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rsignell-usgs/cnaps_example_intake.ipynb

Created November 12, 2023 19:04
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cnaps_example_intake.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "664564e4-d25c-43ea-974c-716beb4f4f00",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"from opendrift.readers import reader_ROMS_intake\n",
"from opendrift.models.oceandrift import OceanDrift\n",
"import intake\n",
"import xarray as xr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5653a5a8-e0fc-4fb2-8c3a-a872ed9e6688",
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"['CNAPS_Forecast_Archive', 'CNAPS_Forecast_Archive_64', 'CNAPS_opendrift']"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"intake_catalog = 'https://mghp.osn.xsede.org/rsignellbucket1/rsignell/testing/cnaps.yml'\n",
"cat = intake.open_catalog(intake_catalog)\n",
"list(cat)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "82624a99-d8d7-4c13-84a3-f885e627fb34",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"dataset = 'CNAPS_opendrift'\n",
"ds = cat[dataset].to_dask()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "44256bf1-d2f5-44b6-b748-56c798599da3",
"metadata": {
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;u&#x27; (ocean_time: 57, s_rho: 36, eta_u: 482, xi_u: 401)&gt;\n",
"dask.array&lt;open_dataset-u, shape=(57, 36, 482, 401), dtype=float32, chunksize=(1, 36, 482, 401), chunktype=numpy.ndarray&gt;\n",
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"Dimensions without coordinates: eta_u, xi_u\n",
"Attributes:\n",
" field: u-velocity, scalar, series\n",
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" location: edge1\n",
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" time: ocean_time\n",
" units: meter second-1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'u'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>ocean_time</span>: 57</li><li><span class='xr-has-index'>s_rho</span>: 36</li><li><span>eta_u</span>: 482</li><li><span>xi_u</span>: 401</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b555018a-a690-4935-801d-a162a4bb1c46' class='xr-array-in' type='checkbox' checked><label for='section-b555018a-a690-4935-801d-a162a4bb1c46' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>dask.array&lt;chunksize=(1, 36, 482, 401), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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"\n",
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" <text x=\"109.859917\" y=\"150.859917\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,109.859917,150.859917)\">36</text>\n",
"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></div></li><li class='xr-section-item'><input id='section-b9971b69-3603-4d8b-9146-f47bd62aa51a' class='xr-section-summary-in' type='checkbox' checked><label for='section-b9971b69-3603-4d8b-9146-f47bd62aa51a' 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>lat_u</span></div><div class='xr-var-dims'>(eta_u, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(482, 401), meta=np.ndarray&gt;</div><input id='attrs-841c40ef-ffa2-4cb8-bf16-97efbe634cb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-841c40ef-ffa2-4cb8-bf16-97efbe634cb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8134acb6-ae9b-453c-8aa4-3ece14090bc7' class='xr-var-data-in' type='checkbox'><label for='data-8134acb6-ae9b-453c-8aa4-3ece14090bc7' 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>field :</span></dt><dd>lat_u, scalar</dd><dt><span>long_name :</span></dt><dd>latitude of U-points</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degree_north</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> 1.47 MiB </td>\n",
" <td> 1.47 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (482, 401) </td>\n",
" <td> (482, 401) </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=\"149\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"99\" y2=\"0\" style=\"stroke-width:2\" />\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lon_u</span></div><div class='xr-var-dims'>(eta_u, xi_u)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(482, 401), meta=np.ndarray&gt;</div><input id='attrs-38812043-08a5-48af-884d-92ecfd0e595c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38812043-08a5-48af-884d-92ecfd0e595c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60169bb9-5272-4652-8e11-7b2f7c46081e' class='xr-var-data-in' type='checkbox'><label for='data-60169bb9-5272-4652-8e11-7b2f7c46081e' 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>field :</span></dt><dd>lon_u, scalar</dd><dt><span>long_name :</span></dt><dd>longitude of U-points</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degree_east</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> 1.47 MiB </td>\n",
" <td> 1.47 MiB </td>\n",
" </tr>\n",
" \n",
" <tr>\n",
" <th> Shape </th>\n",
" <td> (482, 401) </td>\n",
" <td> (482, 401) </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",
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"\n",
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"\n",
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"</svg>\n",
" </td>\n",
" </tr>\n",
"</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ocean_time</span></div><div class='xr-var-dims'>(ocean_time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>5.205e+09 5.205e+09 ... 5.206e+09</div><input id='attrs-7ebc2e76-9f5e-4831-8f74-50b18b243917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ebc2e76-9f5e-4831-8f74-50b18b243917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f96c471-c287-4326-bcb8-60d49fc19579' class='xr-var-data-in' type='checkbox'><label for='data-1f96c471-c287-4326-bcb8-60d49fc19579' 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>calendar :</span></dt><dd>proleptic_gregorian</dd><dt><span>field :</span></dt><dd>time, scalar, series</dd><dt><span>long_name :</span></dt><dd>time since initialization</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>units :</span></dt><dd>seconds since 1858-11-17 00:00:00</dd></dl></div><div class='xr-var-data'><pre>array([5.205082e+09, 5.205092e+09, 5.205103e+09, 5.205114e+09, 5.205125e+09,\n",
" 5.205136e+09, 5.205146e+09, 5.205157e+09, 5.205168e+09, 5.205179e+09,\n",
" 5.205190e+09, 5.205200e+09, 5.205211e+09, 5.205222e+09, 5.205233e+09,\n",
" 5.205244e+09, 5.205254e+09, 5.205265e+09, 5.205276e+09, 5.205287e+09,\n",
" 5.205298e+09, 5.205308e+09, 5.205319e+09, 5.205330e+09, 5.205341e+09,\n",
" 5.205352e+09, 5.205362e+09, 5.205373e+09, 5.205384e+09, 5.205395e+09,\n",
" 5.205406e+09, 5.205416e+09, 5.205427e+09, 5.205438e+09, 5.205449e+09,\n",
" 5.205460e+09, 5.205470e+09, 5.205481e+09, 5.205492e+09, 5.205503e+09,\n",
" 5.205514e+09, 5.205524e+09, 5.205535e+09, 5.205546e+09, 5.205557e+09,\n",
" 5.205568e+09, 5.205578e+09, 5.205589e+09, 5.205600e+09, 5.205611e+09,\n",
" 5.205622e+09, 5.205632e+09, 5.205643e+09, 5.205654e+09, 5.205665e+09,\n",
" 5.205676e+09, 5.205686e+09])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>s_rho</span></div><div class='xr-var-dims'>(s_rho)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.9861 -0.9583 ... -0.01389</div><input id='attrs-a44cc8ad-608a-4c79-b603-7c7d1707b6e5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a44cc8ad-608a-4c79-b603-7c7d1707b6e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93fbc14f-0994-461d-bc4d-ba603cf76721' class='xr-var-data-in' type='checkbox'><label for='data-93fbc14f-0994-461d-bc4d-ba603cf76721' 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>field :</span></dt><dd>s_rho, scalar</dd><dt><span>formula_terms :</span></dt><dd>s: s_rho C: Cs_r eta: zeta depth: h depth_c: hc</dd><dt><span>long_name :</span></dt><dd>S-coordinate at RHO-points</dd><dt><span>positive :</span></dt><dd>up</dd><dt><span>standard_name :</span></dt><dd>ocean_s_coordinate_g2</dd><dt><span>valid_max :</span></dt><dd>0.0</dd><dt><span>valid_min :</span></dt><dd>-1.0</dd></dl></div><div class='xr-var-data'><pre>array([-0.986111, -0.958333, -0.930556, -0.902778, -0.875 , -0.847222,\n",
" -0.819444, -0.791667, -0.763889, -0.736111, -0.708333, -0.680556,\n",
" -0.652778, -0.625 , -0.597222, -0.569444, -0.541667, -0.513889,\n",
" -0.486111, -0.458333, -0.430556, -0.402778, -0.375 , -0.347222,\n",
" -0.319444, -0.291667, -0.263889, -0.236111, -0.208333, -0.180556,\n",
" -0.152778, -0.125 , -0.097222, -0.069444, -0.041667, -0.013889])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0dad42a0-0c9a-4cfc-8a7d-b340974a8862' class='xr-section-summary-in' type='checkbox' ><label for='section-0dad42a0-0c9a-4cfc-8a7d-b340974a8862' class='xr-section-summary' >Indexes: <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-index-name'><div>ocean_time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-844e236b-5f2b-4eb4-8c94-59046a85578a' class='xr-index-data-in' type='checkbox'/><label for='index-844e236b-5f2b-4eb4-8c94-59046a85578a' 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([5205081600.0, 5205092400.0, 5205103200.0, 5205114000.0, 5205124800.0,\n",
" 5205135600.0, 5205146400.0, 5205157200.0, 5205168000.0, 5205178800.0,\n",
" 5205189600.0, 5205200400.0, 5205211200.0, 5205222000.0, 5205232800.0,\n",
" 5205243600.0, 5205254400.0, 5205265200.0, 5205276000.0, 5205286800.0,\n",
" 5205297600.0, 5205308400.0, 5205319200.0, 5205330000.0, 5205340800.0,\n",
" 5205351600.0, 5205362400.0, 5205373200.0, 5205384000.0, 5205394800.0,\n",
" 5205405600.0, 5205416400.0, 5205427200.0, 5205438000.0, 5205448800.0,\n",
" 5205459600.0, 5205470400.0, 5205481200.0, 5205492000.0, 5205502800.0,\n",
" 5205513600.0, 5205524400.0, 5205535200.0, 5205546000.0, 5205556800.0,\n",
" 5205567600.0, 5205578400.0, 5205589200.0, 5205600000.0, 5205610800.0,\n",
" 5205621600.0, 5205632400.0, 5205643200.0, 5205654000.0, 5205664800.0,\n",
" 5205675600.0, 5205686400.0],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;ocean_time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>s_rho</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-edbeb91f-a3e8-42bb-afb0-65f640c97dc5' class='xr-index-data-in' type='checkbox'/><label for='index-edbeb91f-a3e8-42bb-afb0-65f640c97dc5' 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([ -0.986111111111111, -0.9583333333333333, -0.9305555555555555,\n",
" -0.9027777777777777, -0.875, -0.8472222222222222,\n",
" -0.8194444444444444, -0.7916666666666666, -0.7638888888888888,\n",
" -0.736111111111111, -0.7083333333333333, -0.6805555555555555,\n",
" -0.6527777777777778, -0.625, -0.5972222222222222,\n",
" -0.5694444444444444, -0.5416666666666666, -0.5138888888888888,\n",
" -0.4861111111111111, -0.4583333333333333, -0.4305555555555555,\n",
" -0.40277777777777773, -0.375, -0.3472222222222222,\n",
" -0.3194444444444444, -0.29166666666666663, -0.2638888888888889,\n",
" -0.2361111111111111, -0.20833333333333331, -0.18055555555555555,\n",
" -0.15277777777777776, -0.125, -0.09722222222222221,\n",
" -0.06944444444444445, -0.041666666666666664, -0.013888888888888888],\n",
" dtype=&#x27;float64&#x27;, name=&#x27;s_rho&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0cfcbc19-0936-42a9-b7e9-49ad97d4b2aa' class='xr-section-summary-in' type='checkbox' checked><label for='section-0cfcbc19-0936-42a9-b7e9-49ad97d4b2aa' class='xr-section-summary' >Attributes: <span>(6)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>field :</span></dt><dd>u-velocity, scalar, series</dd><dt><span>grid :</span></dt><dd>grid</dd><dt><span>location :</span></dt><dd>edge1</dd><dt><span>long_name :</span></dt><dd>u-momentum component</dd><dt><span>time :</span></dt><dd>ocean_time</dd><dt><span>units :</span></dt><dd>meter second-1</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.DataArray 'u' (ocean_time: 57, s_rho: 36, eta_u: 482, xi_u: 401)>\n",
"dask.array<open_dataset-u, shape=(57, 36, 482, 401), dtype=float32, chunksize=(1, 36, 482, 401), chunktype=numpy.ndarray>\n",
"Coordinates:\n",
" lat_u (eta_u, xi_u) float64 dask.array<chunksize=(482, 401), meta=np.ndarray>\n",
" lon_u (eta_u, xi_u) float64 dask.array<chunksize=(482, 401), meta=np.ndarray>\n",
" * ocean_time (ocean_time) float64 5.205e+09 5.205e+09 ... 5.206e+09 5.206e+09\n",
" * s_rho (s_rho) float64 -0.9861 -0.9583 -0.9306 ... -0.04167 -0.01389\n",
"Dimensions without coordinates: eta_u, xi_u\n",
"Attributes:\n",
" field: u-velocity, scalar, series\n",
" grid: grid\n",
" location: edge1\n",
" long_name: u-momentum component\n",
" time: ocean_time\n",
" units: meter second-1"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.u"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8d2850d7-c351-46d1-b4d7-7d6ea7ce1201",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"12:25:15 INFO opendrift.models.basemodel:528: OpenDriftSimulation initialised (version 1.10.7)\n"
]
}
],
"source": [
"o = OceanDrift(loglevel=20) # Set loglevel to 0 for debug information"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "cc006743-bfb2-45a1-8ec9-50b2b7ea6b9c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"12:25:15 INFO opendrift.readers.reader_ROMS_intake:94: Opening dataset: CNAPS_opendrift\n",
"12:25:18 INFO opendrift.readers.reader_ROMS_intake:173: Read GLS parameters from file.\n",
"12:25:18 WARNING opendrift.readers.basereader.structured:44: No proj string or projection could be derived, using 'fakeproj'. This assumes that the variables are structured and gridded approximately equidistantly on the surface (i.e. in meters). This must be guaranteed by the user. You can get rid of this warning by supplying a valid projection to the reader.\n",
"12:25:18 INFO opendrift.readers.basereader.structured:59: Making interpolator for lon,lat to x,y conversion...\n"
]
}
],
"source": [
"cnaps = reader_ROMS_intake.Reader(intake_catalog=intake_catalog, dataset=dataset)\n",
"o.add_reader(cnaps)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "77a07903-cce1-4d1d-a0d7-85b2c96c314e",
"metadata": {},
"outputs": [],
"source": [
"#%%\n",
"# Creating and adding reader for Nordic 4km current dataset\n",
"#nordic_native = reader_ROMS_native.Reader(o.test_data_folder() +\n",
"# '2Feb2016_Nordic_sigma_3d/Nordic-4km_SLEVELS_avg_00_subset2Feb2016.nc')\n",
"#o.add_reader(nordic_native)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "987800d3-6331-42c3-af26-6c8747331f05",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"12:25:27 INFO opendrift.models.basemodel.environment:219: Adding a dynamical landmask with max. priority based on assumed maximum speed of 1.0 m/s. Adding a customised landmask may be faster...\n",
"12:25:33 INFO opendrift.models.basemodel.environment:246: Fallback values will be used for the following variables which have no readers: \n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tx_wind: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \ty_wind: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tocean_vertical_diffusivity: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_significant_height: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_stokes_drift_x_velocity: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_stokes_drift_y_velocity: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_period_at_variance_spectral_density_maximum: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_mean_period_from_variance_spectral_density_second_frequency_moment: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_to_direction: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_peak_period_from_variance_spectral_density: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_significant_height: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_to_direction: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_mean_period: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_significant_height: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsurface_downward_x_stress: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tsurface_downward_y_stress: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tturbulent_kinetic_energy: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tturbulent_generic_length_scale: 0.000000\n",
"12:25:33 INFO opendrift.models.basemodel.environment:249: \tocean_mixed_layer_thickness: 50.000000\n"
]
}
],
"source": [
"#%%\n",
"# Seed elements at defined positions, depth and time\n",
"o.seed_elements(lon=-69.0, lat=42.0, radius=0, number=10,\n",
" z=np.linspace(0, -150, 10), time=cnaps.start_time)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "cfc73e7a-1538-4bec-b2e9-d3f66e45ff19",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"12:25:33 INFO opendrift.models.basemodel:1816: Duration, steps or end time not specified, running until end of first reader: 2023-11-03 00:00:00\n",
"12:25:33 INFO opendrift.models.basemodel:1816: Duration, steps or end time not specified, running until end of first reader: 2023-11-03 00:00:00\n",
"12:25:33 INFO opendrift.models.basemodel:891: Using existing reader for land_binary_mask\n",
"12:25:34 INFO opendrift.models.basemodel:903: All points are in ocean\n",
"12:25:34 INFO opendrift.models.basemodel.environment:670: ========================\n",
"12:25:34 ERROR opendrift.models.basemodel.environment:671: Index is out of bounds for axis 0 with size 57\n",
"Traceback (most recent call last):\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/models/basemodel/environment.py\", line 646, in get_environment\n",
" reader.get_variables_interpolated(\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/variables.py\", line 889, in get_variables_interpolated\n",
" env, env_profiles = self.get_variables_interpolated_xy(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/variables.py\", line 755, in get_variables_interpolated_xy\n",
" env, env_profiles = self._get_variables_interpolated_(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/structured.py\", line 243, in _get_variables_interpolated_\n",
" self.get_variables(blockvariables_before, time_before,\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/reader_ROMS_intake.py\", line 334, in get_variables\n",
" variables[par] = self.sea_floor_depth_below_sea_level[indy, indx]\n",
" ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/xarray/core/variable.py\", line 811, in __getitem__\n",
" data = as_indexable(self._data)[indexer]\n",
" ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/xarray/core/indexing.py\", line 1446, in __getitem__\n",
" return self.array[key]\n",
" ~~~~~~~~~~^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/dask/array/core.py\", line 1962, in __getitem__\n",
" index2 = normalize_index(index, self.shape)\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/dask/array/slicing.py\", line 917, in normalize_index\n",
" check_index(axis, i, d)\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/dask/array/slicing.py\", line 980, in check_index\n",
" raise IndexError(\n",
"IndexError: Index is out of bounds for axis 0 with size 57\n",
"12:25:34 INFO opendrift.models.basemodel.environment:673: ========================\n",
"12:25:34 INFO opendrift.models.basemodel:1989: 2023-10-27 00:00:00 - step 1 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel.environment:670: ========================\n",
"12:25:37 ERROR opendrift.models.basemodel.environment:671: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n",
"Traceback (most recent call last):\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/models/basemodel/environment.py\", line 646, in get_environment\n",
" reader.get_variables_interpolated(\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/variables.py\", line 889, in get_variables_interpolated\n",
" env, env_profiles = self.get_variables_interpolated_xy(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/variables.py\", line 755, in get_variables_interpolated_xy\n",
" env, env_profiles = self._get_variables_interpolated_(\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/basereader/structured.py\", line 243, in _get_variables_interpolated_\n",
" self.get_variables(blockvariables_before, time_before,\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/reader_ROMS_intake.py\", line 294, in get_variables\n",
" self.z_rho_tot = depth.sdepth(Htot, self.hc, self.Cs_r,\n",
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
" File \"/home/conda/global/e1003848400f1afa7580bb6fa3874f48f20b3522458965117a29babee3516098-20231112-153522-163623-16-opendrift/lib/python3.11/site-packages/opendrift/readers/roppy/depth.py\", line 72, in sdepth\n",
" if Vtransform == 1: # Default transform by Song and Haidvogel\n",
" ^^^^^^^^^^^^^^^\n",
"ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()\n",
"12:25:37 INFO opendrift.models.basemodel.environment:673: ========================\n",
"12:25:37 WARNING opendrift.models.basemodel.environment:678: Reader roms native is discarded after failing more times than allowed (1)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 01:00:00 - step 2 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 02:00:00 - step 3 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 03:00:00 - step 4 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 04:00:00 - step 5 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 05:00:00 - step 6 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 06:00:00 - step 7 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 07:00:00 - step 8 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 08:00:00 - step 9 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 09:00:00 - step 10 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 10:00:00 - step 11 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 11:00:00 - step 12 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 12:00:00 - step 13 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 13:00:00 - step 14 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 14:00:00 - step 15 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 15:00:00 - step 16 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 16:00:00 - step 17 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 17:00:00 - step 18 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 18:00:00 - step 19 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 19:00:00 - step 20 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 20:00:00 - step 21 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 21:00:00 - step 22 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 22:00:00 - step 23 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-27 23:00:00 - step 24 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 00:00:00 - step 25 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 01:00:00 - step 26 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 02:00:00 - step 27 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 03:00:00 - step 28 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 04:00:00 - step 29 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 05:00:00 - step 30 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 06:00:00 - step 31 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 07:00:00 - step 32 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 08:00:00 - step 33 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 09:00:00 - step 34 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 10:00:00 - step 35 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 11:00:00 - step 36 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 12:00:00 - step 37 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 13:00:00 - step 38 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 14:00:00 - step 39 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 15:00:00 - step 40 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 16:00:00 - step 41 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 17:00:00 - step 42 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 18:00:00 - step 43 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 19:00:00 - step 44 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 20:00:00 - step 45 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 21:00:00 - step 46 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 22:00:00 - step 47 of 168 - 10 active elements (0 deactivated)\n",
"12:25:37 INFO opendrift.models.basemodel:1989: 2023-10-28 23:00:00 - step 48 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 00:00:00 - step 49 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 01:00:00 - step 50 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 02:00:00 - step 51 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 03:00:00 - step 52 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 04:00:00 - step 53 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 05:00:00 - step 54 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 06:00:00 - step 55 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 07:00:00 - step 56 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 08:00:00 - step 57 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 09:00:00 - step 58 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 10:00:00 - step 59 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 11:00:00 - step 60 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 12:00:00 - step 61 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 13:00:00 - step 62 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 14:00:00 - step 63 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 15:00:00 - step 64 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 16:00:00 - step 65 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 17:00:00 - step 66 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 18:00:00 - step 67 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 19:00:00 - step 68 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 20:00:00 - step 69 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 21:00:00 - step 70 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 22:00:00 - step 71 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-29 23:00:00 - step 72 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 00:00:00 - step 73 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 01:00:00 - step 74 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 02:00:00 - step 75 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 03:00:00 - step 76 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 04:00:00 - step 77 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 05:00:00 - step 78 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 06:00:00 - step 79 of 168 - 10 active elements (0 deactivated)\n",
"12:25:38 INFO opendrift.models.basemodel:1989: 2023-10-30 07:00:00 - step 80 of 168 - 10 active elements (0 deactivated)\n",
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]
}
],
"source": [
"#%%\n",
"# Running model\n",
"o.run(time_step=3600)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "ce2a6e95-bc4d-4932-bd12-5d9eace48402",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"12:25:39 WARNING opendrift.models.basemodel:2357: Plotting fast. This will make your plots less accurate.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"===========================\n",
"--------------------\n",
"Reader performance:\n",
"--------------------\n",
"global_landmask\n",
" 0:00:00.0 total\n",
" 0:00:00.0 preparing\n",
" 0:00:00.0 reading\n",
" 0:00:00.0 masking\n",
"--------------------\n",
"Performance:\n",
" 24.1 total time\n",
" 17.6 configuration\n",
" 1.0 preparing main loop\n",
" 1.0 moving elements to ocean\n",
" 5.4 main loop\n",
" 0.0 updating elements\n",
" 0.0 cleaning up\n",
"--------------------\n",
"===========================\n",
"Model:\tOceanDrift (OpenDrift version 1.10.7)\n",
"\t10 active Lagrangian3DArray particles (0 deactivated, 0 scheduled)\n",
"-------------------\n",
"Environment variables:\n",
" -----\n",
" land_binary_mask\n",
" 1) global_landmask\n",
" -----\n",
"Readers not added for the following variables:\n",
" ocean_mixed_layer_thickness\n",
" ocean_vertical_diffusivity\n",
" sea_floor_depth_below_sea_level\n",
" sea_surface_swell_wave_peak_period_from_variance_spectral_density\n",
" sea_surface_swell_wave_significant_height\n",
" sea_surface_swell_wave_to_direction\n",
" sea_surface_wave_mean_period_from_variance_spectral_density_second_frequency_moment\n",
" sea_surface_wave_period_at_variance_spectral_density_maximum\n",
" sea_surface_wave_significant_height\n",
" sea_surface_wave_stokes_drift_x_velocity\n",
" sea_surface_wave_stokes_drift_y_velocity\n",
" sea_surface_wind_wave_mean_period\n",
" sea_surface_wind_wave_significant_height\n",
" sea_surface_wind_wave_to_direction\n",
" surface_downward_x_stress\n",
" surface_downward_y_stress\n",
" turbulent_generic_length_scale\n",
" turbulent_kinetic_energy\n",
" upward_sea_water_velocity\n",
" x_sea_water_velocity\n",
" x_wind\n",
" y_sea_water_velocity\n",
" y_wind\n",
"\n",
"Discarded readers:\n",
" roms native (failed more than 1 times)\n",
"\n",
"Time:\n",
"\tStart: 2023-10-27 00:00:00 UTC\n",
"\tPresent: 2023-11-03 00:00:00 UTC\n",
"\tCalculation steps: 168 * 1:00:00 - total time: 7 days, 0:00:00\n",
"\tOutput steps: 169 * 1:00:00\n",
"===========================\n",
"\n"
]
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 1100x740.098 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/plain": [
"(<GeoAxes: title={'center': 'OpenDrift - OceanDrift\\n2023-10-27 00:00 to 2023-11-03 00:00 UTC (169 steps)'}>,\n",
" <Figure size 1100x740.098 with 2 Axes>)"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#%%\n",
"# Print and plot results, with lines colored by particle depth\n",
"print(o)\n",
"o.plot(linecolor='z', fast=True)\n",
"#o.animation()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "global-global-opendrift",
"language": "python",
"name": "conda-env-global-global-opendrift-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.6"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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