imaging_plotter = aplt.ImagingPlotter(imaging=imaging)
imaging_plotter.figures_2d(image=True)
"""
__Plot Customization__
Does the figure display correctly on your computer screen?
If not, you can customize a number of matplotlib setup options using a `MatPlot2D` object in **PyAutoLens**, which
wraps the `matplotlib` methods used to display the image.
(For example, the `Figure` class wraps the `matplotlib` method `plt.figure(), whereas the `Yticks` class wraps
`plt.yticks`).
"""
mat_plot_2d = aplt.MatPlot2D(
figure=aplt.Figure(figsize=(7, 7)),
yticks=aplt.YTicks(fontsize=8),
xticks=aplt.XTicks(fontsize=8),
title=aplt.Title(fontsize=12),
ylabel=aplt.YLabel(fontsize=6),
xlabel=aplt.XLabel(fontsize=6),
)
imaging_plotter = aplt.ImagingPlotter(imaging=imaging, mat_plot_2d=mat_plot_2d)
imaging_plotter.figures_2d(image=True)
"""
Many matplotlib options can be customized, but for now we're only concerned with making sure figures display clear in
your Jupyter Notebooks. Nevertheless, a comprehensive API reference guide of all `matplotlib` wrappers and methods can
be found in the `autolens_workspace/plot` package. You should check this out once you are more familiar with
**PyAutoLens**.
Ideally, we would not specify a new `MatPlot2D` object every time we plot an image, especially as you would be
The benefit of inspecting fits using the aggregator, rather than the files outputs to the hard-disk, is that we can
customize the plots using the PyAutoLens mat_plot_2d.
Below, we create a new function to apply as a generator to do this. However, we use a convenience method available
in the PyAutoLens aggregator package to set up the fit.
"""
fit_agg = al.agg.FitImagingAgg(aggregator=agg)
fit_imaging_gen = fit_agg.max_log_likelihood_gen()
for fit in fit_imaging_gen:
mat_plot_2d = aplt.MatPlot2D(
figure=aplt.Figure(figsize=(12, 12)),
title=aplt.Title(label="Custom Image", fontsize=24),
yticks=aplt.YTicks(fontsize=24),
xticks=aplt.XTicks(fontsize=24),
cmap=aplt.Cmap(norm="log", vmax=1.0, vmin=1.0),
colorbar_tickparams=aplt.ColorbarTickParams(labelsize=20),
units=aplt.Units(in_kpc=True),
)
fit_imaging_plotter = aplt.FitImagingPlotter(fit=fit,
mat_plot_2d=mat_plot_2d)
fit_imaging_plotter.figures_2d(normalized_residual_map=True)
"""
Making this plot for a paper? You can output it to hard disk.
"""
fit_agg = al.agg.FitImagingAgg(aggregator=agg)
fit_imaging_gen = fit_agg.max_log_likelihood_gen()
https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.tick_params.html
https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.yticks.html
https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.xticks.html
"""
tickparams = aplt.TickParams(
axis="y",
which="major",
direction="out",
color="b",
labelsize=20,
labelcolor="r",
length=2,
pad=5,
width=3,
grid_alpha=0.8,
)
yticks = aplt.YTicks(alpha=0.8, fontsize=10, rotation="vertical")
xticks = aplt.XTicks(alpha=0.5, fontsize=5, rotation="horizontal")
mat_plot_2d = aplt.MatPlot2D(tickparams=tickparams,
yticks=yticks,
xticks=xticks)
array_plotter = aplt.Array2DPlotter(array=image, mat_plot_2d=mat_plot_2d)
array_plotter.figure_2d()
"""
Finish.
"""