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. """