Fixed conflicts

Author: PauBadiaM

src/decoupler/_datatype.py

@@ -2,4 +2,4 @@
 import pandas as pd
 from anndata import AnnData
 
-DataType = AnnData | pd.DataFrame | tuple(np.ndarray, np.ndarray, np.ndarray)
+DataType = AnnData | pd.DataFrame | tuple[np.ndarray, np.ndarray, np.ndarray]

src/decoupler/bm/_pp.py

@@ -43,7 +43,7 @@ def _filter(
     net: pd.DataFrame,
     sfilt: bool,
     verbose: bool,
-) -> tuple(AnnData, pd.DataFrame):
+) -> tuple[AnnData, pd.DataFrame]:
     # Remove experiments without sources in net
     srcs = net["source"].unique()
     prts = set()

src/decoupler/bm/_run.py

@@ -30,7 +30,7 @@ def _testsign(
 def _tensor_scores(
     adata: AnnData,
     thr: float,
-) -> tuple(np.ndarray, np.ndarray, np.ndarray, np.ndarray, list):
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, list]:
     # Get unique methods
     has_test = {m.name: m.test for m in _methods}
     has_test = has_test | {"consensus": True}
@@ -79,7 +79,7 @@ def _mask_grps(
     obs: pd.DataFrame,
     groupby: None | list,
     verbose: float,
-) -> tuple(list, list, list):
+) -> tuple[list, list, list]:
     if groupby is not None:
         # Init empty lsts
         msks = []
@@ -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"
-    if isinstance(metrics, None):
-        metrics = ["auc", "fscore", "qrank"]
+    assert isinstance(metrics, str | list) or metrics is None, 'metrics must be str or list'
+    if metrics is None:
+        metrics = ['auc', 'fscore', 'qrank']
     elif isinstance(metrics, str):
         metrics = [metrics]
     if runby == "expr":

src/decoupler/bm/metric/_auc.py

@@ -6,7 +6,7 @@
 def _binary_clf_curve(
     y_true: np.ndarray,
     y_score: np.ndarray,
-) -> tuple(np.ndarray, np.ndarray, np.ndarray):
+) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
     # Sort scores
     idx = np.flip(np.argsort(y_score))
     y_score = y_score[idx]
@@ -78,7 +78,7 @@ def auc(
     y_true: np.ndarray,
     y_score: np.ndarray,
     pi0: float = 0.5,
-) -> tuple(float, float):
+) -> tuple[float, float]:
     """Area Under the Curve."""
     # Normalize to make comparable
     norm = np.nanmax(np.abs(y_score), axis=1)

src/decoupler/bm/metric/_fscore.py

@@ -6,7 +6,7 @@
 def fscore(
     y_true: np.ndarray,
     y_score: np.ndarray,
-) -> tuple(float, float, float):
+) -> tuple[float, float, float]:
     """F-beta score"""
     # Validate
     _validate_bool(y_true=y_true, y_score=y_score)

src/decoupler/bm/metric/_qrank.py

@@ -7,7 +7,7 @@
 def qrank(
     y_true: np.ndarray,
     y_score: np.ndarray,
-) -> tuple(float, float):
+) -> tuple[float, float]:
     """1 - quantile normalized rank"""
     _validate_bool(y_true=y_true, y_score=y_score)
     y_rank = sts.rankdata(y_score, axis=1, nan_policy="omit", method="average")

src/decoupler/ds/_toy.py

@@ -27,7 +27,7 @@ def toy(
     pstime: bool = False,
     seed: int = 42,
     verbose: bool = False,
-) -> tuple(AnnData, pd.DataFrame):
+) -> tuple[AnnData, pd.DataFrame]:
     """
     Generate a toy adata and net for testing.
 

src/decoupler/mt/_aucell.py

@@ -61,7 +61,7 @@ def _func_aucell(
     offsets: np.ndarray,
     n_up: int | float | None = None,
     verbose: bool = False,
-) -> tuple(np.ndarray, None):
+) -> tuple[np.ndarray, None]:
     r"""
     Area Under the Curve for set enrichment within single cells (AUCell) :cite:`aucell`.
 

src/decoupler/mt/_consensus.py

@@ -62,7 +62,7 @@ def _mean_zscores(
 def consensus(
     result: dict | AnnData,
     verbose: bool = False,
-) -> tuple(pd.DataFrame, pd.DataFrame) | None:
+) -> tuple[pd.DataFrame, pd.DataFrame] | None:
     r"""
     Consensus score across methods.
 

src/decoupler/mt/_gsea.py

@@ -40,7 +40,7 @@ def _esrank(
     rnks: np.ndarray,
     set_msk: np.ndarray,
     dec: float,
-) -> tuple(float, int, np.ndarray):
+) -> tuple[float, int, np.ndarray]:
     # Init empty
     mx_value = 0.0
     cum_sum = 0.0
@@ -86,7 +86,7 @@ def _nesrank(
     set_msk: np.ndarray,
     dec: float,
     es: float,
-) -> tuple(float, float):
+) -> tuple[float, float]:
     # Keep old set_msk upstream
     set_msk = set_msk.copy()
     # Compute null
@@ -122,7 +122,7 @@ def _stsgsea(
     starts: np.ndarray,
     offsets: np.ndarray,
     ridx: np.ndarray,
-) -> tuple(np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray):
+) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
     # Sort features
     idx = np.argsort(-row)
     row = row[idx]
@@ -157,7 +157,7 @@ def _func_gsea(
     times: int | float = 1000,
     seed: int | float = 42,
     verbose: bool = False,
-) -> tuple(np.ndarray, np.ndarray):
+) -> tuple[np.ndarray, np.ndarray]:
     r"""
     Gene Set Enrichment Analysis (GSEA) :cite:`gsea`.