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

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

Author: pre-commit-ci[bot]

src/decoupler/bm/_run.py

@@ -139,9 +139,9 @@ def _metric_scores(
     verbose: bool,
     **kwargs,
 ) -> None:
-    assert isinstance(metrics, str | list) or metrics is None, 'metrics must be str or list'
+    assert isinstance(metrics, str | list) or metrics is None, "metrics must be str or list"
     if metrics is None:
-        metrics = ['auc', 'fscore', 'qrank']
+        metrics = ["auc", "fscore", "qrank"]
     elif isinstance(metrics, str):
         metrics = [metrics]
     if runby == "expr":

src/decoupler/mt/_gsva.py

@@ -193,9 +193,7 @@ def _dos_srs(r):
 
 
 @nb.njit(parallel=True, cache=True)
-def _rankmat(
-    mat: np.ndarray
-) -> tuple[np.ndarray, np.ndarray]:
+def _rankmat(mat: np.ndarray) -> tuple[np.ndarray, np.ndarray]:
     n_rows, n_cols = mat.shape
     dos_mat = np.zeros((n_rows, n_cols), dtype=np.int_)
     srs_mat = np.zeros((n_rows, n_cols), dtype=np.int_)

src/decoupler/mt/_run.py

@@ -51,9 +51,9 @@ def _run(
     empty: bool = True,
     bsize: int | float = 250_000,
     verbose: bool = False,
-    **kwargs
+    **kwargs,
 ) -> tuple[pd.DataFrame, pd.DataFrame] | AnnData | None:
-    _log(f'{name} - Running {name}', level='info', verbose=verbose)
+    _log(f"{name} - Running {name}", level="info", verbose=verbose)
     # Process data
     mat, obs, var = extract(data, layer=layer, raw=raw, empty=empty, verbose=verbose)
     sparse = sps.issparse(mat)

src/decoupler/mt/_viper.py

@@ -111,7 +111,7 @@ def _shadow_regulon(
     net: np.ndarray,
     reg_sign: float = 1.96,
     n_targets: int | float = 10,
-    penalty: int | float = 20
+    penalty: int | float = 20,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     # Find significant activities
     msk_sign = np.abs(nes_i) > reg_sign

src/decoupler/pl/_barplot.py

@@ -10,15 +10,12 @@
 
 
 def _set_limits(
-    vmin: int | float,
-    vcenter: int | float,
-    vmax: int | float,
-    values: np.ndarray
+    vmin: int | float, vcenter: int | float, vmax: int | float, values: np.ndarray
 ) -> tuple[float, float, float]:
-    assert np.isfinite(values).all(), 'values in data mut be finite'
-    assert isinstance(vmin, int | float) or vmin is None, 'vmin must be numerical or None'
-    assert isinstance(vcenter, int | float) or vcenter is None, 'vcenter must be numerical or None'
-    assert isinstance(vmax, int | float) or vmax is None, 'vmax must be numerical or None'
+    assert np.isfinite(values).all(), "values in data mut be finite"
+    assert isinstance(vmin, int | float) or vmin is None, "vmin must be numerical or None"
+    assert isinstance(vcenter, int | float) or vcenter is None, "vcenter must be numerical or None"
+    assert isinstance(vmax, int | float) or vmax is None, "vmax must be numerical or None"
     if vmin is None:
         vmin = values.min()
     if vmax is None:

src/decoupler/pl/_leading_edge.py

@@ -12,13 +12,7 @@
 
 @docs.dedent
 def leading_edge(
-    df: pd.DataFrame,
-    net: pd.DataFrame,
-    stat: str,
-    name: str,
-    cmap='RdBu_r',
-    color='#88c544',
-    **kwargs
+    df: pd.DataFrame, net: pd.DataFrame, stat: str, name: str, cmap="RdBu_r", color="#88c544", **kwargs
 ) -> tuple[None | Figure, np.ndarray]:
     """
     Plot the running score of GSEA.

src/decoupler/pl/_obsm.py

@@ -17,13 +17,10 @@ def _input(
     names: str | list | None = None,
     nvar: int | float | list | None = 10,
 ) -> tuple[pd.DataFrame, pd.DataFrame]:
-    assert isinstance(adata, AnnData), 'adata must be adata.AnnData'
-    assert isinstance(uns_key, str) and uns_key in adata.uns, \
-    'uns_key must be str and in adata.uns'
-    assert isinstance(names, str | list) or names is None, \
-    'names must be str, list or None'
-    assert isinstance(nvar, int | float | str | list) or nvar is None, \
-    'nvar must be numeric, list or None'
+    assert isinstance(adata, AnnData), "adata must be adata.AnnData"
+    assert isinstance(uns_key, str) and uns_key in adata.uns, "uns_key must be str and in adata.uns"
+    assert isinstance(names, str | list) or names is None, "names must be str, list or None"
+    assert isinstance(nvar, int | float | str | list) or nvar is None, "nvar must be numeric, list or None"
     # Filter stats by obs names
     stats = adata.uns[uns_key]
     assert hasattr(stats, "key"), "adata.uns[key] must be generated by decoupler.tl.rankby_obsm"

src/decoupler/pp/data.py

@@ -14,11 +14,12 @@ def _extract(
     layer: str | None = None,
     raw: bool = False,
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    assert isinstance(data, list | pd.DataFrame | AnnData), \
-    'mat must be a list of [matrix, samples, features], pd.DataFrame (samples x features)\n\
-    or an AnnData instance'
-    assert layer is None or isinstance(layer, str), 'layer must be str or None'
-    assert isinstance(raw, bool), 'raw must be bool'
+    assert isinstance(data, list | pd.DataFrame | AnnData), (
+        "mat must be a list of [matrix, samples, features], pd.DataFrame (samples x features)\n\
+    or an AnnData instance"
+    )
+    assert layer is None or isinstance(layer, str), "layer must be str or None"
+    assert isinstance(raw, bool), "raw must be bool"
     if isinstance(data, list):
         mat, row, col = data
         mat = np.array(mat)
@@ -44,13 +45,9 @@ def _extract(
 
 
 def _validate_mat(
-    mat: np.ndarray,
-    row: np.ndarray,
-    col: np.ndarray,
-    empty: bool = True,
-    verbose: bool = False
+    mat: np.ndarray, row: np.ndarray, col: np.ndarray, empty: bool = True, verbose: bool = False
 ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
-    assert isinstance(empty, bool), 'empty must be bool'
+    assert isinstance(empty, bool), "empty must be bool"
     # Accept any sparse format but transform to csr
     if sps.issparse(mat) and not isinstance(mat, sps.csr_matrix):
         mat = sps.csr_matrix(mat)