a4-bb26-96e8332a332b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05f4c8b0-79d7-42a4-bb26-96e8332a332b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-447562e1-830e-4c91-984c-2df4eea409ac' class='xr-var-data-in' type='checkbox'><label for='data-447562e1-830e-4c91-984c-2df4eea409ac' 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>30.0</dd><dt><span>crs :</span></dt><dd>EPSG:3577</dd></dl></div><div class='xr-var-data'><pre>array([1539675., 1539705., 1539735., ..., 1581525., 1581555., 1581585.])</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'>3577</div><input id='attrs-85e475a7-9123-4bce-9559-076392ce6cea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85e475a7-9123-4bce-9559-076392ce6cea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ea6caf8-0a33-464d-ac4c-e55d8293d721' class='xr-var-data-in' type='checkbox'><label for='data-3ea6caf8-0a33-464d-ac4c-e55d8293d721' 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;GDA94 / Australian Albers&quot;,GEOGCS[&quot;GDA94&quot;,DATUM[&quot;Geocentric_Datum_of_Australia_1994&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6283&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;4283&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,0],PARAMETER[&quot;longitude_of_center&quot;,132],PARAMETER[&quot;standard_parallel_1&quot;,-18],PARAMETER[&quot;standard_parallel_2&quot;,-36],PARAMETER[&quot;false_easting&quot;,0],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;3577&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>array(3577, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e40c62d1-88c2-4460-b588-df140c26465b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e40c62d1-88c2-4460-b588-df140c26465b' class='xr-section-summary' >Data variables: <span>(8)</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>nbart_blue</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'>dask.array&lt;chunksize=(1, 1632, 1398), meta=np.ndarray&gt;</div><input id='attrs-8c690b6c-86a0-413d-8212-e41bbf5fd109' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8c690b6c-86a0-413d-8212-e41bbf5fd109' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa7cb646-4b0d-46c7-8fe1-8808fa28739d' class='xr-var-data-in' type='checkbox'><label for='data-aa7cb646-4b0d-46c7-8fe1-8808fa28739d' 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>1</dd><dt><span>nodata :</span></dt><dd>-999</dd><dt><span>crs :</span></dt><dd>EPSG:3577</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int16 numpy.ndarray </td>\n",
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       "               &#x27;2023-10-07 23:50:26.184438&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-41aa6b51-e7ea-43fc-b0cf-46f6a6923e5c' class='xr-index-data-in' type='checkbox'/><label for='index-41aa6b51-e7ea-43fc-b0cf-46f6a6923e5c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-3949395.0, -3949425.0, -3949455.0, -3949485.0, -3949515.0,\n",
       "              -3949545.0, -3949575.0, -3949605.0, -3949635.0, -3949665.0,\n",
       "              ...\n",
       "              -3998055.0, -3998085.0, -3998115.0, -3998145.0, -3998175.0,\n",
       "              -3998205.0, -3998235.0, -3998265.0, -3998295.0, -3998325.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1632))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-39e714c7-6304-4c2b-8c3a-3f06555889c5' class='xr-index-data-in' type='checkbox'/><label for='index-39e714c7-6304-4c2b-8c3a-3f06555889c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1539675.0, 1539705.0, 1539735.0, 1539765.0, 1539795.0, 1539825.0,\n",
       "              1539855.0, 1539885.0, 1539915.0, 1539945.0,\n",
       "              ...\n",
       "              1581315.0, 1581345.0, 1581375.0, 1581405.0, 1581435.0, 1581465.0,\n",
       "              1581495.0, 1581525.0, 1581555.0, 1581585.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1398))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5772b4b8-e4ee-4bf9-a07e-4ab59374286a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5772b4b8-e4ee-4bf9-a07e-4ab59374286a' 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:3577</dd><dt><span>grid_mapping :</span></dt><dd>spatial_ref</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:       (time: 3, y: 1632, x: 1398)\n",
       "Coordinates:\n",
       "  * time          (time) datetime64[ns] 2023-09-21T23:50:21.853344 ... 2023-1...\n",
       "  * y             (y) float64 -3.949e+06 -3.949e+06 ... -3.998e+06 -3.998e+06\n",
       "  * x             (x) float64 1.54e+06 1.54e+06 1.54e+06 ... 1.582e+06 1.582e+06\n",
       "    spatial_ref   int32 3577\n",
       "Data variables:\n",
       "    nbart_blue    (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    nbart_green   (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    nbart_red     (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    nbart_nir     (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    nbart_swir_1  (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    nbart_swir_2  (time, y, x) int16 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    oa_fmask      (time, y, x) uint8 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "    fmask         (time, y, x) uint8 dask.array<chunksize=(1, 1632, 1398), meta=np.ndarray>\n",
       "Attributes:\n",
       "    crs:           EPSG:3577\n",
       "    grid_mapping:  spatial_ref"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xrange = 149.264 - 0.2, 149.264 + 0.2\n",
    "yrange = - 35.415 - 0.2, - 35.415 + 0.2\n",
    "timerange = datetime.today() - timedelta(days=21), datetime.today()\n",
    "ds = dc.load(\n",
    "    product=\"ga_ls8c_ard_3\",\n",
    "    x=xrange,\n",
    "    y=yrange,\n",
    "    time=timerange,\n",
    "    # crs=\"EPSG:3577\", # Albers\n",
    "    dask_chunks={\"time\": 1},\n",
    "    measurements=[\n",
    "        \"nbart_blue\",\n",
    "        \"nbart_green\",\n",
    "        \"nbart_red\",\n",
    "        \"nbart_nir\",\n",
    "        \"nbart_swir_1\",\n",
    "        \"nbart_swir_2\",\n",
    "        \"oa_fmask\",\n",
    "    ],\n",
    "    output_crs=\"EPSG:3577\",\n",
    "    resolution=(-30, 30),\n",
    "    group_by=\"solar_day\",\n",
    ")\n",
    "ds['fmask'] = ds['oa_fmask']\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "49015d96-5a13-4ab9-82cd-c2abe6c95b59",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\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",
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       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 1632, x: 1398)&gt;\n",
       "dask.array&lt;where, shape=(1632, 1398), dtype=uint8, chunksize=(1632, 1398), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * y            (y) float64 -3.949e+06 -3.949e+06 ... -3.998e+06 -3.998e+06\n",
       "  * x            (x) float64 1.54e+06 1.54e+06 1.54e+06 ... 1.582e+06 1.582e+06\n",
       "    time         datetime64[ns] 2023-09-21T23:50:21.853344\n",
       "    spatial_ref  int32 3577</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>y</span>: 1632</li><li><span class='xr-has-index'>x</span>: 1398</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-696fc7fd-98e9-4173-bb61-b2d6d677f42e' class='xr-array-in' type='checkbox' checked><label for='section-696fc7fd-98e9-4173-bb61-b2d6d677f42e' 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>dask.array&lt;chunksize=(1632, 1398), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\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> 2.18 MiB </td>\n",
       "                        <td> 2.18 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1632, 1398) </td>\n",
       "                        <td> (1632, 1398) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 40 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"152\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"102\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"102\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"102\" y1=\"0\" x2=\"102\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 102.79411764705884,0.0 102.79411764705884,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"51.397059\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1398</text>\n",
       "  <text x=\"122.794118\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,122.794118,60.000000)\">1632</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-a441d564-29b0-413a-b6dd-c92da5c39525' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a441d564-29b0-413a-b6dd-c92da5c39525' 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'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-3.949e+06 ... -3.998e+06</div><input id='attrs-fa78c8b1-1e5d-4d5e-b9f2-562cd76ec84d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fa78c8b1-1e5d-4d5e-b9f2-562cd76ec84d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65697b08-c677-45c4-a56d-90c1e370b9f6' class='xr-var-data-in' type='checkbox'><label for='data-65697b08-c677-45c4-a56d-90c1e370b9f6' 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>-30.0</dd><dt><span>crs :</span></dt><dd>EPSG:3577</dd></dl></div><div class='xr-var-data'><pre>array([-3949395., -3949425., -3949455., ..., -3998265., -3998295., -3998325.])</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'>1.54e+06 1.54e+06 ... 1.582e+06</div><input id='attrs-b1ea2437-a80e-491a-8382-c2aa512b71f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1ea2437-a80e-491a-8382-c2aa512b71f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc59523f-0f7d-4868-aab0-3cf003fb9a3c' class='xr-var-data-in' type='checkbox'><label for='data-bc59523f-0f7d-4868-aab0-3cf003fb9a3c' 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>30.0</dd><dt><span>crs :</span></dt><dd>EPSG:3577</dd></dl></div><div class='xr-var-data'><pre>array([1539675., 1539705., 1539735., ..., 1581525., 1581555., 1581585.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-09-21T23:50:21.853344</div><input id='attrs-41a8544f-2531-4f03-84a5-ebebdfc3a655' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41a8544f-2531-4f03-84a5-ebebdfc3a655' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fdcf3a4-df13-462b-b3b6-88f70d423acc' class='xr-var-data-in' type='checkbox'><label for='data-1fdcf3a4-df13-462b-b3b6-88f70d423acc' 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;2023-09-21T23:50:21.853344000&#x27;, dtype=&#x27;datetime64[ns]&#x27;)</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'>3577</div><input id='attrs-93f2ad4d-2905-42e5-ac5d-b971c57e32e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93f2ad4d-2905-42e5-ac5d-b971c57e32e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99633e60-a131-4dc8-adc2-fc922e5a47ca' class='xr-var-data-in' type='checkbox'><label for='data-99633e60-a131-4dc8-adc2-fc922e5a47ca' 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;GDA94 / Australian Albers&quot;,GEOGCS[&quot;GDA94&quot;,DATUM[&quot;Geocentric_Datum_of_Australia_1994&quot;,SPHEROID[&quot;GRS 1980&quot;,6378137,298.257222101,AUTHORITY[&quot;EPSG&quot;,&quot;7019&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6283&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;4283&quot;]],PROJECTION[&quot;Albers_Conic_Equal_Area&quot;],PARAMETER[&quot;latitude_of_center&quot;,0],PARAMETER[&quot;longitude_of_center&quot;,132],PARAMETER[&quot;standard_parallel_1&quot;,-18],PARAMETER[&quot;standard_parallel_2&quot;,-36],PARAMETER[&quot;false_easting&quot;,0],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;3577&quot;]]</dd><dt><span>grid_mapping_name :</span></dt><dd>albers_conical_equal_area</dd></dl></div><div class='xr-var-data'><pre>array(3577, dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f89443fa-a46e-4b21-806c-d7ab179672fa' class='xr-section-summary-in' type='checkbox'  ><label for='section-f89443fa-a46e-4b21-806c-d7ab179672fa' class='xr-section-summary' >Indexes: <span>(2)</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-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-be909ce4-4120-4669-8129-d425469732da' class='xr-index-data-in' type='checkbox'/><label for='index-be909ce4-4120-4669-8129-d425469732da' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([-3949395.0, -3949425.0, -3949455.0, -3949485.0, -3949515.0,\n",
       "              -3949545.0, -3949575.0, -3949605.0, -3949635.0, -3949665.0,\n",
       "              ...\n",
       "              -3998055.0, -3998085.0, -3998115.0, -3998145.0, -3998175.0,\n",
       "              -3998205.0, -3998235.0, -3998265.0, -3998295.0, -3998325.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=1632))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2ca5d5c7-96fe-4e23-b567-869ffaa02e8f' class='xr-index-data-in' type='checkbox'/><label for='index-2ca5d5c7-96fe-4e23-b567-869ffaa02e8f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([1539675.0, 1539705.0, 1539735.0, 1539765.0, 1539795.0, 1539825.0,\n",
       "              1539855.0, 1539885.0, 1539915.0, 1539945.0,\n",
       "              ...\n",
       "              1581315.0, 1581345.0, 1581375.0, 1581405.0, 1581435.0, 1581465.0,\n",
       "              1581495.0, 1581525.0, 1581555.0, 1581585.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;, length=1398))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7ff15720-b8e3-4657-90eb-c467e7981ccf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ff15720-b8e3-4657-90eb-c467e7981ccf' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (y: 1632, x: 1398)>\n",
       "dask.array<where, shape=(1632, 1398), dtype=uint8, chunksize=(1632, 1398), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * y            (y) float64 -3.949e+06 -3.949e+06 ... -3.998e+06 -3.998e+06\n",
       "  * x            (x) float64 1.54e+06 1.54e+06 1.54e+06 ... 1.582e+06 1.582e+06\n",
       "    time         datetime64[ns] 2023-09-21T23:50:21.853344\n",
       "    spatial_ref  int32 3577"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wofl = woffles_ard(ds.isel(time=0), dsm=None)\n",
    "wofl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "544320d1-de80-49e3-890b-0f886c569c7e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f1deff53eb0>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_wo(wofl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a9d5201-313c-4914-b826-54041fa9a0bf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}