def test_opening_and_saving(name, catalog_url):
if name == "error":
# Open oceandataset
with pytest.raises(ValueError):
from_catalog(name, catalog_url)
else:
# Open oceandataset
od1 = from_catalog(name, catalog_url)
# Check dimensions
if name not in ["xarray", "HYCOM"]:
dimsList = ["X", "Y", "Xp1", "Yp1"]
assert set(dimsList).issubset(set(od1.dataset.dims))
# Check coordinates
if name == "LLC":
coordsList = ["XC", "YC", "XG", "YG"]
elif name == "HYCOM":
coordsList = ["XC", "YC"]
else:
coordsList = ["XC", "YC", "XG", "YG", "XU", "YU", "XV", "YV"]
assert set(coordsList).issubset(set(od1.dataset.coords))
# Check NaNs
assert all(
[not np.isnan(od1.dataset[coord].values).any() for coord in coordsList]
)
if name == "LLC":
assert type(od1.face_connections["face"]) == dict
assert set(["face"]).issubset(set(od1.dataset.dims))
# Check shift
if name == "xmitgcm_iters":
sizes = od1.dataset.sizes
assert sizes["time"] - sizes["time_midp"] == 1
assert all(
[
"time_midp" in od1.dataset[var].dims
for var in od1.dataset.data_vars
if "ave" in var
]
)
# Save to netcdf
filename = "tmp.nc"
od1.to_netcdf(filename)
# Reopen
if name == "LLC":
args = {"decode_times": False}
else:
args = {}
from_netcdf(filename, **args)
# Clean up
subprocess.call("rm -f " + filename, shell=True)
def test_save_load(od, compute):
path = "test_path"
od.to_netcdf(path=path + ".nc", compute=compute)
open_oceandataset.from_netcdf(path + ".nc")
os.remove(path + ".nc")
od.to_zarr(path=path, compute=compute)
open_oceandataset.from_zarr(path)
shutil.rmtree(path, ignore_errors=True)
def test_save_load(od, compute):
path = 'test_path'
od.to_netcdf(path=path+'.nc', compute=compute)
new_od = open_oceandataset.from_netcdf(path+'.nc')
os.remove(path+'.nc')
od.to_zarr(path=path, compute=compute)
new_od = open_oceandataset.from_zarr(path)
shutil.rmtree(path, ignore_errors=True)
def test_opening_and_saving(name, catalog_url):
if name == "error":
# Open oceandataset
with pytest.raises(ValueError):
from_catalog(name, catalog_url)
else:
# Open oceandataset
od1 = from_catalog(name, catalog_url)
# Check dimensions
if name != "xarray":
dimsList = ["X", "Y", "Xp1", "Yp1"]
assert set(dimsList).issubset(set(od1.dataset.dims))
# Check coordinates
coordsList = ["XC", "YC", "XG", "YG", "XU", "YU", "XV", "YV"]
assert set(coordsList).issubset(set(od1.dataset.coords))
# Check NaNs
assert all([
not np.isnan(od1.dataset[coord].values).any()
for coord in coordsList
])
# Check shift
if name == "xmitgcm_iters":
sizes = od1.dataset.sizes
assert sizes["time"] - sizes["time_midp"] == 1
assert all([
"time_midp" in od1.dataset[var].dims
for var in od1.dataset.data_vars if "ave" in var
])
# Save to netcdf
filename = "tmp.nc"
od1.to_netcdf(filename)
# Reopen
from_netcdf(filename)
# Clean up
subprocess.call("rm -f " + filename, shell=True)
def test_opening_and_saving(name, catalog_url):
if name == 'error':
# Open oceandataset
with pytest.raises(ValueError):
from_catalog(name, catalog_url)
else:
# Open oceandataset
od1 = from_catalog(name, catalog_url)
# Check dimensions
if name != 'xarray':
dimsList = ['X', 'Y', 'Xp1', 'Yp1']
assert set(dimsList).issubset(set(od1.dataset.dims))
# Check coordinates
coordsList = ['XC', 'YC', 'XG', 'YG', 'XU', 'YU', 'XV', 'YV']
assert set(coordsList).issubset(set(od1.dataset.coords))
# Check NaNs
assert all([
not np.isnan(od1.dataset[coord].values).any()
for coord in coordsList
])
# Check shift
if name == 'xmitgcm_iters':
sizes = od1.dataset.sizes
assert sizes['time'] - sizes['time_midp'] == 1
assert all([
'time_midp' in od1.dataset[var].dims
for var in od1.dataset.data_vars if 'ave' in var
])
# Save to netcdf
filename = 'tmp.nc'
od1.to_netcdf(filename)
# Reopen
from_netcdf(filename)
# Clean up
subprocess.call('rm -f ' + filename, shell=True)
def test_from_netcdf():
# Dataset
orig_od = oceandatasets['MITgcm_rect_nc']
# Save
filename = 'tmp_oceandataset.nc'
orig_od.to_netcdf(filename)
# Open
open_od = from_netcdf(filename)
assert orig_od.dataset.equals(open_od.dataset)
assert orig_od._ds.equals(open_od._ds)
# Clean up
import os
os.remove(filename)
import pytest
import xarray as xr
import numpy as np
# From OceanSpy
from oceanspy import open_oceandataset, OceanDataset
from oceanspy.plot import TS_diagram, time_series, horizontal_section, vertical_section
# Matplotlib (keep it below oceanspy)
import matplotlib.pyplot as plt
# Directory
Datadir = "./oceanspy/tests/Data/"
# Test oceandataset
od = open_oceandataset.from_netcdf("{}MITgcm_rect_nc.nc" "".format(Datadir))
# Create mooring, sruvey, and particles
Xmoor = [od.dataset["XC"].min().values, od.dataset["XC"].max().values]
Ymoor = [od.dataset["YC"].min().values, od.dataset["YC"].max().values]
od_moor = od.subsample.mooring_array(Xmoor=Xmoor, Ymoor=Ymoor)
Xsurv = [
od.dataset["XC"].min().values,
od.dataset["XC"].mean().values,
od.dataset["XC"].max().values,
]
Ysurv = [
od.dataset["YC"].min().values,
od.dataset["YC"].mean().values,
od.dataset["YC"].max().values,
mooring_volume_transport,
normal_strain,
potential_density_anomaly,
relative_vorticity,
salt_budget,
shear_strain,
survey_aligned_velocities,
velocity_magnitude,
vertical_relative_vorticity,
)
# Directory
Datadir = "./oceanspy/tests/Data/"
# Create an oceandataset for testing calculus functions
od = open_oceandataset.from_netcdf("{}MITgcm_rect_nc.nc" "".format(Datadir))
# Create an oceandataset for testing calculus functions
od_curv = open_oceandataset.from_netcdf("{}MITgcm_curv_nc.nc"
"".format(Datadir))
# Aliased od
ds = od.dataset
aliases = {var: var + "_alias" for var in ds.data_vars}
ds = ds.rename(aliases)
alias_od = ospy.OceanDataset(ds).set_aliases(aliases)
# Budgets
od_bdg = open_oceandataset.from_netcdf("{}budgets.nc" "".format(Datadir))
import xarray as xr
import numpy as np
# From OceanSpy
from oceanspy import open_oceandataset, OceanDataset
from oceanspy.plot import (TS_diagram, time_series, horizontal_section,
vertical_section)
# Matplotlib (keep it below oceanspy)
import matplotlib.pyplot as plt
# Directory
Datadir = './oceanspy/tests/Data/'
# Test oceandataset
od = open_oceandataset.from_netcdf('{}MITgcm_rect_nc.nc' ''.format(Datadir))
# Create mooring, sruvey, and particles
Xmoor = [od.dataset['XC'].min().values, od.dataset['XC'].max().values]
Ymoor = [od.dataset['YC'].min().values, od.dataset['YC'].max().values]
od_moor = od.subsample.mooring_array(Xmoor=Xmoor, Ymoor=Ymoor)
Xsurv = [
od.dataset['XC'].min().values, od.dataset['XC'].mean().values,
od.dataset['XC'].max().values
]
Ysurv = [
od.dataset['YC'].min().values, od.dataset['YC'].mean().values,
od.dataset['YC'].max().values
]
od_surv = od.subsample.survey_stations(Xsurv=Xsurv, Ysurv=Ysurv)
# TODO: cartesian, and Xp1 Yp1 right are not tested.
import pytest
import numpy as np
import xarray as xr
from numpy.testing import assert_array_equal
# From OceanSpy
from oceanspy import open_oceandataset, OceanDataset
# Directory
Datadir = "./oceanspy/tests/Data/"
# Test oceandataset
MITgcm_curv_nc = open_oceandataset.from_netcdf("{}MITgcm_curv_nc.nc"
"".format(Datadir))
MITgcm_rect_bin = open_oceandataset.from_netcdf("{}MITgcm_rect_bin.nc"
"".format(Datadir))
MITgcm_rect_nc = open_oceandataset.from_netcdf("{}MITgcm_rect_nc.nc"
"".format(Datadir))
# =======
# CUTOUT
# =======
od = MITgcm_curv_nc
moor_od = OceanDataset(od.dataset.expand_dims("mooring"))
Ywarn = od.dataset["YG"].min() - 1
Xwarn = od.dataset["XG"].min() - 1
Zwarn = od.dataset["Zp1"].min() - 1
Twarn = od.dataset["time"].min() - 1
YRange = [od.dataset["YG"].min(), od.dataset["YG"].max()]
XRange = [od.dataset["XG"].min(), od.dataset["XG"].max()]
import pytest
import xarray as xr
import copy
from oceanspy.compute import *
from oceanspy import utils, open_oceandataset
from numpy.testing import assert_array_equal, assert_allclose
import numpy as np
from .test_compute_static import check_params, ds_out_IN_od_out
# Add variables
od_in = open_oceandataset.from_netcdf('./oceanspy/tests/Data/budgets.nc')
def test_heat_budget():
# Compute heat_budget
ds_out = heat_budget(od_in)
for var in ds_out.data_vars:
assert ds_out[var].attrs['units'] == 'degC/s'
check_params(ds_out, var, ['rho0', 'c_p'])
tendH = ds_out['tendH']
check = ds_out['adv_hConvH'] + ds_out['adv_vConvH'] + ds_out[
'dif_vConvH'] + ds_out['kpp_vConvH'] + ds_out['forcH']
assert np.fabs(tendH - check).max().values < 1.e-17
# Test shortcut
od_out = od_in.compute.heat_budget()
ds_out_IN_od_out(ds_out, od_out)
