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
direction.
- Loading and storing exposure information (e.g. date of observation, exposure time).
- Using this information to convert the units of the image to counts, which are the appropriate unit for **arcticpy**.
"""
frame = ac.acs.ImageACS.from_fits(file_path=f"{dataset_path}.fits",
quadrant_letter="A")
"""
We can use the **PyAutoCTI** visualization libary to plot this `Frame2D`.
The image wouldn't not display clearly using the default color scale (which is the default color scale of matplotlib)
so we've updated it below.
"""
plotter = aplt.MatPlot2D(cmap=aplt.Cmap(norm="linear", vmax=2800.0))
aplt.Frame2D(frame=frame, plotter=plotter)
"""
To correct the ACS data, we need a CTI model describing:
- The properties of the `Trap`'s on the CCD that are responsible for CTI by capturing and releasing electrons during
read-out.
- The volume-filling behaviour of electrons in CCD pixels, as this effects whether `Trap`'s are able to captrue them
or not!
**PyAutoCTI** again has in-built tools for automatically loading the CTI model appropriate for correcting ACS data,
using the date of observation loaded in the `ACSFrame` object to choose a model based on the date of observation.
"""
# trap_list = ac.acs.trap_list.from_header(header=header)
# ccd = ac.acs.CCD()