Fixing #1047

[GregoryAshton]

Issue

GitHub #1047

sample_subset_constrained raised a TypeError when sampling with size=None or size=1:

TypeError: only 0-dimensional arrays can be converted to Python scalars

Root Cause

evaluate_constraints returns a numpy array via np.ones_like. When sampling a single point, this is a 1-element array rather than a scalar. The subsequent int(is_valid) call on line 464 fails because int() only accepts 0-dimensional arrays.

Fix

Wrap is_valid with np.squeeze before converting to int, reducing the 1-element array to a 0-d scalar:

# before
n_valid_samples += int(is_valid)

# after
n_valid_samples += int(np.squeeze(is_valid))

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 66.24%. Comparing base (d052622) to head (7f3e429).

Additional details and impacted files

@@           Coverage Diff           @@
##             main    #1061   +/-   ##
=======================================
  Coverage   66.24%   66.24%           
=======================================
  Files          89       89           
  Lines       17064    17064           
  Branches     2642     2642           
=======================================
  Hits        11304    11304           
  Misses       5021     5021           
  Partials      739      739           

Flag	Coverage Δ
python310	66.05% <100.00%> (+0.02%)	⬆️
python311	66.19% <100.00%> (-0.01%)	⬇️
python312	66.21% <100.00%> (-0.03%)	⬇️
python313	66.20% <100.00%> (-0.02%)	⬇️

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[ColmTalbot]

I thought we had fixed this already...

[GregoryAshton]

My apologies @ColmTalbot I may have missed your MR that fixed it. If that is the case, please do close this

[mj-will]

Agreed on the call we would backport this to 2.7.x

[Copilot]

Pull request overview

Fixes a TypeError in PriorDict.sample_subset_constrained when constrained sampling is invoked with size=None or size=1, which occurs in sampler parameter verification paths (e.g., dynesty).

Changes:

• Ensure the constraint validity indicator can be converted to an int by squeezing 1-element numpy arrays to a 0-d scalar before counting valid samples.

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