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FFT filter changes with Numpy v2.0 #592

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Dec 13, 2024
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FFT filter changes with Numpy v2.0 #592

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40dbf12
Include accelerate base tests by default
daurer Dec 13, 2024
cf39bcd
Change expected output in FFT filter tests to reflect support for sin…
daurer Dec 13, 2024
2fbf85d
Adjust tolerance in pycuda fft filter tests
daurer Dec 13, 2024
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1 change: 1 addition & 0 deletions pyproject.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -55,6 +55,7 @@ testpaths = [
"test/ptyscan_tests",
"test/template_tests",
"test/util_tests",
"test/accelerate_tests/base_tests"
]

# this is all BETA according to setuptools
Expand Down
142 changes: 71 additions & 71 deletions test/accelerate_tests/base_tests/array_utils_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -305,42 +305,42 @@ def test_fft_filter(self):
kernel = np.fft.fftn(rk)

output = au.fft_filter(data, kernel, prefactor, postfactor)

known_test_output = np.array([-0.0000000e+00+0.00000000e+00j, -0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00-0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
-0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
-0.0000000e+00+0.00000000e+00j, -0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00-0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00+0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 0.0000000e+00-0.00000000e+00j,
0.0000000e+00+0.00000000e+00j, 6.1097220e-05+2.92563982e-05j,
4.0044695e-05+2.52102855e-05j, 8.9999994e+02+9.00000000e+02j,
8.9999988e+02+8.99999939e+02j, 5.0999994e+02+5.09999939e+02j,
1.9365043e-05+5.84280206e-05j, 3.0681291e-05+2.31116355e-05j,
1.2552022e-05-1.01537153e-05j, -1.4034913e-05-1.17988075e-05j,
-1.9193330e-05+2.72889110e-07j, 1.3895768e-05+1.64778357e-05j,
6.5228807e-05+2.45708943e-05j, 3.8999994e+02+3.89999939e+02j,
8.9999988e+02+8.99999878e+02j, 8.9999982e+02+8.99999878e+02j,
3.0000015e+01+3.00000248e+01j, 3.8863189e-05+3.26705631e-05j,
2.8768281e-06-1.62116921e-05j, -3.2418033e-05-1.97073969e-05j,
-6.6843757e-05+7.19546824e-06j, 6.5036993e-06+3.95851657e-06j,
-2.4053887e-05+9.88548163e-06j, 1.5231475e-05+1.31202614e-06j,
8.7000000e+01+8.70000305e+01j, 6.1035156e-05+0.00000000e+00j,
6.1035156e-05+0.00000000e+00j, -2.4943074e-07+6.62429193e-06j,
1.6712515e-06-2.97475322e-06j, 1.9025241e-05+2.97752194e-07j,
-9.2436176e-07-3.86252796e-05j, -8.8145862e-06-9.89961700e-06j,
-1.5782407e-06+1.01533060e-05j, -4.7593076e-06+2.96332291e-05j])

known_test_output = np.array([-0.00000000e+00+0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
-0.00000000e+00 + 0.00000000e+00j, 0.00000000e+00 - 0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, -0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
-0.00000000e+00+0.00000000e+00j, -0.00000000e+00+0.00000000e+00j,
-0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 8.66422277e-14+4.86768828e-14j,
7.23113320e-14+2.82331542e-14j, 9.00000000e+02+9.00000000e+02j,
9.00000000e+02+9.00000000e+02j, 5.10000000e+02+5.10000000e+02j,
1.41172830e-14+3.62223425e-14j, 2.61684238e-14-4.13866575e-14j,
2.16691314e-14-1.95102733e-14j, -1.36536942e-13-9.94589021e-14j,
-1.42905371e-13-5.77964697e-14j, -5.00005072e-14+4.08620637e-14j,
6.38160272e-14+7.61753583e-14j, 3.90000000e+02+3.90000000e+02j,
9.00000000e+02+9.00000000e+02j, 9.00000000e+02+9.00000000e+02j,
3.00000000e+01+3.00000000e+01j, 8.63255773e-14+7.08532924e-14j,
1.80941313e-14-3.85517154e-14j, 7.84277340e-14-1.32008745e-14j,
-6.57025196e-14-1.72739350e-14j, -6.69570857e-15+6.49622898e-14j,
6.27436466e-15+7.57162569e-14j, 2.01150157e-15+3.65538558e-14j,
8.70000000e+01+8.70000000e+01j, -1.13686838e-13-1.70530257e-13j,
0.00000000e+00-2.27373675e-13j, -1.84492121e-14-9.21502853e-14j,
2.12418687e-14-8.62209232e-14j, 1.20880692e-13+3.86522371e-14j,
1.03754734e-13+9.19851759e-14j, 5.50926123e-14+1.17150422e-13j,
-5.47869215e-14+5.87176511e-14j, -3.52652980e-14+8.44455504e-15j])

np.testing.assert_array_almost_equal(output.flat[::2000], known_test_output)
np.testing.assert_array_almost_equal(output.flat[::2000], known_test_output, decimal=5)

def test_fft_filter_batched(self):
data = np.zeros((2,256, 512), dtype=COMPLEX_TYPE)
Expand All @@ -356,42 +356,42 @@ def test_fft_filter_batched(self):
kernel = np.fft.fftn(rk)

output = au.fft_filter(data, kernel, prefactor, postfactor)
known_test_output = np.array([-0.00000000e+00+0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
-0.00000000e+00 + 0.00000000e+00j, 0.00000000e+00 - 0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, -0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
-0.00000000e+00+0.00000000e+00j, -0.00000000e+00+0.00000000e+00j,
-0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00+0.00000000e+00j,
0.00000000e+00+0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 0.00000000e+00-0.00000000e+00j,
0.00000000e+00-0.00000000e+00j, 8.66422277e-14+4.86768828e-14j,
7.23113320e-14+2.82331542e-14j, 9.00000000e+02+9.00000000e+02j,
9.00000000e+02+9.00000000e+02j, 5.10000000e+02+5.10000000e+02j,
1.41172830e-14+3.62223425e-14j, 2.61684238e-14-4.13866575e-14j,
2.16691314e-14-1.95102733e-14j, -1.36536942e-13-9.94589021e-14j,
-1.42905371e-13-5.77964697e-14j, -5.00005072e-14+4.08620637e-14j,
6.38160272e-14+7.61753583e-14j, 3.90000000e+02+3.90000000e+02j,
9.00000000e+02+9.00000000e+02j, 9.00000000e+02+9.00000000e+02j,
3.00000000e+01+3.00000000e+01j, 8.63255773e-14+7.08532924e-14j,
1.80941313e-14-3.85517154e-14j, 7.84277340e-14-1.32008745e-14j,
-6.57025196e-14-1.72739350e-14j, -6.69570857e-15+6.49622898e-14j,
6.27436466e-15+7.57162569e-14j, 2.01150157e-15+3.65538558e-14j,
8.70000000e+01+8.70000000e+01j, -1.13686838e-13-1.70530257e-13j,
0.00000000e+00-2.27373675e-13j, -1.84492121e-14-9.21502853e-14j,
2.12418687e-14-8.62209232e-14j, 1.20880692e-13+3.86522371e-14j,
1.03754734e-13+9.19851759e-14j, 5.50926123e-14+1.17150422e-13j,
-5.47869215e-14+5.87176511e-14j, -3.52652980e-14+8.44455504e-15j])

np.testing.assert_array_almost_equal(output[1].flat[::2000], known_test_output)

known_test_output = np.array([ 0.00000000e+00-0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
-0.00000000e+00+0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00-0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00-0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00+0.0000000e+00j,
0.00000000e+00+0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00-0.0000000e+00j, 0.00000000e+00-0.0000000e+00j,
0.00000000e+00-0.0000000e+00j, 4.86995195e-05-9.1511911e-06j,
5.89395277e-05+3.6706428e-05j, 8.99999817e+02+9.0000000e+02j,
8.99999817e+02+9.0000000e+02j, 5.09999969e+02+5.0999997e+02j,
6.86399580e-05+5.5245564e-05j, -2.15578075e-06-8.0761157e-07j,
-5.99612467e-05-3.7489859e-05j, -2.08058154e-05-1.7001423e-05j,
-3.15661709e-05-2.0192698e-05j, -1.17410173e-05-2.3929812e-05j,
8.41844594e-05+4.9635066e-05j, 3.90000031e+02+3.9000003e+02j,
8.99999817e+02+8.9999994e+02j, 8.99999817e+02+8.9999994e+02j,
3.00000153e+01+3.0000000e+01j, 4.75842753e-05+1.7961407e-05j,
-1.28229876e-05-3.3492659e-05j, -1.50405585e-05+3.0159079e-05j,
-1.00799960e-04-6.6932058e-05j, -4.90295024e-05-3.6601130e-05j,
-4.48861247e-05-1.4717044e-05j, 2.60417364e-05-8.3221821e-06j,
8.69999847e+01+8.7000046e+01j, 4.31583721e-05+4.3158372e-05j,
4.31583721e-05+4.3158372e-05j, 4.04649109e-06-1.6836095e-05j,
1.37377283e-05+5.2577798e-06j, -2.30404657e-05-3.4596611e-05j,
-1.33214944e-05-3.2517899e-05j, 2.45428764e-05-3.5186855e-07j,
-1.85950885e-05-2.1921931e-05j, -1.65030433e-05-8.0249208e-07j])
np.testing.assert_array_almost_equal(output[1].flat[::2000], known_test_output, decimal=5)


def test_complex_gaussian_filter_fft(self):
Expand Down
12 changes: 6 additions & 6 deletions test/accelerate_tests/cuda_pycuda_tests/array_utils_test.py
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Original file line number Diff line number Diff line change
Expand Up @@ -580,7 +580,7 @@ def test_fft_filter_UNITY(self):

output = au.fft_filter(data, kernel, prefactor, postfactor)

np.testing.assert_allclose(output, data_dev.get(), rtol=1e-5, atol=1e-6)
np.testing.assert_allclose(output, data_dev.get(), rtol=1e-5, atol=1e-5)

def test_fft_filter_batched_UNITY(self):
sh = (2,16, 35)
Expand All @@ -607,7 +607,7 @@ def test_fft_filter_batched_UNITY(self):
output = au.fft_filter(data, kernel, prefactor, postfactor)
print(data_dev.get())

np.testing.assert_allclose(output, data_dev.get(), rtol=1e-5, atol=1e-6)
np.testing.assert_allclose(output, data_dev.get(), rtol=1e-5, atol=1e-5)

def test_complex_gaussian_filter_fft_little_blurring_UNITY(self):
# Arrange
Expand All @@ -624,7 +624,7 @@ def test_complex_gaussian_filter_fft_little_blurring_UNITY(self):
out_exp = au.complex_gaussian_filter_fft(data, mfs)
out = data_dev.get()

np.testing.assert_allclose(out_exp, out, atol=1e-6)
np.testing.assert_allclose(out_exp, out, atol=1e-5)

def test_complex_gaussian_filter_fft_more_blurring_UNITY(self):
# Arrange
Expand All @@ -641,7 +641,7 @@ def test_complex_gaussian_filter_fft_more_blurring_UNITY(self):
out_exp = au.complex_gaussian_filter_fft(data, mfs)
out = data_dev.get()

np.testing.assert_allclose(out_exp, out, atol=1e-6)
np.testing.assert_allclose(out_exp, out, atol=1e-5)

def test_complex_gaussian_filter_fft_nonsquare_UNITY(self):
# Arrange
Expand All @@ -660,7 +660,7 @@ def test_complex_gaussian_filter_fft_nonsquare_UNITY(self):
out_exp = au.complex_gaussian_filter_fft(data, mfs)
out = data_dev.get()

np.testing.assert_allclose(out_exp, out, atol=1e-6)
np.testing.assert_allclose(out_exp, out, atol=1e-5)

def test_complex_gaussian_filter_fft_batched(self):
# Arrange
Expand All @@ -680,4 +680,4 @@ def test_complex_gaussian_filter_fft_batched(self):
out_exp = au.complex_gaussian_filter_fft(data, mfs)
out = data_dev.get()

np.testing.assert_allclose(out_exp, out, atol=1e-6)
np.testing.assert_allclose(out_exp, out, atol=1e-5)
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