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parcels from_xarray_dataset `not_yet_set`
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xarray_not_yet_set.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## xarray + time-evolving depth dimensions"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"\n",
"from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4, ParticleFile\n",
"import numpy as np\n",
"import xarray as xr\n",
"\n",
"#from datetime import timedelta as delta\n",
"#from os import path"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
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"</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
"Dimensions: (x: 100, y: 200, s: 20, t: 10)\n",
"Coordinates:\n",
" * x (x) float64 0.0 0.0102 0.0204 0.03061 ... 0.9794 0.9896 0.9998 1.01\n",
" * y (y) float64 0.0 0.005025 0.01005 0.01508 ... 0.9899 0.995 1.0\n",
" * s (s) float64 -1.0 -0.9474 -0.8947 -0.8421 ... -0.1053 -0.05263 0.0\n",
" * t (t) float64 0.0 0.1111 0.2222 0.3333 ... 0.6667 0.7778 0.8889 1.0\n",
"Data variables:\n",
" z (t, s, y, x) float64 -1.0 -1.0 -1.0 -1.0 -1.0 ... 0.5 0.5 0.5 0.5\n",
" u (t, s, y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 1.5 1.5 1.5 1.5 1.5\n",
" v (t, s, y, x) float64 -0.0 -0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0 0.0\n",
" w (t, s, y, x) float64 -0.0 -0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0 0.0</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-731054a6-d0cb-45f8-b73b-afffd4d201fd' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-731054a6-d0cb-45f8-b73b-afffd4d201fd' 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'>x</span>: 100</li><li><span class='xr-has-index'>y</span>: 200</li><li><span class='xr-has-index'>s</span>: 20</li><li><span class='xr-has-index'>t</span>: 10</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-91f04880-6294-420a-9cdb-244c069d51e3' class='xr-section-summary-in' type='checkbox' checked><label for='section-91f04880-6294-420a-9cdb-244c069d51e3' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0102 0.0204 ... 0.9998 1.01</div><input id='attrs-1d15e8c0-aa8f-405d-aee7-a5fefd7f76ee' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d15e8c0-aa8f-405d-aee7-a5fefd7f76ee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c4c9c32-2f7a-4e8e-a1bf-8dd70b3d8117' class='xr-var-data-in' type='checkbox'><label for='data-4c4c9c32-2f7a-4e8e-a1bf-8dd70b3d8117' 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'></dl></div><div class='xr-var-data'><pre>array([0. , 0.010202, 0.020404, 0.030606, 0.040808, 0.05101 , 0.061212,\n",
" 0.071414, 0.081616, 0.091818, 0.10202 , 0.112222, 0.122424, 0.132626,\n",
" 0.142828, 0.15303 , 0.163232, 0.173434, 0.183636, 0.193838, 0.20404 ,\n",
" 0.214242, 0.224444, 0.234646, 0.244848, 0.255051, 0.265253, 0.275455,\n",
" 0.285657, 0.295859, 0.306061, 0.316263, 0.326465, 0.336667, 0.346869,\n",
" 0.357071, 0.367273, 0.377475, 0.387677, 0.397879, 0.408081, 0.418283,\n",
" 0.428485, 0.438687, 0.448889, 0.459091, 0.469293, 0.479495, 0.489697,\n",
" 0.499899, 0.510101, 0.520303, 0.530505, 0.540707, 0.550909, 0.561111,\n",
" 0.571313, 0.581515, 0.591717, 0.601919, 0.612121, 0.622323, 0.632525,\n",
" 0.642727, 0.652929, 0.663131, 0.673333, 0.683535, 0.693737, 0.703939,\n",
" 0.714141, 0.724343, 0.734545, 0.744747, 0.754949, 0.765152, 0.775354,\n",
" 0.785556, 0.795758, 0.80596 , 0.816162, 0.826364, 0.836566, 0.846768,\n",
" 0.85697 , 0.867172, 0.877374, 0.887576, 0.897778, 0.90798 , 0.918182,\n",
" 0.928384, 0.938586, 0.948788, 0.95899 , 0.969192, 0.979394, 0.989596,\n",
" 0.999798, 1.01 ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.005025 0.01005 ... 0.995 1.0</div><input id='attrs-8c39676c-2415-4841-9d8c-a24d6462e358' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8c39676c-2415-4841-9d8c-a24d6462e358' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f05fc1b4-1003-4eb4-885a-8323256112c4' class='xr-var-data-in' type='checkbox'><label for='data-f05fc1b4-1003-4eb4-885a-8323256112c4' 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'></dl></div><div class='xr-var-data'><pre>array([0. , 0.005025, 0.01005 , 0.015075, 0.020101, 0.025126, 0.030151,\n",
" 0.035176, 0.040201, 0.045226, 0.050251, 0.055276, 0.060302, 0.065327,\n",
" 0.070352, 0.075377, 0.080402, 0.085427, 0.090452, 0.095477, 0.100503,\n",
" 0.105528, 0.110553, 0.115578, 0.120603, 0.125628, 0.130653, 0.135678,\n",
" 0.140704, 0.145729, 0.150754, 0.155779, 0.160804, 0.165829, 0.170854,\n",
" 0.175879, 0.180905, 0.18593 , 0.190955, 0.19598 , 0.201005, 0.20603 ,\n",
" 0.211055, 0.21608 , 0.221106, 0.226131, 0.231156, 0.236181, 0.241206,\n",
" 0.246231, 0.251256, 0.256281, 0.261307, 0.266332, 0.271357, 0.276382,\n",
" 0.281407, 0.286432, 0.291457, 0.296482, 0.301508, 0.306533, 0.311558,\n",
" 0.316583, 0.321608, 0.326633, 0.331658, 0.336683, 0.341709, 0.346734,\n",
" 0.351759, 0.356784, 0.361809, 0.366834, 0.371859, 0.376884, 0.38191 ,\n",
" 0.386935, 0.39196 , 0.396985, 0.40201 , 0.407035, 0.41206 , 0.417085,\n",
" 0.422111, 0.427136, 0.432161, 0.437186, 0.442211, 0.447236, 0.452261,\n",
" 0.457286, 0.462312, 0.467337, 0.472362, 0.477387, 0.482412, 0.487437,\n",
" 0.492462, 0.497487, 0.502513, 0.507538, 0.512563, 0.517588, 0.522613,\n",
" 0.527638, 0.532663, 0.537688, 0.542714, 0.547739, 0.552764, 0.557789,\n",
" 0.562814, 0.567839, 0.572864, 0.577889, 0.582915, 0.58794 , 0.592965,\n",
" 0.59799 , 0.603015, 0.60804 , 0.613065, 0.61809 , 0.623116, 0.628141,\n",
" 0.633166, 0.638191, 0.643216, 0.648241, 0.653266, 0.658291, 0.663317,\n",
" 0.668342, 0.673367, 0.678392, 0.683417, 0.688442, 0.693467, 0.698492,\n",
" 0.703518, 0.708543, 0.713568, 0.718593, 0.723618, 0.728643, 0.733668,\n",
" 0.738693, 0.743719, 0.748744, 0.753769, 0.758794, 0.763819, 0.768844,\n",
" 0.773869, 0.778894, 0.78392 , 0.788945, 0.79397 , 0.798995, 0.80402 ,\n",
" 0.809045, 0.81407 , 0.819095, 0.824121, 0.829146, 0.834171, 0.839196,\n",
" 0.844221, 0.849246, 0.854271, 0.859296, 0.864322, 0.869347, 0.874372,\n",
" 0.879397, 0.884422, 0.889447, 0.894472, 0.899497, 0.904523, 0.909548,\n",
" 0.914573, 0.919598, 0.924623, 0.929648, 0.934673, 0.939698, 0.944724,\n",
" 0.949749, 0.954774, 0.959799, 0.964824, 0.969849, 0.974874, 0.979899,\n",
" 0.984925, 0.98995 , 0.994975, 1. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>s</span></div><div class='xr-var-dims'>(s)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -0.9474 ... -0.05263 0.0</div><input id='attrs-a2db8527-6e56-439b-8d03-3cd33e3c4ca5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2db8527-6e56-439b-8d03-3cd33e3c4ca5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d11f91ba-58f9-4f8d-b6d0-978a8529555f' class='xr-var-data-in' type='checkbox'><label for='data-d11f91ba-58f9-4f8d-b6d0-978a8529555f' 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'></dl></div><div class='xr-var-data'><pre>array([-1. , -0.947368, -0.894737, -0.842105, -0.789474, -0.736842,\n",
" -0.684211, -0.631579, -0.578947, -0.526316, -0.473684, -0.421053,\n",
" -0.368421, -0.315789, -0.263158, -0.210526, -0.157895, -0.105263,\n",
" -0.052632, 0. ])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.1111 0.2222 ... 0.8889 1.0</div><input id='attrs-e35188cc-4913-4101-b088-4e3807d99847' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e35188cc-4913-4101-b088-4e3807d99847' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d22a3e1f-8a00-4a57-8288-392a105d4f5c' class='xr-var-data-in' type='checkbox'><label for='data-d22a3e1f-8a00-4a57-8288-392a105d4f5c' 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'></dl></div><div class='xr-var-data'><pre>array([0. , 0.111111, 0.222222, 0.333333, 0.444444, 0.555556, 0.666667,\n",
" 0.777778, 0.888889, 1. ])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-af089854-dc93-4146-a18f-81bb91a2b7ab' class='xr-section-summary-in' type='checkbox' checked><label for='section-af089854-dc93-4146-a18f-81bb91a2b7ab' class='xr-section-summary' >Data variables: <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>z</span></div><div class='xr-var-dims'>(t, s, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.0 -1.0 -1.0 -1.0 ... 0.5 0.5 0.5</div><input id='attrs-428f2d19-0875-41ab-8b97-0ac2d1795a91' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-428f2d19-0875-41ab-8b97-0ac2d1795a91' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-52606db3-3af9-4ff5-87b7-d2a5c06c409e' class='xr-var-data-in' type='checkbox'><label for='data-52606db3-3af9-4ff5-87b7-d2a5c06c409e' 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'></dl></div><div class='xr-var-data'><pre>array([[[[-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00],\n",
" [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00],\n",
" [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00],\n",
" ...,\n",
" [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00],\n",
" [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00],\n",
" [-1.00000000e+00, -1.00000000e+00, -1.00000000e+00, ...,\n",
" -1.00000000e+00, -1.00000000e+00, -1.00000000e+00]],\n",
"\n",
" [[-9.47368421e-01, -9.47368421e-01, -9.47368421e-01, ...,\n",
" -9.47368421e-01, -9.47368421e-01, -9.47368421e-01],\n",
" [-9.47368421e-01, -9.47368421e-01, -9.47368421e-01, ...,\n",
" -9.47368421e-01, -9.47368421e-01, -9.47368421e-01],\n",
" [-9.47368421e-01, -9.47368421e-01, -9.47368421e-01, ...,\n",
" -9.47368421e-01, -9.47368421e-01, -9.47368421e-01],\n",
"...\n",
" [ 4.21052632e-01, 4.21052632e-01, 4.21052632e-01, ...,\n",
" 4.21052632e-01, 4.21052632e-01, 4.21052632e-01],\n",
" [ 4.21052632e-01, 4.21052632e-01, 4.21052632e-01, ...,\n",
" 4.21052632e-01, 4.21052632e-01, 4.21052632e-01],\n",
" [ 4.21052632e-01, 4.21052632e-01, 4.21052632e-01, ...,\n",
" 4.21052632e-01, 4.21052632e-01, 4.21052632e-01]],\n",
"\n",
" [[ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01],\n",
" [ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01],\n",
" [ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01],\n",
" ...,\n",
" [ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01],\n",
" [ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01],\n",
" [ 5.00000000e-01, 5.00000000e-01, 5.00000000e-01, ...,\n",
" 5.00000000e-01, 5.00000000e-01, 5.00000000e-01]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(t, s, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 1.5 1.5 1.5 1.5</div><input id='attrs-aca67282-2f4d-4e6b-be9b-928070746ea8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aca67282-2f4d-4e6b-be9b-928070746ea8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cac5b8e-047a-480c-9f40-f9384e708199' class='xr-var-data-in' type='checkbox'><label for='data-7cac5b8e-047a-480c-9f40-f9384e708199' 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'></dl></div><div class='xr-var-data'><pre>array([[[[0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" ...,\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ],\n",
" [0. , 0. , 0. , ..., 0. ,\n",
" 0. , 0. ]],\n",
"\n",
" [[0.05263158, 0.05263158, 0.05263158, ..., 0.05263158,\n",
" 0.05263158, 0.05263158],\n",
" [0.05263158, 0.05263158, 0.05263158, ..., 0.05263158,\n",
" 0.05263158, 0.05263158],\n",
" [0.05263158, 0.05263158, 0.05263158, ..., 0.05263158,\n",
" 0.05263158, 0.05263158],\n",
"...\n",
" [1.42105263, 1.42105263, 1.42105263, ..., 1.42105263,\n",
" 1.42105263, 1.42105263],\n",
" [1.42105263, 1.42105263, 1.42105263, ..., 1.42105263,\n",
" 1.42105263, 1.42105263],\n",
" [1.42105263, 1.42105263, 1.42105263, ..., 1.42105263,\n",
" 1.42105263, 1.42105263]],\n",
"\n",
" [[1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ],\n",
" [1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ],\n",
" [1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ],\n",
" ...,\n",
" [1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ],\n",
" [1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ],\n",
" [1.5 , 1.5 , 1.5 , ..., 1.5 ,\n",
" 1.5 , 1.5 ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(t, s, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0</div><input id='attrs-10d72106-bbda-4a11-b13f-3e0083098c9b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-10d72106-bbda-4a11-b13f-3e0083098c9b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b990a231-563d-4aa9-aa69-d5d5a6bee564' class='xr-var-data-in' type='checkbox'><label for='data-b990a231-563d-4aa9-aa69-d5d5a6bee564' 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'></dl></div><div class='xr-var-data'><pre>array([[[[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.]],\n",
"\n",
" [[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.]],\n",
"\n",
" [[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
"...\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>w</span></div><div class='xr-var-dims'>(t, s, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0</div><input id='attrs-a922c699-eee2-4e2d-b37b-17d6a337c820' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a922c699-eee2-4e2d-b37b-17d6a337c820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9de269d7-3d99-482b-8f45-25d455d2e4e6' class='xr-var-data-in' type='checkbox'><label for='data-9de269d7-3d99-482b-8f45-25d455d2e4e6' 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'></dl></div><div class='xr-var-data'><pre>array([[[[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.]],\n",
"\n",
" [[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.]],\n",
"\n",
" [[-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" [-0., -0., -0., ..., -0., -0., -0.],\n",
" ...,\n",
"...\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]],\n",
"\n",
" [[ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" ...,\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.],\n",
" [ 0., 0., 0., ..., 0., 0., 0.]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5b738e44-d8fd-476b-b173-c1b795d172f4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5b738e44-d8fd-476b-b173-c1b795d172f4' class='xr-section-summary' title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (x: 100, y: 200, s: 20, t: 10)\n",
"Coordinates:\n",
" * x (x) float64 0.0 0.0102 0.0204 0.03061 ... 0.9794 0.9896 0.9998 1.01\n",
" * y (y) float64 0.0 0.005025 0.01005 0.01508 ... 0.9899 0.995 1.0\n",
" * s (s) float64 -1.0 -0.9474 -0.8947 -0.8421 ... -0.1053 -0.05263 0.0\n",
" * t (t) float64 0.0 0.1111 0.2222 0.3333 ... 0.6667 0.7778 0.8889 1.0\n",
"Data variables:\n",
" z (t, s, y, x) float64 -1.0 -1.0 -1.0 -1.0 -1.0 ... 0.5 0.5 0.5 0.5\n",
" u (t, s, y, x) float64 0.0 0.0 0.0 0.0 0.0 ... 1.5 1.5 1.5 1.5 1.5\n",
" v (t, s, y, x) float64 -0.0 -0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0 0.0\n",
" w (t, s, y, x) float64 -0.0 -0.0 -0.0 -0.0 -0.0 ... 0.0 0.0 0.0 0.0"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nx, ny, nz, nt = 100, 200, 20, 10\n",
"Lx, Ly, Lz, Lt = 1.01, 1, 1, 1\n",
"ds = xr.Dataset(coords=dict(x=(\"x\", np.linspace(0,Lx,nx)),\n",
" y=(\"y\", np.linspace(0,Ly,ny)),\n",
" s=(\"s\", np.linspace(-Lz,0,nz)),\n",
" t=(\"t\", np.linspace(0,Lt,nt))\n",
" ),\n",
" )\n",
"ds[\"z\"] = Lz*(ds.t*.5*(1+ds.s) + ds.s + 0*ds.y*ds.x)\n",
"ds[\"u\"] = Lz+ds.z\n",
"ds[\"v\"] = 0.*ds.z\n",
"ds[\"w\"] = 0.*ds.z\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'time evolution of the vertical grid')"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1)\n",
"\n",
"_ds = ds.isel(x=0, y=0)\n",
"ax.plot(_ds[\"t\"], _ds[\"z\"], \"k\");\n",
"ax.set_ylabel(\"z\")\n",
"ax.set_xlabel(\"t\")\n",
"ax.set_title(\"time evolution of the vertical grid\")\n",
"\n",
"#ds[\"z\"].isel(x=0, y=0).plot()\n",
"#ds[\"z\"].isel(x=0, y=0).plot()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"variables = {'U': 'u',\n",
" 'V': 'v',\n",
" 'depth': 'z',\n",
" }\n",
"\n",
"dimensions = {'U': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},\n",
" 'V': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'},\n",
" 'depth': {'lon': 'x', 'lat': 'y', 'depth': 'not_yet_set', 'time': 't'}}"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"fieldset = FieldSet.from_xarray_dataset(ds, variables, dimensions, \n",
" mesh='flat', \n",
" allow_time_extrapolation=True,\n",
" )\n",
"fieldset.U.set_depth_from_field(fieldset.depth)\n",
"fieldset.V.set_depth_from_field(fieldset.depth)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"INFO: Compiled ArrayJITParticleAdvectionRK4 ==> /var/folders/pb/vp119fcj1pn3dc3x2dz4dkt80000gn/T/parcels-501/libf9b6b31dece3dcbe8283612a4cfb5cdf_0.so\n"
]
}
],
"source": [
"pset = ParticleSet(fieldset, JITParticle, lon=[0]*2, lat=[0]*2, depth=[-Lz/2, 0])\n",
"pfile = pset.ParticleFile(\"parcel_output\", outputdt=0.2)\n",
"pset.execute(AdvectionRK4, endtime=1, dt=0.1, output_file=pfile)\n",
"\n",
"pfile.export() # export the trajectory data to a netcdf file"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'z')"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds_out = xr.open_dataset('parcel_output.nc')\n",
"\n",
"fig, ax = plt.subplots(1,1)\n",
"ax.plot(ds_out.lon.T, ds_out.z.T, \"-+\")\n",
"ax.grid()\n",
"ax.set_xlabel(\"t\")\n",
"ax.set_ylabel(\"z\")"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'velocity vs parcel trajectory')"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(1,1)\n",
"_ds = ds.isel(x=0,y=0).set_coords(\"z\")\n",
"_ds.u.plot.pcolormesh(x=\"t\", y=\"z\", ax=ax)\n",
"ax.plot(ds_out.time.T/np.timedelta64(1, 's'), ds_out.z.T, \"k-+\")\n",
"ax.grid()\n",
"ax.set_title(\"velocity vs parcel trajectory\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.9.9"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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