import autolens as al
import autolens.plot as aplt
from test_autolens.simulators.imaging import instrument_util
import numpy as np
imaging = instrument_util.load_test_imaging(
dataset_name="light_sersic__source_sersic", instrument="vro")
array = imaging.image
plotter = aplt.Plotter(
figure=aplt.Figure(figsize=(10, 10)),
cmap=aplt.ColorMap(cmap="gray",
norm="symmetric_log",
norm_min=-0.13,
norm_max=20,
linthresh=0.02),
grid_scatterer=aplt.GridScatterer(marker="+", colors="cyan", size=450),
)
grid = al.GridIrregular(grid=[[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]])
print(grid)
vector_field = al.VectorFieldIrregular(vectors=[(1.0, 2.0), (2.0, 1.0)],
grid=[(-1.0, 0.0), (-2.0, 0.0)])
aplt.Array(
array=array.in_2d,
grid=grid,
positions=al.GridIrregularGrouped([(0.0, 1.0), (0.0, 2.0)]),
"""
__Visualization Customization__
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)
"""
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**.