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Created December 9, 2023 22:58
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cnaps_example_intake.ipynb
{
"cells": [
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"cell_type": "markdown",
"id": "2d2f18b7-2ceb-4458-8189-f3b671493e94",
"metadata": {
"tags": []
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"source": [
"# Opendrift from Intake Catalog\n",
"Run Opendrift from an Intake catalog dataset, here a kerchunked collection of CNAPS Native 64-bit offset NetCDF files in an Open Storage Network bucket (object storage with S3 API access)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "664564e4-d25c-43ea-974c-716beb4f4f00",
"metadata": {
"tags": []
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"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": []
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"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": "6eee426c-5e86-4d4c-b4ea-41fc0ac08f5c",
"metadata": {
"tags": []
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"outputs": [],
"source": [
"dataset = 'CNAPS_opendrift'"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c3d45bf2-19ab-4b1f-9f03-8b891e40d46f",
"metadata": {
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{
"data": {
"application/yaml": "CNAPS_opendrift:\n args:\n consolidated: false\n decode_times: false\n drop_variables: dstart\n storage_options:\n fo: s3://rsignellbucket1/jzambon/archive64.json\n lazy: false\n remote_options:\n anon: true\n client_kwargs:\n endpoint_url: https://mghp.osn.xsede.org\n listings_expiry_time: 10\n skip_instance_cache: true\n remote_protocol: s3\n skip_instance_cache: true\n target_options:\n anon: true\n client_kwargs:\n endpoint_url: https://mghp.osn.xsede.org\n listings_expiry_time: 10\n skip_instance_cache: true\n target_protocol: s3\n urlpath: reference://\n description: 'CNAPS US East and Gulf Coast Forecast Archive: original netCDF3-64bit\n offset files'\n driver: intake_xarray.xzarr.ZarrSource\n metadata:\n catalog_dir: https://mghp.osn.xsede.org/rsignellbucket1/rsignell/testing\n",
"text/plain": [
"CNAPS_opendrift:\n",
" args:\n",
" consolidated: false\n",
" decode_times: false\n",
" drop_variables: dstart\n",
" storage_options:\n",
" fo: s3://rsignellbucket1/jzambon/archive64.json\n",
" lazy: false\n",
" remote_options:\n",
" anon: true\n",
" client_kwargs:\n",
" endpoint_url: https://mghp.osn.xsede.org\n",
" listings_expiry_time: 10\n",
" skip_instance_cache: true\n",
" remote_protocol: s3\n",
" skip_instance_cache: true\n",
" target_options:\n",
" anon: true\n",
" client_kwargs:\n",
" endpoint_url: https://mghp.osn.xsede.org\n",
" listings_expiry_time: 10\n",
" skip_instance_cache: true\n",
" target_protocol: s3\n",
" urlpath: reference://\n",
" description: 'CNAPS US East and Gulf Coast Forecast Archive: original netCDF3-64bit\n",
" offset files'\n",
" driver: intake_xarray.xzarr.ZarrSource\n",
" metadata:\n",
" catalog_dir: https://mghp.osn.xsede.org/rsignellbucket1/rsignell/testing\n"
]
},
"metadata": {
"application/json": {
"root": "CNAPS_opendrift"
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},
"output_type": "display_data"
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],
"source": [
"cat[dataset]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "5182e42e-f951-48f2-aca8-7fc726df9c3a",
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"ds = cat[dataset].to_dask() "
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "44256bf1-d2f5-44b6-b748-56c798599da3",
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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",
"Coordinates:\n",
" lat_u (eta_u, xi_u) float64 dask.array&lt;chunksize=(482, 401), meta=np.ndarray&gt;\n",
" lon_u (eta_u, xi_u) float64 dask.array&lt;chunksize=(482, 401), meta=np.ndarray&gt;\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</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-9e31d171-04f4-4aed-9700-f67fe5c2a8ef' class='xr-array-in' type='checkbox' checked><label for='section-9e31d171-04f4-4aed-9700-f67fe5c2a8ef' 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",
" <tr>\n",
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" \n",
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" <th> Dask graph </th>\n",
" <td colspan=\"2\"> 57 chunks in 2 graph layers </td>\n",
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" <line x1=\"38\" y1=\"0\" x2=\"38\" y2=\"25\" />\n",
" <line x1=\"39\" y1=\"0\" x2=\"39\" y2=\"25\" style=\"stroke-width:2\" />\n",
"\n",
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"\n",
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" <text x=\"59.504243\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,59.504243,12.706308)\">1</text>\n",
"\n",
"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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"\n",
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" <text x=\"180.636845\" y=\"161.719833\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >401</text>\n",
" <text x=\"250.553858\" y=\"81.719833\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,250.553858,81.719833)\">482</text>\n",
" <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-ef51b813-3619-4fc0-aa40-6223bf83194f' class='xr-section-summary-in' type='checkbox' checked><label for='section-ef51b813-3619-4fc0-aa40-6223bf83194f' 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-80f861aa-6647-4772-b9a3-dc3e6d16ed90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-80f861aa-6647-4772-b9a3-dc3e6d16ed90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1cc9ed6-6805-4a82-a055-ad7c7e277d3c' class='xr-var-data-in' type='checkbox'><label for='data-c1cc9ed6-6805-4a82-a055-ad7c7e277d3c' 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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"\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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"\n",
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"\n",
" <!-- Text -->\n",
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" <text x=\"119.834025\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,119.834025,60.000000)\">482</text>\n",
"</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-025a99c3-3221-4515-b533-ab7813746106' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-025a99c3-3221-4515-b533-ab7813746106' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e620a68-5b25-4f32-b53f-a7b51076326b' class='xr-var-data-in' type='checkbox'><label for='data-5e620a68-5b25-4f32-b53f-a7b51076326b' 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",
" </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",
" <line x1=\"0\" y1=\"120\" x2=\"99\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
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" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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"\n",
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" <text x=\"49.917012\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >401</text>\n",
" <text x=\"119.834025\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,119.834025,60.000000)\">482</text>\n",
"</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-6c26e43d-a820-475f-8c41-1ce4c17f2bc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c26e43d-a820-475f-8c41-1ce4c17f2bc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ffa3264a-0b8c-41b2-9fb0-9ba354cf7eb1' class='xr-var-data-in' type='checkbox'><label for='data-ffa3264a-0b8c-41b2-9fb0-9ba354cf7eb1' 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-854179e2-0c7e-409a-a95c-571e3e774e83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-854179e2-0c7e-409a-a95c-571e3e774e83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1411f358-d403-4d13-80de-6b732db33891' class='xr-var-data-in' type='checkbox'><label for='data-1411f358-d403-4d13-80de-6b732db33891' 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-b006b4e5-749c-4afd-860a-41b929fa3f1b' class='xr-section-summary-in' type='checkbox' ><label for='section-b006b4e5-749c-4afd-860a-41b929fa3f1b' 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-a3ae2e13-fbdb-48c3-adc7-334076aeae10' class='xr-index-data-in' type='checkbox'/><label for='index-a3ae2e13-fbdb-48c3-adc7-334076aeae10' 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-f29f0437-8fe1-4214-bf49-8e71fb2358a5' class='xr-index-data-in' type='checkbox'/><label for='index-f29f0437-8fe1-4214-bf49-8e71fb2358a5' 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-dfcab13d-cadc-44c7-b6fe-8877acf5aa1d' class='xr-section-summary-in' type='checkbox' checked><label for='section-dfcab13d-cadc-44c7-b6fe-8877acf5aa1d' 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": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds.u"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "8d2850d7-c351-46d1-b4d7-7d6ea7ce1201",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:45:03 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": 8,
"id": "cc006743-bfb2-45a1-8ec9-50b2b7ea6b9c",
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:45:03 INFO opendrift.readers.reader_ROMS_intake:94: Opening dataset: CNAPS_opendrift\n",
"16:45:05 INFO opendrift.readers.reader_ROMS_intake:173: Read GLS parameters from file.\n",
"16:45:05 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",
"16:45:05 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": 9,
"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": 10,
"id": "987800d3-6331-42c3-af26-6c8747331f05",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:45:15 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",
"16:45:20 INFO opendrift.models.basemodel.environment:246: Fallback values will be used for the following variables which have no readers: \n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tx_wind: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \ty_wind: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tocean_vertical_diffusivity: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_significant_height: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_stokes_drift_x_velocity: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_stokes_drift_y_velocity: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_period_at_variance_spectral_density_maximum: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wave_mean_period_from_variance_spectral_density_second_frequency_moment: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_to_direction: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_peak_period_from_variance_spectral_density: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_swell_wave_significant_height: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_to_direction: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_mean_period: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsea_surface_wind_wave_significant_height: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsurface_downward_x_stress: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tsurface_downward_y_stress: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tturbulent_kinetic_energy: 0.000000\n",
"16:45:20 INFO opendrift.models.basemodel.environment:249: \tturbulent_generic_length_scale: 0.000000\n",
"16:45:20 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=-75.50, lat=34.77, radius=0, number=10, \n",
" z=np.linspace(0, -150, 10), time=cnaps.start_time)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "cfc73e7a-1538-4bec-b2e9-d3f66e45ff19",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:45:20 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",
"16:45:20 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",
"16:45:20 INFO opendrift.models.basemodel:891: Using existing reader for land_binary_mask\n",
"16:45:21 INFO opendrift.models.basemodel:903: All points are in ocean\n",
"16:45:22 INFO opendrift.models.basemodel:1989: 2023-10-27 00:00:00 - step 1 of 168 - 10 active elements (0 deactivated)\n",
"16:45:26 INFO opendrift.readers.reader_ROMS_intake:408: Time: 0:00:00.921537\n",
"16:45:28 INFO opendrift.models.basemodel:1989: 2023-10-27 01:00:00 - step 2 of 168 - 10 active elements (0 deactivated)\n",
"16:45:32 INFO opendrift.models.basemodel:1989: 2023-10-27 02:00:00 - step 3 of 168 - 10 active elements (0 deactivated)\n",
"16:45:33 INFO opendrift.models.basemodel:1989: 2023-10-27 03:00:00 - step 4 of 168 - 10 active elements (0 deactivated)\n",
"16:45:33 INFO opendrift.models.basemodel:1989: 2023-10-27 04:00:00 - step 5 of 168 - 10 active elements (0 deactivated)\n",
"16:45:37 INFO opendrift.models.basemodel:1989: 2023-10-27 05:00:00 - step 6 of 168 - 10 active elements (0 deactivated)\n",
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"16:45:38 INFO opendrift.models.basemodel:1989: 2023-10-27 07:00:00 - step 8 of 168 - 10 active elements (0 deactivated)\n",
"16:45:41 INFO opendrift.models.basemodel:1989: 2023-10-27 08:00:00 - step 9 of 168 - 10 active elements (0 deactivated)\n",
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"16:46:44 INFO opendrift.models.basemodel:1989: 2023-10-29 02:00:00 - step 51 of 168 - 10 active elements (0 deactivated)\n",
"16:46:45 WARNING opendrift.readers.basereader.structured:286: Data block from roms native not large enough to cover element positions within timestep. Buffer size (4) must be increased. See `Variables.set_buffer_size`.\n",
"16:46:45 INFO opendrift.models.basemodel:1989: 2023-10-29 03:00:00 - step 52 of 168 - 10 active elements (0 deactivated)\n",
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"16:47:50 INFO opendrift.models.basemodel:1989: 2023-10-30 23:00:00 - step 96 of 168 - 10 active elements (0 deactivated)\n",
"16:47:50 WARNING opendrift.readers.basereader.structured:286: Data block from roms native not large enough to cover element positions within timestep. Buffer size (4) must be increased. See `Variables.set_buffer_size`.\n",
"16:47:50 INFO opendrift.models.basemodel:1989: 2023-10-31 00:00:00 - step 97 of 168 - 10 active elements (0 deactivated)\n",
"16:47:51 INFO opendrift.models.basemodel:1989: 2023-10-31 01:00:00 - step 98 of 168 - 10 active elements (0 deactivated)\n",
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"16:48:04 INFO opendrift.models.basemodel:1989: 2023-10-31 10:00:00 - step 107 of 168 - 10 active elements (0 deactivated)\n",
"16:48:07 INFO opendrift.models.basemodel:1989: 2023-10-31 11:00:00 - step 108 of 168 - 10 active elements (0 deactivated)\n",
"16:48:08 INFO opendrift.models.basemodel:1989: 2023-10-31 12:00:00 - step 109 of 168 - 10 active elements (0 deactivated)\n",
"16:48:08 INFO opendrift.models.basemodel:1989: 2023-10-31 13:00:00 - step 110 of 168 - 10 active elements (0 deactivated)\n",
"16:48:12 INFO opendrift.models.basemodel:1989: 2023-10-31 14:00:00 - step 111 of 168 - 10 active elements (0 deactivated)\n",
"16:48:12 INFO opendrift.models.basemodel:1989: 2023-10-31 15:00:00 - step 112 of 168 - 10 active elements (0 deactivated)\n",
"16:48:12 INFO opendrift.models.basemodel:1989: 2023-10-31 16:00:00 - step 113 of 168 - 10 active elements (0 deactivated)\n",
"16:48:16 INFO opendrift.models.basemodel:1989: 2023-10-31 17:00:00 - step 114 of 168 - 10 active elements (0 deactivated)\n",
"16:48:16 INFO opendrift.models.basemodel:1989: 2023-10-31 18:00:00 - step 115 of 168 - 10 active elements (0 deactivated)\n",
"16:48:17 INFO opendrift.models.basemodel:1989: 2023-10-31 19:00:00 - step 116 of 168 - 10 active elements (0 deactivated)\n",
"16:48:20 INFO opendrift.models.basemodel:1989: 2023-10-31 20:00:00 - step 117 of 168 - 10 active elements (0 deactivated)\n",
"16:48:21 INFO opendrift.models.basemodel:1989: 2023-10-31 21:00:00 - step 118 of 168 - 10 active elements (0 deactivated)\n",
"16:48:21 INFO opendrift.models.basemodel:1989: 2023-10-31 22:00:00 - step 119 of 168 - 10 active elements (0 deactivated)\n",
"16:48:25 INFO opendrift.models.basemodel:1989: 2023-10-31 23:00:00 - step 120 of 168 - 10 active elements (0 deactivated)\n",
"16:48:25 INFO opendrift.models.basemodel:1989: 2023-11-01 00:00:00 - step 121 of 168 - 10 active elements (0 deactivated)\n",
"16:48:25 INFO opendrift.models.basemodel:1989: 2023-11-01 01:00:00 - step 122 of 168 - 10 active elements (0 deactivated)\n",
"16:48:29 INFO opendrift.models.basemodel:1989: 2023-11-01 02:00:00 - step 123 of 168 - 10 active elements (0 deactivated)\n",
"16:48:29 INFO opendrift.models.basemodel:1989: 2023-11-01 03:00:00 - step 124 of 168 - 10 active elements (0 deactivated)\n",
"16:48:30 INFO opendrift.models.basemodel:1989: 2023-11-01 04:00:00 - step 125 of 168 - 10 active elements (0 deactivated)\n",
"16:48:33 INFO opendrift.models.basemodel:1989: 2023-11-01 05:00:00 - step 126 of 168 - 10 active elements (0 deactivated)\n",
"16:48:34 INFO opendrift.models.basemodel:1989: 2023-11-01 06:00:00 - step 127 of 168 - 10 active elements (0 deactivated)\n",
"16:48:34 INFO opendrift.models.basemodel:1989: 2023-11-01 07:00:00 - step 128 of 168 - 10 active elements (0 deactivated)\n",
"16:48:38 INFO opendrift.models.basemodel:1989: 2023-11-01 08:00:00 - step 129 of 168 - 10 active elements (0 deactivated)\n",
"16:48:38 INFO opendrift.models.basemodel:1989: 2023-11-01 09:00:00 - step 130 of 168 - 10 active elements (0 deactivated)\n",
"16:48:38 INFO opendrift.models.basemodel:1989: 2023-11-01 10:00:00 - step 131 of 168 - 10 active elements (0 deactivated)\n",
"16:48:42 INFO opendrift.models.basemodel:1989: 2023-11-01 11:00:00 - step 132 of 168 - 10 active elements (0 deactivated)\n",
"16:48:42 INFO opendrift.models.basemodel:1989: 2023-11-01 12:00:00 - step 133 of 168 - 10 active elements (0 deactivated)\n",
"16:48:43 INFO opendrift.models.basemodel:1989: 2023-11-01 13:00:00 - step 134 of 168 - 10 active elements (0 deactivated)\n",
"16:48:46 INFO opendrift.models.basemodel:1989: 2023-11-01 14:00:00 - step 135 of 168 - 10 active elements (0 deactivated)\n",
"16:48:47 INFO opendrift.models.basemodel:1989: 2023-11-01 15:00:00 - step 136 of 168 - 10 active elements (0 deactivated)\n",
"16:48:47 INFO opendrift.models.basemodel:1989: 2023-11-01 16:00:00 - step 137 of 168 - 10 active elements (0 deactivated)\n",
"16:48:51 INFO opendrift.models.basemodel:1989: 2023-11-01 17:00:00 - step 138 of 168 - 10 active elements (0 deactivated)\n",
"16:48:51 INFO opendrift.models.basemodel:1989: 2023-11-01 18:00:00 - step 139 of 168 - 10 active elements (0 deactivated)\n",
"16:48:52 INFO opendrift.models.basemodel:1989: 2023-11-01 19:00:00 - step 140 of 168 - 10 active elements (0 deactivated)\n",
"16:48:55 INFO opendrift.models.basemodel:1989: 2023-11-01 20:00:00 - step 141 of 168 - 10 active elements (0 deactivated)\n",
"16:48:55 INFO opendrift.models.basemodel:1989: 2023-11-01 21:00:00 - step 142 of 168 - 10 active elements (0 deactivated)\n",
"16:48:56 INFO opendrift.models.basemodel:1989: 2023-11-01 22:00:00 - step 143 of 168 - 10 active elements (0 deactivated)\n",
"16:48:59 INFO opendrift.models.basemodel:1989: 2023-11-01 23:00:00 - step 144 of 168 - 10 active elements (0 deactivated)\n",
"16:49:00 INFO opendrift.models.basemodel:1989: 2023-11-02 00:00:00 - step 145 of 168 - 10 active elements (0 deactivated)\n",
"16:49:00 INFO opendrift.models.basemodel:1989: 2023-11-02 01:00:00 - step 146 of 168 - 10 active elements (0 deactivated)\n",
"16:49:04 INFO opendrift.models.basemodel:1989: 2023-11-02 02:00:00 - step 147 of 168 - 10 active elements (0 deactivated)\n",
"16:49:04 INFO opendrift.models.basemodel:1989: 2023-11-02 03:00:00 - step 148 of 168 - 10 active elements (0 deactivated)\n",
"16:49:05 INFO opendrift.models.basemodel:1989: 2023-11-02 04:00:00 - step 149 of 168 - 10 active elements (0 deactivated)\n",
"16:49:08 INFO opendrift.models.basemodel:1989: 2023-11-02 05:00:00 - step 150 of 168 - 10 active elements (0 deactivated)\n",
"16:49:08 INFO opendrift.models.basemodel:1989: 2023-11-02 06:00:00 - step 151 of 168 - 10 active elements (0 deactivated)\n",
"16:49:09 INFO opendrift.models.basemodel:1989: 2023-11-02 07:00:00 - step 152 of 168 - 10 active elements (0 deactivated)\n",
"16:49:12 INFO opendrift.models.basemodel:1989: 2023-11-02 08:00:00 - step 153 of 168 - 10 active elements (0 deactivated)\n",
"16:49:13 INFO opendrift.models.basemodel:1989: 2023-11-02 09:00:00 - step 154 of 168 - 10 active elements (0 deactivated)\n",
"16:49:13 INFO opendrift.models.basemodel:1989: 2023-11-02 10:00:00 - step 155 of 168 - 10 active elements (0 deactivated)\n",
"16:49:17 INFO opendrift.models.basemodel:1989: 2023-11-02 11:00:00 - step 156 of 168 - 10 active elements (0 deactivated)\n",
"16:49:17 INFO opendrift.models.basemodel:1989: 2023-11-02 12:00:00 - step 157 of 168 - 10 active elements (0 deactivated)\n",
"16:49:18 INFO opendrift.models.basemodel:1989: 2023-11-02 13:00:00 - step 158 of 168 - 10 active elements (0 deactivated)\n",
"16:49:21 INFO opendrift.models.basemodel:1989: 2023-11-02 14:00:00 - step 159 of 168 - 10 active elements (0 deactivated)\n",
"16:49:22 INFO opendrift.models.basemodel:1989: 2023-11-02 15:00:00 - step 160 of 168 - 10 active elements (0 deactivated)\n",
"16:49:22 INFO opendrift.models.basemodel:1989: 2023-11-02 16:00:00 - step 161 of 168 - 10 active elements (0 deactivated)\n",
"16:49:25 INFO opendrift.models.basemodel:1989: 2023-11-02 17:00:00 - step 162 of 168 - 10 active elements (0 deactivated)\n",
"16:49:26 INFO opendrift.models.basemodel:1989: 2023-11-02 18:00:00 - step 163 of 168 - 10 active elements (0 deactivated)\n",
"16:49:26 INFO opendrift.models.basemodel:1989: 2023-11-02 19:00:00 - step 164 of 168 - 10 active elements (0 deactivated)\n",
"16:49:30 INFO opendrift.models.basemodel:1989: 2023-11-02 20:00:00 - step 165 of 168 - 10 active elements (0 deactivated)\n",
"16:49:30 INFO opendrift.models.basemodel:1989: 2023-11-02 21:00:00 - step 166 of 168 - 10 active elements (0 deactivated)\n",
"16:49:31 INFO opendrift.models.basemodel:1989: 2023-11-02 22:00:00 - step 167 of 168 - 10 active elements (0 deactivated)\n",
"16:49:34 INFO opendrift.models.basemodel:1989: 2023-11-02 23:00:00 - step 168 of 168 - 10 active elements (0 deactivated)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 35.5 s, sys: 10.7 s, total: 46.2 s\n",
"Wall time: 4min 14s\n"
]
}
],
"source": [
"%%time\n",
"# Running model\n",
"o.run(time_step=3600)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ce2a6e95-bc4d-4932-bd12-5d9eace48402",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"16:49:35 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",
"roms native\n",
" 0:03:36.1 total\n",
" 0:00:00.0 preparing\n",
" 0:03:01.6 reading\n",
" 0:00:00.4 interpolation\n",
" 0:00:00.0 interpolation_time\n",
" 0:00:00.0 masking\n",
" 0:00:34.4 rotating vectors\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",
" 4:31.3 total time\n",
" 17.0 configuration\n",
" 0.9 preparing main loop\n",
" 0.9 moving elements to ocean\n",
" 4:13.3 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",
" sea_floor_depth_below_sea_level\n",
" upward_sea_water_velocity\n",
" x_sea_water_velocity\n",
" y_sea_water_velocity\n",
" 1) roms native\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_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",
" x_wind\n",
" y_wind\n",
"\n",
"Discarded readers:\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": {
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",
"text/plain": [
"<Figure size 1100x805.304 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 1100x805.304 with 2 Axes>)"
]
},
"execution_count": 12,
"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()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0ccfea24-e52d-440b-af1b-e19a4a3759f1",
"metadata": {},
"outputs": [],
"source": []
}
],
"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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