[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

src/cellarr/CellArrDataset.py

@@ -290,4 +290,6 @@ def get_sample_subset(
         Returns:
             A pandas Dataframe of the subset.
         """
-        return qtd.subset_frame(self._sample_metadata_tdb, subset=subset, columns=columns)
+        return qtd.subset_frame(
+            self._sample_metadata_tdb, subset=subset, columns=columns
+        )

src/cellarr/build_cellarrdataset.py

@@ -223,7 +223,9 @@ def build_cellarrdataset(
         gene_annotation = pd.DataFrame({"cellarr_gene_index": gene_set}, index=gene_set)
     elif isinstance(gene_annotation, list):
         _gene_list = sorted(list(set(gene_annotation)))
-        gene_annotation = pd.DataFrame({"cellarr_gene_index": _gene_list}, index=_gene_list)
+        gene_annotation = pd.DataFrame(
+            {"cellarr_gene_index": _gene_list}, index=_gene_list
+        )
     elif isinstance(gene_annotation, str):
         gene_annotation = pd.read_csv(gene_annotation, index=True, header=True)
         warnings.warn(
@@ -237,8 +239,12 @@ def build_cellarrdataset(
     if not isinstance(gene_annotation, pd.DataFrame):
         raise TypeError("'gene_annotation' must be a pandas dataframe.")
 
-    if len(gene_annotation["cellarr_gene_index"].unique()) != len(gene_annotation["cellarr_gene_index"].tolist()):
-        raise ValueError("'gene_annotation' must contain unique feature ids or gene symbols.")
+    if len(gene_annotation["cellarr_gene_index"].unique()) != len(
+        gene_annotation["cellarr_gene_index"].tolist()
+    ):
+        raise ValueError(
+            "'gene_annotation' must contain unique feature ids or gene symbols."
+        )
 
     gene_annotation.reset_index(drop=True, inplace=True)
 
@@ -249,7 +255,9 @@ def build_cellarrdataset(
             _col_types[col] = "ascii"
 
         _gene_output_uri = f"{output_path}/{gene_annotation_options.tiledb_store_name}"
-        generate_metadata_tiledb_frame(_gene_output_uri, gene_annotation, column_types=_col_types)
+        generate_metadata_tiledb_frame(
+            _gene_output_uri, gene_annotation, column_types=_col_types
+        )
 
         if optimize_tiledb:
             uta.optimize_tiledb_array(_gene_output_uri)
@@ -286,8 +294,12 @@ def build_cellarrdataset(
         for col in sample_metadata.columns:
             _col_types[col] = "ascii"
 
-        _sample_output_uri = f"{output_path}/{sample_metadata_options.tiledb_store_name}"
-        generate_metadata_tiledb_frame(_sample_output_uri, sample_metadata, column_types=_col_types)
+        _sample_output_uri = (
+            f"{output_path}/{sample_metadata_options.tiledb_store_name}"
+        )
+        generate_metadata_tiledb_frame(
+            _sample_output_uri, sample_metadata, column_types=_col_types
+        )
 
         if optimize_tiledb:
             uta.optimize_tiledb_array(_sample_output_uri)
@@ -307,7 +319,9 @@ def build_cellarrdataset(
         _dataset.extend([f"dataset_{idx}" for _ in range(cci)])
 
     if cell_metadata is None:
-        cell_metadata = pd.DataFrame({"cellarr_cell_counts": _cellindex_in_dataset, "cellarr_sample": _dataset})
+        cell_metadata = pd.DataFrame(
+            {"cellarr_cell_counts": _cellindex_in_dataset, "cellarr_sample": _dataset}
+        )
     elif isinstance(cell_metadata, str):
         warnings.warn(
             "Scanning 'cell_metadata' csv file to count number of cells, this may take long",
@@ -329,7 +343,9 @@ def build_cellarrdataset(
         )
     elif isinstance(cell_metadata, pd.DataFrame):
         if sum(cell_counts) != len(cell_metadata):
-            raise ValueError("Number of rows in 'cell_metadata' does not match the number of cells across files.")
+            raise ValueError(
+                "Number of rows in 'cell_metadata' does not match the number of cells across files."
+            )
 
         cell_metadata["cellarr_sample"] = _dataset
 
@@ -339,15 +355,19 @@ def build_cellarrdataset(
 
         if isinstance(cell_metadata, str):
             _cell_metaframe = pd.read_csv(cell_metadata, chunksize=5, header=True)
-            generate_metadata_tiledb_csv(_cell_output_uri, cell_metadata, _cell_metaframe.columns)
+            generate_metadata_tiledb_csv(
+                _cell_output_uri, cell_metadata, _cell_metaframe.columns
+            )
         elif isinstance(cell_metadata, pd.DataFrame):
             _col_types = {}
             for col in cell_metadata.columns:
                 _col_types[col] = "ascii"
 
             _to_write = cell_metadata.astype(str)
 
-            generate_metadata_tiledb_frame(_cell_output_uri, _to_write, column_types=_col_types)
+            generate_metadata_tiledb_frame(
+                _cell_output_uri, _to_write, column_types=_col_types
+            )
 
         if optimize_tiledb:
             uta.optimize_tiledb_array(_cell_output_uri)
@@ -398,7 +418,9 @@ def build_cellarrdataset(
     )
 
 
-def generate_metadata_tiledb_frame(output_uri: str, input: pd.DataFrame, column_types: dict = None):
+def generate_metadata_tiledb_frame(
+    output_uri: str, input: pd.DataFrame, column_types: dict = None
+):
     """Generate metadata tiledb from a :pu:class:`~pandas.DataFrame`.
 
     Args:
@@ -415,7 +437,9 @@ def generate_metadata_tiledb_frame(output_uri: str, input: pd.DataFrame, column_
             Defaults to None.
     """
     _to_write = input.astype(str)
-    utf.create_tiledb_frame_from_dataframe(output_uri, _to_write, column_types=column_types)
+    utf.create_tiledb_frame_from_dataframe(
+        output_uri, _to_write, column_types=column_types
+    )
 
 
 def generate_metadata_tiledb_csv(
@@ -451,7 +475,9 @@ def generate_metadata_tiledb_csv(
 
     for chunk in pd.read_csv(input, chunksize=chunksize, header=True):
         if initfile:
-            utf.create_tiledb_frame_from_column_names(output_uri, chunk.columns, column_dtype)
+            utf.create_tiledb_frame_from_column_names(
+                output_uri, chunk.columns, column_dtype
+            )
             initfile = False
 
         _to_write = chunk.astype(str)

src/cellarr/globalcache.py

@@ -6,8 +6,6 @@
 
 
 def remove_package_cache():
-    """Remove the package cache that contains temporary store for
-    scanned files.
-    """
+    """Remove the package cache that contains temporary store for scanned files."""
     global PACKAGE_SCAN_CACHE
     PACKAGE_SCAN_CACHE = {}

src/cellarr/utils_anndata.py

@@ -119,8 +119,7 @@ def extract_anndata_info(
     num_threads: int = 1,
     force: bool = False,
 ):
-    """Extract and generate the list of unique feature identifiers
-    and cell counts across files.
+    """Extract and generate the list of unique feature identifiers and cell counts across files.
 
     Args:
         h5ad_or_adata:

tests/test_build.py

@@ -69,4 +69,4 @@ def test_build_cellarrdataset():
 
     sfp = tiledb.open(f"{tempdir}/sample_metadata", "r")
     samples = sfp.df[:]
-    assert len(samples) == 2
+    assert len(samples) == 2