32 fix pyright issues

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "pdex"
-version = "0.1.15"
+version = "0.1.16"
 description = "Parallel differential expression for single-cell perturbation sequencing"
 readme = "README.md"
 authors = [{ name = "noam teyssier", email = "noam.teyssier@arcinstitute.org" }]

src/pdex/_single_cell.py

@@ -8,8 +8,8 @@
 import numpy as np
 import pandas as pd
 import polars as pl
-from adjustpy import adjust
-from scipy.sparse import csr_matrix
+from adjustpy import adjust  # type: ignore
+from scipy.sparse import csc_matrix, csr_matrix
 from scipy.stats import anderson_ksamp, mannwhitneyu, ttest_ind
 from tqdm import tqdm
 
@@ -24,13 +24,17 @@
 
 
 def _build_shared_matrix(
-    data: np.ndarray | np.matrix | csr_matrix,
+    data: np.ndarray | np.matrix | csr_matrix | csc_matrix,
 ) -> tuple[str, tuple[int, int], np.dtype]:
     """Create a shared memory matrix from a numpy array."""
     if isinstance(data, np.matrix):
         data = np.asarray(data)
-    elif isinstance(data, csr_matrix):
+    elif isinstance(data, csr_matrix) or isinstance(data, csc_matrix):
         data = data.toarray()
+
+    # data should be a numpy array at this point
+    assert isinstance(data, np.ndarray)
+
     shared_matrix = SharedMemory(create=True, size=data.nbytes)
     matrix = np.ndarray(data.shape, dtype=data.dtype, buffer=shared_matrix.buf)
     matrix[:] = data
@@ -48,8 +52,8 @@ def _combinations_generator(
     target_masks: dict[str, np.ndarray],
     var_indices: dict[str, int],
     reference: str,
-    target_list: list[str],
-    feature_list: list[str],
+    target_list: list[str] | np.ndarray,
+    feature_list: list[str] | np.ndarray,
 ) -> Iterator[tuple]:
     """Generate all combinations of target genes and features."""
     for target in target_list:
@@ -137,10 +141,10 @@ def _process_target_batch_shm(
                     pval, stat = (de_result.pvalue, de_result.statistic)
                 case "anderson":
                     de_result = anderson_ksamp([x_tgt, x_ref], **kwargs)
-                    pval, stat = (de_result.pvalue, de_result.statistic)
+                    pval, stat = (de_result.pvalue, de_result.statistic)  # type: ignore (has attributes pvalue and statistic)
                 case "t-test":
                     de_result = ttest_ind(x_tgt, x_ref, **kwargs)
-                    pval, stat = (de_result.pvalue, de_result.statistic)
+                    pval, stat = (de_result.pvalue, de_result.statistic)  # type: ignore (has attributes pvalue and statistic)
                 case _:
                     raise KeyError(f"Unknown Metric: {metric}")
         except ValueError:
@@ -282,14 +286,14 @@ def parallel_differential_expression(
     if metric not in KNOWN_METRICS:
         raise ValueError(f"Unknown metric: {metric} :: Expecting: {KNOWN_METRICS}")
 
-    unique_targets = adata.obs[groupby_key].unique()
+    unique_targets = np.array(adata.obs[groupby_key].unique())
     if groups is not None:
         unique_targets = [
             target
             for target in unique_targets
             if target in groups or target == reference
         ]
-    unique_features = adata.var.index
+    unique_features = np.array(adata.var.index)
 
     if not is_log1p:
         is_log1p = guess_is_log(adata)
@@ -318,7 +322,7 @@ def parallel_differential_expression(
 
     # Isolate the data matrix from the AnnData object
     logger.info("Creating shared memory memory matrix for parallel computing")
-    (shm_name, shape, dtype) = _build_shared_matrix(data=adata.X)
+    (shm_name, shape, dtype) = _build_shared_matrix(data=adata.X)  # type: ignore
 
     logger.info(f"Creating generator of all combinations: N={n_combinations}")
     combinations = _combinations_generator(

tests/test_pbdex.py

@@ -52,9 +52,9 @@ def build_random_anndata(
         ]
 
     return ad.AnnData(
-        X=np.random.randint(0, MAX_UMI, size=(n_cells, n_genes)),
+        X=np.random.randint(0, int(MAX_UMI), size=(n_cells, n_genes)),
         obs=obs,
-        var=pd.DataFrame(index=[f"gene.{j}" for j in np.arange(N_GENES)]),
+        var=pd.DataFrame(index=np.array([f"gene.{j}" for j in np.arange(N_GENES)])),
     )
 
 

tests/test_pdex.py

@@ -29,7 +29,7 @@ def build_random_anndata(
     if random_state is not None:
         np.random.seed(random_state)
     return ad.AnnData(
-        X=np.random.randint(0, MAX_UMI, size=(n_cells, n_genes)),
+        X=np.random.randint(0, int(MAX_UMI), size=(n_cells, n_genes)),
         obs=pd.DataFrame(
             {
                 pert_col: np.random.choice(
@@ -55,7 +55,7 @@ def test_dex_dense_array():
 
 def test_dex_dense_array_log():
     adata = build_random_anndata()
-    adata.X = np.log1p(adata.X)
+    adata.X = np.log1p(adata.X)  # type: ignore
     results = parallel_differential_expression(
         adata,
         reference=CONTROL_VAR,
@@ -66,7 +66,7 @@ def test_dex_dense_array_log():
 
 def test_dex_dense_array_log_post_agg():
     adata = build_random_anndata()
-    adata.X = np.log1p(adata.X)
+    adata.X = np.log1p(adata.X)  # type: ignore
     results = parallel_differential_expression(
         adata,
         reference=CONTROL_VAR,
@@ -78,7 +78,7 @@ def test_dex_dense_array_log_post_agg():
 
 def test_dex_dense_matrix():
     adata = build_random_anndata()
-    adata.X = np.matrix(adata.X)
+    adata.X = np.matrix(adata.X)  # type: ignore
     results = parallel_differential_expression(
         adata,
         reference=CONTROL_VAR,

tests/test_utils.py

@@ -13,7 +13,7 @@ def build_anndata(log=False) -> ad.AnnData:
     return ad.AnnData(
         X=np.random.random(size=dim)
         if log
-        else np.random.randint(0, MAX_COUNT, size=dim)
+        else np.random.randint(0, int(MAX_COUNT), size=dim)
     )