def test_image_all_1000s():
tester = ThousandsTest(None)
image = FakeImage()
image.data += 5
image.data[:, :] = 1000
image = tester.do_stage(image)
assert image is None
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_no_pixels_saturated():
tester = SaturationTest(None)
image = FakeImage()
image.header['SATURATE'] = 65535
image = tester.do_stage(image)
assert image is not None
assert image.header.get('SATFRAC') == 0.0
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_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_no_offset():
tester = PointingTest(None)
image = FakeImage()
image.header['CRVAL1'] = '1.0'
image.header['CRVAL2'] = '-1.0'
image.header['OFST-RA'] = '0:04:00.00'
image.header['OFST-DEC'] = '-01:00:00.000'
image = tester.do_stage(image)
np.testing.assert_allclose(image.header.get('PNTOFST'), 0.0, atol=1e-7)
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_image_30_percent_1000(set_random_seed):
tester = ThousandsTest(None)
nx = 101
ny = 103
image = FakeImage(nx=nx, ny=ny)
random_pixels_x = np.random.randint(0, nx - 1, size=int(0.3 * nx * ny))
random_pixels_y = np.random.randint(0, ny - 1, size=int(0.3 * nx * ny))
for i in zip(random_pixels_y, random_pixels_x):
image.data[i] = 1000
image = tester.do_stage(image)
assert image is None
def test_large_offset():
tester = PointingTest(None)
image = FakeImage()
image.header['CRVAL1'] = '00:00:00.000'
image.header['CRVAL2'] = '-00:00:00.000'
image.header['OFST-RA'] = '00:00:00.000'
image.header['OFST-DEC'] = '-00:00:10.000'
image = tester.do_stage(image)
assert image.header.get('PNTOFST') == 10.0
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_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_image_10_percent_saturated_rejected(set_random_seed):
tester = SaturationTest(None)
nx = 101
ny = 103
image = FakeImage(nx=nx, ny=ny)
image.header['SATURATE'] = 65535
random_pixels_x = np.random.randint(0, nx - 1, size=int(0.1 * nx * ny))
random_pixels_y = np.random.randint(0, ny - 1, size=int(0.1 * nx * ny))
for i in zip(random_pixels_y, random_pixels_x):
image.data[i] = image.header['SATURATE']
image = tester.do_stage(image)
assert image is None
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_saturation_es_update():
# Set initial values
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
stage.save_qc_results({'Saturated': True, 'saturation_fraction': 0.99}, image)
# Run saturation test
tester = SaturationTest(FakeElasticsearchContext())
image.header['SATURATE'] = 65535
image.data += 5.0
tester.do_stage([image])
# Check info from elasticsearch
results = elasticsearch.Elasticsearch(ES_URL).get_source(
index=stage.ES_INDEX, doc_type=stage.ES_DOC_TYPE, id='test')
assert not results['Saturated']
assert results['saturation_fraction'] == 0.0
def test_image_2_percent_saturated(set_random_seed):
tester = SaturationTest(None)
nx = 101
ny = 103
image = FakeImage(nx=nx, ny=ny)
image.header['SATURATE'] = 65535
random_pixels_x = np.random.randint(0, nx - 1, size=int(0.02 * nx * ny))
random_pixels_y = np.random.randint(0, ny - 1, size=int(0.02 * nx * ny))
for i in zip(random_pixels_y, random_pixels_x):
image.data[i] = image.header['SATURATE']
image = tester.do_stage(image)
assert image is not None
assert np.abs(image.header.get('SATFRAC') - 0.02) < 0.001
def test_raises_exception_if_no_master_calibration(mock_cal, mock_images):
mock_cal.return_value = None
mock_images.return_value = FakeBiasImage()
subtractor = BiasSubtractor(None)
with pytest.raises(MasterCalibrationDoesNotExist):
images = subtractor.do_stage([FakeImage() for x in range(6)])
def test_save_results():
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
output = stage.save_qc_results({}, image, _source=True)
assert '_shards' in output
assert output['_shards']['failed'] == 0
assert 'get' in output
def test_save_qc_results(mock_es):
context = FakeContext()
context.post_to_elasticsearch = True
context.elasticsearch_url = '/'
stage = FakeStage(context)
qc.save_qc_results(stage.runtime_context, {}, FakeImage())
assert mock_es.called
def test_crosstalk():
tester = CrosstalkCorrector(None)
nx = 101
ny = 103
images = [FakeImage(nx=nx, ny=ny) for x in range(6)]
# Add random pixels at 10000 to each of the extensions
for image in images:
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() for image in images]
# Simulate crosstalk
for image in images:
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
images = tester.do_stage(images)
# Assert that we got back the original image
for i, image in enumerate(images):
np.testing.assert_allclose(image.data, expected_image_data[i], atol=2.0, rtol=1e-5)
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_format_qc_results_new_info():
filename, results = qc.format_qc_results(
{
"key1": "value1",
"key2": "value2"
}, FakeImage())
assert results["key1"] == "value1"
assert results["key2"] == "value2"
def test_format_qc_results_numpy_bool():
filename, results = qc.format_qc_results(
{
"normal_bool": True,
"numpy_bool": np.bool_(True)
}, FakeImage())
assert type(results["normal_bool"]) == bool
assert type(results["numpy_bool"]) == bool
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_save_results_with_changed_parameters():
# Push the defualt test image info
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
stage.save_qc_results({}, image)
# Change the image parameters and push again
image.site = "fake_site"
image.instrument = "fake_instrument"
image.epoch = str(int(image.epoch) + 1)
image.dateobs += timedelta(days=1)
# Post to ES and test results
output = stage.save_qc_results({}, image, _source=True)
assert output['result'] == 'updated'
results = output['get']['_source']
assert results['site'] == image.site
assert results['instrument'] == image.instrument
assert results['dayobs'] == image.epoch
assert results['timestamp'] == image.dateobs.strftime("%Y-%m-%dT%H:%M:%S")
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_flags_bad_if_no_master_calibration(mock_cal, mock_frame,
set_random_seed):
mock_cal.return_value = None
nx = 101
ny = 103
context = make_context_with_master_bias(nx=nx, ny=ny)
comparer = BiasComparer(context)
image = comparer.do_stage(FakeImage(nx=nx, ny=ny))
assert image.is_bad is True
def test_format_qc_results_basic_info():
image = FakeImage()
filename, results = qc.format_qc_results({}, image)
assert results['site'] == image.site
assert results['instrument'] == image.camera
assert results['dayobs'] == image.epoch
assert results['@timestamp'] == image.dateobs
assert results['obstype'] == image.obstype
assert filename in image.filename
def test_no_pixels_1000():
tester = ThousandsTest(None)
nx = 101
ny = 103
images = [FakeImage(nx=nx, ny=ny) for x in range(6)]
images = tester.do_stage(images)
assert len(images) == 6
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_header_checker_es_update():
# Set initial values
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
header_checks = ['HeaderBadDecValue', 'HeaderBadRAValue',
'HeaderExptimeNegative', 'HeaderExptimeZero',
'HeaderKeywordsMissing', 'HeaderKeywordsNA']
header_check_booleans = {key: True for key in header_checks}
stage.save_qc_results(header_check_booleans, image)
# Run header sanity test
tester = HeaderSanity(FakeElasticsearchContext())
for key in tester.header_expected_format.keys():
image.header[key] = 1.0
image.header['OBSTYPE'] = 'EXPOSE'
tester.do_stage([image])
# Check info from elasticsearch
results = elasticsearch.Elasticsearch(ES_URL).get_source(
index=stage.ES_INDEX, doc_type=stage.ES_DOC_TYPE, id='test')
for header_check in header_checks:
assert not results[header_check]
def test_pointing_es_update():
# Set initial values
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
stage.save_qc_results({'PointingSevere': True,
'PointingWarning': True,
'pointing_offset': 100.}, image)
# Run pointing test
image.header['CRVAL1'] = '1.0'
image.header['CRVAL2'] = '-1.0'
image.header['OFST-RA'] = '0:04:00.00'
image.header['OFST-DEC'] = '-01:00:00.000'
tester = PointingTest(FakeElasticsearchContext())
tester.do_stage([image])
# Check info from elasticsearch
results = elasticsearch.Elasticsearch(ES_URL).get_source(
index=stage.ES_INDEX, doc_type=stage.ES_DOC_TYPE, id='test')
assert not results['PointingSevere']
assert not results['PointingWarning']
assert results['pointing_offset'] < 1E10
def test_nonzero_but_no_pixels_1000():
tester = ThousandsTest(None)
nx = 101
ny = 103
images = [FakeImage(nx=nx, ny=ny) for x in range(6)]
for image in images:
image.data += 5
images = tester.do_stage(images)
assert len(images) == 6
def test_pattern_noise_es_update():
# Set initial values
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
stage.save_qc_results({'PatternNoise': True}, image)
# Run pattern noise test
tester = PatternNoiseDetector(FakeElasticsearchContext())
tester.do_stage([image])
# Check info from elasticsearch
results = elasticsearch.Elasticsearch(ES_URL).get_source(
index=stage.ES_INDEX, doc_type=stage.ES_DOC_TYPE, id='test')
assert not results['PatternNoise']
def test_all_images_all_1000s():
tester = ThousandsTest(None)
nx = 101
ny = 103
images = [FakeImage(nx=nx, ny=ny) for x in range(6)]
for image in images:
image.data[:, :] = 1000
images = tester.do_stage(images)
assert len(images) == 0
def test_sinistro_1000s_es_update():
# Set initial values
stage = Stage(FakeElasticsearchContext())
image = FakeImage()
stage.save_qc_results({'Error1000s': True, 'fraction_1000s': 0.99}, image)
# Run sinistro 1000s test
tester = ThousandsTest(FakeElasticsearchContext())
tester.do_stage([image])
# Check info from elasticsearch
results = elasticsearch.Elasticsearch(ES_URL).get_source(
index=stage.ES_INDEX, doc_type=stage.ES_DOC_TYPE, id='test')
assert not results['Error1000s']
assert results['fraction_1000s'] == 0.0
def test_no_pixels_saturated():
tester = SaturationTest(None)
nx = 101
ny = 103
images = [FakeImage(nx=nx, ny=ny) for x in range(6)]
for image in images:
image.header['SATURATE'] = 65535
images = tester.do_stage(images)
for image in images:
assert image.header['SATFRAC'][0] == 0.0
assert len(images) == 6
def test_2d_is_not_3d():
test_image = FakeImage()
assert not test_image.data_is_3d()
def test_3d_is_3d():
test_image = FakeImage(n_amps=4)
assert test_image.data_is_3d()
def test_get_n_amps_2d():
n_amps = 4
test_image = FakeImage(n_amps=n_amps)
assert test_image.get_n_amps() == n_amps
def test_get_n_amps_3d():
test_image = FakeImage()
assert test_image.get_n_amps() == 1
def test_get_inner_image_section_3d():
test_image = FakeImage(n_amps=4)
with pytest.raises(ValueError):
test_image.get_inner_image_section()
