def test__subplot_ci_lines__is_output(imaging_ci_7x7, plot_path, plot_patch):
imaging_ci_plotter = aplt.ImagingCIPlotter(
imaging=imaging_ci_7x7,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(plot_path, format="png")),
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(plot_path, format="png")),
)
imaging_ci_plotter.subplot_imaging_ci()
assert path.join(plot_path, "subplot_imaging_ci.png") in plot_patch.paths
imaging_ci_plotter.subplot_1d_ci_line_region(
line_region="parallel_front_edge")
assert (path.join(plot_path, "subplot_1d_ci_parallel_front_edge.png")
in plot_patch.paths)
def visualize_imaging_ci_lines(self, imaging_ci, line_region):
def should_plot(name):
return plot_setting(section="dataset", name=name)
mat_plot_1d = self.mat_plot_1d_from(subfolders=f"imaging_ci")
imaging_ci_plotter = aplt.ImagingCIPlotter(
imaging=imaging_ci, mat_plot_1d=mat_plot_1d, include_2d=self.include_2d
)
if should_plot("subplot_dataset"):
imaging_ci_plotter.subplot_1d_ci_line_region(line_region=line_region)
imaging_ci_plotter.figures_1d_ci_line_region(
line_region=line_region,
image=should_plot("data"),
noise_map=should_plot("noise_map"),
signal_to_noise_map=should_plot("signal_to_noise_map"),
pre_cti_data=should_plot("pre_cti_data"),
)
def test__individual_lines_are_output(imaging_ci_7x7, plot_path, plot_patch):
imaging_ci_plotter = aplt.ImagingCIPlotter(
imaging=imaging_ci_7x7,
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(plot_path, format="png")),
)
imaging_ci_plotter.figures_1d_ci_line_region(
line_region="parallel_front_edge",
image=True,
noise_map=True,
pre_cti_data=True,
signal_to_noise_map=True,
)
assert path.join(plot_path,
"image_parallel_front_edge.png") in plot_patch.paths
assert path.join(plot_path,
"noise_map_parallel_front_edge.png") in plot_patch.paths
assert (path.join(plot_path, "pre_cti_data_parallel_front_edge.png")
in plot_patch.paths)
assert (path.join(plot_path, "signal_to_noise_map_parallel_front_edge.png")
in plot_patch.paths)
plot_patch.paths = []
imaging_ci_plotter.figures_1d_ci_line_region(
line_region="parallel_front_edge", image=True, pre_cti_data=True)
assert path.join(plot_path,
"image_parallel_front_edge.png") in plot_patch.paths
assert (path.join(plot_path, "noise_map_parallel_front_edge.png")
not in plot_patch.paths)
assert (path.join(plot_path, "pre_cti_data_parallel_front_edge.png")
in plot_patch.paths)
assert (path.join(plot_path, "signal_to_noise_map_parallel_front_edge.png")
not in plot_patch.paths)
def test__individual_attributes_are_output(imaging_ci_7x7, plot_path,
plot_patch):
imaging_ci_plotter = aplt.ImagingCIPlotter(
imaging=imaging_ci_7x7,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(plot_path, format="png")),
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(plot_path, format="png")),
)
imaging_ci_7x7.cosmic_ray_map[0, 0] = 1.0
imaging_ci_plotter.figures_2d(
image=True,
noise_map=True,
pre_cti_data=True,
signal_to_noise_map=True,
cosmic_ray_map=True,
)
assert path.join(plot_path, "image_2d.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") in plot_patch.paths
assert path.join(plot_path, "pre_cti_data.png") in plot_patch.paths
assert path.join(plot_path, "signal_to_noise_map.png") in plot_patch.paths
assert path.join(plot_path, "cosmic_ray_map.png") in plot_patch.paths
plot_patch.paths = []
imaging_ci_plotter.figures_2d(image=True, pre_cti_data=True)
assert path.join(plot_path, "image_2d.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") not in plot_patch.paths
assert path.join(plot_path, "pre_cti_data.png") in plot_patch.paths
assert path.join(plot_path,
"signal_to_noise_map.png") not in plot_patch.paths
def visualize_imaging_ci(self, imaging_ci):
def should_plot(name):
return plot_setting(section="dataset", name=name)
mat_plot_2d = self.mat_plot_2d_from(subfolders=f"imaging_ci")
imaging_ci_plotter = aplt.ImagingCIPlotter(
imaging=imaging_ci, mat_plot_2d=mat_plot_2d, include_2d=self.include_2d
)
imaging_ci_plotter.figures_2d(
image=should_plot("data"),
noise_map=should_plot("noise_map"),
inverse_noise_map=should_plot("inverse_noise_map"),
signal_to_noise_map=should_plot("signal_to_noise_map"),
absolute_signal_to_noise_map=should_plot("absolute_signal_to_noise_map"),
potential_chi_squared_map=should_plot("potential_chi_squared_map"),
pre_cti_data=should_plot("pre_cti_data"),
cosmic_ray_map=should_plot("cosmic_ray_map"),
)
if should_plot("subplot_dataset"):
imaging_ci_plotter.subplot_imaging_ci()
simulator.from_layout(
clocker=clocker,
layout=layout_ci,
serial_trap_list=[serial_trap_0, serial_trap_1, serial_trap_2],
serial_ccd=serial_ccd,
) for layout_ci in layout_list
]
"""
__Output__
Output a subplot of the simulated dataset to the dataset path as .png files.
"""
mat_plot_2d = aplt.MatPlot2D(
output=aplt.Output(path=dataset_path, format="png"))
imaging_ci_plotter = aplt.ImagingCIPlotter(imaging=imaging_ci_list[0],
mat_plot_2d=mat_plot_2d)
imaging_ci_plotter.subplot_imaging_ci()
"""
Output the image, noise-map and pre cti image of the charge injection dataset to .fits files.
"""
[
imaging_ci.output_to_fits(
image_path=path.join(
dataset_path,
f"image_{int(imaging_ci.layout.normalization)}.fits"),
noise_map_path=path.join(
dataset_path,
f"noise_map_{int(imaging_ci.layout.normalization)}.fits"),
pre_cti_data_path=path.join(
dataset_path,
f"pre_cti_data_{int(imaging_ci.layout.normalization)}.fits"),
noise_map_path=path.join(
dataset_path, f"noise_map_{layout.normalization}.fits"
),
pre_cti_data_path=path.join(
dataset_path, f"pre_cti_data_{layout.normalization}.fits"
),
layout=layout,
pixel_scales=0.1,
)
for layout in layout_list
]
"""
Lets plot the first `ImagingCI`.
"""
imaging_ci_plotter = aplt.ImagingCIPlotter(imaging=imaging_ci_list[0])
imaging_ci_plotter.subplot_imaging_ci()
"""
__Clocking__
The `Clocker` models the CCD read-out, including CTI.
For parallel clocking, we use 'charge injection mode' which transfers the charge of every pixel over the full CCD.
"""
clocker = ac.Clocker2D(parallel_express=2, parallel_charge_injection_mode=True)
"""
__Model__
We now compose our CTI model, which represents the trap species and CCD volume filling behaviour used to fit the charge
dataset_path, f"noise_map_{pattern.normalization}.fits"
),
pre_cti_data_path=path.join(
dataset_path, f"pre_cti_data_{pattern.normalization}.fits"
),
pixel_scales=0.1,
layout=pattern,
roe_corner=(1, 0),
)
for pattern in line_pattern_list
]
"""
Lets plot the first `DatasetLine` with its `Mask1D`
"""
imaging_ci_plotter = aplt.ImagingCIPlotter(imaging=line_list[0])
imaging_ci_plotter.subplot_imaging_ci()
"""
We now need to mask the data, so that regions where there is no signal (e.g. the edges) are omitted from the fit.
"""
mask_list = [
ac.Mask2D.unmasked(shape_native=line.shape_native, pixel_scales=line.pixel_scales)
for line in line_list
]
"""
The MaskedImaging object combines the dataset with the mask.
"""
masked_line_list = [
ac.ci.ImagingCI(imaging_ci=line, mask=mask)
f"image_{layout.normalization}.fits"),
noise_map_path=path.join(dataset_path,
f"noise_map_{layout.normalization}.fits"),
pre_cti_data_path=path.join(
dataset_path, f"pre_cti_data_{layout.normalization}.fits"),
layout=layout,
pixel_scales=0.1,
) for layout in layout_list
]
"""
__Plotting__
We now pass the first dataset in the imaging to a `ImagingCIPlotter` and call various `figure_*` methods to plot
different attributes.
"""
imaging_ci_plotter = aplt.ImagingCIPlotter(imaging=imaging_ci_list[0])
imaging_ci_plotter.figures_2d(
image=True,
noise_map=True,
pre_cti_data=True,
inverse_noise_map=True,
potential_chi_squared_map=True,
absolute_signal_to_noise_map=True,
)
"""
The `ImagingCIPlotter` may also plot a subplot of all of these attributes.
"""
imaging_ci_plotter.subplot_imaging_ci()
"""
We can also call `figures_1d_*` methods which create 1D plots of regions of the image binned over the parallel or
serial direction.
