Fix tests

Signed-off-by: zethson <[email protected]>

src/multi_condition_comparisions/tl/de.py

@@ -51,7 +51,7 @@ def __init__(
         # Do some sanity checks on the input. Do them after the mask is applied.
 
         # Check that counts have no NaN or Inf values.
-        if np.any(adata.X < 0 or np.isnan(self.adata.X)) or np.any(np.isinf(self.adata.X)):
+        if np.any(np.logical_or(adata.X < 0, np.isnan(self.adata.X))) or np.any(np.isinf(self.adata.X)):
             raise ValueError("Counts cannot contain negative, NaN or Inf values.")
         # Check that counts have numeric values.
         if not np.issubdtype(self.adata.X.dtype, np.number):
@@ -132,7 +132,11 @@ def test_contrasts(self, contrasts: list[str] | dict[str, np.ndarray] | np.ndarr
         for name, contrast in contrasts.items():
             results.append(self._test_single_contrast(contrast, **kwargs).assign(contrast=name))
 
-        return pd.concat(results)
+        results = pd.concat(results)
+        results.rename(columns={"pvalue": "pvals", "padj": "pvals_adj", "log2FoldChange": "logfoldchanges"},
+                       inplace=True)
+
+        return results
 
     def test_reduced(self, modelB: "BaseMethod") -> pd.DataFrame:
         """

tests/test_de.py

@@ -23,44 +23,44 @@ def test_package_has_version():
         ),
     ],
 )
-def test_de(test_adata, statsmodels_stub, kwargs):
+def test_de(test_adata, method_class, kwargs):
     """Check that the method can be initialized and fitted, and perform basic checks on
     the result of test_contrasts."""
-    method = statsmodels_stub(adata=test_adata, design="~condition")  # type: ignore
+    method = method_class(adata=test_adata, design="~condition")  # type: ignore
     method.fit(**kwargs)
     res_df = method.test_contrasts(np.array([0, 1]))
     # Check that the result has the correct number of rows
     assert len(res_df) == test_adata.n_vars
 
 
-def test_pydeseq2de(test_adata):
-    """Check that the pyDESeq2 method can be initialized and fitted and that the test_contrast
-    method returns a dataframe with the correct number of rows.
-
-    Now this is a separate
+def test_pydeseq2_simple(test_adata):
+    """Check that the pyDESeq2 method can be
 
+    1. Initialized
+    2. Fitted
+    3. and that test_contrast returns a DataFrame with the correct number of rows.
     """
     method = PyDESeq2DE(adata=test_adata, design="~condition")
     method.fit()
     res_df = method.test_contrasts(["condition", "A", "B"])
+
     assert len(res_df) == test_adata.n_vars
 
 
-def test_pydeseq2de2(test_adata):
+def test_pydeseq2_complex(test_adata):
     """Check that the pyDESeq2 method can be initialized with a different covariate name and fitted and that the test_contrast
     method returns a dataframe with the correct number of rows.
-
-    Now this is a separate
-
     """
     test_adata.obs["condition1"] = test_adata.obs["condition"].copy()
     method = PyDESeq2DE(adata=test_adata, design="~condition1+group")
     method.fit()
     res_df = method.test_contrasts(["condition1", "A", "B"])
+
     assert len(res_df) == test_adata.n_vars
-    # Check that the index of the result matches the var_names of the adata
+    # Check that the index of the result matches the var_names of the AnnData object
     tm.assert_index_equal(test_adata.var_names, res_df.index, check_order=False, check_names=False)
-    # Check that there is a p-value column
-    assert "pvalue" in res_df.columns
-    # Check that p-values are between 0 and 1
-    assert np.all((0 <= res_df["pvalue"]) & (res_df["pvalue"] <= 1))
+
+    expected_columns = {"pvals", "pvals_adj", "logfoldchanges"}
+    assert expected_columns.issubset(set(res_df.columns))
+    assert np.all((0 <= res_df["pvals"]) & (res_df["pvals"] <= 1))
+