python/examples/nwps.ipynb

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       "Dimensions:    (time: 145, latitude: 156, longitude: 155)\n",
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       "  * time       (time) datetime64[ns] 2023-07-06T18:00:00 ... 2023-07-12T18:00:00\n",
       "  * latitude   (latitude) float64 40.45 40.47 40.49 40.5 ... 43.21 43.23 43.25\n",
       "  * longitude  (longitude) float64 287.5 287.5 287.5 287.6 ... 291.1 291.1 291.2\n",
       "Data variables: (12/18)\n",
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       "    twlwav     (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    erosnp     (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    setup      (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    swell      (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    perpw      (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    ...         ...\n",
       "    dslm       (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    twldt      (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    spc        (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    owashp     (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    runup      (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "    ripcop     (time, latitude, longitude) float64 dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    meta:     Generated with gribberishpy</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-9f0abdaa-5bb9-4bef-947d-7e7c2984a3b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9f0abdaa-5bb9-4bef-947d-7e7c2984a3b1' 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>: 145</li><li><span class='xr-has-index'>latitude</span>: 156</li><li><span class='xr-has-index'>longitude</span>: 155</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-dddfeb84-5a05-464e-b009-dd4e63879b40' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dddfeb84-5a05-464e-b009-dd4e63879b40' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2023-07-06T18:00:00 ... 2023-07-...</div><input id='attrs-1e4407cb-9ac3-4755-a66d-9b52ee1d1149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e4407cb-9ac3-4755-a66d-9b52ee1d1149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a47d07c-c27a-40c5-a67c-1240f9bad796' class='xr-var-data-in' type='checkbox'><label for='data-3a47d07c-c27a-40c5-a67c-1240f9bad796' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>unit :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2023-07-06T18:00:00.000000000&#x27;, &#x27;2023-07-06T19:00:00.000000000&#x27;,\n",
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       "       &#x27;2023-07-08T04:00:00.000000000&#x27;, &#x27;2023-07-08T05:00:00.000000000&#x27;,\n",
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       "       &#x27;2023-07-08T08:00:00.000000000&#x27;, &#x27;2023-07-08T09:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-08T10:00:00.000000000&#x27;, &#x27;2023-07-08T11:00:00.000000000&#x27;,\n",
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       "       &#x27;2023-07-10T16:00:00.000000000&#x27;, &#x27;2023-07-10T17:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-10T18:00:00.000000000&#x27;, &#x27;2023-07-10T19:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-10T20:00:00.000000000&#x27;, &#x27;2023-07-10T21:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-10T22:00:00.000000000&#x27;, &#x27;2023-07-10T23:00:00.000000000&#x27;,\n",
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       "       &#x27;2023-07-11T20:00:00.000000000&#x27;, &#x27;2023-07-11T21:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-11T22:00:00.000000000&#x27;, &#x27;2023-07-11T23:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T00:00:00.000000000&#x27;, &#x27;2023-07-12T01:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T02:00:00.000000000&#x27;, &#x27;2023-07-12T03:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T04:00:00.000000000&#x27;, &#x27;2023-07-12T05:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T06:00:00.000000000&#x27;, &#x27;2023-07-12T07:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T08:00:00.000000000&#x27;, &#x27;2023-07-12T09:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T10:00:00.000000000&#x27;, &#x27;2023-07-12T11:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T12:00:00.000000000&#x27;, &#x27;2023-07-12T13:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T14:00:00.000000000&#x27;, &#x27;2023-07-12T15:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T16:00:00.000000000&#x27;, &#x27;2023-07-12T17:00:00.000000000&#x27;,\n",
       "       &#x27;2023-07-12T18:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</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'>float64</div><div class='xr-var-preview xr-preview'>40.45 40.47 40.49 ... 43.23 43.25</div><input id='attrs-c0ea9900-d917-45bc-90b6-1ba94776d04a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c0ea9900-d917-45bc-90b6-1ba94776d04a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35fb3c87-e629-4c3b-bb60-e6b4ee46ae85' class='xr-var-data-in' type='checkbox'><label for='data-35fb3c87-e629-4c3b-bb60-e6b4ee46ae85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>unit :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd></dl></div><div class='xr-var-data'><pre>array([40.45    , 40.468065, 40.48613 , 40.504195, 40.52226 , 40.540325,\n",
       "       40.55839 , 40.576455, 40.59452 , 40.612585, 40.63065 , 40.648715,\n",
       "       40.66678 , 40.684845, 40.70291 , 40.720975, 40.73904 , 40.757105,\n",
       "       40.77517 , 40.793235, 40.8113  , 40.829365, 40.84743 , 40.865495,\n",
       "       40.88356 , 40.901625, 40.91969 , 40.937755, 40.95582 , 40.973885,\n",
       "       40.99195 , 41.010015, 41.02808 , 41.046145, 41.06421 , 41.082275,\n",
       "       41.10034 , 41.118405, 41.13647 , 41.154535, 41.1726  , 41.190665,\n",
       "       41.20873 , 41.226795, 41.24486 , 41.262925, 41.28099 , 41.299055,\n",
       "       41.31712 , 41.335185, 41.35325 , 41.371315, 41.38938 , 41.407445,\n",
       "       41.42551 , 41.443575, 41.46164 , 41.479705, 41.49777 , 41.515835,\n",
       "       41.5339  , 41.551965, 41.57003 , 41.588095, 41.60616 , 41.624225,\n",
       "       41.64229 , 41.660355, 41.67842 , 41.696485, 41.71455 , 41.732615,\n",
       "       41.75068 , 41.768745, 41.78681 , 41.804875, 41.82294 , 41.841005,\n",
       "       41.85907 , 41.877135, 41.8952  , 41.913265, 41.93133 , 41.949395,\n",
       "       41.96746 , 41.985525, 42.00359 , 42.021655, 42.03972 , 42.057785,\n",
       "       42.07585 , 42.093915, 42.11198 , 42.130045, 42.14811 , 42.166175,\n",
       "       42.18424 , 42.202305, 42.22037 , 42.238435, 42.2565  , 42.274565,\n",
       "       42.29263 , 42.310695, 42.32876 , 42.346825, 42.36489 , 42.382955,\n",
       "       42.40102 , 42.419085, 42.43715 , 42.455215, 42.47328 , 42.491345,\n",
       "       42.50941 , 42.527475, 42.54554 , 42.563605, 42.58167 , 42.599735,\n",
       "       42.6178  , 42.635865, 42.65393 , 42.671995, 42.69006 , 42.708125,\n",
       "       42.72619 , 42.744255, 42.76232 , 42.780385, 42.79845 , 42.816515,\n",
       "       42.83458 , 42.852645, 42.87071 , 42.888775, 42.90684 , 42.924905,\n",
       "       42.94297 , 42.961035, 42.9791  , 42.997165, 43.01523 , 43.033295,\n",
       "       43.05136 , 43.069425, 43.08749 , 43.105555, 43.12362 , 43.141685,\n",
       "       43.15975 , 43.177815, 43.19588 , 43.213945, 43.23201 , 43.250075])</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'>float64</div><div class='xr-var-preview xr-preview'>287.5 287.5 287.5 ... 291.1 291.2</div><input id='attrs-bbad833f-c48a-4678-85bc-77c8c9b908eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbad833f-c48a-4678-85bc-77c8c9b908eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-13c549d7-7e8a-4524-a804-fe0879225c51' class='xr-var-data-in' type='checkbox'><label for='data-13c549d7-7e8a-4524-a804-fe0879225c51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>unit :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd></dl></div><div class='xr-var-data'><pre>array([287.5     , 287.523702, 287.547404, 287.571106, 287.594808, 287.61851 ,\n",
       "       287.642212, 287.665914, 287.689616, 287.713318, 287.73702 , 287.760722,\n",
       "       287.784424, 287.808126, 287.831828, 287.85553 , 287.879232, 287.902934,\n",
       "       287.926636, 287.950338, 287.97404 , 287.997742, 288.021444, 288.045146,\n",
       "       288.068848, 288.09255 , 288.116252, 288.139954, 288.163656, 288.187358,\n",
       "       288.21106 , 288.234762, 288.258464, 288.282166, 288.305868, 288.32957 ,\n",
       "       288.353272, 288.376974, 288.400676, 288.424378, 288.44808 , 288.471782,\n",
       "       288.495484, 288.519186, 288.542888, 288.56659 , 288.590292, 288.613994,\n",
       "       288.637696, 288.661398, 288.6851  , 288.708802, 288.732504, 288.756206,\n",
       "       288.779908, 288.80361 , 288.827312, 288.851014, 288.874716, 288.898418,\n",
       "       288.92212 , 288.945822, 288.969524, 288.993226, 289.016928, 289.04063 ,\n",
       "       289.064332, 289.088034, 289.111736, 289.135438, 289.15914 , 289.182842,\n",
       "       289.206544, 289.230246, 289.253948, 289.27765 , 289.301352, 289.325054,\n",
       "       289.348756, 289.372458, 289.39616 , 289.419862, 289.443564, 289.467266,\n",
       "       289.490968, 289.51467 , 289.538372, 289.562074, 289.585776, 289.609478,\n",
       "       289.63318 , 289.656882, 289.680584, 289.704286, 289.727988, 289.75169 ,\n",
       "       289.775392, 289.799094, 289.822796, 289.846498, 289.8702  , 289.893902,\n",
       "       289.917604, 289.941306, 289.965008, 289.98871 , 290.012412, 290.036114,\n",
       "       290.059816, 290.083518, 290.10722 , 290.130922, 290.154624, 290.178326,\n",
       "       290.202028, 290.22573 , 290.249432, 290.273134, 290.296836, 290.320538,\n",
       "       290.34424 , 290.367942, 290.391644, 290.415346, 290.439048, 290.46275 ,\n",
       "       290.486452, 290.510154, 290.533856, 290.557558, 290.58126 , 290.604962,\n",
       "       290.628664, 290.652366, 290.676068, 290.69977 , 290.723472, 290.747174,\n",
       "       290.770876, 290.794578, 290.81828 , 290.841982, 290.865684, 290.889386,\n",
       "       290.913088, 290.93679 , 290.960492, 290.984194, 291.007896, 291.031598,\n",
       "       291.0553  , 291.079002, 291.102704, 291.126406, 291.150108])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-7aff5165-0d43-445f-a1b6-519f743f2433' class='xr-section-summary-in' type='checkbox'  ><label for='section-7aff5165-0d43-445f-a1b6-519f743f2433' class='xr-section-summary' >Data variables: <span>(18)</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>dirc</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;</div><input id='attrs-9fdf6394-8ed9-4f7b-8f6b-4716b47ead2c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fdf6394-8ed9-4f7b-8f6b-4716b47ead2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de934f72-aa4d-4a56-8d90-4ed5df8bb046' class='xr-var-data-in' type='checkbox'><label for='data-de934f72-aa4d-4a56-8d90-4ed5df8bb046' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>currentdirection</dd><dt><span>long_name :</span></dt><dd>currentdirection</dd><dt><span>unit :</span></dt><dd>degree True</dd><dt><span>coordinates :</span></dt><dd>latitude longitude</dd><dt><span>reference_date :</span></dt><dd>2023-07-06T18:00:00+00:00</dd><dt><span>forecast_date :</span></dt><dd>2023-07-12T17:00:00+00:00</dd><dt><span>time_interval_end :</span></dt><dd></dd><dt><span>fixed_surface_type :</span></dt><dd>ground or water surface</dd><dt><span>fixed_surface_value :</span></dt><dd>0</dd><dt><span>generating_process :</span></dt><dd>forecast</dd><dt><span>statistical_process :</span></dt><dd></dd><dt><span>proj_params :</span></dt><dd>{&#x27;proj&#x27;: &#x27;latlon&#x27;, &#x27;a&#x27;: 6367470.0, &#x27;b&#x27;: 6367470.0}</dd><dt><span>crs :</span></dt><dd>+proj=latlon +a=6367470 +b=6367470</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                <tbody>\n",
       "                    \n",
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       "                        <td> 26.75 MiB </td>\n",
       "                        <td> 26.75 MiB </td>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                <tbody>\n",
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       "                        <td> 26.75 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                        <td> (145, 156, 155) </td>\n",
       "                        <td> (145, 156, 155) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 26.75 MiB </td>\n",
       "                        <td> 26.75 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                        <td> 26.75 MiB </td>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                        <td> 26.75 MiB </td>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>twldt</span></div><div class='xr-var-dims'>(time, latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 156, 155), meta=np.ndarray&gt;</div><input id='attrs-adbc6309-9745-4e64-92c0-590758ada02a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-adbc6309-9745-4e64-92c0-590758ada02a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-62a83ef2-3f3a-4f3e-91d9-4de5f0beccba' class='xr-var-data-in' type='checkbox'><label for='data-62a83ef2-3f3a-4f3e-91d9-4de5f0beccba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>totalwaterlevelabovedunetoe</dd><dt><span>long_name :</span></dt><dd>totalwaterlevelabovedunetoe</dd><dt><span>unit :</span></dt><dd>m</dd><dt><span>coordinates :</span></dt><dd>latitude longitude</dd><dt><span>reference_date :</span></dt><dd>2023-07-06T18:00:00+00:00</dd><dt><span>forecast_date :</span></dt><dd>2023-07-08T19:00:00+00:00</dd><dt><span>time_interval_end :</span></dt><dd></dd><dt><span>fixed_surface_type :</span></dt><dd>ground or water surface</dd><dt><span>fixed_surface_value :</span></dt><dd>0</dd><dt><span>generating_process :</span></dt><dd>forecast</dd><dt><span>statistical_process :</span></dt><dd></dd><dt><span>proj_params :</span></dt><dd>{&#x27;proj&#x27;: &#x27;latlon&#x27;, &#x27;b&#x27;: 6367470.0, &#x27;a&#x27;: 6367470.0}</dd><dt><span>crs :</span></dt><dd>+proj=latlon +a=6367470 +b=6367470</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 26.75 MiB </td>\n",
       "                        <td> 26.75 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (145, 156, 155) </td>\n",
       "                        <td> (145, 156, 155) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                        <th> Data type </th>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                        <td> (145, 156, 155) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
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       "                        <td> 26.75 MiB </td>\n",
       "                        <td> 26.75 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (145, 156, 155) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"244\" height=\"235\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"75\" y2=\"65\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"10\" y1=\"120\" x2=\"75\" y2=\"185\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"10\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"75\" y1=\"65\" x2=\"75\" y2=\"185\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 75.6108597285068,65.6108597285068 75.6108597285068,185.6108597285068 10.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"129\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"75\" y1=\"65\" x2=\"194\" y2=\"65\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"10\" y1=\"0\" x2=\"75\" y2=\"65\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"129\" y1=\"0\" x2=\"194\" y2=\"65\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"10.0,0.0 129.23076923076923,0.0 194.84162895927602,65.6108597285068 75.6108597285068,65.6108597285068\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"75\" y1=\"65\" x2=\"194\" y2=\"65\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"75\" y1=\"185\" x2=\"194\" y2=\"185\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"75\" y1=\"65\" x2=\"75\" y2=\"185\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"194\" y1=\"65\" x2=\"194\" y2=\"185\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"75.6108597285068,65.6108597285068 194.84162895927602,65.6108597285068 194.84162895927602,185.6108597285068 75.6108597285068,185.6108597285068\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"135.226244\" y=\"205.610860\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >155</text>\n",
       "  <text x=\"214.841629\" y=\"125.610860\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,214.841629,125.610860)\">156</text>\n",
       "  <text x=\"32.805430\" y=\"172.805430\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,32.805430,172.805430)\">145</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-aea7875a-4b50-4227-9b4f-c40ab50e17b8' class='xr-section-summary-in' type='checkbox'  ><label for='section-aea7875a-4b50-4227-9b4f-c40ab50e17b8' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f65b351c-59c6-43bc-8901-f6c563eef598' class='xr-index-data-in' type='checkbox'/><label for='index-f65b351c-59c6-43bc-8901-f6c563eef598' 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;2023-07-06 18:00:00&#x27;, &#x27;2023-07-06 19:00:00&#x27;,\n",
       "               &#x27;2023-07-06 20:00:00&#x27;, &#x27;2023-07-06 21:00:00&#x27;,\n",
       "               &#x27;2023-07-06 22:00:00&#x27;, &#x27;2023-07-06 23:00:00&#x27;,\n",
       "               &#x27;2023-07-07 00:00:00&#x27;, &#x27;2023-07-07 01:00:00&#x27;,\n",
       "               &#x27;2023-07-07 02:00:00&#x27;, &#x27;2023-07-07 03:00:00&#x27;,\n",
       "               ...\n",
       "               &#x27;2023-07-12 09:00:00&#x27;, &#x27;2023-07-12 10:00:00&#x27;,\n",
       "               &#x27;2023-07-12 11:00:00&#x27;, &#x27;2023-07-12 12:00:00&#x27;,\n",
       "               &#x27;2023-07-12 13:00:00&#x27;, &#x27;2023-07-12 14:00:00&#x27;,\n",
       "               &#x27;2023-07-12 15:00:00&#x27;, &#x27;2023-07-12 16:00:00&#x27;,\n",
       "               &#x27;2023-07-12 17:00:00&#x27;, &#x27;2023-07-12 18:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=145, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-985a5f29-eaf6-462f-b5b6-727b3c6f651f' class='xr-index-data-in' type='checkbox'/><label for='index-985a5f29-eaf6-462f-b5b6-727b3c6f651f' 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(Float64Index([40.449999999999996, 40.468064999999996, 40.486129999999996,\n",
       "              40.504194999999996, 40.522259999999996, 40.540324999999996,\n",
       "              40.558389999999996, 40.576454999999996, 40.594519999999996,\n",
       "              40.612584999999996,\n",
       "              ...\n",
       "              43.087489999999995, 43.105554999999995, 43.123619999999995,\n",
       "              43.141684999999995, 43.159749999999995, 43.177814999999995,\n",
       "              43.195879999999995, 43.213944999999995, 43.232009999999995,\n",
       "              43.250074999999995],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;latitude&#x27;, length=156))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0761a2a6-712b-4f14-af80-5b93a9121cee' class='xr-index-data-in' type='checkbox'/><label for='index-0761a2a6-712b-4f14-af80-5b93a9121cee' 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(Float64Index([     287.5, 287.523702, 287.547404, 287.571106, 287.594808,\n",
       "               287.61851, 287.642212, 287.665914, 287.689616, 287.713318,\n",
       "              ...\n",
       "               290.93679, 290.960492, 290.984194, 291.007896, 291.031598,\n",
       "                291.0553, 291.079002, 291.102704, 291.126406, 291.150108],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;longitude&#x27;, length=155))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-97d2a97c-89ca-4b43-a94f-5b5cab41444e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-97d2a97c-89ca-4b43-a94f-5b5cab41444e' class='xr-section-summary' >Attributes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>meta :</span></dt><dd>Generated with gribberishpy</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (time: 145, latitude: 156, longitude: 155)\n",
       "Coordinates:\n",
       "  * time       (time) datetime64[ns] 2023-07-06T18:00:00 ... 2023-07-12T18:00:00\n",
       "  * latitude   (latitude) float64 40.45 40.47 40.49 40.5 ... 43.21 43.23 43.25\n",
       "  * longitude  (longitude) float64 287.5 287.5 287.5 287.6 ... 291.1 291.1 291.2\n",
       "Data variables: (12/18)\n",
       "    dirc       (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    twlwav     (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    erosnp     (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    setup      (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    swell      (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    perpw      (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    ...         ...\n",
       "    dslm       (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    twldt      (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    spc        (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    owashp     (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    runup      (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "    ripcop     (time, latitude, longitude) float64 dask.array<chunksize=(145, 156, 155), meta=np.ndarray>\n",
       "Attributes:\n",
       "    meta:     Generated with gribberishpy"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import gribberish\n",
    "import xarray as xr\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ds = xr.open_dataset('box_nwps_CG1_20230706_1800.grib2', engine='gribberish', chunks={})\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x1482bed00>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds.htsgw.isel(time=0).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.040335387081826\n"
     ]
    }
   ],
   "source": [
    "ndbc_44097_latitude = 40.967\n",
    "ndbc_44097_longitude = 360 - 70.167\n",
    "\n",
    "vals = ds.htsgw.interp(latitude=ndbc_44097_latitude, longitude=ndbc_44097_longitude, method='linear')\n",
    "print(vals.max().values)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "env",
   "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": "3.9.11"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}