docs/utils.ipynb

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    "# Utilities for large scale climate data analysis\n",
    "The `xarrayutils.utils` module contains several functions that have proven useful in several of my day to day projects with both observational and model data."
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    "## Linear regression\n",
    "One of the operations many scientists do is calculating a linear trend along a specified dimension (e.g. time) on each grid point of a dataset. `linear_trend` makes this very easy. For demonstration purposes lets load some monthly gridded Argo data from [APDRC](https://apdrc.soest.hawaii.edu/dods/public_data/Argo_Products/monthly_mean/monthly_mixed_layer.info)"
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   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "%matplotlib inline"
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     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: numpy.ndarray size changed, may indicate binary incompatibility. Expected 80 from C header, got 88 from PyObject\n"
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 60, lon: 120, time: 240)\n",
       "Coordinates:\n",
       "  * time     (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n",
       "  * lat      (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon      (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    mld      (time, lat, lon) float32 ...\n",
       "    smld     (time, lat, lon) float32 ...\n",
       "    nmld     (time, lat, lon) float32 ...\n",
       "    ild      (time, lat, lon) float32 ...\n",
       "    sild     (time, lat, lon) float32 ...\n",
       "    nild     (time, lat, lon) float32 ...\n",
       "    ttd      (time, lat, lon) float32 ...\n",
       "    sttd     (time, lat, lon) float32 ...\n",
       "    nttd     (time, lat, lon) float32 ...\n",
       "    blt      (time, lat, lon) float32 ...\n",
       "    sblt     (time, lat, lon) float32 ...\n",
       "    nblt     (time, lat, lon) float32 ...\n",
       "    tid      (time, lat, lon) float32 ...\n",
       "    stid     (time, lat, lon) float32 ...\n",
       "    ntid     (time, lat, lon) float32 ...\n",
       "    mlt      (time, lat, lon) float32 ...\n",
       "    smlt     (time, lat, lon) float32 ...\n",
       "    nmlt     (time, lat, lon) float32 ...\n",
       "    mls      (time, lat, lon) float32 ...\n",
       "    smls     (time, lat, lon) float32 ...\n",
       "    nmls     (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:          3x3 bin-averaged Mixed Layer Monthly mean (from 2001)\n",
       "    Conventions:    COARDS\\nGrADS\n",
       "    dataType:       Grid\n",
       "    documentation:  https://apdrc.soest.hawaii.edu/projects/Argo/index.html\n",
       "    history:        Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser...</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-60474f3c-931d-435c-9b1a-9c4f20bd7fb1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-60474f3c-931d-435c-9b1a-9c4f20bd7fb1' 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>: 60</li><li><span class='xr-has-index'>lon</span>: 120</li><li><span class='xr-has-index'>time</span>: 240</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b3ca7acd-b9c2-4f98-baca-c52f6050976c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b3ca7acd-b9c2-4f98-baca-c52f6050976c' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-15 00:00:00 ... 2020-12-...</div><input id='attrs-a930f0fd-7f55-4276-a4dd-3d7835575641' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a930f0fd-7f55-4276-a4dd-3d7835575641' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2a1e7127-a054-47ff-acc2-b2f53b1d56f8' class='xr-var-data-in' type='checkbox'><label for='data-2a1e7127-a054-47ff-acc2-b2f53b1d56f8' 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>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>240</dd><dt><span>grads_min :</span></dt><dd>00z15jan2001</dd><dt><span>grads_step :</span></dt><dd>1mo</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>00z15jan2001</dd><dt><span>maximum :</span></dt><dd>00z15dec2020</dd><dt><span>resolution :</span></dt><dd>30.435146</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2001, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2001, 3, 15, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeGregorian(2020, 10, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 15, 0, 0, 0, 0)], dtype=object)</pre></div></li><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'>-88.5 -85.5 -82.5 ... 85.5 88.5</div><input id='attrs-b3733d14-fc64-4f83-8a49-3aad329c6138' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3733d14-fc64-4f83-8a49-3aad329c6138' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2de5b551-e1ec-4b23-a006-850746288ba8' class='xr-var-data-in' type='checkbox'><label for='data-2de5b551-e1ec-4b23-a006-850746288ba8' 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>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>60</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-88.5</dd><dt><span>maximum :</span></dt><dd>88.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([-88.5, -85.5, -82.5, -79.5, -76.5, -73.5, -70.5, -67.5, -64.5, -61.5,\n",
       "       -58.5, -55.5, -52.5, -49.5, -46.5, -43.5, -40.5, -37.5, -34.5, -31.5,\n",
       "       -28.5, -25.5, -22.5, -19.5, -16.5, -13.5, -10.5,  -7.5,  -4.5,  -1.5,\n",
       "         1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5])</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'>1.5 4.5 7.5 ... 352.5 355.5 358.5</div><input id='attrs-879d8623-5fec-4bc2-b0a1-c94e33e3ae83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-879d8623-5fec-4bc2-b0a1-c94e33e3ae83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2572d4e9-c053-4f9e-b846-4d6a9850b279' class='xr-var-data-in' type='checkbox'><label for='data-2572d4e9-c053-4f9e-b846-4d6a9850b279' 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>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>120</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>1.5</dd><dt><span>maximum :</span></dt><dd>358.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([  1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5,\n",
       "        91.5,  94.5,  97.5, 100.5, 103.5, 106.5, 109.5, 112.5, 115.5, 118.5,\n",
       "       121.5, 124.5, 127.5, 130.5, 133.5, 136.5, 139.5, 142.5, 145.5, 148.5,\n",
       "       151.5, 154.5, 157.5, 160.5, 163.5, 166.5, 169.5, 172.5, 175.5, 178.5,\n",
       "       181.5, 184.5, 187.5, 190.5, 193.5, 196.5, 199.5, 202.5, 205.5, 208.5,\n",
       "       211.5, 214.5, 217.5, 220.5, 223.5, 226.5, 229.5, 232.5, 235.5, 238.5,\n",
       "       241.5, 244.5, 247.5, 250.5, 253.5, 256.5, 259.5, 262.5, 265.5, 268.5,\n",
       "       271.5, 274.5, 277.5, 280.5, 283.5, 286.5, 289.5, 292.5, 295.5, 298.5,\n",
       "       301.5, 304.5, 307.5, 310.5, 313.5, 316.5, 319.5, 322.5, 325.5, 328.5,\n",
       "       331.5, 334.5, 337.5, 340.5, 343.5, 346.5, 349.5, 352.5, 355.5, 358.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4fa3c94a-f743-4ea8-86ea-088e387eb5f0' class='xr-section-summary-in' type='checkbox'  ><label for='section-4fa3c94a-f743-4ea8-86ea-088e387eb5f0' class='xr-section-summary' >Data variables: <span>(21)</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>mld</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'>...</div><input id='attrs-ee715cbf-5189-4028-8ab7-797707eb4f77' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee715cbf-5189-4028-8ab7-797707eb4f77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8e045bfa-a630-4e23-96f6-5f9743554641' class='xr-var-data-in' type='checkbox'><label for='data-8e045bfa-a630-4e23-96f6-5f9743554641' 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>long_name :</span></dt><dd>mixed layer depth (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smld</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'>...</div><input id='attrs-67fc471e-f453-4fdd-bb4f-da8245b0bb24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67fc471e-f453-4fdd-bb4f-da8245b0bb24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04e13dc2-0aeb-4cfa-bddf-44077f5ae41e' class='xr-var-data-in' type='checkbox'><label for='data-04e13dc2-0aeb-4cfa-bddf-44077f5ae41e' 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>long_name :</span></dt><dd>standard mixed layer depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmld</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'>...</div><input id='attrs-19823b3e-02b2-4571-9ab9-71b09312a19c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-19823b3e-02b2-4571-9ab9-71b09312a19c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56f0c0fc-6bfc-4725-bbd8-f3dbf628b9d2' class='xr-var-data-in' type='checkbox'><label for='data-56f0c0fc-6bfc-4725-bbd8-f3dbf628b9d2' 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>long_name :</span></dt><dd>number of mixed layer depth profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ild</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'>...</div><input id='attrs-0e7c7963-555c-4e9c-8821-6b448c45c967' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e7c7963-555c-4e9c-8821-6b448c45c967' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aed37f9d-ce05-4d7e-8817-b9686d59eefd' class='xr-var-data-in' type='checkbox'><label for='data-aed37f9d-ce05-4d7e-8817-b9686d59eefd' 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>long_name :</span></dt><dd>isothermal layer depth (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sild</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'>...</div><input id='attrs-012661ca-2d63-41d1-a34d-53bc767bb456' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-012661ca-2d63-41d1-a34d-53bc767bb456' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a703886e-17d3-4c3b-80b6-9cd4bd40689d' class='xr-var-data-in' type='checkbox'><label for='data-a703886e-17d3-4c3b-80b6-9cd4bd40689d' 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>long_name :</span></dt><dd>standard isothermal layer depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nild</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'>...</div><input id='attrs-4b1a2249-2549-40e8-9e9f-5ab704f76cd1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b1a2249-2549-40e8-9e9f-5ab704f76cd1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8ba371a-1070-4742-9759-f6f0b5afac3f' class='xr-var-data-in' type='checkbox'><label for='data-a8ba371a-1070-4742-9759-f6f0b5afac3f' 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>long_name :</span></dt><dd>number of isothermal layer depth profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttd</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'>...</div><input id='attrs-67ab5e79-4e9e-4377-a11b-f5723adb7e19' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67ab5e79-4e9e-4377-a11b-f5723adb7e19' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f139ade1-d781-49e2-b93e-7f1b9388f295' class='xr-var-data-in' type='checkbox'><label for='data-f139ade1-d781-49e2-b93e-7f1b9388f295' 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>long_name :</span></dt><dd>top of the thermocline depth (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sttd</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'>...</div><input id='attrs-e277e50a-2912-4c6d-b4c7-db4546872749' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e277e50a-2912-4c6d-b4c7-db4546872749' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19c0855e-399a-4f13-862d-423574ae89c9' class='xr-var-data-in' type='checkbox'><label for='data-19c0855e-399a-4f13-862d-423574ae89c9' 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>long_name :</span></dt><dd>standard top of the thermocline depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nttd</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'>...</div><input id='attrs-a5bda245-3ce4-46b1-bf66-851c3b121e8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a5bda245-3ce4-46b1-bf66-851c3b121e8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc16200b-409b-466c-88e2-2c1a5f053177' class='xr-var-data-in' type='checkbox'><label for='data-dc16200b-409b-466c-88e2-2c1a5f053177' 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>long_name :</span></dt><dd>number of top of the thermocline depth profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blt</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'>...</div><input id='attrs-0a4d85b2-77ba-4d4b-8fdb-228ab2f8c989' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a4d85b2-77ba-4d4b-8fdb-228ab2f8c989' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5847f16f-0493-43ac-b7dd-5f56a1200470' class='xr-var-data-in' type='checkbox'><label for='data-5847f16f-0493-43ac-b7dd-5f56a1200470' 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>long_name :</span></dt><dd>(&gt;0) barrier or (&lt;0) compensated layer thickness (ttd - mld) (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sblt</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'>...</div><input id='attrs-d6122418-39a1-426a-a541-9e9bf8502e49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6122418-39a1-426a-a541-9e9bf8502e49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f43f6a61-c51a-4810-b134-6dd74c5c5d05' class='xr-var-data-in' type='checkbox'><label for='data-f43f6a61-c51a-4810-b134-6dd74c5c5d05' 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>long_name :</span></dt><dd>standard barrier or compensated layer thickness deviation (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nblt</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'>...</div><input id='attrs-7246ed4a-e70a-4654-a901-c1868c8e686f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7246ed4a-e70a-4654-a901-c1868c8e686f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66b587fc-7404-4307-9db7-7350bc8b79b7' class='xr-var-data-in' type='checkbox'><label for='data-66b587fc-7404-4307-9db7-7350bc8b79b7' 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>long_name :</span></dt><dd>number of barrier or compensated layer-2 thickness profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tid</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'>...</div><input id='attrs-a31db3f2-7acf-4a40-a090-2d36727b2ff9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a31db3f2-7acf-4a40-a090-2d36727b2ff9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1655e25d-bdf0-4fc3-a8a2-393bfd517d08' class='xr-var-data-in' type='checkbox'><label for='data-1655e25d-bdf0-4fc3-a8a2-393bfd517d08' 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>long_name :</span></dt><dd>temperature inversion depth (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stid</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'>...</div><input id='attrs-f8d2023c-06f5-438e-86f8-8bb8208a79f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8d2023c-06f5-438e-86f8-8bb8208a79f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ea2fb68-4c2a-4390-9b4f-5b4bcd1acda4' class='xr-var-data-in' type='checkbox'><label for='data-9ea2fb68-4c2a-4390-9b4f-5b4bcd1acda4' 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>long_name :</span></dt><dd>standard temperature inversion depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ntid</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'>...</div><input id='attrs-f07a6ed7-1ec5-4955-8de1-9e1d4e640e35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f07a6ed7-1ec5-4955-8de1-9e1d4e640e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9755084c-5f5d-43fa-abe9-67f2b16abd23' class='xr-var-data-in' type='checkbox'><label for='data-9755084c-5f5d-43fa-abe9-67f2b16abd23' 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>long_name :</span></dt><dd>number of temperature inversion depth profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mlt</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'>...</div><input id='attrs-a83fa2f3-457f-4cec-b120-effec2409ff6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a83fa2f3-457f-4cec-b120-effec2409ff6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc2702b8-91de-4d2f-9043-4d07b9f9a7d5' class='xr-var-data-in' type='checkbox'><label for='data-fc2702b8-91de-4d2f-9043-4d07b9f9a7d5' 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>long_name :</span></dt><dd>mixed layer temperature (degc) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</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'>...</div><input id='attrs-46024e39-ea63-4e4a-9c32-e0b23eeebd51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46024e39-ea63-4e4a-9c32-e0b23eeebd51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37f77110-7d17-414f-9496-f5ace5c9028a' class='xr-var-data-in' type='checkbox'><label for='data-37f77110-7d17-414f-9496-f5ace5c9028a' 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>long_name :</span></dt><dd>standard mixed layer temperature deviation (degc) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmlt</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'>...</div><input id='attrs-5f4f900f-833a-41b0-a998-41087d3fa834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f4f900f-833a-41b0-a998-41087d3fa834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd221685-70fb-4fb5-9846-533a1d2ffddc' class='xr-var-data-in' type='checkbox'><label for='data-cd221685-70fb-4fb5-9846-533a1d2ffddc' 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>long_name :</span></dt><dd>number of mixed layer temperature profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mls</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'>...</div><input id='attrs-85a47dbb-950f-4411-900d-7ac1025f8265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85a47dbb-950f-4411-900d-7ac1025f8265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-387bb384-ecfe-44bd-b2fb-15cae000cfb5' class='xr-var-data-in' type='checkbox'><label for='data-387bb384-ecfe-44bd-b2fb-15cae000cfb5' 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>long_name :</span></dt><dd>mixed layer salinity (psu) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smls</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'>...</div><input id='attrs-efbed3ca-2928-42ff-9ed7-79298cb00f2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efbed3ca-2928-42ff-9ed7-79298cb00f2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7d38c3d-5e05-4766-80f1-4ee3e7cf5947' class='xr-var-data-in' type='checkbox'><label for='data-a7d38c3d-5e05-4766-80f1-4ee3e7cf5947' 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>long_name :</span></dt><dd>standard mixed layer salinity deviation (psu) </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmls</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'>...</div><input id='attrs-67c8c0f8-0420-462e-a803-e754404b9d6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67c8c0f8-0420-462e-a803-e754404b9d6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d908520-ef53-4923-a2e5-643409c1f9c1' class='xr-var-data-in' type='checkbox'><label for='data-7d908520-ef53-4923-a2e5-643409c1f9c1' 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>long_name :</span></dt><dd>number of mixed layer salinity profiles </dd></dl></div><div class='xr-var-data'><pre>[1728000 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8776b974-703c-4032-aa06-35f9b2141527' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8776b974-703c-4032-aa06-35f9b2141527' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>3x3 bin-averaged Mixed Layer Monthly mean (from 2001)</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>documentation :</span></dt><dd>https://apdrc.soest.hawaii.edu/projects/Argo/index.html</dd><dt><span>history :</span></dt><dd>Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Server 2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 60, lon: 120, time: 240)\n",
       "Coordinates:\n",
       "  * time     (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n",
       "  * lat      (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon      (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    mld      (time, lat, lon) float32 ...\n",
       "    smld     (time, lat, lon) float32 ...\n",
       "    nmld     (time, lat, lon) float32 ...\n",
       "    ild      (time, lat, lon) float32 ...\n",
       "    sild     (time, lat, lon) float32 ...\n",
       "    nild     (time, lat, lon) float32 ...\n",
       "    ttd      (time, lat, lon) float32 ...\n",
       "    sttd     (time, lat, lon) float32 ...\n",
       "    nttd     (time, lat, lon) float32 ...\n",
       "    blt      (time, lat, lon) float32 ...\n",
       "    sblt     (time, lat, lon) float32 ...\n",
       "    nblt     (time, lat, lon) float32 ...\n",
       "    tid      (time, lat, lon) float32 ...\n",
       "    stid     (time, lat, lon) float32 ...\n",
       "    ntid     (time, lat, lon) float32 ...\n",
       "    mlt      (time, lat, lon) float32 ...\n",
       "    smlt     (time, lat, lon) float32 ...\n",
       "    nmlt     (time, lat, lon) float32 ...\n",
       "    mls      (time, lat, lon) float32 ...\n",
       "    smls     (time, lat, lon) float32 ...\n",
       "    nmls     (time, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    title:          3x3 bin-averaged Mixed Layer Monthly mean (from 2001)\n",
       "    Conventions:    COARDS\\nGrADS\n",
       "    dataType:       Grid\n",
       "    documentation:  https://apdrc.soest.hawaii.edu/projects/Argo/index.html\n",
       "    history:        Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser..."
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = 'https://apdrc.soest.hawaii.edu:80/dods/public_data/Argo_Products/monthly_mean/monthly_mixed_layer'\n",
    "ds = xr.open_dataset(path, use_cftime=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       ".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:  (lat: 60, lon: 120, time: 240)\n",
       "Coordinates:\n",
       "  * time     (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n",
       "  * lat      (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon      (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    mld      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smld     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmld     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ild      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sild     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nild     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ttd      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sttd     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nttd     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    blt      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sblt     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nblt     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    tid      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    stid     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    ntid     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    mlt      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smlt     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmlt     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    mls      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smls     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmls     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    title:          3x3 bin-averaged Mixed Layer Monthly mean (from 2001)\n",
       "    Conventions:    COARDS\\nGrADS\n",
       "    dataType:       Grid\n",
       "    documentation:  https://apdrc.soest.hawaii.edu/projects/Argo/index.html\n",
       "    history:        Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser...</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-e74efe90-211a-47e2-9a42-f4dfdde491ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e74efe90-211a-47e2-9a42-f4dfdde491ab' 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>: 60</li><li><span class='xr-has-index'>lon</span>: 120</li><li><span class='xr-has-index'>time</span>: 240</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-f05d037d-b5ad-4deb-97d8-c049d06aaa79' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f05d037d-b5ad-4deb-97d8-c049d06aaa79' 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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>2001-01-15 00:00:00 ... 2020-12-...</div><input id='attrs-47df5484-12c1-4450-aa0d-262c4b561922' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47df5484-12c1-4450-aa0d-262c4b561922' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39fe3178-8968-4c79-9cd1-0ab63af0088c' class='xr-var-data-in' type='checkbox'><label for='data-39fe3178-8968-4c79-9cd1-0ab63af0088c' 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>grads_dim :</span></dt><dd>t</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>240</dd><dt><span>grads_min :</span></dt><dd>00z15jan2001</dd><dt><span>grads_step :</span></dt><dd>1mo</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>minimum :</span></dt><dd>00z15jan2001</dd><dt><span>maximum :</span></dt><dd>00z15dec2020</dd><dt><span>resolution :</span></dt><dd>30.435146</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeGregorian(2001, 1, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2001, 2, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2001, 3, 15, 0, 0, 0, 0), ...,\n",
       "       cftime.DatetimeGregorian(2020, 10, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 11, 15, 0, 0, 0, 0),\n",
       "       cftime.DatetimeGregorian(2020, 12, 15, 0, 0, 0, 0)], dtype=object)</pre></div></li><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'>-88.5 -85.5 -82.5 ... 85.5 88.5</div><input id='attrs-37b6aa55-0c0f-49f3-8b23-5ececed8786e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37b6aa55-0c0f-49f3-8b23-5ececed8786e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ccf4ea5-5431-439c-b710-af1ad4ac7c3c' class='xr-var-data-in' type='checkbox'><label for='data-5ccf4ea5-5431-439c-b710-af1ad4ac7c3c' 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>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>60</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-88.5</dd><dt><span>maximum :</span></dt><dd>88.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([-88.5, -85.5, -82.5, -79.5, -76.5, -73.5, -70.5, -67.5, -64.5, -61.5,\n",
       "       -58.5, -55.5, -52.5, -49.5, -46.5, -43.5, -40.5, -37.5, -34.5, -31.5,\n",
       "       -28.5, -25.5, -22.5, -19.5, -16.5, -13.5, -10.5,  -7.5,  -4.5,  -1.5,\n",
       "         1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5])</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'>1.5 4.5 7.5 ... 352.5 355.5 358.5</div><input id='attrs-21fcfb09-81bd-4dd6-a4c3-e1a5b3701575' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21fcfb09-81bd-4dd6-a4c3-e1a5b3701575' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36a9ccb4-6a46-4ba6-9bb4-851e85e2ff0d' class='xr-var-data-in' type='checkbox'><label for='data-36a9ccb4-6a46-4ba6-9bb4-851e85e2ff0d' 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>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>120</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>1.5</dd><dt><span>maximum :</span></dt><dd>358.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([  1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5,\n",
       "        91.5,  94.5,  97.5, 100.5, 103.5, 106.5, 109.5, 112.5, 115.5, 118.5,\n",
       "       121.5, 124.5, 127.5, 130.5, 133.5, 136.5, 139.5, 142.5, 145.5, 148.5,\n",
       "       151.5, 154.5, 157.5, 160.5, 163.5, 166.5, 169.5, 172.5, 175.5, 178.5,\n",
       "       181.5, 184.5, 187.5, 190.5, 193.5, 196.5, 199.5, 202.5, 205.5, 208.5,\n",
       "       211.5, 214.5, 217.5, 220.5, 223.5, 226.5, 229.5, 232.5, 235.5, 238.5,\n",
       "       241.5, 244.5, 247.5, 250.5, 253.5, 256.5, 259.5, 262.5, 265.5, 268.5,\n",
       "       271.5, 274.5, 277.5, 280.5, 283.5, 286.5, 289.5, 292.5, 295.5, 298.5,\n",
       "       301.5, 304.5, 307.5, 310.5, 313.5, 316.5, 319.5, 322.5, 325.5, 328.5,\n",
       "       331.5, 334.5, 337.5, 340.5, 343.5, 346.5, 349.5, 352.5, 355.5, 358.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-472d2cee-3436-4fd4-8aa0-999ae6e2db15' class='xr-section-summary-in' type='checkbox'  ><label for='section-472d2cee-3436-4fd4-8aa0-999ae6e2db15' class='xr-section-summary' >Data variables: <span>(21)</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>mld</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-280a01ca-a1c4-4f61-9ad2-2561d252aa83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-280a01ca-a1c4-4f61-9ad2-2561d252aa83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39f58f1e-fa6f-42ce-8fe5-024ddedcc76f' class='xr-var-data-in' type='checkbox'><label for='data-39f58f1e-fa6f-42ce-8fe5-024ddedcc76f' 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>long_name :</span></dt><dd>mixed layer depth (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smld</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-fee8b029-6df5-43f8-8a0f-f2efb7df37e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fee8b029-6df5-43f8-8a0f-f2efb7df37e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fb521ba-eea0-4abe-b4c4-95b4af7c7a52' class='xr-var-data-in' type='checkbox'><label for='data-8fb521ba-eea0-4abe-b4c4-95b4af7c7a52' 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>long_name :</span></dt><dd>standard mixed layer depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmld</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ae8f9da1-215f-430c-b142-9fc8c98e8b6c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae8f9da1-215f-430c-b142-9fc8c98e8b6c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8183838-66f5-42ac-81b1-dccac410b655' class='xr-var-data-in' type='checkbox'><label for='data-d8183838-66f5-42ac-81b1-dccac410b655' 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>long_name :</span></dt><dd>number of mixed layer depth profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ild</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-5153d8c2-f334-4d5d-babd-30acf9a2f377' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5153d8c2-f334-4d5d-babd-30acf9a2f377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddb6ead2-a5b3-4340-9ab8-9a1d020ed80e' class='xr-var-data-in' type='checkbox'><label for='data-ddb6ead2-a5b3-4340-9ab8-9a1d020ed80e' 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>long_name :</span></dt><dd>isothermal layer depth (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sild</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-28aed956-da37-4e33-b565-80b273304c2d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28aed956-da37-4e33-b565-80b273304c2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8ae6a1d-6752-44df-9243-9a2b6381c81b' class='xr-var-data-in' type='checkbox'><label for='data-d8ae6a1d-6752-44df-9243-9a2b6381c81b' 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>long_name :</span></dt><dd>standard isothermal layer depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nild</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2818daf6-997a-4055-ad6f-58245d3b76fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2818daf6-997a-4055-ad6f-58245d3b76fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79433aec-3a18-4aa7-8315-fed46e59fccb' class='xr-var-data-in' type='checkbox'><label for='data-79433aec-3a18-4aa7-8315-fed46e59fccb' 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>long_name :</span></dt><dd>number of isothermal layer depth profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ttd</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e3a21e7e-0737-4812-9192-c3ced7f10520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3a21e7e-0737-4812-9192-c3ced7f10520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-413f8ea4-f890-43bb-a0ef-5f4e19c9bd97' class='xr-var-data-in' type='checkbox'><label for='data-413f8ea4-f890-43bb-a0ef-5f4e19c9bd97' 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>long_name :</span></dt><dd>top of the thermocline depth (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sttd</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-322ea7b2-45c0-462a-b519-89b9989fc23e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-322ea7b2-45c0-462a-b519-89b9989fc23e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d904aa07-e9c2-4180-bd77-b465d6910094' class='xr-var-data-in' type='checkbox'><label for='data-d904aa07-e9c2-4180-bd77-b465d6910094' 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>long_name :</span></dt><dd>standard top of the thermocline depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nttd</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-003d5704-b2f9-447d-9011-2b8cdc56b608' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-003d5704-b2f9-447d-9011-2b8cdc56b608' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96178664-92be-480a-bf77-03aa45aca855' class='xr-var-data-in' type='checkbox'><label for='data-96178664-92be-480a-bf77-03aa45aca855' 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>long_name :</span></dt><dd>number of top of the thermocline depth profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>blt</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-c8c64e12-3546-4770-a90a-2e9a99bb63f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8c64e12-3546-4770-a90a-2e9a99bb63f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30ee8a2a-b8e6-4842-9457-6dc79f0d91d7' class='xr-var-data-in' type='checkbox'><label for='data-30ee8a2a-b8e6-4842-9457-6dc79f0d91d7' 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>long_name :</span></dt><dd>(&gt;0) barrier or (&lt;0) compensated layer thickness (ttd - mld) (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sblt</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-00229961-12ff-4449-a296-4136503c328b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00229961-12ff-4449-a296-4136503c328b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0cd0ce5-7729-4e33-b71e-ed61f23102ca' class='xr-var-data-in' type='checkbox'><label for='data-c0cd0ce5-7729-4e33-b71e-ed61f23102ca' 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>long_name :</span></dt><dd>standard barrier or compensated layer thickness deviation (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nblt</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-3bc6ec1e-32a3-4393-9e35-9195b5087f48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bc6ec1e-32a3-4393-9e35-9195b5087f48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c36f34be-0ef4-4014-9957-ad769a2f4466' class='xr-var-data-in' type='checkbox'><label for='data-c36f34be-0ef4-4014-9957-ad769a2f4466' 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>long_name :</span></dt><dd>number of barrier or compensated layer-2 thickness profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tid</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-2d6e2e47-ae9f-47c5-8ca7-0fdfc44d7b60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d6e2e47-ae9f-47c5-8ca7-0fdfc44d7b60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ab232ac-473f-4eed-8b73-a0e3d40c17d6' class='xr-var-data-in' type='checkbox'><label for='data-9ab232ac-473f-4eed-8b73-a0e3d40c17d6' 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>long_name :</span></dt><dd>temperature inversion depth (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>stid</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-e18de385-e51b-4e95-aeab-b8aa149f7353' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e18de385-e51b-4e95-aeab-b8aa149f7353' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56f8e29e-1708-4a89-af8c-96683d089b2c' class='xr-var-data-in' type='checkbox'><label for='data-56f8e29e-1708-4a89-af8c-96683d089b2c' 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>long_name :</span></dt><dd>standard temperature inversion depth deviation (m) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ntid</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7e97a4ac-f297-4836-8717-ca7364d052c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e97a4ac-f297-4836-8717-ca7364d052c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f77f1eb5-dd42-4912-82e3-d5b3a691a026' class='xr-var-data-in' type='checkbox'><label for='data-f77f1eb5-dd42-4912-82e3-d5b3a691a026' 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>long_name :</span></dt><dd>number of temperature inversion depth profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mlt</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-6ae50b1a-5302-4b9f-a694-94e5186555d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ae50b1a-5302-4b9f-a694-94e5186555d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f453bf2d-6a3d-48f9-a668-45dec1cf63e1' class='xr-var-data-in' type='checkbox'><label for='data-f453bf2d-6a3d-48f9-a668-45dec1cf63e1' 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>long_name :</span></dt><dd>mixed layer temperature (degc) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smlt</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-0cfd6e19-84fa-40fc-8789-57357820fee0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0cfd6e19-84fa-40fc-8789-57357820fee0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2fc1bf9-c202-4777-b662-8309d795598d' class='xr-var-data-in' type='checkbox'><label for='data-b2fc1bf9-c202-4777-b662-8309d795598d' 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>long_name :</span></dt><dd>standard mixed layer temperature deviation (degc) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmlt</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-9e6ecbd3-f70e-4343-b4cd-2aa568b59d7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9e6ecbd3-f70e-4343-b4cd-2aa568b59d7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eedf4ae8-949c-4cf9-ad1e-fb7fea6e91f0' class='xr-var-data-in' type='checkbox'><label for='data-eedf4ae8-949c-4cf9-ad1e-fb7fea6e91f0' 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>long_name :</span></dt><dd>number of mixed layer temperature profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mls</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-ade164ef-2122-4126-a1d5-afb7e4ca507c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ade164ef-2122-4126-a1d5-afb7e4ca507c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e07ee618-1a98-4b1f-aaa5-2eef4b1e5fa5' class='xr-var-data-in' type='checkbox'><label for='data-e07ee618-1a98-4b1f-aaa5-2eef4b1e5fa5' 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>long_name :</span></dt><dd>mixed layer salinity (psu) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>smls</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'>nan nan nan nan ... nan nan nan nan</div><input id='attrs-d4e2e247-6a00-47dd-8b66-71cc4f80dcc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4e2e247-6a00-47dd-8b66-71cc4f80dcc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34d41757-2807-4eb6-bf44-75297321b2c0' class='xr-var-data-in' type='checkbox'><label for='data-34d41757-2807-4eb6-bf44-75297321b2c0' 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>long_name :</span></dt><dd>standard mixed layer salinity deviation (psu) </dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nmls</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'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-860d136b-8680-44b4-8796-141646e5780a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-860d136b-8680-44b4-8796-141646e5780a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-605cd8f8-d0d1-4ed4-9f2c-642a8bc10b47' class='xr-var-data-in' type='checkbox'><label for='data-605cd8f8-d0d1-4ed4-9f2c-642a8bc10b47' 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>long_name :</span></dt><dd>number of mixed layer salinity profiles </dd></dl></div><div class='xr-var-data'><pre>array([[[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        ...,\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.],\n",
       "        [0., 0., 0., ..., 0., 0., 0.]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-965b45a2-9e07-40e0-9b54-53c0512b091d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-965b45a2-9e07-40e0-9b54-53c0512b091d' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>3x3 bin-averaged Mixed Layer Monthly mean (from 2001)</dd><dt><span>Conventions :</span></dt><dd>COARDS\n",
       "GrADS</dd><dt><span>dataType :</span></dt><dd>Grid</dd><dt><span>documentation :</span></dt><dd>https://apdrc.soest.hawaii.edu/projects/Argo/index.html</dd><dt><span>history :</span></dt><dd>Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Server 2.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 60, lon: 120, time: 240)\n",
       "Coordinates:\n",
       "  * time     (time) object 2001-01-15 00:00:00 ... 2020-12-15 00:00:00\n",
       "  * lat      (lat) float64 -88.5 -85.5 -82.5 -79.5 -76.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon      (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    mld      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smld     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmld     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ild      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sild     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nild     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    ttd      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sttd     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nttd     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    blt      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    sblt     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nblt     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    tid      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    stid     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    ntid     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    mlt      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smlt     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmlt     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "    mls      (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    smls     (time, lat, lon) float32 nan nan nan nan nan ... nan nan nan nan\n",
       "    nmls     (time, lat, lon) float32 0.0 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
       "Attributes:\n",
       "    title:          3x3 bin-averaged Mixed Layer Monthly mean (from 2001)\n",
       "    Conventions:    COARDS\\nGrADS\n",
       "    dataType:       Grid\n",
       "    documentation:  https://apdrc.soest.hawaii.edu/projects/Argo/index.html\n",
       "    history:        Wed Jan 06 09:40:44 HST 2021 : imported by GrADS Data Ser..."
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets find out how much the salinity in each grid point changed over the full period (20 years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xarrayutils.utils import linear_trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       ".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",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
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       "\n",
       "dl.xr-attrs {\n",
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       "}\n",
       "\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",
       "}\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",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (lat: 60, lon: 120)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon        (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    slope      (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n",
       "    intercept  (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n",
       "    r_value    (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n",
       "    p_value    (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n",
       "    std_err    (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045</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-51c817ff-41d8-4bd1-befd-fb36b89de8ae' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-51c817ff-41d8-4bd1-befd-fb36b89de8ae' 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>: 60</li><li><span class='xr-has-index'>lon</span>: 120</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7c6ddc1a-27c3-49d3-8059-e31de78d2530' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7c6ddc1a-27c3-49d3-8059-e31de78d2530' 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'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-88.5 -85.5 -82.5 ... 85.5 88.5</div><input id='attrs-fbec1606-1ea2-4a32-abd7-9df8b91e61d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fbec1606-1ea2-4a32-abd7-9df8b91e61d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2ebc114-0659-4a67-90ee-79ac879a58de' class='xr-var-data-in' type='checkbox'><label for='data-b2ebc114-0659-4a67-90ee-79ac879a58de' 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>grads_dim :</span></dt><dd>y</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>60</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>minimum :</span></dt><dd>-88.5</dd><dt><span>maximum :</span></dt><dd>88.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([-88.5, -85.5, -82.5, -79.5, -76.5, -73.5, -70.5, -67.5, -64.5, -61.5,\n",
       "       -58.5, -55.5, -52.5, -49.5, -46.5, -43.5, -40.5, -37.5, -34.5, -31.5,\n",
       "       -28.5, -25.5, -22.5, -19.5, -16.5, -13.5, -10.5,  -7.5,  -4.5,  -1.5,\n",
       "         1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5])</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'>1.5 4.5 7.5 ... 352.5 355.5 358.5</div><input id='attrs-2b4ac242-7986-41ab-ad86-39b2c40e946c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2b4ac242-7986-41ab-ad86-39b2c40e946c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eddd7570-68e6-43e0-847e-8f438819b0b5' class='xr-var-data-in' type='checkbox'><label for='data-eddd7570-68e6-43e0-847e-8f438819b0b5' 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>grads_dim :</span></dt><dd>x</dd><dt><span>grads_mapping :</span></dt><dd>linear</dd><dt><span>grads_size :</span></dt><dd>120</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>minimum :</span></dt><dd>1.5</dd><dt><span>maximum :</span></dt><dd>358.5</dd><dt><span>resolution :</span></dt><dd>3.0</dd></dl></div><div class='xr-var-data'><pre>array([  1.5,   4.5,   7.5,  10.5,  13.5,  16.5,  19.5,  22.5,  25.5,  28.5,\n",
       "        31.5,  34.5,  37.5,  40.5,  43.5,  46.5,  49.5,  52.5,  55.5,  58.5,\n",
       "        61.5,  64.5,  67.5,  70.5,  73.5,  76.5,  79.5,  82.5,  85.5,  88.5,\n",
       "        91.5,  94.5,  97.5, 100.5, 103.5, 106.5, 109.5, 112.5, 115.5, 118.5,\n",
       "       121.5, 124.5, 127.5, 130.5, 133.5, 136.5, 139.5, 142.5, 145.5, 148.5,\n",
       "       151.5, 154.5, 157.5, 160.5, 163.5, 166.5, 169.5, 172.5, 175.5, 178.5,\n",
       "       181.5, 184.5, 187.5, 190.5, 193.5, 196.5, 199.5, 202.5, 205.5, 208.5,\n",
       "       211.5, 214.5, 217.5, 220.5, 223.5, 226.5, 229.5, 232.5, 235.5, 238.5,\n",
       "       241.5, 244.5, 247.5, 250.5, 253.5, 256.5, 259.5, 262.5, 265.5, 268.5,\n",
       "       271.5, 274.5, 277.5, 280.5, 283.5, 286.5, 289.5, 292.5, 295.5, 298.5,\n",
       "       301.5, 304.5, 307.5, 310.5, 313.5, 316.5, 319.5, 322.5, 325.5, 328.5,\n",
       "       331.5, 334.5, 337.5, 340.5, 343.5, 346.5, 349.5, 352.5, 355.5, 358.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbcf22fc-24ff-4f31-bf83-2156d61aea7a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbcf22fc-24ff-4f31-bf83-2156d61aea7a' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>slope</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.07533 -0.005028</div><input id='attrs-0b111508-0c4c-41db-bb15-8ef0da903519' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0b111508-0c4c-41db-bb15-8ef0da903519' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b14868fc-91c5-4a3c-bf75-590cb7a16774' class='xr-var-data-in' type='checkbox'><label for='data-b14868fc-91c5-4a3c-bf75-590cb7a16774' 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([[        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [-0.17471966, -0.1307214 ,         nan, ...,         nan,\n",
       "                nan, -0.12865409],\n",
       "       [-0.06662766,  0.01246144,  0.02708324, ...,  0.08067257,\n",
       "         0.07533328, -0.00502828]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>intercept</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 22.07 22.88 32.38</div><input id='attrs-ad1bc7d1-ba3b-4223-bf82-4f7cea66b2c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad1bc7d1-ba3b-4223-bf82-4f7cea66b2c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c248c8b5-7762-434a-837e-56bfceed8aa3' class='xr-var-data-in' type='checkbox'><label for='data-c248c8b5-7762-434a-837e-56bfceed8aa3' 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([[        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [51.01670918, 46.24168974,         nan, ...,         nan,\n",
       "                nan, 45.83755191],\n",
       "       [39.08453748, 30.2413176 , 28.87167613, ..., 22.06638106,\n",
       "        22.88234011, 32.3844302 ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r_value</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.9608 1.0 -0.116</div><input id='attrs-43af6028-03a1-4a0c-a704-11d4cadeda11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-43af6028-03a1-4a0c-a704-11d4cadeda11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe74d7fb-3f47-4f11-8552-d2fedbd5fef6' class='xr-var-data-in' type='checkbox'><label for='data-fe74d7fb-3f47-4f11-8552-d2fedbd5fef6' 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([[        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       [        nan,         nan,         nan, ...,         nan,\n",
       "                nan,         nan],\n",
       "       ...,\n",
       "       [        nan,         nan,         nan, ...,  0.        ,\n",
       "                nan,         nan],\n",
       "       [-0.966784  , -0.99593594,  0.        , ...,         nan,\n",
       "         0.        , -0.99963223],\n",
       "       [-0.99116838,  1.        ,  0.98110759, ...,  0.960811  ,\n",
       "         1.        , -0.11597632]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p_value</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.03919 nan 0.884</div><input id='attrs-2e942ca9-eadb-4b52-aaaf-9a49a78bd895' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e942ca9-eadb-4b52-aaaf-9a49a78bd895' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cded2cc2-d11f-43a3-92ba-40c42434b592' class='xr-var-data-in' type='checkbox'><label for='data-cded2cc2-d11f-43a3-92ba-40c42434b592' 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([[       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.16454258, 0.05741457,        nan, ...,        nan,        nan,\n",
       "        0.01726616],\n",
       "       [0.08467113,        nan, 0.12394385, ..., 0.039189  ,        nan,\n",
       "        0.88402368]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>std_err</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.01646 0.0 0.03045</div><input id='attrs-8f8c3c8b-3aae-46a1-ad63-4451fd3af0eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f8c3c8b-3aae-46a1-ad63-4451fd3af0eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7930f908-0e14-414b-a3e6-b7b888f7a7a8' class='xr-var-data-in' type='checkbox'><label for='data-7930f908-0e14-414b-a3e6-b7b888f7a7a8' 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([[       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       ...,\n",
       "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
       "               nan],\n",
       "       [0.04619174, 0.01182139,        nan, ...,        nan,        nan,\n",
       "        0.00349016],\n",
       "       [0.00891418, 0.        , 0.0053405 , ..., 0.01645784, 0.        ,\n",
       "        0.03045052]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c3ca51a-9726-4569-90c0-f25237876a70' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5c3ca51a-9726-4569-90c0-f25237876a70' 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: 60, lon: 120)\n",
       "Coordinates:\n",
       "  * lat        (lat) float64 -88.5 -85.5 -82.5 -79.5 ... 79.5 82.5 85.5 88.5\n",
       "  * lon        (lon) float64 1.5 4.5 7.5 10.5 13.5 ... 349.5 352.5 355.5 358.5\n",
       "Data variables:\n",
       "    slope      (lat, lon) float64 nan nan nan nan ... 0.08067 0.07533 -0.005028\n",
       "    intercept  (lat, lon) float64 nan nan nan nan ... 20.33 22.07 22.88 32.38\n",
       "    r_value    (lat, lon) float64 nan nan nan nan nan ... 1.0 0.9608 1.0 -0.116\n",
       "    p_value    (lat, lon) float64 nan nan nan nan nan ... nan 0.03919 nan 0.884\n",
       "    std_err    (lat, lon) float64 nan nan nan nan ... 0.0 0.01646 0.0 0.03045"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create an array \n",
    "salinity_regressed = linear_trend(ds.mls, 'time')\n",
    "salinity_regressed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can plot the slope as a map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f18a6d33280>"
      ]
     },
     "execution_count": 27,
     "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": [
    "salinity_regressed.slope.plot(robust=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`linear_trend` converts the dimension over which to integrate into logical indicies, so the units of the plot above are (salinity/timestep of the original product), so here PSS/month."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But what about a bit more complex task? Lets find out how mixedlayer salinity and temperature correlate. For this we use `xr_linregress` (for which `linear_trend` is just a thin wrapper):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from xarrayutils.utils import linear_trend, xr_linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f18a6857700>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Mim7HLPXzqcSkatKClGLd5448DTEemOM/JMwtsAPO+0zhhD4t1/ZizV+O2kS9U3E4HI7DlSPNtBUOrlNxtDrJ41XsMzHdH4xW8sHvapVpf+JPATj3ugtar2KtwKOz9O3lmS82BOZ9dPvx0aqOoxlo9FdEknS1KY6oTmXad46JdhUcB8nCxRr4t6tQR1vXVPlmk9TM9gAkWl9CTmc1jaQl+Zf18QPUT9IpTU1jW/dq6Glqgl/mlS16Y+iTqaGtf/xQ09iuXuOnrKgoV5NWaWc1zZRZc1dFqX4Hm9WqrE9mpTUDzZ6jprsfXh5ZiaZo+1AC+0/WLIyn2zE8s34yLlCm8ioNKf5gmYYQ33mSjt1JCHrCX1usvqHyajs6vou2cdXnr2nZAaMDZU2pXgcmU3NYZdXo7xX7/fN56z+/Ckwvuu+sph1cOLgR9SE5ojoVh8PhiBjOpxIS16k4HA5Hk5CDyfx4xOA6FUeLcusrCwPT73+8tt5yntLw2jkL6y3TVvFCg6NtzmqLtGTbauZH16nUxXUqjlan+7hTWPH38/jnV+qsfuL9VQeUSc7U7H3FW3cCkJiqIaNl5f7YjF1bdOxIeq5m9CvepiGp7Xv5AQAvfKQdmRfmO3qYhgRvKPTzHG3L3wVAph27Umn9J54fBSAxWf8q+3erT6ba2v8zOvjS+h4j+uu4F0925Iu16psJHuvyw9M0jPbknu0OWP9wZcX0twAYVea321PfPw7wfSq5qdqOFdV+CvbJudqWJTHqS8ks1fTCZuKF+r3Hl3QxVmm4co7quFVMVh2/o9vvDJR56Ye6z57tIje2KTSRSdLV1nCdisPhcDQFEWLi3S20Lq5FHA6HoykISKwLKa5Lg52KiLwRxjaKjDHXRKY6jrbCooJdDS6vKMxvpZo4HPDBqh08Nyey15w481dIGntTGQR8q4HlAjwUueo42hrVxredfzVsFgApF3wHgHcWqXSH5wsB6DJ4EAB77NiFHgN1nMnoXuqn6NAuMVB2xoodAGy040ESktWGXlFVEyiTZ8eV7Nqr41u+XqD7POaoLoEyFRXqA9iRr5L5mR3VT3LKMT3846jR48hI0X14Y2E6pScBkJPi2+/j7Y0mxcqin3ZqXwA+3+RL8v9p2goAns5ICsx74Rp/fMfhSOlXB94K5mwqAWCMPX+Zqz4CQFJ9X9JL5b0BGJijY37a20GxMeV6XVTM+yhQNqG3yu0nDDtWvwtX6oKg66z7nOcBqK7QcUdfjvl2Uw+pYQRiIhT9JSKno1lwY4FHjTH31lk+CRXWXWdn/dcY85tw1m1tGutUfmaM+aShAiLy6wjWx+FwOA4bIvGmIiKx6MP5KWg6kK9F5A1jzNI6RT81xpzdxHVbjQY7FWPMi41tIJwyrcnMDRppM6SDhhDGxfgjXp9aoFEk5w9UkbkVO/0ETsf1VlFJLwTxT19sAuDYnvqEtaZ4f6BsplXM7d7eD1McHZQj+0iky+VTAeg3TqOa/n7ZyCjWxtFSlL39cGB6y/ufAtDlLE2CFd9L3yJqSnb4K+S2XRULiZyjfhyw2hiz1m73eTSlejgdQ3PWbRHCahER6Q/8GOgRvI4x5sQWqpfjMOeGx74G4P4rRgXmJeT9EoCYlTMAePCCiQBUnjckUKbjJ/8EIHH0yQA8W6QPANOXq1zLdeO6Bcp+P0PDhWWimsQ2dx4PwJ5y3/y1q1xNKpU2hDXFajXtr/TDXu+YrOaXDSUaLpyeqGUK9vjZBCd2U3NN/m7dXl6a/g2276+yv33zV6k1vy3doQ8i1nIWMKEB5GXpA0n7oDTEbZGRNRo23nmGPnTIOarlttOqCwP0s206MFPbcGaBPtgdG6dmTYnz27Z8xTwAihaq+TB77EgAyjZtCpRJufIOAGLK1Nz43ld67SxYFjLXX9ORg/Kp5IjI7KDfU40xU+10F2BT0LJ8YHyIbRwtIguALcCPjDFLDmLdViPcbvYl4BHgX0B1I2UdDoej7XNwMi2FxphQKT7slg7A1Pk9F+hhjNkrImcCrwH9wly3VQm3U6kyxjzceDGHw+E4cojQiPp8oFvQ767o20gAY8zuoOm3ReQfIpITzrqtTWMhxVl28n8i8h3gVSBgEzDGFIVc8SCxzqbZwGZjzNl2vy8APYH1wCXGmOL6twB7yqv4cPWOgHnjYMk9/88AbHz25iatfyTym/dXRLsKjhbiF0l9ALj1N2cCsK9AR6x3PuW4qNXpUEMkYtpfXwP9RKQXsBm4DLiizr46AduMMUZExqHZcXcCJY2t29o09qYyB32V8l6xfhy0zAC9I1SP24BlgBdneCcw3Rhzr4jcaX//JJwNpVtJc6/C8cs+Diy7LkbDSufv0j9GhpXeOOk7jwXKzN+u9tyBVrLjvzZj3X0n+w8Ds3aoBbC82lkCPS69TP0jW3dp+x13tIYCe20MwJsPAGBOuASAjpXq0N2X2jFQZPfJ2qnn7tRsf2f20+eai/L/C0CCHy9BVd+jAZBSHRPTIUX3tWOfH4Cx3MqxnNZHAzG8B8ugCFQqra9jbJ5mjHx2idbrqp7+DcMYvXYGlq0BoDo+F4CUBdMAiB/khwOnl+s+YzpoJsSsYpWhOatrXqBMfGx3AGpMVC0VB832T2cFppNzNfTXCw8umz0dgLju/QNl1v72VwD0OFfdr2tztZ06xftt2ytD/UolldoWR2/XgNOKnSrXUrLI9zmnddHrKnOghnvHddDQ8OQEPzRbatTPZRJSSMjI5b5zdP5N5/oy+HPCP+QGiUT0lzGmSkRuBaahYcGPG2OWiMhNdvkjwEXAzSJSBZQClxljDBBy3WZXqhk0Fv3VC0BEkowxZcHLRCQp9FoHh4h0Bc4Cfg/8wM6eAkyy008CHxNmp+JwOBytQgRlWowxbwNv15n3SND0g8CD4a4bTcLtZr8Ic15T+CtwB1ATNK+jMaYAwH7nhlpRRG4UkdkiMrukaGeoIg6Hw9EiiAgxsbFhfY4kGvOpdEJD1pJFZBS+VakdkNLcnYvI2cB2Y8wcO2L0oLAheVMBBgwbeXjZEQ5jvLE8jsiyfZeazXLbH6h87Gga7/YZxe5X32qx7TuZlgNp7N3tNOAaNKLgT/idym7gpxHY/zHAuTZELgloJyL/AbaJSJ4xpkBE8oBGA8zjYoSOqYn0ydCY9vjC1QDsXzQzUKbK2tpHXD4BgJqZrwFQ8Bd/uM0ndvzC+ckbARj6zn0AVOwZHigzEkj/5t0Hd6RtkEtH+D6C8/7vY8CXgv/ZKToI8uEv1gfK/PmUawD4Yqf+Ed9d7p1Wv0x7K3fSPlnHp3Qr13SxxV11fEPaPv+pr32Bns+UeH2+Sa9Uv0fw/9wboJpt1V3+s7gQgJN6ZwXKZCbpNuOKtB5X9VFfgYn3B7dKtY6lqM7oDEDZa/8AIPVoHfhXPvOdQNnECWcAkLX+c1033e5L/Iqdlr231rzihGwOJX61Vq0pMXu1vTI6qXzOwhI/gvUHL8wH4JGemu73ps/0OMcmdQiUueQXjwPQu52emwz71J60zx8gmVqswyxqSrVTzX9NJQdLt2tsTo+zjw+U3fyB/p+L12mM0K4NrwLQdYIvu9O/c08A3p/yo1rHdOYS/17wzyF5NBuX+TEkjflUnhSRp4HLjTHPRHrnxpi7gLsgoG3zI2PMVSJyP3A1cK/9fj3S+3Y4HI7m4TI/hqJRL5MxpkZEvg1EvFNpgHuBF0XkemAjcHEr7ttRDwmjNCHS7i+chmhLsurmiwLT/R5+uVX3fc7ULwF49eysRko6xL2phCTc0IX3ReRH6NiRQMq8SI1Tsdv6GI3ywhizEzjpYNZPjo9lSF5QFr1uGs6Z/s1hgVm7n7AyIfv1tXr/xvX6Oz0jUKbfO38HoOqM0wCo2KMmljWvf1lrfyO/eTC1a1sM/aGaek5++AeBeWU3/BGAkVbZ9zvPzgVg104/Bni8zXyYZpV5Y6wumwTps2Wkq52qdwcN6X7NrjPWqtzur/DDuMfZzIp9MtX8lRCr28lK9mU9Bqeq2WWnfnF1T3W9Vc/1JetqdmmQx851aoZJ7qDb3bvZN9FkHaXmz9hsNZsULlTzqndT2T5neaBszm4dp5bYrRcAcR20TUpf/mugTEIHjT1JGHECAO2T9NrdeEdDouCtxx+WaVu+OkPNdNVVXwEQl+CbH887ticA2Sk676lrdcB450T/HO3DytBU6v8obcYTABQvXxkoE2OHAezdrKa21E56rj3z19r/+irFVWUaLpxo1aq9N4WCOVsDZYpvUY3banutnNwrQ/e92DdRRgSXpCsk4bbIdfb7lqB5kRyn4nA4HIcXIkjMkRXZFQ5hdSreeBWHw+FwBOE6lQMIV6U4HrgZ8MIwPgb+aYypbKF6ORwOoPCBHwKQ870/tdg+Tn7ws8B0coK7SYaPgHPUH0C45q+HgXjgH/b3N+y8Q8MAHCb33qSxBncX3ARA4jfVxxJb4MtA7Fymki3VlRrbnmAz++3Jbzg97pFAxbzHa/3uvHNbYLqH9W98+Ikmpquu1rGslWW+EENGbgbg+1KSrO+jX8f0QJkCK/Ny/jD1XeRZ/8vp/TVMdWCcfx6M9aFIhbr2qud/DEBNsR+BLjZTYPyXKv2xcY36aDy/CUDFHvX7GFvnJf/5yta9KlCm08L1AOy22Rs7DNH6LJyq0iQpOX74cXIH9dGUF2s4dPJW9dWkHXNaoEz5Et1HxbwPtX49NGS393dvDZQp/uB/tDYrF+k5LSrS89Cnl7bTt49VY8XaIt9HduVwldfZYeX/u5Vrut6qWR8HyrS3ki3752rulbgsbbdgf1W7Xnqudy5VHcTVb+r/cY/1x3Uc4IdblxXr9RRn5X+q7RCAiXdfFiizd8PmwHTuj/7mH9zRFzZ06AePy1EfknA7lbHGmBFBvz+0uv4Oh8NxZCICcW07H05TCLdTqRaRPsaYNQAi0huXV+WI5YNVOxov5Igqy7fpG1V1kPjR/5brW8iSzf7b3tNX1Zfiw9EY4saphCTcTuXHwEcishYdVd8DuLbFatVC3FO+JvSCXn4WupGvTAtZpPOltdPJRDVhwSFC/wmDA9N7rLnECxeu8ZR/R/gBgmmJern1yNEQ4FIb8nl8L39MRIaXqrmdfh+dqKYsqVgPQOUiP3lezFhVnS2frmbNrV8uBnwzFkA3Gy6+Y54qBe/bphHxe/L9TArluzXeOD1Pw5i7TOiq+9rnZ35MylSlgKQMNXN1OWGk3ZfWp2Kf717MnqjqyeUbNOy4Yrfus/DRqYEynSapqgM1VvF6uW4nefTkQJl2YyfS2px4gpq5lltz76SBGvpcXKrHd+6AnEDZ2P1qduxoFQ0o0xts6YYNgTIxmbr+5hnzAUjtpKasThP8UP/FT7wPQEKqnvPKffZ8ZOt2q4LMkPG2TJ+zNMQ7c7iaDWM7+CPqJb8g/ANuDoJz1Icg3Oiv6SLSDxiANuVyY0x5I6s5HA5HG0ZcpxKCgxm5MxpNmhUHjBARjDFPtUitHA6H4zDAmb8OJNyQ4qeBPsB8fF+KAY7YTuXn72gSqd+dMSis8j9N1Ex6503W0dX9pvhJncpKNEpo86f+qOwxb0+PSD0jyY0vzgfgklFdGi7oiDhbfnczcy+8O/D77EEdQ5ar3LHRTmWEtd273lraeCFHaMS9qYQi3DeVMcBgm2nsiGTYCb5K8dheR7Yu0r8+17Dh2CDdo/Qs9TX079IegAwbLhwbJMEyrLNKkXiZFvNsuHaoFNBJJXpznFWpNvkB2br9tIk9A2VqYvTyNedrQtIep9gQ53XzA2VW9VAF6r5Hn68zjPW3xPpROzF7NfCgME2zMSbH6XGtK/EtvGv2qp2/W3ut8xxr9+9+msqF7N3v2/3XJerx9O2vocSVc/UBIf2K7wfKmLnqu6vaoeGvVfs1VDb/iUcDZXKP0wePorkLOVhSbGbFBz/3/Ru9rIK0J3XT34Zyf+s4f2zzAutLmTxEO61je2hIcUebVVPEP5/VKfo/qLLnszJds6OWXvaLQJnU5e8CkJSt18WWr9YCsPDJrwNlkjO1Tbsdo+0/8RdjAT+FccagPoGycZ21rjHdBgJgNquvzFT4oevtx4wn4Tg/xLjFEEHiXfRXXcLtVBYDnYBW8oA5HA7HoY4b/BiKcDuVHGCpiMwCAo9vxphzW6RWDofDcagTwegvETkd+BuaZ/5RY8y9dZZfiZ9SfS9wszFmgV22HtiDuiaqjDFRjRMPt1O5uyUr4TiQm6QnAI+Y9VGrQ68bVXZ90FGamKprVrOTfToixP2faLjyj0/oG+WaHMlERlBSRGKBh4BTgHzgaxF5wxgT7PBaB5xgjCkWkTPQjLfjg5ZPNsYUNrsyESDckOJPGlouIl8aY46OTJUOTV6/1h/LMn/7wafT9cbIrP/J1QC0O/2SwLKUNSpOkDlKRQtuP+OeJtezJZlgsyVOW6wy452zfGmSrpm1O5zyKvVd9MlNC8xLtr6TXXv1ZXfJFh2gN3uDP2akkx3nsrOLjofITVPfzOLtKtmxq9z3XeSmqj17+mr9L+WkqX9jYM6xgTLd4tQHsKJM69otXbe3eY+/HWI08GBjgcq8b7f+kv2V/vhez7ewz/oj1pfUvgY27/Zt+onWJxPXSX0EZoIO6cpL8v9u8XYk9i4rG7PPypbkjhkYKCMp6oPKPlaPZ1iu+kReXOzLvB/A+vm6r54jAbhqdNfAoh/8dxEA71ysx1uYqP6qTzf6gyHH2eM8upvuOws9zpj9Ol6oJtFPdTx9S+0b6tebSgAYGCS7k9T+GACO+blmw8y20jqV1b57tqRc2zTD+qLSCjVgJb1S970lxxfz+CJfr5kL0GumdOncwLJ21/6GVicy5q9xwGpjzFoAEXkemAIEOhVjzBdB5Wei2XgPSSJlEExqykoi0k1EPhKRZSKyRERus/OzROR9EVllvzMb25bD4XC0KhKDxCWE9QFyRGR20OfGoC11ATYF/c638+rjeiA4OYwB3hOROXW2GxUilWGmqVFhVcAPjTFzRSQdmCMi7wPXANONMfeKyJ3Anfj2xEOKqnka3RI36vSo7L/sXR2lHRc0otiLKHqvu+/yOndwpwa3M39zCQBFpU542uEIC+Fg3lQKG/B1SIh5Ie+pIjIZ7VSODZp9jDFmi4jkogkVlxtjZoRbsUgT1bRlxpgCbESZMWaPiCxDe+gpwCRb7ElUaj+qnUpSsm/qOaqdmgtirKnhYOh2s4aVVm/yx6TU7NdxKqZU5Tx+u+vQGDtwwiSVWJkyXFVk51jzxvl2nIpn4gLYU6HmpGobXlpkQ3CD2bq3tgjDyK4aZhpkCSHPZn4ssGUra3QfS3eoaSo3NTFQ1lPMPXewhr8u2b7X1sU3W3mKLd3bqbnpkw167vpn++Y6L8S5n52Xk3JgmGiSDZ/uZGVChuVomcIyXbdPpv+yPner1qO8SpeV2XYqC5KPqe6nEjMdRpyn203U7Sft9gWAPPmZmeNuBmD6TA2zPt1Kp4SiZp9eSzE2dLrb6/8XWHb8ADXDlTz7gO77YhUZPzf/wPuPdL9UJ2yo7rxKNUc++YX/QP3lfK1r2b7aDyLfu9QPv+/STtvl8bla9txBWvf2ib7prJtRU9amcjVImBw1Ae62ZrHUWP+ee05/G85frP+VDe/5sj3DWlk4SpBIqRTnA92CfnclhBKUiAwHHgXOsNlxATDGbLHf20XkVdScFrVOJVLmr1A97cFtQKQnMAr4CuhoOxyv4wn5LxKRG73XyR07nMihw+FoRbzor3A+DfM10E9EeolIAnAZ8EatXYl0B/4LfMMYszJofqq18iAiqcCp6BCQqBHuiPpUoNQYUyMi/YGBwDtBSbq+0ZxKiEga8ApwuzFmd/AAq4YwxkxFoyAYM2bMETsw0+FwRIPIjKg3xlSJyK3ANDSk+HFjzBIRuckufwT4JZAN/MPeH73Q4Y7Aq3ZeHPCsMebdZleqGYRr/poBHGcd5tOB2cClwJUAxpgm94w2q+QrwDPGmP/a2dtEJM8YUyAiecD2+rdwaLP4SjVzZPbXYI1OV998UOtv37UvMJ3bPpW/tRsAwEU/nARA9vjREail43Blq70+sssO27/IYU2ktL+MMW8Db9eZ90jQ9LcIkRTRRoyNqDs/moTbqYgxZr+IXA/83Rhzn4jMa+7ORbvXx4Blxpg/By16A7gauNd+v97cfUWShEx1eq/4iS9lPmBqeI76qtXabDs+9SMEO97yMwCKEzUrXoW1wSfENv7GJnFWLnyzL+u/dYZKYJx6YlDKm8ENd2a9MtRXMaidP69wqB5nV2sXH2B9KZ7yysqdfhbAbOuH8CTST+iptu9dQbLlL1kb/Pg6Mjcj8/wQ1BhrST2ukx7X6+t0H4M7aGhyfNCf2FOJGZCoN9a+NkPgxt2+P6fUtmXajhUAjM7rB0Cw4pAnJeP5Vrpa/0t2jR9qWxLbvlada0SfUHdX6L46p/l/pXFd9HhmbVb/xrpiPYazbPZKgLkFuqxwvx5nb+uTSU7yAyoeyLgIgGHWv3RMbz2+/y5UYYu7TvTTCgSOd8z5gemMtBQyb/bH0MVOV6tJuxvu1mNYoFIxpsr3ieyfrPet0kptt/s/1aCPj79SqRgJuodWlun15Un09OiqF88pffzz271Cz/mpRTan36oDqhzwSHep1HrE9RoKQGa5+qaK33stUDbu0u/qRI1eVz1OjeI4PxGIjY/e/g9Rwu5URORo9M3k+oNctyGOQU1ni0Rkvp33U7QzedF2YhuBiyOwL4fD4YggUruXdQDhdwy3A3cBr1pbX2/go+bu3BjzGfU7+U9q7vYdDoejJTGuUzmAgxlR/4l12Ht2vO+1ZMUON5Zdey6bZ6lZouvRh540fOVWNY/Fd+rTSElHW+GVRVs4Z++XQXOG1VvW0QQE96YSgnCjv45GfR9pQHcRGQF82xjznZas3KHOgKkaV7Ds2vp1NftcfiYAcR01DN2zX+eM92Vf/r5CbdN7y/Jrrft9KwUezLdfvgMASVAb/LpB5wAQGxQx12eIpqqtzO0PQMy+nTRGcrmOFaie8UJg3kWDVXrdVKvNvPzTDwBYNkEH7eakHGhPrrRjMTx/S3qCHx3zYzvuxfNdFO7XtkiL9/+Ynn19XpFup5dN3xtjj69vlj9OZY8dx1CRpONLtu7R7QWPgUhO1W1XoZ1pvHXxxAb5ZjZZH8yAbG3TDbv0d1ms7+vJsuNIlhWqdIg3RsZLkTy/wPcdefIua3aor2f7HvWJZCX741+27tHxH7M36LJhNmVA+yApl5sm6Pmvsf6frGRdNryjSqW0C2pb2evLxABMX1dCXX6cojItLFpEwtEXsqKf+gGrglwzf35LfU9fzVZfSnK61tlLD50SYgzP7edoTqE8K5MTfM63xWqASvxQ/d5rfTU99vo+QKnSum/voJ1e7g6tZ1UHPWdpV/8sUHY3uv8vC3Vf77a7KLDswQNq1tKI+lUctQi3m/0rcBqwE8CqYx7fQnVyOByOw4OYmPA+RxBhH60xZlOdWdUhCzpajIRR10V8m2WlpZSVHrxApuPwpnrDgmhX4bDHiGBi4sL6HG6ISH8RmS4ii+3v4SLy83DWDfdoN4nIRMDYEZ/fA5Y1rbptj14XqwJr8drnACiY4+cy6/ubkwGoSVbzRkmMmlT+OGN9oMyXVn7jhKG19bmKy/x+u+9J6sKKHanxC1KhppUP1qppqzRImqR3lpoazrav5qln/D6wrGLe4yGPIWadhjrH9vUlNra98iwA2cceB8DjXTUI75VXNLz0vSt7Bso+uU7NI4OtKrEnTRIcAjxjQwkAG22I7RUjVP5lW5DMh2fqyUhS09ocq2Tc3WZcTBTf5FZuQ67jqLHbVxNenyDFZE/uZdZ6XRZnw4eP75tzQBusK9bOdek2DffdEBQyPdqq9yZZBWJPiXjjLl1nx25fgqbEmvV2WBPXwDwNtS0q9UOdC0rKapVJs2avIZ18k9tse+xn9NUQ3cQNGiqeWm4fAoJCgWPSMwBIr9J9nNNVpWt2xZ4RKHPZv+cA8N4UrU+vBGuCK/HNVZ4576RjevC384cFHjjeWq3td8dDvo9myhk6Zqpflprj1pdoez02x1edXmtNgHvLtK57bIj5mVb6B2CXDUPPLdFruUu62uNqtum1kJPih3+vKdZtx9vzWFoZ5WfbtutT+RfwY+CfAMaYhSLyLPC7xlYMt0VuAm5BdbnygZH2t8PhcByh2JDicD6HHynGmFl15lWFLFmHcKO/CrGj5x0Oh8OhtOGQ4kIR6YONnRGRiwgznXy40V/9gYdRocehVi3zXGNMo69CrcX8lZvJOPnn7N/pi3vWZ+pxOBxtD0/SKGufH0UZ13lAy+607XYqt6C6igNFZDOaefKqcFaUYLmKeguJfIK1rxljRtl5i40xQ5tc5QgT1y7PpI27/pDoVF5Z5NfBky05upva5D2J+HvfDwiNMtbKlrS3IbqezPr5g3xZjxQbdruuRG3m/UTtz+/s1JBbzw8A8INhOs8s9hN2Jk4K/aJZlb8EgFu7qUbZQ+teCyz7okbDoMdn6jHMKFTb+4Qu6jfJ33Ng7pVPrO8i3vo7vHBagEwbjlroZX7crDIoQ7r4Eih7rc39y5WqOj3O+j5KbDt6tnmAhDitT0WV2tV3Wrn9XUV+4EGJtel37K772F10YFBCqpWhqbbh0Bcc0wOAeUEZKetuO7WdhrR2y0mtVQeAUdb/km0zU+6x2SqXF/jnyEsRsGt/Ra11umf4KRbW2bDlk2wbDM9VX9GO/bq97GTfF5JQoduuSlR/SYzR+ryzdvcBx7vT7vOqYSr+fdOrvnv0wfMGBabTUpIJh8pt6wAwSeoP+sarawPLPB/K5vUlWvd1GwDoNcq/2Rdu1jqK9ZNkdvSzhQIkBYWu59rMoOP7qGTN2C4ZgB8ODuF1KiIyp7m53EePHG5mvv9mWGUTcns0e3/RwI5NjDHG7Gm0sCVcR32KMWZWHfXgsOxrDofD0VZpq+YvEfllnd8AGGMazdkcbqfSZPtaNHlxob4xXDK8c6vs7+GZ6wE/d7rD4YguP3h9Mbcd2xPwRTwjh7TlMSj7gqaTgLMJM+I33E4llH3tkHLct8vO4JSrzuXbx/YC/NHa0WBEUFjofjuCePYWNfV4obZjg5R6E2x4qjci2cuWtylIbfdLm3XxvIFqEntni5om9lrTyg9GpAbKVk3/DwCxQSmG66MqW8M3/7pfr5cXlvuj76+57scA7PqVjnQ+4ZgLAdiwT+u3cNveQFkv1NYze3Vvr/VLifdNNF5Y8Dbb6R7bs7ZaMUC1Ncd65iGvbXJtRsiEWP9PvLxAzSbVXmZFe84ry/2X6OQ03ddeG8LbPiel1jrB9LKhv55qsRcKDFCwS9evyEqxy/Qc983Wdt+8xx/R7mWnPM6a3FYX6bKbB/gPG/fO0/9snm2TEXZf+4NCZEd01vWnry7UfexWs1B7G269Psa3HFQbnZ6zWU0/Hy1TKfyMINPRkqVqUuzeW01t63fWVgUAmPKon0lx+veOIxziO/aq9fvhC/2Q7mcXbwPgf/Z8dumpKu3fOcGXCyq3pkMv4+ZG29ZpdmT+l+uKAmV7ddD29lSx316m21/Rwb/+K2u0/VcFXZ8tQhuWaTHG/Cn4t4j8kTqJw+qj0U5FRGKBm40xJzfFvuZwOBxtkyNKpTgFODDXQgga7VSMMdUiMtpO72usvMPhcBwxtNFORUQW4UvxxQIdgEb9KRC++WueiLwBvESQrS0oU+MhzYWPfxWYfuW68RHffs3qmXaqU4PloolTKXY4IoyVaWmjnB00XQVsM8ZEbvAjkIWKSZ4YNM8ALdapiMjpwN/QXvJRY8y9DZUvK6tixcpC7rGhn8cPUN/DAuuLaA3OtfusqPbt9csLtQ/OTFbb9mdr1GdRVeOHcqdY2/HyKrUBb9+nx7Bksx8OeqnNurhkh9rBvWyC/a1NvyTWD8PMOEnDyWuS1CYfu3trvXVOSq4dNnrFqK7+tA3JLt+n1s7YzSrP0jNdQ4IXxfj7HGRDa0urdHuV1Xp8A7P97Xut0tdmOSyzZcqr/Lb43wr1BXSwIbteiPH0+RoX0qWjbzvfZ6VRjG1LLyQ1Oc1XMu7YQe37GdYG396eh+w037/h+TW88OcTbYbFbu38MonWVzRzs56jnja0tXuCrpNf6bdFhT2uolL1FeSm6T6XV/h/twuHqg/Fk3nxro+yIF/PsFw9Vk+pOcuGEHvyPWkJ/lOydz3ttXI9nj9hzVbfUt2lZ4bWy4ZFJ9uw3D1B2TmHdsvgb+c3TyI/K933qXxrpD5oeedxXDetQ02IkQyLtmtdN9v6jemuZSf38yV1Plql/qWF5eqj/HqRXtsfBPmXvOuhdI/+j/73nqabTMvww44jRoRUihu739ksuX8DzgT2A9cYY+aGs+5B1sNzdNZ1cbQTEYwxRXXXqUu4I+qvPdjKNQfrx3kIOAWVhflaRN4wxixtzXo4HA5H/UTGpxLm/e4MoJ/9jEcHo49vgXvlHPSFIVRvaQjDrxLuiPoHQszeBcw2xrRE/vhxwGqbDAwReR6YArhOxeFwHDJEaJxKOPe7KcBTRkerzxSRDBHJA3qGsW7YGGN6NV6qYcI1fyUBA1GfCsCFwBLgehGZbIy5vbkVqUMXIFhqPx/tnWshIjcCNwLEt+tQd3GLU1BiR2u3+p6bzm/eXxGY/uUpLSxh4TgiOaJSKYTfqeSIyOyg31ONMVPtdDj3u1BluoS5bpMQkUz0zShgNzTGzGhsvXA7lb7AiZ6jRkQeBt5DX7kWHXRtG6e+V6/aM/SkTAVIyu1rSvdUUGjlwp9Y6PsR1k29qO6qEaWyk0pbZFnZkA27/LEGx/dQv8aCrdoBnTpQpTG27/XlS4Z21DEPu6zd2RurcKyV7gDYZn1FiXacRh8rN+7Z4Hfs9/eZ2E7X+2S92p2LS9U3sKHIl3I/GOYV2jE/iYOY0COLspfuA2BWiu/L82Ribh6nPpnXVqjt2/OtgD/+ZrD1c5RZX8on630zbf8c9U14EvhzrE8s0WY93LjFN/XGWemaROuP8Ma0eN8A6XZMR4mVJumRrfuuCPJdeFkzh3byJOr1eIPHxHj+i0mpWte9ySphQ5mOqei+yb9fVG3TVAaSZP0/Vo5ekn1/UFxHzeo4MFHPTeUKTT0wtt+oQBkK9GGzupuO7fCeihNStR2Tg47z04163XsDb28/Xq0UO/b5Y52W7lA/iyc139v64/p1SGvxAcK3T+hKUnIy43/zPgAxQXXPsWOHzj9K/YZfLFe/mpcOYFjQeCFvXn8r5eKdT+/cAby7VMeueOONPDmgsn2RHbtmRDDh+1QKG5BpCed+V1+ZsO6VB4uIfAu4DegKzAcmAF9S268eknC72S5AatDvVKCzMaYaKA+9SrPIB7oF/e4KbKmnrMPhcLQ+xlBdE96nEcK539VXpqXulbcBY4ENxpjJwChgRzgrhvumch8wX0Q+RnvG44F77GDIDw66uo3zNdBPRHoBm4HLgCuaurFbbVIpgAcvHN5AySOLHtdpEq4Nj19Ry2RRNyLM4XCEptmvBEo497s3gFutz2Q8sMsYUyAiO8JYtymUGWPKRAQRSTTGLBeRsOzl4UZ/PSYib6MOJQF+aozxesMfN63ODe6vSkRuBaahYXKPG2OWNLROXHwMmR3TAmGDSTaEdMyg3EhXL0ClfQJZaS0y+yrUvNQzww9p9USgu1rTz7Z9+mI3e2OJX3drZtltFXi98NIvNvo3+n72Nb+TDYXtbcNyPZXpYNGRnaVqRvNkLrzvGTYcE+CD98JP3PnifP/BZ0KPLJaMvwGAP035YWD+Y4/puKi3VhXxjaO6ct2Y7gdsZ8gP/gfAZKsC7HHnZD+gpMiGy3qmmpFWwdgzewQ/9XmZGT3zlSdJEhxGHhtT2zrgmUK2l/iyKsk2A6VnSrTRwwGFaYAJXbUe86r0elq3VvexoVi3k506OFC2JLUfAO0Sa/+9xnXNCEynW3PaXivjs66rmp8yjS+rEp/eU/e5TE1bvW1Gy+WFatrdFSRF1NFeX8XWzOeF8H4dJHHiteHJg9UL6KkVR5qGHkq++uUpB8x7dp5Ky8zP13PTzkryeOdqTFC7nW+zoy6yodKeCa88SCX6rCG1vZxF9j+XFaSG/XkYx9EYhtCh0Qe9nXrudyJyk13+CPA2Gk68Gg0pvrahdZtfK/JFJAN4DXhfRIoJ8w0o3OgvAU4CehtjfiMi3UVkXIjMYBHDGPM22pAOh8NxSBJO6pAwt3PA/c52Jt60oZ5suy1xrzTGnG8n7xaRj4D2wLvhrBuuT+UfwNHA5fb3HjQ22uFwOI5IvDeVcD6HGyLyNxGZCGCM+cQY84YxJqxX23B9KuONMUeJyDy7k2IROSz13b3ok1Cv4eHS1kImJ/91Bu98e2zgt2emAljy53OiUSVHK/PpWl+d+jirKOBoBAPVh2GHESZzgZ/brL+vAi8YY2Y3sg4QfqdSaUduevlUOlDbjB91euSk8q/rx/KjVxfr764aYjhnhR+wUBNC7rw5eHIcW6xcyNTPNQPeCCtFAVBi7d6eL8CzEwczY+5mwPcDedLtSam+fd2TIMm2kiJdrX39ohFqix/ZyZfG2LhL9znRZmgstHIht5/g+y68UMyPlmj45cDvqqp1ajt/Ox6P/e3ZwPSfp9zDsA7Wb9XeHxt08aDGb0ReB5U4Wn0y9z1wF+D7UcDPcNnTSpPE23wV5Vb6pl+WH4Q4L1XbstCGzXrSJHlB2RNXWP9KTqbO83wsXXP84/R8DV5Gyq52/VP7+CHdu8t1/zHWf+PJ2p/RV8vEvvX3QNmEiXqcu9qpr2brXj2fRUE+muQ4PddLtts6W2mZx2ZuCJTpauvsyahMW1xbbic4LHqo9RfkW/ke73oLTrEw2KZkWGTD7q8b283Wr2V8K+HiSQNd3FnbpzipJwA52+YDUJ3hh8LXJGu7DbRlpVrrXrNmnr/BoZN0mQ3lPvUEbdvS7L6BIqFGczeFSJm/DjWMMU8CT1rZlguBP4hId2NMv8bWDdf89QDaW+WKyO+Bz4B7mlphh8PhONwx6JN1OJ/DmL7owPeewPJwVgg3+usZEZmDOusFOM8YE374kMPhcLRB2uiLCiLyB+ACYA3wAvBbY0xJOOs22KkEKVYCbAeeC14WjmKl4/CmomR7rd8v5A5myoY5UaqNozUo361/68R2B2bmbAkCqSPSDyfBI+VwdMKHyTrgaGNMYaiFIjKkvtBlacgmKCLr8KUAugPFdjoD2BgJ8bFIMbpfD/P53+4kZqKmvN1h1PZ+yyu+ikywDfrtmyY2e5/nTP0SgLOtX2NLiTrwvdSzAPlWGqXYynkX2TzZQ0flBcrstOU9ye54O74hWFYiNV1tyROsXPncDcWA7z8Y0sWXqeho7fPj7DxPzuNfX+cHyng29/Z2bMcJPXW7I6vW+mUy1dZd8ZZGNr71/ecDyy7dvrTegIXGBk/e8T+9FjfsVOmaYOn1q8br+JaNti2T7Rgb75jSEvznIE/Opsr6WxLtcQanMJ5rfVgLrW9liPU97AzyI3gy+Kus1Mw5VrKkNCi179uLVHp/ij3Xx/bIAPwxMp0Kvg6ULf5AAx3Sr/2FltmjfispKQiUqS62nfXg4/Xb5uWI27k+UGZDOx1rNn2t3uS99L/D7TEMCkqhW2bHu3jpnDOtnygh1h+nkxVTW/xCamy7L/owMM+MODUw3dqdSvli/T8lDh4HQMVqHbRsKvz/0/58HSqRmKn+ocS+djBzUK74GCuHU12s/tSqrSqbk3Ds+YEy8Z37z2lANiUsho88yrz9YaNSWAB0y05v9v4OJURkrjHmqFDLGnxT8ToNEXkEeMPGQyMiZwAnR7qiDofDcTjRhqO/GqNe0bNwHfVjvQ4FwBjzDnBCc2vlOLLYta+UXfvaVji248hFx6mYsD5tkHoPKtyQ4kIR+TnwH7uxq9BMkIcM+wp28vVv/0O34/VVOu9nfwHgfJsxEWBM5/Yh120ql4zRkExPViXPykssTfCVdM8dpmYuz1TT3pq2VhUFsjIH5DY8dd3l1kQWm+s/DAzvpnXPsqq7XhhsN2v+SgxS1PWUfb19XjxYQ39vGudnddxnzSXJNoS3w241e1VvWR0oUzVrGgCbP5kPQOegEFuobeY6mLE7Wdbc5GUr9Mxg4Ie7lltT5XHW3DffmrG6Zfr7PKm3mmcWbdf1PbPX5t2+ueSiYSrrcXwv3c40m1mya5a/nTR7TjzZF0/OZnxQOO5xNqunJyWSbPflZd40nXzLRtzVdsyPUXNTTYKWiSn36+WZZBI7qzmnas0CACpjfNNdt4Fq4rm6h7aXdNN6SpX+9Soz/PMRv1XjZswO38QGUL7SD7Vd+54KYCTn6nrGmg07jvUlZnZ99ovAdN5drTS+2aovx1rl5uoM/c+WHa+KzZv3+ObRgSP1Ot2ZqZGtiaUaZr02xg9v75Gu/5G4EmvutW295lc/iXjV22J3YRVUuhpjNjVaOAThvqlcjia+f9V+OuCPrnc4HI4jkrY4ot5KwrzWSLF6BzeFG1JchEohOxwOh8PSNi1bgGaXHGuM+TrUQmPMhPpWbPBNRUTubmzP4ZRpa+zZX8qe/c434GjblEz9KSVTfxrtahyyGAw1YX4OQyYDX4rIGhFZKCKLRGRho2vR+JvKt0RkdwPLBdXvvzu8erYc1ZU17C3YS/EKtSnv+taVAHRf5Q+l2Q5MnvdVxPZ5aU/tk4tiVQ4lw5MzL/dtwN2trEqHFG3qrHi1Y3du58vjeyG/c60vZbjNYhccGruiUOU8PJv+0dbe//pCPd5vjvPz9Fxh/UjJceqTibPSJMFyKN3bqZ0+Yel0ACRDbdIxaRmBMkmjNNy1z8DRAOR8+n59TRFWDpb2qVrGC2ceakOeE4OyAO6v0Dr2sDIq260EiyevMicoZcCXNtTW8zd5ZfYGhSgv2Kpt6MnYj7YSOp7sC/jny/OtrA1kSPTf8D25nSttyHOxTVPwwVr1v3y1xncxnmize3qpEYbmqm+kQ2c/jL1PnvoEChJ0bEanTD2PkuWHmldb6ZG4ziqvY+L0mqlY9BkAVUWvBMpWWZ9adVltq8TWr/xU5bvz9foqXK51TbFtXLJ6W6BMhQ1jH3Dx0bQWa9tp5tQe6dpu1bPfASD/mTcB6Hmmnx338wdUKHfYNXptFtvAj/2f+WOxval2PfQ/Ulqox10VdF1EBAPVh/lw+QY4o6krNtap/AtID6OMw+FwHFEY2q75yxizofFSoWlsnMqvm7rhxhCR+4FzUIfPGuBaTwZARO4Crgeqge8ZY6a1VD3CJWHUdYHpnZ871X/HkcPuJ34JQLtrfxOxbb662I9SG2bzzR+OHKamrRYl3JDiluB94C6buewPwF3AT0RkMGpSGwJ0Bj4Qkf7GmOoGtkVaXgbH/GoKBV+qSnG7XmpGMC3wfuqZk5aWqvlgf6UfKjqxZzaDc/0/iRejXmpDZLfV6LoDY4sDZSrTNOx1lR09fkJ3fTnctt8/5EKbpc8bAf/OUjVZlFboK/2qnb6Sqzc9sXsGABt367pp8b6ZKaFYH0S29Z4EQG65hmaW5Q3zy9RY09NuXdb+gm8R1zmsjKIN8rOT+gNw4eNqisy1WQv1ePSY51nFAG8EfN8cDcvtkO6bDbdaJQLP9FRSJzQb/JH0/Tpqm863v7sGhSZnpaop0FN8fnuZtm1wqHPvDnpOF1jz47geGpbrqQufO8APae1WoWHCa+L0Goy318vsLX6oeXZPXZZXtNJWwpq99vnXRdV2DYmVBG2ffQs0JLi8RLezZ6Nvtto4Yz0Aw791HAAVu/UaiE/127bLRF+lF2Db3PXUpdRmslw/TaV4ep89/oAykcYzDVe+/SIABZ/NBWDrfA3/Xv7BC4GyOd3UZPrxr1W1IM1mQO0wOCdQxtQZkdj36gsAiG0fpKT93/ciUve2+qbSHMINKY44xpj3jDGekXMm4A2imAI8b4wpN8asQ9NnjotGHR0Oh6M+jvDBj/UStU6lDtcB79jpLkDwoJt8O+8ARORGEZktIrN37t0fqojD4XC0CMZAZbUJ69McRCRLRN4XkVX2OzNEmW4i8pGILBORJSJyW9Cyu0Vks4jMt58zm1WhRgirUxGR/iIyXUQW29/D7Qj7xtb7QEQWh/hMCSrzM6AKeMabFWJTIc+KMWaqMWaMMWZMdtqByaVCUVCyj4KSfY0XbCJ/+3xt44UcDkcbwFBtwvs0kzuB6TZB1nT7uy5VwA+NMYOACcAt1pXg8RdjzEj7iWg++7qE61P5F/Bj4J8AxpiFIvIs8LuGVjLGNCg6KSJXA2cDJxlfLjkf6BZUrCuwpbEKmpoaKveV0vMSm/52gtpR02P8rMd7KiLjXymxobk5yVY2w/o5XrZ+jpVB/o1eVkZlwVYNUx2cq76Blzb7TX9Md7UCjs6r32E5trP6BHpZX8CrNgtgb6tUuy1ImsQL2X3L+mFyrR+iT6bf8S6p0oedIUlatrq9+hMqq/w/QCXxZKWnQFpk5W08XrlO7fXBQRDX3PU9ewy1ny3ibMjsvKCQYu84zxhqMyzu0TYoKPHbYoDNdrjJqkUn2O0Ehx33s/6SuQXqLxlo17k8SOKnU6qer71W3sYbJe3Nn1PgP6jk5GnYce+91uexZj4AXav8cN9dj6kir5l0mh6LVS3e/uEngTJVNjz45Uem6r6S1OfWqYOex7gk/xqKsf64r//8AQDte+g5S8nxfUdD/vF7ACpnvARA1mANVZZE3++y8ln1NcRa/9vezX7mVF8Hu/mc1dUPly+w/6e8rn0ASO6wrlbde5/qJxvs8rt/AvDRcA0p9trA878A9DpJxdO7/N9jAOyv1mtp6y2XRvAIfPNXKzAFmGSnnwQ+BmppzhhjCoACO71HRJahFp6ltDLhmr9SjDGz6sxrVtC3iJyONsy5xphg29UbwGUikigivYB+QN19OxwOR3Sx41TC+QA5nqnefm48iD11tJ2G13nkNlRYRHoCo4DgQXm32kGMj4cyn0WSgxGU7IOfo/4ibK/YDB4EEoH3Vb+MmcaYm4wxS0TkRbSHrQJuaSzyy+FwOFqbg3xTKWwon4qIfAB0CrHoZwdTJxFJA14BbjfGeAPXHwZ+i1b5t8CfUD92ixBup3ILMBUYKCKb0axgVzVnx8aYvg0s+z3w++Zs3+FwHDp4Y10S8jTIM2bCeVGsTeSIgL8EaNhVICLbRCTPGFMgInmoOEiocvFoh/KMMea/QdveFlTmX8CbEal0PYQrKLkWOFlEUoEYY8yextZpbUxNDVX7y4jN1DfDqljrSwk65+2nPeD/uPSug9r+5k8ePGBehrVxz96i9vTZ61Q2xBsTAbDQSrbvqSMRERckVf+/lWq39iTwPXv/RTYDIUB5tb6sldnxLp2s/Is3VmNA0NiYHdaX4m1v7Q6tX6aVzQdYVKAPMZutHyInRdvLkzPxuHp0eAEQzaFi3uOB6bveUhOwN9bkvMEqY1Ji2+TEoPEgx9lxODtLddmucq17epCvwUtHMML6SV63vihvvA/A9n0qUZ9qZXFy7biV/hn+dmpEl3lSN3lpun6p9UEds3t2oGxVno4VKUzQumaMOB2A+GI/qLF9jZ7HPTM/BiA2SfeZPcx/1iov0mtn4khtg2qbyqBirx5nScFe6pJi5X+y+qlESWZ/3z25+k4NCFr1rgaTTP7DRXpse31/UP9vng3Akn/oPandD/58wD4iQc2s/wWmc73Mjtn6oN7psqv1+3K9BjY8/PdA2crXNKXF2O/rPXjTRypHlZztZ8Fc8T8d+2NqVKqp9x/VJ9V18ii/Ak83/77qRX+1Am8AVwP32u/X6xawcvWPAcuMMX+usyzPM58B5wOLW7KyjeWo/0E98wGoW3mHw+E4UjC02hiUe4EXReR6YCNwMYCIdAYeNcacCRwDfANYJCLz7Xo/tZFe94nISPQRez3w7ZasbGNvKt4j9wBgLNpjgsqrhJec+TDnYJJPtUVG/UyHD837fZP15RxHMG39/9MaLyrGmJ3ASSHmbwHOtNOfUU+KX2PMN1q0gnUIS/tLRN4DjvLMXlbu/qUWr91BULG7lLXvLqRk9WYAUnL19b3Dsb7MhJz1nSZvP9h2utPKpxTZt3Yvm6AnKZJf5P+RPDNObxtKfIyVmfh8ky/+fFoflZiYY01SK7erWeOt5b4Mxx3HqC16y341m3jqxz2MSpSUp/kSFIk2lLU4UdeZtUW3l5Xsn+5SK3viKel6WSJnrvNVnWfOazSSO+J4GS0vHa5hwu0T1ezUR7ReY2v8+lXFashpqpV5uWGoBrVUxvpSLvO2amBh/ywt0ylDv7u390Ntd1pzoWf662lDr722BoiL0XZ6+AuVt7l/kpq2kmO0vlUD/ezaCZtUZqQ0azgAHVZ8rgtyfFOUpwbd7ozLdP3Vqki88jH/b7V1vp5HT4IkZ6iGKm/5ag0A2f38IJ6929SEldZRz+teKwmzb9vyQJnswWpOjbdt6oULd/jmrYEy+997DoBuk3SIQ/E9/rK03z1GU0k+Q01aMYXrASjt7Ss2J29TXeGqHA1x9gywsas1i2u37/vy+0XPPQJAwSw1cVXbYQKV1oQJ0GGQ/heKrEL5+tEath1pyaZWDCk+rAjXUd+d2pm+KoCeEa+Nw+FwHC4Yf6yUwyfcTuVpYJaIvIp20OcDT7VYrRwOh+MQx+C/6Tt8wo3++r2IvAMcZ2dda4yZ13LVannK92pkTWI9o8WXblVTVO/28SGXOyLHO0FmPofjcMGZv0ITVqciIt2BQuDV4HnGmI0tVbGDpaqsmqJVxYx96h8AzLlWbcG71m0NlOm5U6fjp9x24AbqYcMetfAGXztF1vbu+SE88qxtv0uQvd4Lc421EXMrdqoj5uzc8qA1dbpXjdq/8z9Qu3Z5sR+5XTBNbeWdT9Z+fetHaqdfvV2l0tO7dwyULbXzxIYtZy3S407v7IcdX/SdbwIQ00ul7mtS1D4/slOvQJmRNktia5Js5Ua8jI9drXR7TJGG4+793E+tU7RM/RtdLjwPgPJVi3QbI3x7/Th7zmuWalvedIKGmUq5H45bmqxh6Cn7tHOrTD8wL11cWQkAf+ys/ozS19T3kdBRfT+ePD1A9Qj1qXa3EvgVK+cDkJSeFShTU6PXTuVyFYvYu3IFAEN+7+csSbr/Xj2+SZp5s3SH+gja21Dq4HDhpGx9ONq5RCVOti9Q2fz0HD8kvKZCr8Xep6ocSslKbdOc/BWBMqknXwxA/s/Uj/HIM3706QMNijKFxkxTH0hVjJ7XhL7qZ0rJn+uXSVY/4+IS/e1F2w/urWMFa77yI2izz9Yw6P07HgUgrYv6m8qL/fOZ1lX/Y1U2K2TuMA1V9tIDRAxjqHFvKgcQrvnrLfwRH8lAL2AFmvPE4XA4jjgMrRP9dbgRrvlrWPBvETmKFo51djgcjkMdZ/46kCZlfjTGzBWRsZGuTDTY/cQvSezlZzP8sJOO1O2RkVzfKkck6370DSp2a9iqN2LZC00FSLWhrF74675t/ijtMW9Pb61qOg4Byt5reujx4YS+qbhOpS7h+lSCR9bHAEcBO+opHhWSs1MYdtVoKr7SwXpdj+1/QJk1r6vceB/7u6aifqHlT9ar/XpyL7WD90705dS7Wpt7UpHKXZw8VOPfNyRn6PIY3xeyvEzXT/bGq+xXm3xNjW+3r/z0FQAkXoMC4qxkB5l+mfgUtdl7EumePT0hXW3mZTt3Bcp6kh8e3Sdpp9m+jy/lPudXDwNQsrYEgEQr7zHp5b8EymQPUlmLvb9p2ZfSru19f4Tnp4qP0faK37EagC3/+TcA66f74y5yh6kfaffXXwD+OATpd1ygTOw6teknjNaHheInfgtAu1H+M1HiaE2XUDVTx/ZWbPX9cB4J4ycBvi9k93qremG/5fa/BspmfvAQADHpGbr9QeoTKXj6X4EyHU49FYDyzeqWbH+0jnMpX/xFoEyvm1TItnLTKt2elW3JHqp+Ly9lMPiphXcuVT9Or9PUd7H4qS8DZXKHeWM61LdSYb+LP/0oUCbrikGAn1b46jP7Mvp/7x/QHuGS0F+voYreEwCo2aPXb0min/7XS7WdUK5tm2XHKq0t02ugR4X/3zPpul7u3SqBL9P0Oq6pWB8oU1Op/+uOpx6rx7dQx8EM/WaeX7Eff93kYwrUpfVkWg4rwn1TCfZcVqE+llciXx2Hw+E4fHDmrwMJt1NZaoypNYJeRC7mEBtV7zg0qfzqNQCqtulTuZxyAwAxc62oYO/TolEtR4Sp3rQo2lVoVQwRyerY5gi3U7mLAzuQUPOih4Gamhqqduv4kmqbNW/f1p2BIu2666uzZ/ZKPktTCuxL8XPeDLxbTT3HDFETQ9GrGra6Ld5vqsxBPQAotyaa0h0awtv9uFMAqFjrh2EOHaGv4MaqJletWQBAXGc/dDdxrJpCqttpPTpO0FDIwid8vc7iZXpD7nDiZADKFszRugxXc8Xm9z4LlPXMXOkD1ARYU6r+ja2f+WGcOVbt15P18Mwds7/3C387NnQ1NU9lL3YsVgmc7D4afpwVJBMSb0N/O08eB0BlSQkAKUPrTSERYNAWv+5DMrRee95XU9T6hWr+ik9RH1fXY3oHynY+W9V/zXjN8rnscs1Svfn+cYEypzxlLbdWFTjrLM3+Z8p901HsRm2XmvFqBotdqbmNYnqP8Cspeq6rMvXcd7aKuthQWVPih+VWpuqLfdwwNcNV23Dt7PGj/X221zZNP0411SrWq1kv5sSrA2VqytSMGp+t5zOug5rB6DVSly/9PFA2rodeBxn9NVOsZw49+nd+hoqieUu0fvv1XO8vUBOvqQmSL7HH2ff5N2gqMam+YaNmn/4fYyq0vXcn6X8wa+fKQJnyOZqtMu+Y8wGYs1vba0WhXrdbBl4SKDsgRc29OV++CEC1Ve9uf5TftnH91ORWPlNN4bvW6HXbc8rkJh9TSNyI+pA0plJ8BipY1kVEgnTjaUczMz8G7eNHwP1AB2NMoZ13F3A9UA18zxgzrYFNOBwOR6tjcJ1KKBp7U9kCzAbOBeYEzd8DfL+5OxeRbsApqJyzN28wcBk6BqYz8IGI9HfZHx0Ox6GEcW8qIWlMpXgBsEBEnjHGROTNpA5/Ae6gdtKZKcDzxphyYJ2IrAbGAV+GWN/hcESZvfvVXHukBeEbDBVVkVU+bguIacDRJCIvGmMuEZFF1MqhqBhjhjd5xyLnAicZY24TkfXAGGNMoYg8iOar/48t9xjwjjHm5RDbuBG4EaBbh8zRKx7/LVVFGum8bZZmEJzzou/faG+zAO7ao7Iona1vILN3RqDM7vw9tb6rK/QFyQu5BUhI80J/tU8utmG5e8u1360Ienrp1EFtwF62vkEXDbXr+ttr31dt5l5YaPbYkQDEWEkRAIxevDUJur3YvYXaBtW6Xan0fQSeXbxmrfpvds/T8EnPhg6QM1L9LRvfV/+BF47bdfJRgTJr39RlqblqI6+yfipPkj0+KMPisOvVXp1wmWbU3PX3OwDIPG4SdRn9qvpf/r3sr7X2HUx8mvqgBt6o9vTtH6vfJXvUoECZ0lM1lUFqnErgfDxU5VmOv8e3wScOUf9KdbFeF3Gd1SdjYoKep+L0fJoiDQ/e11d9ISl7g0KLPd9JnA3XtuvH7tZ1qtr74dpx+ZqN0GRoCOu6OJWc757sv2zH7Fc/HBvVuR2T2q7WfgB2dbHyLDa7ZGq8Hmd6sYalr4jvESjrSZt44bjZhXr9V6wMkkOxobnbPlejQ6Udd+T5CAH2XflrPfZ4PzNph3YNZ//0OpX4WZpuoqbEz3YrKXpcVTvUrxGf1xOAPYsXBMqkdLThxfY8xKTr/zK+u16jXhgx+HJCUqH7LEtX31ZC0DOvd253lul15YUs9yz3VaXiuw2d01DO+HDo0n+o+fZD4QXB/urUgc3e3+FCY+YvTyTr7KZsXEQ+ADqFWPQz4KfAqaFWCzEvZM9njJkKTAU4ql939x7qcDhaDedTCU1j5i8vr/F3jDE/CV4mIn8AfnLgWrXWPznUfBEZhuqHLbCpibsCc0VkHJAPdAsq3hX17TgcDschg/OphKZB81egkMhcY8xRdeYtbI75q8621uObv4YAz6J+lM7AdKBfY476kd06mvd/cEUgjNYLk9z59fxAma//qaOVPcXgHdZc1T7eNznsta/KHVL0VTxnoIZ+eiYugIEX6WGXrFbziLGjapOtImz/u/xMdWv/8kcA1k1Xk0XRTn1tT43z91kXL1Q3o4cvy9/jDM1gueEdNUkl52YAkHeimnzih/jKvNVbNAx3x3vv6TIb7pt9+rmBMsUfauhpu7G63r75aiLbPs8P9exzy0216lVtFX/jctXUY7K7B5ZVfKFuscRB+oZfuVG3s2uhP3Yh8xg1K6178nkAtnytJhGvjQE6ju4H+ArL6UPUXBg7fBIA5e06B8o+Nk+feSb97RYAUrJt1sMCP6tm7igN3c7wFAh66/ZiOvcJlKlup2aq7dVWtcD+JYrLfJPKHjvaOz5Wr52B2Vr25aVqVusWpEztKSwf013PX89dOqJ7fXvfdOddgwu2qbqul8lzXbGfNXTzHjVX7bX1GNVZt7dku5pmR3f2r48se72WVVozZjs10xWW+scwIEm3HVug4ct7PtPrI82qBQCQq+3V4er/BGYVv/drGqJmjSotm1JrMi72xTZiO9hrJU2VJUycHqdZNz9QZttbei16StsJmRkAFC3WEOp2Pf2R8Mk2RF3y9Px5ChpLH3snUCazn4al544eqGWsmc8baQ+Q+4O/NtsclddvqLnmry+GVfbes4c0eX8ikgW8gCZGXA9cYowpDlFuPRpEVQ1UefsLd/1IEdPQQhG52fpTBojIwqDPOmBhS1TIGLMEeBFYCrwL3OIivxwOx6FGjTGUV9WE9WkmdwLTjTH90IfsOxsoO9kYM7JOB3Yw6zebxnwqzwLvAP9XpyJ7jDFFoVc5eIwxPev8/j3w+0ht3+FwOFqCVjJ/TQEm2ekngY9pxPUQ4fUPisZ8KruAXcDlACKSCyQBaSKSdigl6XI4HK1L92ueBmD9LwY0UrJt0oo+lY6ef9sYU2DvwyGrBLwnIgb4pw1kOpj1I0K4PpVzgD+jPo7tQA9gmTHmkEnSNWbYYDPr9aeo3KhyGTGD1FdQ8eEzgTLxPdWmvfLBxwHofYGGwVbv87PGednwPLpcq7ItVdv9+dWjztJve0Hd+Y7afvt10syKCzf5isEVVWq5+9bRPQEYO1uVahf8891AmXEPqs26fNlsAHbOUxt8/herA2VibUbE3meqBIUnPbEnX02jA671/SWxmWpTNpUably2Rre38FFfjdbbXvluDa9OylRbd97Ynn5bWJ9R7igNw0222QXbn3KeHn97379Rlqr7TC1QH0rV1vVaz1n+8CIvU+P+7drem2epT2TAFP+mlGbt6kkZGsacPvFEAPb2UbmbR6wfBuAHI60cSlHtZ5uy2b7UvpctMaGd+ltSJ6iUTlUH36dSk6jbqRFtk4271SfyVb5/HmOsD8QLtT2nq36/a0NZdgX5Xzz/iKcLNWtTCQD9clIDZfJ3q78k1frz1uxQu3+XLN83s3yL+igWb9RzPK6vhtZutyHx1UHyKueN0HPx0UoNNV9kVbZvP81v23jrp+qXrfuIsYGWLy4qCJSZ3Mf3b03s6U+HwutUVn1Tj9NUWfXjzRsCZdKO18BRE6ch9J6/z1OWBqjcp23h/fc6H6s+S4k50DpfaGV7cob3BSD5DJW1iQkKqa9OVp9k9Rca4iw2VNnz7wAkjJvSbJ9Kbp8h5sI/PBtW2UcuHrkBzZ7rMTXopt9YpOyTxpiMoLLFxpjMugVFpLMxZovtNN4HvmuMmSEiJeGsHynC1f76HTAB+MAYM0pEJmPfXhwOh+NIxGAO5k2lsKFOrL5IWQAR2SYiefYtIw99sA+1jS32e7uIvIoGO80Awlo/UjToqA+i0hizE4gRkRhjzEfAyJarlsPhcBzaeOavcD7N5A3AUxq9mtoKJACISKqIpHvT6BjAxeGuH0nCfVMpEZE0tNd7RkS2EyFBSYfD4TgcMfjm7RbmXuBFEbke1Um8GNTcBTxqjDkT6Ai8asf9xQHPGmPebWj9liJcn0oqUIaOdr8SaA88Y99eDgl6DBpu7nzCl+vOTFY7ap9MX2LCyyrofVfaJ4jNu/3McmNs7P+oWPUnxOxT2/TuzqMCZf6zSGVKclPVTjxjlZpK061ERocgSZePlumbpve0smmt2sdTg8ocP0Lj8D9fotvtZyXnfzy5b6DMLc/PA2D6edb+n6px/zHbdfxL+TJf77N4qc5LyVWzaXIv9YnEjjwpUMYk6XZKHq0dZFe03LeHe/4WDy9FcJX1H6Tm+j6ClBy103tjdhLa6e/Mgb4ESGp/HTewd7lKiKR276q/1/k+kazzvgFAdZaOgYndrA9bplL9HNte82UxZl7wKwDO7ajLdiba8Qk7/LExnvT61v9qlobUTuoniEv1s02mHKf+qJrCfAAqrQx94jg/z0tVjrZhdYxeV4mF6kfbk6m+mc17/Gcs7/ryxoqsK9HrKzHWH5s0e4v6a47KUxmTPTYdw1Gd/DZN3afXw7QivYbfX67XUraVsNmwM0i+P0b9I6N76DlPsplGn/7SP59XTNA2Ldil9dluz68nLwSQm+5fl/931mAaoqxUx738d7neBi7N0u+Ft/04UEbsuJ79hVYfzPru4pL951lv2aArdBxT8mAdEheTq/XNb+dncfWSYnWt0raQaj33UuMfw++Xa/ucP1RdFC/M17HT+yv8DuAv5w1rtk8lu9cgc9qvnwqr7HNXj3MyLcEYY/YF/XyyhericDgchw1OpiU0jeVT2UNo3S0BjDGmXYvUyuFwRJ19z/0OgNTLfx6Y572dONSnUuU6lQMIy/x1ODB61Ajz5YfTeGKlvtJ/uExNB3uCQj2TbRhtnpXUKNqnZb8xzpcbeWOxmr08SeuS/fp6nZ3mmwU22Ix0I7plAHC8DfVcYE0ar85YFyh7/FEaxtjDSrg8/4kuG9nfV15NT9S+vWCX/mEnD9Qw8rQEv88fmqvhyv/4fD3gmyw8E5lncgEYlqOv/zFlWp/9CVrPBdt8c8mEjmrGWWEVTQpsmOpOe7wAMdakckZfNbWllui+dz7zDwDaDx8WKLv+ZZXa6HaSvuF7MjkxNpwTYNN0lYLx5DI8U1l8qt+2NVax2AsBTsrW55bsSRpaXNDfN0nN2FACwOUdNfTWU3KWSt9s55n5pFhDkQvfVPOZJxkDYMrVHORlY6ws0OM0pf4Leuk2lR7Z9JEKSfS7SNePzVbT5ea33g+UTe+mYdH7thbZ49VQ2+5XXBYoUzZMj6PCtkG7fRrWG2OzPerG9XrdlaEmtkXb9fwV7NXje/5rP8z93JEaUvzsTDUlVtnrISPInHX+KL0WH/1Qw3Jzs/WaDL62k22I8z1lvi+3vk6lz3UaUnzuhSohdMckNRFu21cZKDOio+5D7H1mv1Vc/sH/lgfKeH6JTVayJsH+H2rsDTstxb+GBllzYZo1NWdZU6AXfg3wp9PU5Bq7RiWNKvofD0BMkDBHcmpas81RGT0HmUk/fyKssq/fcLQzfzkcDoejAZygZEhcp+JwOBxNwCXpCo3rVBwOR4NM+ssnxFmz2JvXj45ybQ4dnPR9aNqMTyW3zxBzyX3PBcJ65244UNn52mN6Ar6PYJeVMw++MD5ar+vNXKv28J3Wft2/Y3qgjCeTceeJ6s9Ya+XKj8pTv8f2fb4fp1Oa1mfWZrX5jums20mO83OR3fiyhs12teHPJ/XX0NjOQWHHX2wsAWCfDT09ra+WmWtl3vdW+Pv0ZNh7Z+r3Vs8GP9eXOPE4zvqD/vGuytvcdZ6vvJNibyQDrazHmmL1PXjh2kPLVgXK7uuk660rUZ9M13Qts7PUt2P//G2VixlufVE/nKhy9JUv/SFQxvO3pB93BgClc1RaZv5D0wDocdLAQFlPKj2ph5VcGX8+ABWv/jVQxsummZih5ya1r0rrx4z3ZW0CmTJn/U/XWaLno90wP7OD5zupGngCAHElGn5cvUQzUnpZCgHKFnyux7Vbz42X2XL3Ol8OZdd6rVfOcJWazzlZfSxe1kOAapstMbantq0nG1+Roe0Wv8fPTDmnVH0Nuana7t7V5V3j4IfW9rRyMZXW77Wx0Pe1zVqt4fGJ1q9Rbn13XqfywEV+m/RJ0WU1M54DoGiO+ptyTjj+gGOIm3wFAEvL9TwES+DkWb/P/M06r731oXy1RkOUh3TxJf5vGKN+oc83adsWl6n/5rjufrvl2f/cskK9Xh+YoSH2HdL9MPJIhBS36z7QjP3Rv8Iq++FtxzufisPhcDjqxxg/mMDh4zoVh8PhaBKGtmLpiSThan+1CCLyXRFZISJLROS+oPl3ichqu+y0hrbhcDgcUcFAdVVNWJ8jiaj5VKzS8c+As4wx5SKSa9U1BwPP4acT/gDo31j2x6EjRpmX3/uE56zdeO0OjXl//dGXAmWuve0qAEptDL8Xk3/BcD9daad0jXvPTNRl6fa7tNK/MN5drf6Wf32yFoAs6/v4wzkqa9Ex1X8BnFugYx06W7txT9ROvCXWH6eyz267f43a3E28+jDml/p+nBseUQn5KSfoWIB3v9IxCg9eo2ba0RUrAmW9VL6l4y4C4IUlOsbi2c/88TNn2PEzJ/XReuyytvO3l24LlNlqx81M7Kdl3l6g9evdQe3iw7r6tu45dXxYni+qNEgaI7ed2rR/for6NX49Tetcst8f1+CVz7Vtmpak9vWHTtB97X7pH4GyGZNO1+PNV5t5zS5t2/jJvoB27B6V86hctwSAmGGTADAJvsT83jj1R2zfr23gpfp9bsGWQJleNlXxmwt13oVHqcTMhK66bmnQjWPBVr32Tu2dofu2izL3+uNKpELbdv+M1wBIOUH9QQT/H209qtPVfyZrNDVCzT71z3kpDgBMnvp0ahaqDyqucy9b1k+t7Pk3EvqqX6Sys6ZWlip/XE+R6HHG2zFK6dV6LOVvaLuved1PZdCuu47r6foLTZn97taYWusC9M1SP2Gi9SHmJB9oHNlrz/kry/Q6vX6QXl+3TlO/1d4y//o4e5j+V6d+rOf8RCvF8r8vfDmaUYN0nNfwbnrNDMzR7SXG+s/Qx/fJabaPI63rADPyu4+EVfbzO088Ynwq0XxTuRm41xhTDirXbOdPAZ43xpQbY9YBq9EOxuFwOA4pjDFhfY4kotmp9AeOE5GvROQTERlr53cBgjNl5dt5DofDcehgHfXhfI4kWtRR30g2szggE03+NRaVZu6NHw0ZTMizIiI3AjcCdMjrwrId+wLhwTO/1H6p/wknBsq/9b6GwFaWaajhkHE9AfjVG0WBMoU29Dfemr0qStUkcuslviRJdoqayHbkq2lhk5U2udxmfNy2zjchtc/V8OU9RWpGWHauLkub/K1AmeU2pDM5W6U2zvy9mjBOPb5XoEyqNR11zlCzTbrNEOhJdrxJz0DZpaJmieqv1Hyw0GYe7JXnS7V5WQhX7VTz3DtLNDw1K0gyxVOxjbcZ+PbYUOodNmw7Kc4P45zQW4/z89VqgvKkcII5tq/Wa6NVyfVCi6ct8kNjf3GGhgx7CtJf2ayHZ7+iZqcRPa4JlD2qWte/YJxm9IyzEicV6f4l9/RGrfvJR6lp0rs+qnyLCh+uVLPLsI5qJnl6trbb8CDznkc/G1p+Xjfd7p/nq0nwh0P9duvUR81SyaXaFjHlWq9hf/RDsN+4S7OOdj/vewBsrFAzn5dhEvwsk+tLtN3zk/W5q1dnbVtPiRig3z6VXimfqFIwNR9oqGvssRcFylQO0ayXcUVqto1dpaas6l2+2HhGhZ6bdc+pTEv6Xx7Vulgl5/4X3xkoe+0LGkK89F5V0P7OBRr6fGwP/7qYt1WPfa29zs4ZqGHg10ydGSjzPWs2Pt1eH6c8qorbu6wK8xl2KADAUru9YquY3X6ctttVJ/uK3p4yeFaSmm1/9tx8ALp2jbRUocEcYR1GOLRop9JINrObgf8afTecJSI1QA76ZtItqGhXYEuITWDTcU4F6DtkhDu7Doej1TDgOpUQRNP89RpwIoCI9AcS0BzObwCXiUiiiPQC+gGzolVJh8PhCImB6uqasD5HEtEcp/I48LiILAYqgKvtW8sSEXkRWIpml7ylscgvh8PhiAbuTeVA2oxMS8e+Q8xl9z/P0da273H/y34WwFWfqq9i50u3APC0jUL05MIB5k9T2Y3H/3ANAL95YQEAJ47pGijzkZU7eSv1LQDGr1QZ9MumqG14ylDfpu/ZvSdf+xcAbrrtUgBefc+3r3fqmQH4MuWbN6uvxpPGALje2ow9iZQZ1nexq1SdA9dP8DMsetIVvaxMyzorI/NOULjwojXqR/Ik0ncWqK36R988KlBmS4mu18NKpHvy+k+8raHAaRm+7IVHZnud50mUe1kGAdrbuqckxNZa50fH9wxMPzJL/Rlvf63fPzhLfSwfrFC/x5/El5j/S4ba+XvZsNV2Nvy4X7bvz+lTpT6PxUZt+e9bGZJ3F/iSKUVWcv2Z7x4D+FG9iUFSOpf/Q/0P/3eFZgD9xUvqTzh7vFpqLw0KS/94nbbtiE7aBiOT1Ne2Jzk3UCbFbttYH4onDR+/dVmgjFRamflYPa6a3fac2dDgmuLtgbKJw47W7WVqTEv1Mq1vbLZ/LUqW1rFmq4aWS5cBAGxL9q/tDfZ8bdurfsJ4G4a7slDbaJVtK4+HLxpBXf75lR/e68kKfW6zow61kiuf2fMJsHGFLquwsvo//baeh09W6PEVFPly+54f89U7VC7nB6+ppM6PTuoXKPPbd1RWv18n9X/NnKfW80ED/DD+SGRiTMnrZwZc90BYZeffc6YLKXY4HA5Hw7RGSLGIZInI+yKyyn5nhigzQETmB312i8jtdtndIrI5aNmZzapQI7hOxeFwOJqIqQnv00zuBKYbY/oB0+3v2vUwZoUxZqQxZiQwGtgPvBpU5C/ecmPM282uUQO0Ge2vlIRYxvTIZJsNse1os9mdEGS2+t55twLQ7ab/AtBnrIYJX2Uz1gF8+oSO1P2/13QEdro1IV3+2PcDZW57Qs9Vr3PesXNUlXaxVVD91Yl+KDBAUnIyBa/rdXDcfZ8CsHP9ysDy6io1bT3+aw2Wy0pS89Ci7f5r/7gOOm/bPVqPsUD3/wsv69ywPDU5nDs4VHR3/XhZ/k5/ROMkXqh8GYCL06HjHX8/qG0BdPvGvwHI6uKbgRbcW/uh6Wcn9a/17XFON71U+97eMTCvZP1jAJxxzYUAlNtR7fmbfAXcz09Qk1/soCkAfLBEf2/P90ea79yg5s9Xl6ja8S47wr+/DTEG+PM3VPL9hr+peTSns5q23vhcTT0Dc/2yhfvUdOSZC/v9RjMQrrndN7/8ZrNeK6u2qdnRUxX4zgl9AmW8UOfj+thQ2wF6vW7M07Jb9vgj4b1R7E99oqH0A/MmAvCNnr5Z7uKpeh6PHazX27A4DSv/1ydzAmX+/Q01f46PVZNRZY6WPblnO5KSDwwTD8W3x/um2IRR1wEw4BQ9Rw9eoKP4d+71M4weO0BDsEfbEPM3bfbVP52r5uT5W32T25piDTP+0JoYz7fKEJ4iBMB1NgR582415W3wRtbnRTakuBUFJacAk+z0k8DHwE8aKH8SsMYYs6GBMi2Ge1NxOByOpmCgpqomrE8z6WiMKQCw37mNlL8MlboK5lYRWSgij4cyn0US16k4HA5HkzDUmPA+QI6IzA763Bi8JRH5QEQWh/hMOZgaiUgCcC7wUtDsh4E+wEigAPhTc466MdqM+cvhcDhak4Mc/FjYUPRXIwPFt4lInjGmQETygO31lQXOAOYaYwKhnsHTIvIv4M1wK90U2kxIcVz7Lqbd0d9my6s/brBcsF3Ys/fu/uKhRss6oot3riTGD0fuOfEsAB6+TUNQZ1k5mtvHdw6UufJ5DTndtkNt8RdM7A7Ad8b4ZVZ/S6VMBt1zDwBL4nsCsKbIz4h4y29fA+Cnt6sycpYNX15dqHIhY2+4JFD22tFa19QOXW2d1d9x2TmDAmW83Ob7rULvW9PUx/Z/txxd67gvG+HL3h37Bw2JnzRcfWNpSf4z4ffH6bw1N2uGxd+d/EsA3n7q9UCZgZMmAXDJCerzy7Ah3jfdcHetfVbMe5yWpuuVjwWmJ5+hYcle5tNfj1ffx1NrtW2e/tRX1/62zbbqtfsVI/U8BiuDF9lso2tsKP1v39Qw7fsu8KWWxvXIanaIb1JuX9Pj0j+GVXblg+c3eX8icj+w0xhzr4jcCWQZY+6op+zzwDRjzBNB8/I885mIfB8Yb4y5rCl1CQf3puJwOBxNpJUc9fei2ojXAxuBiwFEpDPwqDHmTPs7BTgF+Had9e8TkZHoy9X6EMsjiutUHA6HowkYY6hpBQkWY8xONKKr7vwtwJlBv/cD2SHKfaNFK1gH16k4HA5HEznSZO3Doc34VMaMGWNmz54d7Wo4WghvzMztKb5fos/HHwAwqZc+nFXW6FNj9j03BMpcmqMpBnbZ9AI/fOFnAMS/916gzJrtOg7ixdfV/9Kxe22pH4CxQ3R8zLQPdRxTThe1+3/zRB1Xcs9DHwfK1lT5YzA2P1cryOcAjr5nOgBzXnom5PKG/Btem3gcrj7Aqjkqd7Ss87EAXGMlcX73tA7FeH2DP+7ogS80cKnXA75cf2NtHAoRabZPJbFDH5M35Z6wym547LIjRqbFvak4HA5HUzAGU+O0buviOhWHw+FoAgbXqYTCmb8chwV1TT0AK4vVzOSF/p6yQLMd/vs7tU1Jt+5awYrtKstSZe8BsUHDfru300ye8202wa7tVOJn2EW1TRu7PvR/eyHO0DohuI7IEgnzV0J2L9PxzF+FVTb/P9c685fD4XA4GsDU1PKfORTXqTgcDkcTceavA4lap2IH4zwCJKEZHr9jjJlll90FXA9UA98zxkyLVj0djrbOhN99UOv3zJ/XqxjiCML5VEITzTeV+4BfG2PesUlj7gMmichgVGVzCNAZ+EBE+ruUwkc2ocJlh9t5wzurtDlDVZrk1it/eUDZAbmNy55P7Fk7k2WwD6Uuzo/iwLg3lVBEs1MxgPdPbw9ssdNTgOeNMeXAOhFZDYwDvmz9KjocDkd9GGpcp3IA0exUbgemicgfUQn+iXZ+F2BmULl8O+8ArHz0jQDdu3dvsYo6HA7HAbhxKiFp0U5FRD4AQqUb/BmqZfN9Y8wrInIJ8BhwMiAhyoeMezbGTAWmgoYUR6TSDofDEQbGGGoqXfRXXVq0U2kkR8BTwG3250vAo3Y6H+gWVLQrvmnM4XBEGOeYbzruTeVAopn5cQtwgp0+EVhlp98ALhORRBHpBfQDZkWhfg6Hw1E/1vwVzudIIpo+lRuAv4lIHFCG9Y0YY5aIyIvAUjTU+BYX+eVwOA49nE8lFFHrVIwxnwGj61n2e+D3rVsjh8PhCB9NJ9zy+VQON9yIeofD4WgKxjiZlhC4TsXhcDiagnHjVELhOhWHw+FoAgYw1a5TqUs0o78cDofj8KWVor9E5GIRWSIiNSJSr3y+iJwuIitEZLWI3Bk0P0tE3heRVfY7s1kVagTXqTgcDkeTaLWQ4sXABcCM+gqISCzwEHAGMBi43OooAtwJTDfG9AOm298thutUHA6Ho4m0RqdijFlmjFnRSLFxwGpjzFpjTAXwPKqjiP1+0k4/CZzXrAo1QpvxqcyZM2eviDTW8K1NDlAY7UqE4FCsl6tTeLg6hU9D9erR3I2b/YXTKuY+mhNm8SQRCU5NO9XKTEWKLsCmoN/5wHg73dEYUwBgjCkQkdwI7vcA2kynAqw41NJ1isjsQ61OcGjWy9UpPFydwqel62WMOT1S22pIJ9EY83o4mwgxLyp6iG2pU3E4HI7DkoZ0EsOkIc3EbSKSZ99S8oDtzdxXgzifisPhcBz+fA30E5FeIpKAJjp8wy57A7jaTl8NhPPm02TaUqcSSftkpDgU6wSHZr1cncLD1Sl8DtV6HRQicr6I5ANHA2+JyDQ7v7OIvA1gjKkCbgWmAcuAF40xS+wm7gVOEZFVwCn2d8vV1xiXhsThcDgckaEtvak4HA6HI8q4TsXhcDgcEaNNdCr1yRNEoR7rRWSRiMz3YtJbWyJBRB4Xke0isjhoXr11EJG7bLutEJHTWrFOd4vIZttW80XkzFauUzcR+UhEllkJjNvs/Ki1VQN1inZbJYnILBFZYOv1azs/mm1VX52i2lYONM/y4fwBYoE1QG8gAVgADI5SXdYDOXXm3QfcaafvBP7QwnU4HjgKWNxYHVA5hwVAItDLtmNsK9XpbuBHIcq2Vp3ygKPsdDqw0u47am3VQJ2i3VYCpNnpeOArYEKU26q+OkW1rdzHtIk3lYbkCQ4FWlUiwRgzAygKsw5TgOeNMeXGmHXAarQ9W6NO9dFadSowxsy103vQiJkuRLGtGqhTfbRWWxljzF77M95+DNFtq/rqVB+t0laOtmH+CiVP0NAfsSUxwHsiMkdEbrTzakkkAC0qkVAP9dUh2m13q4gstOYxz3TS6nUSkZ7AKPRp95Boqzp1gii3lYjEish8dODc+8aYqLdVPXWCQ+S6OlJpC53KISNPABxjjDkKVQq9RUSOj1I9wiWabfcw0AcYCRQAf4pGnUQkDXgFuN0Ys7uhoiHmtUi9QtQp6m1ljKk2xoxER2qPE5GhDRRvlXrVU6eot9WRTlvoVBqSJ2hVjDFb7Pd24FX09XqblUZAWkEioR7qq0PU2s4Ys83eFGqAf+GbIlqtTiISj968nzHG/NfOjmpbharTodBWHsaYEuBj4HQOkesquE6HUlsdqbSFTqUheYJWQ0RSRSTdmwZORfMgtKpEQj3UV4c3gMtEJFFEegH9gFmtUSHvZmQ5H22rVquTiAjwGLDMGPPnoEVRa6v66nQItFUHEcmw08nAycByottWIesU7bZycPhHfxljAM5EI2XWoKqe0ahDbzS6ZAGwxKsHkI0mxlllv7NauB7Poa/9lejT2fUN1QH4mW23FcAZrVinp4FFwEL0D5/XynU6FjV/LATm28+Z0WyrBuoU7bYaDsyz+18M/LKxa7sV2qq+OkW1rdzHOJkWh8PhcESOtmD+cjgcDschgutUHA6HwxExXKficDgcjojhOhWHw+FwRAzXqTgcDocjYrhOxeFwOBwRw3UqRxAisrfxUge9zXPFphsQkfNEZHATtvGxiIw5yPIrROTcEMt6SpDEfltHRH4aNJ1s5d4rRCQnmvVyHLm4TsXRLIwxbxhjvJzX56ES463BlcaYFlVOEJHYltx+hAh0KsaYUqNaWE5+xBE1XKdyBCLK/SKyWDSp2KV2/iT7FvCyiCwXkWesdAgicqad95mIPCAib9r514jIgyIyETgXuN8+LfcJfgMRkRwRWW+nk0Xkeask+wKQHFS3U0XkSxGZKyIvWXHFxo5ntGiypi+BW4Lmx9rj/Nru69t2foyI/EM0udObIvK2iFxkl60XkV+KyGfAxfXVx+7zE1FF6mlBGljfE5Gldn/PN1DnVFEV3a9FZJ6ITLHze4rIp3Z/c227IiJ5IjLDtu1iETlORO4FvLeTZ8I6+Q5HSxPtIf3u03ofYK/9vhB4H01w1hHYiCaImgTsQsX2YoAvUemQJFQ2vJdd/zngTTt9DfCgnf43cFHQ/j4GxtjpHGC9nf4B8LidHg5UAWNsmRlAql32E6z8Rp3jCGzX/l4InGCn78cmAwNuBH5upxOB2WiCpouAt+0xdgKKvXqjidbuCKrzAfVBc3d8AXSw8y8NOp4tQKKdzmjgXNwDXOWVQ2WGUoEUIMnO7wfMttM/xJf+iQXSg89pnW2vp06yOPdxn9b6xOE4EjkWeM4YU40qzX4CjAV2A7OMMfkAorkqegJ7gbVGkxuBdio31t3oQXA88ACAMWahiCy08yeg5rPP7QtSAtqx1YuItEdv3p/YWU+jqQdART2He28hQHv0Rn0s8JJRJdutIvJRnc2+0Eh9BgBDgfft/FhU2wy0g3tGRF4DXmug6qcC54rIj+zvJKA72ik9KCIjgWqgv13+NfC4qIrxa8aY+Q1s2+GIGq5TOTIJlVvCozxouhq9Rhoq3xBV+CbWpDrLQonOCZps6fKD2IfUsy1v2XeNMdNqzRQ5q5Ft7muoPiIyDFhijDk6xLpnoZ3mucAvRGSIMaaqnrpdaIxZUWfbdwPbgBFo25WBZs8Uzc9zFvC0iNxvjHmqkeNwOFod51M5MpkBXGp9Dh3Qm2BDMuDLgd6i2QhBzT2h2IPmVvdYD4y20xcFzZ8BXAkgmlhpuJ0/EzhGRPraZSki0p8GMJpLY5eIHGtnXRm0eBpws326R0T6i6Yl+Ay40PpWOqJmv1DUV58VQAcROdrOjxeRISISA3QzxnwE3IGaterzCU0Dvhvksxpl57cHCuxb1DfQtyBEpAew3RjzL1Qe/yhbvtI7PofjUMB1Kkcmr6JmmgXAh6gPYWt9hY0xpcB3gHetA3sb6nupy/PAj63juQ/wR/Sm/gXqn/B4GEizZq87sB2aMWYH6qN5zi6bCQwM43iuBR6yjvrSoPmPAkuBuaJhxv9E37xeQSX4vXlfhTqe+upjjKlAO8k/iMgCVKJ+ItoB/EdEFqGy7H+xnV4ofov6Zhbauv3Wzv8HcLWIzERNX95b0yRgvojMQ31if7Pzp9ptOEe945DASd87wkJE0owxe+2T9UPAKmPMX6JUl4+BHxljZjdjG97xZKOd2jENdayHEzbKbowxpjDadXEcebg3FUe43GAd90tQE80/o1iXIuDfEmLw40Hwpj2eT4HftoUORezgR/QNqCbK1XEcobg3FYejBRGRa4Hb6sz+3BhzS6jyDsfhjutUHA6HwxExnPnL4XA4HBHDdSoOh8PhiBiuU3E4HA5HxHCdisPhcDgixv8DwXcjovPQzRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tempxsalt.r_value.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works in any dimension the dataset has:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/srv/conda/envs/notebook/lib/python3.8/site-packages/xarray/core/computation.py:724: RuntimeWarning: invalid value encountered in sqrt\n",
      "  result_data = func(*input_data)\n"
     ]
    }
   ],
   "source": [
    "tempxsalt = xr_linregress(ds.mlt, ds.mls, dim='lon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f18a5a87850>"
      ]
     },
     "execution_count": 32,
     "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": [
    "tempxsalt.r_value.plot(x='time')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This map shows that in lower latitudes spatial patterns of salinity are generally anticorrlated with temperature and vice versa in the high latitudes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hatch sign agreement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It can often be useful to indicate if the sign along an averaged (or otherwise aggregated) dimension. For instance to show if forced changes have a consistent sign in reference to a multi-member mean. `sign_agreement` makes this easy. Consider this synthetic example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7f18a651b4f0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAosAAADQCAYAAABvPCsKAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAZuklEQVR4nO3df5BlZXng8e/TPT9h+D0DjDAMhkBFFw3gBHVJDCnQRWJlNAFXLV1IsTvrluzGSqySirtralO7S3Y3sdzVdRyRAlIGxURljEREVgsXFovhhzCI4oAoAyMDiAMTYGa6+9k/7hm89Jxzu/vevvfcc/v7qTp17z0/n7n9TM8z73nf80ZmIkmSJJUZqzsASZIkDS+LRUmSJFWyWJQkSVIli0VJkiRVsliUJElSJYtFSZIkVbJYHHER8e2IWFfTtZdGxBciYltEfDciTqwjDo2WmnP6TRFxV0RMRMQFdcSg0VNzTv9xRHw/Iu6NiJsjYm0dcWi4WSyqUkQs6vEUlwDPZOavAh8D/qL3qKTuzUNO/xS4GPib3qORejcPOX03sC4zXwv8LfDfeo9Ko8ZisSYRcWJE/CAiroiIrRHxuYg4NyJujYgfRcSZxX4HR8SVEXFHRNwdEeuL9RdHxFci4qsR8eOIuLT4H+LdEXF7RBzZdrn3RsRtxXVmc94vRsRXgW/0+MdcD1xdvP9b4JyIiB7PqSG1EHI6Mx/JzHuBqV7Oo2ZYIDn9rcx8vvh4O3B8L+fTiMpMlxoW4ERgAngNraL9TuBKIGgVWV8p9vsvwHuL94cDDwIH02rd2AYcAqwCdgHvL/b7GPDB4v23gc8U798EbJ3FebcDR1bE/R3gnpLl3JJ9twLHt31+CFhZ93fvYk5Pi3vWOd12zFXABXV/5y7mdEXcc87p4rhPAP++7u/dZfiWXpuv1ZsfZ+Z9ABFxP3BzZmZE3EfrlxTAW4Dfi4gPFZ+XAScU77+Vmc8Bz0XELuCrxfr7gNe2XedagMy8JSIOjYjDZzjvTZn587KAM/O35vDnK2tFdH7J0TbqOa2FZ0HkdES8F1gH/PZcj9Xos1is156291Ntn6f45c8mgD/IzB+2HxgRr5/l8XBggZYznPcfqwKOiO/Q+l/ydB/KzG9OW7cdWANsj1a/msOA0l9uGhmjntNaeEY+pyPiXOAjwG9n5p4DD9NCZ5/F4Xcj8G/39/WLiNO7OMc/L479TWBXZu7q9ryZ+VuZeVrJUvaP6mbgouL9BcD/yUxbFtXknJbKNDani3N+Gvi9zNzZRdxaACwWh9+fA4uBeyNia/F5rp6JiNuAjbRGKM/XeWfyWeCoiNgG/DFwWR+uoeZpbE5HxG9ExHbgQuDTxW1JqbE5Dfx3YAXwxYi4JyI29+EaariwoUeSJElVbFmUJElSJYtFSZKkBiieu7mz6JZQtj0i4n9Ga+a0eyPijPm4rsWiJElSM1wFnNdh+1uBk4tlA/Cp+bioxaIkSVIDZOYtdH4E3Xrgmmy5HTg8Ilb3et1GPGdx5cqVecLaA+c2/8e9k5XHPPP8vsptu5/fW7lt357yR0xN7u3w6Kmp6jgYG6/cNL5kaeW2xUvLt604aEnlMUcctLhy24rFHf5f8MKzpasnni1fD7Bn1/OV2154caL6uMnqAVVV32L1NwhLx6tnD1y+rDq9lx66vHT9osMOq77Y8kM7RAJ33X33U5m5quNOhZUrV+baE06YeUepRnPJ6WUxnoc0458UjYi1r3vNnI+58847Z53T/bImlueLFTOGPsXe+4EX21ZtysxNczj9ccCjbZ+3F+t2zDXOdo34m33C2rXceuutB6z/f9ufqzzmS9+r/l5uveuxym0/e/Ch0vW7tj9YeczEi7srty1atqJy22HHn1K57dhTTipd/4bTX1F5zIWnVW/7p8dVxzG+9abS9U/e+A+Vxzz0tXsrtz3wg6crtz24u7pQ3z1R/pdnxaLqQveUFdXF86t+7ajKbb9y3qml648+/3crj5k89c2V2wCWH3TQTzru0GbtCSeU5rQ0TOaS04ewiD+g5wYMadY2btky52MiYtY53S97mOKdFY19/zt/8mJmruvh9H2ZOa0RxaIkSdIoCGA8Ku6K9f40w/0zp+13PPB4rye1z6IkSdKABLBkLEqXebAZ+BfFqOg30JoNqKdb0GDLoiRJ0sBE0HVhGBHXAmcDK4vZpD5Ka5YfMnMjcANwPrANeB74w3kI2WJRkiRpUFq3obs7NjPfPcP2BD7Q3dmrWSxKkiQNSBAsruqzOKQsFqU6vPBs5Sh0SVLLxnyk7hDmXS+3oetisShJkjQg+we4NInFoiRJ0oB0fHTOkLJYlCRJGhBvQ0uSJKlS6zZ03VHMjcWiJEnSgAThbWhJM5vYtYudN3yt7jAkqXajOOK5E29DS5IkqZKjoSVJklTJYlGSJEmVImDMPouSJEkqFcF4w4ZDWyxKkqSR8P44se4QZhQBY0vG6w5jTiwWpRrsefYFHv761rrDkKSBWGgjnjuKYGzc29CSJEkqEQHji21ZlCRJUpkIb0NLkiSpXKtl0QEukiRJKhWMjVssAhARa4BrgGOBKWBTZn48Io4EvgCcCDwCvDMzn+lXHJIkabQ0YdRzlRiDMR+d85IJ4E8y866IOAS4MyJuAi4Gbs7MyyPiMuAy4MN9jEMaOi+8OMEDP3i67jAkad444nmWIhhvWJ/FvpW2mbkjM+8q3j8HPAAcB6wHri52uxp4e79ikCRJGiYBjI1F6TKsBtJnMSJOBE4Hvgsck5k7oFVQRsTRFcdsADYArFmzZhBhSn3VntNHht2F1XztOb2CZrWUSLUJbFmcLiJWAH8HfDAzn53tcZm5KTPXZea6latW9S9AaUDac3rFWLN+UUhl2nN6mcWiNCsx1np0TtkyrPravBERi2kVip/LzC8Vq5+IiNVFq+JqYGc/Y5AkSRoaPjrnlyIigM8CD2TmX7Vt2gxcBFxevF7frxgkSVIzNXnEcyfho3Ne5izgfcB9EXFPse5PaRWJ10XEJcBPgQv7GIM0lPZMJg/u3lt3GJI0Z4567tEYQ33LuUzfisXM/L+0Bv2UOadf15UkSRpWEcH44mYNcmxWtJIkSY0WhLehJUmSVCqCsYa1LDartJUkSWqwiGBsyeLSZZbHnxcRP4yIbcVMeNO3nx0RuyLinmL5j73G3KzSVpIkjYxRHfHcUdB1n8WIGAc+CbwZ2A7cERGbM/P703b9Tma+rbdAf8liUarBJLB7YqruMCSplCOe+yh66rN4JrAtMx9unSo+T2sa5enF4rzyNrQkSdKARNFnsWwBVkbElrZlw7TDjwMebfu8vVg33Rsj4nsR8Q8R8U96jdmWRUmSpEEJGF9SWX49lZnrOh99gJz2+S5gbWbujojzga8AJ885zja2LEqSJA1IRBBjY6XLLGwH1rR9Ph54vH2HzHw2M3cX728AFkfEyl5itmVRkiRpUCIYq25ZnMkdwMkR8UrgMeBdwHtefvo4FngiMzMizqTVMPh0DxFbLEqSpP5ZkCOeO2jN4DK7x+RMl5kTEXEpcCMwDlyZmfdHxPuL7RuBC4B/ExETwAvAuzJz+q3qObFYlGowDqxYZC8QDbmJugNQPzniuT69zOBS3Fq+Ydq6jW3vPwF8ousLlLBYlCRJGpQGzuDSrGglSZIaLMZ66rNYi2ZFK0mS1GQRjC3qrs9iXSwWJUmSBmlsvO4I5sRiUZIk9cQRz3MQQXQ5GrouFotSDZaOB6esWFJ3GFJnv6g7APXKEc9DKAIWNev3v8WiJEnSgAQx29lahobFoiRJ0qDYsihJkqRKEYSjoSVJklQqglhsyyIAEXEl8DZgZ2aeWqz7M+BfAU8Wu/1pMW2NJEkaco56ng8B9ll8yVW05ia8Ztr6j2Xm/+jjdaWht3zZIl71a0fVHYbU2e11B6DZctRzg0QQ9llsycxbIvwviCRJ0ksa2GexjnbQSyPi3oi4MiKOqNopIjZExJaI2PLUk09W7SY1RntO/2LfvrrDkXrWntMvMll3OFJDRGsGl7JlSA26WPwUcBJwGrAD+MuqHTNzU2auy8x1K1etGlB4Uv+05/ThDXt6v1SmPaeXMbz/0EnDJIoZXMqWYTXQ0dCZ+cT+9xHxGeDvB3l9SZKkWvmcxc4iYnVm7ig+vgPYOsjrS5Kkzhzx3GcN7LPYz0fnXAucDayMiO3AR4GzI+I0IIFHgH/dr+tLw2zpYQdx0u++tu4wpM5uv63uCNTGEc8jJHx0DgCZ+e6S1Z/t1/UkSZKGX5BjzZoTpVnRSpIkNVnEUI98LmOxKEmSNEjO4CJJkqQyGd6GliRJQ84Rz3UKsFiUNJNFhx7Kqn/21rrDkDr7DxvrjmDBccTzwmDLoiRJkspFtJYGsViUJEkaIFsWJUmSVC4CxptVfjUrWkmSpEYLZ3CRJEn1c8Tz8OrlNnREnAd8HBgHrsjMy6dtj2L7+cDzwMWZeVf30VosSvVYfiiTp7657igk1cARzwtcDzO4RMQ48EngzcB24I6I2JyZ32/b7a3AycXyeuBTxWvXZmwHjYhXl6w7u5eLSpIkLUyth3KXLbNwJrAtMx/OzL3A54H10/ZZD1yTLbcDh0fE6l4ins1N8+si4sPRsjwi/hfwX3u5qCRJ0oIVY+ULrIyILW3LhmlHHgc82vZ5e7FurvvMyWzK2NcDfwHcBhwCfA44q5eLSpIkLURJMEXlcxafysx1HQ4vOzC72GdOZlMs7gNeAJYDy4AfZ+ZULxeVJElamJLJ7Lp22w6saft8PPB4F/vMyWyKxTuA64HfAI4CPh0RF2TmBb1cWJIk9c5Rz82SwFT37Xx3ACdHxCuBx4B3Ae+Zts9m4NKI+Dytu8O7MnNH11dkdsXiJZm5pXj/M2B9RLyvl4tKC93ufVPc9tjuusOQ1CeOeFaVBCa7rBYzcyIiLgVupPXonCsz8/6IeH+xfSNwA63H5myj9eicP+w15hmLxbZCsX3dX/d6YUmSpAUnYbKHHoSZeQOtgrB93ca29wl8oPsrHMjnLEqSJA1IQi99FmthsShJkjRADasVLRYlSZIGJbP7Pot16VuxGBFXAm8DdmbmqcW6I4EvACcCjwDvzMxn+hWDJEmjwBHPo6WXPot16GfL4lXAJ4Br2tZdBtycmZdHxGXF5w/3MQZpKD3z/D6+eE9Pj72SNAQc9ay5aj06p1nV4mym++tKZt4C/Hza6vXA1cX7q4G39+v6kiRJw2gyy5dh1bdiscIx+x8MWbweXbVjRGzYPzfiU08+ObAApX5pz+kXnrX3hZqvPadfZLLucKRGyGIGl7JlWA26WJy1zNyUmesyc93KVavqDkfqWXtOLz/0iLrDkXrWntPLGK87HKkZEianypdhNehi8YmIWA1QvO4c8PUlSZJqk7RGRJctw2rQj87ZDFwEXF68Xj/g60uSNJQc8bxwDPMt5zL9fHTOtcDZwMqI2A58lFaReF1EXAL8FLiwX9eXhtnu5/dy+92OhpaawBHPmk+ZsG+YR7OU6FuxmJnvrth0Tr+uKUmSNMya+OgcZ3CRJEkakCTZNzXEo1lKWCxKkiQNiLehJUmSVCmBfc4NLUmSyjjiWa3nLFosSprBvj17+NmDD9UdhqSCI541KK0+ixaLkiRJKtHqs+gAF0mSJJXw0TmSJEmq5AAXSZIWOAexqJPM9NE5kmY2uXcPu7Y/WHcY0oLiIBYNg1bLon0WJUmSVCZhytvQkiRJKmOfRUmSJFXKTPb66BxJkiSVSZzBRZKkBcFRz+pGJuydsGVR0kymJpl4cXfdUUgjxxHPGnaZabEoSZKkclN9almMiCOBLwAnAo8A78zMZ0r2ewR4DpgEJjJz3UznHpvPQCVJktTZ5FSWLj26DLg5M08Gbi4+V/mdzDxtNoUiWCxKkiQNzFQmeyamSpcerQeuLt5fDby91xPu521oSZKkAZlhgMvKiNjS9nlTZm6a5amPycwdrWvkjog4uioE4BsRkcCnZ3N+i0VJkio44ln9MJmVt5yf6nRrOCK+CRxbsukjc7j8WZn5eFFM3hQRP8jMWzodUEux2E3nSmmkjI2zaNmKuqOQOtpXdwAdOOpZTTXVw2jozDy3altEPBERq4tWxdXAzopzPF687oyILwNnAh2LxTr7LM6pc6UkSVLTJbB3YrJ06dFm4KLi/UXA9dN3iIiDI+KQ/e+BtwBbZzqxt6ElSZIGpI/PWbwcuC4iLgF+ClwIEBGvAK7IzPOBY4AvRwS0asC/ycyvz3TiuorFGTtXRsQGYAPAmjVrBhyeNP/aczqWHFJzNFLv2nN6BeM1RyM1QyZM9GG6v8x8GjinZP3jwPnF+4eBX5/ruesqFmfsXFkUkJsAznjd65o1iaJUoj2nx1YcY06r8dpzelUsNaelWXC6v1nqpnOlJEn94IhnDVLidH8zKjpUjmXmc22dK//ToOOQ6jS+ZCmHHX9K3WFIHb1Q8/Ud8axRlMl8zNYyUHW0LHbVuVKSJKnpMmHSlsXOuu1cKUmS1HzJ1KTFoiRJkkq0Wha9DS1JkqQKWT3d31CyWJQkjTxHPGtoZNpnUdLMFi9dyrGnnFR3GFJHPxvANRzxrIUmEybtsyhJkqQq6aNzJEmSVCa9DS1JkqROpiZtWZQkSVIJH8otSVJNHPGsRkj7LEqahRUHLeGsM46rOwypo3vm6TyOeJZ+KUlHQ0uSJKmCt6ElSZLUiTO4SJIkqZSPzpEkSVK1TKYm9tYdxZxYLEqSGsVRz2qyzGRqn8WipBkccdBifv/XV9cdhtTRJ+ew79rXvYaNW7b0LRZpdCQ5NVl3EHNisShJkjQo3oaWJElSlbRYlCRJUrVkytvQkiRJKmPLoiRJ88ARzxpZjoaenYg4D/g4MA5ckZmX1xGHVJeDl4zzxuMPqTsMSdKg5VTjWhbHBn3BiBin9USGtwKvBt4dEa8edBySJEmDlkBOTZUuw6qOlsUzgW2Z+TBARHweWA98v4ZYJEmSBqeBfRYH3rIIHAc82vZ5e7HuZSJiQ0RsiYgtTz355MCCk/rFnNaoac/pJ81paVYyk8l9e0uXXkTEhRFxf0RMRcS6DvudFxE/jIhtEXHZbM5dR7EYJevygBWZmzJzXWauW7lq1QDCkvrLnNaoac/pVea0NDvZmsGlbOnRVuD3gVuqdui2K2Adt6G3A2vaPh8PPF5DHJIkSQPWn9vQmfkAQERZm9xLuuoKGJkHNOr1VUQsAh4EzgEeA+4A3pOZ93c45kngJ8XHlcBT/Y5zFozj5YwD1mbmrJpXzOmOjOPlzOnuGcfLGccccrpfIuLrtL6DMsuAF9s+b8rMTXM8/7eBD2XmAZO1R8QFwHmZ+S+Lz+8DXp+Zl3Y658BbFjNzIiIuBW6k9eicKzsVisUxL/1gI2JLZlbeix8U4zCOXpjTxtG0OGZiThtH0+KoS2ae1+2xEfFN4NiSTR/JzOtnc4qykGY6qJbnLGbmDcANdVxbkiSpiTLz3B5P0VVXwDoGuEiSJGnw7gBOjohXRsQS4F3A5pkOamKxOKd7931kHC9nHN0blpiN4+WMo3vDErNxvJxxjLCIeEdEbAfeCHwtIm4s1r8iIm6AVldAYH9XwAeA62bqCgg1DHCRJElSczSxZVGSJEkDYrEoSZKkSo0qFruZoqZPcTwSEfdFxD0RccBzjPp43SsjYmdEbG1bd2RE3BQRPypej6gpjj+LiMeK7+SeiDi/zzGsiYhvRcQDxfRGf1SsH/j30Qtz2pxuu545Pb9xmNM153RxzZHI64WuMcVidDlFTR/9TmaeNuBnRV0FTH8+02XAzZl5MnBz8bmOOAA+VnwnpxWPR+qnCeBPMvNVwBuADxT5UMf30RVzGjCn25nT88+cPtAgcxpGIK/VoGKRtilqMnMvsH+KmgUjM28Bfj5t9Xrg6uL91cDba4pjoDJzR2beVbx/jtaoruOo4fvogTltTrfHYE6PAHP6gDhGIa8XvCYVi8cBj7Z93l6sq0MC34iIOyNiQ00x7HdMZu6A1l9K4OgaY7k0Iu4tbn8M7JZCRJwInA58l+H6PmZiTpcbpp+hOT035nS5YfoZ1pLT0Oi8XvCaVCx2NUVNn5yVmWfQutXygYh4U01xDJNPAScBpwE7gL8cxEUjYgXwd8AHM/PZQVxzHpnTw82cnjtzerjVktPQ+Lxe8JpULHY1RU0/ZObjxetO4Mu0br3U5YmIWA1QvO6sI4jMfCIzJzNzCvgMA/hOImIxrV8+n8vMLxWrh+L7mCVzutxQ/AzN6a6Y0+WG4mdYR07DSOT1gtekYrGrKWrmW0QcHBGH7H8PvAXY2vmovtoMXFS8vwiYzUTi827/X/rCO+jzdxIRAXwWeCAz/6pt01B8H7NkTpcbip+hOd0Vc7rcUPwMB53TxTVHIa+VmY1ZgPOBB4GHgI/UFMOvAN8rlvsHGQdwLa1bB/to/Q/+EuAoWiPJflS8HllTHH8N3AfcS+uXwOo+x/CbtG5v3QvcUyzn1/F99PjnMKfN6f0xmNPzF4M5PQQ5XcQxEnm90Ben+5MkSVKlJt2GliRJ0oBZLEqSJKmSxaIkSZIqWSxKkiSpksWiJEmSKlksSpIkqZLFoiRJkipZLC4gEfHnEfFHbZ//c0T8uzpjknphTmvUmNMaRj6UewGJiBOBL2XmGRExRuvJ+Wdm5tP1RiZ1x5zWqDGnNYwW1R2ABiczH4mIpyPidOAY4G5/AanJzGmNGnNaw8hiceG5ArgYOBa4st5QpHlhTmvUmNMaKt6GXmAiYgmtieQXAydn5mTNIUk9Mac1asxpDRtbFheYzNwbEd8CfuEvII0Cc1qjxpzWsLFYXGCKDtNvAC6sOxZpPpjTGjXmtIaNj85ZQCLi1cA24ObM/FHd8Ui9Mqc1asxpDSP7LEqSJKmSLYuSJEmqZLEoSZKkShaLkiRJqmSxKEmSpEoWi5IkSar0/wF/uwih6HHdfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.linspace(-np.pi, np.pi, 25)\n",
    "y = np.linspace(-np.pi, np.pi, 25)\n",
    "xx, yy = np.meshgrid(x,y)\n",
    "\n",
    "data1 = np.sin(xx)\n",
    "data2 = np.sin(yy)\n",
    "data3 = np.ones_like(xx)\n",
    "\n",
    "np.fill_diagonal(data3,np.nan)\n",
    "np.fill_diagonal(data3[1:],np.nan)\n",
    "np.fill_diagonal(data3[:,1:],np.nan)\n",
    "np.fill_diagonal(data3[:,2:],np.nan)\n",
    "\n",
    "da = xr.DataArray(np.array([data1, data2, data3]), dims=['member','x', 'y'])\n",
    "\n",
    "da.plot(col='member')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the mean of these fields, suggests that values increase in the upper-left, upper-right, lower-right quadrant, and the missing values in the third layer distort the mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f18a6545520>"
      ]
     },
     "execution_count": 34,
     "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": [
    "da.mean('member').plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets produce a mask to see where all elements along the `member` dimension have the same sign:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f18a6172940>"
      ]
     },
     "execution_count": 35,
     "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": [
    "from xarrayutils.utils import sign_agreement\n",
    "\n",
    "sign_agreement(da, da.mean('member'), 'member', threshold=1.0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You could use this information to indicate the areas of the average, where the members do not agree by hatching:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f18a66cb2b0>"
      ]
     },
     "execution_count": 36,
     "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": [
    "da.mean('member').plot()\n",
    "sign_agreement(\n",
    "    da, da.mean('member'), 'member'\n",
    ").plot.contourf(\n",
    "    colors='none',\n",
    "    hatches=['..', None],\n",
    "    levels=[0,0.5],\n",
    "    add_colorbar=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Masking values in the mixed layer\n",
    "\n",
    "Sometimes it is helpful to analyze data by excluding the values in the mixed layer. This can be easily done with `mask_mixedlayer`. Let's see how:\n",
    "\n",
    "First load a CMIP6 dataset from the cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "--> The keys in the returned dictionary of datasets are constructed as follows:\n",
      "\t'activity_id.institution_id.source_id.experiment_id.table_id.grid_label'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "    <div>\n",
       "        <style>\n",
       "            /* Turns off some styling */\n",
       "            progress {\n",
       "                /* gets rid of default border in Firefox and Opera. */\n",
       "                border: none;\n",
       "                /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
       "                background-size: auto;\n",
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       "            .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
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       "      <progress value='1' class='' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
       "      100.00% [1/1 00:00<00:00]\n",
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   "source": [
    "import intake\n",
    "url = \"https://raw.githubusercontent.com/NCAR/intake-esm-datastore/master/catalogs/pangeo-cmip6.json\"\n",
    "col = intake.open_esm_datastore(url)\n",
    "cat = col.search(\n",
    "    table_id='Omon',\n",
    "    grid_label='gn',\n",
    "    experiment_id='historical',\n",
    "    member_id='r1i1p1f1',\n",
    "    variable_id=['thetao','mlotst'],#, \n",
    "    source_id=[\"ACCESS-ESM1-5\"]\n",
    ")\n",
    "ddict = cat.to_dataset_dict(\n",
    "    zarr_kwargs={'consolidated':True, 'decode_times':True},\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id           (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i) float32 dask.array&lt;chunksize=(1, 196, 300, 360), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices) float64 dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices) float64 dask.array&lt;chunksize=(300, 360, 4), meta=np.ndarray&gt;\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    grid:                    native atmosphere N96 grid (145x192 latxlon)\n",
       "    forcing_index:           1\n",
       "    table_id:                Omon\n",
       "    variant_label:           r1i1p1f1\n",
       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
       "    tracking_id:             hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n",
       "    initialization_index:    1\n",
       "    physics_index:           1\n",
       "    run_variant:             forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n",
       "    branch_time_in_parent:   21915.0\n",
       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
       "    cmor_version:            3.4.0\n",
       "    grid_label:              gn\n",
       "    nominal_resolution:      250 km\n",
       "    intake_esm_varname:      mlotst\\nthetao\n",
       "    experiment_id:           historical\n",
       "    realization_index:       1\n",
       "    parent_activity_id:      CMIP\n",
       "    sub_experiment_id:       none\n",
       "    source_id:               ACCESS-ESM1-5\n",
       "    branch_time_in_child:    0.0\n",
       "    parent_experiment_id:    piControl\n",
       "    activity_id:             CMIP\n",
       "    data_specs_version:      01.00.30\n",
       "    realm:                   ocean\n",
       "    product:                 model-output\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    experiment:              all-forcing simulation of the recent past\n",
       "    mip_era:                 CMIP6\n",
       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
       "    branch_method:           standard\n",
       "    frequency:               mon\n",
       "    institution_id:          CSIRO\n",
       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_time_units:       days since 0101-1-1\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
       "    sub_experiment:          none\n",
       "    source_type:             AOGCM\n",
       "    version:                 v20191115\n",
       "    parent_source_id:        ACCESS-ESM1-5\n",
       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn</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-ba98bbd3-835d-46dd-ba59-ea7cde005bc5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ba98bbd3-835d-46dd-ba59-ea7cde005bc5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 300</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-81471af6-642b-4a81-b75f-3d57fa6d3cb8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-81471af6-642b-4a81-b75f-3d57fa6d3cb8' class='xr-section-summary' >Coordinates: <span>(9)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-f44a573c-d6ba-4a6f-a53b-3ddcf6f5526f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f44a573c-d6ba-4a6f-a53b-3ddcf6f5526f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b6ff55c-cf0f-4e96-91a4-fbc3831600e8' class='xr-var-data-in' type='checkbox'><label for='data-7b6ff55c-cf0f-4e96-91a4-fbc3831600e8' 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>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-85f67281-b934-494e-b469-62de56ce0c63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85f67281-b934-494e-b469-62de56ce0c63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de7e481a-37a4-4dc2-8611-f966bc96215a' class='xr-var-data-in' type='checkbox'><label for='data-de7e481a-37a4-4dc2-8611-f966bc96215a' 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>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 297, 298, 299], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-32438597-857f-4e3c-a1b2-2911f135685d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32438597-857f-4e3c-a1b2-2911f135685d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b275c52e-911f-40c1-acf3-d7430ff1693c' class='xr-var-data-in' type='checkbox'><label for='data-b275c52e-911f-40c1-acf3-d7430ff1693c' 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>bounds :</span></dt><dd>vertices_latitude</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'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "  <thead>\n",
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       "    <tr><th> Shape </th><td> (300, 360) </td> <td> (300, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-009a1894-a6c8-4750-9be2-8ded5375cc4e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-009a1894-a6c8-4750-9be2-8ded5375cc4e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebe64f2-d1db-48ed-b629-40d29d0ee2d7' class='xr-var-data-in' type='checkbox'><label for='data-8ebe64f2-d1db-48ed-b629-40d29d0ee2d7' 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>bounds :</span></dt><dd>vertices_longitude</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'><table>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-ab9b1b73-268e-4a4e-84af-1a2cbb28d144' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab9b1b73-268e-4a4e-84af-1a2cbb28d144' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55724f72-8ccb-4256-a3c3-5549aa548d04' class='xr-var-data-in' type='checkbox'><label for='data-55724f72-8ccb-4256-a3c3-5549aa548d04' 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;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-05553b84-4551-4e73-94e8-c10736a841f6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05553b84-4551-4e73-94e8-c10736a841f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7151dabf-8811-416f-9699-98211840beb7' class='xr-var-data-in' type='checkbox'><label for='data-7151dabf-8811-416f-9699-98211840beb7' 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'><table>\n",
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       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-4671b7e8-80ef-4894-9afb-57fcba4f7d15' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4671b7e8-80ef-4894-9afb-57fcba4f7d15' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02119c6e-7797-4290-ba68-a0c9b82dc289' class='xr-var-data-in' type='checkbox'><label for='data-02119c6e-7797-4290-ba68-a0c9b82dc289' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 ... 5.499e+03 5.831e+03</div><input id='attrs-286e2cf4-d66d-4692-b352-a428dc54fbcd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-286e2cf4-d66d-4692-b352-a428dc54fbcd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61edc8e9-c8ca-4be7-999f-5729054f872f' class='xr-var-data-in' type='checkbox'><label for='data-61edc8e9-c8ca-4be7-999f-5729054f872f' 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>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01,\n",
       "       5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01,\n",
       "       1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02,\n",
       "       1.550000e+02, 1.650000e+02, 1.750000e+02, 1.850000e+02, 1.950000e+02,\n",
       "       2.050000e+02, 2.168468e+02, 2.413490e+02, 2.807807e+02, 3.432505e+02,\n",
       "       4.273156e+02, 5.367156e+02, 6.654141e+02, 8.127816e+02, 9.690651e+02,\n",
       "       1.130935e+03, 1.289605e+03, 1.455770e+03, 1.622926e+03, 1.801558e+03,\n",
       "       1.984855e+03, 2.182905e+03, 2.388417e+03, 2.610935e+03, 2.842564e+03,\n",
       "       3.092205e+03, 3.351295e+03, 3.628058e+03, 3.913264e+03, 4.214495e+03,\n",
       "       4.521918e+03, 4.842566e+03, 5.166130e+03, 5.499245e+03, 5.831294e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-0c723a21-9e25-4056-8c9b-0ddfdb371c92' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c723a21-9e25-4056-8c9b-0ddfdb371c92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4044aa02-7a08-4d62-aff3-94136cc2ad33' class='xr-var-data-in' type='checkbox'><label for='data-4044aa02-7a08-4d62-aff3-94136cc2ad33' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(member_id, time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-f8683fdf-e6d6-441c-b16c-99cf7114d44a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f8683fdf-e6d6-441c-b16c-99cf7114d44a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21057553-3baf-4182-a1b2-26fd3a022b25' class='xr-var-data-in' type='checkbox'><label for='data-21057553-3baf-4182-a1b2-26fd3a022b25' 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: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "aerosol: CLASSIC (v1.0)\n",
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       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fde\n",
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       "<xarray.Dataset>\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
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       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
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       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
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       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
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       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
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       "    intake_esm_varname:      mlotst\\nthetao\n",
       "    experiment_id:           historical\n",
       "    realization_index:       1\n",
       "    parent_activity_id:      CMIP\n",
       "    sub_experiment_id:       none\n",
       "    source_id:               ACCESS-ESM1-5\n",
       "    branch_time_in_child:    0.0\n",
       "    parent_experiment_id:    piControl\n",
       "    activity_id:             CMIP\n",
       "    data_specs_version:      01.00.30\n",
       "    realm:                   ocean\n",
       "    product:                 model-output\n",
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       "    experiment:              all-forcing simulation of the recent past\n",
       "    mip_era:                 CMIP6\n",
       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
       "    branch_method:           standard\n",
       "    frequency:               mon\n",
       "    institution_id:          CSIRO\n",
       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_time_units:       days since 0101-1-1\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
       "    sub_experiment:          none\n",
       "    source_type:             AOGCM\n",
       "    version:                 v20191115\n",
       "    parent_source_id:        ACCESS-ESM1-5\n",
       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = ddict['CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn']\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can remove the values in the mixed layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/xarrayutils/xarrayutils/utils.py:805: UserWarning: Cell bounds [{z_bounds}] not found in input. Masking is performed with cell centers, which might be less accurate\n",
      "  warnings.warn(\n"
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       "  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:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id           (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array&lt;chunksize=(1, 196, 300, 360, 50), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    grid:                                 native atmosphere N96 grid (145x192...\n",
       "    forcing_index:                        1\n",
       "    table_id:                             Omon\n",
       "    variant_label:                        r1i1p1f1\n",
       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
       "    tracking_id:                          hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n",
       "    initialization_index:                 1\n",
       "    physics_index:                        1\n",
       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
       "    branch_time_in_parent:                21915.0\n",
       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
       "    cmor_version:                         3.4.0\n",
       "    grid_label:                           gn\n",
       "    nominal_resolution:                   250 km\n",
       "    intake_esm_varname:                   mlotst\\nthetao\n",
       "    experiment_id:                        historical\n",
       "    realization_index:                    1\n",
       "    parent_activity_id:                   CMIP\n",
       "    sub_experiment_id:                    none\n",
       "    source_id:                            ACCESS-ESM1-5\n",
       "    branch_time_in_child:                 0.0\n",
       "    parent_experiment_id:                 piControl\n",
       "    activity_id:                          CMIP\n",
       "    data_specs_version:                   01.00.30\n",
       "    realm:                                ocean\n",
       "    product:                              model-output\n",
       "    Conventions:                          CF-1.7 CMIP-6.2\n",
       "    experiment:                           all-forcing simulation of the recen...\n",
       "    mip_era:                              CMIP6\n",
       "    institution:                          Commonwealth Scientific and Industr...\n",
       "    branch_method:                        standard\n",
       "    frequency:                            mon\n",
       "    institution_id:                       CSIRO\n",
       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
       "    parent_variant_label:                 r1i1p1f1\n",
       "    parent_mip_era:                       CMIP6\n",
       "    parent_time_units:                    days since 0101-1-1\n",
       "    further_info_url:                     https://furtherinfo.es-doc.org/CMIP...\n",
       "    sub_experiment:                       none\n",
       "    source_type:                          AOGCM\n",
       "    version:                              v20191115\n",
       "    parent_source_id:                     ACCESS-ESM1-5\n",
       "    license:                              CMIP6 model data produced by CSIRO ...\n",
       "    intake_esm_dataset_key:               CMIP.CSIRO.ACCESS-ESM1-5.historical...\n",
       "    mixed_layer_values_removed_based_on:  lev</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-8873b060-c334-4ba9-a6d0-d854fa24babc' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8873b060-c334-4ba9-a6d0-d854fa24babc' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 300</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d477e8bd-a0b9-404b-bd2c-b83cd50bf89d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d477e8bd-a0b9-404b-bd2c-b83cd50bf89d' class='xr-section-summary' >Coordinates: <span>(9)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-f56fa3db-6fde-4336-83e3-dbf7c2dbfb3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f56fa3db-6fde-4336-83e3-dbf7c2dbfb3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86d2db49-c4e6-4711-ab57-4e36292a43ac' class='xr-var-data-in' type='checkbox'><label for='data-86d2db49-c4e6-4711-ab57-4e36292a43ac' 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>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-5f255333-0583-4870-ac5f-227c8a8e8d58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f255333-0583-4870-ac5f-227c8a8e8d58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa5c1c0b-7184-429d-8ce0-cd9a117c989d' class='xr-var-data-in' type='checkbox'><label for='data-aa5c1c0b-7184-429d-8ce0-cd9a117c989d' 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>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 297, 298, 299], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-a43dd28d-5fe0-4ff9-8854-65e476c09da8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a43dd28d-5fe0-4ff9-8854-65e476c09da8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b9be4d4-853e-48ba-a7e2-47408ff661d8' class='xr-var-data-in' type='checkbox'><label for='data-4b9be4d4-853e-48ba-a7e2-47408ff661d8' 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>bounds :</span></dt><dd>vertices_latitude</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'><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> 864.00 kB </td> <td> 864.00 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (300, 360) </td> <td> (300, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</td>\n",
       "<td>\n",
       "<svg width=\"170\" height=\"150\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"100\" x2=\"120\" y2=\"100\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"100\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"100\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,100.0 0.0,100.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"120.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"140.000000\" y=\"50.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,140.000000,50.000000)\">300</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-48437529-9c47-4a71-bd88-b97c869b0c81' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48437529-9c47-4a71-bd88-b97c869b0c81' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-235a60b4-60ed-4fb7-a436-ff47e32b34bb' class='xr-var-data-in' type='checkbox'><label for='data-235a60b4-60ed-4fb7-a436-ff47e32b34bb' 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>bounds :</span></dt><dd>vertices_longitude</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'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
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       "</table></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'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-d9197295-f1e3-448b-8d50-c9efb2a268a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d9197295-f1e3-448b-8d50-c9efb2a268a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71520a4c-6666-4bb5-a749-78f9f20a4f6a' class='xr-var-data-in' type='checkbox'><label for='data-71520a4c-6666-4bb5-a749-78f9f20a4f6a' 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;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-fdc8e679-2f39-4555-bdd9-32a0cbad437a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdc8e679-2f39-4555-bdd9-32a0cbad437a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb13f8cb-0698-4578-b58d-3c79b9db7a74' class='xr-var-data-in' type='checkbox'><label for='data-eb13f8cb-0698-4578-b58d-3c79b9db7a74' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-8043ced0-099c-45cc-9a62-c8292d462e77' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8043ced0-099c-45cc-9a62-c8292d462e77' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b21dadbf-6213-4fc0-aa56-fad508ce0f2f' class='xr-var-data-in' type='checkbox'><label for='data-b21dadbf-6213-4fc0-aa56-fad508ce0f2f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 ... 5.499e+03 5.831e+03</div><input id='attrs-a7258240-7e8d-403e-ad11-6fe4177e06f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a7258240-7e8d-403e-ad11-6fe4177e06f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73806e43-7732-4b89-9edc-30ab067c1d59' class='xr-var-data-in' type='checkbox'><label for='data-73806e43-7732-4b89-9edc-30ab067c1d59' 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>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01,\n",
       "       5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01,\n",
       "       1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02,\n",
       "       1.550000e+02, 1.650000e+02, 1.750000e+02, 1.850000e+02, 1.950000e+02,\n",
       "       2.050000e+02, 2.168468e+02, 2.413490e+02, 2.807807e+02, 3.432505e+02,\n",
       "       4.273156e+02, 5.367156e+02, 6.654141e+02, 8.127816e+02, 9.690651e+02,\n",
       "       1.130935e+03, 1.289605e+03, 1.455770e+03, 1.622926e+03, 1.801558e+03,\n",
       "       1.984855e+03, 2.182905e+03, 2.388417e+03, 2.610935e+03, 2.842564e+03,\n",
       "       3.092205e+03, 3.351295e+03, 3.628058e+03, 3.913264e+03, 4.214495e+03,\n",
       "       4.521918e+03, 4.842566e+03, 5.166130e+03, 5.499245e+03, 5.831294e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-07ce209c-04ef-42a8-bd68-d9cf6ac3b057' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-07ce209c-04ef-42a8-bd68-d9cf6ac3b057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-db02dbb6-d255-4fba-9395-0034e1b718cc' class='xr-var-data-in' type='checkbox'><label for='data-db02dbb6-d255-4fba-9395-0034e1b718cc' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_longitude</span></div><div class='xr-var-dims'>(j, i, vertices, lev, member_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;</div><input id='attrs-f4558384-d444-403c-a901-9894ad5c3b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4558384-d444-403c-a901-9894ad5c3b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48a4d8b7-fbbd-4dc5-b4db-5c1f06375299' class='xr-var-data-in' type='checkbox'><label for='data-48a4d8b7-fbbd-4dc5-b4db-5c1f06375299' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(member_id, time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-756b3b78-5fb0-44f5-a1a4-4c14a107ad10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-756b3b78-5fb0-44f5-a1a4-4c14a107ad10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b5dc9a3-9fa2-4e85-9b2c-4553cc00618e' class='xr-var-data-in' type='checkbox'><label for='data-4b5dc9a3-9fa2-4e85-9b2c-4553cc00618e' 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: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
       "atmosChem: none\n",
       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fde\n",
       "hdl:21.14100/5c349831-3686-44bd-a668-b5473685d419\n",
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       "hdl:21.14100/79363e12-71ec-45ff-8ac7-5d9caaf123be\n",
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       "hdl:21.14100/b292c680-e324-487d-89af-5e95579a6adf\n",
       "hdl:21.14100/5533bdc5-d82b-48ec-acd2-d4f07903f67c\n",
       "hdl:21.14100/c26507e7-0f70-402c-9f87-741919c26350</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>branch_time_in_parent :</span></dt><dd>21915.0</dd><dt><span>history :</span></dt><dd>2019-11-15T15:38:06Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.\n",
       "2019-11-15T15:08:04Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>intake_esm_varname :</span></dt><dd>mlotst\n",
       "thetao</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>version :</span></dt><dd>v20191115</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/).  Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file).  The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn</dd><dt><span>mixed_layer_values_removed_based_on :</span></dt><dd>lev</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    grid:                                 native atmosphere N96 grid (145x192...\n",
       "    forcing_index:                        1\n",
       "    table_id:                             Omon\n",
       "    variant_label:                        r1i1p1f1\n",
       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
       "    tracking_id:                          hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n",
       "    initialization_index:                 1\n",
       "    physics_index:                        1\n",
       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
       "    branch_time_in_parent:                21915.0\n",
       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
       "    cmor_version:                         3.4.0\n",
       "    grid_label:                           gn\n",
       "    nominal_resolution:                   250 km\n",
       "    intake_esm_varname:                   mlotst\\nthetao\n",
       "    experiment_id:                        historical\n",
       "    realization_index:                    1\n",
       "    parent_activity_id:                   CMIP\n",
       "    sub_experiment_id:                    none\n",
       "    source_id:                            ACCESS-ESM1-5\n",
       "    branch_time_in_child:                 0.0\n",
       "    parent_experiment_id:                 piControl\n",
       "    activity_id:                          CMIP\n",
       "    data_specs_version:                   01.00.30\n",
       "    realm:                                ocean\n",
       "    product:                              model-output\n",
       "    Conventions:                          CF-1.7 CMIP-6.2\n",
       "    experiment:                           all-forcing simulation of the recen...\n",
       "    mip_era:                              CMIP6\n",
       "    institution:                          Commonwealth Scientific and Industr...\n",
       "    branch_method:                        standard\n",
       "    frequency:                            mon\n",
       "    institution_id:                       CSIRO\n",
       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
       "    parent_variant_label:                 r1i1p1f1\n",
       "    parent_mip_era:                       CMIP6\n",
       "    parent_time_units:                    days since 0101-1-1\n",
       "    further_info_url:                     https://furtherinfo.es-doc.org/CMIP...\n",
       "    sub_experiment:                       none\n",
       "    source_type:                          AOGCM\n",
       "    version:                              v20191115\n",
       "    parent_source_id:                     ACCESS-ESM1-5\n",
       "    license:                              CMIP6 model data produced by CSIRO ...\n",
       "    intake_esm_dataset_key:               CMIP.CSIRO.ACCESS-ESM1-5.historical...\n",
       "    mixed_layer_values_removed_based_on:  lev"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xarrayutils.utils import mask_mixedlayer\n",
    "ds_wo_ml = mask_mixedlayer(ds, ds.mlotst)\n",
    "ds_wo_ml"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or to have the mixed layer values only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jovyan/xarrayutils/xarrayutils/utils.py:805: UserWarning: Cell bounds [{z_bounds}] not found in input. Masking is performed with cell centers, which might be less accurate\n",
      "  warnings.warn(\n"
     ]
    },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "    longitude           (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;\n",
       "  * member_id           (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array&lt;chunksize=(1, 196, 300, 360, 50), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    grid:                                 native atmosphere N96 grid (145x192...\n",
       "    forcing_index:                        1\n",
       "    table_id:                             Omon\n",
       "    variant_label:                        r1i1p1f1\n",
       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
       "    tracking_id:                          hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n",
       "    initialization_index:                 1\n",
       "    physics_index:                        1\n",
       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
       "    branch_time_in_parent:                21915.0\n",
       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
       "    cmor_version:                         3.4.0\n",
       "    grid_label:                           gn\n",
       "    nominal_resolution:                   250 km\n",
       "    intake_esm_varname:                   mlotst\\nthetao\n",
       "    experiment_id:                        historical\n",
       "    realization_index:                    1\n",
       "    parent_activity_id:                   CMIP\n",
       "    sub_experiment_id:                    none\n",
       "    source_id:                            ACCESS-ESM1-5\n",
       "    branch_time_in_child:                 0.0\n",
       "    parent_experiment_id:                 piControl\n",
       "    activity_id:                          CMIP\n",
       "    data_specs_version:                   01.00.30\n",
       "    realm:                                ocean\n",
       "    product:                              model-output\n",
       "    Conventions:                          CF-1.7 CMIP-6.2\n",
       "    experiment:                           all-forcing simulation of the recen...\n",
       "    mip_era:                              CMIP6\n",
       "    institution:                          Commonwealth Scientific and Industr...\n",
       "    branch_method:                        standard\n",
       "    frequency:                            mon\n",
       "    institution_id:                       CSIRO\n",
       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
       "    parent_variant_label:                 r1i1p1f1\n",
       "    parent_mip_era:                       CMIP6\n",
       "    parent_time_units:                    days since 0101-1-1\n",
       "    further_info_url:                     https://furtherinfo.es-doc.org/CMIP...\n",
       "    sub_experiment:                       none\n",
       "    source_type:                          AOGCM\n",
       "    version:                              v20191115\n",
       "    parent_source_id:                     ACCESS-ESM1-5\n",
       "    license:                              CMIP6 model data produced by CSIRO ...\n",
       "    intake_esm_dataset_key:               CMIP.CSIRO.ACCESS-ESM1-5.historical...\n",
       "    mixed_layer_values_removed_based_on:  lev</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-0f96a560-8caa-47de-b5e5-a6ba9a40b42c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0f96a560-8caa-47de-b5e5-a6ba9a40b42c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 300</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1075f840-9699-4a07-8458-b533a2db8678' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1075f840-9699-4a07-8458-b533a2db8678' class='xr-section-summary' >Coordinates: <span>(9)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-c7ad7386-98b8-4af5-bc6a-b56dbcf100ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7ad7386-98b8-4af5-bc6a-b56dbcf100ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9afbbdd9-9043-4848-9baa-af5e974e3ad1' class='xr-var-data-in' type='checkbox'><label for='data-9afbbdd9-9043-4848-9baa-af5e974e3ad1' 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>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-362e9b5c-ace9-462e-8c91-97abc875fdcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-362e9b5c-ace9-462e-8c91-97abc875fdcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2932453-62b8-49af-af0f-8d396b611dea' class='xr-var-data-in' type='checkbox'><label for='data-c2932453-62b8-49af-af0f-8d396b611dea' 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>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 297, 298, 299], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-e7cb4daf-9697-4f93-9cac-f492d131c50b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7cb4daf-9697-4f93-9cac-f492d131c50b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92d9ad2c-0f00-4fa4-83ae-cb5f5e002a8c' class='xr-var-data-in' type='checkbox'><label for='data-92d9ad2c-0f00-4fa4-83ae-cb5f5e002a8c' 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>bounds :</span></dt><dd>vertices_latitude</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'><table>\n",
       "<tr>\n",
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       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-129d9c75-dc05-4781-bf2c-be5689d60c07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-129d9c75-dc05-4781-bf2c-be5689d60c07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2837e44-f3ec-4234-a871-4567aa4c415d' class='xr-var-data-in' type='checkbox'><label for='data-a2837e44-f3ec-4234-a871-4567aa4c415d' 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>bounds :</span></dt><dd>vertices_longitude</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'><table>\n",
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       "</table></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'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-2a496fe6-1460-4692-9c7d-b106a38f2961' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a496fe6-1460-4692-9c7d-b106a38f2961' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa011e72-1b48-423b-88ea-d487beb9872e' class='xr-var-data-in' type='checkbox'><label for='data-fa011e72-1b48-423b-88ea-d487beb9872e' 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;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-785a8956-a86d-43a9-a507-c34d34c946e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-785a8956-a86d-43a9-a507-c34d34c946e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3040c6ee-dc2c-421e-95ad-3a6fe49c9a6b' class='xr-var-data-in' type='checkbox'><label for='data-3040c6ee-dc2c-421e-95ad-3a6fe49c9a6b' 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'><table>\n",
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       "    <tr><th> Shape </th><td> (1980, 2) </td> <td> (1980, 2) </td></tr>\n",
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       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-12adc4df-6465-4b72-aef3-af5e7a7d2c2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12adc4df-6465-4b72-aef3-af5e7a7d2c2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a921713b-35ab-4b9e-8f4c-dbc0a7fe3ade' class='xr-var-data-in' type='checkbox'><label for='data-a921713b-35ab-4b9e-8f4c-dbc0a7fe3ade' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 ... 5.499e+03 5.831e+03</div><input id='attrs-5caa6939-7cfb-4a4c-be96-bb4f95cd7cfb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5caa6939-7cfb-4a4c-be96-bb4f95cd7cfb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac85146-1943-432f-91d4-9e95bec73243' class='xr-var-data-in' type='checkbox'><label for='data-aac85146-1943-432f-91d4-9e95bec73243' 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>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01,\n",
       "       5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01,\n",
       "       1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02,\n",
       "       1.550000e+02, 1.650000e+02, 1.750000e+02, 1.850000e+02, 1.950000e+02,\n",
       "       2.050000e+02, 2.168468e+02, 2.413490e+02, 2.807807e+02, 3.432505e+02,\n",
       "       4.273156e+02, 5.367156e+02, 6.654141e+02, 8.127816e+02, 9.690651e+02,\n",
       "       1.130935e+03, 1.289605e+03, 1.455770e+03, 1.622926e+03, 1.801558e+03,\n",
       "       1.984855e+03, 2.182905e+03, 2.388417e+03, 2.610935e+03, 2.842564e+03,\n",
       "       3.092205e+03, 3.351295e+03, 3.628058e+03, 3.913264e+03, 4.214495e+03,\n",
       "       4.521918e+03, 4.842566e+03, 5.166130e+03, 5.499245e+03, 5.831294e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-1144678b-00d7-460f-bc17-35b175e7b6c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1144678b-00d7-460f-bc17-35b175e7b6c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e63aa9a5-ca26-4046-abe0-df0471f6dc7a' class='xr-var-data-in' type='checkbox'><label for='data-e63aa9a5-ca26-4046-abe0-df0471f6dc7a' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vertices_longitude</span></div><div class='xr-var-dims'>(j, i, vertices, lev, member_id, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray&gt;</div><input id='attrs-95b389da-bd8f-4a88-ae7f-0505c12b1048' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95b389da-bd8f-4a88-ae7f-0505c12b1048' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-647ea18f-dba3-46cb-9f48-bda1132bb7e6' class='xr-var-data-in' type='checkbox'><label for='data-647ea18f-dba3-46cb-9f48-bda1132bb7e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(member_id, time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-4d120cbb-169b-4ecd-b94b-39350d3a354c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d120cbb-169b-4ecd-b94b-39350d3a354c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-366d411c-a14c-4f61-9278-77eb7bafc2f6' class='xr-var-data-in' type='checkbox'><label for='data-366d411c-a14c-4f61-9278-77eb7bafc2f6' 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: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
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       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fde\n",
       "hdl:21.14100/5c349831-3686-44bd-a668-b5473685d419\n",
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       "hdl:21.14100/c26507e7-0f70-402c-9f87-741919c26350</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>branch_time_in_parent :</span></dt><dd>21915.0</dd><dt><span>history :</span></dt><dd>2019-11-15T15:38:06Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.\n",
       "2019-11-15T15:08:04Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>intake_esm_varname :</span></dt><dd>mlotst\n",
       "thetao</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>version :</span></dt><dd>v20191115</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/).  Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file).  The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn</dd><dt><span>mixed_layer_values_removed_based_on :</span></dt><dd>lev</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices, lev, member_id, time) float64 dask.array<chunksize=(300, 360, 4, 50, 1, 196), meta=np.ndarray>\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    grid:                                 native atmosphere N96 grid (145x192...\n",
       "    forcing_index:                        1\n",
       "    table_id:                             Omon\n",
       "    variant_label:                        r1i1p1f1\n",
       "    source:                               ACCESS-ESM1.5 (2019): \\naerosol: CL...\n",
       "    tracking_id:                          hdl:21.14100/4ca9ce2d-6374-4e22-8ad...\n",
       "    initialization_index:                 1\n",
       "    physics_index:                        1\n",
       "    run_variant:                          forcing: GHG, Oz, SA, Sl, Vl, BC, O...\n",
       "    branch_time_in_parent:                21915.0\n",
       "    history:                              2019-11-15T15:38:06Z ; CMOR rewrote...\n",
       "    cmor_version:                         3.4.0\n",
       "    grid_label:                           gn\n",
       "    nominal_resolution:                   250 km\n",
       "    intake_esm_varname:                   mlotst\\nthetao\n",
       "    experiment_id:                        historical\n",
       "    realization_index:                    1\n",
       "    parent_activity_id:                   CMIP\n",
       "    sub_experiment_id:                    none\n",
       "    source_id:                            ACCESS-ESM1-5\n",
       "    branch_time_in_child:                 0.0\n",
       "    parent_experiment_id:                 piControl\n",
       "    activity_id:                          CMIP\n",
       "    data_specs_version:                   01.00.30\n",
       "    realm:                                ocean\n",
       "    product:                              model-output\n",
       "    Conventions:                          CF-1.7 CMIP-6.2\n",
       "    experiment:                           all-forcing simulation of the recen...\n",
       "    mip_era:                              CMIP6\n",
       "    institution:                          Commonwealth Scientific and Industr...\n",
       "    branch_method:                        standard\n",
       "    frequency:                            mon\n",
       "    institution_id:                       CSIRO\n",
       "    title:                                ACCESS-ESM1-5 output prepared for C...\n",
       "    table_info:                           Creation Date:(30 April 2019) MD5:e...\n",
       "    parent_variant_label:                 r1i1p1f1\n",
       "    parent_mip_era:                       CMIP6\n",
       "    parent_time_units:                    days since 0101-1-1\n",
       "    further_info_url:                     https://furtherinfo.es-doc.org/CMIP...\n",
       "    sub_experiment:                       none\n",
       "    source_type:                          AOGCM\n",
       "    version:                              v20191115\n",
       "    parent_source_id:                     ACCESS-ESM1-5\n",
       "    license:                              CMIP6 model data produced by CSIRO ...\n",
       "    intake_esm_dataset_key:               CMIP.CSIRO.ACCESS-ESM1-5.historical...\n",
       "    mixed_layer_values_removed_based_on:  lev"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xarrayutils.utils import mask_mixedlayer\n",
    "ds_ml_only = mask_mixedlayer(ds, ds.mlotst, mask='inside')\n",
    "ds_ml_only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "roi = dict(i=150, time=0, j=slice(50, 200))\n",
    "plt.figure(figsize=[10, 15])\n",
    "for di, (data, label) in enumerate(zip([ds, ds_wo_ml, ds_ml_only], ['full data', 'mixed layer removed', 'mixed layer only'])):\n",
    "    plt.subplot(3,1,di+1)\n",
    "    data.thetao.isel(lev=slice(0,20),**roi).plot(yincrease=False)\n",
    "    ds.mlotst.isel(**roi).plot(x='j')\n",
    "    plt.title(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case the cell bounds are not available for the model output, but `mask_mixedlayer` has the option to take those into account and e.g. remove cells only if the lower boundary is within the mixed layer. All you need to do is correctly specify `z_bounds` with the variable/coordinate name of the cell bounds."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Removing bottom values\n",
    "\n",
    "For similar reasons you might sometimes want to mask out the values closest to the ocean bottom. `remove_bottom_values` will mask out the bottom-most grid cell at every location. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
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       "    longitude           (j, i) float64 dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
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       "  * member_id           (member_id) &lt;U8 &#x27;r1i1p1f1&#x27;\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array&lt;chunksize=(1, 196, 300, 360, 50), meta=np.ndarray&gt;\n",
       "    vertices_latitude   (j, i, vertices, lev) float64 dask.array&lt;chunksize=(300, 360, 4, 50), meta=np.ndarray&gt;\n",
       "    vertices_longitude  (j, i, vertices, lev) float64 dask.array&lt;chunksize=(300, 360, 4, 50), meta=np.ndarray&gt;\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    grid:                    native atmosphere N96 grid (145x192 latxlon)\n",
       "    forcing_index:           1\n",
       "    table_id:                Omon\n",
       "    variant_label:           r1i1p1f1\n",
       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
       "    tracking_id:             hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n",
       "    initialization_index:    1\n",
       "    physics_index:           1\n",
       "    run_variant:             forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n",
       "    branch_time_in_parent:   21915.0\n",
       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
       "    cmor_version:            3.4.0\n",
       "    grid_label:              gn\n",
       "    nominal_resolution:      250 km\n",
       "    intake_esm_varname:      mlotst\\nthetao\n",
       "    experiment_id:           historical\n",
       "    realization_index:       1\n",
       "    parent_activity_id:      CMIP\n",
       "    sub_experiment_id:       none\n",
       "    source_id:               ACCESS-ESM1-5\n",
       "    branch_time_in_child:    0.0\n",
       "    parent_experiment_id:    piControl\n",
       "    activity_id:             CMIP\n",
       "    data_specs_version:      01.00.30\n",
       "    realm:                   ocean\n",
       "    product:                 model-output\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    experiment:              all-forcing simulation of the recent past\n",
       "    mip_era:                 CMIP6\n",
       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
       "    branch_method:           standard\n",
       "    frequency:               mon\n",
       "    institution_id:          CSIRO\n",
       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_time_units:       days since 0101-1-1\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
       "    sub_experiment:          none\n",
       "    source_type:             AOGCM\n",
       "    version:                 v20191115\n",
       "    parent_source_id:        ACCESS-ESM1-5\n",
       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn</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-3afd639d-6b82-4ee2-92c7-caa55f57c652' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3afd639d-6b82-4ee2-92c7-caa55f57c652' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</li><li><span class='xr-has-index'>i</span>: 360</li><li><span class='xr-has-index'>j</span>: 300</li><li><span class='xr-has-index'>lev</span>: 50</li><li><span class='xr-has-index'>member_id</span>: 1</li><li><span class='xr-has-index'>time</span>: 1980</li><li><span>vertices</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8e5826c-a29f-4361-a8ff-0fa8f2b78142' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8e5826c-a29f-4361-a8ff-0fa8f2b78142' class='xr-section-summary' >Coordinates: <span>(9)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 355 356 357 358 359</div><input id='attrs-572a2436-1688-4592-8607-45d819440126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-572a2436-1688-4592-8607-45d819440126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b468fb4-3c53-4ca9-8e7d-8079ac936dc5' class='xr-var-data-in' type='checkbox'><label for='data-9b468fb4-3c53-4ca9-8e7d-8079ac936dc5' 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>long_name :</span></dt><dd>cell index along first dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 357, 358, 359], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>j</span></div><div class='xr-var-dims'>(j)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 295 296 297 298 299</div><input id='attrs-df8bc075-254c-4786-a43f-4b488eda690b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-df8bc075-254c-4786-a43f-4b488eda690b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64d1a358-50c9-469f-8db9-60f0baca1136' class='xr-var-data-in' type='checkbox'><label for='data-64d1a358-50c9-469f-8db9-60f0baca1136' 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>long_name :</span></dt><dd>cell index along second dimension</dd><dt><span>units :</span></dt><dd>1</dd></dl></div><div class='xr-var-data'><pre>array([  0,   1,   2, ..., 297, 298, 299], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-672f6b05-7ad3-4121-b0e9-5e94f12247de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-672f6b05-7ad3-4121-b0e9-5e94f12247de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f3a5b1-5417-4e58-94e2-de10abd27150' class='xr-var-data-in' type='checkbox'><label for='data-84f3a5b1-5417-4e58-94e2-de10abd27150' 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>bounds :</span></dt><dd>vertices_latitude</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'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><th> Shape </th><td> (300, 360) </td> <td> (300, 360) </td></tr>\n",
       "    <tr><th> Count </th><td> 5 Tasks </td><td> 1 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(j, i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(300, 360), meta=np.ndarray&gt;</div><input id='attrs-b9c54753-4521-4a7d-889d-4187369bfe4c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b9c54753-4521-4a7d-889d-4187369bfe4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c16d6242-f262-4e90-b6e3-a0df1600e535' class='xr-var-data-in' type='checkbox'><label for='data-c16d6242-f262-4e90-b6e3-a0df1600e535' 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>bounds :</span></dt><dd>vertices_longitude</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'><table>\n",
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       "<td>\n",
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       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table>\n",
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       "<td>\n",
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       "</td>\n",
       "</tr>\n",
       "</table></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'>1850-01-16T12:00:00 ... 2014-12-...</div><input id='attrs-aa9e130f-cf73-4bb3-bbd7-5ce226e13e17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa9e130f-cf73-4bb3-bbd7-5ce226e13e17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7882a4e6-7873-4ecd-a1ca-b9034fb1b311' class='xr-var-data-in' type='checkbox'><label for='data-7882a4e6-7873-4ecd-a1ca-b9034fb1b311' 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;1850-01-16T12:00:00.000000000&#x27;, &#x27;1850-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;1850-03-16T12:00:00.000000000&#x27;, ..., &#x27;2014-10-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2014-11-16T00:00:00.000000000&#x27;, &#x27;2014-12-16T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1980, 2), meta=np.ndarray&gt;</div><input id='attrs-1038b274-9dd0-44cd-9aa5-5432973a9f62' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1038b274-9dd0-44cd-9aa5-5432973a9f62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aedea39e-aff2-4151-91a9-23da019faab1' class='xr-var-data-in' type='checkbox'><label for='data-aedea39e-aff2-4151-91a9-23da019faab1' 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'><table>\n",
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       "\n",
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       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">1980</text>\n",
       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>member_id</span></div><div class='xr-var-dims'>(member_id)</div><div class='xr-var-dtype'>&lt;U8</div><div class='xr-var-preview xr-preview'>&#x27;r1i1p1f1&#x27;</div><input id='attrs-e5f3197f-fd89-46f4-95d7-9679b4914c8f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e5f3197f-fd89-46f4-95d7-9679b4914c8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d78db549-1a47-42d3-8040-8d4d78353dc5' class='xr-var-data-in' type='checkbox'><label for='data-d78db549-1a47-42d3-8040-8d4d78353dc5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;r1i1p1f1&#x27;], dtype=&#x27;&lt;U8&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.0 15.0 ... 5.499e+03 5.831e+03</div><input id='attrs-bd4563b6-c093-4e8d-b15c-e6bb1152b6a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd4563b6-c093-4e8d-b15c-e6bb1152b6a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74f1a116-1f49-43bb-84d4-c47109604868' class='xr-var-data-in' type='checkbox'><label for='data-74f1a116-1f49-43bb-84d4-c47109604868' 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>Z</dd><dt><span>bounds :</span></dt><dd>lev_bnds</dd><dt><span>long_name :</span></dt><dd>ocean depth coordinate</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([5.000000e+00, 1.500000e+01, 2.500000e+01, 3.500000e+01, 4.500000e+01,\n",
       "       5.500000e+01, 6.500000e+01, 7.500000e+01, 8.500000e+01, 9.500000e+01,\n",
       "       1.050000e+02, 1.150000e+02, 1.250000e+02, 1.350000e+02, 1.450000e+02,\n",
       "       1.550000e+02, 1.650000e+02, 1.750000e+02, 1.850000e+02, 1.950000e+02,\n",
       "       2.050000e+02, 2.168468e+02, 2.413490e+02, 2.807807e+02, 3.432505e+02,\n",
       "       4.273156e+02, 5.367156e+02, 6.654141e+02, 8.127816e+02, 9.690651e+02,\n",
       "       1.130935e+03, 1.289605e+03, 1.455770e+03, 1.622926e+03, 1.801558e+03,\n",
       "       1.984855e+03, 2.182905e+03, 2.388417e+03, 2.610935e+03, 2.842564e+03,\n",
       "       3.092205e+03, 3.351295e+03, 3.628058e+03, 3.913264e+03, 4.214495e+03,\n",
       "       4.521918e+03, 4.842566e+03, 5.166130e+03, 5.499245e+03, 5.831294e+03])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lev_bnds</span></div><div class='xr-var-dims'>(lev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(50, 2), meta=np.ndarray&gt;</div><input id='attrs-2e38fc64-7937-490a-9d0f-318c7e49085f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2e38fc64-7937-490a-9d0f-318c7e49085f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10689b18-763a-4e3f-afde-5efedcb5fb85' class='xr-var-data-in' type='checkbox'><label for='data-10689b18-763a-4e3f-afde-5efedcb5fb85' 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'><table>\n",
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       "  <tbody>\n",
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       "    <tr><th> Shape </th><td> (50, 2) </td> <td> (50, 2) </td></tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>thetao</span></div><div class='xr-var-dims'>(member_id, time, lev, j, i)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 5, 50, 300, 360), meta=np.ndarray&gt;</div><input id='attrs-9c033036-de25-45d7-bd1a-5bdcee16125c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c033036-de25-45d7-bd1a-5bdcee16125c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-207804b8-8b06-47cb-b2df-ee4955f52006' class='xr-var-data-in' type='checkbox'><label for='data-207804b8-8b06-47cb-b2df-ee4955f52006' 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: areacello volume: volcello</dd><dt><span>cell_methods :</span></dt><dd>area: mean where sea time: mean</dd><dt><span>comment :</span></dt><dd>Diagnostic should be contributed even for models using conservative temperature as prognostic field.</dd><dt><span>long_name :</span></dt><dd>Sea Water Potential Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_potential_temperature</dd><dt><span>units :</span></dt><dd>degC</dd></dl></div><div class='xr-var-data'><table>\n",
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       "aerosol: CLASSIC (v1.0)\n",
       "atmos: HadGAM2 (r1.1, N96; 192 x 145 longitude/latitude; 38 levels; top level 39255 m)\n",
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       "land: CABLE2.4\n",
       "landIce: none\n",
       "ocean: ACCESS-OM2 (MOM5, tripolar primarily 1deg; 360 x 300 longitude/latitude; 50 levels; top grid cell 0-10 m)\n",
       "ocnBgchem: WOMBAT (same grid as ocean)\n",
       "seaIce: CICE4.1 (same grid as ocean)</dd><dt><span>tracking_id :</span></dt><dd>hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fde\n",
       "hdl:21.14100/5c349831-3686-44bd-a668-b5473685d419\n",
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       "hdl:21.14100/c26507e7-0f70-402c-9f87-741919c26350</dd><dt><span>initialization_index :</span></dt><dd>1</dd><dt><span>physics_index :</span></dt><dd>1</dd><dt><span>run_variant :</span></dt><dd>forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2, N2O, CH4, CFC11, CFC12, CFC113, HCFC22, HFC125, HFC134a)</dd><dt><span>branch_time_in_parent :</span></dt><dd>21915.0</dd><dt><span>history :</span></dt><dd>2019-11-15T15:38:06Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.\n",
       "2019-11-15T15:08:04Z ; CMOR rewrote data to be consistent with CMIP6, CF-1.7 CMIP-6.2 and CF standards.</dd><dt><span>cmor_version :</span></dt><dd>3.4.0</dd><dt><span>grid_label :</span></dt><dd>gn</dd><dt><span>nominal_resolution :</span></dt><dd>250 km</dd><dt><span>intake_esm_varname :</span></dt><dd>mlotst\n",
       "thetao</dd><dt><span>experiment_id :</span></dt><dd>historical</dd><dt><span>realization_index :</span></dt><dd>1</dd><dt><span>parent_activity_id :</span></dt><dd>CMIP</dd><dt><span>sub_experiment_id :</span></dt><dd>none</dd><dt><span>source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>branch_time_in_child :</span></dt><dd>0.0</dd><dt><span>parent_experiment_id :</span></dt><dd>piControl</dd><dt><span>activity_id :</span></dt><dd>CMIP</dd><dt><span>data_specs_version :</span></dt><dd>01.00.30</dd><dt><span>realm :</span></dt><dd>ocean</dd><dt><span>product :</span></dt><dd>model-output</dd><dt><span>Conventions :</span></dt><dd>CF-1.7 CMIP-6.2</dd><dt><span>experiment :</span></dt><dd>all-forcing simulation of the recent past</dd><dt><span>mip_era :</span></dt><dd>CMIP6</dd><dt><span>institution :</span></dt><dd>Commonwealth Scientific and Industrial Research Organisation, Aspendale, Victoria 3195, Australia</dd><dt><span>branch_method :</span></dt><dd>standard</dd><dt><span>frequency :</span></dt><dd>mon</dd><dt><span>institution_id :</span></dt><dd>CSIRO</dd><dt><span>title :</span></dt><dd>ACCESS-ESM1-5 output prepared for CMIP6</dd><dt><span>table_info :</span></dt><dd>Creation Date:(30 April 2019) MD5:e14f55f257cceafb2523e41244962371</dd><dt><span>parent_variant_label :</span></dt><dd>r1i1p1f1</dd><dt><span>parent_mip_era :</span></dt><dd>CMIP6</dd><dt><span>parent_time_units :</span></dt><dd>days since 0101-1-1</dd><dt><span>further_info_url :</span></dt><dd>https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCESS-ESM1-5.historical.none.r1i1p1f1</dd><dt><span>sub_experiment :</span></dt><dd>none</dd><dt><span>source_type :</span></dt><dd>AOGCM</dd><dt><span>version :</span></dt><dd>v20191115</dd><dt><span>parent_source_id :</span></dt><dd>ACCESS-ESM1-5</dd><dt><span>license :</span></dt><dd>CMIP6 model data produced by CSIRO is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (https://creativecommons.org/licenses/).  Consult https://pcmdi.llnl.gov/CMIP6/TermsOfUse for terms of use governing CMIP6 output, including citation requirements and proper acknowledgment.  Further information about this data, including some limitations, can be found via the further_info_url (recorded as a global attribute in this file).  The data producers and data providers make no warranty, either express or implied, including, but not limited to, warranties of merchantability and fitness for a particular purpose. All liabilities arising from the supply of the information (including any liability arising in negligence) are excluded to the fullest extent permitted by law.</dd><dt><span>intake_esm_dataset_key :</span></dt><dd>CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:             (bnds: 2, i: 360, j: 300, lev: 50, member_id: 1, time: 1980, vertices: 4)\n",
       "Coordinates:\n",
       "  * i                   (i) int32 0 1 2 3 4 5 6 ... 353 354 355 356 357 358 359\n",
       "  * j                   (j) int32 0 1 2 3 4 5 6 ... 293 294 295 296 297 298 299\n",
       "    latitude            (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "    longitude           (j, i) float64 dask.array<chunksize=(300, 360), meta=np.ndarray>\n",
       "  * time                (time) datetime64[ns] 1850-01-16T12:00:00 ... 2014-12...\n",
       "    time_bnds           (time, bnds) datetime64[ns] dask.array<chunksize=(1980, 2), meta=np.ndarray>\n",
       "  * member_id           (member_id) <U8 'r1i1p1f1'\n",
       "  * lev                 (lev) float64 5.0 15.0 25.0 ... 5.499e+03 5.831e+03\n",
       "    lev_bnds            (lev, bnds) float64 dask.array<chunksize=(50, 2), meta=np.ndarray>\n",
       "Dimensions without coordinates: bnds, vertices\n",
       "Data variables:\n",
       "    mlotst              (member_id, time, j, i, lev) float32 dask.array<chunksize=(1, 196, 300, 360, 50), meta=np.ndarray>\n",
       "    vertices_latitude   (j, i, vertices, lev) float64 dask.array<chunksize=(300, 360, 4, 50), meta=np.ndarray>\n",
       "    vertices_longitude  (j, i, vertices, lev) float64 dask.array<chunksize=(300, 360, 4, 50), meta=np.ndarray>\n",
       "    thetao              (member_id, time, lev, j, i) float32 dask.array<chunksize=(1, 5, 50, 300, 360), meta=np.ndarray>\n",
       "Attributes:\n",
       "    grid:                    native atmosphere N96 grid (145x192 latxlon)\n",
       "    forcing_index:           1\n",
       "    table_id:                Omon\n",
       "    variant_label:           r1i1p1f1\n",
       "    source:                  ACCESS-ESM1.5 (2019): \\naerosol: CLASSIC (v1.0)\\...\n",
       "    tracking_id:             hdl:21.14100/4ca9ce2d-6374-4e22-8ad3-1a5a6dfb8fd...\n",
       "    initialization_index:    1\n",
       "    physics_index:           1\n",
       "    run_variant:             forcing: GHG, Oz, SA, Sl, Vl, BC, OC, (GHG = CO2...\n",
       "    branch_time_in_parent:   21915.0\n",
       "    history:                 2019-11-15T15:38:06Z ; CMOR rewrote data to be c...\n",
       "    cmor_version:            3.4.0\n",
       "    grid_label:              gn\n",
       "    nominal_resolution:      250 km\n",
       "    intake_esm_varname:      mlotst\\nthetao\n",
       "    experiment_id:           historical\n",
       "    realization_index:       1\n",
       "    parent_activity_id:      CMIP\n",
       "    sub_experiment_id:       none\n",
       "    source_id:               ACCESS-ESM1-5\n",
       "    branch_time_in_child:    0.0\n",
       "    parent_experiment_id:    piControl\n",
       "    activity_id:             CMIP\n",
       "    data_specs_version:      01.00.30\n",
       "    realm:                   ocean\n",
       "    product:                 model-output\n",
       "    Conventions:             CF-1.7 CMIP-6.2\n",
       "    experiment:              all-forcing simulation of the recent past\n",
       "    mip_era:                 CMIP6\n",
       "    institution:             Commonwealth Scientific and Industrial Research ...\n",
       "    branch_method:           standard\n",
       "    frequency:               mon\n",
       "    institution_id:          CSIRO\n",
       "    title:                   ACCESS-ESM1-5 output prepared for CMIP6\n",
       "    table_info:              Creation Date:(30 April 2019) MD5:e14f55f257ccea...\n",
       "    parent_variant_label:    r1i1p1f1\n",
       "    parent_mip_era:          CMIP6\n",
       "    parent_time_units:       days since 0101-1-1\n",
       "    further_info_url:        https://furtherinfo.es-doc.org/CMIP6.CSIRO.ACCES...\n",
       "    sub_experiment:          none\n",
       "    source_type:             AOGCM\n",
       "    version:                 v20191115\n",
       "    parent_source_id:        ACCESS-ESM1-5\n",
       "    license:                 CMIP6 model data produced by CSIRO is licensed u...\n",
       "    intake_esm_dataset_key:  CMIP.CSIRO.ACCESS-ESM1-5.historical.Omon.gn"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from xarrayutils.utils import remove_bottom_values\n",
    "ds_bot_removed = remove_bottom_values(ds)\n",
    "ds_bot_removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roi = dict(i=150, time=0, j=slice(50, 100))\n",
    "plt.figure(figsize=[10, 15])\n",
    "for di, (data, label) in enumerate(zip([ds, ds_bot_removed], ['full data', 'bottom layer removed'])):\n",
    "    plt.subplot(2,1,di+1)\n",
    "    data.thetao.isel(lev=slice(38, -2),**roi).plot(yincrease=False, robust=True)\n",
    "    plt.title(label)\n",
    "    plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}