{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "sticky-exhibit",
   "metadata": {},
   "source": [
    "# Demo\n",
    "\n",
    "Author: Cindy Chiao\n",
    "Last Modified: Nov 16, 2021\n",
    "\n",
    "## What is xbatcher? \n",
    "Xbatcher is a small library for iterating through xarray objects (DataArrays and Datasets) in batches. The goal is to make it easy to feed xarray objects to machine learning libraries such as Keras and PyTorch. \n",
    "\n",
    "## What is included in this notebook?\n",
    "* showcase current abilities with example data \n",
    "* brief discussion of current development track and ideas for future work "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "banner-importance",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import xbatcher\n",
    "import fsspec"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "equipped-sense",
   "metadata": {},
   "source": [
    "## Example data\n",
    "\n",
    "Here we will load an example dataset from a global climate model. The data is from the _historical_ experiment from CMIP6 and represents 60 days of daily max air temperature. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dutch-grave",
   "metadata": {},
   "outputs": [
    {
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 145, lon: 192, time: 60)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
       "  * time     (time) datetime64[ns] 1850-01-01T12:00:00 ... 1850-03-01T12:00:00\n",
       "Data variables:\n",
       "    tasmax   (time, lat, lon) float32 dask.array&lt;chunksize=(60, 145, 192), meta=np.ndarray&gt;</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e0329886-7c6d-45fc-a9d4-e7a8a39eb425' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e0329886-7c6d-45fc-a9d4-e7a8a39eb425' 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>: 145</li><li><span class='xr-has-index'>lon</span>: 192</li><li><span class='xr-has-index'>time</span>: 60</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a753e3c0-9924-4ce6-a0de-ddf5fe37bb9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a753e3c0-9924-4ce6-a0de-ddf5fe37bb9d' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -88.75 -87.5 ... 88.75 90.0</div><input id='attrs-2a293b90-81fb-4b71-ab00-4eb287855b64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a293b90-81fb-4b71-ab00-4eb287855b64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43c40763-c6a6-483e-b8f7-553bfcd1c324' class='xr-var-data-in' type='checkbox'><label for='data-43c40763-c6a6-483e-b8f7-553bfcd1c324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>lat_bnds</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,\n",
       "       -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,\n",
       "       -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,\n",
       "       -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,\n",
       "       -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,\n",
       "       -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,\n",
       "       -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,\n",
       "       -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,\n",
       "         0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,\n",
       "        11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,\n",
       "        22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,\n",
       "        33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,\n",
       "        45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,\n",
       "        56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,\n",
       "        67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,\n",
       "        78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,\n",
       "        90.  ])</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'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-95373103-a2da-411b-bbb5-cdb15a0e2672' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95373103-a2da-411b-bbb5-cdb15a0e2672' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65f1d241-f925-4647-adaf-a6bdf0ce4ed9' class='xr-var-data-in' type='checkbox'><label for='data-65f1d241-f925-4647-adaf-a6bdf0ce4ed9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>lon_bnds</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,\n",
       "        15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,\n",
       "        30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,\n",
       "        45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,\n",
       "        60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,\n",
       "        75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,\n",
       "        90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,\n",
       "       105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,\n",
       "       120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,\n",
       "       135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,\n",
       "       150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,\n",
       "       165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,\n",
       "       180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,\n",
       "       195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,\n",
       "       210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,\n",
       "       225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,\n",
       "       240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,\n",
       "       255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,\n",
       "       270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,\n",
       "       285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,\n",
       "       300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,\n",
       "       315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,\n",
       "       330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,\n",
       "       345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-01-01T12:00:00 ... 1850-03-...</div><input id='attrs-da6cb118-cf40-40a5-b439-485475cfec39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da6cb118-cf40-40a5-b439-485475cfec39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe862cf-483b-4958-8980-277a695d3855' class='xr-var-data-in' type='checkbox'><label for='data-ebe862cf-483b-4958-8980-277a695d3855' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-01T12:00:00.000000000&#x27;, &#x27;1850-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-03T12:00:00.000000000&#x27;, &#x27;1850-01-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-05T12:00:00.000000000&#x27;, &#x27;1850-01-06T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-07T12:00:00.000000000&#x27;, &#x27;1850-01-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-09T12:00:00.000000000&#x27;, &#x27;1850-01-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-11T12:00:00.000000000&#x27;, &#x27;1850-01-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-13T12:00:00.000000000&#x27;, &#x27;1850-01-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-15T12:00:00.000000000&#x27;, &#x27;1850-01-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-17T12:00:00.000000000&#x27;, &#x27;1850-01-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-19T12:00:00.000000000&#x27;, &#x27;1850-01-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-21T12:00:00.000000000&#x27;, &#x27;1850-01-22T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-23T12:00:00.000000000&#x27;, &#x27;1850-01-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-25T12:00:00.000000000&#x27;, &#x27;1850-01-26T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-27T12:00:00.000000000&#x27;, &#x27;1850-01-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-29T12:00:00.000000000&#x27;, &#x27;1850-01-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-31T12:00:00.000000000&#x27;, &#x27;1850-02-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-02T12:00:00.000000000&#x27;, &#x27;1850-02-03T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-04T12:00:00.000000000&#x27;, &#x27;1850-02-05T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-06T12:00:00.000000000&#x27;, &#x27;1850-02-07T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-08T12:00:00.000000000&#x27;, &#x27;1850-02-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-12T12:00:00.000000000&#x27;, &#x27;1850-02-13T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-14T12:00:00.000000000&#x27;, &#x27;1850-02-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-16T12:00:00.000000000&#x27;, &#x27;1850-02-17T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-18T12:00:00.000000000&#x27;, &#x27;1850-02-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-22T12:00:00.000000000&#x27;, &#x27;1850-02-23T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-24T12:00:00.000000000&#x27;, &#x27;1850-02-25T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-26T12:00:00.000000000&#x27;, &#x27;1850-02-27T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-28T12:00:00.000000000&#x27;, &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67f45a53-2463-4172-8c0e-3ccae65363c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67f45a53-2463-4172-8c0e-3ccae65363c4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(60, 145, 192), meta=np.ndarray&gt;</div><input id='attrs-48e64beb-d0af-4ba5-ac51-4267164ef2e2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48e64beb-d0af-4ba5-ac51-4267164ef2e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d75c0697-4ff1-407c-8aac-528bcff556da' class='xr-var-data-in' type='checkbox'><label for='data-d75c0697-4ff1-407c-8aac-528bcff556da' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 6.68 MB </td> <td> 6.68 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (60, 145, 192) </td> <td> (60, 145, 192) </td></tr>\n",
       "    <tr><th> Count </th><td> 2 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"205\" height=\"166\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"35\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"90\" x2=\"35\" y2=\"116\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"90\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"35\" y2=\"116\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 35.69394773840388,25.69394773840388 35.69394773840388,116.31894773840388 10.0,90.625\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"130\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"35\" y1=\"25\" x2=\"155\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
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       "  <!-- Vertical lines -->\n",
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       "  <line x1=\"155\" y1=\"25\" x2=\"155\" y2=\"116\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"95.693948\" y=\"136.318948\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >192</text>\n",
       "  <text x=\"175.693948\" y=\"71.006448\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,175.693948,71.006448)\">145</text>\n",
       "  <text x=\"12.846974\" y=\"123.471974\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.846974,123.471974)\">60</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9b48390f-cab4-4259-b15e-fd245dcd4163' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9b48390f-cab4-4259-b15e-fd245dcd4163' 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>\n",
       "Dimensions:  (lat: 145, lon: 192, time: 60)\n",
       "Coordinates:\n",
       "  * lat      (lat) float64 -90.0 -88.75 -87.5 -86.25 ... 86.25 87.5 88.75 90.0\n",
       "  * lon      (lon) float64 0.0 1.875 3.75 5.625 7.5 ... 352.5 354.4 356.2 358.1\n",
       "  * time     (time) datetime64[ns] 1850-01-01T12:00:00 ... 1850-03-01T12:00:00\n",
       "Data variables:\n",
       "    tasmax   (time, lat, lon) float32 dask.array<chunksize=(60, 145, 192), meta=np.ndarray>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "store = fsspec.get_mapper(\n",
    "    \"az://carbonplan-share/example_cmip6_data.zarr\", account_name=\"carbonplan\"\n",
    ")\n",
    "ds = xr.open_zarr(store, consolidated=True)\n",
    "\n",
    "# the attributes contain a lot of useful information, but clutter the print out when we inspect the outputs\n",
    "# throughout this demo, clearing it to avoid confusion\n",
    "ds.attrs = {}\n",
    "\n",
    "# inspect the dataset\n",
    "display(ds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "applicable-diesel",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f95fc348550>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the first time dimension\n",
    "ds.isel(time=0).tasmax.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "animated-marsh",
   "metadata": {},
   "source": [
    "## Batch generation\n",
    "\n",
    "Xbatcher's `BatchGenerator` can be used to generate batches with several arguments controlling the exact behavior.\n",
    "\n",
    "The `input_dims` argument takes a dictionary specifying the size of the inputs in each dimension. For example, `{'time': 10}` means that each of the input sample will have 10 time points, while all other dimensions are flattened to a \"sample\" dimension\n",
    "\n",
    "Note that even though `ds` in this case only has one variable, the function can operate on multiple variables at the same time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "attempted-cooling",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6 batches\n"
     ]
    },
    {
     "data": {
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       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (sample: 27840, time: 10)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-02-20T12:00:00 ... 1850-03-01T12:00:00\n",
       "  * sample   (sample) MultiIndex\n",
       "  - lat      (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
       "  - lon      (sample) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "Data variables:\n",
       "    tasmax   (sample, time) float32 226.1 226.2 224.0 ... 251.5 245.5 242.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8f274e39-e53d-41d9-a015-3a569f4cee5f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8f274e39-e53d-41d9-a015-3a569f4cee5f' 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'>sample</span>: 27840</li><li><span class='xr-has-index'>time</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-43334732-3e6f-41ee-9071-8a121169d0a9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-43334732-3e6f-41ee-9071-8a121169d0a9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-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'>1850-02-20T12:00:00 ... 1850-03-...</div><input id='attrs-4e79c0de-ae49-4c3e-b8e9-f7eda9730d8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e79c0de-ae49-4c3e-b8e9-f7eda9730d8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df594458-8b65-41d1-93fa-9024738cbd6a' class='xr-var-data-in' type='checkbox'><label for='data-df594458-8b65-41d1-93fa-9024738cbd6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-22T12:00:00.000000000&#x27;, &#x27;1850-02-23T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-24T12:00:00.000000000&#x27;, &#x27;1850-02-25T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-26T12:00:00.000000000&#x27;, &#x27;1850-02-27T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-02-28T12:00:00.000000000&#x27;, &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>MultiIndex</div><div class='xr-var-preview xr-preview'>(lat, lon)</div><input id='attrs-433cdd80-b2a5-4f99-941b-fcc468679710' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-433cdd80-b2a5-4f99-941b-fcc468679710' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9af39ae-3635-4481-9a24-b17b1ee393a1' class='xr-var-data-in' type='checkbox'><label for='data-b9af39ae-3635-4481-9a24-b17b1ee393a1' 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([(-90.0, 0.0), (-90.0, 1.875), (-90.0, 3.75), ..., (90.0, 354.375),\n",
       "       (90.0, 356.25), (90.0, 358.125)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-c65eadbd-2560-4c56-be83-75de9caee509' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c65eadbd-2560-4c56-be83-75de9caee509' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c27f72a6-98e0-468e-a34d-287a406506c2' class='xr-var-data-in' type='checkbox'><label for='data-c27f72a6-98e0-468e-a34d-287a406506c2' 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([-90., -90., -90., ...,  90.,  90.,  90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-502d2c1c-e383-446d-b5ce-416d14122efd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-502d2c1c-e383-446d-b5ce-416d14122efd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-676e26d4-5294-480a-beda-d9f2b12a576f' class='xr-var-data-in' type='checkbox'><label for='data-676e26d4-5294-480a-beda-d9f2b12a576f' 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.   ,   1.875,   3.75 , ..., 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a569e179-c1fa-4772-9e3f-a4c65a2339ee' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a569e179-c1fa-4772-9e3f-a4c65a2339ee' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(sample, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>226.1 226.2 224.0 ... 245.5 242.9</div><input id='attrs-2c360f2b-9a21-4b60-9afb-8b10432ffedb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c360f2b-9a21-4b60-9afb-8b10432ffedb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d08bb73-7120-4a84-8dcb-a777e8558223' class='xr-var-data-in' type='checkbox'><label for='data-9d08bb73-7120-4a84-8dcb-a777e8558223' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[226.11433, 226.19003, 224.03416, ..., 226.06456, 226.33026,\n",
       "        226.99207],\n",
       "       [226.11433, 226.19003, 224.03416, ..., 226.06456, 226.33026,\n",
       "        226.99207],\n",
       "       [226.11433, 226.19003, 224.03416, ..., 226.06456, 226.33026,\n",
       "        226.99207],\n",
       "       ...,\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7a4257f6-a3fb-4bc6-9aaf-279d0220c7e2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7a4257f6-a3fb-4bc6-9aaf-279d0220c7e2' 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>\n",
       "Dimensions:  (sample: 27840, time: 10)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-02-20T12:00:00 ... 1850-03-01T12:00:00\n",
       "  * sample   (sample) MultiIndex\n",
       "  - lat      (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
       "  - lon      (sample) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "Data variables:\n",
       "    tasmax   (sample, time) float32 226.1 226.2 224.0 ... 251.5 245.5 242.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_timepoint_in_each_sample = 10\n",
    "\n",
    "bgen = xbatcher.BatchGenerator(\n",
    "    ds=ds,\n",
    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
    ")\n",
    "\n",
    "n_batch = 0\n",
    "for batch in bgen:\n",
    "    n_batch += 1\n",
    "\n",
    "print(f\"{n_batch} batches\")\n",
    "display(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "digital-night",
   "metadata": {},
   "source": [
    "We can verify that the outputs have the expected shapes. \n",
    "\n",
    "For example, there are 60 time points in our input dataset, we're asking 10 timepoints in each batch, thus expecting 6 batches "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "integral-theta",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expecting 6.0 batches, getting 6 batches\n"
     ]
    }
   ],
   "source": [
    "expected_n_batch = len(ds.time) / n_timepoint_in_each_sample\n",
    "print(f\"Expecting {expected_n_batch} batches, getting {n_batch} batches\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "usual-kennedy",
   "metadata": {},
   "source": [
    "There are 145 lat points and 192 lon points, thus we're expecting 145 * 192 = 27840 samples in a batch."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "incomplete-native",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expecting 27840 samples per batch, getting 27840 samples per batch\n"
     ]
    }
   ],
   "source": [
    "expected_batch_size = len(ds.lat) * len(ds.lon)\n",
    "print(\n",
    "    f\"Expecting {expected_batch_size} samples per batch, getting {len(batch.sample)} samples per batch\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "durable-gazette",
   "metadata": {},
   "source": [
    "## Controlling the size/shape of batches\n",
    "\n",
    "We can use `batch_dims` and `concat_input_dims` options to control how many sample ends up in each batch. For example, we can specify 10 time points for each sample, but 20 time points in each batch this should yield half as many batches and twice as many samples in a batch as the example above note the difference in dimension name in this case "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sophisticated-legislation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 batches\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (sample: 55680, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (sample, time_input) datetime64[ns] 1850-02-10T12:00:00 ... ...\n",
       "  * sample       (sample) MultiIndex\n",
       "  - input_batch  (sample) int64 0 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1 1\n",
       "  - lat          (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "  - lon          (sample) float64 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
       "Dimensions without coordinates: time_input\n",
       "Data variables:\n",
       "    tasmax       (sample, time_input) float32 238.8 235.2 234.7 ... 245.5 242.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-4b587083-78b7-4bff-8cb9-0b4db44d9d3c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-4b587083-78b7-4bff-8cb9-0b4db44d9d3c' 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'>sample</span>: 55680</li><li><span>time_input</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0419356d-c153-4bdf-9323-fa2081550853' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0419356d-c153-4bdf-9323-fa2081550853' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(sample, time_input)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-02-10T12:00:00 ... 1850-03-...</div><input id='attrs-6d3d60fd-fde2-4182-b211-db3e89b61be6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d3d60fd-fde2-4182-b211-db3e89b61be6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb369593-8e31-417a-a40d-ed94c21814e6' class='xr-var-data-in' type='checkbox'><label for='data-eb369593-8e31-417a-a40d-ed94c21814e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>MultiIndex</div><div class='xr-var-preview xr-preview'>(input_batch, lat, lon)</div><input id='attrs-61cecbd9-a826-4213-a5c2-0e8903cb18a6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-61cecbd9-a826-4213-a5c2-0e8903cb18a6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-890b0e1c-0778-40c6-8f30-b89935d0afee' class='xr-var-data-in' type='checkbox'><label for='data-890b0e1c-0778-40c6-8f30-b89935d0afee' 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, -90.0, 0.0), (0, -90.0, 1.875), (0, -90.0, 3.75), ...,\n",
       "       (1, 90.0, 354.375), (1, 90.0, 356.25), (1, 90.0, 358.125)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>input_batch</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-2bd06b8c-6704-4b94-a5df-fc05fcc69afc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bd06b8c-6704-4b94-a5df-fc05fcc69afc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32819fd3-4907-419a-9f68-843fbc687e62' class='xr-var-data-in' type='checkbox'><label for='data-32819fd3-4907-419a-9f68-843fbc687e62' 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, 0, 0, ..., 1, 1, 1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-84919ac9-bd65-4e41-a1c6-a1e809220f9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-84919ac9-bd65-4e41-a1c6-a1e809220f9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1477cbc-5155-4559-84cd-a3ce38a36953' class='xr-var-data-in' type='checkbox'><label for='data-e1477cbc-5155-4559-84cd-a3ce38a36953' 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([-90., -90., -90., ...,  90.,  90.,  90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-0c3ad14c-8e86-4dcd-a19e-a2c0a890d812' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c3ad14c-8e86-4dcd-a19e-a2c0a890d812' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe733e0-9bb7-4a63-92c9-7b93983df3a9' class='xr-var-data-in' type='checkbox'><label for='data-1fe733e0-9bb7-4a63-92c9-7b93983df3a9' 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.   ,   1.875,   3.75 , ..., 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-70301a46-2e78-4552-99ca-9b3033e9a98c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-70301a46-2e78-4552-99ca-9b3033e9a98c' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(sample, time_input)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>238.8 235.2 234.7 ... 245.5 242.9</div><input id='attrs-06219633-9721-4815-b85e-14015d39e409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-06219633-9721-4815-b85e-14015d39e409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6352be13-b8a4-48d3-a9ba-a4c469c82a96' class='xr-var-data-in' type='checkbox'><label for='data-6352be13-b8a4-48d3-a9ba-a4c469c82a96' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       [238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       [238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       ...,\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-84d70c96-e029-43aa-9e30-783d67b7ce85' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-84d70c96-e029-43aa-9e30-783d67b7ce85' 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>\n",
       "Dimensions:      (sample: 55680, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (sample, time_input) datetime64[ns] 1850-02-10T12:00:00 ... ...\n",
       "  * sample       (sample) MultiIndex\n",
       "  - input_batch  (sample) int64 0 0 0 0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1 1 1 1\n",
       "  - lat          (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "  - lon          (sample) float64 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
       "Dimensions without coordinates: time_input\n",
       "Data variables:\n",
       "    tasmax       (sample, time_input) float32 238.8 235.2 234.7 ... 245.5 242.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_timepoint_in_each_sample = 10\n",
    "n_timepoint_in_each_batch = 20\n",
    "\n",
    "bgen = xbatcher.BatchGenerator(\n",
    "    ds=ds,\n",
    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
    "    batch_dims={\"time\": n_timepoint_in_each_batch},\n",
    "    concat_input_dims=True,\n",
    ")\n",
    "\n",
    "n_batch = 0\n",
    "for batch in bgen:\n",
    "    n_batch += 1\n",
    "\n",
    "print(f\"{n_batch} batches\")\n",
    "display(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "spectacular-reading",
   "metadata": {},
   "source": [
    "## Last batch behavior\n",
    "\n",
    "If the input ds is not divisible by the specified `input_dims`, the remainder will be discarded instead of having a fractional batch. See https://github.com/xarray-contrib/xbatcher/issues/5 for more on this topic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "residential-income",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "last time point in ds    is 1850-03-01T12:00:00.000000000\n",
      "last time point in batch is 1850-01-31T12:00:00.000000000\n"
     ]
    },
    {
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       "  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",
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       "\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",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
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       "\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",
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       "\n",
       ".xr-icon-database,\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (sample: 27840, time: 31)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01T12:00:00 ... 1850-01-31T12:00:00\n",
       "  * sample   (sample) MultiIndex\n",
       "  - lat      (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
       "  - lon      (sample) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "Data variables:\n",
       "    tasmax   (sample, time) float32 252.6 250.9 250.4 ... 257.6 256.9 243.3</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1d7be293-028c-4257-a10f-8411303e3bc9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1d7be293-028c-4257-a10f-8411303e3bc9' 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'>sample</span>: 27840</li><li><span class='xr-has-index'>time</span>: 31</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3c0b3d49-7b30-4899-adb9-b97f80d1abfb' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c0b3d49-7b30-4899-adb9-b97f80d1abfb' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-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'>1850-01-01T12:00:00 ... 1850-01-...</div><input id='attrs-3cea082b-9b0f-430f-8c4b-474a987f0c4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3cea082b-9b0f-430f-8c4b-474a987f0c4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02e4659a-3377-4ace-a634-78f9aa70325b' class='xr-var-data-in' type='checkbox'><label for='data-02e4659a-3377-4ace-a634-78f9aa70325b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1850-01-01T12:00:00.000000000&#x27;, &#x27;1850-01-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-03T12:00:00.000000000&#x27;, &#x27;1850-01-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-05T12:00:00.000000000&#x27;, &#x27;1850-01-06T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-07T12:00:00.000000000&#x27;, &#x27;1850-01-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-09T12:00:00.000000000&#x27;, &#x27;1850-01-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-11T12:00:00.000000000&#x27;, &#x27;1850-01-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-13T12:00:00.000000000&#x27;, &#x27;1850-01-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-15T12:00:00.000000000&#x27;, &#x27;1850-01-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-17T12:00:00.000000000&#x27;, &#x27;1850-01-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-19T12:00:00.000000000&#x27;, &#x27;1850-01-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-21T12:00:00.000000000&#x27;, &#x27;1850-01-22T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-23T12:00:00.000000000&#x27;, &#x27;1850-01-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-25T12:00:00.000000000&#x27;, &#x27;1850-01-26T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-27T12:00:00.000000000&#x27;, &#x27;1850-01-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-29T12:00:00.000000000&#x27;, &#x27;1850-01-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;1850-01-31T12:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>MultiIndex</div><div class='xr-var-preview xr-preview'>(lat, lon)</div><input id='attrs-ad56b3ff-b0b4-4c18-8ffc-6e5c98fc93c3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad56b3ff-b0b4-4c18-8ffc-6e5c98fc93c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42111a17-3e94-412c-aa7f-a002c8d21e7e' class='xr-var-data-in' type='checkbox'><label for='data-42111a17-3e94-412c-aa7f-a002c8d21e7e' 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([(-90.0, 0.0), (-90.0, 1.875), (-90.0, 3.75), ..., (90.0, 354.375),\n",
       "       (90.0, 356.25), (90.0, 358.125)], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-2c87f8f0-aee4-4499-a5a8-7999c46341ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c87f8f0-aee4-4499-a5a8-7999c46341ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8065262d-d9a7-4dda-bf1d-83f746a113bd' class='xr-var-data-in' type='checkbox'><label for='data-8065262d-d9a7-4dda-bf1d-83f746a113bd' 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([-90., -90., -90., ...,  90.,  90.,  90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-4681d7ab-3cff-470f-84a0-4bf422154247' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4681d7ab-3cff-470f-84a0-4bf422154247' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6891a577-e2c9-4950-8de3-76498a601bda' class='xr-var-data-in' type='checkbox'><label for='data-6891a577-e2c9-4950-8de3-76498a601bda' 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.   ,   1.875,   3.75 , ..., 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0b2320e9-1567-4f64-a6a0-81bf9fbf3ee4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0b2320e9-1567-4f64-a6a0-81bf9fbf3ee4' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(sample, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>252.6 250.9 250.4 ... 256.9 243.3</div><input id='attrs-fce93e33-24ea-40b6-823a-1192c7c15736' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fce93e33-24ea-40b6-823a-1192c7c15736' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb6bc3f0-1811-44a6-b9b6-21ac80d3fda3' class='xr-var-data-in' type='checkbox'><label for='data-cb6bc3f0-1811-44a6-b9b6-21ac80d3fda3' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[252.5753 , 250.88838, 250.43263, ..., 242.25215, 241.18654,\n",
       "        239.78615],\n",
       "       [252.5753 , 250.88838, 250.43263, ..., 242.25215, 241.18654,\n",
       "        239.78615],\n",
       "       [252.5753 , 250.88838, 250.43263, ..., 242.25215, 241.18654,\n",
       "        239.78615],\n",
       "       ...,\n",
       "       [247.44861, 248.71996, 241.68065, ..., 257.61612, 256.9255 ,\n",
       "        243.33928],\n",
       "       [247.44861, 248.71996, 241.68065, ..., 257.61612, 256.9255 ,\n",
       "        243.33928],\n",
       "       [247.44861, 248.71996, 241.68065, ..., 257.61612, 256.9255 ,\n",
       "        243.33928]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-551906bf-f764-42e4-87aa-e0a034b6113f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-551906bf-f764-42e4-87aa-e0a034b6113f' 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>\n",
       "Dimensions:  (sample: 27840, time: 31)\n",
       "Coordinates:\n",
       "  * time     (time) datetime64[ns] 1850-01-01T12:00:00 ... 1850-01-31T12:00:00\n",
       "  * sample   (sample) MultiIndex\n",
       "  - lat      (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0 90.0\n",
       "  - lon      (sample) float64 0.0 1.875 3.75 5.625 ... 352.5 354.4 356.2 358.1\n",
       "Data variables:\n",
       "    tasmax   (sample, time) float32 252.6 250.9 250.4 ... 257.6 256.9 243.3"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "n_timepoint_in_batch = 31\n",
    "\n",
    "bgen = xbatcher.BatchGenerator(ds=ds, input_dims={\"time\": n_timepoint_in_batch})\n",
    "\n",
    "for batch in bgen:\n",
    "    print(f\"last time point in ds    is {ds.time[-1].values}\")\n",
    "    print(f\"last time point in batch is {batch.time[-1].values}\")\n",
    "display(batch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "competitive-islam",
   "metadata": {},
   "source": [
    "## Overlapping inputs\n",
    "\n",
    "In the example above, all samples have distinct time points. That is, for any lat/lon pixel, sample 1 has time points 1-10, sample 2 has time point 11-20, and they do not overlap \n",
    "however, in many machine learning applications, we will want overlapping samples (e.g. sample 1 has time points 1-10, sample 2 has time points 2-11, and so on). We can use the `input_overlap` argument to get this behavior."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "cleared-custody",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3 batches\n"
     ]
    },
    {
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       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (sample: 306240, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (sample, time_input) datetime64[ns] 1850-02-10T12:00:00 ... ...\n",
       "  * sample       (sample) MultiIndex\n",
       "  - input_batch  (sample) int64 0 0 0 0 0 0 0 0 0 ... 10 10 10 10 10 10 10 10 10\n",
       "  - lat          (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "  - lon          (sample) float64 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
       "Dimensions without coordinates: time_input\n",
       "Data variables:\n",
       "    tasmax       (sample, time_input) float32 238.8 235.2 234.7 ... 245.5 242.9</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1ab6df59-74ed-458d-bb8d-cc23f581da47' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1ab6df59-74ed-458d-bb8d-cc23f581da47' 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'>sample</span>: 306240</li><li><span>time_input</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39f2d82b-c886-496d-9d89-0606c0dcfa42' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39f2d82b-c886-496d-9d89-0606c0dcfa42' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(sample, time_input)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-02-10T12:00:00 ... 1850-03-...</div><input id='attrs-fd3bf000-f36b-4020-85da-7f18404c30c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd3bf000-f36b-4020-85da-7f18404c30c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45da1f8a-d9ea-4d31-8eb4-b49d6bbb5227' class='xr-var-data-in' type='checkbox'><label for='data-45da1f8a-d9ea-4d31-8eb4-b49d6bbb5227' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       ...,\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, ...,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-03-01T12:00:00.000000000&#x27;]], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>sample</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>MultiIndex</div><div class='xr-var-preview xr-preview'>(input_batch, lat, lon)</div><input id='attrs-1489f5f5-9076-4c49-8b98-fc3f0bd70191' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1489f5f5-9076-4c49-8b98-fc3f0bd70191' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7512f02e-017e-430e-8213-7a6511424025' class='xr-var-data-in' type='checkbox'><label for='data-7512f02e-017e-430e-8213-7a6511424025' 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, -90.0, 0.0), (0, -90.0, 1.875), (0, -90.0, 3.75), ...,\n",
       "       (10, 90.0, 354.375), (10, 90.0, 356.25), (10, 90.0, 358.125)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>input_batch</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 ... 10 10 10 10 10 10</div><input id='attrs-3d9ffe01-6c8b-4c6b-893b-ea96508a0519' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d9ffe01-6c8b-4c6b-893b-ea96508a0519' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc539c32-a5f8-4274-b837-844c8db4f4f3' class='xr-var-data-in' type='checkbox'><label for='data-fc539c32-a5f8-4274-b837-844c8db4f4f3' 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,  0,  0, ..., 10, 10, 10])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lat</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-90.0 -90.0 -90.0 ... 90.0 90.0</div><input id='attrs-10c8972f-1231-41cb-8b86-29a1eebbef41' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10c8972f-1231-41cb-8b86-29a1eebbef41' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-448b4aec-e51b-4b97-8850-23d87dc8ddc2' class='xr-var-data-in' type='checkbox'><label for='data-448b4aec-e51b-4b97-8850-23d87dc8ddc2' 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([-90., -90., -90., ...,  90.,  90.,  90.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon</span></div><div class='xr-var-dims'>(sample)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.875 3.75 ... 356.2 358.1</div><input id='attrs-aa82a84a-7647-4ca2-b19a-f882a17de917' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aa82a84a-7647-4ca2-b19a-f882a17de917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-539e7d5d-947e-4f35-9d2b-f5be8e0894e5' class='xr-var-data-in' type='checkbox'><label for='data-539e7d5d-947e-4f35-9d2b-f5be8e0894e5' 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.   ,   1.875,   3.75 , ..., 354.375, 356.25 , 358.125])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0d04eca-b278-4329-9b93-9cc269801d70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a0d04eca-b278-4329-9b93-9cc269801d70' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(sample, time_input)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>238.8 235.2 234.7 ... 245.5 242.9</div><input id='attrs-d97c6756-affe-417e-9b54-2ac5b4b1d1d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d97c6756-affe-417e-9b54-2ac5b4b1d1d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff65c55-c41a-48f0-a5cd-cd19f04d8d8c' class='xr-var-data-in' type='checkbox'><label for='data-7ff65c55-c41a-48f0-a5cd-cd19f04d8d8c' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       [238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       [238.76602, 235.20686, 234.71843, ..., 234.75949, 228.80817,\n",
       "        225.40395],\n",
       "       ...,\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855],\n",
       "       [238.86842, 240.81023, 242.40994, ..., 251.47948, 245.45798,\n",
       "        242.93855]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9deaade4-ca86-41d3-9bcf-5ebe23d198f2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9deaade4-ca86-41d3-9bcf-5ebe23d198f2' 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>\n",
       "Dimensions:      (sample: 306240, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (sample, time_input) datetime64[ns] 1850-02-10T12:00:00 ... ...\n",
       "  * sample       (sample) MultiIndex\n",
       "  - input_batch  (sample) int64 0 0 0 0 0 0 0 0 0 ... 10 10 10 10 10 10 10 10 10\n",
       "  - lat          (sample) float64 -90.0 -90.0 -90.0 -90.0 ... 90.0 90.0 90.0\n",
       "  - lon          (sample) float64 0.0 1.875 3.75 5.625 ... 354.4 356.2 358.1\n",
       "Dimensions without coordinates: time_input\n",
       "Data variables:\n",
       "    tasmax       (sample, time_input) float32 238.8 235.2 234.7 ... 245.5 242.9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_timepoint_in_each_sample = 10\n",
    "n_timepoint_in_each_batch = 20\n",
    "input_overlap = 9\n",
    "\n",
    "bgen = xbatcher.BatchGenerator(\n",
    "    ds=ds,\n",
    "    input_dims={\"time\": n_timepoint_in_each_sample},\n",
    "    batch_dims={\"time\": n_timepoint_in_each_batch},\n",
    "    concat_input_dims=True,\n",
    "    input_overlap={\"time\": input_overlap},\n",
    ")\n",
    "\n",
    "n_batch = 0\n",
    "for batch in bgen:\n",
    "    n_batch += 1\n",
    "\n",
    "print(f\"{n_batch} batches\")\n",
    "batch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "harmful-benefit",
   "metadata": {},
   "source": [
    "We can inspect the samples in a batch for a lat/lon pixel, noting that the overlap only applies within a batch and not across. Thus, within the 20 time points in a batch, we can get 11 samples each with 10 time points and 9 time points allowed to overlap."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "earlier-warehouse",
   "metadata": {},
   "outputs": [
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       "Dimensions:      (input_batch: 11, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (input_batch, time_input) datetime64[ns] 1850-02-10T12:00:00...\n",
       "  * input_batch  (input_batch) int64 0 1 2 3 4 5 6 7 8 9 10\n",
       "Dimensions without coordinates: time_input\n",
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       "    tasmax       (input_batch, time_input) float32 238.8 235.2 ... 226.3 227.0</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8b244791-7492-4916-b700-9af16fed6698' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b244791-7492-4916-b700-9af16fed6698' 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'>input_batch</span>: 11</li><li><span>time_input</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-da117993-f4fa-4790-a550-f49cc7a61c56' class='xr-section-summary-in' type='checkbox'  checked><label for='section-da117993-f4fa-4790-a550-f49cc7a61c56' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(input_batch, time_input)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1850-02-10T12:00:00 ... 1850-03-...</div><input id='attrs-c57e741c-ed9b-4147-9b64-4facf6832b48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c57e741c-ed9b-4147-9b64-4facf6832b48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1340f8a1-e7f0-4a3e-a593-0977968c405f' class='xr-var-data-in' type='checkbox'><label for='data-1340f8a1-e7f0-4a3e-a593-0977968c405f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>bounds :</span></dt><dd>time_bnds</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([[&#x27;1850-02-10T12:00:00.000000000&#x27;, &#x27;1850-02-11T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-12T12:00:00.000000000&#x27;, &#x27;1850-02-13T12:00:00.000000000&#x27;,\n",
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       "        &#x27;1850-02-16T12:00:00.000000000&#x27;, &#x27;1850-02-17T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-18T12:00:00.000000000&#x27;, &#x27;1850-02-19T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-11T12:00:00.000000000&#x27;, &#x27;1850-02-12T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-13T12:00:00.000000000&#x27;, &#x27;1850-02-14T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-15T12:00:00.000000000&#x27;, &#x27;1850-02-16T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;, &#x27;1850-02-20T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-12T12:00:00.000000000&#x27;, &#x27;1850-02-13T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-14T12:00:00.000000000&#x27;, &#x27;1850-02-15T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-16T12:00:00.000000000&#x27;, &#x27;1850-02-17T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-18T12:00:00.000000000&#x27;, &#x27;1850-02-19T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-13T12:00:00.000000000&#x27;, &#x27;1850-02-14T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-15T12:00:00.000000000&#x27;, &#x27;1850-02-16T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-17T12:00:00.000000000&#x27;, &#x27;1850-02-18T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;, &#x27;1850-02-20T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-21T12:00:00.000000000&#x27;, &#x27;1850-02-22T12:00:00.000000000&#x27;],\n",
       "...\n",
       "        &#x27;1850-02-19T12:00:00.000000000&#x27;, &#x27;1850-02-20T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-21T12:00:00.000000000&#x27;, &#x27;1850-02-22T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-23T12:00:00.000000000&#x27;, &#x27;1850-02-24T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-25T12:00:00.000000000&#x27;, &#x27;1850-02-26T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-18T12:00:00.000000000&#x27;, &#x27;1850-02-19T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, &#x27;1850-02-23T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-24T12:00:00.000000000&#x27;, &#x27;1850-02-25T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-26T12:00:00.000000000&#x27;, &#x27;1850-02-27T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-19T12:00:00.000000000&#x27;, &#x27;1850-02-20T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-21T12:00:00.000000000&#x27;, &#x27;1850-02-22T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-23T12:00:00.000000000&#x27;, &#x27;1850-02-24T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-25T12:00:00.000000000&#x27;, &#x27;1850-02-26T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-27T12:00:00.000000000&#x27;, &#x27;1850-02-28T12:00:00.000000000&#x27;],\n",
       "       [&#x27;1850-02-20T12:00:00.000000000&#x27;, &#x27;1850-02-21T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-22T12:00:00.000000000&#x27;, &#x27;1850-02-23T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-24T12:00:00.000000000&#x27;, &#x27;1850-02-25T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-26T12:00:00.000000000&#x27;, &#x27;1850-02-27T12:00:00.000000000&#x27;,\n",
       "        &#x27;1850-02-28T12:00:00.000000000&#x27;, &#x27;1850-03-01T12:00:00.000000000&#x27;]],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>input_batch</span></div><div class='xr-var-dims'>(input_batch)</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 10</div><input id='attrs-2b182476-f8f7-4e02-9e33-eba21acf9315' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2b182476-f8f7-4e02-9e33-eba21acf9315' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0562951-98dc-4a62-a87c-73759c2ba023' class='xr-var-data-in' type='checkbox'><label for='data-a0562951-98dc-4a62-a87c-73759c2ba023' 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, 10])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bd3c87a7-2a7a-45df-b0c6-a03758df0658' class='xr-section-summary-in' type='checkbox'  checked><label for='section-bd3c87a7-2a7a-45df-b0c6-a03758df0658' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>tasmax</span></div><div class='xr-var-dims'>(input_batch, time_input)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>238.8 235.2 234.7 ... 226.3 227.0</div><input id='attrs-05fa49e9-f486-4e9d-be11-75698aa7dc76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05fa49e9-f486-4e9d-be11-75698aa7dc76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7969c52-790e-4260-92ba-e9e92fafc798' class='xr-var-data-in' type='checkbox'><label for='data-f7969c52-790e-4260-92ba-e9e92fafc798' 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>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>cell_methods :</span></dt><dd>area: mean time: maximum</dd><dt><span>comment :</span></dt><dd>maximum near-surface (usually, 2 meter) air temperature (add cell_method attribute &#x27;time: max&#x27;)</dd><dt><span>coordinates :</span></dt><dd>height</dd><dt><span>history :</span></dt><dd>2019-11-15T17:28:16Z altered by CMOR: Treated scalar dimension: &#x27;height&#x27;. 2019-11-15T17:28:16Z altered by CMOR: replaced missing value flag (-1.07374e+09) with standard missing value (1e+20).</dd><dt><span>long_name :</span></dt><dd>Daily Maximum Near-Surface Air Temperature</dd><dt><span>standard_name :</span></dt><dd>air_temperature</dd><dt><span>units :</span></dt><dd>K</dd></dl></div><div class='xr-var-data'><pre>array([[238.76602, 235.20686, 234.71843, 233.27286, 235.85324, 236.41687,\n",
       "        235.92152, 234.75949, 228.80817, 225.40395],\n",
       "       [235.20686, 234.71843, 233.27286, 235.85324, 236.41687, 235.92152,\n",
       "        234.75949, 228.80817, 225.40395, 226.11433],\n",
       "       [234.71843, 233.27286, 235.85324, 236.41687, 235.92152, 234.75949,\n",
       "        228.80817, 225.40395, 226.11433, 226.19003],\n",
       "       [233.27286, 235.85324, 236.41687, 235.92152, 234.75949, 228.80817,\n",
       "        225.40395, 226.11433, 226.19003, 224.03416],\n",
       "       [235.85324, 236.41687, 235.92152, 234.75949, 228.80817, 225.40395,\n",
       "        226.11433, 226.19003, 224.03416, 240.60652],\n",
       "       [236.41687, 235.92152, 234.75949, 228.80817, 225.40395, 226.11433,\n",
       "        226.19003, 224.03416, 240.60652, 241.64258],\n",
       "       [235.92152, 234.75949, 228.80817, 225.40395, 226.11433, 226.19003,\n",
       "        224.03416, 240.60652, 241.64258, 240.7453 ],\n",
       "       [234.75949, 228.80817, 225.40395, 226.11433, 226.19003, 224.03416,\n",
       "        240.60652, 241.64258, 240.7453 , 233.43279],\n",
       "       [228.80817, 225.40395, 226.11433, 226.19003, 224.03416, 240.60652,\n",
       "        241.64258, 240.7453 , 233.43279, 226.06456],\n",
       "       [225.40395, 226.11433, 226.19003, 224.03416, 240.60652, 241.64258,\n",
       "        240.7453 , 233.43279, 226.06456, 226.33026],\n",
       "       [226.11433, 226.19003, 224.03416, 240.60652, 241.64258, 240.7453 ,\n",
       "        233.43279, 226.06456, 226.33026, 226.99207]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-46e941e1-b83b-4126-b113-908cc20f25c1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-46e941e1-b83b-4126-b113-908cc20f25c1' 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>\n",
       "Dimensions:      (input_batch: 11, time_input: 10)\n",
       "Coordinates:\n",
       "    time         (input_batch, time_input) datetime64[ns] 1850-02-10T12:00:00...\n",
       "  * input_batch  (input_batch) int64 0 1 2 3 4 5 6 7 8 9 10\n",
       "Dimensions without coordinates: time_input\n",
       "Data variables:\n",
       "    tasmax       (input_batch, time_input) float32 238.8 235.2 ... 226.3 227.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sample 1 goes from 1850-02-10T12:00:00.000000000 to 1850-02-19T12:00:00.000000000\n",
      "sample 2 goes from 1850-02-11T12:00:00.000000000 to 1850-02-20T12:00:00.000000000\n"
     ]
    }
   ],
   "source": [
    "lat = -90\n",
    "lon = 0\n",
    "pixel = batch.sel(lat=lat, lon=lon)\n",
    "display(pixel)\n",
    "\n",
    "print(\n",
    "    f\"sample 1 goes from {pixel.isel(input_batch=0).time[0].values} to {pixel.isel(input_batch=0).time[-1].values}\"\n",
    ")\n",
    "print(\n",
    "    f\"sample 2 goes from {pixel.isel(input_batch=1).time[0].values} to {pixel.isel(input_batch=1).time[-1].values}\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arranged-telephone",
   "metadata": {},
   "source": [
    "## Example applications\n",
    "\n",
    "These batches can then be used to train a downstream machine learning model while preserving the indices of these sample. \n",
    "\n",
    "As an example, let's say we want to train a simple CNN model to predict the max air temprature for each day at each lat/lon pixel. To predict the temperature at lat/lon/time of (i, j, t), we'll use features including the temperature of a 9 x 9 grid centered at (i, j), from times t-10 to t-1 (shape of input should be (n_samples_in_each_batch, 9, 9, 9)). Note that in this example, we subset the dataset to a smaller domain for efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "consolidated-chocolate",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch 0\n",
      "feature shape (600, 9, 9, 9)\n",
      "label shape (600,)\n",
      "shape of lat of each sample (600,)\n",
      "\n",
      "batch 1\n",
      "feature shape (600, 9, 9, 9)\n",
      "label shape (600,)\n",
      "shape of lat of each sample (600,)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bgen = xbatcher.BatchGenerator(\n",
    "    ds=ds[[\"tasmax\"]].isel(lat=slice(0, 18), lon=slice(0, 18), time=slice(0, 30)),\n",
    "    input_dims={\"lat\": 9, \"lon\": 9, \"time\": 10},\n",
    "    batch_dims={\"lat\": 18, \"lon\": 18, \"time\": 15},\n",
    "    concat_input_dims=True,\n",
    "    input_overlap={\"lat\": 8, \"lon\": 8, \"time\": 9},\n",
    ")\n",
    "\n",
    "for i, batch in enumerate(bgen):\n",
    "    print(f\"batch {i}\")\n",
    "    # make sure the ordering of dimension is consistent\n",
    "    batch = batch.transpose(\"input_batch\", \"lat_input\", \"lon_input\", \"time_input\")\n",
    "\n",
    "    # only use the first 9 time points as features, since the last time point is the label to be predicted\n",
    "    features = batch.tasmax.isel(time_input=slice(0, 9))\n",
    "    # select the center pixel at the last time point to be the label to be predicted\n",
    "    # the actual lat/lon/time for each of the sample can be accessed in labels.coords\n",
    "    labels = batch.tasmax.isel(lat_input=5, lon_input=5, time_input=9)\n",
    "\n",
    "    print(\"feature shape\", features.shape)\n",
    "    print(\"label shape\", labels.shape)\n",
    "    print(\"shape of lat of each sample\", labels.coords[\"lat\"].shape)\n",
    "    print(\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legislative-closer",
   "metadata": {},
   "source": [
    "We can also use the Xarray's \"stack\" method to transform these into 2D inputs (n_samples, n_features) suitable for other machine learning algorithms implemented in libraries such as [sklearn](https://scikit-learn.org/stable/) and [xgboost](https://xgboost.readthedocs.io/en/stable/). In this case, we are expecting 9 x 9 x 9 = 729 features total."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "advisory-chicken",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "batch 0\n",
      "feature shape (600, 729)\n",
      "label shape (600,)\n",
      "shape of lat of each sample (600,) \n",
      "\n",
      "batch 1\n",
      "feature shape (600, 729)\n",
      "label shape (600,)\n",
      "shape of lat of each sample (600,) \n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i, batch in enumerate(bgen):\n",
    "    print(f\"batch {i}\")\n",
    "    # make sure the ordering of dimension is consistent\n",
    "    batch = batch.transpose(\"input_batch\", \"lat_input\", \"lon_input\", \"time_input\")\n",
    "\n",
    "    # only use the first 9 time points as features, since the last time point is the label to be predicted\n",
    "    features = batch.tasmax.isel(time_input=slice(0, 9))\n",
    "    features = features.stack(features=[\"lat_input\", \"lon_input\", \"time_input\"])\n",
    "\n",
    "    # select the center pixel at the last time point to be the label to be predicted\n",
    "    # the actual lat/lon/time for each of the sample can be accessed in labels.coords\n",
    "    labels = batch.tasmax.isel(lat_input=5, lon_input=5, time_input=9)\n",
    "\n",
    "    print(\"feature shape\", features.shape)\n",
    "    print(\"label shape\", labels.shape)\n",
    "    print(\"shape of lat of each sample\", labels.coords[\"lat\"].shape, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "persistent-culture",
   "metadata": {},
   "source": [
    "## What's next?\n",
    "\n",
    "There are many additional useful features that were yet to be implemented in the context of batch generation for downstream machine learning model training purposes. One of the current efforts is to improve the set of data loaders. \n",
    "\n",
    "Additional features of interest can include: \n",
    "\n",
    "1. Handling overlaps across batches. The common use case of batching in machine learning training involves generating all samples, then group them into batches. When overlap is enabled, this yields different results compared to first generating batches then creating possible samples within each batch. \n",
    "\n",
    "2. Shuffling/randomization of samples across batches. It is often desirable for each batch to be grouped randomly instead of along a specific dimension. \n",
    "\n",
    "3. Be efficient in terms of memory usage. In the case where overlap is enabled, each sample would comprised of mostly repetitive values compared to adjacent samples. It would be beneficial if each batch/sample is generated lazily to avoid storing these extra duplicative values. \n",
    "\n",
    "4. Handling preprocessing steps. For example, data augmentation, scaling/normalization, outlier detection, etc. \n",
    "\n",
    "\n",
    "More thoughts on 1. and 2. can be found in [this issue](https://github.com/xarray-contrib/xbatcher/issues/30). Interested users are welcomed to comment or submit other issues in GitHub. "
   ]
  }
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