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Fix flake8 issues
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I ran `black -l 80 -S ./test_inverse_distortion.py`

Signed-off-by: Patrick Avery <[email protected]>
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@psavery
psavery committed Nov 23, 2023
1 parent 1ade6aa commit f235676
Showing 1 changed file with 107 additions and 75 deletions.
182 changes: 107 additions & 75 deletions tests/test_inverse_distortion.py
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Original file line number Diff line number Diff line change
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test_dir = os.path.dirname(os.path.abspath(__file__))

RHO_MAX = 204.8
params = [-2.277777438488093e-05, -8.763805995117837e-05, -0.00047451698761967085]

big_test_in = np.array([[ 47.031483 , -5.2170362],
[ 60.0171 , 27.218563 ],
[ 60.697784 , 25.48354 ],
[ 56.90082 , -35.88738 ],
[ 55.631718 , -37.62758 ],
[ 41.258152 , -63.237328 ],
[ 78.00906 , -8.576369 ],
[ 77.7207 , -10.315189 ],
[ 73.20562 , 32.426453 ],
[ 92.81449 , -13.717096 ],
[101.26153 , 18.790167 ],
[ 33.428936 , 99.08392 ],
[101.51741 , 17.049505 ],
[ 85.12195 , 59.84272 ],
[-44.172375 , 11.69572 ],
[-57.850574 , -19.013659 ],
[-57.169926 , -20.749386 ],
[-52.772987 , 44.117252 ],
[-54.043003 , 42.37685 ],
[-74.88209 , 16.800816 ],
[-70.36986 , -25.959877 ],
[-38.385185 , 69.727234 ],
[-75.17097 , 15.061297 ],
[-89.98957 , 20.207851 ],
[-98.708176 , -10.574363 ],
[-82.30401 , -53.390797 ],
[-98.45244 , -12.316093 ]], dtype=np.float32)

big_test_out = np.array([[ 47.03288205, -5.21719147],
[ 60.02002476, 27.21988891],
[ 60.70080301, 25.48480692],
[ 56.90341105, -35.88901181],
[ 55.63418288, -37.62924612],
[ 41.26039938, -63.24077218],
[ 78.01561783, -8.57708985],
[ 77.72717551, -10.31604838],
[ 73.21095917, 32.42881771],
[ 92.82553762, -13.71872887],
[101.27584945, 18.79282435],
[ 33.43310185, 99.09627097],
[101.5318596 , 17.05193195],
[ 85.13101706, 59.84909563],
[-44.17351296, 11.69602109],
[-57.85318107, -19.01451522],
[-57.17243759, -20.75029751],
[-52.77530392, 44.11918895],
[-54.04540365, 42.37873249],
[-74.88783308, 16.80210463],
[-70.37458323, -25.96162026],
[-38.38762237, 69.73166096],
[-75.17679039, 15.06246417],
[-89.99957766, 20.21009857],
[-98.72150605, -10.57579081],
[-82.31199676, -53.39597868],
[-98.46565139, -12.31774635]])
params = [
-2.277777438488093e-05,
-8.763805995117837e-05,
-0.00047451698761967085,
]

big_test_in = np.array(
[
[47.031483, -5.2170362],
[60.0171, 27.218563],
[60.697784, 25.48354],
[56.90082, -35.88738],
[55.631718, -37.62758],
[41.258152, -63.237328],
[78.00906, -8.576369],
[77.7207, -10.315189],
[73.20562, 32.426453],
[92.81449, -13.717096],
[101.26153, 18.790167],
[33.428936, 99.08392],
[101.51741, 17.049505],
[85.12195, 59.84272],
[-44.172375, 11.69572],
[-57.850574, -19.013659],
[-57.169926, -20.749386],
[-52.772987, 44.117252],
[-54.043003, 42.37685],
[-74.88209, 16.800816],
[-70.36986, -25.959877],
[-38.385185, 69.727234],
[-75.17097, 15.061297],
[-89.98957, 20.207851],
[-98.708176, -10.574363],
[-82.30401, -53.390797],
[-98.45244, -12.316093],
],
dtype=np.float32,
)

big_test_out = np.array(
[
[47.03288205, -5.21719147],
[60.02002476, 27.21988891],
[60.70080301, 25.48480692],
[56.90341105, -35.88901181],
[55.63418288, -37.62924612],
[41.26039938, -63.24077218],
[78.01561783, -8.57708985],
[77.72717551, -10.31604838],
[73.21095917, 32.42881771],
[92.82553762, -13.71872887],
[101.27584945, 18.79282435],
[33.43310185, 99.09627097],
[101.5318596, 17.05193195],
[85.13101706, 59.84909563],
[-44.17351296, 11.69602109],
[-57.85318107, -19.01451522],
[-57.17243759, -20.75029751],
[-52.77530392, 44.11918895],
[-54.04540365, 42.37873249],
[-74.88783308, 16.80210463],
[-70.37458323, -25.96162026],
[-38.38762237, 69.73166096],
[-75.17679039, 15.06246417],
[-89.99957766, 20.21009857],
[-98.72150605, -10.57579081],
[-82.31199676, -53.39597868],
[-98.46565139, -12.31774635],
]
)


def test_known_values():
xy_in = np.array([[140.40087891, 117.74253845]])
expected_output = np.array([[140.44540352, 117.77987754]])
xy_out = inverse_distortion.ge_41rt_inverse_distortion(xy_in, RHO_MAX, params)
assert np.allclose(xy_out, expected_output)
xy_in = np.array([[140.40087891, 117.74253845]])
expected_output = np.array([[140.44540352, 117.77987754]])
xy_out = inverse_distortion.ge_41rt_inverse_distortion(
xy_in, RHO_MAX, params
)
assert np.allclose(xy_out, expected_output)


def test_big_input():
xy_out = inverse_distortion.ge_41rt_inverse_distortion(big_test_in, RHO_MAX, params)
assert np.allclose(xy_out, big_test_out)
xy_out = inverse_distortion.ge_41rt_inverse_distortion(
big_test_in, RHO_MAX, params
)
assert np.allclose(xy_out, big_test_out)


def test_large_input():
xy_in = np.array([[1e5, 1e5]])
xy_out = inverse_distortion.ge_41rt_inverse_distortion(xy_in, RHO_MAX, params)
# No specific expected output here, just ensure it doesn't fail
assert xy_out.shape == xy_in.shape
xy_in = np.array([[1e5, 1e5]])
xy_out = inverse_distortion.ge_41rt_inverse_distortion(
xy_in, RHO_MAX, params
)
# No specific expected output here, just ensure it doesn't fail
assert xy_out.shape == xy_in.shape


def test_logged_data():
# Load logged data
with open(os.path.join(test_dir, "data", "inverse_distortion_in_out.pkl"), 'rb') as f:
with open(
os.path.join(test_dir, "data", "inverse_distortion_in_out.pkl"), 'rb'
) as f:
logged_data = pickle.load(f)

logged_inputs = logged_data['inputs']
logged_outputs = logged_data['outputs']
logged_params = logged_data['params']

for xy_in, xy_out_expected, params in zip(logged_inputs, logged_outputs, logged_params):
xy_out = inverse_distortion.ge_41rt_inverse_distortion(xy_in, RHO_MAX, params)
for xy_in, xy_out_expected, params in zip(
logged_inputs, logged_outputs, logged_params
):
xy_out = inverse_distortion.ge_41rt_inverse_distortion(
xy_in, RHO_MAX, params
)
assert np.allclose(xy_out, xy_out_expected, atol=1e-7)


def test_random_values():
np.random.seed(42)
xy_in = np.random.rand(10, 2) * 200
xy_out = inverse_distortion.ge_41rt_inverse_distortion(xy_in, RHO_MAX, params)
# Verify function does not raise any exception
assert xy_out.shape == xy_in.shape
np.random.seed(42)
xy_in = np.random.rand(10, 2) * 200
xy_out = inverse_distortion.ge_41rt_inverse_distortion(
xy_in, RHO_MAX, params
)
# Verify function does not raise any exception
assert xy_out.shape == xy_in.shape

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