[akashdeshmukh-git]

dndm_from_sample() took the width of every bin from the first one, so dn/dm was wrong in all other bins when bins were given as non-uniform edges, which the bins parameter allows.

Summary

Type of change

• Bug fix
• New feature
• Documentation
• Maintenance (refactoring, CI, dependencies, etc.)

Linked issues

Checklist

I have

• Added or updated tests covering this change.
• Updated docstrings and/or docs where relevant.

Summary by Sourcery

Fix dndm_from_sample to calculate mass functions correctly for non-uniform bins.

Bug Fixes:

• Correct dn/dm normalization for samples binned with non-uniform mass-bin edges by using each bin’s width.
• Add regression coverage for uneven-bin mass functions.

[sourcery-ai]

Reviewer's guide (collapsed on small PRs)

Reviewer's Guide

Fix dndm_from_sample so each bin is normalized by its own width in log10 mass, and add a regression test confirming correct dn/dm values for non-uniform bin edges.

Flow diagram for per-bin dndm normalization

flowchart LR
    M["Sample masses m"] --> H["np.histogram(np.log10(m), bins, weights=nm)"]
    H --> E["Bin edges"]
    E --> D["np.diff(edges)"]
    E --> C["Bin centres"]
    D --> N["Normalize each bin by its own width"]
    C --> N
    N --> R["dndm_from_sample result"]

Loading

File-Level Changes

Change	Details	Files
Normalize each histogram bin using its own logarithmic width.	Replace the single width derived from the first two bin centres with the per-bin widths from adjacent edges. Preserve the existing dn/dm conversion while correcting results for non-uniform bins.	src/hmf/helpers/sample.py
Add regression coverage for non-uniform bin edges.	Generate a uniformly distributed log-mass sample with a known analytic dn/dm. Verify the calculated values across uneven bins using a tight numerical tolerance.	tests/test_sample.py

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[sourcery-ai]

Hey - I've found 2 issues

Prompt for AI Agents

Please address the comments from this code review:

## Individual Comments

### Comment 1
<location path="src/hmf/helpers/sample.py" line_range="108" />
<code_context>
     hist, edges = np.histogram(np.log10(m), bins, weights=nm)
     centres = (edges[1:] + edges[:-1]) / 2
-    dx = centres[1] - centres[0]
+    dx = np.diff(edges)
     hist = hist.astype("float") / (10**centres * float(V) * dx * np.log(10))

</code_context>
<issue_to_address>
**Concurrent bin changes skew normalization**

When another thread mutates the ndarray passed as `bins` between the `centres` calculation and `np.diff(edges)`, `np.histogram` returns the supplied edge array, so `dx` can read newly mutated widths while `hist` and `centres` reflect the earlier edges, producing a misnormalized result.

Copy caller-provided bin edges before passing them to `np.histogram`, and use that snapshot throughout the calculation.
</issue_to_address>

### Comment 2
<location path="tests/test_sample.py" line_range="71" />
<code_context>
+    centres, hist = dndm_from_sample(m, volume, bins=bins)
+
+    expected = n / (volume * 3 * 10**centres * np.log(10))
+    assert np.allclose(hist, expected, rtol=1e-10)
</code_context>
<issue_to_address>
**Uneven-bin test misses the bug**

When the old equal-width normalization is used, `np.allclose` uses its default `atol=1e-8`, much larger than the expected histogram values in the higher-mass bins (about `1e-10`). The old equal-width normalization therefore still satisfies this assertion, so the regression test passes with the bug present.

Set an explicit `atol` small enough for these values (or zero) so the test detects the incorrect bin normalization.
</issue_to_address>

Sourcery assessment

Approval pending. 2 findings to address first.

Blocking findings: src/hmf/helpers/sample.py:108, tests/test_sample.py:71

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[sourcery-ai]

🟡 Medium · Concurrent bin changes skew normalization

When another thread mutates the ndarray passed as bins between the centres calculation and np.diff(edges), np.histogram returns the supplied edge array, so dx can read newly mutated widths while hist and centres reflect the earlier edges, producing a misnormalized result.

Copy caller-provided bin edges before passing them to np.histogram, and use that snapshot throughout the calculation.

Prompt for AI agents

In `src/hmf/helpers/sample.py` at line 108:

**Concurrent bin changes skew normalization**

When another thread mutates the ndarray passed as `bins` between the `centres` calculation and `np.diff(edges)`, `np.histogram` returns the supplied edge array, so `dx` can read newly mutated widths while `hist` and `centres` reflect the earlier edges, producing a misnormalized result.

Copy caller-provided bin edges before passing them to `np.histogram`, and use that snapshot throughout the calculation.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 99.60%. Comparing base (3b61ccd) to head (7225984).
⚠️ Report is 1 commits behind head on main.

Additional details and impacted files

@@           Coverage Diff           @@
##             main     #427   +/-   ##
=======================================
  Coverage   99.60%   99.60%           
=======================================
  Files          62       62           
  Lines        7819     7819           
=======================================
  Hits         7788     7788           
  Misses         31       31           

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[akashdeshmukh-git]

Tolerance: fixed in 200473c with an explicit atol=0. Thread safety: not applicable. The function only uses the edges array returned by np.histogram, never bins after that call.

[steven-murray]

Thank you @akashdeshmukh-git !