def test_crosstalk(set_random_seed):
tester = CrosstalkCorrector(None)
nx = 101
ny = 103
image = FakeImage(nx=nx, ny=ny)
# Add random pixels at 10000 to each of the extensions
for amp in range(4):
image.data = np.ones((4, ny, nx)) * 1000.0
random_pixels_x = np.random.randint(0, nx - 1, size=int(0.05 * nx * ny))
random_pixels_y = np.random.randint(0, ny - 1, size=int(0.05 * nx * ny))
for i in zip(random_pixels_y, random_pixels_x):
image.data[amp][i] = 10000
expected_image_data = image.data.copy()
# Simulate crosstalk
original_data = image.data.copy()
for j in range(4):
for i in range(4):
if i != j:
crosstalk_coeff = np.random.uniform(0.0, 0.01)
image.header['CRSTLK{i}{j}'.format(i=i+1, j=j+1)] = crosstalk_coeff
image.data[j] += original_data[i] * crosstalk_coeff
# Try to remove it
image = tester.do_stage(image)
# Assert that we got back the original image
np.testing.assert_allclose(image.data, expected_image_data, atol=2.0, rtol=1e-5)
def test_pattern_noise_on_2d_image(set_random_seed):
image = FakeImage()
image.data = generate_data(has_pattern_noise=True)
detector = pattern_noise.PatternNoiseDetector(None)
logger.error = mock.MagicMock()
detector.do_stage(image)
assert logger.error.called
def test_get_inner_quarter_default():
test_image = FakeImage()
test_image.data = np.random.randint(0, 1000, size=test_image.data.shape)
# get inner quarter manually
inner_nx = round(test_image.nx * 0.25)
inner_ny = round(test_image.ny * 0.25)
inner_quarter = test_image.data[inner_ny:-inner_ny, inner_nx:-inner_nx]
np.testing.assert_array_equal(test_image.get_inner_image_section(), inner_quarter)
def test_get_inner_quarter_default():
test_image = FakeImage()
test_image.data = np.random.randint(0, 1000, size=test_image.data.shape)
# get inner quarter manually
inner_nx = round(test_image.nx * 0.25)
inner_ny = round(test_image.ny * 0.25)
inner_quarter = test_image.data[inner_ny:-inner_ny, inner_nx:-inner_nx]
np.testing.assert_array_equal(test_image.get_inner_image_section(), inner_quarter)
def test_pattern_noise_on_2d_image(set_random_seed):
image = FakeImage()
image.data = generate_data(has_pattern_noise=True)
detector = pattern_noise.PatternNoiseDetector(None)
logger.error = mock.MagicMock()
detector.do_stage(image)
assert logger.error.called
def test_pattern_noise_on_3d_image(mock_save_qc):
data = 100.0 * np.sin(np.arange(1000000 * 4) / 0.1) + 1000.0 + np.random.normal(0.0, 10.0, size=1000000 * 4)
data = data.reshape(4, 1000, 1000)
image = FakeImage()
image.data = data
detector = pattern_noise.PatternNoiseDetector(None)
_ = detector.do_stage([image])
assert mock_save_qc.called
def test_bias_master_level_subtraction_is_reasonable(set_random_seed):
input_bias = 2000.0
read_noise = 15.0
subtractor = BiasMasterLevelSubtractor(None)
image = FakeImage()
image.data = np.random.normal(input_bias, read_noise, size=(image.ny, image.nx))
image = subtractor.do_stage(image)
np.testing.assert_allclose(np.zeros(image.data.shape), image.data, atol=8 * read_noise)
np.testing.assert_allclose(image.header.get('BIASLVL'), input_bias, atol=1.0)
def test_dark_normalization_is_reasonable(set_random_seed):
nx = 101
ny = 103
normalizer = DarkNormalizer(None)
data = np.random.normal(30.0, 10, size=(ny, nx))
image = FakeImage()
image.data = data.copy()
image = normalizer.do_stage(image)
np.testing.assert_allclose(image.data, data / image.exptime, 1e-5)
def test_pattern_noise_in_only_one_quadrant(mock_save_qc):
data = np.random.normal(0.0, 10.0, size=1000000 * 4) + 1000.0
data = data.reshape(4, 1000, 1000)
data[3] += 100.0 * np.sin(np.arange(1e6) / 0.1).reshape(1000, 1000)
image = FakeImage()
image.data = data
detector = pattern_noise.PatternNoiseDetector(None)
_ = detector.do_stage([image])
assert mock_save_qc.called
def test_does_not_reject_noisy_image(mock_cal, mock_frame, set_random_seed):
mock_cal.return_value = 'test.fits'
master_readnoise = 3.0
nx = 101
ny = 103
context = make_context_with_master_bias(readnoise=master_readnoise, nx=nx, ny=ny)
comparer = BiasComparer(context)
image = FakeImage(image_multiplier=0.0)
image.data = np.random.normal(0.0, image.readnoise, size=(ny, nx))
image = comparer.do_stage(image)
assert image.is_bad is False
def test_does_not_reject_noisy_image(mock_cal, mock_frame, set_random_seed):
mock_cal.return_value = 'test.fits'
master_readnoise = 3.0
nx = 101
ny = 103
context = make_context_with_master_bias(readnoise=master_readnoise,
nx=nx,
ny=ny)
comparer = BiasComparer(context)
image = FakeImage(image_multiplier=0.0)
image.data = np.random.normal(0.0, image.readnoise, size=(ny, nx))
image = comparer.do_stage(image)
assert image.is_bad is False
def test_flat_normalization_is_reasonable(set_random_seed):
flat_variation = 0.05
input_level = 10000.0
nx = 101
ny = 103
normalizer = FlatNormalizer(None)
image = FakeImage()
flat_pattern = np.random.normal(1.0, flat_variation, size=(ny, nx))
image.data = np.random.poisson(flat_pattern * input_level).astype(float)
image = normalizer.do_stage(image)
# For right now, we only use a quarter of the image to calculate the flat normalization
# because real ccds have crazy stuff at the edges, so the S/N is cut down by a factor of 2
# Assume 50% slop because the variation in the pattern does not decrease like sqrt(n)
assert np.abs(image.header['FLATLVL'] - input_level) < (3.0 * flat_variation * input_level / (nx * ny) ** 0.5)
assert np.abs(np.mean(image.data) - 1.0) <= 3.0 * flat_variation / (nx * ny) ** 0.5
def test_does_flag_bad_image(mock_cal, mock_frame, set_random_seed):
mock_cal.return_value = 'test.fits'
master_readnoise = 3.0
nx = 101
ny = 103
context = make_context_with_master_bias(readnoise=master_readnoise, nx=nx, ny=ny)
comparer = BiasComparer(context)
image = FakeImage(image_multiplier=0.0)
image.data = np.random.normal(0.0, image.readnoise, size=(ny, nx))
x_indexes = np.random.choice(np.arange(nx), size=2000)
y_indexes = np.random.choice(np.arange(ny), size=2000)
for x, y in zip(x_indexes, y_indexes):
image.data[y, x] = np.random.normal(100, image.readnoise)
image = comparer.do_stage(image)
assert image.is_bad
def test_does_flag_bad_image(mock_cal, mock_frame, set_random_seed):
mock_cal.return_value = 'test.fits'
master_readnoise = 3.0
nx = 101
ny = 103
context = make_context_with_master_bias(readnoise=master_readnoise,
nx=nx,
ny=ny)
comparer = BiasComparer(context)
image = FakeImage(image_multiplier=0.0)
image.data = np.random.normal(0.0, image.readnoise, size=(ny, nx))
x_indexes = np.random.choice(np.arange(nx), size=2000)
y_indexes = np.random.choice(np.arange(ny), size=2000)
for x, y in zip(x_indexes, y_indexes):
image.data[y, x] = np.random.normal(100, image.readnoise)
image = comparer.do_stage(image)
assert image.is_bad
def test_crosstalk(set_random_seed):
tester = CrosstalkCorrector(None)
nx = 101
ny = 103
image = FakeImage(nx=nx, ny=ny)
# Add random pixels at 10000 to each of the extensions
for amp in range(4):
image.data = np.ones((4, ny, nx)) * 1000.0
random_pixels_x = np.random.randint(0,
nx - 1,
size=int(0.05 * nx * ny))
random_pixels_y = np.random.randint(0,
ny - 1,
size=int(0.05 * nx * ny))
for i in zip(random_pixels_y, random_pixels_x):
image.data[amp][i] = 10000
expected_image_data = image.data.copy()
# Simulate crosstalk
original_data = image.data.copy()
for j in range(4):
for i in range(4):
if i != j:
crosstalk_coeff = np.random.uniform(0.0, 0.01)
image.header['CRSTLK{i}{j}'.format(i=i + 1,
j=j + 1)] = crosstalk_coeff
image.data[j] += original_data[i] * crosstalk_coeff
# Try to remove it
image = tester.do_stage(image)
# Assert that we got back the original image
np.testing.assert_allclose(image.data,
expected_image_data,
atol=2.0,
rtol=1e-5)