fix: suppress ty 0.0.27 type errors and move prior_count after as_pandas

- _math.py: update prange type: ignore to bare form (ty 0.0.27 changed
  error code from attr-defined to not-iterable for nb.prange)
- __init__.py: update adata.X subscript type: ignore to bare form (ty
  0.0.27 stricter on ZappyArray subscripting); move prior_count param
  after as_pandas to preserve positional backward compatibility
- test_pdex.py: add isinstance narrowing before DataFrame.equals() call
  to satisfy ty's union type check
- CLAUDE.md: update signature to match new param order

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: sullivanj91

CLAUDE.md

@@ -36,7 +36,7 @@ uv run ty check
 
 ### Core Pipeline (`src/pdex/__init__.py`)
 
-The main entry point is `pdex(adata, groupby, mode, threads, is_log1p, geometric_mean, prior_count, as_pandas, **kwargs)`, which:
+The main entry point is `pdex(adata, groupby, mode, threads, is_log1p, geometric_mean, as_pandas, prior_count, **kwargs)`, which:
 
 1. Validates the `groupby` column in `adata.obs`
 2. Extracts unique groups (filters NaN and empty strings)

src/pdex/__init__.py

@@ -129,9 +129,9 @@ def _isolate_matrix(
     if adata.X is None:
         raise ValueError("AnnData object does not have a matrix.")
     if mask_y is None:
-        result = adata.X[mask_x]  # type: ignore[not-subscriptable]
+        result = adata.X[mask_x]  # type: ignore
     else:
-        result = adata.X[mask_x, mask_y]  # type: ignore[not-subscriptable]
+        result = adata.X[mask_x, mask_y]  # type: ignore
 
     # Fast path: already in-memory
     if isinstance(result, (np.ndarray, csr_matrix)):
@@ -150,8 +150,8 @@ def pdex(
     threads: int = 0,
     is_log1p: bool | None = None,
     geometric_mean: bool = True,
-    prior_count: float = 0.0,
     as_pandas: bool = False,
+    prior_count: float = 0.0,
     **kwargs,
 ) -> pl.DataFrame | pd.DataFrame:
     """Run parallel differential expression analysis on single-cell data.
@@ -199,15 +199,15 @@ def pdex(
         ``is_log1p=True`` the data is back-transformed before averaging
         (``mean(expm1(X))``) so the output is consistent regardless of input
         format.
+    as_pandas:
+        If ``True``, return a :class:`pandas.DataFrame` instead of a
+        :class:`polars.DataFrame`. Requires ``pyarrow``.
     prior_count:
         Pseudocount added to both ``target_mean`` and ``ref_mean`` before computing
         ``fold_change``. When ``prior_count > 0``, extreme fold changes from near-zero
         reference means (scRNA-seq sparsity artifact) are dampened toward zero.
         Has no effect on the Mann-Whitney U p-value or FDR.
         Default ``0.0`` preserves existing behaviour.
-    as_pandas:
-        If ``True``, return a :class:`pandas.DataFrame` instead of a
-        :class:`polars.DataFrame`. Requires ``pyarrow``.
     **kwargs:
         Mode-specific keyword arguments:
 

src/pdex/_math.py

@@ -14,7 +14,7 @@ def _log1p_col_mean(matrix: np.ndarray) -> np.ndarray:
     """Mean of log1p(X) across rows (axis=0) for a dense 2-D array."""
     n_rows, n_cols = matrix.shape
     result = np.zeros(n_cols)
-    for j in nb.prange(n_cols):  # type: ignore[attr-defined]
+    for j in nb.prange(n_cols):  # type: ignore
         s = 0.0
         for i in range(n_rows):
             s += np.log1p(matrix[i, j])
@@ -26,7 +26,7 @@ def _log1p_col_mean(matrix: np.ndarray) -> np.ndarray:
 def _expm1_vec(x: np.ndarray) -> np.ndarray:
     """Element-wise expm1 over a 1-D array."""
     result = np.empty_like(x)
-    for i in nb.prange(len(x)):  # type: ignore[attr-defined]
+    for i in nb.prange(len(x)):  # type: ignore
         result[i] = np.expm1(x[i])
     return result
 
@@ -36,7 +36,7 @@ def _expm1_vec_mean(matrix: np.ndarray) -> np.ndarray:
     """Mean of expm1(X) across rows (axis=0) for a dense 2-D array."""
     n_rows, n_cols = matrix.shape
     result = np.zeros(n_cols)
-    for j in nb.prange(n_cols):  # type: ignore[attr-defined]
+    for j in nb.prange(n_cols):  # type: ignore
         s = 0.0
         for i in range(n_rows):
             s += np.expm1(matrix[i, j])

tests/test_pdex.py

@@ -148,6 +148,8 @@ def test_prior_count_zero_matches_default(self, small_adata):
         explicit_result = pdex(
             small_adata, groupby="guide", is_log1p=False, prior_count=0.0
         )
+        assert isinstance(default_result, pl.DataFrame)
+        assert isinstance(explicit_result, pl.DataFrame)
         assert default_result.equals(explicit_result)