Fix: Handle zero valid samples in importance_reweight to avoid ValueError crash

[jackieblaum]

Summary

This PR adds a guard in importance_reweight to handle cases where all log weights are NaN or Inf, which previously caused a crash at .max(). If no valid samples are found, the function now returns zero weights as a safe fallback.

What this fixes

Previously, if all samples were invalid (e.g., failed simulations, out-of-support prior samples), the code would hit:

log_weights -= log_weights[~bad].max()

leading to:

ValueError: zero-size array to reduction operation maximum which has no identity

Changes

• Added a check for empty valid samples after masking out NaN/Inf log weights.
• If no valid samples are found, zero weights are returned to prevent a crash.
• Added a warning printout for diagnostics.

Related Issue

Fixes #39

Notes

This is a minimal fix to avoid the crash. A future enhancement could include diagnostics when invalid samples dominate, and possibly resampling strategies.

[Copilot]

Pull Request Overview

This PR fixes a crash in the importance_reweight function that occurs when all log weights are NaN or Inf values. The fix adds a guard to check for zero valid samples and returns zero weights as a safe fallback instead of crashing.

• Added validation to handle empty arrays before calling .max() operation
• Returns zero weights when no valid samples are available
• Added diagnostic warning message for debugging purposes

[Copilot]

Using print() for warnings is not ideal. Consider using the logging module or warnings.warn() for better control over diagnostic output and to follow Python best practices.

Suggested change

	print("All log weights are NaN or Inf — skipping this round!")
	warnings.warn("All log weights are NaN or Inf — skipping this round!")