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   "cell_type": "markdown",
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    "# Example to improve code design\n",
    "**Claire Carouge, CLEX CMS**\n",
    "\n",
    "We are often asked how to organise a code for tasks such as:\n",
    "* performing the same analysis on different models or grid resolutions\n",
    "* performing an analysis on one dataset for different options (e.g. different seasons)\n",
    "\n",
    "The principle to apply in all cases is to avoid repeating code. In Python and most of other programming languages, to avoid repetition one can use dictionaries and/or functions and/or classes.\n",
    "\n",
    "In this blog, we are going to use a small example to illustrate those techniques. Those techniques can often be used interchangeably. Usually, functions and classes result in more flexible and reusable code.\n",
    "\n",
    "Note: the analysis chosen here does not need to use dictionaries and functions to be done. We only do so here to illustrate the generic process. The direct way to perform the analysis is noted at the end of this notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "bf4138ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy.crs as ccrs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9999091",
   "metadata": {},
   "source": [
    "## Badly organised code\n",
    "The following code is correct as it gives the correct answer but it is prone to errors because the code is repeated. This increases the risk of typos. For more complex analyses, it can also make it harder to test as it is not modular. And it can be time-consuming to add extra data to the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "96c35c4b",
   "metadata": {},
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tsl&#x27; (time: 1980, lat: 96, lon: 96)&gt;\n",
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       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as &quot;missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tsl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-93c1cd00-410d-45a8-882d-a453f510a25e' class='xr-array-in' type='checkbox' checked><label for='section-93c1cd00-410d-45a8-882d-a453f510a25e' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1980, 96, 96), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-dac59d0c-c8a0-45f2-adc2-94633a9a5865' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dac59d0c-c8a0-45f2-adc2-94633a9a5865' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-0ea933e1-117e-441a-b8ed-de42793a74c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ea933e1-117e-441a-b8ed-de42793a74c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d96e84af-bb16-4a60-ba0d-dfd95478591b' class='xr-var-data-in' type='checkbox'><label for='data-d96e84af-bb16-4a60-ba0d-dfd95478591b' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_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.      , -88.10526 , -86.210526, -84.31579 , -82.42105 , -80.52631 ,\n",
       "       -78.63158 , -76.73684 , -74.8421  , -72.947365, -71.052635, -69.1579  ,\n",
       "       -67.26316 , -65.36842 , -63.473682, -61.57895 , -59.68421 , -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.31579 , -46.42105 ,\n",
       "       -44.526318, -42.63158 , -40.736843, -38.842106, -36.94737 , -35.05263 ,\n",
       "       -33.157894, -31.263159, -29.368422, -27.473684, -25.578947, -23.68421 ,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.68421 ,  25.578947,  27.473684,  29.368422,  31.263159,  33.157894,\n",
       "        35.05263 ,  36.94737 ,  38.842106,  40.736843,  42.63158 ,  44.526318,\n",
       "        46.42105 ,  48.31579 ,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.68421 ,  61.57895 ,  63.473682,  65.36842 ,  67.26316 ,\n",
       "        69.1579  ,  71.052635,  72.947365,  74.8421  ,  76.73684 ,  78.63158 ,\n",
       "        80.52631 ,  82.42105 ,  84.31579 ,  86.210526,  88.10526 ,  90.      ],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>360.0 3.75 7.5 ... 352.5 356.2</div><input id='attrs-2ad50e4f-634f-436f-b615-670b3692f397' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ad50e4f-634f-436f-b615-670b3692f397' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63caac69-3a1f-4ae4-a2db-b3ebb45516fd' class='xr-var-data-in' type='checkbox'><label for='data-63caac69-3a1f-4ae4-a2db-b3ebb45516fd' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([360.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01419</div><input id='attrs-3e6425e0-c1b0-4755-a8bc-f7a489c30e8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e6425e0-c1b0-4755-a8bc-f7a489c30e8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca8e72ea-46ed-4fa1-8922-00fd8b1a3422' class='xr-var-data-in' type='checkbox'><label for='data-ca8e72ea-46ed-4fa1-8922-00fd8b1a3422' 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>name :</span></dt><dd>solth</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>long_name :</span></dt><dd>Thermal soil levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0.01418965, dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-29ac0158-08af-42cf-807b-e863aed5a5f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-29ac0158-08af-42cf-807b-e863aed5a5f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ede985a-8ab4-44c2-9a9b-d6deda044e0d' class='xr-var-data-in' type='checkbox'><label for='data-2ede985a-8ab4-44c2-9a9b-d6deda044e0d' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-134750fe-f835-400b-9aa4-b5a7e78b744c' class='xr-section-summary-in' type='checkbox'  ><label for='section-134750fe-f835-400b-9aa4-b5a7e78b744c' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature of Soil</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: mean where land time: mean</dd><dt><span>interval_operation :</span></dt><dd>1800 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>soil_temperature</dd><dt><span>description :</span></dt><dd>Temperature of each soil layer.  Reported as &quot;missing&quot; for grid cells occupied entirely by &quot;sea&quot;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tsl' (time: 1980, lat: 96, lon: 96)>\n",
       "dask.array<getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as \"missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella"
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       ".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",
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       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\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",
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       "  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",
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       "\n",
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       "  padding: 0;\n",
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       "  grid-template-columns: 125px auto;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
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       "  width: auto;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tsl&#x27; (time: 1980, lat: 96, lon: 96)&gt;\n",
       "dask.array&lt;getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as &quot;missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tsl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-565a041b-548a-438a-9eb3-a594b4aefc02' class='xr-array-in' type='checkbox' checked><label for='section-565a041b-548a-438a-9eb3-a594b4aefc02' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1980, 96, 96), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 4 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"164\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"34\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 80.58823529411765,70.58823529411765 80.58823529411765,104.9007638365262 10.0,34.31252854240854\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"114\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"44\" y1=\"0\" x2=\"114\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 44.31252854240854,0.0 114.9007638365262,70.58823529411765 80.58823529411765,70.58823529411765\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"114\" y2=\"70\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"104\" x2=\"114\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"114\" y1=\"70\" x2=\"114\" y2=\"104\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"80.58823529411765,70.58823529411765 114.9007638365262,70.58823529411765 114.9007638365262,104.9007638365262 80.58823529411765,104.9007638365262\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"97.744500\" y=\"124.900764\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >96</text>\n",
       "  <text x=\"134.900764\" y=\"87.744500\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,134.900764,87.744500)\">96</text>\n",
       "  <text x=\"35.294118\" y=\"89.606646\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.606646)\">1980</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-f62b3f27-b637-4ef1-855b-d4e5a2633e2d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f62b3f27-b637-4ef1-855b-d4e5a2633e2d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-0daa0dd0-78d4-48e8-a4be-71060dc6ecad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0daa0dd0-78d4-48e8-a4be-71060dc6ecad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc67d83a-daee-4b9a-bb3c-70616f9b9093' class='xr-var-data-in' type='checkbox'><label for='data-cc67d83a-daee-4b9a-bb3c-70616f9b9093' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_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.      , -88.10526 , -86.210526, -84.31579 , -82.42105 , -80.52631 ,\n",
       "       -78.63158 , -76.73684 , -74.8421  , -72.947365, -71.052635, -69.1579  ,\n",
       "       -67.26316 , -65.36842 , -63.473682, -61.57895 , -59.68421 , -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.31579 , -46.42105 ,\n",
       "       -44.526318, -42.63158 , -40.736843, -38.842106, -36.94737 , -35.05263 ,\n",
       "       -33.157894, -31.263159, -29.368422, -27.473684, -25.578947, -23.68421 ,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.68421 ,  25.578947,  27.473684,  29.368422,  31.263159,  33.157894,\n",
       "        35.05263 ,  36.94737 ,  38.842106,  40.736843,  42.63158 ,  44.526318,\n",
       "        46.42105 ,  48.31579 ,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.68421 ,  61.57895 ,  63.473682,  65.36842 ,  67.26316 ,\n",
       "        69.1579  ,  71.052635,  72.947365,  74.8421  ,  76.73684 ,  78.63158 ,\n",
       "        80.52631 ,  82.42105 ,  84.31579 ,  86.210526,  88.10526 ,  90.      ],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>360.0 3.75 7.5 ... 352.5 356.2</div><input id='attrs-de798f6a-25d7-4ce1-8d70-79997e5947c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de798f6a-25d7-4ce1-8d70-79997e5947c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5df5864f-3bbd-4db6-b39e-bea6163fb533' class='xr-var-data-in' type='checkbox'><label for='data-5df5864f-3bbd-4db6-b39e-bea6163fb533' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([360.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01419</div><input id='attrs-89f49f53-5ce8-4356-8e11-2355f633a35d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89f49f53-5ce8-4356-8e11-2355f633a35d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-535a0b08-9ba0-45de-b7f6-9ea87bea47e0' class='xr-var-data-in' type='checkbox'><label for='data-535a0b08-9ba0-45de-b7f6-9ea87bea47e0' 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>name :</span></dt><dd>solth</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>long_name :</span></dt><dd>Thermal soil levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0.01418965, dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-e302af05-3504-42f3-809b-72f6b5108d0a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e302af05-3504-42f3-809b-72f6b5108d0a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea00227-5927-40c3-b089-b7d9a2ddf0ba' class='xr-var-data-in' type='checkbox'><label for='data-6ea00227-5927-40c3-b089-b7d9a2ddf0ba' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76ae401b-396e-4921-9df1-0cf02a695452' class='xr-section-summary-in' type='checkbox'  ><label for='section-76ae401b-396e-4921-9df1-0cf02a695452' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature of Soil</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: mean where land time: mean</dd><dt><span>interval_operation :</span></dt><dd>1800 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>soil_temperature</dd><dt><span>description :</span></dt><dd>Temperature of each soil layer.  Reported as &quot;missing&quot; for grid cells occupied entirely by &quot;sea&quot;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tsl' (time: 1980, lat: 96, lon: 96)>\n",
       "dask.array<getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as \"missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-21.07/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1395: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<cartopy.mpl.feature_artist.FeatureArtist at 0x7f532dedfd30>"
      ]
     },
     "execution_count": 2,
     "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": [
    "# Read in the data we need\n",
    "\n",
    "## Paths to 2 CMIP6 model experiments\n",
    "IPSL_path = Path(\"/g/data/oi10/replicas/CMIP6/CMIP/IPSL/IPSL-CM5A2-INCA/\")\n",
    "historical_path = IPSL_path/\"historical/r1i1p1f1/Lmon/tsl/gr/v20200729/\"\n",
    "picontrol_path  = IPSL_path/\"piControl/r1i1p1f1/Lmon/tsl/gr/v20210216/\"\n",
    "## Find all netcdf files in those directories. \n",
    "historical_path = sorted(historical_path.glob(\"*.nc\"))\n",
    "picontrol_path  = sorted(picontrol_path.glob(\"*.nc\"))\n",
    "\n",
    "historical_ds = xr.open_mfdataset(historical_path)\n",
    "picontrol_ds  = xr.open_mfdataset(picontrol_path)\n",
    "\n",
    "# Select the data to cover the same space and period for both datasets\n",
    "## Select the surface only\n",
    "historical_tsl = historical_ds[\"tsl\"].isel(solth=0)\n",
    "picontrol_tsl  = picontrol_ds[\"tsl\"].isel(solth=0)\n",
    "\n",
    "## Restrict the data to the longest common time window.\n",
    "picontrol_tsl = picontrol_tsl.sel(time=historical_tsl.time)\n",
    "\n",
    "# Check our data\n",
    "## Use display() to get a nice display of the DataArray instead of print()\n",
    "display(historical_tsl)\n",
    "display(picontrol_tsl)\n",
    "\n",
    "# Do our analysis: calculate a percentile for each season\n",
    "time = historical_tsl.time\n",
    "hist_pct_djf = historical_tsl.where(time.dt.season == 'DJF').quantile(0.9, dim=[\"time\"])\n",
    "hist_pct_mam = historical_tsl.where(time.dt.season == 'MAM').quantile(0.9, dim=[\"time\"])\n",
    "hist_pct_jja = historical_tsl.where(time.dt.season == 'JJA').quantile(0.9, dim=[\"time\"])\n",
    "hist_pct_son = historical_tsl.where(time.dt.season == 'SON').quantile(0.9, dim=[\"time\"])\n",
    "pic_pct_djf  = picontrol_tsl.where(time.dt.season == 'DJF').quantile(0.9, dim=[\"time\"])\n",
    "pic_pct_mam  = picontrol_tsl.where(time.dt.season == 'MAM').quantile(0.9, dim=[\"time\"])\n",
    "pic_pct_jja  = picontrol_tsl.where(time.dt.season == 'JJA').quantile(0.9, dim=[\"time\"])\n",
    "pic_pct_son  = picontrol_tsl.where(time.dt.season == 'SON').quantile(0.9, dim=[\"time\"])\n",
    "\n",
    "# Check out results with a plot. We remove the first longitude because it's not in sorted order otherwise.\n",
    "fg=hist_pct_djf.isel(lon=slice(1,None)).plot(transform=ccrs.PlateCarree(),\n",
    "    subplot_kws={\n",
    "        \"projection\": ccrs.PlateCarree(central_longitude=180.)\n",
    "    }\n",
    ")\n",
    "fg.axes.coastlines()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10e9aea1",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "The code has three parts:\n",
    "* read in the data\n",
    "* select the data\n",
    "* perform the analysis\n",
    "\n",
    "There are also some checks done either with displaying the data or plotting the data.\n",
    "\n",
    "The code has a lot of similar lines with only an option that changes between the lines. It was quite long to write as each time I had to be careful to use the correct name. \n",
    "\n",
    "#### Reading the data\n",
    "The part to read the data is essentially composed of 2 sets of copied lines, changing only the inputs: once for the historical data path and once for the pi-Control data path.\n",
    "\n",
    "#### Select the data\n",
    "This part is very short. Although the part to select only the surface layer (`isel(solth=0)`) is repeated, it is only one line so it's fine to keep.\n",
    "\n",
    "#### Perform the analysis\n",
    "In this case, we have 2 sets of four lines. For each dataset, we have a different line for each season. In short, we are performing the same analysis on 2 datasets for 4 options each time. We could simplify that with a function that would calculate the analysis for all 4 options at once.\n",
    "\n",
    "Now let's see how we can rewrite the code in a more modular way and limiting copied lines\n",
    "\n",
    "## Better code structure\n",
    "### Reading and select the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "333f46d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to read in the data\n",
    "def get_var(path, varname):\n",
    "    \"\"\"Reads in all the netcdf files in the input path and return the data for the selected variable\n",
    "    path: pathlib.Path, path to the netcdf files\n",
    "    varname: string, name of the variable to select in the netcdf files\"\"\"\n",
    "    \n",
    "    netcdf_files = sorted(path.glob(\"*.nc\"))\n",
    "    ds = xr.open_mfdataset(netcdf_files)\n",
    "    \n",
    "    return ds[varname]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "92a0dcde",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tsl&#x27; (time: 1980, lat: 96, lon: 96)&gt;\n",
       "dask.array&lt;getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as &quot;missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tsl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-7b3e88b3-59f1-4bb0-a9eb-9bb5e021974a' class='xr-array-in' type='checkbox' checked><label for='section-7b3e88b3-59f1-4bb0-a9eb-9bb5e021974a' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1980, 96, 96), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"164\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"80\" y2=\"70\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"34\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"80\" y1=\"70\" x2=\"80\" y2=\"104\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"97.744500\" y=\"124.900764\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >96</text>\n",
       "  <text x=\"134.900764\" y=\"87.744500\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,134.900764,87.744500)\">96</text>\n",
       "  <text x=\"35.294118\" y=\"89.606646\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.606646)\">1980</text>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-369d8253-512c-41cf-856b-9abe8395ac9d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-369d8253-512c-41cf-856b-9abe8395ac9d' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-da8d8761-d0db-4b49-9b4f-e4cf3fc3f36f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da8d8761-d0db-4b49-9b4f-e4cf3fc3f36f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4907d5e0-b926-4958-975a-7aa2fb2751ca' class='xr-var-data-in' type='checkbox'><label for='data-4907d5e0-b926-4958-975a-7aa2fb2751ca' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_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.      , -88.10526 , -86.210526, -84.31579 , -82.42105 , -80.52631 ,\n",
       "       -78.63158 , -76.73684 , -74.8421  , -72.947365, -71.052635, -69.1579  ,\n",
       "       -67.26316 , -65.36842 , -63.473682, -61.57895 , -59.68421 , -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.31579 , -46.42105 ,\n",
       "       -44.526318, -42.63158 , -40.736843, -38.842106, -36.94737 , -35.05263 ,\n",
       "       -33.157894, -31.263159, -29.368422, -27.473684, -25.578947, -23.68421 ,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.68421 ,  25.578947,  27.473684,  29.368422,  31.263159,  33.157894,\n",
       "        35.05263 ,  36.94737 ,  38.842106,  40.736843,  42.63158 ,  44.526318,\n",
       "        46.42105 ,  48.31579 ,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.68421 ,  61.57895 ,  63.473682,  65.36842 ,  67.26316 ,\n",
       "        69.1579  ,  71.052635,  72.947365,  74.8421  ,  76.73684 ,  78.63158 ,\n",
       "        80.52631 ,  82.42105 ,  84.31579 ,  86.210526,  88.10526 ,  90.      ],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>360.0 3.75 7.5 ... 352.5 356.2</div><input id='attrs-8e53287f-d5db-4d96-974e-0faf7298ee85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e53287f-d5db-4d96-974e-0faf7298ee85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4312f035-ac05-40a1-9328-a9f626531734' class='xr-var-data-in' type='checkbox'><label for='data-4312f035-ac05-40a1-9328-a9f626531734' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([360.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01419</div><input id='attrs-e36d4df2-eb55-468b-8396-075bc608a70c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e36d4df2-eb55-468b-8396-075bc608a70c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6b0a46d-c274-409e-8305-2da973a05780' class='xr-var-data-in' type='checkbox'><label for='data-b6b0a46d-c274-409e-8305-2da973a05780' 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>name :</span></dt><dd>solth</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>long_name :</span></dt><dd>Thermal soil levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0.01418965, dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-e44ef2b7-a401-4c24-9a25-47261fa39494' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e44ef2b7-a401-4c24-9a25-47261fa39494' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-def8d5e4-f7e9-4212-a6c8-659cebcac346' class='xr-var-data-in' type='checkbox'><label for='data-def8d5e4-f7e9-4212-a6c8-659cebcac346' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3db6920-c30b-4b90-8249-1adf68f7ac69' class='xr-section-summary-in' type='checkbox'  ><label for='section-c3db6920-c30b-4b90-8249-1adf68f7ac69' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature of Soil</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: mean where land time: mean</dd><dt><span>interval_operation :</span></dt><dd>1800 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>soil_temperature</dd><dt><span>description :</span></dt><dd>Temperature of each soil layer.  Reported as &quot;missing&quot; for grid cells occupied entirely by &quot;sea&quot;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'tsl' (time: 1980, lat: 96, lon: 96)>\n",
       "dask.array<getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as \"missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella"
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tsl&#x27; (time: 1980, lat: 96, lon: 96)&gt;\n",
       "dask.array&lt;getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as &quot;missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tsl'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1980</li><li><span class='xr-has-index'>lat</span>: 96</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c5b152e5-cb0a-4ba0-abfb-ef48d439b385' class='xr-array-in' type='checkbox' checked><label for='section-c5b152e5-cb0a-4ba0-abfb-ef48d439b385' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1980, 96, 96), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                        <td> 69.61 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                        <td> (1980, 96, 96) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 4 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"164\" height=\"154\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <text x=\"35.294118\" y=\"89.606646\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,35.294118,89.606646)\">1980</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-ff9bfa72-77cf-4f5a-acc3-04dd43c24a7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ff9bfa72-77cf-4f5a-acc3-04dd43c24a7a' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-90.0 -88.11 -86.21 ... 88.11 90.0</div><input id='attrs-f47d5aff-0fb9-402d-b672-538789140e07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f47d5aff-0fb9-402d-b672-538789140e07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59f4825c-838e-4051-833b-ef862128f316' class='xr-var-data-in' type='checkbox'><label for='data-59f4825c-838e-4051-833b-ef862128f316' 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>standard_name :</span></dt><dd>latitude</dd><dt><span>long_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.      , -88.10526 , -86.210526, -84.31579 , -82.42105 , -80.52631 ,\n",
       "       -78.63158 , -76.73684 , -74.8421  , -72.947365, -71.052635, -69.1579  ,\n",
       "       -67.26316 , -65.36842 , -63.473682, -61.57895 , -59.68421 , -57.789474,\n",
       "       -55.894737, -54.      , -52.105263, -50.210526, -48.31579 , -46.42105 ,\n",
       "       -44.526318, -42.63158 , -40.736843, -38.842106, -36.94737 , -35.05263 ,\n",
       "       -33.157894, -31.263159, -29.368422, -27.473684, -25.578947, -23.68421 ,\n",
       "       -21.789474, -19.894737, -18.      , -16.105263, -14.210526, -12.315789,\n",
       "       -10.421053,  -8.526316,  -6.631579,  -4.736842,  -2.842105,  -0.947368,\n",
       "         0.947368,   2.842105,   4.736842,   6.631579,   8.526316,  10.421053,\n",
       "        12.315789,  14.210526,  16.105263,  18.      ,  19.894737,  21.789474,\n",
       "        23.68421 ,  25.578947,  27.473684,  29.368422,  31.263159,  33.157894,\n",
       "        35.05263 ,  36.94737 ,  38.842106,  40.736843,  42.63158 ,  44.526318,\n",
       "        46.42105 ,  48.31579 ,  50.210526,  52.105263,  54.      ,  55.894737,\n",
       "        57.789474,  59.68421 ,  61.57895 ,  63.473682,  65.36842 ,  67.26316 ,\n",
       "        69.1579  ,  71.052635,  72.947365,  74.8421  ,  76.73684 ,  78.63158 ,\n",
       "        80.52631 ,  82.42105 ,  84.31579 ,  86.210526,  88.10526 ,  90.      ],\n",
       "      dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>360.0 3.75 7.5 ... 352.5 356.2</div><input id='attrs-1c0eb279-38bd-4ad0-bce6-0bb52c0f4919' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c0eb279-38bd-4ad0-bce6-0bb52c0f4919' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4b0a395-1e1e-4a97-851a-8c5ce1b6c89a' class='xr-var-data-in' type='checkbox'><label for='data-f4b0a395-1e1e-4a97-851a-8c5ce1b6c89a' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([360.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>solth</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.01419</div><input id='attrs-42d59ebb-4b6a-4ed0-989f-22f935f7406f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-42d59ebb-4b6a-4ed0-989f-22f935f7406f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cafe17be-c123-4ccf-8ec9-74a32d6b9c2a' class='xr-var-data-in' type='checkbox'><label for='data-cafe17be-c123-4ccf-8ec9-74a32d6b9c2a' 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>name :</span></dt><dd>solth</dd><dt><span>standard_name :</span></dt><dd>model_level_number</dd><dt><span>long_name :</span></dt><dd>Thermal soil levels</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array(0.01418965, dtype=float32)</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'>object</div><div class='xr-var-preview xr-preview'>1850-01-16 12:00:00 ... 2014-12-...</div><input id='attrs-94064d4f-58a9-48a5-b7ee-93114f8f0b3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94064d4f-58a9-48a5-b7ee-93114f8f0b3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa748523-5358-4384-a75c-35f5c4fb74e3' class='xr-var-data-in' type='checkbox'><label for='data-fa748523-5358-4384-a75c-35f5c4fb74e3' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1850-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_bounds</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1850, 1, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 2, 15, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1850, 3, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       ...,\n",
       "       cftime.DatetimeNoLeap(2014, 10, 16, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 11, 16, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(2014, 12, 16, 12, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ea32e915-1941-4008-a6cd-0f38c04c01f9' class='xr-section-summary-in' type='checkbox'  ><label for='section-ea32e915-1941-4008-a6cd-0f38c04c01f9' class='xr-section-summary' >Attributes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>Temperature of Soil</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>cell_methods :</span></dt><dd>area: mean where land time: mean</dd><dt><span>interval_operation :</span></dt><dd>1800 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>standard_name :</span></dt><dd>soil_temperature</dd><dt><span>description :</span></dt><dd>Temperature of each soil layer.  Reported as &quot;missing&quot; for grid cells occupied entirely by &quot;sea&quot;.</dd><dt><span>history :</span></dt><dd>none</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'tsl' (time: 1980, lat: 96, lon: 96)>\n",
       "dask.array<getitem, shape=(1980, 96, 96), dtype=float32, chunksize=(1980, 96, 96), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lat      (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 84.32 86.21 88.11 90.0\n",
       "  * lon      (lon) float32 360.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "    solth    float32 0.01419\n",
       "  * time     (time) object 1850-01-16 12:00:00 ... 2014-12-16 12:00:00\n",
       "Attributes:\n",
       "    long_name:           Temperature of Soil\n",
       "    units:               K\n",
       "    online_operation:    average\n",
       "    cell_methods:        area: mean where land time: mean\n",
       "    interval_operation:  1800 s\n",
       "    interval_write:      1 month\n",
       "    standard_name:       soil_temperature\n",
       "    description:         Temperature of each soil layer.  Reported as \"missin...\n",
       "    history:             none\n",
       "    cell_measures:       area: areacella"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Paths to 2 CMIP6 model experiments\n",
    "IPSL_path = Path(\"/g/data/oi10/replicas/CMIP6/CMIP/IPSL/IPSL-CM5A2-INCA/\")\n",
    "\n",
    "variable = 'tsl'\n",
    "\n",
    "experiments = {\n",
    "    \"historical\": IPSL_path / \"historical/r1i1p1f1/Lmon\" / variable / \"gr/v20200729/\",\n",
    "    \"picontrol\": IPSL_path / \"piControl/r1i1p1f1/Lmon\" / variable / \"gr/v20210216/\",\n",
    "}\n",
    "\n",
    "# Dictionary to store the experiments with the experiment name as their key and data\n",
    "# as the value\n",
    "data = {}\n",
    "for expt, path in experiments.items():\n",
    "    # Find all netcdf files in those directories and restrict the data to the surface layer.\n",
    "    data[expt] = get_var(path, variable).isel(solth=0)\n",
    "\n",
    "# Restrict the data to the longest common time window.\n",
    "data[\"picontrol\"] = data[\"picontrol\"].sel(time=data[\"historical\"].time)\n",
    "\n",
    "for expt in data.keys():\n",
    "    display(data[expt])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1db7886c",
   "metadata": {},
   "source": [
    "First, we define a function to read in the data to reduce the number of lines copied.\n",
    "We also define two dictionaries:\n",
    "* A user-defined dictionary, `experiments`, that stores the name of the experiments and the paths to the data.\n",
    "* A computed dictionary, `data`, that associates the data to each corresponding experiment\n",
    "\n",
    "This structures allows us to expand to more experiments more easily. One just needs to add an experiment name and path to the `experiments` dictionary. Everything else is then computed for all datasets via a loop through the keys of the `experiments` or `data` dictionaries."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9827b94b",
   "metadata": {},
   "source": [
    "### Analysis\n",
    "For our analysis, we will define dictionaries and functions again. This will reduce the number of copied lines from 8 to 0.\n",
    "\n",
    "In this way, we also remove any hard-written reference to the name of the experiments and it will work for any number of experiments. The final dictionary `pct_dict` contains a dictionary associated with each key, i.e. a dictionary of dictionaries. The keys of `pct_dict` are the names of the experiments originally defined in `experiments`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ee82cc37",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'DJF': <xarray.DataArray 'tsl' (lat: 96, lon: 96)>\n",
      "dask.array<getitem, shape=(96, 96), dtype=float64, chunksize=(96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9, 'JJA': <xarray.DataArray 'tsl' (lat: 96, lon: 96)>\n",
      "dask.array<getitem, shape=(96, 96), dtype=float64, chunksize=(96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9, 'MAM': <xarray.DataArray 'tsl' (lat: 96, lon: 96)>\n",
      "dask.array<getitem, shape=(96, 96), dtype=float64, chunksize=(96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9, 'SON': <xarray.DataArray 'tsl' (lat: 96, lon: 96)>\n",
      "dask.array<getitem, shape=(96, 96), dtype=float64, chunksize=(96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9}\n"
     ]
    }
   ],
   "source": [
    "# Do our analysis: calculate a percentile for each season\n",
    "def season_quantile(data, quantile):\n",
    "    \"\"\"Calculate a quantile for each season in the input data\n",
    "    \n",
    "    data: DataArray, data with a dimension named 'time'\n",
    "    quantile: scalar, quantile to calculate for the data\"\"\"\n",
    "    \n",
    "    seas_pct = {}\n",
    "    for season, seas_data in data.groupby('time.season'):\n",
    "        seas_pct[season] = seas_data.quantile(0.9, dim=\"time\")\n",
    "\n",
    "    return seas_pct\n",
    "\n",
    "pct_dict_dict={}\n",
    "for expt in data.keys():\n",
    "    pct_dict_dict[expt] = season_quantile(data[expt], 0.9)\n",
    "\n",
    "print(pct_dict_dict[\"historical\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cba45643",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Plot the data\n",
    "The easiest way to perform a panel plot of all the percentiles is to get the data in a DataArray instead of a dictionary. \n",
    "\n",
    "For this, we give two solutions:\n",
    "* a function to concatenate a dictionary with DataArrays as values into a single DataArray. We concatenate along a new dimension \"season\" and use the keys of the `seas_pct` dictionaries from `season_quantile` as the coordinate.\n",
    "* a different implementation of `season_quantile` to output a DataArray instead of a dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "79be3e81",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'tsl' (season: 4, lat: 96, lon: 96)>\n",
      "dask.array<concatenate, shape=(4, 96, 96), dtype=float64, chunksize=(1, 96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9\n",
      "  * season    (season) <U3 'DJF' 'JJA' 'MAM' 'SON'\n"
     ]
    }
   ],
   "source": [
    "def dictionary_to_dataarray(input_dic, new_dim_name):\n",
    "    \"\"\"Concatenate the data from a dictionary to a DataArray along a new dimension.\n",
    "    The keys of the dictionary are used to create a coordinate for this new dimension\"\"\"\n",
    "    \n",
    "    # Concatenate all the data in one DataArray along a new dimension\n",
    "    da = xr.concat([data for data in input_dic.values()], dim=new_dim_name)\n",
    "\n",
    "    # Add the dictionary keys to create a coordinate for the new dimension. \n",
    "    # Not necessary but nice to have.\n",
    "    da = da.assign_coords({new_dim_name:list(input_dic.keys())})\n",
    "    \n",
    "    return da\n",
    "    \n",
    "pct_dict = {}\n",
    "for expt in data.keys():\n",
    "    pct_dict[expt] = dictionary_to_dataarray(pct_dict_dict[expt], \"season\")\n",
    "\n",
    "print(pct_dict[\"historical\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f8a69336",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.DataArray 'tsl' (season: 4, lat: 96, lon: 96)>\n",
      "dask.array<concatenate, shape=(4, 96, 96), dtype=float64, chunksize=(1, 96, 96), chunktype=numpy.ndarray>\n",
      "Coordinates:\n",
      "  * lat       (lat) float32 -90.0 -88.11 -86.21 -84.32 ... 86.21 88.11 90.0\n",
      "  * lon       (lon) float32 360.0 3.75 7.5 11.25 ... 345.0 348.8 352.5 356.2\n",
      "    quantile  float64 0.9\n",
      "  * season    (season) <U3 'DJF' 'JJA' 'MAM' 'SON'\n"
     ]
    }
   ],
   "source": [
    "# Modify season_quantile to output a DataArray\n",
    "def season_quantile_DA(data, quantile):\n",
    "    \"\"\"Calculate a quantile for each season in the input data\n",
    "    \n",
    "    data: DataArray, data with a dimension named 'time'\n",
    "    quantile: scalar, quantile to calculate for the data\"\"\"\n",
    "    \n",
    "    seas_pct = []\n",
    "    seas_coords = []\n",
    "    for season, seas_data in data.groupby('time.season'):\n",
    "        seas_coords.append(season)\n",
    "        seas_pct.append(seas_data.quantile(0.9, dim=\"time\"))\n",
    "\n",
    "    # Concatenate list to DataArray along a `season` dimension and \n",
    "    # add coordinate for this new dimension\n",
    "    da = xr.concat(seas_pct,dim=\"season\")\n",
    "    da = da.assign_coords({\"season\":seas_coords})\n",
    "\n",
    "    return da\n",
    "\n",
    "pct_dict={}\n",
    "for expt in data.keys():\n",
    "    pct_dict[expt] = season_quantile_DA(data[expt], 0.9)\n",
    "\n",
    "print(pct_dict[\"historical\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96860b49",
   "metadata": {},
   "source": [
    "We could also simply call `dictionary_to_dataarray` from `season_quantile` to transform the dictionary into a dataarray just before returning the result. All of these ways to do it are quite equivalent. If you know you want a DataArray, you may as well write a function that gives you a DataArray. \n",
    "\n",
    "### Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e2363550",
   "metadata": {},
   "outputs": [],
   "source": [
    "def season_plot(da):\n",
    "    \n",
    "    # Plot using the season dimension to create the panel plot\n",
    "    fg=da.isel(lon=slice(1,None)).plot(col=\"season\", col_wrap=2,\n",
    "        transform=ccrs.PlateCarree(),\n",
    "        subplot_kws={\n",
    "            \"projection\": ccrs.PlateCarree(central_longitude=180.)\n",
    "        },\n",
    "        figsize=(10,4),\n",
    "    )\n",
    "    print(type(fg))\n",
    "    fg.map(lambda: plt.gca().coastlines())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "93d4c1e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/hh5/public/apps/miniconda3/envs/analysis3-21.07/lib/python3.9/site-packages/numpy/lib/nanfunctions.py:1395: RuntimeWarning: All-NaN slice encountered\n",
      "  result = np.apply_along_axis(_nanquantile_1d, axis, a, q,\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xarray.plot.facetgrid.FacetGrid'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_plot(pct_dict[\"historical\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "47c1d73d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'xarray.plot.facetgrid.FacetGrid'>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "season_plot(pct_dict[\"picontrol\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50b59108",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Conclusion\n",
    "When you wonder how to organise your code, keep those principles in mind:\n",
    "* reduce the number of lines that are copied with a single option changed.\n",
    "* simplify adding an extra case to the analysis. For example, in the final version of the code, we only need to add one line to the `experiments` dictionary to run the analysis on 3 datasets instead of 2.\n",
    "\n",
    "Dictionaries and functions are often useful to reach those goals. The final version of the code isn't necessarily shorter but it is more flexible and contains functions that could prove useful to re-use in other analyses.\n",
    "\n",
    "## Disclaimer\n",
    "For this specific analysis we don't need the dictionary or function. We only do so here to create a simple example for people to understand. Normally, the seasonal quantiles can be calculated with:\n",
    "\n",
    "`hist_pct = historical[\"tsl\"].groupby('time.season').quantile(0.9, dim=\"time\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d5174bf1",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "9f2763deb7ce183fe4750a8fd67ef23caa5e3aa8cfb16820e8aad52af6ddba1f"
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  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}