def test__fit_sub_plot_real_space(
fit_interferometer_x2_plane_7x7,
fit_interferometer_x2_plane_inversion_7x7,
include_2d_all,
plot_path,
plot_patch,
):
fit_interferometer_plotter = aplt.FitInterferometerPlotter(
fit=fit_interferometer_x2_plane_7x7,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(output=aplt.Output(plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(plot_path, format="png")),
)
fit_interferometer_plotter.subplot_fit_real_space()
assert path.join(plot_path, "subplot_fit_real_space.png") in plot_patch.paths
plot_patch.paths = []
fit_interferometer_plotter = aplt.FitInterferometerPlotter(
fit=fit_interferometer_x2_plane_inversion_7x7,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(output=aplt.Output(plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(plot_path, format="png")),
)
fit_interferometer_plotter.subplot_fit_real_space()
assert path.join(plot_path, "subplot_fit_real_space.png") in plot_patch.paths
def test__subplot_fit_point(fit_point_dataset_x2_plane, include_2d_all,
plot_path, plot_patch):
fit_point_plotter = aplt.FitPointDatasetPlotter(
fit=fit_point_dataset_x2_plane,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(path=plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")),
)
fit_point_plotter.subplot_fit_point()
assert path.join(plot_path, "subplot_fit_point.png") in plot_patch.paths
def test__subplot_point_dataset(point_dataset, include_2d_all, plot_path,
plot_patch):
point_dataset_plotter = aplt.PointDatasetPlotter(
point_dataset=point_dataset,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(path=plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")),
)
point_dataset_plotter.subplot_point_dataset()
assert path.join(plot_path,
"subplot_point_dataset.png") in plot_patch.paths
def test__fit_point_quantities_are_output(fit_point_dataset_x2_plane,
include_2d_all, plot_path,
plot_patch):
fit_point_plotter = aplt.FitPointDatasetPlotter(
fit=fit_point_dataset_x2_plane,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(path=plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")),
)
fit_point_plotter.figures_2d(positions=True, fluxes=True)
assert path.join(plot_path,
"fit_point_dataset_positions.png") in plot_patch.paths
assert path.join(plot_path,
"fit_point_dataset_fluxes.png") in plot_patch.paths
plot_patch.paths = []
fit_point_plotter.figures_2d(positions=True, fluxes=False)
assert path.join(plot_path,
"fit_point_dataset_positions.png") in plot_patch.paths
assert path.join(plot_path,
"fit_point_dataset_fluxes.png") not in plot_patch.paths
plot_patch.paths = []
fit_point_dataset_x2_plane.point_dataset.fluxes = None
fit_point_plotter = aplt.FitPointDatasetPlotter(
fit=fit_point_dataset_x2_plane,
include_2d=include_2d_all,
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")),
)
fit_point_plotter.figures_2d(positions=True, fluxes=True)
assert path.join(plot_path,
"fit_point_dataset_positions.png") in plot_patch.paths
assert path.join(plot_path,
"fit_point_dataset_fluxes.png") not in plot_patch.paths
def test__subplot_point_dict(point_dict, include_2d_all, plot_path,
plot_patch):
point_dict_plotter = aplt.PointDictPlotter(
point_dict=point_dict,
include_2d=include_2d_all,
mat_plot_1d=aplt.MatPlot1D(
output=aplt.Output(path=plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(
output=aplt.Output(path=plot_path, format="png")),
)
point_dict_plotter.subplot_positions()
assert path.join(plot_path,
"subplot_point_dict_positions.png") in plot_patch.paths
point_dict_plotter.subplot_fluxes()
assert path.join(plot_path,
"subplot_point_dict_fluxes.png") in plot_patch.paths
yx_plotter = aplt.YX1DPlotter(y=y, x=x)
yx_plotter.figure_1d()
"""
We can fill between two lines line on the y vs x plot using the `FillBetween` matplotlib wrapper object which wraps
the following method(s):
plt.fill_between: https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.fill_between.html
The input`match_color_to_yx=True` ensures the filled area is the same as the line that is plotted, as opposed
to a different color. This ensures that shaded regions can easily be paired in color to the plot when it is
appropriate (e.g. plotted the errors of the line).
"""
y1 = al.Array1D.manual_native(array=[0.5, 1.5, 2.5, 3.5, 4.5],
pixel_scales=1.0)
y2 = al.Array1D.manual_native(array=[1.5, 2.5, 3.5, 4.5, 5.5],
pixel_scales=1.0)
mat_plot_1d = aplt.MatPlot1D(
fill_between=aplt.FillBetween(match_color_to_yx=True, alpha=0.3))
visuals_1d = aplt.Visuals1D(shaded_region=[y1, y2])
yx_plotter = aplt.YX1DPlotter(y=y,
x=x,
mat_plot_1d=mat_plot_1d,
visuals_1d=visuals_1d)
yx_plotter.figure_1d()
"""
Finish.
"""
value=grid.pixel_scale),
fluxes=fluxes_1,
fluxes_noise_map=al.ValuesIrregular(values=[1.0, 1.0, 1.0, 1.0]),
)
point_dict = al.PointDict(
point_dataset_list=[point_dataset_0, point_dataset_1])
point_dict.output_to_json(file_path=path.join(dataset_path, "point_dict.json"),
overwrite=True)
"""
__Visualize__
Output a subplot of the simulated point source dictionary and the tracer's quantities to the dataset path as .png files.
"""
mat_plot_1d = aplt.MatPlot1D(
output=aplt.Output(path=dataset_path, format="png"))
mat_plot_2d = aplt.MatPlot2D(
output=aplt.Output(path=dataset_path, format="png"))
point_dict_plotter = aplt.PointDictPlotter(point_dict=point_dict,
mat_plot_1d=mat_plot_1d,
mat_plot_2d=mat_plot_2d)
point_dict_plotter.subplot_positions()
point_dict_plotter.subplot_fluxes()
tracer_plotter = aplt.TracerPlotter(tracer=tracer,
grid=grid,
mat_plot_2d=mat_plot_2d)
tracer_plotter.subplot_tracer()
tracer_plotter.subplot_plane_images()
"""
def test__fit_quantities_are_output(
fit_interferometer_x2_plane_7x7, plot_path, plot_patch
):
fit_interferometer_plotter = aplt.FitInterferometerPlotter(
fit=fit_interferometer_x2_plane_7x7,
mat_plot_1d=aplt.MatPlot1D(output=aplt.Output(path=plot_path, format="png")),
mat_plot_2d=aplt.MatPlot2D(output=aplt.Output(path=plot_path, format="png")),
)
fit_interferometer_plotter.figures_2d(
visibilities=True,
noise_map=True,
signal_to_noise_map=True,
model_visibilities=True,
residual_map_real=True,
residual_map_imag=True,
normalized_residual_map_real=True,
normalized_residual_map_imag=True,
chi_squared_map_real=True,
chi_squared_map_imag=True,
image=True,
)
assert path.join(plot_path, "visibilities.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") in plot_patch.paths
assert path.join(plot_path, "signal_to_noise_map.png") in plot_patch.paths
assert path.join(plot_path, "model_visibilities.png") in plot_patch.paths
assert (
path.join(plot_path, "real_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "real_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "real_normalized_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "imag_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "imag_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "imag_normalized_residual_map_vs_uv_distances.png")
in plot_patch.paths
)
assert path.join(plot_path, "image_2d.png") in plot_patch.paths
plot_patch.paths = []
fit_interferometer_plotter.figures_2d(
visibilities=True,
noise_map=False,
signal_to_noise_map=False,
model_visibilities=True,
chi_squared_map_real=True,
chi_squared_map_imag=True,
)
assert path.join(plot_path, "visibilities.png") in plot_patch.paths
assert path.join(plot_path, "noise_map.png") not in plot_patch.paths
assert path.join(plot_path, "signal_to_noise_map.png") not in plot_patch.paths
assert path.join(plot_path, "model_visibilities.png") in plot_patch.paths
assert (
path.join(plot_path, "real_residual_map_vs_uv_distances.png")
not in plot_patch.paths
)
assert (
path.join(plot_path, "real_normalized_residual_map_vs_uv_distances.png")
not in plot_patch.paths
)
assert (
path.join(plot_path, "real_chi_squared_map_vs_uv_distances.png")
in plot_patch.paths
)
assert (
path.join(plot_path, "imag_residual_map_vs_uv_distances.png")
not in plot_patch.paths
)
assert (
path.join(plot_path, "imag_normalized_residual_map_vs_uv_distances.png")
not in plot_patch.paths
)
assert (
path.join(plot_path, "imag_chi_squared_map_vs_uv_distances.png")
in plot_patch.paths
)
mass_1 = al.mp.EllPowerLaw(
centre=(0.0, 0.0),
einstein_radius=1.0,
elliptical_comps=al.convert.elliptical_comps_from(axis_ratio=0.7,
angle=45.0),
slope=2.1,
)
"""
We also need the 2D grid the `MassProfile`'s are evaluated on.
"""
grid = al.Grid2D.uniform(shape_native=(100, 100), pixel_scales=0.05)
"""
We now pass the mass profiles and grid to a `MassProfilePlotter` and create a `MultiYX1DPlotter` which will be
used to plot both of their convergences in 1D on the same figure.
"""
mat_plot_1d = aplt.MatPlot1D(yx_plot=aplt.YXPlot(plot_axis_type="semilogy"))
mass_profile_plotter_0 = aplt.MassProfilePlotter(mass_profile=mass_0,
grid=grid,
mat_plot_1d=mat_plot_1d)
mass_profile_plotter_1 = aplt.MassProfilePlotter(mass_profile=mass_1,
grid=grid,
mat_plot_1d=mat_plot_1d)
"""
We use these plotters to create a `MultiYX1DPlotter` which plot both of their convergences in 1D on the same figure.
"""
multi_plotter = aplt.MultiYX1DPlotter(
plotter_list=[mass_profile_plotter_0, mass_profile_plotter_1])
"""
We now use the multi plotter to plot the convergences, where:
mass_profile_plotter = aplt.MassProfilePlotter(mass_profile=mass,
grid=grid,
include_1d=include_1d)
mass_profile_plotter.figures_1d(convergence=True)
"""
The appearance of the einstein radius is customized using a `EinsteinRadiusAXVLine` object.
To plot the einstein radius as a vertical line this wraps the following matplotlib method:
plt.axvline: https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.axvline.html
"""
einstein_radius_axvline = aplt.EinsteinRadiusAXVLine(linestyle="-.",
c="r",
linewidth=20)
mat_plot_1d = aplt.MatPlot1D(einstein_radius_axvline=einstein_radius_axvline)
mass_profile_plotter = aplt.MassProfilePlotter(mass_profile=mass,
grid=grid,
mat_plot_1d=mat_plot_1d,
include_1d=include_1d)
mass_profile_plotter.figures_1d(convergence=True)
"""
To plot the einstein radius manually, we can pass it into a` Visuals1D` object.
"""
visuals_1d = aplt.Visuals1D(einstein_radius=mass.einstein_radius_from_grid(
grid=grid))
mass_profile_plotter = aplt.MassProfilePlotter(mass_profile=mass,
grid=grid,
visuals_1d=visuals_1d)
light_profile=bulge, grid=grid, include_1d=include_1d
)
light_profile_plotter.figures_1d(image=True)
"""
The appearance of the half-light radius is customized using a `HalfLightRadiusAXVLine` object.
To plot the half-light radius as a vertical line this wraps the following matplotlib method:
plt.axvline: https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.axvline.html
"""
half_light_radius_axvline = aplt.HalfLightRadiusAXVLine(
linestyle="-.", c="r", linewidth=20
)
mat_plot_1d = aplt.MatPlot1D(half_light_radius_axvline=half_light_radius_axvline)
light_profile_plotter = aplt.LightProfilePlotter(
light_profile=bulge, grid=grid, mat_plot_1d=mat_plot_1d, include_1d=include_1d
)
light_profile_plotter.figures_1d(image=True)
"""
To plot the half-light radius manually, we can pass it into a` Visuals1D` object.
"""
visuals_1d = aplt.Visuals1D(half_light_radius=bulge.half_light_radius)
light_profile_plotter = aplt.LightProfilePlotter(
light_profile=bulge, grid=grid, visuals_1d=visuals_1d
)
light_profile_plotter.figures_1d(image=True)
