print(mapper.source_grid_slim[0])
print("Source Grid2D Pixel 2")
print(source_plane_grid[1])
print(mapper.source_grid_slim[1])
print("etc.")
"""
We can over-lay this grid on the figure, which is starting to look a bit less boring now!
"""
include_2d = aplt.Include2D(mapper_source_grid_slim=True,
mapper_source_pixelization_grid=True)
mapper_plotter = aplt.MapperPlotter(mapper=mapper, include_2d=include_2d)
mapper_plotter.set_title("Even less Boring Grid2D of Rectangular Pixels")
mapper_plotter.figure_2d()
mat_plot_2d = aplt.MatPlot2D(axis=aplt.Axis(extent=[-0.3, 0.3, -0.3, 0.3]))
mapper_plotter = aplt.MapperPlotter(mapper=mapper,
mat_plot_2d=mat_plot_2d,
include_2d=include_2d)
mapper_plotter.set_title("Zoomed Grid2D of Rectangular Pixels")
mapper_plotter.figure_2d()
"""
Finally, the mapper`s `pixeliation_grid` has lots of information about the pixelization, for example, the arc-second
size and dimensions.
"""
print(mapper.source_pixelization_grid.shape_native_scaled)
print(mapper.source_pixelization_grid.scaled_maxima)
print(mapper.source_pixelization_grid.scaled_minima)
"""
__Wrap Up__
plane_plotter = aplt.PlanePlotter(plane=source_plane,
grid=source_plane_grid,
mat_plot_2d=mat_plot_2d)
plane_plotter.figures_2d(plane_grid=True)
"""
The source-plane looks very interesting! We can see it is not regular, not uniform, and has an aestetically pleasing
visual appearance. Remember that every coordinate on this source-plane grid (e.g. every black dot) corresponds to a
coordinate on the image-plane grid that has been deflected by our mass profile; this is strong gravitational lensing
in action!
We can zoom in on the `centre` of the source-plane (remembering the lens galaxy was centred at (0.0", 0.0")) to
reveal a 'diamond like' structure with a fractal like appearance.
"""
mat_plot_2d = aplt.MatPlot2D(
title=aplt.Title(label="Source-plane Grid2D Zoomed"),
axis=aplt.Axis(extent=[-0.1, 0.1, -0.1, 0.1]),
)
plane_plotter = aplt.PlanePlotter(plane=source_plane,
grid=source_plane_grid,
mat_plot_2d=mat_plot_2d)
plane_plotter.figures_2d(plane_grid=True)
"""
__Mappings__
Lets plot the image and source planes next to one another and highlight specific points on both. The coloring of the
highlighted points therefore shows how specific image pixels **map** to the source-plane (and visa versa).
This is the first time we have used the `Visuals2D` object, which allows the appearance of **PyAutoLens** figures to
be customized. We'll see this object crop up throughout the **HowToLens** lectures, and a full description of all
# workspace_path = str(here())
# %cd $workspace_path
# print(f"Working Directory has been set to `{workspace_path}`")
from os import path
import autolens as al
import autolens.plot as aplt
"""
First, lets load an example Hubble Space Telescope image of a real strong lens as an `Array2D`.
"""
dataset_path = path.join("dataset", "slacs", "slacs1430+4105")
image_path = path.join(dataset_path, "image.fits")
image = al.Array2D.from_fits(file_path=image_path, hdu=0, pixel_scales=0.03)
"""
We can customize the figure using the `Axis` matplotlib wrapper object which wraps the following method(s):
plt.axis: https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.pyplot.axis.html
"""
array_plotter = aplt.Array2DPlotter(array=image)
array_plotter.figure_2d()
axis = aplt.Axis(extent=[-1.0, 1.0, -1.0, 1.0])
mat_plot_2d = aplt.MatPlot2D(axis=axis)
array_plotter = aplt.Array2DPlotter(array=image, mat_plot_2d=mat_plot_2d)
array_plotter.figure_2d()
"""
Finish.
"""
