groupby-apply.ipynb

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      "flox  : 0.6.2.dev6+gc370b5d\n",
      "xarray: 2022.9.0\n",
      "sys   : 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:36:39) [GCC 10.4.0]\n",
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      "\n"
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   "source": [
    "%load_ext watermark\n",
    "\n",
    "import dask.array \n",
    "import flox\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import xarray as xr\n",
    "\n",
    "%watermark -iv"
   ]
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   "source": [
    "## Groupby: smaller\n",
    "\n",
    "The original problem is 3D : `time, lat, lon`. I've made it 4d so it's easy to scale up: just increase `NZ` and adjust chunk size `ZCHUNKS`"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (time: 14532, z: 1, lat: 720, lon: 1440)&gt;\n",
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       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1981-09-01T12:00:00 ... 2021-06-14T12:00:00\n",
       "Dimensions without coordinates: z, lat, lon</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'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 14532</li><li><span>z</span>: 1</li><li><span>lat</span>: 720</li><li><span>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e4032a64-1921-4ad2-9fee-194cb1ba67f7' class='xr-array-in' type='checkbox' checked><label for='section-e4032a64-1921-4ad2-9fee-194cb1ba67f7' 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=(20, 1, 720, 1440), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <td> 112.26 GiB </td>\n",
       "                        <td> 158.20 MiB </td>\n",
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       "        </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-794f5593-b1da-44c8-b692-ee955e0759d4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-794f5593-b1da-44c8-b692-ee955e0759d4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span 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'>1981-09-01T12:00:00 ... 2021-06-...</div><input id='attrs-a65f0e97-e387-439a-984f-d4efe13f6d9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a65f0e97-e387-439a-984f-d4efe13f6d9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59c0b8c1-3b6f-4789-911f-20feec9634ca' class='xr-var-data-in' type='checkbox'><label for='data-59c0b8c1-3b6f-4789-911f-20feec9634ca' 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;1981-09-01T12:00:00.000000000&#x27;, &#x27;1981-09-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1981-09-03T12:00:00.000000000&#x27;, ..., &#x27;2021-06-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-06-13T12:00:00.000000000&#x27;, &#x27;2021-06-14T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e0c4f3db-30e5-4877-bd44-7aa09780d3ea' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0c4f3db-30e5-4877-bd44-7aa09780d3ea' 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 'sst' (time: 14532, z: 1, lat: 720, lon: 1440)>\n",
       "dask.array<random_sample, shape=(14532, 1, 720, 1440), dtype=float64, chunksize=(20, 1, 720, 1440), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1981-09-01T12:00:00 ... 2021-06-14T12:00:00\n",
       "Dimensions without coordinates: z, lat, lon"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NZ = 1\n",
    "ZCHUNKS= -1\n",
    "\n",
    "oisst = xr.DataArray(\n",
    "    dask.array.random.random((14532, NZ, 720, 1440), chunks=(20, ZCHUNKS, -1, -1)),\n",
    "    dims=(\"time\", \"z\", \"lat\", \"lon\"),\n",
    "    coords={\"time\": pd.date_range(\"1981-09-01 12:00\", \"2021-06-14 12:00\", freq=\"D\")},\n",
    "    name=\"sst\",\n",
    ")\n",
    "oisst"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d5a6507-0fda-4b4e-b79c-2f078bf4331e",
   "metadata": {},
   "source": [
    "## apply / reduce\n",
    "\n",
    "`reduce` is like `apply` but the user function receives the underlying array (e.g. `dask.array`) time, not an Xarray object.\n",
    "\n",
    "`apply` is the more common use-case but `reduce` lowers it down to a array problem and remove some of the xarray pieces."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93c1395b-d55a-4580-9e74-ddbf039818ba",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (dayofyear: 366, z: 1, lat: 720, lon: 1440)&gt;\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (dayofyear: 366, z: 1, lat: 720, lon: 1440)>\n",
       "dask.array<stack, shape=(366, 1, 720, 1440), dtype=float64, chunksize=(1, 1, 720, 1440), chunktype=numpy.ndarray>\n",
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     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "oisst.groupby(\"time.dayofyear\").reduce(np.mean)"
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   "id": "95a4f1a7-e3f0-4309-b06e-14866062fb48",
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   "source": [
    "meidan is epic; `dask.array.median` is rechunking to one chunk along `time`"
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   "execution_count": 4,
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      ],
      "text/plain": [
       "<xarray.DataArray 'sst' (dayofyear: 366, z: 1, lat: 720, lon: 1440)>\n",
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     "execution_count": 4,
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   "id": "75b80c0a-3b3b-48ea-a29e-b290173a373a",
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    {
     "name": "stderr",
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       "  display: none;\n",
       "}\n",
       "\n",
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       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
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       "\n",
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       ".xr-header > ul {\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
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       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-item input:enabled + label {\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "  color: var(--xr-font-color0);\n",
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       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
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       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
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       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
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       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
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       "\n",
       ".xr-array-wrap {\n",
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       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
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       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
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       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
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       "\n",
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
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       "  display: inline-block;\n",
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       ".xr-dim-list:before {\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  vertical-align: middle;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;sst&#x27; (month: 12, z: 1, lat: 720, lon: 1440)&gt;\n",
       "dask.array&lt;stack, shape=(12, 1, 720, 1440), dtype=float64, chunksize=(1, 1, 81, 166), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: z, lat, lon</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'>'sst'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>month</span>: 12</li><li><span>z</span>: 1</li><li><span>lat</span>: 720</li><li><span>lon</span>: 1440</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-5f3eb199-97d6-4293-8e11-ba943b18c30d' class='xr-array-in' type='checkbox' checked><label for='section-5f3eb199-97d6-4293-8e11-ba943b18c30d' 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=(1, 1, 81, 166), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 94.92 MiB </td>\n",
       "                        <td> 105.05 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (12, 1, 720, 1440) </td>\n",
       "                        <td> (1, 1, 81, 166) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 74 Graph Layers </td>\n",
       "                        <td> 13068 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-eb81d92d-1e9c-4252-9f3d-4990c112cc76' class='xr-section-summary-in' type='checkbox'  checked><label for='section-eb81d92d-1e9c-4252-9f3d-4990c112cc76' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>month</span></div><div class='xr-var-dims'>(month)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 7 8 9 10 11 12</div><input id='attrs-4125d9d9-13f2-4ca6-9955-c591d792f529' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4125d9d9-13f2-4ca6-9955-c591d792f529' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd199b4d-262d-4a47-a973-b45393c7428b' class='xr-var-data-in' type='checkbox'><label for='data-fd199b4d-262d-4a47-a973-b45393c7428b' 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([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b779ef8-17ab-4a72-9862-49c2b9a5c924' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b779ef8-17ab-4a72-9862-49c2b9a5c924' 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 'sst' (month: 12, z: 1, lat: 720, lon: 1440)>\n",
       "dask.array<stack, shape=(12, 1, 720, 1440), dtype=float64, chunksize=(1, 1, 81, 166), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * month    (month) int64 1 2 3 4 5 6 7 8 9 10 11 12\n",
       "Dimensions without coordinates: z, lat, lon"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oisst.groupby(\"time.month\").reduce(np.median)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea96096e-d5c5-41f8-b714-6758c0cb08c1",
   "metadata": {},
   "source": [
    "### What's happening?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38389fa2-a5fc-4229-8ee6-55191a63233b",
   "metadata": {},
   "source": [
    "We index the underlying dask array `num_groups` times with different indexers : `np.array[ndim=1, int]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1e30b37f-68ec-468f-87b0-609d976cd51a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1: 1240 indices\n",
      "2: 1130 indices\n",
      "3: 1240 indices\n",
      "4: 1200 indices\n",
      "5: 1240 indices\n",
      "6: 1184 indices\n",
      "7: 1209 indices\n",
      "8: 1209 indices\n",
      "9: 1200 indices\n",
      "10: 1240 indices\n",
      "11: 1200 indices\n",
      "12: 1240 indices\n"
     ]
    }
   ],
   "source": [
    "gb = oisst.groupby(\"time.month\")\n",
    "for month, idxr in zip(gb._unique_coord.data, gb._group_indices):\n",
    "    print(f\"{month}: {len(idxr)} indices\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "572eb5cb-191e-4dbf-8abf-498dfe0ff6b6",
   "metadata": {},
   "source": [
    "So something like"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c70a2609-af20-43bb-b7f6-788c8c4d2d6d",
   "metadata": {},
   "outputs": [
    {
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    "np.take(oisst.data, indices=idxr, axis=oisst.get_axis_num(\"time\"))"
   ]
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  {
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   "id": "e3da9ad5-f98f-43aa-96ad-bebd6dcb834b",
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   "source": [
    "for each group, apply the operation, then concatenate to form the final result"
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  },
  {
   "cell_type": "markdown",
   "id": "6146909d-5674-46e6-b55d-30e9a5cadbbb",
   "metadata": {},
   "source": [
    "For `\"time.dayofyear\"` we do this 366 times."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "218180ae-c0be-4e97-821e-f2d375db747d",
   "metadata": {},
   "source": [
    "## Groupby: bigger\n",
    "\n",
    "To scale this just change the start date. The actual dataset begins in 1979"
   ]
  },
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   "cell_type": "code",
   "execution_count": 8,
   "id": "b05d9c2e-8ccb-48b0-a64a-c7dfa8e2e95d",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;random_sample-20a5c0c0d6023800094d5b8de9b21c45&#x27; (time: 2928,\n",
       "                                                                    y: 15360,\n",
       "                                                                    x: 18432)&gt;\n",
       "dask.array&lt;random_sample, shape=(2928, 15360, 18432), dtype=float64, chunksize=(224, 350, 350), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Dimensions without coordinates: y, x</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'>'random_sample-20a5c0c0d6023800094d5b8de9b21c45'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2928</li><li><span>y</span>: 15360</li><li><span>x</span>: 18432</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bb858465-c293-4f29-a44b-4338408af470' class='xr-array-in' type='checkbox' checked><label for='section-bb858465-c293-4f29-a44b-4338408af470' 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=(224, 350, 350), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 6.03 TiB </td>\n",
       "                        <td> 209.35 MiB </td>\n",
       "                    </tr>\n",
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       "       &#x27;2020-01-01T06:00:00.000000000&#x27;, ..., &#x27;2020-12-31T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T18:00:00.000000000&#x27;, &#x27;2020-12-31T21:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2646c6bf-ae8b-4c53-9ba2-6488e971763c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2646c6bf-ae8b-4c53-9ba2-6488e971763c' 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 'random_sample-20a5c0c0d6023800094d5b8de9b21c45' (time: 2928,\n",
       "                                                                    y: 15360,\n",
       "                                                                    x: 18432)>\n",
       "dask.array<random_sample, shape=(2928, 15360, 18432), dtype=float64, chunksize=(224, 350, 350), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# actually 30 years 1979 - 2020\n",
    "time = pd.date_range(\"2020-01-01\", \"2020-12-31 23:59\", freq=\"3H\")\n",
    "nwm = xr.DataArray(\n",
    "    dims=(\"time\", \"y\", \"x\"), \n",
    "    data=dask.array.random.random(\n",
    "        (len(time), 15360, 18432), chunks=(224, 350, 350)\n",
    "    ),\n",
    "    coords={\"time\": time}\n",
    ")\n",
    "nwm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77236e2c-75c6-4441-ab1a-a68a30b5be74",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;random_sample-20a5c0c0d6023800094d5b8de9b21c45&#x27; (\n",
       "                                                                    dayofyear: 366,\n",
       "                                                                    y: 15360,\n",
       "                                                                    x: 18432)&gt;\n",
       "dask.array&lt;stack, shape=(366, 15360, 18432), dtype=float64, chunksize=(1, 350, 350), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * dayofyear  (dayofyear) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366\n",
       "Dimensions without coordinates: y, x</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'>'random_sample-20a5c0c0d6023800094d5b8de9b21c45'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>dayofyear</span>: 366</li><li><span>y</span>: 15360</li><li><span>x</span>: 18432</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b11ab9ac-678c-43a5-8963-526f2ec3c0ae' class='xr-array-in' type='checkbox' checked><label for='section-b11ab9ac-678c-43a5-8963-526f2ec3c0ae' 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=(1, 350, 350), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-0f7c1313-eacc-4a5c-b93c-b0a9b1b0c36a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0f7c1313-eacc-4a5c-b93c-b0a9b1b0c36a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dayofyear</span></div><div class='xr-var-dims'>(dayofyear)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4 5 6 ... 362 363 364 365 366</div><input id='attrs-5a2924b1-7f9d-4297-9dca-97d55cc3212a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5a2924b1-7f9d-4297-9dca-97d55cc3212a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6b9eb1b-368f-4b94-a0d0-53bba2f12916' class='xr-var-data-in' type='checkbox'><label for='data-b6b9eb1b-368f-4b94-a0d0-53bba2f12916' 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([  1,   2,   3, ..., 364, 365, 366])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f587e17-2256-4936-9475-1314bfc2b580' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5f587e17-2256-4936-9475-1314bfc2b580' 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 'random_sample-20a5c0c0d6023800094d5b8de9b21c45' (\n",
       "                                                                    dayofyear: 366,\n",
       "                                                                    y: 15360,\n",
       "                                                                    x: 18432)>\n",
       "dask.array<stack, shape=(366, 15360, 18432), dtype=float64, chunksize=(1, 350, 350), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * dayofyear  (dayofyear) int64 1 2 3 4 5 6 7 8 ... 360 361 362 363 364 365 366\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# default xarray approach - \"shuffle\"\n",
    "nwm.groupby(\"time.dayofyear\").reduce(np.mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46ca9c09-ae49-4b5b-b35f-2cd2fda65c01",
   "metadata": {},
   "source": [
    "###  Solution?\n",
    "\n",
    "We can make this a single indexing problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f4539d94-9644-4e40-a967-f68d8f5e79e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "gb = oisst.groupby(\"time.month\")\n",
    "all_indexes = np.concatenate(gb._group_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "73ad62fe-744a-473b-b0fa-8939a02e0059",
   "metadata": {},
   "source": [
    "The simplest way to shuffle"
   ]
  },
  {
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   "execution_count": 11,
   "id": "8e7d9600-5331-406f-a890-05949d2b6474",
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     "execution_count": 11,
     "metadata": {},
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   ],
   "source": [
    "shuffled = oisst.data[all_indexes, ...]\n",
    "shuffled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3337398c-0af8-4752-8990-18f7a6176c16",
   "metadata": {},
   "source": [
    "Ooh we can make use of https://github.com/dask/dask/pull/3901"
   ]
  },
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   ],
   "source": [
    "from dask.array.slicing import shuffle_slice\n",
    "\n",
    "shuffled = shuffle_slice(oisst.data, all_indexes)\n",
    "shuffled"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce5ce074-1ea4-4593-acb1-409547685565",
   "metadata": {},
   "source": [
    "Since all elements in a group are sequential in `shuffled` we can just slice them out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "a4e50d1c-17a3-4819-be2b-f696a20f9b6d",
   "metadata": {},
   "outputs": [],
   "source": [
    "group_len = [len(idxr) for idxr in gb._group_indices]\n",
    "edges = np.cumsum([0, *group_len])\n",
    "\n",
    "results = []\n",
    "for left, right in zip(edges[:-1], edges[1:]):\n",
    "    group = shuffled[slice(left, right), ...]\n",
    "    results.append(group.mean(axis=0))"
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   "execution_count": 64,
   "id": "3161b97d-6ab9-49ac-910f-ec71a5bda6a8",
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      "text/plain": [
       "dask.array<concatenate, shape=(12, 720, 1440), dtype=float64, chunksize=(1, 720, 1440), chunktype=numpy.ndarray>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "dask.array.concatenate(results, axis=0)"
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  },
  {
   "cell_type": "markdown",
   "id": "dc33677a-5d12-4f30-9f91-2103b63469a6",
   "metadata": {},
   "source": [
    "compare to"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59112d06-c81e-4732-afee-21e746b74b78",
   "metadata": {},
   "source": [
    "## (incomplete) NWM 2D groupby\n",
    "\n",
    "mimics county aggregation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "37b528fd-f784-42b2-b5ee-685e0afa1ff1",
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;random_sample-20a5c0c0d6023800094d5b8de9b21c45&#x27; (time: 2928,\n",
       "                                                                    y: 15360,\n",
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       "       &#x27;2020-01-01T06:00:00.000000000&#x27;, ..., &#x27;2020-12-31T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-12-31T18:00:00.000000000&#x27;, &#x27;2020-12-31T21:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5979956b-5582-43da-97d9-212617e0f14b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5979956b-5582-43da-97d9-212617e0f14b' 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>"
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      "text/plain": [
       "<xarray.DataArray 'random_sample-20a5c0c0d6023800094d5b8de9b21c45' (time: 2928,\n",
       "                                                                    y: 15360,\n",
       "                                                                    x: 18432)>\n",
       "dask.array<random_sample, shape=(2928, 15360, 18432), dtype=float64, chunksize=(224, 350, 350), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 2020-01-01 ... 2020-12-31T21:00:00\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nwm"
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  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "549d5351-e3ef-46be-b2f4-e76970bce628",
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   "outputs": [
    {
     "data": {
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       "1024"
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     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "32*32"
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  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ba3b3fad-c3af-497d-a250-29ae1e88303f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.repeat(np.arange(64), nwm.sizes[\"x\"]//64)\n",
    "y = np.repeat(np.arange(64), nwm.sizes[\"y\"]//64)"
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  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "7a038ba6-305f-4bc7-b122-b080f4e301b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "county = xr.DataArray(x[np.newaxis, :] * y[:, np.newaxis], dims=(\"y\", \"x\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "10b592b3-bfaa-4a80-8ddf-cf15cef6f79c",
   "metadata": {},
   "outputs": [
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       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (y: 15360, x: 18432)&gt;\n",
       "array([[   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       ...,\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969]])\n",
       "Dimensions without coordinates: y, x</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>y</span>: 15360</li><li><span>x</span>: 18432</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-e0939c11-36ab-4d10-8a8c-969ae0283865' class='xr-array-in' type='checkbox' checked><label for='section-e0939c11-36ab-4d10-8a8c-969ae0283865' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>0 0 0 0 0 0 0 0 0 0 ... 3969 3969 3969 3969 3969 3969 3969 3969 3969</span></div><div class='xr-array-data'><pre>array([[   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       ...,\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969]])</pre></div></div></li><li class='xr-section-item'><input id='section-f7d6e1e4-207f-4404-a915-2bca5e7fff91' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f7d6e1e4-207f-4404-a915-2bca5e7fff91' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-9d1d918c-bc8e-42e7-be09-9425441e48e5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9d1d918c-bc8e-42e7-be09-9425441e48e5' 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: 15360, x: 18432)>\n",
       "array([[   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       [   0,    0,    0, ...,    0,    0,    0],\n",
       "       ...,\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969],\n",
       "       [   0,    0,    0, ..., 3969, 3969, 3969]])\n",
       "Dimensions without coordinates: y, x"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "508e651b-5795-4930-a555-6c64138ca0ac",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "nwm.groupby(county)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e3275193-e09a-41f9-b4a8-dc55dbb08cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2aaef0edcf70>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 418,
       "width": 517
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "plt.imshow(county, cmap=mpl.cm.Dark2)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:miniconda3-ncar-challenge]",
   "language": "python",
   "name": "conda-env-miniconda3-ncar-challenge-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}