RG_Lee.ipynb

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    "import matplotlib.pyplot as plt  # plotting library\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np  # numerical library\n",
    "import xarray as xr  # netCDF library\n",
    "\n",
    "import cartopy  # Map projections libary\n",
    "import cartopy.crs as ccrs  # Projections list\n",
    "\n",
    "import cftime\n",
    "import datetime\n",
    "\n",
    "import os"
   ]
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    "# Some defaults:\n",
    "plt.rcParams['figure.figsize'] = (12, 5)  # Default plot size"
   ]
  },
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    "if os.uname().nodename.lower().find('eemmmbp') != -1:\n",
    "    data_dir = '/Users/pvb/Dropbox/Oceanografia/Data/Climatologias/RG_ArgoClim/NC/'\n",
    "elif os.uname().nodename.lower().find('rossby') != -1:\n",
    "    data_dir = '/data/shareddata/Climatologias/RG_ArgoClim/NC/'"
   ]
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    "# Load anomaly data until 2019 and the mean T and S\n",
    "fileSalt2019 = data_dir+'RG_ArgoClim_Salinity_2019.nc'\n",
    "fileTemp2019 = data_dir+'RG_ArgoClim_Temperature_2019.nc'\n",
    "\n",
    "XrTemp2019 = xr.open_dataset(fileTemp2019, decode_times=False)\n",
    "XrSalt2019 = xr.open_dataset(fileSalt2019, decode_times=False)"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 2GB\n",
       "Dimensions:                   (LONGITUDE: 360, LATITUDE: 145, PRESSURE: 58,\n",
       "                               TIME: 180)\n",
       "Coordinates:\n",
       "  * LONGITUDE                 (LONGITUDE) float32 1kB 20.5 21.5 ... 378.5 379.5\n",
       "  * LATITUDE                  (LATITUDE) float32 580B -64.5 -63.5 ... 78.5 79.5\n",
       "  * PRESSURE                  (PRESSURE) float32 232B 2.5 10.0 ... 1.975e+03\n",
       "  * TIME                      (TIME) float32 720B 0.5 1.5 2.5 ... 178.5 179.5\n",
       "Data variables:\n",
       "    ARGO_TEMPERATURE_MEAN     (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ...\n",
       "    ARGO_TEMPERATURE_ANOMALY  (TIME, PRESSURE, LATITUDE, LONGITUDE) float32 2GB ...\n",
       "    BATHYMETRY_MASK           (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ...\n",
       "    MAPPING_MASK              (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e1bfefc9-bcea-41cc-9501-afd71dbff571' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e1bfefc9-bcea-41cc-9501-afd71dbff571' 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'>LONGITUDE</span>: 360</li><li><span class='xr-has-index'>LATITUDE</span>: 145</li><li><span class='xr-has-index'>PRESSURE</span>: 58</li><li><span class='xr-has-index'>TIME</span>: 180</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-e8c57d38-ead6-49d2-92d8-730779692d70' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e8c57d38-ead6-49d2-92d8-730779692d70' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LONGITUDE</span></div><div class='xr-var-dims'>(LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 378.5 379.5</div><input id='attrs-c0fe0501-da5a-4a64-b81a-4ddf9c9d4b3c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0fe0501-da5a-4a64-b81a-4ddf9c9d4b3c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2205acbf-b858-4d93-8c2f-e7d25e24af45' class='xr-var-data-in' type='checkbox'><label for='data-2205acbf-b858-4d93-8c2f-e7d25e24af45' 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><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 20.5,  21.5,  22.5, ..., 377.5, 378.5, 379.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LATITUDE</span></div><div class='xr-var-dims'>(LATITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-64.5 -63.5 -62.5 ... 78.5 79.5</div><input id='attrs-98022e60-35ec-473f-be7c-8c5ed02a6854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98022e60-35ec-473f-be7c-8c5ed02a6854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d7aa1345-e09d-4096-ad6a-d2203e4eafa2' class='xr-var-data-in' type='checkbox'><label for='data-d7aa1345-e09d-4096-ad6a-d2203e4eafa2' 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_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5,\n",
       "       -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
       "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5,\n",
       "       -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5,\n",
       "       -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,\n",
       "        -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,\n",
       "         5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,\n",
       "        15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,\n",
       "        25.5,  26.5,  27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,\n",
       "        35.5,  36.5,  37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,\n",
       "        45.5,  46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
       "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,  64.5,\n",
       "        65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,  73.5,  74.5,\n",
       "        75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>PRESSURE</span></div><div class='xr-var-dims'>(PRESSURE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 1.9e+03 1.975e+03</div><input id='attrs-9b4b75f3-f4d6-4448-afbc-4752cab03c45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b4b75f3-f4d6-4448-afbc-4752cab03c45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fe00817-0785-474c-83ea-61ff391e68e5' class='xr-var-data-in' type='checkbox'><label for='data-1fe00817-0785-474c-83ea-61ff391e68e5' 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>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>uneven</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([   2.5,   10. ,   20. ,   30. ,   40. ,   50. ,   60. ,   70. ,   80. ,\n",
       "         90. ,  100. ,  110. ,  120. ,  130. ,  140. ,  150. ,  160. ,  170. ,\n",
       "        182.5,  200. ,  220. ,  240. ,  260. ,  280. ,  300. ,  320. ,  340. ,\n",
       "        360. ,  380. ,  400. ,  420. ,  440. ,  462.5,  500. ,  550. ,  600. ,\n",
       "        650. ,  700. ,  750. ,  800. ,  850. ,  900. ,  950. , 1000. , 1050. ,\n",
       "       1100. , 1150. , 1200. , 1250. , 1300. , 1350. , 1412.5, 1500. , 1600. ,\n",
       "       1700. , 1800. , 1900. , 1975. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 177.5 178.5 179.5</div><input id='attrs-4ad99ff3-80db-414f-9cd6-f23db8d1ea0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ad99ff3-80db-414f-9cd6-f23db8d1ea0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4580fce7-3f2c-4714-a3fe-b3f70890abab' class='xr-var-data-in' type='checkbox'><label for='data-4580fce7-3f2c-4714-a3fe-b3f70890abab' 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>months since 2004-01-01 00:00:00</dd><dt><span>time_origin :</span></dt><dd>01-JAN-2004 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "        10.5,  11.5,  12.5,  13.5,  14.5,  15.5,  16.5,  17.5,  18.5,  19.5,\n",
       "        20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "        30.5,  31.5,  32.5,  33.5,  34.5,  35.5,  36.5,  37.5,  38.5,  39.5,\n",
       "        40.5,  41.5,  42.5,  43.5,  44.5,  45.5,  46.5,  47.5,  48.5,  49.5,\n",
       "        50.5,  51.5,  52.5,  53.5,  54.5,  55.5,  56.5,  57.5,  58.5,  59.5,\n",
       "        60.5,  61.5,  62.5,  63.5,  64.5,  65.5,  66.5,  67.5,  68.5,  69.5,\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5,\n",
       "        80.5,  81.5,  82.5,  83.5,  84.5,  85.5,  86.5,  87.5,  88.5,  89.5,\n",
       "        90.5,  91.5,  92.5,  93.5,  94.5,  95.5,  96.5,  97.5,  98.5,  99.5,\n",
       "       100.5, 101.5, 102.5, 103.5, 104.5, 105.5, 106.5, 107.5, 108.5, 109.5,\n",
       "       110.5, 111.5, 112.5, 113.5, 114.5, 115.5, 116.5, 117.5, 118.5, 119.5,\n",
       "       120.5, 121.5, 122.5, 123.5, 124.5, 125.5, 126.5, 127.5, 128.5, 129.5,\n",
       "       130.5, 131.5, 132.5, 133.5, 134.5, 135.5, 136.5, 137.5, 138.5, 139.5,\n",
       "       140.5, 141.5, 142.5, 143.5, 144.5, 145.5, 146.5, 147.5, 148.5, 149.5,\n",
       "       150.5, 151.5, 152.5, 153.5, 154.5, 155.5, 156.5, 157.5, 158.5, 159.5,\n",
       "       160.5, 161.5, 162.5, 163.5, 164.5, 165.5, 166.5, 167.5, 168.5, 169.5,\n",
       "       170.5, 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b10e45d0-2260-490d-b87b-13da9548fe77' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b10e45d0-2260-490d-b87b-13da9548fe77' class='xr-section-summary' >Data variables: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>ARGO_TEMPERATURE_MEAN</span></div><div class='xr-var-dims'>(PRESSURE, LATITUDE, LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7626fc95-b5ee-4d9b-a3e0-bcaf59927a18' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7626fc95-b5ee-4d9b-a3e0-bcaf59927a18' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fc6e633-6276-424f-8c1c-74ec7e01440c' class='xr-var-data-in' type='checkbox'><label for='data-0fc6e633-6276-424f-8c1c-74ec7e01440c' 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>degree celcius (ITS-90)</dd><dt><span>long_name :</span></dt><dd>ARGO TEMPERATURE MEAN Jan 2004 - Dec 2018 (15.0 year) RG CLIMATOLOGY</dd></dl></div><div class='xr-var-data'><pre>[3027600 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ARGO_TEMPERATURE_ANOMALY</span></div><div class='xr-var-dims'>(TIME, PRESSURE, LATITUDE, LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7152d9ef-eda0-495b-9d30-fa3c3ef29681' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7152d9ef-eda0-495b-9d30-fa3c3ef29681' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-753ddcc7-ccc6-4aac-a7c3-7dbde5660fea' class='xr-var-data-in' type='checkbox'><label for='data-753ddcc7-ccc6-4aac-a7c3-7dbde5660fea' 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>degree celcius (ITS-90)</dd><dt><span>long_name :</span></dt><dd>ARGO TEMPERATURE ANOMALY defined by Jan 2004 - Dec 2018 (15.0 year) RG CLIMATOLOGY</dd></dl></div><div class='xr-var-data'><pre>[544968000 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>BATHYMETRY_MASK</span></div><div class='xr-var-dims'>(PRESSURE, LATITUDE, LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-79edf933-af2c-4bef-aef5-dd96cc5174c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79edf933-af2c-4bef-aef5-dd96cc5174c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b583437-e00a-455d-8369-74f2211320a4' class='xr-var-data-in' type='checkbox'><label for='data-3b583437-e00a-455d-8369-74f2211320a4' 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>BATHYMETRY MASK</dd></dl></div><div class='xr-var-data'><pre>[3027600 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>MAPPING_MASK</span></div><div class='xr-var-dims'>(PRESSURE, LATITUDE, LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7b6d587a-17df-40b7-93ef-e4f25ecdbdf7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b6d587a-17df-40b7-93ef-e4f25ecdbdf7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b4525a2-267d-4fe4-9514-4a1e4fd044ee' class='xr-var-data-in' type='checkbox'><label for='data-4b4525a2-267d-4fe4-9514-4a1e4fd044ee' 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>MAPPING MASK: pressure limits of mapping can be shallower than 2000dbar in marginal seas </dd></dl></div><div class='xr-var-data'><pre>[3027600 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fae2c033-8197-4f09-82a4-6ee3723b2486' class='xr-section-summary-in' type='checkbox'  ><label for='section-fae2c033-8197-4f09-82a4-6ee3723b2486' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>LONGITUDE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-df7af77e-2421-4d4e-aa35-b65984df278b' class='xr-index-data-in' type='checkbox'/><label for='index-df7af77e-2421-4d4e-aa35-b65984df278b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "       ...\n",
       "       370.5, 371.5, 372.5, 373.5, 374.5, 375.5, 376.5, 377.5, 378.5, 379.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;LONGITUDE&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>LATITUDE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-07ba519a-3a0d-4647-8874-4a5feb54cead' class='xr-index-data-in' type='checkbox'/><label for='index-07ba519a-3a0d-4647-8874-4a5feb54cead' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       ...\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;LATITUDE&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>PRESSURE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5b5b35da-32ff-47b6-a0e2-7440b6f9f459' class='xr-index-data-in' type='checkbox'/><label for='index-5b5b35da-32ff-47b6-a0e2-7440b6f9f459' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   30.0,   40.0,   50.0,   60.0,   70.0,   80.0,\n",
       "         90.0,  100.0,  110.0,  120.0,  130.0,  140.0,  150.0,  160.0,  170.0,\n",
       "        182.5,  200.0,  220.0,  240.0,  260.0,  280.0,  300.0,  320.0,  340.0,\n",
       "        360.0,  380.0,  400.0,  420.0,  440.0,  462.5,  500.0,  550.0,  600.0,\n",
       "        650.0,  700.0,  750.0,  800.0,  850.0,  900.0,  950.0, 1000.0, 1050.0,\n",
       "       1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1412.5, 1500.0, 1600.0,\n",
       "       1700.0, 1800.0, 1900.0, 1975.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;PRESSURE&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>TIME</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1937099e-b74d-4a30-8cc3-4a93d29508c0' class='xr-index-data-in' type='checkbox'/><label for='index-1937099e-b74d-4a30-8cc3-4a93d29508c0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "       ...\n",
       "       170.5, 171.5, 172.5, 173.5, 174.5, 175.5, 176.5, 177.5, 178.5, 179.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;TIME&#x27;, length=180))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b41840fc-a4f6-4d3e-adc6-40510aad9d0f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b41840fc-a4f6-4d3e-adc6-40510aad9d0f' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 2GB\n",
       "Dimensions:                   (LONGITUDE: 360, LATITUDE: 145, PRESSURE: 58,\n",
       "                               TIME: 180)\n",
       "Coordinates:\n",
       "  * LONGITUDE                 (LONGITUDE) float32 1kB 20.5 21.5 ... 378.5 379.5\n",
       "  * LATITUDE                  (LATITUDE) float32 580B -64.5 -63.5 ... 78.5 79.5\n",
       "  * PRESSURE                  (PRESSURE) float32 232B 2.5 10.0 ... 1.975e+03\n",
       "  * TIME                      (TIME) float32 720B 0.5 1.5 2.5 ... 178.5 179.5\n",
       "Data variables:\n",
       "    ARGO_TEMPERATURE_MEAN     (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ...\n",
       "    ARGO_TEMPERATURE_ANOMALY  (TIME, PRESSURE, LATITUDE, LONGITUDE) float32 2GB ...\n",
       "    BATHYMETRY_MASK           (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ...\n",
       "    MAPPING_MASK              (PRESSURE, LATITUDE, LONGITUDE) float32 12MB ..."
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XrTemp2019"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab07151d-77d3-4633-95bd-ed6a7c723bc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "T_mean = XrTemp2019.ARGO_TEMPERATURE_MEAN\n",
    "S_mean = XrSalt2019.ARGO_SALINITY_MEAN\n",
    "T0_anom = XrTemp2019.ARGO_TEMPERATURE_ANOMALY\n",
    "S0_anom = XrSalt2019.ARGO_SALINITY_ANOMALY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2132fa6f-c131-4258-aedb-2660613be4fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "files = []\n",
    "for iy in range(2019,2024):\n",
    "    for im in range(1,13):\n",
    "        files.append(data_dir+\"RG_ArgoClim_%04d%02d_2019.nc\"%(iy,im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ed650a8e-19a0-454a-b897-36cd7fad3c38",
   "metadata": {},
   "outputs": [],
   "source": [
    "iy = 2024\n",
    "for im in range(1,4):\n",
    "    files.append(data_dir+\"RG_ArgoClim_%04d%02d_2019.nc\"%(iy,im))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c48e104c-74f0-42da-b07f-59e2c92d6adb",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = xr.open_mfdataset(files, decode_times=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f16c652f-2d94-4049-a53a-b613d84b4c06",
   "metadata": {},
   "outputs": [],
   "source": [
    "T1_anom = data.ARGO_TEMPERATURE_ANOMALY\n",
    "S1_anom = data.ARGO_SALINITY_ANOMALY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "ac4bbc74-77d1-4327-8473-2644b4c2bd71",
   "metadata": {},
   "outputs": [],
   "source": [
    "T_anom=xr.concat([T0_anom,T1_anom],dim='TIME')\n",
    "S_anom=xr.concat([S0_anom,S1_anom],dim='TIME')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5ef6fc02-83b6-4861-9493-2cfec47ec9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "T=T_anom+T_mean\n",
    "S=S_anom+S_mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1ea5e951-997f-41e7-9a14-2e2401d0e900",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (TIME: 243, PRESSURE: 58, LATITUDE: 145, LONGITUDE: 360)&gt; Size: 3GB\n",
       "dask.array&lt;add, shape=(243, 58, 145, 360), dtype=float32, chunksize=(83, 58, 83, 83), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * LONGITUDE  (LONGITUDE) float32 1kB 20.5 21.5 22.5 23.5 ... 377.5 378.5 379.5\n",
       "  * LATITUDE   (LATITUDE) float32 580B -64.5 -63.5 -62.5 ... 77.5 78.5 79.5\n",
       "  * PRESSURE   (PRESSURE) float32 232B 2.5 10.0 20.0 ... 1.9e+03 1.975e+03\n",
       "  * TIME       (TIME) float32 972B 0.5 1.5 2.5 3.5 ... 239.5 240.5 241.5 242.5</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>TIME</span>: 243</li><li><span class='xr-has-index'>PRESSURE</span>: 58</li><li><span class='xr-has-index'>LATITUDE</span>: 145</li><li><span class='xr-has-index'>LONGITUDE</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cadbfecc-a18d-4366-90a1-f7f93748faee' class='xr-array-in' type='checkbox' checked><label for='section-cadbfecc-a18d-4366-90a1-f7f93748faee' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.74 GiB </td>\n",
       "                        <td> 126.51 MiB </td>\n",
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       "  <text x=\"153.072423\" y=\"80.405756\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,153.072423,80.405756)\">58</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-f71262c5-4a73-4916-9e4e-78c832bc8bd7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f71262c5-4a73-4916-9e4e-78c832bc8bd7' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LONGITUDE</span></div><div class='xr-var-dims'>(LONGITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 378.5 379.5</div><input id='attrs-652aa0cb-8c5b-4559-a5e8-d50cb7501af6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-652aa0cb-8c5b-4559-a5e8-d50cb7501af6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cce87f89-3662-4850-9e7a-4e895193c18e' class='xr-var-data-in' type='checkbox'><label for='data-cce87f89-3662-4850-9e7a-4e895193c18e' 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><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 20.5,  21.5,  22.5, ..., 377.5, 378.5, 379.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>LATITUDE</span></div><div class='xr-var-dims'>(LATITUDE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-64.5 -63.5 -62.5 ... 78.5 79.5</div><input id='attrs-dc66c3ca-0ead-43c2-b77a-5dced6ccea3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dc66c3ca-0ead-43c2-b77a-5dced6ccea3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad75283f-2d68-45d2-87fe-6c0d2093a5c7' class='xr-var-data-in' type='checkbox'><label for='data-ad75283f-2d68-45d2-87fe-6c0d2093a5c7' 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_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5,\n",
       "       -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
       "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5,\n",
       "       -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5,\n",
       "       -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,\n",
       "        -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,\n",
       "         5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,\n",
       "        15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,\n",
       "        25.5,  26.5,  27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,\n",
       "        35.5,  36.5,  37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,\n",
       "        45.5,  46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
       "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,  64.5,\n",
       "        65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,  73.5,  74.5,\n",
       "        75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>PRESSURE</span></div><div class='xr-var-dims'>(PRESSURE)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 1.9e+03 1.975e+03</div><input id='attrs-60f255c4-a7b0-421c-9e92-2b2eae4c75de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60f255c4-a7b0-421c-9e92-2b2eae4c75de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c9a5a23-4e3c-4211-81e7-2c093230c69d' class='xr-var-data-in' type='checkbox'><label for='data-4c9a5a23-4e3c-4211-81e7-2c093230c69d' 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>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>uneven</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([   2.5,   10. ,   20. ,   30. ,   40. ,   50. ,   60. ,   70. ,   80. ,\n",
       "         90. ,  100. ,  110. ,  120. ,  130. ,  140. ,  150. ,  160. ,  170. ,\n",
       "        182.5,  200. ,  220. ,  240. ,  260. ,  280. ,  300. ,  320. ,  340. ,\n",
       "        360. ,  380. ,  400. ,  420. ,  440. ,  462.5,  500. ,  550. ,  600. ,\n",
       "        650. ,  700. ,  750. ,  800. ,  850. ,  900. ,  950. , 1000. , 1050. ,\n",
       "       1100. , 1150. , 1200. , 1250. , 1300. , 1350. , 1412.5, 1500. , 1600. ,\n",
       "       1700. , 1800. , 1900. , 1975. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>TIME</span></div><div class='xr-var-dims'>(TIME)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.5 1.5 2.5 ... 240.5 241.5 242.5</div><input id='attrs-f3a8158c-a9cd-437b-a069-8609d7597066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3a8158c-a9cd-437b-a069-8609d7597066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-245300b8-1d13-4270-a446-df4a8e9ed8d2' class='xr-var-data-in' type='checkbox'><label for='data-245300b8-1d13-4270-a446-df4a8e9ed8d2' 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>months since 2004-01-01 00:00:00</dd><dt><span>time_origin :</span></dt><dd>01-JAN-2004 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([  0.5,   1.5,   2.5, ..., 240.5, 241.5, 242.5], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fafa3400-4dec-4734-868a-95e250497859' class='xr-section-summary-in' type='checkbox'  ><label for='section-fafa3400-4dec-4734-868a-95e250497859' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>LONGITUDE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c193ecc2-b528-4308-85ba-eaa243ad1d36' class='xr-index-data-in' type='checkbox'/><label for='index-c193ecc2-b528-4308-85ba-eaa243ad1d36' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "       ...\n",
       "       370.5, 371.5, 372.5, 373.5, 374.5, 375.5, 376.5, 377.5, 378.5, 379.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;LONGITUDE&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>LATITUDE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e50a5438-3feb-4379-bdc3-a3979013730a' class='xr-index-data-in' type='checkbox'/><label for='index-e50a5438-3feb-4379-bdc3-a3979013730a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       ...\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;LATITUDE&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>PRESSURE</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d805e6e4-dd41-4fd4-9b00-9fe0af3eab39' class='xr-index-data-in' type='checkbox'/><label for='index-d805e6e4-dd41-4fd4-9b00-9fe0af3eab39' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   30.0,   40.0,   50.0,   60.0,   70.0,   80.0,\n",
       "         90.0,  100.0,  110.0,  120.0,  130.0,  140.0,  150.0,  160.0,  170.0,\n",
       "        182.5,  200.0,  220.0,  240.0,  260.0,  280.0,  300.0,  320.0,  340.0,\n",
       "        360.0,  380.0,  400.0,  420.0,  440.0,  462.5,  500.0,  550.0,  600.0,\n",
       "        650.0,  700.0,  750.0,  800.0,  850.0,  900.0,  950.0, 1000.0, 1050.0,\n",
       "       1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1412.5, 1500.0, 1600.0,\n",
       "       1700.0, 1800.0, 1900.0, 1975.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;PRESSURE&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>TIME</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd4439ca-6c9f-497d-a784-868d61c9920f' class='xr-index-data-in' type='checkbox'/><label for='index-bd4439ca-6c9f-497d-a784-868d61c9920f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  0.5,   1.5,   2.5,   3.5,   4.5,   5.5,   6.5,   7.5,   8.5,   9.5,\n",
       "       ...\n",
       "       233.5, 234.5, 235.5, 236.5, 237.5, 238.5, 239.5, 240.5, 241.5, 242.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;TIME&#x27;, length=243))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3b57c756-42e7-486e-a3dd-a2227efd2c59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3b57c756-42e7-486e-a3dd-a2227efd2c59' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (TIME: 243, PRESSURE: 58, LATITUDE: 145, LONGITUDE: 360)> Size: 3GB\n",
       "dask.array<add, shape=(243, 58, 145, 360), dtype=float32, chunksize=(83, 58, 83, 83), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * LONGITUDE  (LONGITUDE) float32 1kB 20.5 21.5 22.5 23.5 ... 377.5 378.5 379.5\n",
       "  * LATITUDE   (LATITUDE) float32 580B -64.5 -63.5 -62.5 ... 77.5 78.5 79.5\n",
       "  * PRESSURE   (PRESSURE) float32 232B 2.5 10.0 20.0 ... 1.9e+03 1.975e+03\n",
       "  * TIME       (TIME) float32 972B 0.5 1.5 2.5 3.5 ... 239.5 240.5 241.5 242.5"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "fe89e39e-ae88-4aff-b793-59219ab71150",
   "metadata": {},
   "outputs": [],
   "source": [
    "time = pd.date_range(start=\"2004-01-01\", periods=T.shape[0],freq='MS')+pd.DateOffset(days=14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b714a810-fca4-486d-924b-e367692e2e3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = T.assign_coords(TIME=(\"TIME\", time))\n",
    "S = S.assign_coords(TIME=(\"TIME\", time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5b8d0884-d281-401d-b570-3ba93a34080b",
   "metadata": {},
   "outputs": [],
   "source": [
    "T = T.rename({'LONGITUDE':'lon','LATITUDE':'lat', 'PRESSURE':'pre', 'TIME':'time'})\n",
    "S = S.rename({'LONGITUDE':'lon','LATITUDE':'lat', 'PRESSURE':'pre', 'TIME':'time'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "cba3e066-7338-47ec-93bd-9d8583677b57",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray (time: 243, pre: 58, lat: 145, lon: 360)&gt; Size: 3GB\n",
       "dask.array&lt;add, shape=(243, 58, 145, 360), dtype=float32, chunksize=(83, 58, 83, 83), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 1kB 20.5 21.5 22.5 23.5 ... 376.5 377.5 378.5 379.5\n",
       "  * lat      (lat) float32 580B -64.5 -63.5 -62.5 -61.5 ... 76.5 77.5 78.5 79.5\n",
       "  * pre      (pre) float32 232B 2.5 10.0 20.0 30.0 ... 1.8e+03 1.9e+03 1.975e+03\n",
       "  * time     (time) datetime64[ns] 2kB 2004-01-15 2004-02-15 ... 2024-03-15</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'></div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 243</li><li><span class='xr-has-index'>pre</span>: 58</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>lon</span>: 360</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c66bd565-6f10-4bff-aaed-3a851d07a700' class='xr-array-in' type='checkbox' checked><label for='section-c66bd565-6f10-4bff-aaed-3a851d07a700' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.74 GiB </td>\n",
       "                        <td> 126.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (243, 58, 145, 360) </td>\n",
       "                        <td> (83, 58, 83, 83) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 660 chunks in 133 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
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       "  <text x=\"235.144845\" y=\"92.478179\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >360</text>\n",
       "  <text x=\"315.144845\" y=\"48.311512\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,315.144845,48.311512)\">145</text>\n",
       "  <text x=\"153.072423\" y=\"80.405756\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,153.072423,80.405756)\">58</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-8975d551-51ac-4a7e-8419-aef0fb12f816' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8975d551-51ac-4a7e-8419-aef0fb12f816' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 378.5 379.5</div><input id='attrs-93d92939-de71-45e0-b0b5-7f928d31762b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93d92939-de71-45e0-b0b5-7f928d31762b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c41826b-5214-424d-a681-928b130d0acd' class='xr-var-data-in' type='checkbox'><label for='data-7c41826b-5214-424d-a681-928b130d0acd' 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><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 20.5,  21.5,  22.5, ..., 377.5, 378.5, 379.5], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-64.5 -63.5 -62.5 ... 78.5 79.5</div><input id='attrs-508e9207-7aaa-4903-809c-fc42875dae96' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-508e9207-7aaa-4903-809c-fc42875dae96' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3728afc7-0438-4caa-bdab-a544d3232592' class='xr-var-data-in' type='checkbox'><label for='data-3728afc7-0438-4caa-bdab-a544d3232592' 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_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5,\n",
       "       -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
       "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5,\n",
       "       -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5,\n",
       "       -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,\n",
       "        -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,\n",
       "         5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,\n",
       "        15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,\n",
       "        25.5,  26.5,  27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,\n",
       "        35.5,  36.5,  37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,\n",
       "        45.5,  46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
       "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,  64.5,\n",
       "        65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,  73.5,  74.5,\n",
       "        75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pre</span></div><div class='xr-var-dims'>(pre)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 1.9e+03 1.975e+03</div><input id='attrs-2e7eb93f-6729-47e3-ad66-f5c240f04a5b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e7eb93f-6729-47e3-ad66-f5c240f04a5b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2923b21-0d6d-403b-98ab-1db767d05695' class='xr-var-data-in' type='checkbox'><label for='data-c2923b21-0d6d-403b-98ab-1db767d05695' 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>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>uneven</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([   2.5,   10. ,   20. ,   30. ,   40. ,   50. ,   60. ,   70. ,   80. ,\n",
       "         90. ,  100. ,  110. ,  120. ,  130. ,  140. ,  150. ,  160. ,  170. ,\n",
       "        182.5,  200. ,  220. ,  240. ,  260. ,  280. ,  300. ,  320. ,  340. ,\n",
       "        360. ,  380. ,  400. ,  420. ,  440. ,  462.5,  500. ,  550. ,  600. ,\n",
       "        650. ,  700. ,  750. ,  800. ,  850. ,  900. ,  950. , 1000. , 1050. ,\n",
       "       1100. , 1150. , 1200. , 1250. , 1300. , 1350. , 1412.5, 1500. , 1600. ,\n",
       "       1700. , 1800. , 1900. , 1975. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2004-01-15 ... 2024-03-15</div><input id='attrs-371cdac1-f675-42df-918d-688c595bb4ba' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-371cdac1-f675-42df-918d-688c595bb4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbcd45ba-6c0c-479d-8bf8-e6bf1c9103a3' class='xr-var-data-in' type='checkbox'><label for='data-dbcd45ba-6c0c-479d-8bf8-e6bf1c9103a3' 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;2004-01-15T00:00:00.000000000&#x27;, &#x27;2004-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-03-15T00:00:00.000000000&#x27;, ..., &#x27;2024-01-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-15T00:00:00.000000000&#x27;, &#x27;2024-03-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c2958715-b535-4068-b8a4-08ebd0a280ed' class='xr-section-summary-in' type='checkbox'  ><label for='section-c2958715-b535-4068-b8a4-08ebd0a280ed' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c88e41a-54a6-4fe1-b69d-acd029519af5' class='xr-index-data-in' type='checkbox'/><label for='index-1c88e41a-54a6-4fe1-b69d-acd029519af5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "       ...\n",
       "       370.5, 371.5, 372.5, 373.5, 374.5, 375.5, 376.5, 377.5, 378.5, 379.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-55060b82-90d5-4f65-b5f6-1058a5f236f6' class='xr-index-data-in' type='checkbox'/><label for='index-55060b82-90d5-4f65-b5f6-1058a5f236f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       ...\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pre</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38e546d4-35cc-4d23-bddf-0875074c9bad' class='xr-index-data-in' type='checkbox'/><label for='index-38e546d4-35cc-4d23-bddf-0875074c9bad' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   30.0,   40.0,   50.0,   60.0,   70.0,   80.0,\n",
       "         90.0,  100.0,  110.0,  120.0,  130.0,  140.0,  150.0,  160.0,  170.0,\n",
       "        182.5,  200.0,  220.0,  240.0,  260.0,  280.0,  300.0,  320.0,  340.0,\n",
       "        360.0,  380.0,  400.0,  420.0,  440.0,  462.5,  500.0,  550.0,  600.0,\n",
       "        650.0,  700.0,  750.0,  800.0,  850.0,  900.0,  950.0, 1000.0, 1050.0,\n",
       "       1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1412.5, 1500.0, 1600.0,\n",
       "       1700.0, 1800.0, 1900.0, 1975.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;pre&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aa978dbe-81a9-41a6-92e2-cbe765e94281' class='xr-index-data-in' type='checkbox'/><label for='index-aa978dbe-81a9-41a6-92e2-cbe765e94281' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2004-01-15&#x27;, &#x27;2004-02-15&#x27;, &#x27;2004-03-15&#x27;, &#x27;2004-04-15&#x27;,\n",
       "               &#x27;2004-05-15&#x27;, &#x27;2004-06-15&#x27;, &#x27;2004-07-15&#x27;, &#x27;2004-08-15&#x27;,\n",
       "               &#x27;2004-09-15&#x27;, &#x27;2004-10-15&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-06-15&#x27;, &#x27;2023-07-15&#x27;, &#x27;2023-08-15&#x27;, &#x27;2023-09-15&#x27;,\n",
       "               &#x27;2023-10-15&#x27;, &#x27;2023-11-15&#x27;, &#x27;2023-12-15&#x27;, &#x27;2024-01-15&#x27;,\n",
       "               &#x27;2024-02-15&#x27;, &#x27;2024-03-15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=243, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d93fdf60-62b7-4075-91aa-b2cf2d4150a9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d93fdf60-62b7-4075-91aa-b2cf2d4150a9' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray (time: 243, pre: 58, lat: 145, lon: 360)> Size: 3GB\n",
       "dask.array<add, shape=(243, 58, 145, 360), dtype=float32, chunksize=(83, 58, 83, 83), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * lon      (lon) float32 1kB 20.5 21.5 22.5 23.5 ... 376.5 377.5 378.5 379.5\n",
       "  * lat      (lat) float32 580B -64.5 -63.5 -62.5 -61.5 ... 76.5 77.5 78.5 79.5\n",
       "  * pre      (pre) float32 232B 2.5 10.0 20.0 30.0 ... 1.8e+03 1.9e+03 1.975e+03\n",
       "  * time     (time) datetime64[ns] 2kB 2004-01-15 2004-02-15 ... 2024-03-15"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Save one DataArray as dataset\n",
    "RG = T.to_dataset(name = 'temperature')\n",
    "# Add second DataArray to existing dataset (ds)\n",
    "RG['salinity'] = S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a8550243-d617-426f-be59-da592ab4ff15",
   "metadata": {},
   "outputs": [
    {
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       "  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-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\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",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 6GB\n",
       "Dimensions:      (lon: 360, lat: 145, pre: 58, time: 243)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 1kB 20.5 21.5 22.5 23.5 ... 377.5 378.5 379.5\n",
       "  * lat          (lat) float32 580B -64.5 -63.5 -62.5 -61.5 ... 77.5 78.5 79.5\n",
       "  * pre          (pre) float32 232B 2.5 10.0 20.0 ... 1.8e+03 1.9e+03 1.975e+03\n",
       "  * time         (time) datetime64[ns] 2kB 2004-01-15 2004-02-15 ... 2024-03-15\n",
       "Data variables:\n",
       "    temperature  (time, pre, lat, lon) float32 3GB dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;\n",
       "    salinity     (time, pre, lat, lon) float32 3GB dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-e2c5bde2-d3c4-48b2-81f5-7d67b60a93fe' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e2c5bde2-d3c4-48b2-81f5-7d67b60a93fe' 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'>lon</span>: 360</li><li><span class='xr-has-index'>lat</span>: 145</li><li><span class='xr-has-index'>pre</span>: 58</li><li><span class='xr-has-index'>time</span>: 243</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-a4ee1243-ffdc-4665-9dfa-b896dac8fb9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-a4ee1243-ffdc-4665-9dfa-b896dac8fb9c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.5 21.5 22.5 ... 378.5 379.5</div><input id='attrs-ca4be2a6-2f55-4212-8f9e-9af3219a8002' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ca4be2a6-2f55-4212-8f9e-9af3219a8002' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72f297ba-916b-4b6b-b038-8dc383c15e76' class='xr-var-data-in' type='checkbox'><label for='data-72f297ba-916b-4b6b-b038-8dc383c15e76' 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><dt><span>modulo :</span></dt><dd>360.0</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([ 20.5,  21.5,  22.5, ..., 377.5, 378.5, 379.5], dtype=float32)</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'>float32</div><div class='xr-var-preview xr-preview'>-64.5 -63.5 -62.5 ... 78.5 79.5</div><input id='attrs-a38063ca-2fc7-4ab3-8323-2aae4927f9e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a38063ca-2fc7-4ab3-8323-2aae4927f9e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d003a37a-5057-4ddb-8df2-857fc7d06a87' class='xr-var-data-in' type='checkbox'><label for='data-d003a37a-5057-4ddb-8df2-857fc7d06a87' 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_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       -54.5, -53.5, -52.5, -51.5, -50.5, -49.5, -48.5, -47.5, -46.5, -45.5,\n",
       "       -44.5, -43.5, -42.5, -41.5, -40.5, -39.5, -38.5, -37.5, -36.5, -35.5,\n",
       "       -34.5, -33.5, -32.5, -31.5, -30.5, -29.5, -28.5, -27.5, -26.5, -25.5,\n",
       "       -24.5, -23.5, -22.5, -21.5, -20.5, -19.5, -18.5, -17.5, -16.5, -15.5,\n",
       "       -14.5, -13.5, -12.5, -11.5, -10.5,  -9.5,  -8.5,  -7.5,  -6.5,  -5.5,\n",
       "        -4.5,  -3.5,  -2.5,  -1.5,  -0.5,   0.5,   1.5,   2.5,   3.5,   4.5,\n",
       "         5.5,   6.5,   7.5,   8.5,   9.5,  10.5,  11.5,  12.5,  13.5,  14.5,\n",
       "        15.5,  16.5,  17.5,  18.5,  19.5,  20.5,  21.5,  22.5,  23.5,  24.5,\n",
       "        25.5,  26.5,  27.5,  28.5,  29.5,  30.5,  31.5,  32.5,  33.5,  34.5,\n",
       "        35.5,  36.5,  37.5,  38.5,  39.5,  40.5,  41.5,  42.5,  43.5,  44.5,\n",
       "        45.5,  46.5,  47.5,  48.5,  49.5,  50.5,  51.5,  52.5,  53.5,  54.5,\n",
       "        55.5,  56.5,  57.5,  58.5,  59.5,  60.5,  61.5,  62.5,  63.5,  64.5,\n",
       "        65.5,  66.5,  67.5,  68.5,  69.5,  70.5,  71.5,  72.5,  73.5,  74.5,\n",
       "        75.5,  76.5,  77.5,  78.5,  79.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pre</span></div><div class='xr-var-dims'>(pre)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>2.5 10.0 20.0 ... 1.9e+03 1.975e+03</div><input id='attrs-48628087-8b25-4b30-998a-7403e4449127' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48628087-8b25-4b30-998a-7403e4449127' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-426553a5-1b8a-4967-90e1-65846e18ff32' class='xr-var-data-in' type='checkbox'><label for='data-426553a5-1b8a-4967-90e1-65846e18ff32' 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>dbar</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>point_spacing :</span></dt><dd>uneven</dd><dt><span>axis :</span></dt><dd>Z</dd></dl></div><div class='xr-var-data'><pre>array([   2.5,   10. ,   20. ,   30. ,   40. ,   50. ,   60. ,   70. ,   80. ,\n",
       "         90. ,  100. ,  110. ,  120. ,  130. ,  140. ,  150. ,  160. ,  170. ,\n",
       "        182.5,  200. ,  220. ,  240. ,  260. ,  280. ,  300. ,  320. ,  340. ,\n",
       "        360. ,  380. ,  400. ,  420. ,  440. ,  462.5,  500. ,  550. ,  600. ,\n",
       "        650. ,  700. ,  750. ,  800. ,  850. ,  900. ,  950. , 1000. , 1050. ,\n",
       "       1100. , 1150. , 1200. , 1250. , 1300. , 1350. , 1412.5, 1500. , 1600. ,\n",
       "       1700. , 1800. , 1900. , 1975. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2004-01-15 ... 2024-03-15</div><input id='attrs-121b24d0-6028-4bbc-88f4-221d20bfe689' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-121b24d0-6028-4bbc-88f4-221d20bfe689' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f82eb7b5-984e-4392-ba45-b1400ee26865' class='xr-var-data-in' type='checkbox'><label for='data-f82eb7b5-984e-4392-ba45-b1400ee26865' 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;2004-01-15T00:00:00.000000000&#x27;, &#x27;2004-02-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2004-03-15T00:00:00.000000000&#x27;, ..., &#x27;2024-01-15T00:00:00.000000000&#x27;,\n",
       "       &#x27;2024-02-15T00:00:00.000000000&#x27;, &#x27;2024-03-15T00:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76443818-e840-4a94-97e9-29c146df5738' class='xr-section-summary-in' type='checkbox'  checked><label for='section-76443818-e840-4a94-97e9-29c146df5738' class='xr-section-summary' >Data variables: <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>temperature</span></div><div class='xr-var-dims'>(time, pre, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;</div><input id='attrs-e91fdfac-8b0f-4ecc-94d1-eb55f9f2e31d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e91fdfac-8b0f-4ecc-94d1-eb55f9f2e31d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a3c9d6e-6bf7-434b-b693-46d2ece30392' class='xr-var-data-in' type='checkbox'><label for='data-3a3c9d6e-6bf7-434b-b693-46d2ece30392' 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",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.74 GiB </td>\n",
       "                        <td> 126.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (243, 58, 145, 360) </td>\n",
       "                        <td> (83, 58, 83, 83) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 660 chunks in 133 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"496\" height=\"122\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"81\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"25\" x2=\"81\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time, pre, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(83, 58, 83, 83), meta=np.ndarray&gt;</div><input id='attrs-9f3a2899-ffd9-443f-a0ef-d020acae714f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9f3a2899-ffd9-443f-a0ef-d020acae714f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98732e3b-fd48-4e5b-9a06-9db06e356de5' class='xr-var-data-in' type='checkbox'><label for='data-98732e3b-fd48-4e5b-9a06-9db06e356de5' 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",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.74 GiB </td>\n",
       "                        <td> 126.51 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (243, 58, 145, 360) </td>\n",
       "                        <td> (83, 58, 83, 83) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 660 chunks in 133 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "  <text x=\"153.072423\" y=\"80.405756\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,153.072423,80.405756)\">58</text>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-c82d50ca-e776-45a3-b595-b97b8b048dc0' class='xr-section-summary-in' type='checkbox'  ><label for='section-c82d50ca-e776-45a3-b595-b97b8b048dc0' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b868f95f-8f83-4af6-b678-f792e2d3bd3c' class='xr-index-data-in' type='checkbox'/><label for='index-b868f95f-8f83-4af6-b678-f792e2d3bd3c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 20.5,  21.5,  22.5,  23.5,  24.5,  25.5,  26.5,  27.5,  28.5,  29.5,\n",
       "       ...\n",
       "       370.5, 371.5, 372.5, 373.5, 374.5, 375.5, 376.5, 377.5, 378.5, 379.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lon&#x27;, length=360))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5aba2063-30bb-4a29-95db-cf85e19591ea' class='xr-index-data-in' type='checkbox'/><label for='index-5aba2063-30bb-4a29-95db-cf85e19591ea' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-64.5, -63.5, -62.5, -61.5, -60.5, -59.5, -58.5, -57.5, -56.5, -55.5,\n",
       "       ...\n",
       "        70.5,  71.5,  72.5,  73.5,  74.5,  75.5,  76.5,  77.5,  78.5,  79.5],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lat&#x27;, length=145))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pre</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8b5d2ad8-466d-4043-9f3e-c4d5736b82a8' class='xr-index-data-in' type='checkbox'/><label for='index-8b5d2ad8-466d-4043-9f3e-c4d5736b82a8' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   2.5,   10.0,   20.0,   30.0,   40.0,   50.0,   60.0,   70.0,   80.0,\n",
       "         90.0,  100.0,  110.0,  120.0,  130.0,  140.0,  150.0,  160.0,  170.0,\n",
       "        182.5,  200.0,  220.0,  240.0,  260.0,  280.0,  300.0,  320.0,  340.0,\n",
       "        360.0,  380.0,  400.0,  420.0,  440.0,  462.5,  500.0,  550.0,  600.0,\n",
       "        650.0,  700.0,  750.0,  800.0,  850.0,  900.0,  950.0, 1000.0, 1050.0,\n",
       "       1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1412.5, 1500.0, 1600.0,\n",
       "       1700.0, 1800.0, 1900.0, 1975.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;pre&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-84f18647-e4ca-43ea-aaa5-0b88fc6d4597' class='xr-index-data-in' type='checkbox'/><label for='index-84f18647-e4ca-43ea-aaa5-0b88fc6d4597' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2004-01-15&#x27;, &#x27;2004-02-15&#x27;, &#x27;2004-03-15&#x27;, &#x27;2004-04-15&#x27;,\n",
       "               &#x27;2004-05-15&#x27;, &#x27;2004-06-15&#x27;, &#x27;2004-07-15&#x27;, &#x27;2004-08-15&#x27;,\n",
       "               &#x27;2004-09-15&#x27;, &#x27;2004-10-15&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-06-15&#x27;, &#x27;2023-07-15&#x27;, &#x27;2023-08-15&#x27;, &#x27;2023-09-15&#x27;,\n",
       "               &#x27;2023-10-15&#x27;, &#x27;2023-11-15&#x27;, &#x27;2023-12-15&#x27;, &#x27;2024-01-15&#x27;,\n",
       "               &#x27;2024-02-15&#x27;, &#x27;2024-03-15&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=243, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-92621a6c-5100-49ec-b99f-178d562ada18' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-92621a6c-5100-49ec-b99f-178d562ada18' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 6GB\n",
       "Dimensions:      (lon: 360, lat: 145, pre: 58, time: 243)\n",
       "Coordinates:\n",
       "  * lon          (lon) float32 1kB 20.5 21.5 22.5 23.5 ... 377.5 378.5 379.5\n",
       "  * lat          (lat) float32 580B -64.5 -63.5 -62.5 -61.5 ... 77.5 78.5 79.5\n",
       "  * pre          (pre) float32 232B 2.5 10.0 20.0 ... 1.8e+03 1.9e+03 1.975e+03\n",
       "  * time         (time) datetime64[ns] 2kB 2004-01-15 2004-02-15 ... 2024-03-15\n",
       "Data variables:\n",
       "    temperature  (time, pre, lat, lon) float32 3GB dask.array<chunksize=(83, 58, 83, 83), meta=np.ndarray>\n",
       "    salinity     (time, pre, lat, lon) float32 3GB dask.array<chunksize=(83, 58, 83, 83), meta=np.ndarray>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RG"
   ]
  },
  {
   "cell_type": "raw",
   "id": "7f401901-433f-44bf-bea1-c2494dccfc77",
   "metadata": {},
   "source": [
    "Antiguo cuando no sabia como solucioanr el problema del tiempo\n",
    "temperature=T.values\n",
    "salinity=S.values\n",
    "lon=T.LONGITUDE.values\n",
    "lat=T.LATITUDE.values\n",
    "pre=T.PRESSURE.values\n",
    "time = pd.date_range(start=\"2004-01-01\", periods=T.shape[0],freq='MS')+pd.DateOffset(days=14)\n",
    "reference_time = pd.Timestamp(\"2004-04-01\")\n",
    "\n",
    "RG = xr.Dataset(\n",
    "    data_vars=dict(\n",
    "        temperature=([\"time\",\"pre\",\"lat\",\"lon\"], temperature),\n",
    "        salinity=([\"time\",\"pre\",\"lat\",\"lon\"], salinity),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        lon=([\"lon\"], lon),\n",
    "        lat=([\"lat\"], lat),\n",
    "        pre=([\"pre\"], pre),\n",
    "        time=time,\n",
    "    ),\n",
    "    attrs=dict(description=\"Roemmich-Gilson Argo Climatology\"),\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "06352184-606e-4e59-82ec-ba7b592bec1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "492c9d5a-97e3-4ce2-bae3-b03550a43eb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1 , 1 , figsize = (14,8))\n",
    "ax.plot(RG.time,\n",
    "        RG.temperature.sel(lat=slice( -60, 60)).mean(dim='lat').mean(dim='lon').sel(pre=200, method='nearest'),\n",
    "        label = '200')\n",
    "ax.grid(linestyle='-', linewidth=.9);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d56174ae-5a90-4a6a-b3ff-067e1fadb9b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "RG.to_netcdf('./Data/RG_Global_'+str(RG.time[0].dt.strftime('%Y-%m').values)+'_'+str(RG.time[-1].dt.strftime('%Y-%m').values)+'.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ce9a74-be90-419c-a1bd-52e62a033215",
   "metadata": {},
   "outputs": [],
   "source": [
    "RG2 = RG.interp(pre=np.arange(2.5,1980,10), method=\"linear\")"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "parcels",
   "language": "python",
   "name": "parcels"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}