FEAT: Use nested sampling weights in plot_corner and quantiles#1075
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christianescamilla15-cell wants to merge 1 commit intobilby-dev:mainfrom
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FEAT: Use nested sampling weights in plot_corner and quantiles#1075christianescamilla15-cell wants to merge 1 commit intobilby-dev:mainfrom
christianescamilla15-cell wants to merge 1 commit intobilby-dev:mainfrom
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Adds an `use_nested_samples` option to `Result.plot_corner` which uses the weighted nested samples rather than the resampled posterior. This reduces the Monte-Carlo noise from resampling in the marginal distributions and produces smoother corner plots when the underlying sampler provides nested samples with weights. Also extends `Result.get_one_dimensional_median_and_error_bar` with optional `samples` and `weights` arguments and introduces a `_weighted_quantile` helper (linear interpolation of the weighted empirical CDF) so the 1D title quantiles use the same weights as the plot when `use_nested_samples=True`. Closes bilby-dev#563 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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Summary
Closes #563.
use_nested_sampleskeyword toResult.plot_corner. When enabled, the full nested-sampling chain is passed tocorner.corneralong with the per-sampleweightscolumn, rather than using the resampled posterior. This avoids the Monte-Carlo tail noise introduced by resampling and produces smoother marginal distributions.Result.get_one_dimensional_median_and_error_barwith optionalsamplesandweightsarguments so the 1D title quantiles can be computed from the same weighted samples that are plotted._weighted_quantilehelper that computes quantiles via linear interpolation of the weighted empirical CDF. With uniform weights it reproducesnumpy.percentileto within floating-point precision.Raises a clear
ValueErrorwhenuse_nested_samples=Trueis passed but the result has no nested samples or noweightscolumn in the nested samples data frame.Test plan
test_plot_corner_use_nested_samplescovering successful use.weightscolumn._weighted_quantile(uniform weights matchnp.percentile, edge cases, shape validation).get_one_dimensional_median_and_error_baracceptssamples/weightsand returns sensible medians.