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Meancurrents.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"from glob import glob\n",
"import threading\n",
"\n",
"import numpy as np\n",
"import dask.dataframe as dd\n",
"from dask import delayed\n",
"import pandas as pd\n",
"import xarray as xr\n",
"\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"#import matplotlib.animation as anima\n",
"\n",
"import crocosi.postp as pp\n",
"from crocosi.jet import set_relevant_time\n",
"import phdequinox.croco_drifter as cdr"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from dask.distributed import Client, LocalCluster\n",
"#\n",
"#cluster = LocalCluster()\n",
"from dask_jobqueue import PBSCluster\n",
"cluster = PBSCluster()\n",
"w = cluster.scale(jobs=2)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from dask.distributed import Client\n",
"#client = Client() # set up local cluster on your laptop\n",
"client = Client(cluster) # with distributed cluster"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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"<table style=\"border: 2px solid white;\">\n",
"<tr>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Client</h3>\n",
"<ul style=\"text-align: left; list-style: none; margin: 0; padding: 0;\">\n",
" <li><b>Scheduler: </b>tcp://10.148.0.6:42635</li>\n",
" <li><b>Dashboard: </b><a href='http://10.148.0.6:8787/status' target='_blank'>http://10.148.0.6:8787/status</a>\n",
"</ul>\n",
"</td>\n",
"<td style=\"vertical-align: top; border: 0px solid white\">\n",
"<h3 style=\"text-align: left;\">Cluster</h3>\n",
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"client"
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{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"root_path = '/home/datawork-lops-osi/equinox/jetn/old/'\n",
"#root_path = '/home1/datawork/slgentil/'#jet_cfg1_wp75_4km_1500a2000j_floats_lev50\n",
"run = 'jet_cfg1_wp75_4km_1500a2000j_itide/'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Standard model output : Statistics"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Analysing directory /home/datawork-lops-osi/equinox/jetn/old/jet_cfg1_wp75_4km_1500a2000j_itide/\n",
"Found 5 segments\n",
"Search for parameters in croco.in :\n",
"Parameters detected in output.mpi :\n",
"Opening datasets: grid / surf\n",
"Grid size: (L ,M, N) = (258, 722, 50)\n"
]
}
],
"source": [
"gparams = {'f0': 1.0313e-4, 'beta': 1.6186e-11}\n",
"\n",
"# AP here:\n",
"r = pp.Run(root_path+run, outputs=['surf'], tdir_max=5, verbose=2)\n",
"r['surf'] = r['surf'].assign_coords(time=r['surf'].time_instant.chunk({'time':None}))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<xarray.Dataset>\n",
"Dimensions: (time: 2000, x_rho: 52, x_u: 52, y_rho: 145, y_v: 145)\n",
"Coordinates:\n",
" * y_v (y_v) float32 0.0 20000.0 40000.0 ... 2860000.0 2880000.0\n",
" f_rho (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" s_rho_slice0 float32 -0.01\n",
" y_u (y_rho) float32 dask.array<chunksize=(145,), meta=np.ndarray>\n",
" * y_rho (y_rho) float32 -2000.0 18000.0 ... 2858000.0 2878000.0\n",
" f (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" * x_rho (x_rho) float32 -2000.0 18000.0 38000.0 ... 998000.0 1018000.0\n",
" * time (time) float64 dask.array<chunksize=(200,), meta=np.ndarray>\n",
" * x_u (x_u) float32 0.0 20000.0 40000.0 ... 1000000.0 1020000.0\n",
" time_counter (time) float64 1.5e+03 1.5e+03 1.501e+03 ... 2e+03 2e+03\n",
" x_v (x_rho) float32 dask.array<chunksize=(52,), meta=np.ndarray>\n",
"Data variables:\n",
" u (time, y_rho, x_u) float32 dask.array<chunksize=(200, 145, 52), meta=np.ndarray>\n",
" v (time, y_v, x_rho) float32 dask.array<chunksize=(200, 145, 52), meta=np.ndarray>\n",
"Attributes:\n",
" name: surf\n",
" description: Created by xios\n",
" title: Created by xios\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Feb-03 10:28:55 GMT\n",
" uuid: 979d973b-3250-4199-96c2-7dff8e4f5076\n",
"dataset size: 0 GB\n"
]
}
],
"source": [
"V = ['u', 'v']\n",
"dij = 5\n",
"dti = 36\n",
"ds = (r['surf'][V].isel({'x_rho': slice(0,None,dij), 'x_u': slice(0,None,dij),\n",
" 'y_rho': slice(0,None,dij), 'y_v': slice(0,None,dij),\n",
" 'time': slice(0,None,dti)})\n",
" ) #.squeeze() # AP here: pourquoi squeeze ?\n",
"print(ds)\n",
"print('dataset size: %.0f GB' %(ds.nbytes/1e9))"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
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"</style><div class='xr-wrap'><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-e96661a6-dbe2-4c1b-bd42-2af770bf9935' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e96661a6-dbe2-4c1b-bd42-2af770bf9935' class='xr-section-summary' title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 2000</li><li><span class='xr-has-index'>x_rho</span>: 52</li><li><span class='xr-has-index'>y_rho</span>: 145</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c0ec2bfd-2d05-4185-b505-4f79e690feaa' class='xr-section-summary-in' type='checkbox' checked><label for='section-c0ec2bfd-2d05-4185-b505-4f79e690feaa' class='xr-section-summary' >Coordinates: <span>(7)</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>f_rho</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.96396e-05 ... 0.00012629559</div><input id='attrs-80c0d937-ab7c-467f-ba68-02927d7cc045' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-80c0d937-ab7c-467f-ba68-02927d7cc045' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c4ca478-ab76-46f0-88f4-fdc2a9645655' class='xr-var-data-in' type='checkbox'><label for='data-0c4ca478-ab76-46f0-88f4-fdc2a9645655' 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><pre class='xr-var-data'>array([7.96395980e-05, 7.99635964e-05, 8.02876020e-05, 8.06116004e-05,\n",
" 8.09355988e-05, 8.12595972e-05, 8.15835956e-05, 8.19076013e-05,\n",
" 8.22315997e-05, 8.25555981e-05, 8.28795964e-05, 8.32036021e-05,\n",
" 8.35276005e-05, 8.38515989e-05, 8.41755973e-05, 8.44995957e-05,\n",
" 8.48236014e-05, 8.51475997e-05, 8.54715981e-05, 8.57955965e-05,\n",
" 8.61196022e-05, 8.64436006e-05, 8.67675990e-05, 8.70915974e-05,\n",
" 8.74155958e-05, 8.77396014e-05, 8.80635998e-05, 8.83875982e-05,\n",
" 8.87115966e-05, 8.90356023e-05, 8.93596007e-05, 8.96835991e-05,\n",
" 9.00075975e-05, 9.03315959e-05, 9.06556015e-05, 9.09795999e-05,\n",
" 9.13035983e-05, 9.16275967e-05, 9.19516024e-05, 9.22756008e-05,\n",
" 9.25995992e-05, 9.29235975e-05, 9.32475959e-05, 9.35716016e-05,\n",
" 9.38956000e-05, 9.42195984e-05, 9.45435968e-05, 9.48676025e-05,\n",
" 9.51916008e-05, 9.55155992e-05, 9.58395976e-05, 9.61635960e-05,\n",
" 9.64876017e-05, 9.68116001e-05, 9.71355985e-05, 9.74595969e-05,\n",
" 9.77835953e-05, 9.81076009e-05, 9.84315993e-05, 9.87555977e-05,\n",
" 9.90795961e-05, 9.94036018e-05, 9.97276002e-05, 1.00051599e-04,\n",
" 1.00375597e-04, 1.00699595e-04, 1.01023601e-04, 1.01347599e-04,\n",
" 1.01671598e-04, 1.01995596e-04, 1.02319602e-04, 1.02643600e-04,\n",
" 1.02967599e-04, 1.03291597e-04, 1.03615595e-04, 1.03939601e-04,\n",
" 1.04263599e-04, 1.04587598e-04, 1.04911596e-04, 1.05235602e-04,\n",
" 1.05559600e-04, 1.05883599e-04, 1.06207597e-04, 1.06531596e-04,\n",
" 1.06855601e-04, 1.07179600e-04, 1.07503598e-04, 1.07827596e-04,\n",
" 1.08151595e-04, 1.08475600e-04, 1.08799599e-04, 1.09123597e-04,\n",
" 1.09447596e-04, 1.09771601e-04, 1.10095600e-04, 1.10419598e-04,\n",
" 1.10743596e-04, 1.11067595e-04, 1.11391601e-04, 1.11715599e-04,\n",
" 1.12039597e-04, 1.12363596e-04, 1.12687601e-04, 1.13011600e-04,\n",
" 1.13335598e-04, 1.13659597e-04, 1.13983595e-04, 1.14307601e-04,\n",
" 1.14631599e-04, 1.14955597e-04, 1.15279596e-04, 1.15603601e-04,\n",
" 1.15927600e-04, 1.16251598e-04, 1.16575597e-04, 1.16899595e-04,\n",
" 1.17223601e-04, 1.17547599e-04, 1.17871597e-04, 1.18195596e-04,\n",
" 1.18519602e-04, 1.18843600e-04, 1.19167598e-04, 1.19491597e-04,\n",
" 1.19815595e-04, 1.20139601e-04, 1.20463599e-04, 1.20787598e-04,\n",
" 1.21111596e-04, 1.21435602e-04, 1.21759600e-04, 1.22083598e-04,\n",
" 1.22407597e-04, 1.22731595e-04, 1.23055594e-04, 1.23379592e-04,\n",
" 1.23703590e-04, 1.24027603e-04, 1.24351602e-04, 1.24675600e-04,\n",
" 1.24999598e-04, 1.25323597e-04, 1.25647595e-04, 1.25971594e-04,\n",
" 1.26295592e-04], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho_slice0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01</div><input id='attrs-4ea10b83-543a-41ea-8fe9-c9ff3722389e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ea10b83-543a-41ea-8fe9-c9ff3722389e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-313b6a40-40a0-49a2-8f9d-c96a015d1a94' class='xr-var-data-in' type='checkbox'><label for='data-313b6a40-40a0-49a2-8f9d-c96a015d1a94' 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>name :</span></dt><dd>s_rho_slice0</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>s_rho_slice0</dd><dt><span>long_name :</span></dt><dd>s_rho_slice0</dd></dl></div><pre class='xr-var-data'>array(-0.01, dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_rho</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 20000.0 ... 2860000.0 2880000.0</div><input id='attrs-496d4373-bdb3-40ef-a4e6-b0cc33611c55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-496d4373-bdb3-40ef-a4e6-b0cc33611c55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d14f51-9f83-4baa-84e2-f70933165215' class='xr-var-data-in' type='checkbox'><label for='data-b0d14f51-9f83-4baa-84e2-f70933165215' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>y_v</dd><dt><span>long_name :</span></dt><dd>y_v</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><pre class='xr-var-data'>array([ 0., 20000., 40000., 60000., 80000., 100000., 120000.,\n",
" 140000., 160000., 180000., 200000., 220000., 240000., 260000.,\n",
" 280000., 300000., 320000., 340000., 360000., 380000., 400000.,\n",
" 420000., 440000., 460000., 480000., 500000., 520000., 540000.,\n",
" 560000., 580000., 600000., 620000., 640000., 660000., 680000.,\n",
" 700000., 720000., 740000., 760000., 780000., 800000., 820000.,\n",
" 840000., 860000., 880000., 900000., 920000., 940000., 960000.,\n",
" 980000., 1000000., 1020000., 1040000., 1060000., 1080000., 1100000.,\n",
" 1120000., 1140000., 1160000., 1180000., 1200000., 1220000., 1240000.,\n",
" 1260000., 1280000., 1300000., 1320000., 1340000., 1360000., 1380000.,\n",
" 1400000., 1420000., 1440000., 1460000., 1480000., 1500000., 1520000.,\n",
" 1540000., 1560000., 1580000., 1600000., 1620000., 1640000., 1660000.,\n",
" 1680000., 1700000., 1720000., 1740000., 1760000., 1780000., 1800000.,\n",
" 1820000., 1840000., 1860000., 1880000., 1900000., 1920000., 1940000.,\n",
" 1960000., 1980000., 2000000., 2020000., 2040000., 2060000., 2080000.,\n",
" 2100000., 2120000., 2140000., 2160000., 2180000., 2200000., 2220000.,\n",
" 2240000., 2260000., 2280000., 2300000., 2320000., 2340000., 2360000.,\n",
" 2380000., 2400000., 2420000., 2440000., 2460000., 2480000., 2500000.,\n",
" 2520000., 2540000., 2560000., 2580000., 2600000., 2620000., 2640000.,\n",
" 2660000., 2680000., 2700000., 2720000., 2740000., 2760000., 2780000.,\n",
" 2800000., 2820000., 2840000., 2860000., 2880000.], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.96396e-05 ... 0.00012629559</div><input id='attrs-34a781fa-56e6-4403-ba44-f0b8e22e8de1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34a781fa-56e6-4403-ba44-f0b8e22e8de1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6d8cc498-6747-446e-8092-a2ffde53f72e' class='xr-var-data-in' type='checkbox'><label for='data-6d8cc498-6747-446e-8092-a2ffde53f72e' 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><pre class='xr-var-data'>array([7.96395980e-05, 7.99635964e-05, 8.02876020e-05, 8.06116004e-05,\n",
" 8.09355988e-05, 8.12595972e-05, 8.15835956e-05, 8.19076013e-05,\n",
" 8.22315997e-05, 8.25555981e-05, 8.28795964e-05, 8.32036021e-05,\n",
" 8.35276005e-05, 8.38515989e-05, 8.41755973e-05, 8.44995957e-05,\n",
" 8.48236014e-05, 8.51475997e-05, 8.54715981e-05, 8.57955965e-05,\n",
" 8.61196022e-05, 8.64436006e-05, 8.67675990e-05, 8.70915974e-05,\n",
" 8.74155958e-05, 8.77396014e-05, 8.80635998e-05, 8.83875982e-05,\n",
" 8.87115966e-05, 8.90356023e-05, 8.93596007e-05, 8.96835991e-05,\n",
" 9.00075975e-05, 9.03315959e-05, 9.06556015e-05, 9.09795999e-05,\n",
" 9.13035983e-05, 9.16275967e-05, 9.19516024e-05, 9.22756008e-05,\n",
" 9.25995992e-05, 9.29235975e-05, 9.32475959e-05, 9.35716016e-05,\n",
" 9.38956000e-05, 9.42195984e-05, 9.45435968e-05, 9.48676025e-05,\n",
" 9.51916008e-05, 9.55155992e-05, 9.58395976e-05, 9.61635960e-05,\n",
" 9.64876017e-05, 9.68116001e-05, 9.71355985e-05, 9.74595969e-05,\n",
" 9.77835953e-05, 9.81076009e-05, 9.84315993e-05, 9.87555977e-05,\n",
" 9.90795961e-05, 9.94036018e-05, 9.97276002e-05, 1.00051599e-04,\n",
" 1.00375597e-04, 1.00699595e-04, 1.01023601e-04, 1.01347599e-04,\n",
" 1.01671598e-04, 1.01995596e-04, 1.02319602e-04, 1.02643600e-04,\n",
" 1.02967599e-04, 1.03291597e-04, 1.03615595e-04, 1.03939601e-04,\n",
" 1.04263599e-04, 1.04587598e-04, 1.04911596e-04, 1.05235602e-04,\n",
" 1.05559600e-04, 1.05883599e-04, 1.06207597e-04, 1.06531596e-04,\n",
" 1.06855601e-04, 1.07179600e-04, 1.07503598e-04, 1.07827596e-04,\n",
" 1.08151595e-04, 1.08475600e-04, 1.08799599e-04, 1.09123597e-04,\n",
" 1.09447596e-04, 1.09771601e-04, 1.10095600e-04, 1.10419598e-04,\n",
" 1.10743596e-04, 1.11067595e-04, 1.11391601e-04, 1.11715599e-04,\n",
" 1.12039597e-04, 1.12363596e-04, 1.12687601e-04, 1.13011600e-04,\n",
" 1.13335598e-04, 1.13659597e-04, 1.13983595e-04, 1.14307601e-04,\n",
" 1.14631599e-04, 1.14955597e-04, 1.15279596e-04, 1.15603601e-04,\n",
" 1.15927600e-04, 1.16251598e-04, 1.16575597e-04, 1.16899595e-04,\n",
" 1.17223601e-04, 1.17547599e-04, 1.17871597e-04, 1.18195596e-04,\n",
" 1.18519602e-04, 1.18843600e-04, 1.19167598e-04, 1.19491597e-04,\n",
" 1.19815595e-04, 1.20139601e-04, 1.20463599e-04, 1.20787598e-04,\n",
" 1.21111596e-04, 1.21435602e-04, 1.21759600e-04, 1.22083598e-04,\n",
" 1.22407597e-04, 1.22731595e-04, 1.23055594e-04, 1.23379592e-04,\n",
" 1.23703590e-04, 1.24027603e-04, 1.24351602e-04, 1.24675600e-04,\n",
" 1.24999598e-04, 1.25323597e-04, 1.25647595e-04, 1.25971594e-04,\n",
" 1.26295592e-04], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_rho</span></div><div class='xr-var-dims'>(x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 20000.0 ... 1000000.0 1020000.0</div><input id='attrs-193dc7eb-b1aa-461e-ab32-9da36cd50f65' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-193dc7eb-b1aa-461e-ab32-9da36cd50f65' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-03a9286d-6145-4bed-96ae-c31c4b166240' class='xr-var-data-in' type='checkbox'><label for='data-03a9286d-6145-4bed-96ae-c31c4b166240' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>x_u</dd><dt><span>long_name :</span></dt><dd>x_u</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><pre class='xr-var-data'>array([ 0., 20000., 40000., 60000., 80000., 100000., 120000.,\n",
" 140000., 160000., 180000., 200000., 220000., 240000., 260000.,\n",
" 280000., 300000., 320000., 340000., 360000., 380000., 400000.,\n",
" 420000., 440000., 460000., 480000., 500000., 520000., 540000.,\n",
" 560000., 580000., 600000., 620000., 640000., 660000., 680000.,\n",
" 700000., 720000., 740000., 760000., 780000., 800000., 820000.,\n",
" 840000., 860000., 880000., 900000., 920000., 940000., 960000.,\n",
" 980000., 1000000., 1020000.], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5e+03 1.5e+03 ... 2e+03 2e+03</div><input id='attrs-4b14a031-7cce-471c-81cc-52286f549da8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b14a031-7cce-471c-81cc-52286f549da8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4cc86f4-4851-477c-9826-72b75f9386bb' class='xr-var-data-in' type='checkbox'><label for='data-b4cc86f4-4851-477c-9826-72b75f9386bb' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>time_instant</dd><dt><span>long_name :</span></dt><dd>time_instant</dd></dl></div><pre class='xr-var-data'>array([1500.006944, 1500.256944, 1500.506944, ..., 1999.256944, 1999.506944,\n",
" 1999.756944])</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>time_counter</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5e+03 1.5e+03 ... 2e+03 2e+03</div><input id='attrs-b0a34d8c-794f-4899-a823-2adf51832576' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0a34d8c-794f-4899-a823-2adf51832576' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67ae75f7-4007-4c47-97e1-991348dd83f7' class='xr-var-data-in' type='checkbox'><label for='data-67ae75f7-4007-4c47-97e1-991348dd83f7' 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>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>time_counter</dd><dt><span>long_name :</span></dt><dd>time_counter</dd></dl></div><pre class='xr-var-data'>array([1500.006944, 1500.256944, 1500.506944, ..., 1999.256944, 1999.506944,\n",
" 1999.756944])</pre></li></ul></div></li><li class='xr-section-item'><input id='section-87d53a0d-bd01-4799-a7cb-e3f760f5b15d' class='xr-section-summary-in' type='checkbox' checked><label for='section-87d53a0d-bd01-4799-a7cb-e3f760f5b15d' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 145, 52), meta=np.ndarray&gt;</div><input id='attrs-6d391f77-c3de-414d-bc00-9b9c55b90544' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d391f77-c3de-414d-bc00-9b9c55b90544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-61f6c9fa-faf5-48be-8316-0edbd35b74a7' class='xr-var-data-in' type='checkbox'><label for='data-61f6c9fa-faf5-48be-8316-0edbd35b74a7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>u-momentum component</dd><dt><span>units :</span></dt><dd>meter second-1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>interval_operation :</span></dt><dd>600 s</dd><dt><span>interval_write :</span></dt><dd>600 s</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 60.32 MB </td> <td> 6.03 MB </td></tr>\n",
" <tr><th> Shape </th><td> (2000, 145, 52) </td> <td> (200, 145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 10 Tasks </td><td> 10 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"161\" height=\"157\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(200, 145, 52), meta=np.ndarray&gt;</div><input id='attrs-d31bfdb4-13ca-4fe7-9cdf-4a34e019375e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d31bfdb4-13ca-4fe7-9cdf-4a34e019375e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c581db4d-a7fb-4ad8-9a58-25c09c1221e2' class='xr-var-data-in' type='checkbox'><label for='data-c581db4d-a7fb-4ad8-9a58-25c09c1221e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>long_name :</span></dt><dd>v-momentum component</dd><dt><span>units :</span></dt><dd>meter second-1</dd><dt><span>online_operation :</span></dt><dd>instant</dd><dt><span>interval_operation :</span></dt><dd>600 s</dd><dt><span>interval_write :</span></dt><dd>600 s</dd><dt><span>cell_methods :</span></dt><dd>time: point</dd></dl></div><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 60.32 MB </td> <td> 6.03 MB </td></tr>\n",
" <tr><th> Shape </th><td> (2000, 145, 52) </td> <td> (200, 145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 10 Tasks </td><td> 10 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
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"\n",
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"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-ff54d87b-d68f-4801-8b82-9f022ec6a406' class='xr-section-summary-in' type='checkbox' checked><label for='section-ff54d87b-d68f-4801-8b82-9f022ec6a406' 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>name :</span></dt><dd>surf</dd><dt><span>description :</span></dt><dd>Created by xios</dd><dt><span>title :</span></dt><dd>Created by xios</dd><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>timeStamp :</span></dt><dd>2020-Feb-03 10:28:55 GMT</dd><dt><span>uuid :</span></dt><dd>979d973b-3250-4199-96c2-7dff8e4f5076</dd></dl></div></li></ul></div></div>"
],
"text/plain": [
"<xarray.Dataset>\n",
"Dimensions: (time: 2000, x_rho: 52, y_rho: 145)\n",
"Coordinates:\n",
" f_rho (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" s_rho_slice0 float32 -0.01\n",
" * y_rho (y_rho) float32 0.0 20000.0 40000.0 ... 2860000.0 2880000.0\n",
" f (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" * x_rho (x_rho) float32 0.0 20000.0 40000.0 ... 1000000.0 1020000.0\n",
" * time (time) float64 1.5e+03 1.5e+03 1.501e+03 ... 2e+03 2e+03\n",
" time_counter (time) float64 1.5e+03 1.5e+03 1.501e+03 ... 2e+03 2e+03\n",
"Data variables:\n",
" u (time, y_rho, x_rho) float32 dask.array<chunksize=(200, 145, 52), meta=np.ndarray>\n",
" v (time, y_rho, x_rho) float32 dask.array<chunksize=(200, 145, 52), meta=np.ndarray>\n",
"Attributes:\n",
" name: surf\n",
" description: Created by xios\n",
" title: Created by xios\n",
" Conventions: CF-1.6\n",
" timeStamp: 2020-Feb-03 10:28:55 GMT\n",
" uuid: 979d973b-3250-4199-96c2-7dff8e4f5076"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ds = ds.persist()\n",
"ds.coords['y_rho'] = (ds['y_rho']+2000)\n",
"ds.coords['x_rho'] = (ds['x_rho']+2000)\n",
"ds['u'] = ds.u.rename({'x_u':'x_rho'})\n",
"ds['v'] = ds.v.rename({'y_v':'y_rho'})\n",
"ds = ds.drop(['x_u','y_v','x_v','y_u'])\n",
"ds"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2aaaedaad510>"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"ds.u.isel(time=slice(0,4)).mean('time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2aaaedb73410>"
]
},
"execution_count": 17,
"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": [
"ds.u.isel(time=slice(0,40)).mean('time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2aaaec7b4e10>"
]
},
"execution_count": 18,
"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": [
"ds.u.isel(time=slice(0,400)).mean('time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.collections.QuadMesh at 0x2aaaedd43050>"
]
},
"execution_count": 19,
"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": [
"ds.u.isel(time=slice(0,2000)).mean('time').plot()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
"<defs>\n",
"<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
"<title>Show/Hide data repr</title>\n",
"<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
"<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
"<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
"</symbol>\n",
"<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
"<title>Show/Hide attributes</title>\n",
"<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
"<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
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"</symbol>\n",
"</defs>\n",
"</svg>\n",
"<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
" *\n",
" */\n",
"\n",
":root {\n",
" --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
" --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
" --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
" --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
" --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
" --xr-background-color: var(--jp-layout-color0, white);\n",
" --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
" --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
"}\n",
"\n",
".xr-wrap {\n",
" min-width: 300px;\n",
" max-width: 700px;\n",
"}\n",
"\n",
".xr-header {\n",
" padding-top: 6px;\n",
" padding-bottom: 6px;\n",
" margin-bottom: 4px;\n",
" border-bottom: solid 1px var(--xr-border-color);\n",
"}\n",
"\n",
".xr-header > div,\n",
".xr-header > ul {\n",
" display: inline;\n",
" margin-top: 0;\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-obj-type,\n",
".xr-array-name {\n",
" margin-left: 2px;\n",
" margin-right: 10px;\n",
"}\n",
"\n",
".xr-obj-type {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-sections {\n",
" padding-left: 0 !important;\n",
" display: grid;\n",
" grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
"}\n",
"\n",
".xr-section-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-section-item input {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-item input + label {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label {\n",
" cursor: pointer;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-item input:enabled + label:hover {\n",
" color: var(--xr-font-color0);\n",
"}\n",
"\n",
".xr-section-summary {\n",
" grid-column: 1;\n",
" color: var(--xr-font-color2);\n",
" font-weight: 500;\n",
"}\n",
"\n",
".xr-section-summary > span {\n",
" display: inline-block;\n",
" padding-left: 0.5em;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label {\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-section-summary-in + label:before {\n",
" display: inline-block;\n",
" content: '►';\n",
" font-size: 11px;\n",
" width: 15px;\n",
" text-align: center;\n",
"}\n",
"\n",
".xr-section-summary-in:disabled + label:before {\n",
" color: var(--xr-disabled-color);\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label:before {\n",
" content: '▼';\n",
"}\n",
"\n",
".xr-section-summary-in:checked + label > span {\n",
" display: none;\n",
"}\n",
"\n",
".xr-section-summary,\n",
".xr-section-inline-details {\n",
" padding-top: 4px;\n",
" padding-bottom: 4px;\n",
"}\n",
"\n",
".xr-section-inline-details {\n",
" grid-column: 2 / -1;\n",
"}\n",
"\n",
".xr-section-details {\n",
" display: none;\n",
" grid-column: 1 / -1;\n",
" margin-bottom: 5px;\n",
"}\n",
"\n",
".xr-section-summary-in:checked ~ .xr-section-details {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-array-wrap {\n",
" grid-column: 1 / -1;\n",
" display: grid;\n",
" grid-template-columns: 20px auto;\n",
"}\n",
"\n",
".xr-array-wrap > label {\n",
" grid-column: 1;\n",
" vertical-align: top;\n",
"}\n",
"\n",
".xr-preview {\n",
" color: var(--xr-font-color3);\n",
"}\n",
"\n",
".xr-array-preview,\n",
".xr-array-data {\n",
" padding: 0 5px !important;\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-array-data,\n",
".xr-array-in:checked ~ .xr-array-preview {\n",
" display: none;\n",
"}\n",
"\n",
".xr-array-in:checked ~ .xr-array-data,\n",
".xr-array-preview {\n",
" display: inline-block;\n",
"}\n",
"\n",
".xr-dim-list {\n",
" display: inline-block !important;\n",
" list-style: none;\n",
" padding: 0 !important;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list li {\n",
" display: inline-block;\n",
" padding: 0;\n",
" margin: 0;\n",
"}\n",
"\n",
".xr-dim-list:before {\n",
" content: '(';\n",
"}\n",
"\n",
".xr-dim-list:after {\n",
" content: ')';\n",
"}\n",
"\n",
".xr-dim-list li:not(:last-child):after {\n",
" content: ',';\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-has-index {\n",
" font-weight: bold;\n",
"}\n",
"\n",
".xr-var-list,\n",
".xr-var-item {\n",
" display: contents;\n",
"}\n",
"\n",
".xr-var-item > div,\n",
".xr-var-item label,\n",
".xr-var-item > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-even);\n",
" margin-bottom: 0;\n",
"}\n",
"\n",
".xr-var-item > .xr-var-name:hover span {\n",
" padding-right: 5px;\n",
"}\n",
"\n",
".xr-var-list > li:nth-child(odd) > div,\n",
".xr-var-list > li:nth-child(odd) > label,\n",
".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
" background-color: var(--xr-background-color-row-odd);\n",
"}\n",
"\n",
".xr-var-name {\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-var-dims {\n",
" grid-column: 2;\n",
"}\n",
"\n",
".xr-var-dtype {\n",
" grid-column: 3;\n",
" text-align: right;\n",
" color: var(--xr-font-color2);\n",
"}\n",
"\n",
".xr-var-preview {\n",
" grid-column: 4;\n",
"}\n",
"\n",
".xr-var-name,\n",
".xr-var-dims,\n",
".xr-var-dtype,\n",
".xr-preview,\n",
".xr-attrs dt {\n",
" white-space: nowrap;\n",
" overflow: hidden;\n",
" text-overflow: ellipsis;\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-var-name:hover,\n",
".xr-var-dims:hover,\n",
".xr-var-dtype:hover,\n",
".xr-attrs dt:hover {\n",
" overflow: visible;\n",
" width: auto;\n",
" z-index: 1;\n",
"}\n",
"\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" display: none;\n",
" background-color: var(--xr-background-color) !important;\n",
" padding-bottom: 5px !important;\n",
"}\n",
"\n",
".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
".xr-var-data-in:checked ~ .xr-var-data {\n",
" display: block;\n",
"}\n",
"\n",
".xr-var-data > table {\n",
" float: right;\n",
"}\n",
"\n",
".xr-var-name span,\n",
".xr-var-data,\n",
".xr-attrs {\n",
" padding-left: 25px !important;\n",
"}\n",
"\n",
".xr-attrs,\n",
".xr-var-attrs,\n",
".xr-var-data {\n",
" grid-column: 1 / -1;\n",
"}\n",
"\n",
"dl.xr-attrs {\n",
" padding: 0;\n",
" margin: 0;\n",
" display: grid;\n",
" grid-template-columns: 125px auto;\n",
"}\n",
"\n",
".xr-attrs dt, dd {\n",
" padding: 0;\n",
" margin: 0;\n",
" float: left;\n",
" padding-right: 10px;\n",
" width: auto;\n",
"}\n",
"\n",
".xr-attrs dt {\n",
" font-weight: normal;\n",
" grid-column: 1;\n",
"}\n",
"\n",
".xr-attrs dt:hover span {\n",
" display: inline-block;\n",
" background: var(--xr-background-color);\n",
" padding-right: 10px;\n",
"}\n",
"\n",
".xr-attrs dd {\n",
" grid-column: 2;\n",
" white-space: pre-wrap;\n",
" word-break: break-all;\n",
"}\n",
"\n",
".xr-icon-database,\n",
".xr-icon-file-text2 {\n",
" display: inline-block;\n",
" vertical-align: middle;\n",
" width: 1em;\n",
" height: 1.5em !important;\n",
" stroke-width: 0;\n",
" stroke: currentColor;\n",
" fill: currentColor;\n",
"}\n",
"</style><div class='xr-wrap'><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-40d7013b-c931-44e2-99d7-2804eecf51b6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-40d7013b-c931-44e2-99d7-2804eecf51b6' 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_rho</span>: 52</li><li><span class='xr-has-index'>y_rho</span>: 145</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-380aca05-a270-40ea-8689-81c0b4dac82d' class='xr-section-summary-in' type='checkbox' checked><label for='section-380aca05-a270-40ea-8689-81c0b4dac82d' class='xr-section-summary' >Coordinates: <span>(5)</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>f_rho</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.96396e-05 ... 0.00012629559</div><input id='attrs-f4751865-e168-4217-8436-251758d66f28' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4751865-e168-4217-8436-251758d66f28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2569de61-5e83-4411-944d-01f0632fc86f' class='xr-var-data-in' type='checkbox'><label for='data-2569de61-5e83-4411-944d-01f0632fc86f' 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><pre class='xr-var-data'>array([7.96395980e-05, 7.99635964e-05, 8.02876020e-05, 8.06116004e-05,\n",
" 8.09355988e-05, 8.12595972e-05, 8.15835956e-05, 8.19076013e-05,\n",
" 8.22315997e-05, 8.25555981e-05, 8.28795964e-05, 8.32036021e-05,\n",
" 8.35276005e-05, 8.38515989e-05, 8.41755973e-05, 8.44995957e-05,\n",
" 8.48236014e-05, 8.51475997e-05, 8.54715981e-05, 8.57955965e-05,\n",
" 8.61196022e-05, 8.64436006e-05, 8.67675990e-05, 8.70915974e-05,\n",
" 8.74155958e-05, 8.77396014e-05, 8.80635998e-05, 8.83875982e-05,\n",
" 8.87115966e-05, 8.90356023e-05, 8.93596007e-05, 8.96835991e-05,\n",
" 9.00075975e-05, 9.03315959e-05, 9.06556015e-05, 9.09795999e-05,\n",
" 9.13035983e-05, 9.16275967e-05, 9.19516024e-05, 9.22756008e-05,\n",
" 9.25995992e-05, 9.29235975e-05, 9.32475959e-05, 9.35716016e-05,\n",
" 9.38956000e-05, 9.42195984e-05, 9.45435968e-05, 9.48676025e-05,\n",
" 9.51916008e-05, 9.55155992e-05, 9.58395976e-05, 9.61635960e-05,\n",
" 9.64876017e-05, 9.68116001e-05, 9.71355985e-05, 9.74595969e-05,\n",
" 9.77835953e-05, 9.81076009e-05, 9.84315993e-05, 9.87555977e-05,\n",
" 9.90795961e-05, 9.94036018e-05, 9.97276002e-05, 1.00051599e-04,\n",
" 1.00375597e-04, 1.00699595e-04, 1.01023601e-04, 1.01347599e-04,\n",
" 1.01671598e-04, 1.01995596e-04, 1.02319602e-04, 1.02643600e-04,\n",
" 1.02967599e-04, 1.03291597e-04, 1.03615595e-04, 1.03939601e-04,\n",
" 1.04263599e-04, 1.04587598e-04, 1.04911596e-04, 1.05235602e-04,\n",
" 1.05559600e-04, 1.05883599e-04, 1.06207597e-04, 1.06531596e-04,\n",
" 1.06855601e-04, 1.07179600e-04, 1.07503598e-04, 1.07827596e-04,\n",
" 1.08151595e-04, 1.08475600e-04, 1.08799599e-04, 1.09123597e-04,\n",
" 1.09447596e-04, 1.09771601e-04, 1.10095600e-04, 1.10419598e-04,\n",
" 1.10743596e-04, 1.11067595e-04, 1.11391601e-04, 1.11715599e-04,\n",
" 1.12039597e-04, 1.12363596e-04, 1.12687601e-04, 1.13011600e-04,\n",
" 1.13335598e-04, 1.13659597e-04, 1.13983595e-04, 1.14307601e-04,\n",
" 1.14631599e-04, 1.14955597e-04, 1.15279596e-04, 1.15603601e-04,\n",
" 1.15927600e-04, 1.16251598e-04, 1.16575597e-04, 1.16899595e-04,\n",
" 1.17223601e-04, 1.17547599e-04, 1.17871597e-04, 1.18195596e-04,\n",
" 1.18519602e-04, 1.18843600e-04, 1.19167598e-04, 1.19491597e-04,\n",
" 1.19815595e-04, 1.20139601e-04, 1.20463599e-04, 1.20787598e-04,\n",
" 1.21111596e-04, 1.21435602e-04, 1.21759600e-04, 1.22083598e-04,\n",
" 1.22407597e-04, 1.22731595e-04, 1.23055594e-04, 1.23379592e-04,\n",
" 1.23703590e-04, 1.24027603e-04, 1.24351602e-04, 1.24675600e-04,\n",
" 1.24999598e-04, 1.25323597e-04, 1.25647595e-04, 1.25971594e-04,\n",
" 1.26295592e-04], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>s_rho_slice0</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01</div><input id='attrs-2e251376-7516-46b2-9540-9c64684a57b0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e251376-7516-46b2-9540-9c64684a57b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1911917-24c4-4cb1-99bd-3333b1236ede' class='xr-var-data-in' type='checkbox'><label for='data-c1911917-24c4-4cb1-99bd-3333b1236ede' 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>name :</span></dt><dd>s_rho_slice0</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>standard_name :</span></dt><dd>s_rho_slice0</dd><dt><span>long_name :</span></dt><dd>s_rho_slice0</dd></dl></div><pre class='xr-var-data'>array(-0.01, dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y_rho</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 20000.0 ... 2860000.0 2880000.0</div><input id='attrs-0464354f-3d06-4bb5-a2db-a09a1c2c17f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0464354f-3d06-4bb5-a2db-a09a1c2c17f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b567bda-c016-4627-8caa-b3de326893d2' class='xr-var-data-in' type='checkbox'><label for='data-9b567bda-c016-4627-8caa-b3de326893d2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>y_v</dd><dt><span>long_name :</span></dt><dd>y_v</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><pre class='xr-var-data'>array([ 0., 20000., 40000., 60000., 80000., 100000., 120000.,\n",
" 140000., 160000., 180000., 200000., 220000., 240000., 260000.,\n",
" 280000., 300000., 320000., 340000., 360000., 380000., 400000.,\n",
" 420000., 440000., 460000., 480000., 500000., 520000., 540000.,\n",
" 560000., 580000., 600000., 620000., 640000., 660000., 680000.,\n",
" 700000., 720000., 740000., 760000., 780000., 800000., 820000.,\n",
" 840000., 860000., 880000., 900000., 920000., 940000., 960000.,\n",
" 980000., 1000000., 1020000., 1040000., 1060000., 1080000., 1100000.,\n",
" 1120000., 1140000., 1160000., 1180000., 1200000., 1220000., 1240000.,\n",
" 1260000., 1280000., 1300000., 1320000., 1340000., 1360000., 1380000.,\n",
" 1400000., 1420000., 1440000., 1460000., 1480000., 1500000., 1520000.,\n",
" 1540000., 1560000., 1580000., 1600000., 1620000., 1640000., 1660000.,\n",
" 1680000., 1700000., 1720000., 1740000., 1760000., 1780000., 1800000.,\n",
" 1820000., 1840000., 1860000., 1880000., 1900000., 1920000., 1940000.,\n",
" 1960000., 1980000., 2000000., 2020000., 2040000., 2060000., 2080000.,\n",
" 2100000., 2120000., 2140000., 2160000., 2180000., 2200000., 2220000.,\n",
" 2240000., 2260000., 2280000., 2300000., 2320000., 2340000., 2360000.,\n",
" 2380000., 2400000., 2420000., 2440000., 2460000., 2480000., 2500000.,\n",
" 2520000., 2540000., 2560000., 2580000., 2600000., 2620000., 2640000.,\n",
" 2660000., 2680000., 2700000., 2720000., 2740000., 2760000., 2780000.,\n",
" 2800000., 2820000., 2840000., 2860000., 2880000.], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span>f</span></div><div class='xr-var-dims'>(y_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.96396e-05 ... 0.00012629559</div><input id='attrs-4b1e87f9-b789-45cb-aec8-36eb616b725b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4b1e87f9-b789-45cb-aec8-36eb616b725b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ece1a6d5-2338-4ab1-a046-4878a46bc385' class='xr-var-data-in' type='checkbox'><label for='data-ece1a6d5-2338-4ab1-a046-4878a46bc385' 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><pre class='xr-var-data'>array([7.96395980e-05, 7.99635964e-05, 8.02876020e-05, 8.06116004e-05,\n",
" 8.09355988e-05, 8.12595972e-05, 8.15835956e-05, 8.19076013e-05,\n",
" 8.22315997e-05, 8.25555981e-05, 8.28795964e-05, 8.32036021e-05,\n",
" 8.35276005e-05, 8.38515989e-05, 8.41755973e-05, 8.44995957e-05,\n",
" 8.48236014e-05, 8.51475997e-05, 8.54715981e-05, 8.57955965e-05,\n",
" 8.61196022e-05, 8.64436006e-05, 8.67675990e-05, 8.70915974e-05,\n",
" 8.74155958e-05, 8.77396014e-05, 8.80635998e-05, 8.83875982e-05,\n",
" 8.87115966e-05, 8.90356023e-05, 8.93596007e-05, 8.96835991e-05,\n",
" 9.00075975e-05, 9.03315959e-05, 9.06556015e-05, 9.09795999e-05,\n",
" 9.13035983e-05, 9.16275967e-05, 9.19516024e-05, 9.22756008e-05,\n",
" 9.25995992e-05, 9.29235975e-05, 9.32475959e-05, 9.35716016e-05,\n",
" 9.38956000e-05, 9.42195984e-05, 9.45435968e-05, 9.48676025e-05,\n",
" 9.51916008e-05, 9.55155992e-05, 9.58395976e-05, 9.61635960e-05,\n",
" 9.64876017e-05, 9.68116001e-05, 9.71355985e-05, 9.74595969e-05,\n",
" 9.77835953e-05, 9.81076009e-05, 9.84315993e-05, 9.87555977e-05,\n",
" 9.90795961e-05, 9.94036018e-05, 9.97276002e-05, 1.00051599e-04,\n",
" 1.00375597e-04, 1.00699595e-04, 1.01023601e-04, 1.01347599e-04,\n",
" 1.01671598e-04, 1.01995596e-04, 1.02319602e-04, 1.02643600e-04,\n",
" 1.02967599e-04, 1.03291597e-04, 1.03615595e-04, 1.03939601e-04,\n",
" 1.04263599e-04, 1.04587598e-04, 1.04911596e-04, 1.05235602e-04,\n",
" 1.05559600e-04, 1.05883599e-04, 1.06207597e-04, 1.06531596e-04,\n",
" 1.06855601e-04, 1.07179600e-04, 1.07503598e-04, 1.07827596e-04,\n",
" 1.08151595e-04, 1.08475600e-04, 1.08799599e-04, 1.09123597e-04,\n",
" 1.09447596e-04, 1.09771601e-04, 1.10095600e-04, 1.10419598e-04,\n",
" 1.10743596e-04, 1.11067595e-04, 1.11391601e-04, 1.11715599e-04,\n",
" 1.12039597e-04, 1.12363596e-04, 1.12687601e-04, 1.13011600e-04,\n",
" 1.13335598e-04, 1.13659597e-04, 1.13983595e-04, 1.14307601e-04,\n",
" 1.14631599e-04, 1.14955597e-04, 1.15279596e-04, 1.15603601e-04,\n",
" 1.15927600e-04, 1.16251598e-04, 1.16575597e-04, 1.16899595e-04,\n",
" 1.17223601e-04, 1.17547599e-04, 1.17871597e-04, 1.18195596e-04,\n",
" 1.18519602e-04, 1.18843600e-04, 1.19167598e-04, 1.19491597e-04,\n",
" 1.19815595e-04, 1.20139601e-04, 1.20463599e-04, 1.20787598e-04,\n",
" 1.21111596e-04, 1.21435602e-04, 1.21759600e-04, 1.22083598e-04,\n",
" 1.22407597e-04, 1.22731595e-04, 1.23055594e-04, 1.23379592e-04,\n",
" 1.23703590e-04, 1.24027603e-04, 1.24351602e-04, 1.24675600e-04,\n",
" 1.24999598e-04, 1.25323597e-04, 1.25647595e-04, 1.25971594e-04,\n",
" 1.26295592e-04], dtype=float32)</pre></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x_rho</span></div><div class='xr-var-dims'>(x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 20000.0 ... 1000000.0 1020000.0</div><input id='attrs-c2fa78ca-58b2-4142-9123-7a3b0ddd136f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2fa78ca-58b2-4142-9123-7a3b0ddd136f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e22eaa28-9f84-4276-a7a2-d69d0d663136' class='xr-var-data-in' type='checkbox'><label for='data-e22eaa28-9f84-4276-a7a2-d69d0d663136' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>x_u</dd><dt><span>long_name :</span></dt><dd>x_u</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><pre class='xr-var-data'>array([ 0., 20000., 40000., 60000., 80000., 100000., 120000.,\n",
" 140000., 160000., 180000., 200000., 220000., 240000., 260000.,\n",
" 280000., 300000., 320000., 340000., 360000., 380000., 400000.,\n",
" 420000., 440000., 460000., 480000., 500000., 520000., 540000.,\n",
" 560000., 580000., 600000., 620000., 640000., 660000., 680000.,\n",
" 700000., 720000., 740000., 760000., 780000., 800000., 820000.,\n",
" 840000., 860000., 880000., 900000., 920000., 940000., 960000.,\n",
" 980000., 1000000., 1020000.], dtype=float32)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-bd469292-2300-41ac-a249-159939a5e653' class='xr-section-summary-in' type='checkbox' checked><label for='section-bd469292-2300-41ac-a249-159939a5e653' 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>u_tmean</span></div><div class='xr-var-dims'>(y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 52), meta=np.ndarray&gt;</div><input id='attrs-3d30664e-f89c-4b68-b9d6-55ad5b37e60f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3d30664e-f89c-4b68-b9d6-55ad5b37e60f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e351d1c-3acf-4476-8aaf-9794c1fc658c' class='xr-var-data-in' type='checkbox'><label for='data-7e351d1c-3acf-4476-8aaf-9794c1fc658c' 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><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 30.16 kB </td> <td> 30.16 kB </td></tr>\n",
" <tr><th> Shape </th><td> (145, 52) </td> <td> (145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 24 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"94\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.000000,0.000000 44.108955,0.000000 44.108955,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"22.054477\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
" <text x=\"64.108955\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,64.108955,60.000000)\">145</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>v_tmean</span></div><div class='xr-var-dims'>(y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 52), meta=np.ndarray&gt;</div><input id='attrs-34d41dd9-ce08-4a3f-9f39-8454adaf7d48' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-34d41dd9-ce08-4a3f-9f39-8454adaf7d48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-12e19f4c-1fda-4184-ba4d-b864353bd7bd' class='xr-var-data-in' type='checkbox'><label for='data-12e19f4c-1fda-4184-ba4d-b864353bd7bd' 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><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 30.16 kB </td> <td> 30.16 kB </td></tr>\n",
" <tr><th> Shape </th><td> (145, 52) </td> <td> (145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 24 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"94\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.000000,0.000000 44.108955,0.000000 44.108955,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"22.054477\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
" <text x=\"64.108955\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,64.108955,60.000000)\">145</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>u_tstd</span></div><div class='xr-var-dims'>(y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 52), meta=np.ndarray&gt;</div><input id='attrs-c6c50f29-d8de-4a9e-9549-176fa6ed7450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c6c50f29-d8de-4a9e-9549-176fa6ed7450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a23bb9a-eee7-450d-a71f-e191a3e2031d' class='xr-var-data-in' type='checkbox'><label for='data-4a23bb9a-eee7-450d-a71f-e191a3e2031d' 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><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 30.16 kB </td> <td> 30.16 kB </td></tr>\n",
" <tr><th> Shape </th><td> (145, 52) </td> <td> (145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 25 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"94\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.000000,0.000000 44.108955,0.000000 44.108955,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"22.054477\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
" <text x=\"64.108955\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,64.108955,60.000000)\">145</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>v_tstd</span></div><div class='xr-var-dims'>(y_rho, x_rho)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(145, 52), meta=np.ndarray&gt;</div><input id='attrs-023d092c-32b0-4f58-a693-9118d72f14c0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-023d092c-32b0-4f58-a693-9118d72f14c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2649cd26-0cd3-4e4b-8ad2-be2ed0808cd6' class='xr-var-data-in' type='checkbox'><label for='data-2649cd26-0cd3-4e4b-8ad2-be2ed0808cd6' 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><pre class='xr-var-data'><table>\n",
"<tr>\n",
"<td>\n",
"<table>\n",
" <thead>\n",
" <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr><th> Bytes </th><td> 30.16 kB </td> <td> 30.16 kB </td></tr>\n",
" <tr><th> Shape </th><td> (145, 52) </td> <td> (145, 52) </td></tr>\n",
" <tr><th> Count </th><td> 25 Tasks </td><td> 1 Chunks </td></tr>\n",
" <tr><th> Type </th><td> float32 </td><td> numpy.ndarray </td></tr>\n",
" </tbody>\n",
"</table>\n",
"</td>\n",
"<td>\n",
"<svg width=\"94\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
"\n",
" <!-- Horizontal lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"44\" y2=\"0\" style=\"stroke-width:2\" />\n",
" <line x1=\"0\" y1=\"120\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Vertical lines -->\n",
" <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
" <line x1=\"44\" y1=\"0\" x2=\"44\" y2=\"120\" style=\"stroke-width:2\" />\n",
"\n",
" <!-- Colored Rectangle -->\n",
" <polygon points=\"0.000000,0.000000 44.108955,0.000000 44.108955,120.000000 0.000000,120.000000\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
"\n",
" <!-- Text -->\n",
" <text x=\"22.054477\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >52</text>\n",
" <text x=\"64.108955\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,64.108955,60.000000)\">145</text>\n",
"</svg>\n",
"</td>\n",
"</tr>\n",
"</table></pre></li></ul></div></li><li class='xr-section-item'><input id='section-13d50dc5-50f4-477f-98db-9e3b9b53378d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-13d50dc5-50f4-477f-98db-9e3b9b53378d' 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_rho: 52, y_rho: 145)\n",
"Coordinates:\n",
" f_rho (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" s_rho_slice0 float32 -0.01\n",
" * y_rho (y_rho) float32 0.0 20000.0 40000.0 ... 2860000.0 2880000.0\n",
" f (y_rho) float32 7.96396e-05 7.99636e-05 ... 0.00012629559\n",
" * x_rho (x_rho) float32 0.0 20000.0 40000.0 ... 1000000.0 1020000.0\n",
"Data variables:\n",
" u_tmean (y_rho, x_rho) float32 dask.array<chunksize=(145, 52), meta=np.ndarray>\n",
" v_tmean (y_rho, x_rho) float32 dask.array<chunksize=(145, 52), meta=np.ndarray>\n",
" u_tstd (y_rho, x_rho) float32 dask.array<chunksize=(145, 52), meta=np.ndarray>\n",
" v_tstd (y_rho, x_rho) float32 dask.array<chunksize=(145, 52), meta=np.ndarray>"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"stats_surf = xr.merge([ds.mean('time').rename({v:v+'_tmean' for v in ds}), \n",
" ds.std('time').rename({v:v+'_tstd' for v in ds})\n",
" ])\n",
"stats_surf\n",
"\n",
"# AP: mean('time') mieux que mean(axis=0)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"cluster.close()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.6"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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