fix linting

Author: mffrank

grassp/io/read.py

@@ -153,21 +153,21 @@ def read_maxquant(*args, **kwargs) -> anndata.AnnData:
     """
     Wrapper for protdata.read_maxquant that transposes the AnnData object.
     """
-    adata = protdata.read_maxquant(*args, **kwargs)
+    adata = protdata.io.read_maxquant(*args, **kwargs)
     return adata.T
 
 
 def read_fragpipe(*args, **kwargs) -> anndata.AnnData:
     """
     Wrapper for protdata.read_fragpipe that transposes the AnnData object.
     """
-    adata = protdata.read_fragpipe(*args, **kwargs)
+    adata = protdata.io.read_fragpipe(*args, **kwargs)
     return adata.T
 
 
 def read_diann(*args, **kwargs) -> anndata.AnnData:
     """
     Wrapper for protdata.read_diann that transposes the AnnData object.
     """
-    adata = protdata.read_diann(*args, **kwargs)
+    adata = protdata.io.read_diann(*args, **kwargs)
     return adata.T

grassp/preprocessing/enrichment.py

@@ -116,7 +116,7 @@ def calculate_enrichment_vs_untagged(
                     "Multiple samples found for condition: " + experimental_condition
                 )
             data_aggr.layers["pvals"][:, aggr_mask] = pv[:, None]
-            data_aggr.X[:, aggr_mask] = lfc[:, None]
+            data_aggr[:, aggr_mask].X = lfc[:, None]
 
     # Now remove the untagged samples
     if drop_untagged:
@@ -215,7 +215,7 @@ def calculate_enrichment_vs_all(
         lfc = np.median(intensities_ip, axis=1) - np.median(intensities_control, axis=1)
         aggr_mask = data_aggr.var["_experimental_condition"] == experimental_condition
         data_aggr.layers["pvals"][:, aggr_mask] = pv[:, None]
-        data_aggr.X[:, aggr_mask] = lfc[:, None]
+        data_aggr[:, aggr_mask].X = lfc[:, None]
         data_aggr.var.loc[aggr_mask, "enriched_vs"] = ",".join(
             data_aggr.var_names[control_mask]
         )

grassp/tools/clustering.py

@@ -12,7 +12,7 @@
 import scanpy as sc
 
 from numpy.linalg import det, eigvals, inv
-from scipy.special import gammaln
+from scipy.special import gammaln  # pylint: disable=no-name-in-module
 from scipy.stats import multivariate_normal