create_assim_files.ipynb

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     "text": [
      "invalid value encountered in cast (/opt/miniconda3/envs/pyfvcom/lib/python3.12/site-packages/utide/harmonics.py:16)\n",
      "invalid value encountered in cast (/opt/miniconda3/envs/pyfvcom/lib/python3.12/site-packages/utide/harmonics.py:17)\n"
     ]
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   "source": [
    "from scipy.io import loadmat\n",
    "import xarray as xr\n",
    "import numpy as np\n",
    "\n",
    "from datetime import datetime\n",
    "import PyFVCOM as pf\n",
    "\n",
    "# Load an FVCOM model output and plot a surface.\n",
    "from PyFVCOM.read import FileReader\n",
    "from PyFVCOM.plot import Plotter\n",
    "from cmocean import cm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
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   "execution_count": 2,
   "metadata": {},
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   "source": [
    "# Load the .mat file\n",
    "mat_data = loadmat('mobj_2005.mat')"
   ]
  },
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   "execution_count": 3,
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     "text": [
      "dict_keys(['__header__', '__version__', '__globals__', 'Mobj'])\n"
     ]
    }
   ],
   "source": [
    "# Explorar las claves del diccionario para ver qué variables están disponibles\n",
    "print(mat_data.keys())\n",
    "\n",
    "# Acceder a la variable 'Mobj'\n",
    "Mobj = mat_data['Mobj']\n",
    "\n",
    "# Ahora puedes acceder a los atributos dentro de Mobj. Si lonc está allí:\n",
    "lonc = Mobj['lonc'][0][0]  # Esto puede variar según cómo esté almacenado\n",
    "latc = Mobj['latc'][0][0]  # Esto puede variar según cómo esté almacenado\n",
    "\n",
    "lon = Mobj['lon'][0][0]  # Esto puede variar según cómo esté almacenado\n",
    "lat = Mobj['lat'][0][0]  # Esto puede variar según cómo esté almacenado\n",
    "\n",
    "fvcom_lonlat = np.array((lon, lat))"
   ]
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   "source": [
    "# Define a start, end and sampling interval for the tidal data\n",
    "start = datetime.strptime('2005-06-30', '%Y-%m-%d')\n",
    "end = datetime.strptime('2005-09-29', '%Y-%m-%d')\n",
    "interval = 1 / 24  # 1 hourly in units of days\n",
    "model = pf.preproc.Model(start, end, 'Golfo_v8_gridscope_fixedv6.2dm', sampling=interval,\n",
    "                         native_coordinates='spherical', zone='20N')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Creating xarray.Dataset from Data3hr/2005*12.nc\n"
     ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 303MB\n",
       "Dimensions:   (time: 90, lat: 163, lon: 126, depth: 40)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 720B 2005-07-01T12:00:00 ... 2005-09-28T1...\n",
       "  * lat       (lat) float64 1kB 19.04 19.12 19.2 19.28 ... 31.84 31.92 32.0\n",
       "  * lon       (lon) float64 1kB -115.0 -115.0 -114.9 ... -105.2 -105.1 -105.0\n",
       "  * depth     (depth) float64 320B 0.0 2.0 4.0 6.0 ... 2.5e+03 3e+03 4e+03 5e+03\n",
       "Data variables:\n",
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       "    salinity  (time, depth, lat, lon) float32 296MB dask.array&lt;chunksize=(1, 6, 163, 126), meta=np.ndarray&gt;\n",
       "Attributes: (12/14)\n",
       "    classification_level:      UNCLASSIFIED\n",
       "    distribution_statement:    Approved for public release. Distribution unli...\n",
       "    downgrade_date:            not applicable\n",
       "    classification_authority:  not applicable\n",
       "    institution:               Naval Oceanographic Office\n",
       "    source:                    HYCOM archive file\n",
       "    ...                        ...\n",
       "    Conventions:               CF-1.6 NAVO_netcdf_v1.1\n",
       "    History:                   Translated to CF-1.0 Conventions by Netcdf-Jav...\n",
       "    geospatial_lat_min:        19.040000915527344\n",
       "    geospatial_lat_max:        32.0\n",
       "    geospatial_lon_min:        -115.03997802734375\n",
       "    geospatial_lon_max:        -105.03997802734375</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-54aa4710-63b6-48b2-91db-25d9da16c8c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-54aa4710-63b6-48b2-91db-25d9da16c8c0' 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'>time</span>: 90</li><li><span class='xr-has-index'>lat</span>: 163</li><li><span class='xr-has-index'>lon</span>: 126</li><li><span class='xr-has-index'>depth</span>: 40</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c516091-291d-4e86-bfa6-2d21e4c9529b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c516091-291d-4e86-bfa6-2d21e4c9529b' 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'>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'>2005-07-01T12:00:00 ... 2005-09-...</div><input id='attrs-f36666d1-b6ca-4e11-944c-46c774af5e5d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f36666d1-b6ca-4e11-944c-46c774af5e5d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ed0b034-41ef-4cbc-bd45-07aa630a75d6' class='xr-var-data-in' type='checkbox'><label for='data-1ed0b034-41ef-4cbc-bd45-07aa630a75d6' 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>Valid Time</dd><dt><span>time_origin :</span></dt><dd>2000-01-01 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>NAVO_code :</span></dt><dd>13</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2005-07-01T12:00:00.000000000&#x27;, &#x27;2005-07-02T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-03T12:00:00.000000000&#x27;, &#x27;2005-07-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-05T12:00:00.000000000&#x27;, &#x27;2005-07-06T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-07T12:00:00.000000000&#x27;, &#x27;2005-07-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-09T12:00:00.000000000&#x27;, &#x27;2005-07-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-11T12:00:00.000000000&#x27;, &#x27;2005-07-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-13T12:00:00.000000000&#x27;, &#x27;2005-07-14T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-15T09:00:00.000000000&#x27;, &#x27;2005-07-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-17T12:00:00.000000000&#x27;, &#x27;2005-07-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-19T12:00:00.000000000&#x27;, &#x27;2005-07-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-21T12:00:00.000000000&#x27;, &#x27;2005-07-22T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-23T12:00:00.000000000&#x27;, &#x27;2005-07-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-25T12:00:00.000000000&#x27;, &#x27;2005-07-26T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-27T12:00:00.000000000&#x27;, &#x27;2005-07-28T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-29T12:00:00.000000000&#x27;, &#x27;2005-07-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-07-31T12:00:00.000000000&#x27;, &#x27;2005-08-01T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-02T12:00:00.000000000&#x27;, &#x27;2005-08-03T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-04T12:00:00.000000000&#x27;, &#x27;2005-08-05T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-06T12:00:00.000000000&#x27;, &#x27;2005-08-07T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-08T12:00:00.000000000&#x27;, &#x27;2005-08-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-10T12:00:00.000000000&#x27;, &#x27;2005-08-11T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-12T12:00:00.000000000&#x27;, &#x27;2005-08-13T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-14T12:00:00.000000000&#x27;, &#x27;2005-08-15T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-16T12:00:00.000000000&#x27;, &#x27;2005-08-17T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-18T12:00:00.000000000&#x27;, &#x27;2005-08-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-20T12:00:00.000000000&#x27;, &#x27;2005-08-21T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-22T12:00:00.000000000&#x27;, &#x27;2005-08-23T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-24T12:00:00.000000000&#x27;, &#x27;2005-08-25T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-26T12:00:00.000000000&#x27;, &#x27;2005-08-27T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-28T12:00:00.000000000&#x27;, &#x27;2005-08-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-08-30T12:00:00.000000000&#x27;, &#x27;2005-08-31T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-01T09:00:00.000000000&#x27;, &#x27;2005-09-02T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-03T12:00:00.000000000&#x27;, &#x27;2005-09-04T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-05T12:00:00.000000000&#x27;, &#x27;2005-09-06T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-07T12:00:00.000000000&#x27;, &#x27;2005-09-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-09T12:00:00.000000000&#x27;, &#x27;2005-09-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-11T09:00:00.000000000&#x27;, &#x27;2005-09-12T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-13T12:00:00.000000000&#x27;, &#x27;2005-09-14T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-15T12:00:00.000000000&#x27;, &#x27;2005-09-16T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-17T12:00:00.000000000&#x27;, &#x27;2005-09-18T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-19T12:00:00.000000000&#x27;, &#x27;2005-09-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-21T12:00:00.000000000&#x27;, &#x27;2005-09-22T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-23T12:00:00.000000000&#x27;, &#x27;2005-09-24T12:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-25T12:00:00.000000000&#x27;, &#x27;2005-09-26T09:00:00.000000000&#x27;,\n",
       "       &#x27;2005-09-27T12:00:00.000000000&#x27;, &#x27;2005-09-28T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>19.04 19.12 19.2 ... 31.92 32.0</div><input id='attrs-c28665a6-46f2-432a-ba60-450a5f04434b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c28665a6-46f2-432a-ba60-450a5f04434b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c08891f-792a-43bb-8ac8-89d08de2bfdf' class='xr-var-data-in' type='checkbox'><label for='data-8c08891f-792a-43bb-8ac8-89d08de2bfdf' 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>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>NAVO_code :</span></dt><dd>1</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([19.040001, 19.120001, 19.200001, 19.280001, 19.360001, 19.440001,\n",
       "       19.52    , 19.6     , 19.68    , 19.76    , 19.84    , 19.92    ,\n",
       "       20.      , 20.08    , 20.16    , 20.24    , 20.32    , 20.4     ,\n",
       "       20.48    , 20.559999, 20.639999, 20.719999, 20.799999, 20.879999,\n",
       "       20.959999, 21.040001, 21.120001, 21.200001, 21.280001, 21.360001,\n",
       "       21.440001, 21.52    , 21.6     , 21.68    , 21.76    , 21.84    ,\n",
       "       21.92    , 22.      , 22.08    , 22.16    , 22.24    , 22.32    ,\n",
       "       22.4     , 22.48    , 22.559999, 22.639999, 22.719999, 22.799999,\n",
       "       22.879999, 22.959999, 23.040001, 23.120001, 23.200001, 23.280001,\n",
       "       23.360001, 23.440001, 23.52    , 23.6     , 23.68    , 23.76    ,\n",
       "       23.84    , 23.92    , 24.      , 24.08    , 24.16    , 24.24    ,\n",
       "       24.32    , 24.4     , 24.48    , 24.559999, 24.639999, 24.719999,\n",
       "       24.799999, 24.879999, 24.959999, 25.040001, 25.120001, 25.200001,\n",
       "       25.280001, 25.360001, 25.440001, 25.52    , 25.6     , 25.68    ,\n",
       "       25.76    , 25.84    , 25.92    , 26.      , 26.08    , 26.16    ,\n",
       "       26.24    , 26.32    , 26.4     , 26.48    , 26.559999, 26.639999,\n",
       "       26.719999, 26.799999, 26.879999, 26.959999, 27.040001, 27.120001,\n",
       "       27.200001, 27.280001, 27.360001, 27.440001, 27.52    , 27.6     ,\n",
       "       27.68    , 27.76    , 27.84    , 27.92    , 28.      , 28.08    ,\n",
       "       28.16    , 28.24    , 28.32    , 28.4     , 28.48    , 28.559999,\n",
       "       28.639999, 28.719999, 28.799999, 28.879999, 28.959999, 29.040001,\n",
       "       29.120001, 29.200001, 29.280001, 29.360001, 29.440001, 29.52    ,\n",
       "       29.6     , 29.68    , 29.76    , 29.84    , 29.92    , 30.      ,\n",
       "       30.08    , 30.16    , 30.24    , 30.32    , 30.4     , 30.48    ,\n",
       "       30.559999, 30.639999, 30.719999, 30.799999, 30.879999, 30.959999,\n",
       "       31.040001, 31.120001, 31.200001, 31.280001, 31.360001, 31.440001,\n",
       "       31.52    , 31.6     , 31.68    , 31.76    , 31.84    , 31.92    ,\n",
       "       32.      ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-115.0 -115.0 ... -105.1 -105.0</div><input id='attrs-ec19a7ea-74b9-4d0f-9378-f456e3d8050c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec19a7ea-74b9-4d0f-9378-f456e3d8050c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36fcb1b0-cae5-400f-89ef-4b3320bb51f2' class='xr-var-data-in' type='checkbox'><label for='data-36fcb1b0-cae5-400f-89ef-4b3320bb51f2' 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>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>modulo :</span></dt><dd>360 degrees</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>NAVO_code :</span></dt><dd>2</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([-115.039978, -114.960022, -114.880005, -114.799988, -114.719971,\n",
       "       -114.640015, -114.559998, -114.47998 , -114.400024, -114.320007,\n",
       "       -114.23999 , -114.160034, -114.080017, -114.      , -113.919983,\n",
       "       -113.839966, -113.76001 , -113.679993, -113.599976, -113.52002 ,\n",
       "       -113.440002, -113.359985, -113.280029, -113.200012, -113.119995,\n",
       "       -113.039978, -112.960022, -112.880005, -112.799988, -112.719971,\n",
       "       -112.640015, -112.559998, -112.47998 , -112.400024, -112.320007,\n",
       "       -112.23999 , -112.160034, -112.080017, -112.      , -111.919983,\n",
       "       -111.839966, -111.76001 , -111.679993, -111.599976, -111.52002 ,\n",
       "       -111.440002, -111.359985, -111.280029, -111.200012, -111.119995,\n",
       "       -111.039978, -110.960022, -110.880005, -110.799988, -110.719971,\n",
       "       -110.640015, -110.559998, -110.47998 , -110.400024, -110.320007,\n",
       "       -110.23999 , -110.160034, -110.080017, -110.      , -109.919983,\n",
       "       -109.839966, -109.76001 , -109.679993, -109.599976, -109.52002 ,\n",
       "       -109.440002, -109.359985, -109.280029, -109.200012, -109.119995,\n",
       "       -109.039978, -108.960022, -108.880005, -108.799988, -108.719971,\n",
       "       -108.640015, -108.559998, -108.47998 , -108.400024, -108.320007,\n",
       "       -108.23999 , -108.160034, -108.080017, -108.      , -107.919983,\n",
       "       -107.839966, -107.76001 , -107.679993, -107.599976, -107.52002 ,\n",
       "       -107.440002, -107.359985, -107.280029, -107.200012, -107.119995,\n",
       "       -107.039978, -106.960022, -106.880005, -106.799988, -106.719971,\n",
       "       -106.640015, -106.559998, -106.47998 , -106.400024, -106.320007,\n",
       "       -106.23999 , -106.160034, -106.080017, -106.      , -105.919983,\n",
       "       -105.839966, -105.76001 , -105.679993, -105.599976, -105.52002 ,\n",
       "       -105.440002, -105.359985, -105.280029, -105.200012, -105.119995,\n",
       "       -105.039978])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 2.0 4.0 ... 3e+03 4e+03 5e+03</div><input id='attrs-52dc0ad8-8fca-4c2c-8e02-2bece89ef5f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52dc0ad8-8fca-4c2c-8e02-2bece89ef5f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66860bbf-f07d-4826-bc4e-656d9c99bfe1' class='xr-var-data-in' type='checkbox'><label for='data-66860bbf-f07d-4826-bc4e-656d9c99bfe1' 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>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>NAVO_code :</span></dt><dd>5</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Height</dd><dt><span>_CoordinateZisPositive :</span></dt><dd>down</dd></dl></div><div class='xr-var-data'><pre>array([0.00e+00, 2.00e+00, 4.00e+00, 6.00e+00, 8.00e+00, 1.00e+01, 1.20e+01,\n",
       "       1.50e+01, 2.00e+01, 2.50e+01, 3.00e+01, 3.50e+01, 4.00e+01, 4.50e+01,\n",
       "       5.00e+01, 6.00e+01, 7.00e+01, 8.00e+01, 9.00e+01, 1.00e+02, 1.25e+02,\n",
       "       1.50e+02, 2.00e+02, 2.50e+02, 3.00e+02, 3.50e+02, 4.00e+02, 5.00e+02,\n",
       "       6.00e+02, 7.00e+02, 8.00e+02, 9.00e+02, 1.00e+03, 1.25e+03, 1.50e+03,\n",
       "       2.00e+03, 2.50e+03, 3.00e+03, 4.00e+03, 5.00e+03])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-96de4671-e2ce-4979-8c1a-4d23dd867419' class='xr-section-summary-in' type='checkbox'  checked><label for='section-96de4671-e2ce-4979-8c1a-4d23dd867419' 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>surf_el</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 163, 126), meta=np.ndarray&gt;</div><input id='attrs-50b39f10-a7fb-4bd3-a4ae-b6d277ed9fc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50b39f10-a7fb-4bd3-a4ae-b6d277ed9fc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecd7fbde-b5b5-41d3-8688-e250a8496835' class='xr-var-data-in' type='checkbox'><label for='data-ecd7fbde-b5b5-41d3-8688-e250a8496835' 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>_CoordinateAxes :</span></dt><dd>time lat lon </dd><dt><span>long_name :</span></dt><dd>Water Surface Elevation</dd><dt><span>standard_name :</span></dt><dd>sea_surface_elevation</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>NAVO_code :</span></dt><dd>32</dd></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> 7.05 MiB </td>\n",
       "                        <td> 80.23 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (90, 163, 126) </td>\n",
       "                        <td> (1, 163, 126) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 90 chunks in 181 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=\"191\" height=\"208\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "    </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, depth, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 6, 163, 126), meta=np.ndarray&gt;</div><input id='attrs-12abd9c0-c85b-405c-bbd8-61a505be1312' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12abd9c0-c85b-405c-bbd8-61a505be1312' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97e9b48a-0deb-479e-942a-940cbef2e53b' class='xr-var-data-in' type='checkbox'><label for='data-97e9b48a-0deb-479e-942a-940cbef2e53b' 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>_CoordinateAxes :</span></dt><dd>time depth lat lon </dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd><dt><span>units :</span></dt><dd>psu</dd><dt><span>NAVO_code :</span></dt><dd>16</dd></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> 282.05 MiB </td>\n",
       "                        <td> 481.36 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (90, 40, 163, 126) </td>\n",
       "                        <td> (1, 6, 163, 126) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 630 chunks in 181 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",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-eb7ec0c9-38d2-4901-97a0-553e2d7d4aee' class='xr-section-summary-in' type='checkbox'  ><label for='section-eb7ec0c9-38d2-4901-97a0-553e2d7d4aee' 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>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-630dc5e7-f5c1-41ed-936a-5f0b6ee09c44' class='xr-index-data-in' type='checkbox'/><label for='index-630dc5e7-f5c1-41ed-936a-5f0b6ee09c44' 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;2005-07-01 12:00:00&#x27;, &#x27;2005-07-02 12:00:00&#x27;,\n",
       "               &#x27;2005-07-03 12:00:00&#x27;, &#x27;2005-07-04 12:00:00&#x27;,\n",
       "               &#x27;2005-07-05 12:00:00&#x27;, &#x27;2005-07-06 12:00:00&#x27;,\n",
       "               &#x27;2005-07-07 12:00:00&#x27;, &#x27;2005-07-08 12:00:00&#x27;,\n",
       "               &#x27;2005-07-09 12:00:00&#x27;, &#x27;2005-07-10 12:00:00&#x27;,\n",
       "               &#x27;2005-07-11 12:00:00&#x27;, &#x27;2005-07-12 12:00:00&#x27;,\n",
       "               &#x27;2005-07-13 12:00:00&#x27;, &#x27;2005-07-14 12:00:00&#x27;,\n",
       "               &#x27;2005-07-15 09:00:00&#x27;, &#x27;2005-07-16 12:00:00&#x27;,\n",
       "               &#x27;2005-07-17 12:00:00&#x27;, &#x27;2005-07-18 12:00:00&#x27;,\n",
       "               &#x27;2005-07-19 12:00:00&#x27;, &#x27;2005-07-20 12:00:00&#x27;,\n",
       "               &#x27;2005-07-21 12:00:00&#x27;, &#x27;2005-07-22 12:00:00&#x27;,\n",
       "               &#x27;2005-07-23 12:00:00&#x27;, &#x27;2005-07-24 12:00:00&#x27;,\n",
       "               &#x27;2005-07-25 12:00:00&#x27;, &#x27;2005-07-26 12:00:00&#x27;,\n",
       "               &#x27;2005-07-27 12:00:00&#x27;, &#x27;2005-07-28 12:00:00&#x27;,\n",
       "               &#x27;2005-07-29 12:00:00&#x27;, &#x27;2005-07-30 12:00:00&#x27;,\n",
       "               &#x27;2005-07-31 12:00:00&#x27;, &#x27;2005-08-01 12:00:00&#x27;,\n",
       "               &#x27;2005-08-02 12:00:00&#x27;, &#x27;2005-08-03 12:00:00&#x27;,\n",
       "               &#x27;2005-08-04 12:00:00&#x27;, &#x27;2005-08-05 12:00:00&#x27;,\n",
       "               &#x27;2005-08-06 12:00:00&#x27;, &#x27;2005-08-07 12:00:00&#x27;,\n",
       "               &#x27;2005-08-08 12:00:00&#x27;, &#x27;2005-08-09 12:00:00&#x27;,\n",
       "               &#x27;2005-08-10 12:00:00&#x27;, &#x27;2005-08-11 09:00:00&#x27;,\n",
       "               &#x27;2005-08-12 12:00:00&#x27;, &#x27;2005-08-13 12:00:00&#x27;,\n",
       "               &#x27;2005-08-14 12:00:00&#x27;, &#x27;2005-08-15 12:00:00&#x27;,\n",
       "               &#x27;2005-08-16 12:00:00&#x27;, &#x27;2005-08-17 12:00:00&#x27;,\n",
       "               &#x27;2005-08-18 12:00:00&#x27;, &#x27;2005-08-19 12:00:00&#x27;,\n",
       "               &#x27;2005-08-20 12:00:00&#x27;, &#x27;2005-08-21 12:00:00&#x27;,\n",
       "               &#x27;2005-08-22 12:00:00&#x27;, &#x27;2005-08-23 12:00:00&#x27;,\n",
       "               &#x27;2005-08-24 12:00:00&#x27;, &#x27;2005-08-25 12:00:00&#x27;,\n",
       "               &#x27;2005-08-26 12:00:00&#x27;, &#x27;2005-08-27 12:00:00&#x27;,\n",
       "               &#x27;2005-08-28 12:00:00&#x27;, &#x27;2005-08-29 12:00:00&#x27;,\n",
       "               &#x27;2005-08-30 12:00:00&#x27;, &#x27;2005-08-31 12:00:00&#x27;,\n",
       "               &#x27;2005-09-01 09:00:00&#x27;, &#x27;2005-09-02 09:00:00&#x27;,\n",
       "               &#x27;2005-09-03 12:00:00&#x27;, &#x27;2005-09-04 12:00:00&#x27;,\n",
       "               &#x27;2005-09-05 12:00:00&#x27;, &#x27;2005-09-06 12:00:00&#x27;,\n",
       "               &#x27;2005-09-07 12:00:00&#x27;, &#x27;2005-09-08 12:00:00&#x27;,\n",
       "               &#x27;2005-09-09 12:00:00&#x27;, &#x27;2005-09-10 12:00:00&#x27;,\n",
       "               &#x27;2005-09-11 09:00:00&#x27;, &#x27;2005-09-12 12:00:00&#x27;,\n",
       "               &#x27;2005-09-13 12:00:00&#x27;, &#x27;2005-09-14 09:00:00&#x27;,\n",
       "               &#x27;2005-09-15 12:00:00&#x27;, &#x27;2005-09-16 12:00:00&#x27;,\n",
       "               &#x27;2005-09-17 12:00:00&#x27;, &#x27;2005-09-18 09:00:00&#x27;,\n",
       "               &#x27;2005-09-19 12:00:00&#x27;, &#x27;2005-09-20 12:00:00&#x27;,\n",
       "               &#x27;2005-09-21 12:00:00&#x27;, &#x27;2005-09-22 09:00:00&#x27;,\n",
       "               &#x27;2005-09-23 12:00:00&#x27;, &#x27;2005-09-24 12:00:00&#x27;,\n",
       "               &#x27;2005-09-25 12:00:00&#x27;, &#x27;2005-09-26 09:00:00&#x27;,\n",
       "               &#x27;2005-09-27 12:00:00&#x27;, &#x27;2005-09-28 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</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-ce17127a-b478-4d4f-b272-07fc72c5639a' class='xr-index-data-in' type='checkbox'/><label for='index-ce17127a-b478-4d4f-b272-07fc72c5639a' 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([19.040000915527344,   19.1200008392334, 19.200000762939453,\n",
       "       19.280000686645508, 19.360000610351562, 19.440000534057617,\n",
       "       19.520000457763672, 19.600000381469727,  19.68000030517578,\n",
       "       19.760000228881836,\n",
       "       ...\n",
       "       31.280000686645508, 31.360000610351562, 31.440000534057617,\n",
       "       31.520000457763672, 31.600000381469727,  31.68000030517578,\n",
       "       31.760000228881836,  31.84000015258789, 31.920000076293945,\n",
       "                     32.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=163))</pre></div></li><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-7f8bd2ac-8974-4a9f-8545-210ad6224d45' class='xr-index-data-in' type='checkbox'/><label for='index-7f8bd2ac-8974-4a9f-8545-210ad6224d45' 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([-115.03997802734375, -114.96002197265625,  -114.8800048828125,\n",
       "       -114.79998779296875,   -114.719970703125,  -114.6400146484375,\n",
       "       -114.55999755859375,    -114.47998046875,  -114.4000244140625,\n",
       "       -114.32000732421875,\n",
       "       ...\n",
       "         -105.760009765625, -105.67999267578125,  -105.5999755859375,\n",
       "          -105.52001953125, -105.44000244140625,  -105.3599853515625,\n",
       "         -105.280029296875, -105.20001220703125,  -105.1199951171875,\n",
       "       -105.03997802734375],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=126))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>depth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-696d9659-7f0b-445e-a636-a96ee90b58c5' class='xr-index-data-in' type='checkbox'/><label for='index-696d9659-7f0b-445e-a636-a96ee90b58c5' 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([   0.0,    2.0,    4.0,    6.0,    8.0,   10.0,   12.0,   15.0,   20.0,\n",
       "         25.0,   30.0,   35.0,   40.0,   45.0,   50.0,   60.0,   70.0,   80.0,\n",
       "         90.0,  100.0,  125.0,  150.0,  200.0,  250.0,  300.0,  350.0,  400.0,\n",
       "        500.0,  600.0,  700.0,  800.0,  900.0, 1000.0, 1250.0, 1500.0, 2000.0,\n",
       "       2500.0, 3000.0, 4000.0, 5000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;depth&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c3baf8eb-8096-45f4-a54f-8e56993fa040' class='xr-section-summary-in' type='checkbox'  ><label for='section-c3baf8eb-8096-45f4-a54f-8e56993fa040' class='xr-section-summary' >Attributes: <span>(14)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>classification_level :</span></dt><dd>UNCLASSIFIED</dd><dt><span>distribution_statement :</span></dt><dd>Approved for public release. Distribution unlimited.</dd><dt><span>downgrade_date :</span></dt><dd>not applicable</dd><dt><span>classification_authority :</span></dt><dd>not applicable</dd><dt><span>institution :</span></dt><dd>Naval Oceanographic Office</dd><dt><span>source :</span></dt><dd>HYCOM archive file</dd><dt><span>history :</span></dt><dd>archv2ncdf3z</dd><dt><span>field_type :</span></dt><dd>instantaneous</dd><dt><span>Conventions :</span></dt><dd>CF-1.6 NAVO_netcdf_v1.1</dd><dt><span>History :</span></dt><dd>Translated to CF-1.0 Conventions by Netcdf-Java CDM (CFGridWriter2)\n",
       "Original Dataset = GLBv0.08/expt_53.X/data/2005; Translation Date = 2024-10-24T22:59:16.338Z</dd><dt><span>geospatial_lat_min :</span></dt><dd>19.040000915527344</dd><dt><span>geospatial_lat_max :</span></dt><dd>32.0</dd><dt><span>geospatial_lon_min :</span></dt><dd>-115.03997802734375</dd><dt><span>geospatial_lon_max :</span></dt><dd>-105.03997802734375</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 303MB\n",
       "Dimensions:   (time: 90, lat: 163, lon: 126, depth: 40)\n",
       "Coordinates:\n",
       "  * time      (time) datetime64[ns] 720B 2005-07-01T12:00:00 ... 2005-09-28T1...\n",
       "  * lat       (lat) float64 1kB 19.04 19.12 19.2 19.28 ... 31.84 31.92 32.0\n",
       "  * lon       (lon) float64 1kB -115.0 -115.0 -114.9 ... -105.2 -105.1 -105.0\n",
       "  * depth     (depth) float64 320B 0.0 2.0 4.0 6.0 ... 2.5e+03 3e+03 4e+03 5e+03\n",
       "Data variables:\n",
       "    surf_el   (time, lat, lon) float32 7MB dask.array<chunksize=(1, 163, 126), meta=np.ndarray>\n",
       "    salinity  (time, depth, lat, lon) float32 296MB dask.array<chunksize=(1, 6, 163, 126), meta=np.ndarray>\n",
       "Attributes: (12/14)\n",
       "    classification_level:      UNCLASSIFIED\n",
       "    distribution_statement:    Approved for public release. Distribution unli...\n",
       "    downgrade_date:            not applicable\n",
       "    classification_authority:  not applicable\n",
       "    institution:               Naval Oceanographic Office\n",
       "    source:                    HYCOM archive file\n",
       "    ...                        ...\n",
       "    Conventions:               CF-1.6 NAVO_netcdf_v1.1\n",
       "    History:                   Translated to CF-1.0 Conventions by Netcdf-Jav...\n",
       "    geospatial_lat_min:        19.040000915527344\n",
       "    geospatial_lat_max:        32.0\n",
       "    geospatial_lon_min:        -115.03997802734375\n",
       "    geospatial_lon_max:        -105.03997802734375"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncfiles = 'Data3hr/2005*12.nc'\n",
    "\n",
    "with xr.open_mfdataset(ncfiles, parallel=True,combine='nested', concat_dim=\"time\", data_vars='minimal', \\\n",
    "                       coords='minimal', compat='override') as dsts:\n",
    "    print(\"Creating xarray.Dataset from \" + str(ncfiles))\n",
    "\n",
    "dsts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((90, 163, 126), (90, 163, 126), 163)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ssh_values = dsts['surf_el'].values\n",
    "sss_values = dsts['salinity'][:,0,:,:].values\n",
    "ssh_lon = dsts['lon'].values\n",
    "ssh_lat = dsts['lat'].values\n",
    "hycom_times = dsts['time'].values\n",
    "\n",
    "np.shape(ssh_values), np.shape(sss_values), len(ssh_lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "ft = pf.preproc.RegularGridInterpolator((ssh_lon, ssh_lat), ssh_values.T, method='linear', fill_value=None)\n",
    "interp_ssh = ft(fvcom_lonlat.T)\n",
    "\n",
    "interp_ssh = np.squeeze(interp_ssh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "fvcom_lonlat = np.squeeze(fvcom_lonlat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El número de NaN en interp_ssh es: 172800\n"
     ]
    }
   ],
   "source": [
    "# Contar el número total de NaN en interp_ssh\n",
    "num_nans = np.isnan(interp_ssh).sum()\n",
    "print(f\"El número de NaN en interp_ssh es: {num_nans}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.interpolate import griddata\n",
    "\n",
    "# Verificar si existen NaN y rellenarlos\n",
    "for t in range(interp_ssh.shape[1]):  # Iterar sobre cada tiempo\n",
    "    # Crear una máscara para los valores NaN en el tiempo `t`\n",
    "    mask = np.isnan(interp_ssh[:, t])\n",
    "    \n",
    "    if np.any(mask):\n",
    "        # Usar solo los nodos válidos para interpolación\n",
    "        interp_ssh[mask, t] = griddata(\n",
    "            fvcom_lonlat.T[~mask],           # Coordenadas de nodos sin NaN\n",
    "            interp_ssh[~mask, t],          # Valores en esos nodos\n",
    "            fvcom_lonlat.T[mask],            # Coordenadas de nodos con NaN\n",
    "            method='nearest'               # Método de interpolación\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El número de NaN en interp_ssh es: 0\n"
     ]
    }
   ],
   "source": [
    "# Contar el número total de NaN en interp_ssh\n",
    "num_nans = np.isnan(interp_ssh).sum()\n",
    "print(f\"El número de NaN en interp_ssh es: {num_nans}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2005-09-29T00:00:00.000000'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.time.Times[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2005-07-01T12:00:00.000000',\n",
       " '2005-07-02T12:00:00.000000',\n",
       " '2005-07-03T12:00:00.000000',\n",
       " '2005-07-04T12:00:00.000000',\n",
       " '2005-07-05T12:00:00.000000',\n",
       " '2005-07-06T12:00:00.000000',\n",
       " '2005-07-07T12:00:00.000000',\n",
       " '2005-07-08T12:00:00.000000',\n",
       " '2005-07-09T12:00:00.000000',\n",
       " '2005-07-10T12:00:00.000000',\n",
       " '2005-07-11T12:00:00.000000',\n",
       " '2005-07-12T12:00:00.000000',\n",
       " '2005-07-13T12:00:00.000000',\n",
       " '2005-07-14T12:00:00.000000',\n",
       " '2005-07-15T12:00:00.000000',\n",
       " '2005-07-16T12:00:00.000000',\n",
       " '2005-07-17T12:00:00.000000',\n",
       " '2005-07-18T12:00:00.000000',\n",
       " '2005-07-19T12:00:00.000000',\n",
       " '2005-07-20T12:00:00.000000',\n",
       " '2005-07-21T12:00:00.000000',\n",
       " '2005-07-22T12:00:00.000000',\n",
       " '2005-07-23T12:00:00.000000',\n",
       " '2005-07-24T12:00:00.000000',\n",
       " '2005-07-25T12:00:00.000000',\n",
       " '2005-07-26T12:00:00.000000',\n",
       " '2005-07-27T12:00:00.000000',\n",
       " '2005-07-28T12:00:00.000000',\n",
       " '2005-07-29T12:00:00.000000',\n",
       " '2005-07-30T12:00:00.000000',\n",
       " '2005-07-31T12:00:00.000000',\n",
       " '2005-08-01T12:00:00.000000',\n",
       " '2005-08-02T12:00:00.000000',\n",
       " '2005-08-03T12:00:00.000000',\n",
       " '2005-08-04T12:00:00.000000',\n",
       " '2005-08-05T12:00:00.000000',\n",
       " '2005-08-06T12:00:00.000000',\n",
       " '2005-08-07T12:00:00.000000',\n",
       " '2005-08-08T12:00:00.000000',\n",
       " '2005-08-09T12:00:00.000000',\n",
       " '2005-08-10T12:00:00.000000',\n",
       " '2005-08-11T12:00:00.000000',\n",
       " '2005-08-12T12:00:00.000000',\n",
       " '2005-08-13T12:00:00.000000',\n",
       " '2005-08-14T12:00:00.000000',\n",
       " '2005-08-15T12:00:00.000000',\n",
       " '2005-08-16T12:00:00.000000',\n",
       " '2005-08-17T12:00:00.000000',\n",
       " '2005-08-18T12:00:00.000000',\n",
       " '2005-08-19T12:00:00.000000',\n",
       " '2005-08-20T12:00:00.000000',\n",
       " '2005-08-21T12:00:00.000000',\n",
       " '2005-08-22T12:00:00.000000',\n",
       " '2005-08-23T12:00:00.000000',\n",
       " '2005-08-24T12:00:00.000000',\n",
       " '2005-08-25T12:00:00.000000',\n",
       " '2005-08-26T12:00:00.000000',\n",
       " '2005-08-27T12:00:00.000000',\n",
       " '2005-08-28T12:00:00.000000',\n",
       " '2005-08-29T12:00:00.000000',\n",
       " '2005-08-30T12:00:00.000000',\n",
       " '2005-08-31T12:00:00.000000',\n",
       " '2005-09-01T12:00:00.000000',\n",
       " '2005-09-02T12:00:00.000000',\n",
       " '2005-09-03T12:00:00.000000',\n",
       " '2005-09-04T12:00:00.000000',\n",
       " '2005-09-05T12:00:00.000000',\n",
       " '2005-09-06T12:00:00.000000',\n",
       " '2005-09-07T12:00:00.000000',\n",
       " '2005-09-08T12:00:00.000000',\n",
       " '2005-09-09T12:00:00.000000',\n",
       " '2005-09-10T12:00:00.000000',\n",
       " '2005-09-11T12:00:00.000000',\n",
       " '2005-09-12T12:00:00.000000',\n",
       " '2005-09-13T12:00:00.000000',\n",
       " '2005-09-14T12:00:00.000000',\n",
       " '2005-09-15T12:00:00.000000',\n",
       " '2005-09-16T12:00:00.000000',\n",
       " '2005-09-17T12:00:00.000000',\n",
       " '2005-09-18T12:00:00.000000',\n",
       " '2005-09-19T12:00:00.000000',\n",
       " '2005-09-20T12:00:00.000000',\n",
       " '2005-09-21T12:00:00.000000',\n",
       " '2005-09-22T12:00:00.000000',\n",
       " '2005-09-23T12:00:00.000000',\n",
       " '2005-09-24T12:00:00.000000',\n",
       " '2005-09-25T12:00:00.000000',\n",
       " '2005-09-26T12:00:00.000000',\n",
       " '2005-09-27T12:00:00.000000',\n",
       " '2005-09-28T12:00:00.000000']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Crear un nuevo array con fechas cada 3 elementos, comenzando desde el índice 17\n",
    "Times_cada_3 = model.time.Times[29:-8:24]\n",
    "time_cada_3 = model.time.time[29:-8:24]\n",
    "Itime_cada_3 = model.time.Itime[29:-8:24]\n",
    "Itime2_cada_3 = model.time.Itime2[29:-8:24]\n",
    "\n",
    "Times_cada_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "lons = np.squeeze(lon)\n",
    "lats = np.squeeze(lat)\n",
    "\n",
    "# Desanidar el array para obtener el valor numérico\n",
    "nodes = Mobj['nVerts'][0].item()\n",
    "\n",
    "# Si necesitas aplanarlo aún más, usa flatten()\n",
    "nodes = nodes.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2005/07/01 12:00:00.000000',\n",
       " '2005/07/02 12:00:00.000000',\n",
       " '2005/07/03 12:00:00.000000',\n",
       " '2005/07/04 12:00:00.000000',\n",
       " '2005/07/05 12:00:00.000000',\n",
       " '2005/07/06 12:00:00.000000',\n",
       " '2005/07/07 12:00:00.000000',\n",
       " '2005/07/08 12:00:00.000000',\n",
       " '2005/07/09 12:00:00.000000',\n",
       " '2005/07/10 12:00:00.000000',\n",
       " '2005/07/11 12:00:00.000000',\n",
       " '2005/07/12 12:00:00.000000',\n",
       " '2005/07/13 12:00:00.000000',\n",
       " '2005/07/14 12:00:00.000000',\n",
       " '2005/07/15 12:00:00.000000',\n",
       " '2005/07/16 12:00:00.000000',\n",
       " '2005/07/17 12:00:00.000000',\n",
       " '2005/07/18 12:00:00.000000',\n",
       " '2005/07/19 12:00:00.000000',\n",
       " '2005/07/20 12:00:00.000000',\n",
       " '2005/07/21 12:00:00.000000',\n",
       " '2005/07/22 12:00:00.000000',\n",
       " '2005/07/23 12:00:00.000000',\n",
       " '2005/07/24 12:00:00.000000',\n",
       " '2005/07/25 12:00:00.000000',\n",
       " '2005/07/26 12:00:00.000000',\n",
       " '2005/07/27 12:00:00.000000',\n",
       " '2005/07/28 12:00:00.000000',\n",
       " '2005/07/29 12:00:00.000000',\n",
       " '2005/07/30 12:00:00.000000',\n",
       " '2005/07/31 12:00:00.000000',\n",
       " '2005/08/01 12:00:00.000000',\n",
       " '2005/08/02 12:00:00.000000',\n",
       " '2005/08/03 12:00:00.000000',\n",
       " '2005/08/04 12:00:00.000000',\n",
       " '2005/08/05 12:00:00.000000',\n",
       " '2005/08/06 12:00:00.000000',\n",
       " '2005/08/07 12:00:00.000000',\n",
       " '2005/08/08 12:00:00.000000',\n",
       " '2005/08/09 12:00:00.000000',\n",
       " '2005/08/10 12:00:00.000000',\n",
       " '2005/08/11 12:00:00.000000',\n",
       " '2005/08/12 12:00:00.000000',\n",
       " '2005/08/13 12:00:00.000000',\n",
       " '2005/08/14 12:00:00.000000',\n",
       " '2005/08/15 12:00:00.000000',\n",
       " '2005/08/16 12:00:00.000000',\n",
       " '2005/08/17 12:00:00.000000',\n",
       " '2005/08/18 12:00:00.000000',\n",
       " '2005/08/19 12:00:00.000000',\n",
       " '2005/08/20 12:00:00.000000',\n",
       " '2005/08/21 12:00:00.000000',\n",
       " '2005/08/22 12:00:00.000000',\n",
       " '2005/08/23 12:00:00.000000',\n",
       " '2005/08/24 12:00:00.000000',\n",
       " '2005/08/25 12:00:00.000000',\n",
       " '2005/08/26 12:00:00.000000',\n",
       " '2005/08/27 12:00:00.000000',\n",
       " '2005/08/28 12:00:00.000000',\n",
       " '2005/08/29 12:00:00.000000',\n",
       " '2005/08/30 12:00:00.000000',\n",
       " '2005/08/31 12:00:00.000000',\n",
       " '2005/09/01 12:00:00.000000',\n",
       " '2005/09/02 12:00:00.000000',\n",
       " '2005/09/03 12:00:00.000000',\n",
       " '2005/09/04 12:00:00.000000',\n",
       " '2005/09/05 12:00:00.000000',\n",
       " '2005/09/06 12:00:00.000000',\n",
       " '2005/09/07 12:00:00.000000',\n",
       " '2005/09/08 12:00:00.000000',\n",
       " '2005/09/09 12:00:00.000000',\n",
       " '2005/09/10 12:00:00.000000',\n",
       " '2005/09/11 12:00:00.000000',\n",
       " '2005/09/12 12:00:00.000000',\n",
       " '2005/09/13 12:00:00.000000',\n",
       " '2005/09/14 12:00:00.000000',\n",
       " '2005/09/15 12:00:00.000000',\n",
       " '2005/09/16 12:00:00.000000',\n",
       " '2005/09/17 12:00:00.000000',\n",
       " '2005/09/18 12:00:00.000000',\n",
       " '2005/09/19 12:00:00.000000',\n",
       " '2005/09/20 12:00:00.000000',\n",
       " '2005/09/21 12:00:00.000000',\n",
       " '2005/09/22 12:00:00.000000',\n",
       " '2005/09/23 12:00:00.000000',\n",
       " '2005/09/24 12:00:00.000000',\n",
       " '2005/09/25 12:00:00.000000',\n",
       " '2005/09/26 12:00:00.000000',\n",
       " '2005/09/27 12:00:00.000000',\n",
       " '2005/09/28 12:00:00.000000']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convertir al nuevo formato\n",
    "tiempos_convertidos = [\n",
    "    datetime.strptime(t, \"%Y-%m-%dT%H:%M:%S.%f\").strftime(\"%Y/%m/%d %H:%M:%S.%f\")\n",
    "    for t in Times_cada_3\n",
    "]\n",
    "\n",
    "tiempos_convertidos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from netCDF4 import Dataset\n",
    "\n",
    "# Datos de ejemplo\n",
    "node = 5734  # Número de nodos\n",
    "nt = min(len(time_cada_3), len(Itime_cada_3), len(Itime2_cada_3), len(Times_cada_3), interp_ssh.shape[1])\n",
    "\n",
    "# Crear el archivo NetCDF\n",
    "ncid = Dataset('2005_sshgrd.nc', 'w', format='NETCDF4')\n",
    "\n",
    "# Definir las dimensiones\n",
    "node_dimid = ncid.createDimension('node', node)\n",
    "time_dimid = ncid.createDimension('time', None)  # Dimensión ilimitada para el tiempo\n",
    "DateStrLen_dimid = ncid.createDimension('DateStrLen', 26)\n",
    "\n",
    "# Definir lon y lat (no tienen la dimensión 'time')\n",
    "lon_varid = ncid.createVariable('lon', 'f4', ('node',))\n",
    "lon_varid.long_name = 'nodal longitude'\n",
    "lon_varid.units = 'degrees_east'\n",
    "    \n",
    "lat_varid = ncid.createVariable('lat', 'f4', ('node',))\n",
    "lat_varid.long_name = 'nodal latitude'\n",
    "lat_varid.units = 'degrees_north'\n",
    "\n",
    "# Definir el tiempo y las variables relacionadas (manteniendo 'time' como última dimensión)\n",
    "time_varid = ncid.createVariable('time', 'f4', ('time',))\n",
    "time_varid.long_name = 'time'\n",
    "time_varid.units = 'days since 1858-11-17 00:00:00'\n",
    "time_varid.format = 'modified julian day (MJD)'\n",
    "time_varid.time_zone = 'UTC'\n",
    "\n",
    "Itime_varid = ncid.createVariable('Itime', 'i4', ('time',))\n",
    "Itime_varid.units = 'days since 1858-11-17 00:00:00'\n",
    "Itime_varid.format = 'modified julian day (MJD)'\n",
    "Itime_varid.time_zone = 'UTC'\n",
    "\n",
    "Itime2_varid = ncid.createVariable('Itime2', 'i4', ('time',))\n",
    "Itime2_varid.units = 'msec since 00:00:00'\n",
    "Itime2_varid.time_zone = 'UTC'\n",
    "\n",
    "Times_varid = ncid.createVariable('Times', 'S1', ('time', 'DateStrLen'))\n",
    "Times_varid.time_zone = 'UTC'\n",
    "\n",
    "# Definir la altura de la superficie del mar (SSH) con 'time' al final\n",
    "ssh_varid = ncid.createVariable('ssh', 'f4', ('time', 'node'))\n",
    "ssh_varid.long_name = 'Water Surface Height'\n",
    "ssh_varid.units = 'meters'\n",
    "\n",
    "\n",
    "# Atributos globales\n",
    "ncid.source = 'FVCOM grid (unstructured) surface forcing'\n",
    "\n",
    "# Escribir los datos en las variables\n",
    "ncid.variables['lon'][:] = lons\n",
    "ncid.variables['lat'][:] = lats\n",
    "\n",
    "# Escribir los datos en las variables usando el bucle corregido\n",
    "for it in range(nt):\n",
    "    ncid.variables['time'][it] = time_cada_3[it]\n",
    "    ncid.variables['Itime'][it] = Itime_cada_3[it]\n",
    "    ncid.variables['Itime2'][it] = Itime2_cada_3[it]\n",
    "    \n",
    "    # Usar sólo un índice para Times_cada_3 ya convertido en array de NumPy\n",
    "    ncid.variables['Times'][it, :] = list(tiempos_convertidos[it])  \n",
    "    ncid.variables['ssh'][it, :] = interp_ssh[:, it].T \n",
    "\n",
    "# Cerrar el archivo NetCDF\n",
    "ncid.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an object which holds the model outputs. We're only loading\n",
    "# surface elevation and temperature for the first 200 time steps.\n",
    "fvcom = FileReader('1year_fixed_8c_gs_2005_0001.nc',  variables=['zeta', 'temp'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, '2005-09-28T12:00:00.000000000')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 787.402x787.402 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make a plot of the surface elevation.\n",
    "plot = Plotter(fvcom,\n",
    "               figsize=(20, 20),\n",
    "               res='i',\n",
    "               tick_inc=(4, 2),\n",
    "               cb_label='{} ({})'.format(fvcom.atts.zeta.long_name,\n",
    "                                         fvcom.atts.zeta.units),\n",
    "               cmap=cm.balance)\n",
    "plot.plot_field(interp_ssh[:, 10])\n",
    "plot.axes.set_title(hycom_times[-1])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyfvcom",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}