def test_gain_header_0():
gain_normalizer = GainNormalizer(None)
fake_images = [FakeGainImage() for x in range(6)]
for image in fake_images:
image.gain = 0.0
images = gain_normalizer.do_stage(fake_images)
assert len(images) == 0
def test_gain_is_empty_list():
gain_normalizer = GainNormalizer(None)
fake_images = [FakeGainImage() for x in range(6)]
for image in fake_images:
image.gain = []
images = gain_normalizer.do_stage(fake_images)
assert len(images) == 0
def test_gain_is_empty_list():
gain_normalizer = GainNormalizer(None)
fake_images = [FakeGainImage() for x in range(6)]
for image in fake_images:
image.gain = []
images = gain_normalizer.do_stage(fake_images)
assert len(images) == 0
def test_gain_header_0():
gain_normalizer = GainNormalizer(None)
fake_images = [FakeGainImage() for x in range(6)]
for image in fake_images:
image.gain = 0.0
images = gain_normalizer.do_stage(fake_images)
assert len(images) == 0
def test_gain_datacube(set_random_seed):
n_amplifiers = 4
nx, ny = 101, 103
saturation = 65536
max_linearity = 60000
# These tests will fail if the gain is a numpy array because it will try to check element by
# element which raises and exception here.
input_gains = list(np.random.uniform(0.5, 2.5, size=n_amplifiers))
input_data = np.random.normal(10, 1, size=(n_amplifiers, ny, nx))
image = FakeGainImage(nx=nx, ny=ny)
image.gain = input_gains
image.data = input_data.copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(image)
for i in range(n_amplifiers):
np.testing.assert_allclose(image.data[i],
input_data[i] * input_gains[i])
np.testing.assert_allclose(image.header['SATURATE'],
saturation * min(input_gains))
np.testing.assert_allclose(image.header['MAXLIN'],
max_linearity * min(input_gains))
def test_gain_datacube():
n_amplifiers = 4
nx, ny = 101, 103
n_images = 6
saturation = 65536
max_linearity = 60000
# These tests will fail if the gain is a numpy array because it will try to check element by
# element which raises and exception here.
input_gains = [list(np.random.uniform(0.5, 2.5, size=n_amplifiers)) for i in range(n_images)]
input_data = [np.random.normal(10, 1, size=(n_amplifiers, ny, nx)) for i in range(n_images)]
fake_images = [FakeGainImage(nx=nx, ny=ny) for i in range(n_images)]
for i, image in enumerate(fake_images):
image.gain = input_gains[i]
image.data = input_data[i].copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
output_images = gain_normalizer.do_stage(fake_images)
for i, image in enumerate(output_images):
for j in range(n_amplifiers):
np.testing.assert_allclose(image.data[j], input_data[i][j] * input_gains[i][j])
np.testing.assert_allclose(image.header['SATURATE'], saturation * min(input_gains[i]))
np.testing.assert_allclose(image.header['MAXLIN'], max_linearity * min(input_gains[i]))
def test_gain_1d():
nx, ny = 101, 103
n_images = 6
saturation = 65536
max_linearity = 60000
input_gains = np.random.uniform(0.5, 2.5, size=n_images)
input_data = [
np.random.normal(10, 1, size=(ny, nx)) for i in range(n_images)
]
fake_images = [FakeGainImage(nx=nx, ny=ny) for x in range(n_images)]
for i, image in enumerate(fake_images):
image.gain = input_gains[i]
image.data = input_data[i].copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
output_images = gain_normalizer.do_stage(fake_images)
for i, image in enumerate(output_images):
np.testing.assert_allclose(image.data, input_data[i] * input_gains[i])
np.testing.assert_allclose(image.header['SATURATE'],
saturation * input_gains[i])
np.testing.assert_allclose(image.header['MAXLIN'],
max_linearity * input_gains[i])
def test_gain_1d(set_random_seed):
nx, ny = 101, 103
saturation = 65536
max_linearity = 60000
input_gains = np.random.uniform(0.5, 2.5)
input_data = np.random.normal(10, 1, size=(ny, nx))
image = FakeGainImage(nx=nx, ny=ny)
image.gain = input_gains
image.data = input_data.copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(image)
np.testing.assert_allclose(image.data, input_data * input_gains)
np.testing.assert_allclose(image.header['SATURATE'], saturation * input_gains)
np.testing.assert_allclose(image.header['MAXLIN'], max_linearity * input_gains)
def test_gain_1d(set_random_seed):
nx, ny = 101, 103
saturation = 65536
max_linearity = 60000
input_gains = np.random.uniform(0.5, 2.5)
input_data = np.random.normal(10, 1, size=(ny, nx))
image = FakeGainImage(nx=nx, ny=ny)
image.gain = input_gains
image.data = input_data.copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(image)
np.testing.assert_allclose(image.data, input_data * input_gains)
np.testing.assert_allclose(image.header['SATURATE'],
saturation * input_gains)
np.testing.assert_allclose(image.header['MAXLIN'],
max_linearity * input_gains)
def test_gain_1d():
nx, ny = 101, 103
n_images = 6
saturation = 65536
max_linearity = 60000
input_gains = np.random.uniform(0.5, 2.5, size=n_images)
input_data = [np.random.normal(10, 1, size=(ny, nx)) for i in range(n_images)]
fake_images = [FakeGainImage(nx=nx, ny=ny) for x in range(n_images)]
for i, image in enumerate(fake_images):
image.gain = input_gains[i]
image.data = input_data[i].copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
output_images = gain_normalizer.do_stage(fake_images)
for i, image in enumerate(output_images):
np.testing.assert_allclose(image.data, input_data[i] * input_gains[i])
np.testing.assert_allclose(image.header['SATURATE'], saturation * input_gains[i])
np.testing.assert_allclose(image.header['MAXLIN'], max_linearity * input_gains[i])
def test_gain_datacube():
n_amplifiers = 4
nx, ny = 101, 103
n_images = 6
saturation = 65536
max_linearity = 60000
# These tests will fail if the gain is a numpy array because it will try to check element by
# element which raises and exception here.
input_gains = [
list(np.random.uniform(0.5, 2.5, size=n_amplifiers))
for i in range(n_images)
]
input_data = [
np.random.normal(10, 1, size=(n_amplifiers, ny, nx))
for i in range(n_images)
]
fake_images = [FakeGainImage(nx=nx, ny=ny) for i in range(n_images)]
for i, image in enumerate(fake_images):
image.gain = input_gains[i]
image.data = input_data[i].copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
output_images = gain_normalizer.do_stage(fake_images)
for i, image in enumerate(output_images):
for j in range(n_amplifiers):
np.testing.assert_allclose(image.data[j],
input_data[i][j] * input_gains[i][j])
np.testing.assert_allclose(image.header['SATURATE'],
saturation * min(input_gains[i]))
np.testing.assert_allclose(image.header['MAXLIN'],
max_linearity * min(input_gains[i]))
def test_gain_datacube(set_random_seed):
n_amplifiers = 4
nx, ny = 101, 103
saturation = 65536
max_linearity = 60000
# These tests will fail if the gain is a numpy array because it will try to check element by
# element which raises and exception here.
input_gains = list(np.random.uniform(0.5, 2.5, size=n_amplifiers))
input_data = np.random.normal(10, 1, size=(n_amplifiers, ny, nx))
image = FakeGainImage(nx=nx, ny=ny)
image.gain = input_gains
image.data = input_data.copy()
image.header['SATURATE'] = saturation
image.header['MAXLIN'] = max_linearity
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(image)
for i in range(n_amplifiers):
np.testing.assert_allclose(image.data[i], input_data[i] * input_gains[i])
np.testing.assert_allclose(image.header['SATURATE'], saturation * min(input_gains))
np.testing.assert_allclose(image.header['MAXLIN'], max_linearity * min(input_gains))
def test_gain_header_0():
gain_normalizer = GainNormalizer(None)
image = FakeGainImage()
image.gain = 0.0
image = gain_normalizer.do_stage(image)
assert image is None
def test_null_input_image():
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.run(None)
assert image is None
def test_gain_header_0():
gain_normalizer = GainNormalizer(None)
image = FakeGainImage()
image.gain = 0.0
image = gain_normalizer.do_stage(image)
assert image is None
def test_gain_is_empty_list():
gain_normalizer = GainNormalizer(None)
image = FakeGainImage()
image.gain = []
image = gain_normalizer.do_stage(image)
assert image is None
def test_null_input_image():
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.run(None)
assert image is None
def test_gain_header_missing():
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(FakeGainImage())
assert image is None
def test_no_input_images():
gain_normalizer = GainNormalizer(None)
images = gain_normalizer.do_stage([])
assert len(images) == 0
def test_gain_is_empty_list():
gain_normalizer = GainNormalizer(None)
image = FakeGainImage()
image.gain = []
image = gain_normalizer.do_stage(image)
assert image is None
def test_group_by_keywords():
gain_normalizer = GainNormalizer(None)
assert gain_normalizer.group_by_keywords is None
def test_gain_header_missing():
gain_normalizer = GainNormalizer(None)
images = gain_normalizer.do_stage([FakeGainImage() for x in range(6)])
assert len(images) == 0
def test_gain_header_missing():
gain_normalizer = GainNormalizer(None)
images = gain_normalizer.do_stage([FakeGainImage() for x in range(6)])
assert len(images) == 0
def test_no_input_images():
gain_normalizer = GainNormalizer(None)
images = gain_normalizer.do_stage([])
assert len(images) == 0
def test_gain_header_missing():
gain_normalizer = GainNormalizer(None)
image = gain_normalizer.do_stage(FakeGainImage())
assert image is None