def test__masked_front_edge_from_layout():
layout = ac.Layout1DLine(shape_1d=(5, ), region_list=[(1, 4)])
mask = ac.Mask1DLine.masked_front_edge_from_layout(
layout=layout,
settings=ac.SettingsMask1DLine(front_edge_pixels=(0, 2)),
pixel_scales=0.1,
)
assert type(mask) == ac.Mask1DLine
assert (mask == np.array([False, True, True, False, False])).all()
layout = ac.Layout1DLine(shape_1d=(9, ), region_list=[(1, 4), (6, 9)])
mask = ac.Mask1DLine.masked_front_edge_from_layout(
layout=layout,
settings=ac.SettingsMask1DLine(front_edge_pixels=(1, 3)),
pixel_scales=0.1,
)
assert type(mask) == ac.Mask1DLine
assert (mask == np.array(
[False, False, True, True, False, False, False, True, True])).all()
def test__array_1d_of_trails_from():
layout = ac.Layout1DLine(shape_1d=(4, ),
region_list=[(0, 2)],
prescan=(0, 1),
overscan=(0, 1))
array = ac.Array1D.manual_native(array=[0.0, 1.0, 2.0, 3.0],
pixel_scales=1.0)
array_extracted = layout.array_1d_of_trails_from(array=array)
assert (array_extracted == np.array([0.0, 0.0, 2.0, 3.0])).all()
layout = ac.Layout1DLine(shape_1d=(5, ),
region_list=[(0, 2)],
prescan=(2, 3),
overscan=(3, 4))
array_extracted = layout.array_1d_of_trails_from(array=array)
assert (array_extracted == np.array([0.0, 0.0, 0.0, 0.0])).all()
layout = ac.Layout1DLine(shape_1d=(4, ),
region_list=[(0, 1), (2, 3)],
prescan=(2, 3),
overscan=(2, 3))
array_extracted = layout.array_1d_of_trails_from(array=array)
assert (array_extracted.native == np.array([0.0, 1.0, 0.0, 3.0])).all()
def test__trail_size_to_array_edge():
layout = ac.Layout1DLine(shape_1d=(5, ),
region_list=[ac.Region1D(region=(0, 3))])
assert layout.trail_size_to_array_edge == 2
layout = ac.Layout1DLine(shape_1d=(7, ),
region_list=[ac.Region1D(region=(0, 3))])
assert layout.trail_size_to_array_edge == 4
layout = ac.Layout1DLine(
shape_1d=(15, ),
region_list=[
ac.Region1D(region=(0, 2)),
ac.Region1D(region=(5, 8)),
ac.Region1D(region=(11, 14)),
],
)
assert layout.trail_size_to_array_edge == 1
layout = ac.Layout1DLine(
shape_1d=(20, ),
region_list=[
ac.Region1D(region=(0, 2)),
ac.Region1D(region=(5, 8)),
ac.Region1D(region=(11, 14)),
],
)
assert layout.trail_size_to_array_edge == 6
def test__from_post_cti_data(self, clocker_1d, traps_x2, ccd):
layout = ac.Layout1DLine(shape_1d=(5,), region_list=[(0, 5)])
simulator = ac.SimulatorDatasetLine(
pixel_scales=1.0,
normalization=10.0,
read_noise=4.0,
add_poisson_noise=False,
noise_seed=1,
)
dataset_line = simulator.from_layout(
layout=layout, clocker=clocker_1d, trap_list=traps_x2, ccd=ccd
)
pre_cti_data = simulator.pre_cti_data_from(
layout=layout, pixel_scales=1.0
).native
post_cti_data = clocker_1d.add_cti(
data=pre_cti_data, trap_list=traps_x2, ccd=ccd
)
dataset_line_via_post_cti_data = simulator.from_post_cti_data(
post_cti_data=post_cti_data, pre_cti_data=pre_cti_data.native, layout=layout
)
assert (dataset_line.data == dataset_line_via_post_cti_data.data).all()
assert (
dataset_line.noise_map == dataset_line_via_post_cti_data.noise_map
).all()
assert (
dataset_line.pre_cti_data == dataset_line_via_post_cti_data.pre_cti_data
).all()
def test__array_1d_of_non_regions_from():
layout = ac.Layout1DLine(shape_1d=(5, ), region_list=[(0, 3)])
array = ac.Array1D.manual_native(array=[0.0, 1.0, 2.0, 3.0],
pixel_scales=1.0)
array_extracted = layout.array_1d_of_non_regions_from(array=array)
assert (array_extracted == np.array([0.0, 0.0, 0.0, 3.0])).all()
layout = ac.Layout1DLine(shape_1d=(5, ), region_list=[(0, 1), (3, 4)])
array_extracted = layout.array_1d_of_non_regions_from(array=array)
assert (array_extracted == np.array([0.0, 1.0, 2.0, 0.0])).all()
def test__array_1d_of_edges_and_epers_from(array):
layout = ac.Layout1DLine(shape_1d=(10, ), region_list=[(0, 4)])
extracted_array = layout.array_1d_of_edges_and_epers_from(
array=array, fpr_pixels=(0, 2), trails_pixels=(0, 2))
assert (extracted_array == np.array(
[0.0, 1.0, 0.0, 0.0, 4.0, 5.0, 0.0, 0.0, 0.0, 0.0])).all()
layout = ac.Layout1DLine(shape_1d=(10, ), region_list=[(0, 1), (3, 4)])
extracted_array = layout.array_1d_of_edges_and_epers_from(
array=array, fpr_pixels=(0, 1), trails_pixels=(0, 1))
assert (extracted_array == np.array(
[0.0, 1.0, 0.0, 3.0, 4.0, 0.0, 0.0, 0.0, 0.0, 0.0])).all()
def test__pre_cti_data_from(self):
simulator = ac.SimulatorDatasetLine(normalization=10.0, pixel_scales=1.0)
layout = ac.Layout1DLine(shape_1d=(3,), region_list=[(0, 2)])
pre_cti_data = simulator.pre_cti_data_from(layout=layout, pixel_scales=1.0)
print(pre_cti_data)
assert (pre_cti_data.native == np.array([10.0, 10.0, 0.0])).all()
def test__masked_front_edges_and_epers_from_layout():
unmasked = ac.Mask1DLine.unmasked(shape_slim=(5, ), pixel_scales=1.0)
layout = ac.Layout1DLine(shape_1d=(5, ), region_list=[(1, 3)])
mask = ac.Mask1DLine.masked_fprs_and_epers_from(
mask=unmasked,
layout=layout,
settings=ac.SettingsMask1DLine(front_edge_pixels=(1, 2),
trails_pixels=(0, 1)),
pixel_scales=0.1,
)
assert (mask == np.array([False, False, True, True, False])).all()
def test__no_instrumental_effects_input__only_cti_simulated(
self, clocker_1d, traps_x2, ccd
):
layout = ac.Layout1DLine(shape_1d=(5,), region_list=[(0, 5)])
simulator = ac.SimulatorDatasetLine(
pixel_scales=1.0, normalization=10.0, add_poisson_noise=False
)
dataset_line = simulator.from_layout(
layout=layout, clocker=clocker_1d, trap_list=traps_x2, ccd=ccd
)
assert dataset_line.data == pytest.approx(
np.array([9.43, 9.43, 9.43, 9.43, 9.43]), 1e-1
)
assert dataset_line.layout == layout
def test__include_read_noise__is_added_after_cti(self, clocker_1d, traps_x2, ccd):
layout = ac.Layout1DLine(shape_1d=(5,), region_list=[(0, 5)])
simulator = ac.SimulatorDatasetLine(
pixel_scales=1.0,
normalization=10.0,
read_noise=1.0,
add_poisson_noise=False,
noise_seed=1,
)
dataset_line = simulator.from_layout(
layout=layout, clocker=clocker_1d, trap_list=traps_x2, ccd=ccd
)
# Use seed to give us a known read noises map we'll test_autocti for
assert dataset_line.data == pytest.approx(
np.array([11.05513, 9.36790, 9.47129, 8.92700, 10.8654]), 1e-2
)
assert dataset_line.layout == layout
def test__from_fits__load_pre_cti_data_data_from_the_layout_ci_and_data(self):
create_fits(fits_path=fits_path)
layout_ci = ac.Layout1DLine(shape_1d=(7,), region_list=[(0, 7)])
imaging = ac.DatasetLine.from_fits(
pixel_scales=1.0,
layout=layout_ci,
data_path=path.join(fits_path, "3_ones.fits"),
data_hdu=0,
noise_map_path=path.join(fits_path, "3_twos.fits"),
noise_map_hdu=0,
pre_cti_data_path=path.join(fits_path, "3_threes.fits"),
pre_cti_data_hdu=0,
)
assert (imaging.data.native == np.ones((7,))).all()
assert (imaging.noise_map.native == 2.0 * np.ones((7,))).all()
assert (imaging.pre_cti_data.native == 3.0 * np.ones((7,))).all()
assert imaging.layout == layout_ci
clean_fits(fits_path=fits_path)
__Normalizations__
We require the normalization of the charge in every cti line dataset, as the names of the files are tagged with this.
"""
normalization_list = [100, 5000, 25000, 84700]
"""
__Layout__
We use the regions and normalization_list above to create the `Layout1DLine` of every cti-line we fit. This is used
for visualizing the model-fit.
"""
layout_list = [
ac.Layout1DLine(
shape_1d=shape_native,
region_list=region_1d_list,
normalization=normalization,
prescan=prescan,
overscan=overscan,
) for normalization in normalization_list
]
"""
__Dataset__
We now load every cti-line dataset, including a noise-map and pre-CTI line containing the data before read-out and
therefore without CTI. This uses a`DatasetLine` object.
"""
dataset_line_list = [
ac.DatasetLine.from_fits(
data_path=path.join(dataset_path, f"data_{layout.normalization}.fits"),
noise_map_path=path.join(dataset_path,
f"noise_map_{layout.normalization}.fits"),
# Test noise
noise = np.ones_like(post_cti) * 0.01
# Conversion value for HST ACS
pixel_scale = 0.05
# Convert to AutoCTI objects
post_cti = ac.Array1D.manual_native(array=post_cti, pixel_scales=pixel_scale)
noise = ac.Array1D.manual_native(array=noise, pixel_scales=pixel_scale)
pre_cti = ac.Array1D.manual_native(array=pre_cti, pixel_scales=pixel_scale)
shape_native = (length, 1)
region_1d_list = [(warm_index, warm_index + 1)]
normalization = np.sum(pre_cti)
layout = ac.Layout1DLine(shape_1d=shape_native,
region_list=region_1d_list,
normalization=normalization)
dataset_line = ac.DatasetLine(data=post_cti,
noise_map=noise,
pre_cti_data=pre_cti,
layout=layout)
# Set up fitting CTI model
clocker = ac.Clocker1D(express=express)
trap_0 = af.Model(ac.TrapInstantCapture)
trap_0.density = af.UniformPrior(lower_limit=0.0, upper_limit=20.0)
trap_0.release_timescale = af.UniformPrior(lower_limit=0.0, upper_limit=10.0)
trap_0.fractional_volume_none_exposed = 0.0
trap_0.fractional_volume_full_exposed = 0.0