def test_parallel_search_no_cython(self, osgs):
"""Test the parallel resampling until the cython calls."""
from pyresample.gradient import parallel_gradient_search as pgs
data = da.ones((100, 100), chunks=(25, 50))
src_x = da.ones((100, 100), chunks=(25, 50))
src_y = da.ones((100, 100), chunks=(25, 50))
dst_x = da.ones((180, 360), chunks=(90, 90))
dst_y = da.ones((180, 360), chunks=(90, 90))
def fake_gradient_resample_data(*args, **kwargs):
assert(kwargs['method'] == 'bilinear')
assert(args[0].shape == (1, 25, 50))
for arg in args[1:7]:
assert(arg.shape == (25, 50))
for arg in args[7:]:
assert(arg.shape == (90, 90))
return args[7][np.newaxis, :, :]
osgs.side_effect = fake_gradient_resample_data
res = pgs(data, src_x, src_y, dst_x, dst_y)
res = res.compute(scheduler='single-threaded')
assert(res.shape == (180, 360))
data = da.ones((3, 100, 100), chunks=(3, 25, 50))
def fake_gradient_resample_data(*args, **kwargs):
assert(kwargs['method'] == 'bilinear')
assert(args[0].shape == (3, 25, 50))
for arg in args[1:7]:
assert(arg.shape == (25, 50))
for arg in args[7:]:
assert(arg.shape == (90, 90))
return args[7][np.newaxis, :, :]
def test_parallel_resampling_no_blockwise(self, blockwise):
"""Test the parallel resampling until the blockwise call."""
from pyresample.gradient import parallel_gradient_search as pgs
data = da.ones((100, 100), chunks=(25, 50))
src_x = da.ones((100, 100), chunks=(25, 50))
src_y = da.ones((100, 100), chunks=(25, 50))
dst_x = da.ones((180, 360), chunks=(90, 90))
dst_y = da.ones((180, 360), chunks=(90, 90))
def fake_blockwise(*args, **kwargs):
return args[2]
blockwise.side_effect = fake_blockwise
res = pgs(data, src_x, src_y, dst_x, dst_y)
assert(res.shape == (25, 50))
def test_parallel_search_no_blocks(self, grd):
"""Test the parallel resampling until the blocked calls."""
from pyresample.gradient import parallel_gradient_search as pgs
data = da.ones((100, 100), chunks=(25, 50))
src_x = da.ones((100, 100), chunks=(25, 50))
src_y = da.ones((100, 100), chunks=(25, 50))
dst_x = da.ones((180, 360), chunks=(90, 90))
dst_y = da.ones((180, 360), chunks=(90, 90))
def fake_gradient_resample_data(*args, **kwargs):
assert(kwargs['method'] == 'bilinear')
return args[7][np.newaxis, :, :, np.newaxis]
grd.side_effect = fake_gradient_resample_data
res = pgs(data, src_x, src_y, dst_x, dst_y)
res = res.compute(scheduler='single-threaded')
assert(res.shape == (180, 360))