Fix non-C-contiguous matrix reads in Python bindings

[colganwi]

Problem

The Python bindings silently misread non-C-contiguous NumPy arrays, corrupting the likelihood. numpy_to_character_matrix, numpy_to_observation_matrix, and numpy_to_mutation_priors in python/src/numpy_conversion.cpp indexed the raw buffer assuming a C-contiguous (row-major) layout:

result[i][j] = ptr[i * num_chars + j];   // hardcoded row-major strides

If the input is not C-contiguous — a transposed view, a strided slice, or a pandas DataFrame.values (frequently Fortran-ordered) — the wrong elements are read and the character matrix is effectively transposed/scrambled, with no error raised.

Symptoms

• the true (generating) tree scores lower than incorrect trees;
• topology_search degrades a good starting tree instead of improving it (e.g. a perfect NJ tree was driven to ~0.74 normalized RF);
• permuting the cell→leaf assignment can increase the likelihood;
• joint log-likelihood ≠ sum of per-character log-likelihoods, and depends on column order.

Feeding a matrix straight from pandas (df.values) reliably triggers it.

Fixes #1.

Fix

Read the buffers using their actual buf.strides so any memory layout is handled correctly (no copy). Applied to all three converters.

Tests

Added TestMemoryLayoutIndependence in tests/python/test_likelihood.py:

• test_c_and_fortran_order_agree — C- vs F-contiguous copies give identical likelihood
• test_noncontiguous_view_matches_copy — strided (non-contiguous) view matches a contiguous copy
• test_joint_equals_sum_over_characters — joint == sum of per-character likelihoods with heterogeneous alphabet sizes
• test_column_order_invariance — likelihood is invariant to column ordering

All four fail on main and pass with this change.

Verification

• Full Python test suite passes (tests/python/test_likelihood.py, tests/python/test_em.py): 28 passed.
• Regression check on examples/n250_character_matrix is unchanged (it was already C-contiguous): true tree correctly scores highest.
• End-to-end on simulated lineage-tracing data (pandas-derived, F-contiguous matrix): with the fix, correctly-aligned data beats a row-shuffled assignment by ~485 log-likelihood units, and topology_search now improves or preserves the NJ tree (0.000 / 0.037→0.000 / 0.000 normalized RF over 3 seeds) instead of degrading it.

🤖 Generated with Claude Code