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WIP fix for 821: test spectral smooth on windows #823

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merged 5 commits into from
Jun 12, 2022
Merged

WIP fix for 821: test spectral smooth on windows #823

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1c36928
make the test for issue 821 more granular so we can see where the
keflavich Jun 12, 2022
b4a36f4
fix an idiom
keflavich Jun 12, 2022
d3b09a1
correct the test
keflavich Jun 12, 2022
3f69c11
fix a second test
keflavich Jun 12, 2022
30de5ea
fix missing fixture import; see https://github.com/pydata/xarray/pull…
keflavich Jun 12, 2022
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2 changes: 1 addition & 1 deletion spectral_cube/tests/test_dask.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,7 @@
from astropy.utils import data

try:
from distributed.utils_test import client, loop, cluster_fixture # noqa
from distributed.utils_test import client, loop, cluster_fixture, cleanup # noqa
DISTRIBUTED_INSTALLED = True
except ImportError:
DISTRIBUTED_INSTALLED = False
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47 changes: 32 additions & 15 deletions spectral_cube/tests/test_regrid.py
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Expand Up @@ -133,18 +133,36 @@ def test_spectral_smooth(data_522_delta, use_dask):

cube, data = cube_and_raw(data_522_delta, use_dask=use_dask)

result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), use_memmap=False)
kernel = convolution.Gaussian1DKernel(1.0)

result = cube.spectral_smooth(kernel=kernel, use_memmap=False)

# check that all values come out right from the cube creation
np.testing.assert_almost_equal(cube[2,:,:].value, 1.0)
np.testing.assert_almost_equal(cube.unitless_filled_data[:2,:,:], 0.0)
np.testing.assert_almost_equal(cube.unitless_filled_data[3:,:,:], 0.0)

# make sure the kernel comes out right; the convolution test will fail if this is wrong
assert kernel.array.size == 9
# this was the old astropy normalization
# We don't actually need the kernel to match these values, but I'm leaving this here
# as a note for future us:
# https://github.com/astropy/astropy/pull/13299
# the error came about because we were using two different kernel sizes, which resulted in
# two different normalizations after the correction in 13299
# Before 13299, normalization was not guaranteed.
#np.testing.assert_almost_equal(kernel.array[2:-2],
# np.array([0.05399097, 0.24197072, 0.39894228, 0.24197072, 0.05399097]))

np.testing.assert_almost_equal(result[:,0,0].value,
convolution.Gaussian1DKernel(1.0,
x_size=5).array,
kernel.array[2:-2],
4)

result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), use_memmap=True)
# second test with memmap=True
result = cube.spectral_smooth(kernel=kernel, use_memmap=True)

np.testing.assert_almost_equal(result[:,0,0].value,
convolution.Gaussian1DKernel(1.0,
x_size=5).array,
kernel.array[2:-2],
4)

def test_catch_kernel_with_units(data_522_delta, use_dask):
Expand All @@ -165,19 +183,19 @@ def test_spectral_smooth_4cores(data_522_delta):

cube, data = cube_and_raw(data_522_delta, use_dask=False)

result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), num_cores=4, use_memmap=True)
kernel = convolution.Gaussian1DKernel(1.0)
result = cube.spectral_smooth(kernel=kernel, num_cores=4, use_memmap=True)

assert kernel.array.size == 9
np.testing.assert_almost_equal(result[:,0,0].value,
convolution.Gaussian1DKernel(1.0,
x_size=5).array,
kernel.array[2:-2],
4)

# this is one way to test non-parallel mode
result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0), num_cores=4, use_memmap=False)
result = cube.spectral_smooth(kernel=kernel, num_cores=4, use_memmap=False)

np.testing.assert_almost_equal(result[:,0,0].value,
convolution.Gaussian1DKernel(1.0,
x_size=5).array,
kernel.array[2:-2],
4)

# num_cores = 4 is a contradiction with parallel=False, so we want to make
Expand All @@ -187,12 +205,11 @@ def test_spectral_smooth_4cores(data_522_delta):
"multiple cores were: these are incompatible "
"options. Either specify num_cores=1 or "
"parallel=True")):
result = cube.spectral_smooth(kernel=convolution.Gaussian1DKernel(1.0),
result = cube.spectral_smooth(kernel=kernel,
num_cores=4, parallel=False)

np.testing.assert_almost_equal(result[:,0,0].value,
convolution.Gaussian1DKernel(1.0,
x_size=5).array,
kernel.array[2:-2],
4)


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