[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
     hooks:
       - id: pyproject-fmt
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.14.7
+    rev: v0.14.8
     hooks:
       - id: ruff-check
         types_or: [python, pyi, jupyter]

pyproject.toml

@@ -4,7 +4,7 @@ requires = [ "hatchling" ]
 
 [project]
 name = "decoupler"
-version = "2.1.2"
+version = "2.1.3"
 description = "Python package to perform enrichment analysis from omics data."
 readme = "README.md"
 license = { file = "LICENSE" }

src/decoupler/ds/_toy.py

@@ -113,7 +113,9 @@ def toy(
         _log(m, level="info", verbose=verbose)
         pst = np.arange(adata.n_obs)
         pst = pst / pst.max()
-        adata.X[:, :8] = adata.X[:, :8] + (adata.X[:, :8] * pst.reshape(-1, 1))
+        X = adata.X.copy()
+        X[:, :8] = adata.X[:, :8] + (adata.X[:, :8] * pst.reshape(-1, 1))
+        adata.X = X
         adata.obs["pstime"] = pst
     m = f"toy - generated AnnData with shape={adata.shape}"
     _log(m, level="info", verbose=verbose)
@@ -167,5 +169,7 @@ def toy_bench(shuffle_r: float = 0.25, seed: int = 42, verbose: bool = False, **
         _log(m, level="info", verbose=verbose)
         idxs = rng.choice(idxs, n_shuffle, replace=False)
         r_idxs = rng.choice(idxs, idxs.size, replace=False)
-        adata.X[r_idxs, :] = adata.X[idxs, :]
+        X = adata.X.copy()
+        X[r_idxs, :] = adata.X[idxs, :]
+        adata.X = X
     return adata, net

src/decoupler/pp/net.py

@@ -306,10 +306,14 @@ def shuffle_net(
     # Shuffle
     if target:
         rng = np.random.default_rng(seed=seed)
-        rng.shuffle(rnet["target"].values)
+        vals = rnet["target"].values.copy()
+        rng.shuffle(vals)
+        rnet["target"] = vals
     if weight:
         rng = np.random.default_rng(seed=seed + int(not same_seed))
-        rng.shuffle(rnet["weight"].values)
+        vals = rnet["weight"].values.copy()
+        rng.shuffle(vals)
+        rnet["weight"] = vals
     return rnet.drop_duplicates(["source", "target"], keep="first")
 
 

tests/op/test_dtype.py

@@ -16,7 +16,7 @@ def test_infer_dtypes():
     df["b"] = df["b"].astype("string")
     idf = dc.op._dtype._infer_dtypes(df.copy())
     assert pd.api.types.is_numeric_dtype(idf["a"])
-    assert idf["b"].dtype == "object"
+    assert (idf["b"].dtype == "object") or (idf["b"].dtype == "str")
     assert pd.api.types.is_bool_dtype(idf["c"])
     assert pd.api.types.is_bool_dtype(idf["d"])
     assert pd.api.types.is_numeric_dtype(idf["e"])

tests/pp/test_anndata.py

@@ -70,14 +70,22 @@ def test_pseudobulk(
 ):
     adata, _ = dc.ds.toy(nobs=10000, nvar=500, bval=2, seed=42, verbose=False)
     adata.layers["counts"] = adata.X.round()
-    adata.obs["sample"] = adata.obs["sample"].astype("object")
+    adata.obs["sample"] = adata.obs["sample"]
     adata.obs["dose"] = rng.choice(["low", "medium", "high"], size=adata.n_obs, replace=True)
     if empty:
-        adata.X[:, 3] = 0.0
-        adata.layers["counts"][:, 3] = 0.0
+        X = adata.X.copy()
+        X[:, 3] = 0.0
+        adata.X = X
+        X = adata.layers["counts"].copy()
+        X[:, 3] = 0.0
+        adata.layers["counts"] = X
         msk = adata.obs["sample"] == "S01"
-        adata.X[msk, :] = 0.0
-        adata.layers["counts"][msk, :] = 0.0
+        X = adata.X.copy()
+        X[msk, :] = 0.0
+        adata.X = X
+        X = adata.layers["counts"].copy()
+        X[msk, :] = 0.0
+        adata.layers["counts"] = X
     if sparse:
         adata.X = sps.csr_matrix(adata.X)
     if mode == "sum":