本文整理汇总了Python中xarray.Dataset.to_netcdf方法的典型用法代码示例。如果您正苦于以下问题:Python Dataset.to_netcdf方法的具体用法?Python Dataset.to_netcdf怎么用?Python Dataset.to_netcdf使用的例子?那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类xarray.Dataset
的用法示例。
在下文中一共展示了Dataset.to_netcdf方法的7个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_open_and_do_math
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_open_and_do_math(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp)
with open_mfdataset(tmp) as ds:
actual = 1.0 * ds
self.assertDatasetAllClose(original, actual)
示例2: test_coordinates_encoding
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_coordinates_encoding(self):
def equals_latlon(obj):
return obj == 'lat lon' or obj == 'lon lat'
original = Dataset({'temp': ('x', [0, 1]), 'precip': ('x', [0, -1])},
{'lat': ('x', [2, 3]), 'lon': ('x', [4, 5])})
with self.roundtrip(original) as actual:
self.assertDatasetIdentical(actual, original)
with create_tmp_file() as tmp_file:
original.to_netcdf(tmp_file)
with open_dataset(tmp_file, decode_coords=False) as ds:
self.assertTrue(equals_latlon(ds['temp'].attrs['coordinates']))
self.assertTrue(equals_latlon(ds['precip'].attrs['coordinates']))
self.assertNotIn('coordinates', ds.attrs)
self.assertNotIn('coordinates', ds['lat'].attrs)
self.assertNotIn('coordinates', ds['lon'].attrs)
modified = original.drop(['temp', 'precip'])
with self.roundtrip(modified) as actual:
self.assertDatasetIdentical(actual, modified)
with create_tmp_file() as tmp_file:
modified.to_netcdf(tmp_file)
with open_dataset(tmp_file, decode_coords=False) as ds:
self.assertTrue(equals_latlon(ds.attrs['coordinates']))
self.assertNotIn('coordinates', ds['lat'].attrs)
self.assertNotIn('coordinates', ds['lon'].attrs)
示例3: test_weakrefs
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_weakrefs(self):
example = Dataset({'foo': ('x', np.arange(5.0))})
expected = example.rename({'foo': 'bar', 'x': 'y'})
with create_tmp_file() as tmp_file:
example.to_netcdf(tmp_file, engine='scipy')
on_disk = open_dataset(tmp_file, engine='pynio')
actual = on_disk.rename({'foo': 'bar', 'x': 'y'})
del on_disk # trigger garbage collection
self.assertDatasetIdentical(actual, expected)
示例4: test_preprocess_mfdataset
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_preprocess_mfdataset(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp)
def preprocess(ds):
return ds.assign_coords(z=0)
expected = preprocess(original)
with open_mfdataset(tmp, preprocess=preprocess) as actual:
self.assertDatasetIdentical(expected, actual)
示例5: test_open_dataset
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_open_dataset(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp)
with open_dataset(tmp, chunks={'x': 5}) as actual:
self.assertIsInstance(actual.foo.variable.data, da.Array)
self.assertEqual(actual.foo.variable.data.chunks, ((5, 5),))
self.assertDatasetIdentical(original, actual)
with open_dataset(tmp, chunks=5) as actual:
self.assertDatasetIdentical(original, actual)
with open_dataset(tmp) as actual:
self.assertIsInstance(actual.foo.variable.data, np.ndarray)
self.assertDatasetIdentical(original, actual)
示例6: test_lock
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def test_lock(self):
original = Dataset({'foo': ('x', np.random.randn(10))})
with create_tmp_file() as tmp:
original.to_netcdf(tmp, format='NETCDF3_CLASSIC')
with open_dataset(tmp, chunks=10) as ds:
task = ds.foo.data.dask[ds.foo.data.name, 0]
self.assertIsInstance(task[-1], type(Lock()))
with open_mfdataset(tmp) as ds:
task = ds.foo.data.dask[ds.foo.data.name, 0]
self.assertIsInstance(task[-1], type(Lock()))
with open_mfdataset(tmp, engine='scipy') as ds:
task = ds.foo.data.dask[ds.foo.data.name, 0]
self.assertNotIsInstance(task[-1], type(Lock()))
示例7: ensembles2dataset_dask
# 需要导入模块: from xarray import Dataset [as 别名]
# 或者: from xarray.Dataset import to_netcdf [as 别名]
def ensembles2dataset_dask(ensdict, ncfpath, dsattrs={}, chunks=10,
verbose=True, print_every=1000):
"""
Convert a dictionary of ensembles into an xarray Dataset object
using dask.delayed to keep memory usage feasible.
"""
mms2ms = 1e-3
n=0
# fbadens = np.array(ensdict_aux)==None
# nt = len(ensdict) - np.sum(fbadens)
# embed()
ensdict0 = None
while ensdict0 is None:
ensdict0 = ensdict[n].compute()
n+=1
nz = ensdict0['fixed_leader_janus']['number_of_cells']
fixj = ensdict0['fixed_leader_janus'].compute()
fix5 = ensdict0['fixed_leader_beam5'].compute()
# Add ping offset to get beam 5's timestamps.
dt5 = fix5['ping_offset_time'] # In milliseconds.
dt5 = np.array(Timedelta(dt5, unit='ms'))
th = fixj['beam_angle']
assert th==25 # Always 25 degrees.
th = th*np.pi/180.
Cth = np.cos(th)
# Construct along-beam/vertical axes.
cm2m = 1e-2
r1janus = fixj['bin_1_distance']*cm2m
r1b5 = fix5['bin_1_distance']*cm2m
ncj = fixj['number_of_cells']
nc5 = fix5['number_of_cells']
lcj = fixj['depth_cell_length']*cm2m
lc5 = fix5['depth_cell_length']*cm2m
Lj = ncj*lcj # Distance from center of bin 1 to the center of last bin (Janus).
L5 = nc5*lc5 # Distance from center of bin 1 to the center of last bin (beam 5).
rb = r1janus + np.arange(0, Lj, lcj) # Distance from xducer head
# (Janus).
zab = Cth*rb # Vertical distance from xducer head
# (Janus).
zab5 = r1b5 + np.arange(0, L5, lc5) # Distance from xducer head, also
# depth for the vertical beam.
rb = IndexVariable('z', rb, attrs={'units':'meters', 'long_name':"along-beam distance from the xducer's face to the center of the bins, for beams 1-4 (Janus)"})
zab = IndexVariable('z', zab, attrs={'units':'meters', 'long_name':"vertical distance from the instrument's head to the center of the bins, for beams 1-4 (Janus)"})
zab5 = IndexVariable('z5', zab5, attrs={'units':'meters', 'long_name':"vertical distance from xducer face to the center of the bins, for beam 5 (vertical)"})
ensdict = from_sequence(ensdict)
tjanus = ensdict.map_partitions(_alloc_timestamp_parts)
t5 = _addtarr(tjanus, dt5)
if verbose: print("Unpacking timestamps.")
time = IndexVariable('time', tjanus.compute(), attrs={'long_name':'timestamps for beams 1-4 (Janus)'})
time5 = IndexVariable('time5', t5.compute(), attrs={'long_name':'timestamps for beam 5 (vertical)'})
if verbose: print("Done unpacking timestamps.")
coords0 = dict(time=time)
coords = dict(z=zab, time=time, rb=rb)
coords5 = dict(z5=zab5, time5=time5)
dims = ['z', 'time']
dims5 = ['z5', 'time5']
dims0 = ['time']
coordsdict = coords0
if verbose: print("Allocating heading, pitch, roll.")
kwda = dict(coords=coordsdict, dims=dims0, attrs=dict(units=unit, long_name=lname))
svars = ['heading', 'pitch', 'roll']
long_names = svars
units = ['degrees']*3
grp = 'variable_leader_janus'
vars1d = dict()
for vname,lname,unit in zip(svars,long_names,units):
if verbose: print(vname)
wrk = ensdict.map_partitions(_alloc_hpr, grp, vname)
# wrk = darr.from_array(np.array(wrk.compute()), chunks=chunks)
wrk2 = delayed(_bag2DataArray)(wrk, chunks)(**kwda)
vars1d.update({vname:wrk2})
del(wrk, wrk2)
ds2hpr = Dataset(data_vars=vars1d, coords=coordsdict)
ds2hpr = ds2hpr.to_netcdf(ncfpath, compute=False, mode='w')
if verbose: print("Saving heading, pitch, roll.")
ds2hpr.compute()
if verbose: print("Done saving heading, pitch, roll.")
del(ds2hpr)
coordsdict = coords5
# Load beam 5 variables into memory to
# be able to put them in a chunked DataArray.
if verbose: print("Allocating beam 5 variables.")
grps = ['velocity_beam5', 'correlation_beam5', 'echo_intensity_beam5']
long_names = ['Beam 5 velocity', 'Beam 5 correlation', 'Beam 5 echo amplitude']
units = ['mm/s, positive toward xducer face', 'unitless', 'dB']
vars5 = dict()
for grp,lname,unit in zip(grps,long_names,units):
#.........这里部分代码省略.........