Rubin's repeated observations of cluster-scale strong lenses let us study their image structure and time delays. RubinsCSSL collects our simulations and analysis for these systems, with optional PyCS3 tools for fitting time delays.
The PyCS3 time-delay fitting modules use the time-delays extra.
python -m pip install -e '.[time-delays]'Set RUBINSCSSL_PATH to the checkout directory when the package is used from a different location. If it is unset, the package uses its checkout root. data/ and visuals/ are ignored by Git for local inputs and generated figures.
Install .[plots] for the corner, static band-performance, and interactive
Bokeh plots.
python -m pip install -e '.[plots]'plot_band_performance() and plot_band_performance_bokeh() render the same
assessability, conditional-accuracy, and end-to-end fractions from the summary
table. The HTML version adds hover values and a legend that can hide series.
Both plotting functions and make_population_corner_suite() accept
typeplotback='dark' for black backgrounds with white axes. The default is
white with black axes, or the theme selected by an enclosing TDpy
plot_style() block. Returned figures and saved files share the selected theme
without changing later plots.
The synthetic feature-pipeline notebook
passes explicitly toy dual-image curves and toy fit assessments through
build_population_analysis_tables(). It generates static and Bokeh band
summaries under the ignored visuals/ directory. These values exercise the
feature and plotting APIs; they are not Rubin observations, PyCS3 fits, or a
measurement of time-delay recovery performance. The small deterministic
example runs quickly and needs no PyCS3 installation.