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_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)
import xarray as xr
# Oceanspy modules
from oceanspy import (
AVAILABLE_PARAMETERS,
DEFAULT_PARAMETERS,
OCEANSPY_AXES,
OceanDataset,
open_oceandataset,
)
# Directory
Datadir = "./oceanspy/tests/Data/"
od = open_oceandataset.from_netcdf("{}MITgcm_rect_nc.nc" "".format(Datadir))
ECCO_url = "{}catalog_ECCO.yaml".format(Datadir)
ECCOod = open_oceandataset.from_catalog("LLC", ECCO_url)
# Remove global attributes
ds = od.dataset
ds.attrs = {}
clean_od = OceanDataset(ds)
# Aliased od
ds = od.dataset
aliases = {var: var + "_alias" for var in ds.data_vars}
ds = ds.rename(aliases)
alias_od = OceanDataset(ds).set_aliases(aliases)
# Grid only wihtout time_midp
ds = clean_od.dataset
ds = ds.drop_vars(ds.data_vars)
# 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))
ECCO_url = "{}catalog_ECCO.yaml".format(Datadir)
ECCOod = open_oceandataset.from_catalog('LLC', ECCO_url)
# =======
# 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()]
ZRange = [od.dataset["Zp1"].min(), od.dataset["Zp1"].max()]
timeRange = [od.dataset["time"].values[0], od.dataset["time"].values[-1]]