[FIX] ASR: tolerate flat/degenerate segments in fit_eeg_distribution

[sappelhoff]

fit_eeg_distribution's vectorized grid-search histogram divides by a per-column baseline-relative value that is zero when a run of samples is tied (e.g. a flatlined / disconnected electrode): cols = nbins / newX[mcurr] → inf, then H = newX[:m] * cols → nan (0·inf). Casting nan to int yields INT64_MIN, and np.bincount then raises ValueError: 'list' argument must have no negative elements.

A flat segment is exactly the dropout case max_dropout_fraction is meant to tolerate, so the fit should degrade gracefully rather than crash. This makes the histogram binning ignore non-finite entries (count them in no bin), so a degenerate grid column just yields a poor — but finite — objective and the grid search continues. Results on finite input are unchanged.

The crash is reachable through the primary public entry point: ASR().fit(X) with a single dead channel currently raises ValueError.

Testing

Adds three tests: the direct fit_eeg_distribution crash on a tied/flat vector; ASR().fit() with a dead channel; and a guard that the result on clean data is numerically identical (same mu, sig, alpha, beta). Full tests/test_asr.py + tests/test_cov.py pass; ruff clean.

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Note: includes the nonlinear_eigenspace reproducibility fix from #118 so the riemann CI test (test_asr_class[*-riemann]) is stable regardless of what this PR changes. Once #118 merges first, that commit drops out on rebase.

[codecov]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 83.12%. Comparing base (107cd1b) to head (85c2a6d).

Additional details and impacted files

@@           Coverage Diff           @@
##           master     #120   +/-   ##
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  Coverage   83.11%   83.12%           
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  Files          25       25           
  Lines        2825     2826    +1     
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+ Hits         2348     2349    +1     
  Misses        477      477           

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