本文整理汇总了Python中xarray.DataArray.compute方法的典型用法代码示例。如果您正苦于以下问题:Python DataArray.compute方法的具体用法?Python DataArray.compute怎么用?Python DataArray.compute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.DataArray
的用法示例。
在下文中一共展示了DataArray.compute方法的4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_dataarray_pickle
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import compute [as 别名]
def test_dataarray_pickle(self):
# Test that pickling/unpickling does not convert the dask
# backend to numpy
a1 = DataArray(build_dask_array())
a1.compute()
self.assertFalse(a1._in_memory)
self.assertEquals(kernel_call_count, 1)
a2 = pickle.loads(pickle.dumps(a1))
self.assertEquals(kernel_call_count, 1)
self.assertDataArrayIdentical(a1, a2)
self.assertFalse(a1._in_memory)
self.assertFalse(a2._in_memory)
示例2: test_expand_without_dims
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import compute [as 别名]
def test_expand_without_dims(self):
from satpy.resample import NativeResampler
import numpy as np
import dask.array as da
from xarray import DataArray
from pyresample.geometry import AreaDefinition
from pyresample.utils import proj4_str_to_dict
ds1 = DataArray(da.zeros((100, 50), chunks=85))
proj_dict = proj4_str_to_dict('+proj=lcc +datum=WGS84 +ellps=WGS84 '
'+lon_0=-95. +lat_0=25 +lat_1=25 '
'+units=m +no_defs')
target = AreaDefinition(
'test',
'test',
'test',
proj_dict,
x_size=100,
y_size=200,
area_extent=(-1000., -1500., 1000., 1500.),
)
# source geo def doesn't actually matter
resampler = NativeResampler(None, target)
new_arr = resampler.resample(ds1)
self.assertEqual(new_arr.shape, (200, 100))
new_arr2 = resampler.resample(ds1.compute())
self.assertTrue(np.all(new_arr == new_arr2))
示例3: test_dataarray_pickle
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import compute [as 别名]
def test_dataarray_pickle(self):
# Test that pickling/unpickling converts the dask backend
# to numpy in neither the data variable nor the non-index coords
data = build_dask_array('data')
nonindex_coord = build_dask_array('coord')
a1 = DataArray(data, dims=['x'], coords={'y': ('x', nonindex_coord)})
a1.compute()
assert not a1._in_memory
assert not a1.coords['y']._in_memory
assert kernel_call_count == 2
a2 = pickle.loads(pickle.dumps(a1))
assert kernel_call_count == 2
assert_identical(a1, a2)
assert not a1._in_memory
assert not a2._in_memory
assert not a1.coords['y']._in_memory
assert not a2.coords['y']._in_memory
示例4: ensembles2dataset_dask
# 需要导入模块: from xarray import DataArray [as 别名]
# 或者: from xarray.DataArray import compute [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.........