time_series_global_O2_DICP_ORCA05.ipynb

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   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import xarray as xr\n",
    "import pandas as pd\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import axes\n",
    "import numpy as np\n",
    "import cartopy\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/scratch/usr/shkifmmp/notebooks/shared-notebooks\n"
     ]
    }
   ],
   "source": [
    "!pwd"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define paths to data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# path_05_cy1='/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1/OUT/yearly/'\n",
    "# path_05_cy2='/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle2/OUT/yearly/'\n",
    "# path_05_cy3='/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle3/OUT/yearly/'\n",
    "# path_05_cy4='/scratch/usr/shklvn09/OLD_WORK1/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle4/OUT/yearly/'\n",
    "# path_05_cy5='/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP002-cycle5/OUT/yearly/'\n",
    "\n",
    "# path_05_cy1_new='/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.LP10-KLP001-cycle1/OUT/'\n",
    "# path_05_cy2_new='/scratch/usr/shklvn09/SCRATCH/ORCA05.L46.LIM2vp.CFCSF6.MOPS.LP10-KLP001-cycle2/OUT/'\n",
    "path_test = '/scratch/usr/shkifmmp/notebooks/shared-notebooks/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       "    long_name:           oxygen\n",
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       "    interval_operation:  2160 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 2160 s) time_counter: mean</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'O2'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time_counter</span>: 61</li><li><span class='xr-has-index'>deptht</span>: 46</li><li><span>y</span>: 511</li><li><span>x</span>: 722</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-602391fa-7012-40fe-84e4-a7a9449afaf1' class='xr-array-in' type='checkbox' checked><label for='section-602391fa-7012-40fe-84e4-a7a9449afaf1' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 46, 100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nav_lon</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(100, 100), meta=np.ndarray&gt;</div><input id='attrs-b7e3e515-e742-4fdc-9dcd-43a52f28e287' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7e3e515-e742-4fdc-9dcd-43a52f28e287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f2cf5fee-da48-4cbb-b9e2-d86f450960af' class='xr-var-data-in' type='checkbox'><label for='data-f2cf5fee-da48-4cbb-b9e2-d86f450960af' 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>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>deptht</span></div><div class='xr-var-dims'>(deptht)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.047 9.454 ... 5.625e+03 5.875e+03</div><input id='attrs-0230dc0a-3fa5-47f3-ba7b-26673894b850' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0230dc0a-3fa5-47f3-ba7b-26673894b850' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70631eca-0cd8-4db5-94c3-b46a2cd7c5da' class='xr-var-data-in' type='checkbox'><label for='data-70631eca-0cd8-4db5-94c3-b46a2cd7c5da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>name :</span></dt><dd>deptht</dd><dt><span>long_name :</span></dt><dd>Vertical T levels</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>bounds :</span></dt><dd>deptht_bounds</dd></dl></div><div class='xr-var-data'><pre>array([3.046773e+00, 9.454049e+00, 1.636397e+01, 2.389871e+01, 3.220929e+01,\n",
       "       4.148185e+01, 5.194513e+01, 6.387905e+01, 7.762451e+01, 9.359412e+01,\n",
       "       1.122835e+02, 1.342823e+02, 1.602840e+02, 1.910925e+02, 2.276233e+02,\n",
       "       2.708962e+02, 3.220169e+02, 3.821444e+02, 4.524429e+02, 5.340197e+02,\n",
       "       6.278525e+02, 7.347150e+02, 8.551112e+02, 9.892289e+02, 1.136922e+03,\n",
       "       1.297724e+03, 1.470893e+03, 1.655472e+03, 1.850365e+03, 2.054414e+03,\n",
       "       2.266454e+03, 2.485371e+03, 2.710133e+03, 2.939812e+03, 3.173588e+03,\n",
       "       3.410756e+03, 3.650712e+03, 3.892950e+03, 4.137047e+03, 4.382654e+03,\n",
       "       4.629485e+03, 4.877303e+03, 5.125919e+03, 5.375177e+03, 5.624952e+03,\n",
       "       5.875141e+03], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_centered</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1,), meta=np.ndarray&gt;</div><input id='attrs-3bee38a2-7a11-4032-994e-d2ad1f274402' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bee38a2-7a11-4032-994e-d2ad1f274402' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-286325cf-121e-41bd-8dbb-a4fa637df55c' class='xr-var-data-in' type='checkbox'><label for='data-286325cf-121e-41bd-8dbb-a4fa637df55c' 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>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_centered_bounds</dd><dt><span>cell_methods :</span></dt><dd>time_counter: mean</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</svg>\n",
       "</td>\n",
       "</tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_counter</span></div><div class='xr-var-dims'>(time_counter)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>1958-07-01T17:00:00 ... 2018-07-...</div><input id='attrs-7506946b-bd7b-46be-9ef9-2830202daaaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7506946b-bd7b-46be-9ef9-2830202daaaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7789fd23-5e18-4fd0-813c-0c55c252e2e6' class='xr-var-data-in' type='checkbox'><label for='data-7789fd23-5e18-4fd0-813c-0c55c252e2e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time axis</dd><dt><span>time_origin :</span></dt><dd>1900-01-01 00:00:00</dd><dt><span>bounds :</span></dt><dd>time_counter_bounds</dd><dt><span>cell_methods :</span></dt><dd>time_counter: mean</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;1958-07-01T17:00:00.000000000&#x27;, &#x27;1959-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1960-07-01T14:00:00.000000000&#x27;, &#x27;1961-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1962-07-01T17:00:00.000000000&#x27;, &#x27;1963-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1964-07-01T14:00:00.000000000&#x27;, &#x27;1965-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1966-07-01T17:00:00.000000000&#x27;, &#x27;1967-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1968-07-01T14:00:00.000000000&#x27;, &#x27;1969-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1970-07-01T17:00:00.000000000&#x27;, &#x27;1971-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1972-07-01T14:00:00.000000000&#x27;, &#x27;1973-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1974-07-01T17:00:00.000000000&#x27;, &#x27;1975-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1976-07-01T14:00:00.000000000&#x27;, &#x27;1977-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1978-07-01T17:00:00.000000000&#x27;, &#x27;1979-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1980-07-01T14:00:00.000000000&#x27;, &#x27;1981-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1982-07-01T17:00:00.000000000&#x27;, &#x27;1983-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1984-07-01T14:00:00.000000000&#x27;, &#x27;1985-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1986-07-01T17:00:00.000000000&#x27;, &#x27;1987-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1988-07-01T14:00:00.000000000&#x27;, &#x27;1989-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1990-07-01T17:00:00.000000000&#x27;, &#x27;1991-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1992-07-01T14:00:00.000000000&#x27;, &#x27;1993-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1994-07-01T17:00:00.000000000&#x27;, &#x27;1995-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1996-07-01T14:00:00.000000000&#x27;, &#x27;1997-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;1998-07-01T17:00:00.000000000&#x27;, &#x27;1999-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2000-07-01T14:00:00.000000000&#x27;, &#x27;2001-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2002-07-01T17:00:00.000000000&#x27;, &#x27;2003-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2004-07-01T14:00:00.000000000&#x27;, &#x27;2005-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2006-07-01T17:00:00.000000000&#x27;, &#x27;2007-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2008-07-01T14:00:00.000000000&#x27;, &#x27;2009-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2010-07-01T17:00:00.000000000&#x27;, &#x27;2011-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2012-07-01T14:00:00.000000000&#x27;, &#x27;2013-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2014-07-01T17:00:00.000000000&#x27;, &#x27;2015-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2016-07-01T14:00:00.000000000&#x27;, &#x27;2017-07-01T17:00:00.000000000&#x27;,\n",
       "       &#x27;2018-07-01T17:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4af728bc-aa84-4496-8d27-8b6f233beca7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4af728bc-aa84-4496-8d27-8b6f233beca7' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>oxygen</dd><dt><span>units :</span></dt><dd>mmol/m3</dd><dt><span>online_operation :</span></dt><dd>average</dd><dt><span>interval_operation :</span></dt><dd>2160 s</dd><dt><span>interval_write :</span></dt><dd>1 month</dd><dt><span>cell_methods :</span></dt><dd>time: mean (interval: 2160 s) time_counter: mean</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'O2' (time_counter: 61, deptht: 46, y: 511, x: 722)>\n",
       "dask.array<concatenate, shape=(61, 46, 511, 722), dtype=float32, chunksize=(1, 46, 100, 100), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    nav_lat        (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "    nav_lon        (y, x) float32 dask.array<chunksize=(100, 100), meta=np.ndarray>\n",
       "  * deptht         (deptht) float32 3.047 9.454 16.36 ... 5.625e+03 5.875e+03\n",
       "    time_centered  (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "  * time_counter   (time_counter) datetime64[ns] 1958-07-01T17:00:00 ... 2018...\n",
       "Dimensions without coordinates: y, x\n",
       "Attributes:\n",
       "    long_name:           oxygen\n",
       "    units:               mmol/m3\n",
       "    online_operation:    average\n",
       "    interval_operation:  2160 s\n",
       "    interval_write:      1 month\n",
       "    cell_methods:        time: mean (interval: 2160 s) time_counter: mean"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2_05_cy1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### O2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "ename": "OSError",
     "evalue": "no files to open",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mOSError\u001b[0m                                   Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-13-2be76fb250e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# o2_05_cy2 = xr.open_mfdataset(paths = sorted(glob(path_05_cy2 + 'OR*_O2.nc')), concat_dim = 'time_counter',\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m o2_05_cy3 = xr.open_mfdataset(paths = sorted(glob(path_05_cy3 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n\u001b[0m\u001b[1;32m      6\u001b[0m                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# o2_05_cy4 = xr.open_mfdataset(paths = sorted(glob(path_05_cy4 + 'OR*_O2.nc')), concat_dim = 'time_counter',\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/miniconda3/envs/py3_eddy/lib/python3.8/site-packages/xarray/backends/api.py\u001b[0m in \u001b[0;36mopen_mfdataset\u001b[0;34m(paths, chunks, concat_dim, compat, preprocess, engine, data_vars, coords, combine, parallel, join, attrs_file, combine_attrs, **kwargs)\u001b[0m\n\u001b[1;32m    871\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    872\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mpaths\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 873\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mOSError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"no files to open\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    874\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    875\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcombine\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"nested\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mOSError\u001b[0m: no files to open"
     ]
    }
   ],
   "source": [
    "\n",
    "o2_05_cy1 = xr.open_mfdataset(paths = sorted(glob(path_05_cy1 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "# o2_05_cy2 = xr.open_mfdataset(paths = sorted(glob(path_05_cy2 + 'OR*_O2.nc')), concat_dim = 'time_counter',\n",
    "#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "# o2_05_cy3 = xr.open_mfdataset(paths = sorted(glob(path_05_cy3 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "# o2_05_cy4 = xr.open_mfdataset(paths = sorted(glob(path_05_cy4 + 'OR*_O2.nc')), concat_dim = 'time_counter',                       \n",
    "#                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "# o2_05_cy5 = xr.open_mfdataset(paths = sorted(glob(path_05_cy5 + 'OR*_O2.nc')), concat_dim = 'time_counter', \n",
    "#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "\n",
    "\n",
    "\n",
    "# o2_05_cy1_new = xr.open_mfdataset(paths = sorted(glob(path_05_cy1_new + 'OR*1y*mops*.nc')), concat_dim = 'time_counter', \n",
    "#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2\n",
    "# o2_05_cy2_new = xr.open_mfdataset(paths = sorted(glob(path_05_cy2_new + 'OR*1y*mops*.nc')), concat_dim = 'time_counter', \n",
    "#                                   chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).O2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### DICP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dicp_05_cy1 = xr.open_mfdataset(paths = sorted(glob(path_05_cy1 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "dicp_05_cy2 = xr.open_mfdataset(paths = sorted(glob(path_05_cy2 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "dicp_05_cy3 = xr.open_mfdataset(paths = sorted(glob(path_05_cy3 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "dicp_05_cy4 = xr.open_mfdataset(paths = sorted(glob(path_05_cy4 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "dicp_05_cy5 = xr.open_mfdataset(paths = sorted(glob(path_05_cy5 + 'OR*_DICP.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "\n",
    "dicp_05_cy1_new = xr.open_mfdataset(paths = sorted(glob(path_05_cy1_new + 'OR*1y*mops*.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n",
    "dicp_05_cy2_new = xr.open_mfdataset(paths = sorted(glob(path_05_cy2_new + 'OR*1y*mops*.nc')), concat_dim = 'time_counter', \n",
    "                                  chunks={\"time_counter\":1, \"y\":100, \"x\":100, \"deptht\":46}).DICP\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61, 46, 511, 722)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2_05_cy1_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "maskdir = '/scratch/usr/shklvn09/NUDATA/ORCA/05-data/meshmask/mesh_mask.nc'\n",
    "tmask_05 = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).tmask.squeeze()\n",
    "e1t_05 = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e1t.squeeze()\n",
    "e2t_05 = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e2t.squeeze()\n",
    "e3t_05 = xr.open_dataset(maskdir,\n",
    "                      chunks={\"z\":46, \"y\":100, \"x\":100}).e3t_0.squeeze()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tmask_05, e3t_05 = tmask_05.rename({'z':'deptht'}), e3t_05.rename({'z':'deptht'}) #renaming it for mask and tracers to have same dimension name\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
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       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
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       "\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;tmask&#x27; (deptht: 46, y: 511, x: 722)&gt;\n",
       "dask.array&lt;getitem, shape=(46, 511, 722), dtype=int8, chunksize=(46, 100, 100), chunktype=numpy.ndarray&gt;\n",
       "Dimensions without coordinates: deptht, y, x</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'tmask'</div><ul class='xr-dim-list'><li><span>deptht</span>: 46</li><li><span>y</span>: 511</li><li><span>x</span>: 722</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-52b7eb41-0c85-4819-ad12-7605c3aef123' class='xr-array-in' type='checkbox' checked><label for='section-52b7eb41-0c85-4819-ad12-7605c3aef123' 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=(46, 100, 100), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "    <tr><th> Shape </th><td> (46, 511, 722) </td> <td> (46, 100, 100) </td></tr>\n",
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       "<xarray.DataArray 'tmask' (deptht: 46, y: 511, x: 722)>\n",
       "dask.array<getitem, shape=(46, 511, 722), dtype=int8, chunksize=(46, 100, 100), chunktype=numpy.ndarray>\n",
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     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tmask_05"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compute global integrals and concatenate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#vol_05=(e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')).squeeze() \n",
    "# o2_05_cy1_int = (o2_05_cy1.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "# o2_05_cy2_int = (o2_05_cy2.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "# o2_05_cy3_int = (o2_05_cy3.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "# o2_05_cy4_int = (o2_05_cy4.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "# o2_05_cy5_int = (o2_05_cy5.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "\n",
    "# #o2_05_cy1_int_load = xr.load_dataset(path_05_cy1 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1_global_O2_integral.nc')\n",
    "# #o2_05_cy2_int = xr.open_dataset(path_05_cy2 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle2_global_O2_integral.nc')\n",
    "# #o2_05_cy3_int = xr.open_dataset(path_05_cy3 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle3_global_O2_integral.nc')\n",
    "# #o2_05_cy4_int = xr.open_dataset(path_05_cy4 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle4_global_O2_integral.nc')\n",
    "# #o2_05_cy5_int = xr.open_dataset(path_05_cy5 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle5_global_O2_integral.nc')\n",
    "\n",
    "\n",
    "# o2_05_int=np.hstack((np.array(o2_05_cy1_int.values).squeeze(), \n",
    "#                        np.array(o2_05_cy2_int.values).squeeze(),\n",
    "#                        np.array(o2_05_cy3_int.values).squeeze(),\n",
    "#                        np.array(o2_05_cy4_int.values).squeeze(),\n",
    "#                        np.array(o2_05_cy5_int.values).squeeze()))\n",
    "\n",
    "# #o2_05_ave=o2_05_int/np.array(vol_05)\n",
    "\n",
    "#testing \n",
    "o2_05_int=np.hstack((np.array(o2_05_cy1_int.values).squeeze(), \n",
    "                       np.array(o2_05_cy1_int.values).squeeze()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "       Dimensions:        (time_counter: 61)\n",
       "       Coordinates:\n",
       "           time_centered  (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "         * time_counter   (time_counter) datetime64[ns] 1958-07-01T17:00:00 ... 2018...\n",
       "       Data variables:\n",
       "           o2_integral    (time_counter) float64 dask.array<chunksize=(1,), meta=np.ndarray>>      ,\n",
       "       <bound method Mapping.values of <xarray.Dataset>\n",
       "       Dimensions:        (time_counter: 61)\n",
       "       Coordinates:\n",
       "           time_centered  (time_counter) datetime64[ns] dask.array<chunksize=(1,), meta=np.ndarray>\n",
       "         * time_counter   (time_counter) datetime64[ns] 1958-07-01T17:00:00 ... 2018...\n",
       "       Data variables:\n",
       "           o2_integral    (time_counter) float64 dask.array<chunksize=(1,), meta=np.ndarray>>      ],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o2_05_int"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the integrated fields on disk \n",
    "o2_05_cy1_int = o2_05_cy1_int.to_dataset(name='o2_integral')\n",
    "o2_05_cy1_int.to_netcdf(path_test + 'ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1_global_O2_integral.nc', mode='w')\n",
    "#o2_05_cy2_int.to_netcdf(path_05_cy2 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle2_global_O2_integral.nc', mode='w')\n",
    "#o2_05_cy3_int.to_netcdf(path_05_cy3 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle3_global_O2_integral.nc', mode='w')\n",
    "#o2_05_cy4_int.to_netcdf(path_05_cy4 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle4_global_O2_integral.nc', mode='w')\n",
    "#o2_05_cy5_int.to_netcdf(path_05_cy5 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle5_global_O2_integral.nc', mode='w')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "o2_05_cy1_int_load = xr.load_dataset(path_05_cy1 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1_global_O2_integral.nc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "pippo=o2_05_cy1_int_load.__xarray_dataarray_variable__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(61,)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pippo.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicp_05_cy1_int = (dicp_05_cy1.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "dicp_05_cy2_int = (dicp_05_cy2.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "dicp_05_cy3_int = (dicp_05_cy3.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "dicp_05_cy4_int = (dicp_05_cy4.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht'))\n",
    "dicp_05_cy5_int = (dicp_05_cy5.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "\n",
    "#dicp_05_cy1_int = xr.open_dataset(path_05_cy1 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1_global_dicp_integral.nc')\n",
    "#dicp_05_cy2_int = xr.open_dataset(path_05_cy2 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle2_global_dicp_integral.nc')\n",
    "#dicp_05_cy3_int = xr.open_dataset(path_05_cy3 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle3_global_dicp_integral.nc')\n",
    "#dicp_05_cy4_int = xr.open_dataset(path_05_cy4 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle4_global_dicp_integral.nc')\n",
    "#dicp_05_cy5_int = xr.open_dataset(path_05_cy5 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle5_global_dicp_integral.nc')\n",
    "\n",
    "\n",
    "\n",
    "dicp_05_int=np.hstack((np.array(dicp_05_cy1_int.values).squeeze(), \n",
    "                       np.array(dicp_05_cy2_int.values).squeeze(),\n",
    "                       np.array(dicp_05_cy3_int.values).squeeze(),\n",
    "                       np.array(dicp_05_cy4_int.values).squeeze(),\n",
    "                       np.array(dicp_05_cy5_int.values).squeeze()))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Store the integrated fields on disk \n",
    "#dicp_05_cy1_int.to_netcdf(path_05_cy1 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle1_global_dicp_integral.nc', mode='w')\n",
    "#dicp_05_cy2_int.to_netcdf(path_05_cy2 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle2_global_dicp_integral.nc', mode='w')\n",
    "#dicp_05_cy3_int.to_netcdf(path_05_cy3 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle3_global_dicp_integral.nc', mode='w')\n",
    "#dicp_05_cy4_int.to_netcdf(path_05_cy4 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle4_global_dicp_integral.nc', mode='w')\n",
    "#dicp_05_cy5_int.to_netcdf(path_05_cy5 + 'integrals/ORCA05.L46.LIM2vp.CFCSF6.MOPS.B.LP09-KLP001-cycle5_global_dicp_integral.nc', mode='w')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicp_05_cy1_int_new = (dicp_05_cy1_new.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "o2_05_cy1_int_new = (o2_05_cy1_new.where(tmask_05) * e1t_05 * e2t_05 * e3t_05).sum(('x','y','deptht')) \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dicp_05_cy1_new.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting O2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "ax = plt.gca()\n",
    "ax.plot(np.arange(0, 297),o2_05_int, color='black', linewidth=2, label='LP09')\n",
    "ax.plot(np.arange(0, 61),o2_05_cy1_int_new, color='red', linewidth=2, label='LP10')\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"mmol\", fontsize = 16)\n",
    "ax.set_title(\"Global O2 inventory\", fontsize = 18)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "axes.Axes.tick_params(ax, axis='both' ,labelsize=14)\n",
    "ax.grid()\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=12)\n",
    "\n",
    "\n",
    "locs, labels = plt.xticks()\n",
    "plt.xticks(np.arange(0, 305, step=61), ('cycle01', 'cycle02', 'cycle03', 'cycle04', 'cycle05'), rotation=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "#savepath = '/scratch/usr/shklvn09/notebooks/shared-notebooks/figures/Paper_CO2/'\n",
    "#plt.savefig(savepath + 'DICP_glob_inv_ORCA05_ORCA025_ORION10.png', format='png', facecolor=\"w\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plotting DICP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "ax = plt.gca()\n",
    "ax.plot(np.arange(0, 305),dicp_05_int, color='black', linewidth=2, label='LP09')\n",
    "ax.plot(np.arange(0, 61),dicp_05_cy1_int_new, color='red', linewidth=2, label='LP10')\n",
    "\n",
    "\n",
    "ax.set_ylabel(\"mol\", fontsize = 16)\n",
    "ax.set_title(\"Global DICP inventory\", fontsize = 18)\n",
    "\n",
    "plt.xticks(fontsize=14)\n",
    "plt.yticks(fontsize=14)\n",
    "axes.Axes.tick_params(ax, axis='both' ,labelsize=14)\n",
    "ax.grid()\n",
    "ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0., fontsize=12)\n",
    "\n",
    "\n",
    "locs, labels = plt.xticks()\n",
    "plt.xticks(np.arange(0, 305, step=61), ('cycle01', 'cycle02', 'cycle03', 'cycle04', 'cycle05'), rotation=25)\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "#savepath = '/scratch/usr/shklvn09/notebooks/shared-notebooks/figures/Paper_CO2/'\n",
    "#plt.savefig(savepath + 'DICP_glob_inv_ORCA05_ORCA025_ORION10.png', format='png', facecolor=\"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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