[pre-commit.ci] pre-commit autoupdate

[pre-commit-ci]

updates:

• github.com/pycqa/flake8: 7.0.0 → 7.1.0

[codecov]

Codecov Report

All modified and coverable lines are covered by tests ✅

Project coverage is 86.83%. Comparing base (9b5e229) to head (902ba24).

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #100   +/-   ##
=======================================
  Coverage   86.83%   86.83%           
=======================================
  Files          14       14           
  Lines        1193     1193           
  Branches      172      172           
=======================================
  Hits         1036     1036           
  Misses        105      105           
  Partials       52       52           

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[sk1p]

@matbryan52 do you have an idea about the test failure? It seems that we are missing the tolerance by just a tiny bit:

E           Not equal to tolerance rtol=1e-07, atol=0.5
E           
E           Mismatched elements: 1 / 2 (50%)
E           Max absolute difference: 0.5083443
E           Max relative difference: 0.15369503
E            x: array([ 0.832502, 29.035461], dtype=float32)
E            y: array([ 0.98369 , 28.527117]

Is something going on, or do we need to tweak the tolerance?

(note that it's not related to numpy 2.0, as hdbscan installs a numpy version <2)

[matbryan52]

@matbryan52 do you have an idea about the test failure? It seems that we are missing the tolerance by just a tiny bit:

E           Not equal to tolerance rtol=1e-07, atol=0.5
E           
E           Mismatched elements: 1 / 2 (50%)
E           Max absolute difference: 0.5083443
E           Max relative difference: 0.15369503
E            x: array([ 0.832502, 29.035461], dtype=float32)
E            y: array([ 0.98369 , 28.527117]

Is something going on, or do we need to tweak the tolerance?

(note that it's not related to numpy 2.0, as hdbscan installs a numpy version <2)

I have a feeling it's a tolerance issue, but it must be a rare one. The large tolerance is due to the small-ish number of peaks in the fake CBED frame and the fact that cbed_frame has to centre its peaks on integer pixels despite using floating (and random) a/b vectors:

        zero = shape / 2 + np.random.uniform(-1, 1, size=2)
        a = np.array([27.17, 0.]) + np.random.uniform(-1, 1, size=2)
        b = np.array([0., 29.19]) + np.random.uniform(-1, 1, size=2)

At a later time we could modify the testcase to be more precise, but for now I would not worry.

[sk1p]

As the re-trigger worked fine, I created an issue for the tolerance, and I'll go ahead and merge this one.