visualization-HFR-CMEMS.ipynb

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    "from netCDF4 import Dataset\n",
    "import netCDF4 as netcdf\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt, animation\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.ticker as mticker\n",
    "import matplotlib as mpl\n",
    "import cmocean as cmo\n",
    "\n",
    "#mapping\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from cartopy.io import shapereader\n",
    "from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER\n",
    "from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
    "from scipy.interpolate import griddata\n",
    "\n",
    "import xarray as xr\n",
    "import xarray.ufuncs as xu\n",
    "\n",
    "from datetime import datetime\n",
    "from matplotlib.path import Path\n",
    "from matplotlib.patches import Polygon\n",
    "from matplotlib import rc\n",
    "font = {'weight':'bold'}\n",
    "rc('font',**font)\n",
    "\n",
    "# This is needed to display graphics calculated outside of jupyter notebook\n",
    "from IPython.display import HTML, display\n",
    "#import geoviews as gv\n",
    "#import holoviews as hv\n",
    "#import geoviews.feature as gf\n",
    "#gv.extension('bokeh', 'matplotlib')"
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    "def subset(lonw,lone,latn,lats,data,date1,date2):\n",
    "    mask_lon = (data.lon >= lonw) & (data.lon <= lone)\n",
    "    mask_lat = (data.lat >= lats) & (data.lat <= latn)\n",
    "    %time new_data = data.sel(time=slice(date1,date2)).where(mask_lon & mask_lat, drop=True)\n",
    "    return new_data"
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   "source": [
    "# import HFR dataset\n",
    "aggr_url = \"https://hfrnet-tds.ucsd.edu/thredds/dodsC/HFR/USEGC/6km/hourly/RTV/HFRADAR_US_East_and_Gulf_Coast_6km_Resolution_Hourly_RTV_best.ncd\"\n",
    "data_h = xr.open_dataset(aggr_url)\n",
    "\n",
    "# import CMEMS outputs\n",
    "aggr_url = \"./data/CMEMS-global-analysis-forecast-phy-001-024-hourly-u-v.nc\"\n",
    "data_m = xr.open_dataset(aggr_url)"
   ]
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     "text": [
      "CPU times: user 474 ms, sys: 163 ms, total: 637 ms\n",
      "Wall time: 21.1 s\n"
     ]
    }
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   "source": [
    "# subset the dataset to the area and time period of interest\n",
    "\n",
    "lonw, lone = -76.3, -73   # westernmonst and easternmost longitude\n",
    "lats, latn = 36.5, 39.5 # southernmonst and northernmost latitude\n",
    "date1 = \"2020-05-01\"    # initial date\n",
    "date2 = \"2020-06-01\"    # final date\n",
    "\n",
    "# create mask\n",
    "mask_lon = (data_h.lon >= lonw) & (data_h.lon <= lone)\n",
    "mask_lat = (data_h.lat >= lats) & (data_h.lat <= latn)\n",
    "\n",
    "# subset\n",
    "%time data_sub_h = data_h.sel(time=slice(date1, date2)).where(mask_lon & mask_lat, drop=True)"
   ]
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     "text": [
      "CPU times: user 25.1 ms, sys: 3.46 ms, total: 28.5 ms\n",
      "Wall time: 25.9 ms\n"
     ]
    }
   ],
   "source": [
    "mask_lon = (data_m.longitude >= lonw) & (data_m.longitude <= lone)\n",
    "mask_lat = (data_m.latitude >= lats) & (data_m.latitude <= latn)\n",
    "%time data_sub_m = data_m.sel(time=slice(date1, date2)).where(mask_lon & mask_lat, drop=True)\n",
    "\n",
    "#data.groupby(\"time.month\").mean(dim='time')\n",
    "#data_sub # 'subseted' dataset"
   ]
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (time: 744, depth: 1, latitude: 37, longitude: 37)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494\n",
       "  * latitude   (latitude) float32 36.5 36.58 36.67 36.75 ... 39.33 39.42 39.5\n",
       "  * time       (time) datetime64[ns] 2020-05-01T00:30:00 ... 2020-05-31T23:30:00\n",
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       "    uo         (time, depth, latitude, longitude) float32 nan ... -0.04151\n",
       "Attributes: (12/25)\n",
       "    title:                              hourly mean fields from Global Ocean ...\n",
       "    easting:                            longitude\n",
       "    northing:                           latitude\n",
       "    history:                            2021/08/05 00:11:43 MERCATOR OCEAN Ne...\n",
       "    source:                             MERCATOR PSY4QV3R1\n",
       "    institution:                        MERCATOR OCEAN\n",
       "    ...                                 ...\n",
       "    FROM_ORIGINAL_FILE__longitude_max:  179.91667\n",
       "    FROM_ORIGINAL_FILE__latitude_min:   -80.0\n",
       "    FROM_ORIGINAL_FILE__latitude_max:   90.0\n",
       "    z_min:                              0.494025\n",
       "    z_max:                              0.494025\n",
       "    _CoordSysBuilder:                   ucar.nc2.dataset.conv.CF1Convention</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-99e3734c-3b73-4356-8948-7f60802a1b7e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-99e3734c-3b73-4356-8948-7f60802a1b7e' 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>: 744</li><li><span class='xr-has-index'>depth</span>: 1</li><li><span class='xr-has-index'>latitude</span>: 37</li><li><span class='xr-has-index'>longitude</span>: 37</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4e248402-264a-4bc7-ad02-90224eb694a6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4e248402-264a-4bc7-ad02-90224eb694a6' 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'>depth</span></div><div class='xr-var-dims'>(depth)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.494</div><input id='attrs-f722f896-f56b-4278-b434-496e2ad2a528' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f722f896-f56b-4278-b434-496e2ad2a528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-383e4cab-b04e-4806-aa25-88055f5186de' class='xr-var-data-in' type='checkbox'><label for='data-383e4cab-b04e-4806-aa25-88055f5186de' 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>valid_min :</span></dt><dd>0.494025</dd><dt><span>valid_max :</span></dt><dd>0.494025</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>unit_long :</span></dt><dd>Meters</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>axis :</span></dt><dd>Z</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([0.494025], 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'>36.5 36.58 36.67 ... 39.42 39.5</div><input id='attrs-4b191406-4729-4eee-8ac8-921e58ffcbcf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b191406-4729-4eee-8ac8-921e58ffcbcf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-551d52fd-d0af-4e4f-a9f1-c6ae21f773a6' class='xr-var-data-in' type='checkbox'><label for='data-551d52fd-d0af-4e4f-a9f1-c6ae21f773a6' 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>valid_min :</span></dt><dd>36.5</dd><dt><span>valid_max :</span></dt><dd>39.5</dd><dt><span>step :</span></dt><dd>0.08333588</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>unit_long :</span></dt><dd>Degrees North</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lat</dd></dl></div><div class='xr-var-data'><pre>array([36.5     , 36.583332, 36.666668, 36.75    , 36.833332, 36.916668,\n",
       "       37.      , 37.083332, 37.166668, 37.25    , 37.333332, 37.416668,\n",
       "       37.5     , 37.583332, 37.666668, 37.75    , 37.833332, 37.916668,\n",
       "       38.      , 38.083332, 38.166668, 38.25    , 38.333332, 38.416668,\n",
       "       38.5     , 38.583332, 38.666668, 38.75    , 38.833332, 38.916668,\n",
       "       39.      , 39.083332, 39.166668, 39.25    , 39.333332, 39.416668,\n",
       "       39.5     ], 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'>2020-05-01T00:30:00 ... 2020-05-...</div><input id='attrs-e790516e-25be-4174-b349-e8965867b672' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e790516e-25be-4174-b349-e8965867b672' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5178e7d0-d9fe-47f8-b0c4-32394aa4c90c' class='xr-var-data-in' type='checkbox'><label for='data-5178e7d0-d9fe-47f8-b0c4-32394aa4c90c' 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>Time (hours since 1950-01-01)</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Time</dd><dt><span>valid_min :</span></dt><dd>616512.5</dd><dt><span>valid_max :</span></dt><dd>617255.5</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-05-01T00:30:00.000000000&#x27;, &#x27;2020-05-01T01:30:00.000000000&#x27;,\n",
       "       &#x27;2020-05-01T02:30:00.000000000&#x27;, ..., &#x27;2020-05-31T21:30:00.000000000&#x27;,\n",
       "       &#x27;2020-05-31T22:30:00.000000000&#x27;, &#x27;2020-05-31T23:30:00.000000000&#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'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-76.0 -75.92 ... -73.08 -73.0</div><input id='attrs-0265efc5-70c2-4822-bd46-c62d806d4712' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0265efc5-70c2-4822-bd46-c62d806d4712' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53e11fe5-c993-4190-8b1b-6e124cde0234' class='xr-var-data-in' type='checkbox'><label for='data-53e11fe5-c993-4190-8b1b-6e124cde0234' 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>valid_min :</span></dt><dd>-76.0</dd><dt><span>valid_max :</span></dt><dd>-73.0</dd><dt><span>step :</span></dt><dd>0.08332825</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>unit_long :</span></dt><dd>Degrees East</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>_CoordinateAxisType :</span></dt><dd>Lon</dd></dl></div><div class='xr-var-data'><pre>array([-76.      , -75.916664, -75.833336, -75.75    , -75.666664, -75.583336,\n",
       "       -75.5     , -75.416664, -75.333336, -75.25    , -75.166664, -75.083336,\n",
       "       -75.      , -74.916664, -74.833336, -74.75    , -74.666664, -74.583336,\n",
       "       -74.5     , -74.416664, -74.333336, -74.25    , -74.166664, -74.083336,\n",
       "       -74.      , -73.916664, -73.833336, -73.75    , -73.666664, -73.583336,\n",
       "       -73.5     , -73.416664, -73.333336, -73.25    , -73.166664, -73.083336,\n",
       "       -73.      ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3922e10b-940a-43cb-9188-f2e200e02c4a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3922e10b-940a-43cb-9188-f2e200e02c4a' 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>vo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan 0.4047 ... -0.05249 -0.047</div><input id='attrs-2c6e7af4-333e-40be-a196-3fa2a30a1e0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6e7af4-333e-40be-a196-3fa2a30a1e0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d9056f8-a317-4767-9720-c3f1ddc12c92' class='xr-var-data-in' type='checkbox'><label for='data-0d9056f8-a317-4767-9720-c3f1ddc12c92' 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>Northward velocity</dd><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[[        nan,  0.40467542,  0.4461806 , ...,  0.6305124 ,\n",
       "           0.57130647,  0.51698357],\n",
       "         [        nan,  0.474868  ,  0.5340739 , ...,  0.7245094 ,\n",
       "           0.6787317 ,  0.64149904],\n",
       "         [        nan,  0.4937895 ,  0.54750204, ...,  0.73793757,\n",
       "           0.70925015,  0.68910795],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ..., -0.06714072,\n",
       "          -0.10315256, -0.11841182],\n",
       "         [-0.01831111,         nan,         nan, ..., -0.07751702,\n",
       "          -0.11658071, -0.13367108],\n",
       "         [-0.02502518,         nan,         nan, ..., -0.08056886,\n",
       "          -0.11597034, -0.1391644 ]]],\n",
       "\n",
       "\n",
       "       [[[        nan,  0.32166508,  0.37537766, ...,  0.606708  ,\n",
       "           0.54933316,  0.49806207],\n",
       "         [        nan,  0.37842953,  0.45167392, ...,  0.7080294 ,\n",
       "           0.662862  ,  0.6286813 ],\n",
       "         [        nan,  0.37781915,  0.4608295 , ...,  0.72511977,\n",
       "...\n",
       "          -0.02319407, -0.00793481],\n",
       "         [-0.0122074 ,         nan,         nan, ..., -0.06042665,\n",
       "          -0.03906369, -0.02563555],\n",
       "         [-0.0122074 ,         nan,         nan, ..., -0.06836146,\n",
       "          -0.04882962, -0.0408948 ]]],\n",
       "\n",
       "\n",
       "       [[[        nan, -0.17029329, -0.15686513, ...,  0.84292126,\n",
       "           0.74221015,  0.66408277],\n",
       "         [        nan, -0.16296884, -0.1513718 , ...,  1.0309153 ,\n",
       "           0.9265419 ,  0.8197272 ],\n",
       "         [        nan, -0.14526811, -0.10376293, ...,  1.0968353 ,\n",
       "           1.0376294 ,  0.9637745 ],\n",
       "         ...,\n",
       "         [        nan,         nan,         nan, ..., -0.04333628,\n",
       "          -0.02502518, -0.00976592],\n",
       "         [-0.01037629,         nan,         nan, ..., -0.05981628,\n",
       "          -0.04211554, -0.02929777],\n",
       "         [-0.01098666,         nan,         nan, ..., -0.06714072,\n",
       "          -0.05249184, -0.0469985 ]]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uo</span></div><div class='xr-var-dims'>(time, depth, latitude, longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan -0.07447 ... -0.0531 -0.04151</div><input id='attrs-ba647f6f-904c-4e9d-b05a-13a33a64b1d7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba647f6f-904c-4e9d-b05a-13a33a64b1d7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6b47766-e664-4558-b9f8-ea4737145888' class='xr-var-data-in' type='checkbox'><label for='data-f6b47766-e664-4558-b9f8-ea4737145888' 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>Eastward velocity</dd><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>unit_long :</span></dt><dd>Meters per second</dd><dt><span>cell_methods :</span></dt><dd>area: mean</dd></dl></div><div class='xr-var-data'><pre>array([[[[            nan, -7.44651631e-02, -1.08035520e-01, ...,\n",
       "           6.53096080e-01,  5.42619109e-01,  4.32752460e-01],\n",
       "         [            nan, -4.02844325e-02, -5.24918362e-02, ...,\n",
       "           9.17996764e-01,  8.50856066e-01,  7.69676805e-01],\n",
       "         [            nan, -3.41807306e-02, -9.76592302e-03, ...,\n",
       "           1.22806478e+00,  1.22135079e+00,  1.19083226e+00],\n",
       "         ...,\n",
       "         [            nan,             nan,             nan, ...,\n",
       "          -1.23294778e-01, -1.15359962e-01, -9.33866352e-02],\n",
       "         [-1.06204413e-01,             nan,             nan, ...,\n",
       "          -1.20853297e-01, -1.10477000e-01, -9.03347880e-02],\n",
       "         [-1.01321451e-01,             nan,             nan, ...,\n",
       "          -6.34784997e-02, -6.95822015e-02, -6.89718276e-02]]],\n",
       "\n",
       "\n",
       "       [[[            nan, -5.67644276e-02, -8.17896053e-02, ...,\n",
       "           6.73848689e-01,  5.58488727e-01,  4.44959879e-01],\n",
       "         [            nan, -1.70903653e-02, -2.44148076e-02, ...,\n",
       "           9.30204153e-01,  8.58180463e-01,  7.73339033e-01],\n",
       "         [            nan, -3.05185094e-03,  2.80770287e-02, ...,\n",
       "...\n",
       "         [ 1.64799951e-02,             nan,             nan, ...,\n",
       "          -7.08029419e-02, -5.73747978e-02, -4.88296151e-02],\n",
       "         [ 1.58696249e-02,             nan,             nan, ...,\n",
       "          -6.95822015e-02, -5.79851680e-02, -4.63881344e-02]]],\n",
       "\n",
       "\n",
       "       [[[            nan,  5.49333170e-03, -7.93481246e-03, ...,\n",
       "           1.09378338e+00,  9.72319722e-01,  8.69777501e-01],\n",
       "         [            nan, -1.89214759e-02, -3.90636921e-02, ...,\n",
       "           1.33121741e+00,  1.22135079e+00,  1.09561443e+00],\n",
       "         [            nan, -3.78429517e-02, -5.98162785e-02, ...,\n",
       "           1.56926179e+00,  1.52104247e+00,  1.42460406e+00],\n",
       "         ...,\n",
       "         [            nan,             nan,             nan, ...,\n",
       "          -4.94399853e-02, -4.27259132e-02, -3.72325815e-02],\n",
       "         [ 1.28177740e-02,             nan,             nan, ...,\n",
       "          -6.46992400e-02, -5.00503555e-02, -4.21155430e-02],\n",
       "         [ 1.22074038e-02,             nan,             nan, ...,\n",
       "          -6.53096139e-02, -5.31022064e-02, -4.15051728e-02]]]],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab97a280-39b7-41b1-9d72-7984f8f398d5' class='xr-section-summary-in' type='checkbox'  ><label for='section-ab97a280-39b7-41b1-9d72-7984f8f398d5' class='xr-section-summary' >Attributes: <span>(25)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>title :</span></dt><dd>hourly mean fields from Global Ocean Physics Analysis and Forecast updated Daily</dd><dt><span>easting :</span></dt><dd>longitude</dd><dt><span>northing :</span></dt><dd>latitude</dd><dt><span>history :</span></dt><dd>2021/08/05 00:11:43 MERCATOR OCEAN Netcdf creation</dd><dt><span>source :</span></dt><dd>MERCATOR PSY4QV3R1</dd><dt><span>institution :</span></dt><dd>MERCATOR OCEAN</dd><dt><span>references :</span></dt><dd>http://www.mercator-ocean.fr</dd><dt><span>comment :</span></dt><dd>CMEMS product</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>domain_name :</span></dt><dd>GL12</dd><dt><span>FROM_ORIGINAL_FILE__field_type :</span></dt><dd>mean</dd><dt><span>field_date :</span></dt><dd>2021-08-14 00:00:00</dd><dt><span>field_julian_date :</span></dt><dd>26158.0</dd><dt><span>julian_day_unit :</span></dt><dd>days since 1950-01-01 00:00:00</dd><dt><span>forecast_range :</span></dt><dd>9-day_forecast</dd><dt><span>forecast_type :</span></dt><dd>forecast</dd><dt><span>bulletin_date :</span></dt><dd>2021-08-05 00:00:00</dd><dt><span>bulletin_type :</span></dt><dd>operational</dd><dt><span>FROM_ORIGINAL_FILE__longitude_min :</span></dt><dd>-180.0</dd><dt><span>FROM_ORIGINAL_FILE__longitude_max :</span></dt><dd>179.91667</dd><dt><span>FROM_ORIGINAL_FILE__latitude_min :</span></dt><dd>-80.0</dd><dt><span>FROM_ORIGINAL_FILE__latitude_max :</span></dt><dd>90.0</dd><dt><span>z_min :</span></dt><dd>0.494025</dd><dt><span>z_max :</span></dt><dd>0.494025</dd><dt><span>_CoordSysBuilder :</span></dt><dd>ucar.nc2.dataset.conv.CF1Convention</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 744, depth: 1, latitude: 37, longitude: 37)\n",
       "Coordinates:\n",
       "  * depth      (depth) float32 0.494\n",
       "  * latitude   (latitude) float32 36.5 36.58 36.67 36.75 ... 39.33 39.42 39.5\n",
       "  * time       (time) datetime64[ns] 2020-05-01T00:30:00 ... 2020-05-31T23:30:00\n",
       "  * longitude  (longitude) float32 -76.0 -75.92 -75.83 ... -73.17 -73.08 -73.0\n",
       "Data variables:\n",
       "    vo         (time, depth, latitude, longitude) float32 nan 0.4047 ... -0.047\n",
       "    uo         (time, depth, latitude, longitude) float32 nan ... -0.04151\n",
       "Attributes: (12/25)\n",
       "    title:                              hourly mean fields from Global Ocean ...\n",
       "    easting:                            longitude\n",
       "    northing:                           latitude\n",
       "    history:                            2021/08/05 00:11:43 MERCATOR OCEAN Ne...\n",
       "    source:                             MERCATOR PSY4QV3R1\n",
       "    institution:                        MERCATOR OCEAN\n",
       "    ...                                 ...\n",
       "    FROM_ORIGINAL_FILE__longitude_max:  179.91667\n",
       "    FROM_ORIGINAL_FILE__latitude_min:   -80.0\n",
       "    FROM_ORIGINAL_FILE__latitude_max:   90.0\n",
       "    z_min:                              0.494025\n",
       "    z_max:                              0.494025\n",
       "    _CoordSysBuilder:                   ucar.nc2.dataset.conv.CF1Convention"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_sub_m # "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b6df90a2-31e0-4134-91d3-1b0f328916d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# select a specific date and time\n",
    "date = '2020-05-05T09:00:00'\n",
    "datah = data_sub_h.sel(time=date,method='nearest')\n",
    "datam = data_sub_m.sel(time=date,method='nearest') # mimics the model output - to be merged with Teresa and Maurício's code\n",
    "# need to import the satellite data s well"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cde34a0e-4c4e-49ca-a4db-157be0330d27",
   "metadata": {},
   "outputs": [],
   "source": [
    "datah, datam = data_sub_h, data_sub_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "59ef95c1-9a1d-48c3-ae03-e28b18da07c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the magnitude of surface currents\n",
    "Magh = np.sqrt(datah.u**2+datah.v**2)\n",
    "Magm = np.sqrt(datam.uo**2+datam.vo**2)\n",
    "\n",
    "# create the mesh\n",
    "xh, yh = np.meshgrid(datah.lon.values,datah.lat.values)\n",
    "xm, ym = np.meshgrid(datam.longitude.values,datam.latitude.values)\n",
    "\n",
    "# mask data\n",
    "Magh = np.ma.masked_invalid(Magh)\n",
    "Magm = np.ma.masked_invalid(Magm).squeeze()\n",
    "Uh = np.ma.masked_invalid(datah.u.values)\n",
    "Vh = np.ma.masked_invalid(datah.v.values)\n",
    "Um = np.ma.masked_invalid(datam.uo.values).squeeze()\n",
    "Vm = np.ma.masked_invalid(datam.vo.values).squeeze()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0c4c649d-de60-463a-80f7-459a935b4c8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Angleh = np.pi/2 - np.arctan2(Uh/Magh, Vh/Magh)\n",
    "#Anglem = np.pi/2 - np.arctan2(Um/magm, Vm/Magm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "71e16088-4bfd-459b-999a-32c8dbecb158",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_map(lonw,lone,lats,latn,ax,flag=0):\n",
    "    ax.set_extent([lonw, lone, lats, latn])\n",
    "\n",
    "    ax.xaxis.set_major_formatter(LongitudeFormatter())\n",
    "    ax.yaxis.set_major_formatter(LatitudeFormatter())\n",
    "\n",
    "    stp = 1\n",
    "    lon_ticks = np.around(np.arange(int(lonw),int(lone)+stp),1) #[-75, -74, -73, -72, -71, -70, -69]\n",
    "    lat_ticks = np.around(np.arange(lats,latn+stp),1)\n",
    "\n",
    "    ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())\n",
    "    ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())\n",
    "\n",
    "    ax.xaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                             direction='in', top='on', pad=7)\n",
    "    ax.yaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                             direction='in', right='on',pad=7)\n",
    "    \n",
    "    if flag == 0: ax.tick_params(labelleft=True,labelbottom=True,labeltop=False,labelright=False)  \n",
    "    elif flag == 1: ax.tick_params(labelleft=False,labelbottom=True,labeltop=False,labelright=False)  \n",
    "    else: ax.tick_params(labelleft=False,labelbottom=True,labeltop=False,labelright=True)  \n",
    "\n",
    "    ax.add_feature(cfeature.LAND.with_scale('10m'), facecolor='.5', alpha=0.4)\n",
    "    ax.coastlines(resolution='10m',linewidth=0.3)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7bb9233d-35d8-418c-ac72-44fdab328539",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.gridspec as gridspec\n",
    "gs = gridspec.GridSpec(13,3)\n",
    "#i = 508\n",
    "i=0\n",
    "proj = ccrs.PlateCarree()\n",
    "#ax = fig.add_subplot(1,3,1,projection=proj)\n",
    "fig = plt.figure(1,figsize=(16,6))\n",
    "ax1 = fig.add_subplot(gs[:12,0],projection=proj)\n",
    "ax2 = fig.add_subplot(gs[:12,1],projection=proj)\n",
    "ax3 = fig.add_subplot(gs[:12,2],projection=proj)\n",
    "ax4 = fig.add_subplot(gs[12,1])\n",
    "ax5 = fig.add_subplot(gs[12,2])\n",
    "ax1 = make_map(lonw,lone,lats,latn,ax1,flag=0)\n",
    "\n",
    "uh = griddata((xh.ravel(),yh.ravel()),Uh[i].ravel(),(xm,ym))\n",
    "vh = griddata((xh.ravel(),yh.ravel()),Vh[i].ravel(),(xm,ym))\n",
    "magh = griddata((xh.ravel(),yh.ravel()),Magh[i].ravel(),(xm,ym))\n",
    "\n",
    "um, vm, magm = Um[i], Vm[i], Magm[i]\n",
    "angleh = np.pi/2 - np.arctan2(uh/magh, vh/magh)\n",
    "anglem = np.pi/2 - np.arctan2(um/magm, vm/magm)\n",
    "\n",
    "# plot current vectors\n",
    "c1, c2 = 'C1', 'C0'\n",
    "cf1=ax1.quiver(xm,ym,uh,vh,color=c1,#magh,cmap=cmo.cm.tempo,\n",
    "             scale=10,width=.004,zorder=2)\n",
    "cf2=ax1.quiver(xm,ym,um,vm,color=c2,#magm,cmap=cmo.cm.amp,\n",
    "             scale=10,width=.003,zorder=1)\n",
    "\n",
    "# add the scale for the currents magnitude\n",
    "qk = ax1.quiverkey(cf1, -75.7, 38.9, 0.5, label='HFR: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                   coordinates='data', labelcolor=c1)\n",
    "qk = ax1.quiverkey(cf2, -75.7, 38.6, 0.5, label='CMEMS: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                   coordinates='data', labelcolor=c2)\n",
    "\n",
    "# time stamp\n",
    "the_time = np.datetime_as_string(datah.time.values[i], unit='h')#'%Y-%m-%d')\n",
    "the_time = the_time[:10]+' %sH'%the_time[11:]\n",
    "#ax1.text(-75.6,39.25,'Time: {:}'.format(the_time),fontsize=13,va='center', ha='center',\n",
    "#        bbox=dict(facecolor='w', alpha=.6),transform=proj)\n",
    "fig.suptitle('Time: {:}'.format(the_time),fontsize=13,fontweight='bold',y=.85)\n",
    "\n",
    "\n",
    "#ax = fig.add_subplot(1,3,2,projection=proj)\n",
    "ax2 = make_map(lonw,lone,lats,latn,ax2,flag=1)\n",
    "cf = ax2.pcolormesh(xm,ym,magm-magh,vmin=-1,vmax=1,cmap=cmo.cm.balance)\n",
    "#cax,kw = mpl.colorbar.make_axes(ax2,location='bottom',pad=0.05,aspect=5)\n",
    "out=fig.colorbar(cf,cax=ax4,ticks=np.linspace(-1,1,7),\n",
    "                 format='%.2f',extend='both',**kw)\n",
    "out.set_label('magnitude difference [m s$^{-1}$]',size=12,fontweight='bold')\n",
    "out.ax.tick_params(direction='in',size=2., width=1)\n",
    "\n",
    "#ax = fig.add_subplot(1,3,3,projection=proj)\n",
    "ax3 = make_map(lonw,lone,lats,latn,ax3,flag=2)\n",
    "cf = ax3.pcolormesh(xm,ym,anglem-angleh,vmin=-5,vmax=5,cmap=cmo.cm.balance)\n",
    "\n",
    "out=fig.colorbar(cf,cax=ax5,ticks=np.linspace(-5,5,7),\n",
    "                 format='%.2f',extend='both',**kw)\n",
    "out.set_label('direction difference [1]',size=12,fontweight='bold')\n",
    "out.ax.tick_params(direction='in',size=2., width=1)\n",
    "#out.ax.tick_params(direction='in',size=2., width=1)\n",
    "#print(i)\n",
    "plt.show()\n",
    "plt.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6580580-b900-4e4c-b429-2786c3ae2212",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.nanmin(magm-magh)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbf972f6-bd5c-4d8a-998b-ce7f9e2566b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# conventional map (cartopy)\n",
    "proj = ccrs.PlateCarree()\n",
    "\n",
    "#for i in range(5):\n",
    "i = 0\n",
    "fig = plt.figure(1,figsize=(8,12))\n",
    "ax = fig.add_subplot(1,2,1,projection=proj)\n",
    "ax.set_extent([lonw, lone, lats, latn])\n",
    "\n",
    "ax.xaxis.set_major_formatter(LongitudeFormatter())\n",
    "ax.yaxis.set_major_formatter(LatitudeFormatter())\n",
    "\n",
    "stp = 1\n",
    "lon_ticks = np.around(np.arange(int(lonw),int(lone)+stp),1) #[-75, -74, -73, -72, -71, -70, -69]\n",
    "lat_ticks = np.around(np.arange(lats,latn+stp),1)\n",
    "\n",
    "ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())\n",
    "ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())\n",
    "\n",
    "ax.xaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                         direction='in', top='on', pad=7)\n",
    "ax.yaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                         direction='in', right='on',pad=7)\n",
    "\n",
    "ax.tick_params(labelleft=True,labelbottom=False,labeltop=True,labelright=False)  \n",
    "ax.add_feature(cfeature.LAND.with_scale('10m'), facecolor='0', alpha=0.4)\n",
    "ax.coastlines(resolution='10m',linewidth=0.3)\n",
    "\n",
    "#vmax = np.nanmean(magh)+2*np.nanstd(magh)\n",
    "#bounds = np.linspace(0,vmax,5)\n",
    "#norm = mpl.colors.Normalize(vmin=0., vmax=vmax)\n",
    "\n",
    "uh = griddata((xh.ravel(),yh.ravel()),Uh[i].ravel(),(xm,ym))\n",
    "vh = griddata((xh.ravel(),yh.ravel()),Vh[i].ravel(),(xm,ym))\n",
    "magh = griddata((xh.ravel(),yh.ravel()),Magh[i].ravel(),(xm,ym))\n",
    "\n",
    "um, vm, magm = Um[i], Vm[i], Magm[i]\n",
    "\n",
    "# plot current vectors\n",
    "c1, c2 = 'C1', 'C0'\n",
    "cf1=ax.quiver(xm,ym,uh,vh,color=c1,#magh,cmap=cmo.cm.tempo,\n",
    "             scale=10,width=.004,zorder=2)\n",
    "cf2=ax.quiver(xm,ym,um,vm,color=c2,#magm,cmap=cmo.cm.amp,\n",
    "             scale=10,width=.003,zorder=1)\n",
    "\n",
    "# add the scale for the currents magnitude\n",
    "qk = ax.quiverkey(cf1, -75.7, 38.9, 0.5, label='HFR: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                   coordinates='data', labelcolor=c1)\n",
    "qk = ax.quiverkey(cf2, -75.7, 38.72, 0.5, label='CMEMS: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                   coordinates='data', labelcolor=c2)\n",
    "\n",
    "# time stamp\n",
    "the_time = np.datetime_as_string(datah.time.values[i], unit='h')#'%Y-%m-%d')\n",
    "the_time = the_time[:10]+' %sH'%the_time[11:]\n",
    "ax.text(-75.6,39.25,'Time: {:}'.format(the_time),fontsize=13,va='center', ha='center',\n",
    "        bbox=dict(facecolor='w', alpha=.6),transform=proj)\n",
    "\n",
    "#print(i)\n",
    "plt.show()\n",
    "plt.close()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ef8986-b7e2-46d5-b92e-7822856096e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create map\n",
    "proj = ccrs.PlateCarree()                     # map projection\n",
    "fig = plt.figure(1, figsize=(8,12))           # create figure object\n",
    "ax = fig.add_subplot(1,1,1,projection=proj)\n",
    "ax.set_extent([lonw, lone, lats, latn])\n",
    "\n",
    "ax.xaxis.set_major_formatter(LongitudeFormatter())\n",
    "ax.yaxis.set_major_formatter(LatitudeFormatter())\n",
    "\n",
    "stp = 1\n",
    "lon_ticks = np.around(np.arange(int(lonw),int(lone)+1),stp) #[-75, -74, -73, -72, -71, -70, -69]\n",
    "lat_ticks = np.around(np.arange(lats,latn+1),stp)\n",
    "\n",
    "ax.set_xticks(lon_ticks, crs=ccrs.PlateCarree())\n",
    "ax.set_yticks(lat_ticks, crs=ccrs.PlateCarree())\n",
    "\n",
    "ax.xaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                         direction='in', top='on', pad=7)\n",
    "ax.yaxis.set_tick_params(which='major', size=2., width=1, \n",
    "                         direction='in', right='on',pad=7)\n",
    "\n",
    "ax.tick_params(labelleft=True,labelbottom=False,labeltop=True,labelright=False)  \n",
    "ax.add_feature(cfeature.LAND.with_scale('10m'), facecolor='k', alpha=0.4)\n",
    "ax.coastlines(resolution='10m',linewidth=0.3)\n",
    "\n",
    "#vmax = np.nanmean(magh)+2*np.nanstd(magh)\n",
    "#bounds = np.linspace(0,vmax,5)\n",
    "#norm = mpl.colors.Normalize(vmin=0., vmax=vmax)\n",
    "\n",
    "def plot_map(i):\n",
    "    uh = griddata((xh.ravel(),yh.ravel()),Uh[i].ravel(),(xm,ym))\n",
    "    vh = griddata((xh.ravel(),yh.ravel()),Vh[i].ravel(),(xm,ym))\n",
    "    magh = griddata((xh.ravel(),yh.ravel()),Magh[i].ravel(),(xm,ym))\n",
    "    \n",
    "    um, vm, magm = Um[i], Vm[i], Magm[i]\n",
    "\n",
    "    # plot current vectors\n",
    "    c1, c2 = 'C1', 'C0'\n",
    "    cf1=ax.quiver(xm,ym,uh,vh,color=c1,#magh,cmap=cmo.cm.tempo,\n",
    "                 scale=10,width=.004,zorder=2)\n",
    "    cf2=ax.quiver(xm,ym,um,vm,color=c2,#magm,cmap=cmo.cm.amp,\n",
    "                 scale=10,width=.003,zorder=1)\n",
    "\n",
    "    # add the scale for the currents magnitude\n",
    "    qk = ax.quiverkey(cf1, 0.25, 0.65, 0.5, label='HFR: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                       coordinates='figure', labelcolor=c1)\n",
    "    qk = ax.quiverkey(cf2, 0.25, 0.62, 0.5, label='CMEMS: 0.5 m s$^{-1}$', labelpos='N',\n",
    "                       coordinates='figure', labelcolor=c2)\n",
    "\n",
    "    # time stamp\n",
    "    the_time = np.datetime_as_string(datah.time.values[i], unit='h')#'%Y-%m-%d')\n",
    "    the_time = the_time[:10]+' %sH'%the_time[11:]\n",
    "    ax.text(-75.6,39.25,'Time: {:}'.format(the_time),fontsize=13,va='center', ha='center',\n",
    "                 bbox=dict(facecolor='w', alpha=.6),\n",
    "             transform=proj)\n",
    "    plt.show()\n",
    "    #plt.close()\n",
    "    \n",
    "ani = animation.FuncAnimation(\n",
    "    plt.gcf(),             # figure\n",
    "    plot_map,         # name of the function above\n",
    "    frames=range(Vm.shape[0]),       # Could also be iterable or list\n",
    "    interval=200,     # ms between frames\n",
    "    blit=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd568fbc-a0b2-4a60-8ad5-fea37a5c619e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ani"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0fddeac3-a1ff-469d-b6ca-e79497400350",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ani' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_2288/2254786362.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mHTML\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mani\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_jshtml\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'ani' is not defined"
     ]
    }
   ],
   "source": [
    "HTML(ani.to_jshtml())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "455114ad-a6f4-4e2a-ba15-4850c453fe1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "proj = ccrs.PlateCarree()\n",
    "\n",
    "stph, stpm = 2, 2 #subsample the vectorfield\n",
    "\n",
    "# compute angles of the vector\n",
    "angleh = np.pi/2 - np.arctan2(uh/magh, vh/magh)\n",
    "anglem = np.pi/2 - np.arctan2(um/magm, vm/magm)\n",
    "\n",
    "# holoviews objects\n",
    "#hfr = gv.VectorField((xm[::stph,::stph], ym[::stph,::stph],           # HFR vectorfield\n",
    "#                      angleh[::stph,::stph], magh[::stph,::stph]), \n",
    "#                     crs=proj)\n",
    "#mdl = gv.VectorField((xm[::stpm,::stpm], ym[::stpm,::stpm],           # 'model' vectorfield\n",
    "#                    anglem[::stpm,::stpm], magm[::stpm,::stpm]), \n",
    "#                     crs=proj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6be56fd3-b26f-457a-a164-2b2f9e9820b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#tiles = gv.tile_sources.CartoLight                # map object - I really didn't like any of them\n",
    "\n",
    "# plot interactive map\n",
    "#hfr.opts(magnitude='Magnitude',color='red',     \n",
    "#         padding=.1, colorbar=True, line_width=2,\n",
    "#         height=350, width=350) * \\\n",
    "#mdl.opts(magnitude='Magnitude',color='black',\n",
    "#         padding=.1, colorbar=True, line_width=2,\n",
    "#         height=350, width=350) * tiles.opts(alpha=1,fontsize='0pt')\n",
    "# not sure which colors to choose.... bur colormaps for each dataset does not look good\n",
    "#arquivo track satélite 228!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "880a0eb9-3c61-4459-bd08-4743ec341cb7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#hv.help(hv.Tiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95c79395-0009-43a2-b323-f4057179dcd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# time holograph test\n",
    "#kdims = ['time', 'lon', 'lat']\n",
    "#vdims = ['u','v']\n",
    "#data_gv = gv.Dataset(data_sub, kdims=kdims, vdims=vdims)\n",
    "#hv.Dimension.type_formatters[np.datetime64] = '%Y-%m-%d'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e49a4e61-08b1-4955-816d-defa1689df09",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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