area_statistics.ipynb

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    "# Cookie cutter\n",
    "\n",
    "The aim of this notebook is to produce summary statistics for Hazards over NCRA regions, with the flexibility to apply the method to any shapefile region.\n",
    "\n",
    "Typical statistics include median, mean, min, max, 10th, 90th percentiles\n",
    "\n",
    "This method has used guidance from [https://github.com/aus-ref-clim-data-nci/shapefiles/blob/master/python_tutorial.ipynb]"
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   "source": [
    "<div>\n",
    "<img src=\"cookie_cutter.jpg\" width=\"500\" title=\"Cookie cutter\"/>\n",
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   "cell_type": "markdown",
   "id": "80d8e644-4aae-4694-8a64-d103ae097212",
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   "source": [
    "## Step 1 - access needed packages"
   ]
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   "execution_count": 1,
   "id": "36cc4a65-2673-412c-8759-0d5ecd7d3f54",
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   "outputs": [],
   "source": [
    "# navigate to correct working directory"
   ]
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   "execution_count": 2,
   "id": "f5a14c8e-f291-474a-86d3-39637c7f2728",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/g/data/mn51/users/gt3409/plotting_maps\n"
     ]
    }
   ],
   "source": [
    "cd /g/data/mn51/users/gt3409/plotting_maps/"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "# import needed packages\n",
    "from acs_area_statistics import acs_regional_stats, get_regions\n",
    "\n",
    "regions = get_regions([\"ncra_regions\", \"australia\"])"
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
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   "source": [
    "import xarray as xr\n",
    "import geopandas as gpd\n",
    "import regionmask\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n"
   ]
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   "execution_count": 5,
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      "text/plain": [
       "<regionmask.Regions 'ncra_regions-australia'>\n",
       "overlap:  True\n",
       "\n",
       "Regions:\n",
       "0  WANorth                          Western Australia North\n",
       "1  WASouth                          Western Australia South\n",
       "2      NSW New South Wales and Australian Capital Territory\n",
       "3      VIC                                         Victoria\n",
       "4       SA                                  South Australia\n",
       "5      TAS                                         Tasmania\n",
       "6       NT                               Northern Territory\n",
       "7 QLDNorth                                 Queensland North\n",
       "8 QLDSouth                                 Queensland South\n",
       "9      AUS                                        Australia\n",
       "\n",
       "[10 regions]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "# read in the shapefile with regions you will use\n",
    "# from acs_plotting_maps import regions_dict\n",
    "# ncra_gdf = gpd.read_file(f'/g/data/ia39/aus-ref-clim-data-nci/shapefiles/data/ncra_regions/ncra_regions.shp')\n",
    "# ncra_gdf[\"abbrevs\"]=['VIC', 'NT','TAS', 'SA', 'NSW', 'WAN', 'WAS', 'SQ', 'NQ']\n",
    "# regions = regionmask.from_geopandas(ncra_gdf, names=\"NAME\", abbrevs=\"abbrevs\")\n",
    "regions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66539853-e59a-4545-8f10-45a1031fb763",
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   "source": [
    "## Step 2 - Load and prepare data\n",
    "\n",
    "Open the data you need. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e86d1c80-1914-4a98-af00-2111bcdd1900",
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   "outputs": [
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     "text": [
      "CPU times: user 3.58 s, sys: 1.35 s, total: 4.93 s\n",
      "Wall time: 7.11 s\n"
     ]
    }
   ],
   "source": [
    "%%time \n",
    "# open Hazard data\n",
    "# this is a slowish part of the code. It is likely to be reused if you're interested in calculating multiple statistics\n",
    "filename = \"/g/data/ia39/ncra/bushfire/fire_climate_classes_AGCD-05i_MM_ssp370_v1-r1-ACS-NRNBC_GWL12.nc\"\n",
    "ds = xr.open_dataset(filename, use_cftime = True,)\n",
    "# ds=ds.rename({\"latitude\":\"lat\", \"longitude\": \"lon\"})\n",
    "# ds = ds.assign_coords(lat = ds.lat.astype(\"double\").round(3), lon = ds.lon.astype(\"double\").round(3))"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 5MB\n",
       "Dimensions:                      (lat: 691, lon: 886, class: 5)\n",
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       "  * lon                          (lon) float64 7kB 112.0 112.0 ... 156.2 156.2\n",
       "    clim_period                  &lt;U6 24B ...\n",
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       "    class_code                   (class) int64 40B ...\n",
       "    class_long_description       (class) &lt;U23 460B ...\n",
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       "    colour_map                   (class) &lt;U7 140B ...\n",
       "Attributes: (12/13)\n",
       "    data_source:                  BARPA ia39\n",
       "    notebook:                     stern_dehoedt_ernst_2000_BARPA.ipynb\n",
       "    Global_Warming_Level:         GWL1.2\n",
       "    reference:                    Stern, H., De Hoedt, G., &amp; Ernst, J. (2000)...\n",
       "    GCM_model:                    EC-Earth3, CESM2, ACCESS-CM2, CMCC-ESM2, CN...\n",
       "    Experiment_ID:                ssp370, ssp370, ssp370, ssp370, ssp370, ssp...\n",
       "    ...                           ...\n",
       "    RCM_model:                    CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN...\n",
       "    RCM_model_version_ID:         v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-M...\n",
       "    start_year:                   2004, 2010, 2010, 2012, 2016, 2015, 2011, 2...\n",
       "    end_year:                     2023, 2029, 2029, 2031, 2035, 2034, 2030, 2...\n",
       "    GWL:                          GWL1.2\n",
       "    bias_correction_description:  MRNBC-AGCD</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-b9656b2f-50dd-4335-978c-51d977bebec9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b9656b2f-50dd-4335-978c-51d977bebec9' 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'>lat</span>: 691</li><li><span class='xr-has-index'>lon</span>: 886</li><li><span class='xr-has-index'>class</span>: 5</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-639f47f7-1d51-44a1-9c9c-0a82e1667126' class='xr-section-summary-in' type='checkbox'  checked><label for='section-639f47f7-1d51-44a1-9c9c-0a82e1667126' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.5 -44.45 -44.4 ... -10.05 -10.0</div><input id='attrs-1e5a631a-9c13-4e44-b669-c6d7900c7160' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e5a631a-9c13-4e44-b669-c6d7900c7160' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22276d08-639e-4cf9-8cc5-e549155ca372' class='xr-var-data-in' type='checkbox'><label for='data-22276d08-639e-4cf9-8cc5-e549155ca372' 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'></dl></div><div class='xr-var-data'><pre>array([-44.5 , -44.45, -44.4 , ..., -10.1 , -10.05, -10.  ])</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'>112.0 112.0 112.1 ... 156.2 156.2</div><input id='attrs-74f86161-e736-4482-8853-bfe97182a0fa' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74f86161-e736-4482-8853-bfe97182a0fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-864e6119-d6c2-4872-91d9-fb8e46c2a7b3' class='xr-var-data-in' type='checkbox'><label for='data-864e6119-d6c2-4872-91d9-fb8e46c2a7b3' 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'></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 156.15, 156.2 , 156.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clim_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4e836d1e-bb20-4f81-ae9d-d58a8256a868' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e836d1e-bb20-4f81-ae9d-d58a8256a868' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-833e8883-7c45-493f-a104-a8ca73f34882' class='xr-var-data-in' type='checkbox'><label for='data-833e8883-7c45-493f-a104-a8ca73f34882' 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'></dl></div><div class='xr-var-data'><pre>[1 values with dtype=&lt;U6]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>class</span></div><div class='xr-var-dims'>(class)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4</div><input id='attrs-aac81fb2-9969-45f0-9976-db13ac6d0204' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aac81fb2-9969-45f0-9976-db13ac6d0204' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c7633e6-60c8-4354-9e72-0aeb09119113' class='xr-var-data-in' type='checkbox'><label for='data-3c7633e6-60c8-4354-9e72-0aeb09119113' 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'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6a4f6f92-3526-4d86-b143-0df0293fc27b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6a4f6f92-3526-4d86-b143-0df0293fc27b' class='xr-section-summary' >Data variables: <span>(5)</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>fire_climate_class</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b46fbda1-fffb-40fa-8f66-ec6cb45e70e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b46fbda1-fffb-40fa-8f66-ec6cb45e70e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5414a2bd-88d3-489e-b31b-e1fb793e4904' class='xr-var-data-in' type='checkbox'><label for='data-5414a2bd-88d3-489e-b31b-e1fb793e4904' 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'></dl></div><div class='xr-var-data'><pre>[612226 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>class_code</span></div><div class='xr-var-dims'>(class)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a1583bf0-67b4-4bed-9d06-944d1c743c33' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1583bf0-67b4-4bed-9d06-944d1c743c33' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e87ef58a-8087-4b07-a510-e206452bd82f' class='xr-var-data-in' type='checkbox'><label for='data-e87ef58a-8087-4b07-a510-e206452bd82f' 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>description :</span></dt><dd>These values are used in the &#x27;fire_climate_class&#x27; geodata variable to represent the fire climate classes</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>class_long_description</span></div><div class='xr-var-dims'>(class)</div><div class='xr-var-dtype'>&lt;U23</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-8704c320-dd5c-4e3b-8d90-6d72399b1fbc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8704c320-dd5c-4e3b-8d90-6d72399b1fbc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8a6e64df-73bc-4011-8e7e-6eb60d8abd20' class='xr-var-data-in' type='checkbox'><label for='data-8a6e64df-73bc-4011-8e7e-6eb60d8abd20' 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>description :</span></dt><dd>The names of the fire climate classes</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=&lt;U23]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stern_dehoedt_minor_classes</span></div><div class='xr-var-dims'>(class)</div><div class='xr-var-dtype'>&lt;U50</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-04d9a815-5bbf-4690-9802-49f6e91ce69e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-04d9a815-5bbf-4690-9802-49f6e91ce69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97ee114-44c4-490b-b75c-36813a383210' class='xr-var-data-in' type='checkbox'><label for='data-d97ee114-44c4-490b-b75c-36813a383210' 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>description :</span></dt><dd>The Stern-deHoedt-Ernst (2000) minor class codes comprising each fire climate class</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=&lt;U50]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>colour_map</span></div><div class='xr-var-dims'>(class)</div><div class='xr-var-dtype'>&lt;U7</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-139b6e71-4f2e-4a69-bb49-4be349b9077f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-139b6e71-4f2e-4a69-bb49-4be349b9077f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32033cb0-4016-48bc-a807-298172658670' class='xr-var-data-in' type='checkbox'><label for='data-32033cb0-4016-48bc-a807-298172658670' 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>description :</span></dt><dd>These hex codes are the recommended colours to use when illustrating the fire climate classes</dd><dt><span>json :</span></dt><dd>{&quot;100&quot;: &quot;#84a19b&quot;, &quot;101&quot;: &quot;#e0d7c6&quot;, &quot;102&quot;: &quot;#486136&quot;, &quot;103&quot;: &quot;#737932&quot;, &quot;104&quot;: &quot;#a18a6e&quot;}</dd></dl></div><div class='xr-var-data'><pre>[5 values with dtype=&lt;U7]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e30459f4-10ce-4316-8d59-4fa3f1a91d00' class='xr-section-summary-in' type='checkbox'  ><label for='section-e30459f4-10ce-4316-8d59-4fa3f1a91d00' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f33b13b4-aa74-4423-8961-d113ef89e725' class='xr-index-data-in' type='checkbox'/><label for='index-f33b13b4-aa74-4423-8961-d113ef89e725' 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(Index([ -44.5, -44.45,  -44.4, -44.35,  -44.3, -44.25,  -44.2, -44.15,  -44.1,\n",
       "       -44.05,\n",
       "       ...\n",
       "       -10.45,  -10.4, -10.35,  -10.3, -10.25,  -10.2, -10.15,  -10.1, -10.05,\n",
       "        -10.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=691))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8915d50-fd57-40c0-8dbd-1118c4c6bad1' class='xr-index-data-in' type='checkbox'/><label for='index-b8915d50-fd57-40c0-8dbd-1118c4c6bad1' 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(Index([ 112.0, 112.05,  112.1, 112.15,  112.2, 112.25,  112.3, 112.35,  112.4,\n",
       "       112.45,\n",
       "       ...\n",
       "        155.8, 155.85,  155.9, 155.95,  156.0, 156.05,  156.1, 156.15,  156.2,\n",
       "       156.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=886))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>class</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e80b1b7-e10d-4f7a-9900-ed7db0f628c9' class='xr-index-data-in' type='checkbox'/><label for='index-4e80b1b7-e10d-4f7a-9900-ed7db0f628c9' 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(Index([0, 1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;class&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b2c6ce21-e280-43d5-8e3a-9b35c3031c35' class='xr-section-summary-in' type='checkbox'  ><label for='section-b2c6ce21-e280-43d5-8e3a-9b35c3031c35' class='xr-section-summary' >Attributes: <span>(13)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>data_source :</span></dt><dd>BARPA ia39</dd><dt><span>notebook :</span></dt><dd>stern_dehoedt_ernst_2000_BARPA.ipynb</dd><dt><span>Global_Warming_Level :</span></dt><dd>GWL1.2</dd><dt><span>reference :</span></dt><dd>Stern, H., De Hoedt, G., &amp; Ernst, J. (2000). Objective classification of Australian climates. Australian Meteorological Magazine, 49(2), 87–96.</dd><dt><span>GCM_model :</span></dt><dd>EC-Earth3, CESM2, ACCESS-CM2, CMCC-ESM2, CNRM-ESM2-1, ACCESS-ESM1-5, MPI-ESM1-2-HR, EC-Earth3, CESM2, ACCESS-CM2, NorESM2-MM, CMCC-ESM2, ACCESS-ESM1-5</dd><dt><span>Experiment_ID :</span></dt><dd>ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370, ssp370</dd><dt><span>Ensemble :</span></dt><dd>r1i1p1f1, r11i1p1f1, r4i1p1f1, r1i1p1f1, r1i1p1f2, r6i1p1f1, r1i1p1f1, r1i1p1f1, r11i1p1f1, r4i1p1f1, r1i1p1f1, r1i1p1f1, r6i1p1f1</dd><dt><span>RCM_model :</span></dt><dd>CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN, BARPA-R, BARPA-R, BARPA-R, BARPA-R, BARPA-R, BARPA-R, BARPA-R</dd><dt><span>RCM_model_version_ID :</span></dt><dd>v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-MRNBC-AGCD-1960-2022</dd><dt><span>start_year :</span></dt><dd>2004, 2010, 2010, 2012, 2016, 2015, 2011, 2004, 2010, 2010, 2029, 2012, 2015</dd><dt><span>end_year :</span></dt><dd>2023, 2029, 2029, 2031, 2035, 2034, 2030, 2023, 2029, 2029, 2048, 2031, 2034</dd><dt><span>GWL :</span></dt><dd>GWL1.2</dd><dt><span>bias_correction_description :</span></dt><dd>MRNBC-AGCD</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 5MB\n",
       "Dimensions:                      (lat: 691, lon: 886, class: 5)\n",
       "Coordinates:\n",
       "  * lat                          (lat) float64 6kB -44.5 -44.45 ... -10.05 -10.0\n",
       "  * lon                          (lon) float64 7kB 112.0 112.0 ... 156.2 156.2\n",
       "    clim_period                  <U6 24B ...\n",
       "  * class                        (class) int64 40B 0 1 2 3 4\n",
       "Data variables:\n",
       "    fire_climate_class           (lat, lon) float64 5MB ...\n",
       "    class_code                   (class) int64 40B ...\n",
       "    class_long_description       (class) <U23 460B ...\n",
       "    stern_dehoedt_minor_classes  (class) <U50 1kB ...\n",
       "    colour_map                   (class) <U7 140B ...\n",
       "Attributes: (12/13)\n",
       "    data_source:                  BARPA ia39\n",
       "    notebook:                     stern_dehoedt_ernst_2000_BARPA.ipynb\n",
       "    Global_Warming_Level:         GWL1.2\n",
       "    reference:                    Stern, H., De Hoedt, G., & Ernst, J. (2000)...\n",
       "    GCM_model:                    EC-Earth3, CESM2, ACCESS-CM2, CMCC-ESM2, CN...\n",
       "    Experiment_ID:                ssp370, ssp370, ssp370, ssp370, ssp370, ssp...\n",
       "    ...                           ...\n",
       "    RCM_model:                    CCAM-v2203-SN, CCAM-v2203-SN, CCAM-v2203-SN...\n",
       "    RCM_model_version_ID:         v1-r1-ACS-MRNBC-AGCD-1960-2022, v1-r1-ACS-M...\n",
       "    start_year:                   2004, 2010, 2010, 2012, 2016, 2015, 2011, 2...\n",
       "    end_year:                     2023, 2029, 2029, 2031, 2035, 2034, 2030, 2...\n",
       "    GWL:                          GWL1.2\n",
       "    bias_correction_description:  MRNBC-AGCD"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07e98607-e185-4888-80ab-55997ad193d9",
   "metadata": {},
   "source": [
    "## Step 3 - Create your mask\n",
    "\n",
    "There's a range of ways to calculate your mask:\n",
    "- **regions.mask_3D(ds)** will create a \"mask\" array that is ```True``` for all grid cells whose centre point falls within a particular state/territory and ```False``` elsewhere\n",
    "- **regions.mask_3D_frac_approx(ds)** will calculate the fraction of each grid cell that overlaps with each shape\n",
    "- **mask_10pct** you may create a mask from the fraction values we need to decide on a minimum overlap threshold. eg ```mask_10pct = frac >= 0.1```\n",
    "\n",
    "Fractional masking```regions.mask_3D_frac_approx(ds)``` which will weigh each lat lon grid by the approximate fraction of the area that lay within the shapefile geometery. It  is probably the most accurate, but more expensive than the simple mask_3D.\n",
    "\n",
    "The masking methods return an xr.DataArray with three dimensions:  **region**, **lat**, and **lon**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "974715bc-7e93-435a-9da8-b523b35624cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create your mask\n",
    "# you only need one mask. here are two examples. \n",
    "# you can also is a keyword fro the function to calculate the mask, but if you're performing multiple calculations, this can be slow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4b6b3284-ba6e-4e35-a08a-b7e6edeec318",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 20.8 s, sys: 1.95 s, total: 22.8 s\n",
      "Wall time: 22.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# calculate weighted mask this is a very slow part of the code and\n",
    "# can be reused for any datasets using the same regions and the same lat lon\n",
    "mask_frac = regions.mask_3D_frac_approx(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b3a5c780-0410-4aa0-a952-fc82aef32433",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.51 s, sys: 188 ms, total: 4.7 s\n",
      "Wall time: 4.71 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "mask_centred = regions.mask_3D(ds)"
   ]
  },
  {
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   "id": "e1862601-a652-4127-b0df-e61ed0b1099d",
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   "source": [
    "## Step 4: Use function\n",
    "To apply mask calculate a statistic to summarise each region "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "2fe93240-ed15-4f9d-8a6b-65464320d7da",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the stats using the acs_region_fractional_stats function\n",
    "# the function can infer/calculate many of the inputs, but it will be faster to explicitly specify them in the function, eg var, dims, mask  \n",
    "\n",
    "# start, end = (\"1991\", \"2010\") \n",
    "dims = ( \"lat\", \"lon\")\n",
    "# var = \"pr\"\n",
    "var = \"fire_climate_class\"\n",
    "how = [\"mean\", \"min\", \"max\", \"mode\"]\n",
    "outfile=\"out.csv\"\n",
    "mask = mask_centred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fcc0e6de-dc00-4db8-b000-c3824d203212",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 3.97 s, sys: 1.84 s, total: 5.82 s\n",
      "Wall time: 6.42 s\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 3kB\n",
       "Dimensions:                  (region: 10)\n",
       "Coordinates:\n",
       "    clim_period              &lt;U6 24B &#x27;GWL1.2&#x27;\n",
       "  * region                   (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
       "    abbrevs                  (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;\n",
       "    names                    (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... ...\n",
       "Data variables:\n",
       "    fire_climate_class_mean  (region) float64 80B 101.0 101.4 ... 101.2 101.3\n",
       "    fire_climate_class_min   (region) float64 80B 100.0 101.0 ... 100.0 100.0\n",
       "    fire_climate_class_max   (region) float64 80B 102.0 104.0 ... 104.0 104.0\n",
       "    fire_climate_class_mode  (region) float64 80B 101.0 101.0 ... 101.0 101.0</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-deb3924c-9af0-4b56-844d-4e73b5891db6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-deb3924c-9af0-4b56-844d-4e73b5891db6' 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'>region</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dc063c00-133b-47e8-a720-f59d756415a4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc063c00-133b-47e8-a720-f59d756415a4' 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>clim_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;GWL1.2&#x27;</div><input id='attrs-dda1b4c4-a617-4105-bbe3-6346acb83333' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dda1b4c4-a617-4105-bbe3-6346acb83333' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d15e4fc-bab8-47cb-9857-665241edd4d3' class='xr-var-data-in' type='checkbox'><label for='data-3d15e4fc-bab8-47cb-9857-665241edd4d3' 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'></dl></div><div class='xr-var-data'><pre>array(&#x27;GWL1.2&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-33ef91e9-2131-4226-9a96-befdab65c456' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-33ef91e9-2131-4226-9a96-befdab65c456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a193e64-cc51-4854-9cfd-fb344e52d239' class='xr-var-data-in' type='checkbox'><label for='data-1a193e64-cc51-4854-9cfd-fb344e52d239' 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'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-44a78673-bd5e-41f0-97d3-80780f661fc9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44a78673-bd5e-41f0-97d3-80780f661fc9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aea59018-a158-4190-a810-46b1a6750331' class='xr-var-data-in' type='checkbox'><label for='data-aea59018-a158-4190-a810-46b1a6750331' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-2b29fb8e-8af0-462b-9082-844906bb8303' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b29fb8e-8af0-462b-9082-844906bb8303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7186b20d-e151-46cb-b2c9-8a4c21661684' class='xr-var-data-in' type='checkbox'><label for='data-7186b20d-e151-46cb-b2c9-8a4c21661684' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-955b59a5-ea53-4f71-a4eb-950b68a09a5c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-955b59a5-ea53-4f71-a4eb-950b68a09a5c' 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>fire_climate_class_mean</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>101.0 101.4 102.0 ... 101.2 101.3</div><input id='attrs-0a0c0797-8a6d-42bd-a159-fa71c8804a87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0a0c0797-8a6d-42bd-a159-fa71c8804a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4649900e-41e6-4517-a888-5023821662ca' class='xr-var-data-in' type='checkbox'><label for='data-4649900e-41e6-4517-a888-5023821662ca' 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'></dl></div><div class='xr-var-data'><pre>array([100.96876395, 101.35157822, 102.00501792, 102.84284524,\n",
       "       101.33042181, 102.07211067, 100.87863498, 100.91624733,\n",
       "       101.17274228, 101.26589614])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fire_climate_class_min</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>100.0 101.0 101.0 ... 100.0 100.0</div><input id='attrs-716e9037-b6da-423b-93d4-30814646f1a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-716e9037-b6da-423b-93d4-30814646f1a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-117aa777-669b-4614-b145-5ead42f3009a' class='xr-var-data-in' type='checkbox'><label for='data-117aa777-669b-4614-b145-5ead42f3009a' 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'></dl></div><div class='xr-var-data'><pre>array([100., 101., 101., 101., 101., 102., 100., 100., 100., 100.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fire_climate_class_max</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>102.0 104.0 104.0 ... 104.0 104.0</div><input id='attrs-c55129a0-759f-415f-aed7-7575d5c377c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c55129a0-759f-415f-aed7-7575d5c377c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5b50a68-61ab-4e24-b0c7-9d5a883d3a06' class='xr-var-data-in' type='checkbox'><label for='data-c5b50a68-61ab-4e24-b0c7-9d5a883d3a06' 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'></dl></div><div class='xr-var-data'><pre>array([102., 104., 104., 104., 104., 104., 101., 104., 104., 104.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fire_climate_class_mode</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>101.0 101.0 101.0 ... 101.0 101.0</div><input id='attrs-99112ef5-08c5-41b5-8bfa-d925538931f1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-99112ef5-08c5-41b5-8bfa-d925538931f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe8b6338-df70-416a-9d25-4e4a389567ab' class='xr-var-data-in' type='checkbox'><label for='data-fe8b6338-df70-416a-9d25-4e4a389567ab' 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'></dl></div><div class='xr-var-data'><pre>array([101., 101., 101., 102., 101., 102., 101., 101., 101., 101.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82d9c2d4-b003-4f70-9c02-459d3b232e3b' class='xr-section-summary-in' type='checkbox'  ><label for='section-82d9c2d4-b003-4f70-9c02-459d3b232e3b' class='xr-section-summary' >Indexes: <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-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f550ff76-cb58-4ccf-95ec-aa426e332a63' class='xr-index-data-in' type='checkbox'/><label for='index-f550ff76-cb58-4ccf-95ec-aa426e332a63' 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(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8983b1a2-ab0b-42dd-95f4-b2ab47263943' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8983b1a2-ab0b-42dd-95f4-b2ab47263943' 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.Dataset> Size: 3kB\n",
       "Dimensions:                  (region: 10)\n",
       "Coordinates:\n",
       "    clim_period              <U6 24B 'GWL1.2'\n",
       "  * region                   (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
       "    abbrevs                  (region) <U8 320B 'WANorth' 'WASouth' ... 'AUS'\n",
       "    names                    (region) <U48 2kB 'Western Australia North' ... ...\n",
       "Data variables:\n",
       "    fire_climate_class_mean  (region) float64 80B 101.0 101.4 ... 101.2 101.3\n",
       "    fire_climate_class_min   (region) float64 80B 100.0 101.0 ... 100.0 100.0\n",
       "    fire_climate_class_max   (region) float64 80B 102.0 104.0 ... 104.0 104.0\n",
       "    fire_climate_class_mode  (region) float64 80B 101.0 101.0 ... 101.0 101.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "da_summary = acs_regional_stats(ds=ds, infile=filename, var=var, mask = mask, dims=dims, how = how, outfile=outfile)\n",
    "da_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "eabe7ce9-fb4a-4c57-aa96-6b867dbc9d09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# da_summary = acs_regional_stats(ds=ds, mask = \"fractional\", how = [\"mean\", \"min\", \"max\"], outfile=\"out.csv\")\n",
    "# da_summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c2efcd07-7754-40d1-9bbd-ec959fd7ee41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x14ca1980b6a0>"
      ]
     },
     "execution_count": 14,
     "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": [
    "ds[var].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12391ec5-bd59-4a19-a51a-06e1426b08f2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ce3bacc-8264-47b9-8ed7-7b13bc4f29e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ef68fa6-4a78-4e01-a8fc-9660ead4ad0b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "360bcde7-81f2-4520-b43c-595bdc468d42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9a60dac-2cb0-4b4c-8acc-f0cd3738b91f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ad5eea9f-55df-47fd-8a5e-a41ec003086f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "\n",
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       ".xr-attrs dd {\n",
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       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
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       "  grid-column: 2;\n",
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       "\n",
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       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
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       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;fire_climate_class&#x27; (lat: 691, lon: 886, region: 10)&gt; Size: 49MB\n",
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 6kB -44.5 -44.45 -44.4 ... -10.1 -10.05 -10.0\n",
       "  * lon          (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
       "    clim_period  &lt;U6 24B &#x27;GWL1.2&#x27;\n",
       "  * region       (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
       "    abbrevs      (region) &lt;U8 320B &#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;QLDSouth&#x27; &#x27;AUS&#x27;\n",
       "    names        (region) &lt;U48 2kB &#x27;Western Australia North&#x27; ... &#x27;Australia&#x27;</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'>'fire_climate_class'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 691</li><li><span class='xr-has-index'>lon</span>: 886</li><li><span class='xr-has-index'>region</span>: 10</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd5ca375-a3ed-48db-8927-c602f60428f7' class='xr-array-in' type='checkbox' checked><label for='section-bd5ca375-a3ed-48db-8927-c602f60428f7' 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>nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan</span></div><div class='xr-array-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
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       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])</pre></div></div></li><li class='xr-section-item'><input id='section-a8cd1186-29f7-464e-9acc-358fe5372382' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a8cd1186-29f7-464e-9acc-358fe5372382' class='xr-section-summary' >Coordinates: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-44.5 -44.45 -44.4 ... -10.05 -10.0</div><input id='attrs-b401c9d8-f279-494d-96f7-1f1aee32b81c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b401c9d8-f279-494d-96f7-1f1aee32b81c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd4a7bb9-e41b-493d-b6eb-589e1b3d5712' class='xr-var-data-in' type='checkbox'><label for='data-dd4a7bb9-e41b-493d-b6eb-589e1b3d5712' 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'></dl></div><div class='xr-var-data'><pre>array([-44.5 , -44.45, -44.4 , ..., -10.1 , -10.05, -10.  ])</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'>112.0 112.0 112.1 ... 156.2 156.2</div><input id='attrs-bb85e0ee-eea8-4c76-8905-772edd252eb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bb85e0ee-eea8-4c76-8905-772edd252eb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b29ffe70-3b17-46b1-b1be-b41cc271dd80' class='xr-var-data-in' type='checkbox'><label for='data-b29ffe70-3b17-46b1-b1be-b41cc271dd80' 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'></dl></div><div class='xr-var-data'><pre>array([112.  , 112.05, 112.1 , ..., 156.15, 156.2 , 156.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>clim_period</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>&lt;U6</div><div class='xr-var-preview xr-preview'>&#x27;GWL1.2&#x27;</div><input id='attrs-9ac07e35-6040-42bb-934e-27fe8682eb1d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9ac07e35-6040-42bb-934e-27fe8682eb1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-287af6e5-8ef2-47b4-bf0a-793706543ffb' class='xr-var-data-in' type='checkbox'><label for='data-287af6e5-8ef2-47b4-bf0a-793706543ffb' 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'></dl></div><div class='xr-var-data'><pre>array(&#x27;GWL1.2&#x27;, dtype=&#x27;&lt;U6&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>region</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 7 8 9</div><input id='attrs-27863f3f-3738-4d91-ae36-d2b7dbd8151f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27863f3f-3738-4d91-ae36-d2b7dbd8151f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a933b65-19e2-4e80-9f9f-b0c698329e3f' class='xr-var-data-in' type='checkbox'><label for='data-3a933b65-19e2-4e80-9f9f-b0c698329e3f' 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'></dl></div><div class='xr-var-data'><pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>abbrevs</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;WANorth&#x27; &#x27;WASouth&#x27; ... &#x27;AUS&#x27;</div><input id='attrs-b82d2005-e5cf-4946-84b8-839525eb194a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b82d2005-e5cf-4946-84b8-839525eb194a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9052b1f-97cb-41fe-a221-f5183109ba96' class='xr-var-data-in' type='checkbox'><label for='data-d9052b1f-97cb-41fe-a221-f5183109ba96' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;WANorth&#x27;, &#x27;WASouth&#x27;, &#x27;NSW&#x27;, &#x27;VIC&#x27;, &#x27;SA&#x27;, &#x27;TAS&#x27;, &#x27;NT&#x27;, &#x27;QLDNorth&#x27;,\n",
       "       &#x27;QLDSouth&#x27;, &#x27;AUS&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>names</span></div><div class='xr-var-dims'>(region)</div><div class='xr-var-dtype'>&lt;U48</div><div class='xr-var-preview xr-preview'>&#x27;Western Australia North&#x27; ... &#x27;A...</div><input id='attrs-4041c7be-24ba-4d84-b888-3bee5d543cab' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4041c7be-24ba-4d84-b888-3bee5d543cab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e57439f9-0cff-41aa-baa0-5d998dbc948e' class='xr-var-data-in' type='checkbox'><label for='data-e57439f9-0cff-41aa-baa0-5d998dbc948e' 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'></dl></div><div class='xr-var-data'><pre>array([&#x27;Western Australia North&#x27;, &#x27;Western Australia South&#x27;,\n",
       "       &#x27;New South Wales and Australian Capital Territory&#x27;, &#x27;Victoria&#x27;,\n",
       "       &#x27;South Australia&#x27;, &#x27;Tasmania&#x27;, &#x27;Northern Territory&#x27;,\n",
       "       &#x27;Queensland North&#x27;, &#x27;Queensland South&#x27;, &#x27;Australia&#x27;], dtype=&#x27;&lt;U48&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a63b9837-2b07-4ffd-b901-2083e83d6e12' class='xr-section-summary-in' type='checkbox'  ><label for='section-a63b9837-2b07-4ffd-b901-2083e83d6e12' class='xr-section-summary' >Indexes: <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-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f94d1013-ef04-4d58-849f-c9d104c73960' class='xr-index-data-in' type='checkbox'/><label for='index-f94d1013-ef04-4d58-849f-c9d104c73960' 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(Index([ -44.5, -44.45,  -44.4, -44.35,  -44.3, -44.25,  -44.2, -44.15,  -44.1,\n",
       "       -44.05,\n",
       "       ...\n",
       "       -10.45,  -10.4, -10.35,  -10.3, -10.25,  -10.2, -10.15,  -10.1, -10.05,\n",
       "        -10.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=691))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e76213fa-e896-438c-b06c-5aab08a67808' class='xr-index-data-in' type='checkbox'/><label for='index-e76213fa-e896-438c-b06c-5aab08a67808' 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(Index([ 112.0, 112.05,  112.1, 112.15,  112.2, 112.25,  112.3, 112.35,  112.4,\n",
       "       112.45,\n",
       "       ...\n",
       "        155.8, 155.85,  155.9, 155.95,  156.0, 156.05,  156.1, 156.15,  156.2,\n",
       "       156.25],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=886))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>region</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-98dca043-7128-42a9-8889-ccb115cf5a78' class='xr-index-data-in' type='checkbox'/><label for='index-98dca043-7128-42a9-8889-ccb115cf5a78' 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(Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=&#x27;int64&#x27;, name=&#x27;region&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-746df215-2fc1-4463-8b61-2d7746d1cc43' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-746df215-2fc1-4463-8b61-2d7746d1cc43' 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 'fire_climate_class' (lat: 691, lon: 886, region: 10)> Size: 49MB\n",
       "array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]])\n",
       "Coordinates:\n",
       "  * lat          (lat) float64 6kB -44.5 -44.45 -44.4 ... -10.1 -10.05 -10.0\n",
       "  * lon          (lon) float64 7kB 112.0 112.0 112.1 112.2 ... 156.2 156.2 156.2\n",
       "    clim_period  <U6 24B 'GWL1.2'\n",
       "  * region       (region) int64 80B 0 1 2 3 4 5 6 7 8 9\n",
       "    abbrevs      (region) <U8 320B 'WANorth' 'WASouth' ... 'QLDSouth' 'AUS'\n",
       "    names        (region) <U48 2kB 'Western Australia North' ... 'Australia'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_masked = ds[var].where(mask_centred)\n",
    "ds_masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8aa1af82-cc2b-4d6a-bbe2-a8754008d6f6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'props' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mprops\u001b[49m[i]\u001b[38;5;241m.\u001b[39mto_dict()\n",
      "\u001b[0;31mNameError\u001b[0m: name 'props' is not defined"
     ]
    }
   ],
   "source": [
    "props[i].to_dict()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2628544a-3220-4737-9ddc-0a0d811a526b",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55b85e89-ec83-4f04-9677-fadab6a8a954",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_masked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89e8940e-bc6e-4659-98a4-657760656fbe",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"AI\"\n",
    "ds_ai = xr.open_dataset(\"/g/data/ia39/ncra/drought_aridity/ai/AI-atmospheric-based_NHP1-AUS-5_rcp85_bias-adjusted_2D_GWL20_percentiles10-50-90.nc\")\n",
    "\n",
    "ds=ds_ai.sel(quantile = 0.5)\n",
    "\n",
    "mask = regions.mask_3D(ds)\n",
    "ds_masked = ds.where(mask)[var]\n",
    "\n",
    "stat = \"proportions\"\n",
    "bins = [0.0, 0.05, 0.2, 0.5, 0.65]\n",
    "bin_labels =  [\"Hyper-arid\", \"Arid\", \"Semi-arid\", \"Dry sub-humid\"]\n",
    "\n",
    "df_masked = ds_masked.to_dataframe()\n",
    "df_masked[\"category\"] = pd.cut(df_masked[var], bins, labels=bin_labels, ordered=True)\n",
    "proportion_dict = {}\n",
    "props = df_masked.groupby(\"region\").value_counts([\"category\"], normalize=True).round(4)\n",
    "for i in range(len(ds_masked.region)):\n",
    "    proportion_dict[i] = props[i].to_dict() \n",
    "    # limit to first 20 categories\n",
    "    proportion_dict[i]  = dict(list(proportion_dict[i].items())[0: 20]) \n",
    "    \n",
    "d = {\"coords\": {\n",
    "        \"region\": {\"dims\": \"region\", \"data\": list(proportion_dict.keys()),}\n",
    "    },\n",
    "     \"dims\": \"region\",\n",
    "    \"data\":list(proportion_dict.values()),\n",
    "    \"name\": stat}\n",
    "df = xr.DataArray.from_dict(d).rename(f\"{var}_cat_{stat}\")\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da131ccd-ff5b-49d1-b90a-5371657234e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "dict(list(test_dict.items())[0: 20]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fce147d-7db6-4571-992f-5e2d43a104d9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "proportion_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64a45424-8de0-492b-a6c3-e5a2055b6c98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "696c2e3a-0606-40da-826e-7c5d8131a57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_masked.to_dataframe().groupby([\"region\"])[var].agg(pd.Series.mode).to_xarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b60850e-1b89-4190-b9e6-b4afc2340a15",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ee499ac-aa1a-4079-b2e5-9410de7aa3ab",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d1993a74-3dfb-4b96-b812-71bc61f9722f",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_weighted = ds[var].weighted(mask )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dab8a236-ec0c-4ab4-8766-f92ae197900a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_weighted.sum(dim=(\"lat\", \"lon\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4413f1aa-8ece-43d4-ae71-7bf48213ce17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# mode calculation for each region\n",
    "xr.merge([ds_masked.to_dataframe().groupby(['region'])[var].agg(pd.Series.mode).to_xarray().rename(\"mode\"), ds_weighted.sum(dim=(\"lat\", \"lon\")).rename(\"sum\")],)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3ea8ea5-8fee-4659-b0cf-33a338a20d7e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ec55bbb-7359-4a27-91a7-72abd47769bc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7553ceb7-34f7-42e9-9be2-101d5feb96b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# count_df = pd.DataFrame(df_masked.value_counts(normalize=True)).droplevel(\"names\")\n",
    "# count_df.sort_index(level=\"abbrevs\", sort_remaining=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb43b929-1008-4a75-8a86-14bdf65dc022",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f08e2166-ef4b-44e0-879b-f375c3e9bb24",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_masked = ds.where(mask_centred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e90f455d-7e78-4c89-8f12-952105774a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ds.AI.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a211fcf6-d20f-472d-83ad-fcb4e5b65aa4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = ds.where(mask_centred).to_dataframe()\n",
    "df[\"category\"] = pd.cut(df[var], bins, labels=bin_labels, ordered=True)\n",
    "df.groupby(\"region\")[\"category\"].agg(pd.Series.mode).to_xarray().rename(\"mode\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea3653ce-adb0-403e-a99c-d9f29fae43ca",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2881fbf-42aa-49a9-8429-cd44298a2667",
   "metadata": {},
   "outputs": [],
   "source": [
    "filename = \"/g/data/ia39/ncra/drought_aridity/ai/AI-atmospheric-based_NHP1-AUS-5_ACCESS1-0_rcp45_r1i1p1__r240x120-ISIMIP2b_2D_GWL12.nc\"\n",
    "ds = xr.open_dataset(filename, use_cftime = True,)\n",
    "var=\"AI\"\n",
    "dims=(\"lat\", \"long\",)\n",
    "bins=  [0., 0.05, 0.2, 0.5, 0.65]\n",
    "bin_labels =[\"Hyper-arid\", \"Arid\", \"Semi-arid\", \"Dry sub-humid\"]\n",
    "mask_centred = regions.mask_3D(ds)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b712eba6-cf5d-425b-af56-a322f8aaa00f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = acs_regional_stats(ds=ds, var=var,  mask = mask_centred, how = [\"min\", \"max\", \"mode\"], dims = (\"lat\", \"lon\"), bins = bins, bin_labels=bin_labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "37394965-9dd8-4f1a-b35a-0d72dbd665ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90950427-8147-4d73-8b3e-a193032d058c",
   "metadata": {},
   "outputs": [],
   "source": [
    "if bins is not None:\n",
    "    df = ds_masked.to_dataframe()\n",
    "    df[\"category\"] = pd.cut(df[var], bins, labels=bin_labels, ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "15fe4496-675d-43b6-a5d9-9d396835f360",
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31ada746-166a-4c7d-98b2-28d073e74e50",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds_weighted = ds[var].weighted(mask)\n",
    "dims=(\"lat\", \"lon\")\n",
    "ds_weighted.sum(dim=dims)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd1a3bc9-5d9f-42a7-8754-d0309eec1382",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da0841ab-2f12-40eb-a53a-bdbae26bb26a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e9bb5f3-29b0-4d4c-bfd7-41f60240c1a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "lat_weights = np.cos(np.deg2rad(ds['lat']))\n",
    "ds_weighted = ds[var].weighted(mask * lat_weights)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4cd89733-3b87-4913-a7d0-4d119c5b4f66",
   "metadata": {},
   "outputs": [],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d973eac8-46cf-48d3-b0de-93c04a19d513",
   "metadata": {},
   "outputs": [],
   "source": [
    "var = \"fire_climate_class\"\n",
    "# ds_masked.to_dataframe().groupby(['region'])[var].agg(pd.Series.mode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a60dc1c-414f-40c4-b257-e4026e15274d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = acs_regional_stats(ds=ds, var=var,  mask = mask_frac, how = [\"min\", \"mode\"], dims = (\"lat\", \"lon\"))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa8c37be-397b-44c6-a1c9-fc3e547ac297",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6df762c-9a7c-474e-b3f6-770bf834a12b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e17a0e33-4086-45c4-afe8-6172fb556345",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8347e93a-f12e-4b77-9e1b-8dfd4ece9bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = dims, how = [\"mean\", \"min\", \"max\"], outfile = \"out.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ae004d9-0ec0-45fd-9e80-18f4c60e7e19",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47e08e93-6acc-4e41-b7ea-9895507cc116",
   "metadata": {},
   "outputs": [],
   "source": [
    "## extra things"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9e30e43-8b8b-4388-bf70-01cc4e379b43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bb7f03f-c030-4ad7-aceb-edba66a070da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1a1f8d0e-e654-4005-ac9b-f4dc19399caf",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03ae37cc-3dfa-4104-9f5d-a5572b1ebe2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "da_p90 = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"quantile\", quantile=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cc0aa69-98b0-457a-b052-aceb7cb825ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"mean\")\n",
    "da_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2598e67f-0902-474f-ad01-07f157e34ba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_max = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"max\")\n",
    "da_max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "163fd56e-31f4-4ff5-8e97-e45a2e63044f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e13e6f3e-7bb0-452c-8446-8354c0be304a",
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.merge([da_mean, da_max, da_p90])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c3c47a09-1872-4b2d-ae49-638d3a7e308f",
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.merge([da_mean, da_max, da_p90]).to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "06a4f581-b077-454e-8ae2-e8a7bb1ea500",
   "metadata": {},
   "outputs": [],
   "source": [
    "xr.merge([da_mean, da_max, da_p90])[[\"names\", \"pr_p90\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "adca2a5d-3bfc-4bc1-9b9a-010053725afd",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# select one region\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"mean\", select_abbr=[\"VIC\"])\n",
    "da_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e2db0ae-09d4-4501-88ef-c877454206d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9d0bac23-d26e-40fe-bb49-5564883ad226",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# select one region and get timeseries\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"lat\", \"lon\"), how = \"mean\", select_abbr=[\"VIC\"])\n",
    "da_mean.squeeze()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "756b3f35-fbdc-4e4b-ada0-3f24f8d5b5bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "da_mean.squeeze().plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f67ac84-c231-4667-982a-b44ae16d5eaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask = \"fractional\", start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"mean\")\n",
    "da_mean.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0e9911a-eeb5-4fd6-8f28-54748e3f86d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask = \"centred\", start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"mean\")\n",
    "da_mean.to_dataframe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05a1744e-f645-4a40-8763-f10faa4ae084",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask = \"min_overlap\", start=start, end=end, dims = (\"time\", \"lat\", \"lon\"), how = \"mean\", overlap_threshold=0.3)\n",
    "da_mean.to_dataframe()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2847e115-f27d-4180-9d45-424c7a214723",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"lat\", \"lon\"), how = \"mean\")\n",
    "da_mean.to_dataframe(dim_order=[\"region\", \"time\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd98005b-4b0b-4a60-8b59-e4dea9ed22a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "da_mean.sel(region=(regions.abbrevs.index(\"NSW\"))).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c581530-8826-4167-b641-9568c4b572eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# regional mean\n",
    "da_mean = acs_regional_stats(ds=ds, var=var, mask =mask_frac, start=start, end=end, dims = (\"lat\", \"lon\"), how = \"mean\")\n",
    "da_mean.to_dataframe(dim_order=[\"region\", \"time\"])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3431df93-2d49-48e1-9404-1bce97e3dc4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb369f15-5433-4cb6-89b8-48347f0efdef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the mean rx1day value from the 20 years for the global warming level (GWL)\n",
    "ds = ds.sel(time = slice(start, end))\n",
    "da_mean = ds[\"pr\"].mean(dim=\"time\")\n",
    "da_mean.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "47a656b7-eed9-4d66-bf70-a6eb2b304f90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "289de12d-c969-43fd-9df4-f20e820bc75a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cc0883f-497a-4698-9297-ec900e56673b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import needed packages\n",
    "from acs_area_statistics import acs_regional_stats, regions\n",
    "import xarray as xr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5674b686-a481-49b2-971d-369bfda6909f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the stats using the acs_region_fractional_stats function\n",
    "# Find the mean value for each region\n",
    "filename = \"/g/data/ia39/ncra/extratropical_storms/5km/lows_AGCD-05i_ACCESS-CM2_historical_r4i1p1f1_BOM_BARPA-R_v1-r1_annual.nc\"\n",
    "ds = xr.open_dataset(filename, use_cftime = True,)\n",
    "var = \"low_freq\"\n",
    "mask_frac = regions.mask_3D_frac_approx(ds)\n",
    "start, end = (\"1991\", \"2010\")\n",
    "dims = (\"time\", \"lat\", \"lon\",)\n",
    "\n",
    "da_mean = acs_regional_stats(ds=ds,var=var, mask=mask_frac, start=start, end=end, dims = dims, how = \"mean\")\n",
    "da_mean"
   ]
  }
 ],
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