tutorial_0_visualization.md

✏️ This page is auto-generated from scripts/chapter_1_introduction/tutorial_0_visualization.py — do not edit it directly. It shows the example fully executed, with its real output images. Run it yourself via the Python script or the Jupyter notebook.

Tutorial 0: Visualization

In this tutorial, we quickly cover visualization in PyAutoLens and make sure images display clearly in your Jupyter notebook and on your computer screen.

Contents

• Directories: PyAutoLens assumes the working directory is autolens_workspace on your hard-disk.
• Dataset: Load and plot the strong lens dataset.
• Subplots: In addition to plotting individual figures, PyAutoLens can plot subplots which show multiple.
• Plot Customization: Does the figure display correctly on your computer screen?
• Overlays: Overlays such as critical curves and image positions are added using the lines= and positions=.
• Wrap Up: Summary of the script and next steps.

from autoconf import jax_wrapper  # Sets JAX environment before other imports

from autoconf import setup_notebook; setup_notebook()

Working Directory has been set to `HowToLens`

If the printed working directory does not match the workspace path on your computer, you can manually set it as follows (the example below shows the path I would use on my laptop. The code is commented out so you do not use this path in this tutorial!

# workspace_path = "/Users/Jammy/Code/PyAuto/autolens_workspace"
# #%cd $workspace_path
# print(f"Working Directory has been set to `{workspace_path}`")

Dataset

The dataset_path specifies where the dataset is located, which is the directory autolens_workspace/dataset/imaging/simple__no_lens_light.

There are many example simulated images of strong lenses in this directory that will be used throughout the HowToLens lectures.

from pathlib import Path

import autolens as al
import autolens.plot as aplt

dataset_path = Path("dataset") / "imaging" / "simple__no_lens_light"

We now load this dataset from .fits files and create an instance of an Imaging object.

dataset = al.Imaging.from_fits(
    data_path=dataset_path / "data.fits",
    noise_map_path=dataset_path / "noise_map.fits",
    psf_path=dataset_path / "psf.fits",
    pixel_scales=0.1,
)

We can plot an image with aplt.plot_array(), passing the data array and a title.

aplt.plot_array(array=dataset.data, title="Dataset Image")

Subplots

In addition to plotting individual figures, PyAutoLens can plot subplots which show multiple views of the dataset at once.

The aplt.subplot_imaging_dataset() function plots the data, noise-map and PSF together.

aplt.subplot_imaging_dataset(dataset=dataset)

Plot Customization

Does the figure display correctly on your computer screen?

If not, the default matplotlib settings can be customized via the config files in:

autolens_workspace/config/visualize/

Key config entries:

• mat_wrap.yaml -> Figure -> figure: -> figsize
• mat_wrap.yaml -> YLabel -> figure: -> fontsize
• mat_wrap.yaml -> XLabel -> figure: -> fontsize
• mat_wrap.yaml -> TickParams -> figure: -> labelsize
• mat_wrap.yaml -> YTicks -> figure: -> labelsize
• mat_wrap.yaml -> XTicks -> figure: -> labelsize

For quick one-off adjustments you can pass title=, colormap=, and use_log10= directly:

aplt.plot_array(array=dataset.data, title="Dataset Image (Log10)", use_log10=True)

Overlays

Overlays such as critical curves and image positions are added using the lines= and positions= keyword arguments.

For example, we can compute the critical curves of a tracer and overlay them on the image.

grid = al.Grid2D.uniform(shape_native=(100, 100), pixel_scales=0.05)

lens_galaxy = al.Galaxy(
    redshift=0.5,
    mass=al.mp.Isothermal(centre=(0.0, 0.0), einstein_radius=1.6, ell_comps=(0.0, 0.0)),
)

source_galaxy = al.Galaxy(
    redshift=1.0,
    bulge=al.lp.SersicCoreSph(
        centre=(0.0, 0.0), intensity=1.0, effective_radius=0.5, sersic_index=2.0
    ),
)

tracer = al.Tracer(galaxies=[lens_galaxy, source_galaxy])

tangential_critical_curve_list = al.LensCalc.from_tracer(
    tracer=tracer
).tangential_critical_curve_list_from(grid=grid)

aplt.plot_array(
    array=tracer.image_2d_from(grid=grid),
    title="Tracer Image with Critical Curves",
    lines=tangential_critical_curve_list,
)

Wrap Up

Throughout the lectures you'll see lots more visuals plotted on figures and subplots.

The key plotting functions you'll use are:

• aplt.plot_array(array, title, ...) — plot any 2D array.
• aplt.plot_grid(grid, title, ...) — plot a 2D grid of coordinates.
• aplt.subplot_imaging_dataset(dataset) — multi-panel dataset overview.
• aplt.subplot_tracer(tracer, grid) — multi-panel tracer overview.
• aplt.subplot_fit_imaging(fit) — multi-panel fit overview.

Great! Hopefully, visualization in PyAutoLens is displaying nicely for us to get on with the HowToLens lecture series.