make some methods private, minor refactorings, get rid of overview()

alphastats/DataSet.py

@@ -52,6 +52,11 @@ def __init__(
             metadata_path (str, optional): path to metadata file. Defaults to None.
             sample_column (str, optional): column in metadata file indicating the sample IDs. Defaults to None.
 
+        Attributes of a DataSet instance:
+            DataSet().rawinput: Raw Protein data.
+            DataSet().mat:      Processed data matrix with ProteinIDs/ProteinGroups as columns and samples as rows. All computations are performed on this matrix.
+            DataSet().metadata: Metadata for the samples in the matrix. Metadata will be matched with DataSet().mat when needed (for instance Volcano Plot).
+
         """
         self._check_loader(loader=loader)
 
@@ -64,14 +69,14 @@ def __init__(
         self.gene_names: str = loader.gene_names
 
         # include filtering before
-        self.create_matrix()
+        self._create_matrix()
         self._check_matrix_values()
 
         self.metadata: pd.DataFrame
         self.sample: str
         if metadata_path is not None:
             self.sample = sample_column
-            self.metadata = self.load_metadata(file_path=metadata_path)
+            self.metadata = self._load_metadata(file_path=metadata_path)
             self._remove_misc_samples_in_metadata()
         else:
             self.sample = "sample"
@@ -95,7 +100,6 @@ def __init__(
         self.preprocessed: bool = False
 
         print("DataSet has been created.")
-        self.overview()
 
     def preprocess(
         self,
@@ -133,7 +137,7 @@ def preprocess(
 
     def reset_preprocessing(self):
         """Reset all preprocessing steps"""
-        self.create_matrix()
+        self._create_matrix()
         self.preprocessing_info = Preprocess.init_preprocessing_info(
             num_samples=self.mat.shape[0],
             num_protein_groups=self.mat.shape[1],
@@ -207,7 +211,7 @@ def _subset(self):
         )
         return self.mat[self.mat.index.isin(self.metadata[self.sample].tolist())]
 
-    def create_matrix(self):
+    def _create_matrix(self):
         """
         Creates a matrix of the Outputfile, with columns displaying features (Proteins) and
         rows the samples.
@@ -233,12 +237,14 @@ def create_matrix(self):
         mat.replace([np.inf, -np.inf], np.nan, inplace=True)
 
         # remove proteins with only zero  # TODO this is re-done in preprocessing
-        self.mat = mat.loc[:, (mat != 0).any(axis=0)]
-        self.mat = self.mat.astype(float)
+        mat = mat.loc[:, (mat != 0).any(axis=0)]
 
+        self.mat = mat.astype(float)
         self.rawmat = mat
 
-    def load_metadata(self, file_path: Union[pd.DataFrame, str]) -> pd.DataFrame:
+    def _load_metadata(
+        self, file_path: Union[pd.DataFrame, str]
+    ) -> Optional[pd.DataFrame]:
         """Load metadata either xlsx, txt, csv or txt file
 
         Args:
@@ -261,25 +267,15 @@ def load_metadata(self, file_path: Union[pd.DataFrame, str]) -> pd.DataFrame:
         elif file_path.endswith(".csv"):
             df = pd.read_csv(file_path)
         else:
-            df = None
             logging.warning(
                 "WARNING: Metadata could not be read. \nMetadata has to be a .xslx, .tsv, .csv or .txt file"
             )
-            return
+            return None
+
         if df is not None and self.sample not in df.columns:
             logging.error(f"sample_column: {self.sample} not found in {file_path}")
 
         # check whether sample labeling matches protein data
         #  warnings.warn("WARNING: Sample names do not match sample labelling in protein data")
         df.columns = df.columns.astype(str)
         return df
-
-    def overview(self):
-        """Print overview of the DataSet"""
-        dataset_overview = (
-            "Attributes of the DataSet can be accessed using: \n"
-            + "DataSet.rawinput:\t Raw Protein data.\n"
-            + "DataSet.mat:\t\tProcessed data matrix with ProteinIDs/ProteinGroups as columns and samples as rows. All computations are performed on this matrix.\n"
-            + "DataSet.metadata:\tMetadata for the samples in the matrix. Metadata will be matched with DataSet.mat when needed (for instance Volcano Plot)."
-        )
-        print(dataset_overview)

tests/test_DataSet.py

@@ -80,14 +80,14 @@ def test_load_metadata_missing_sample_column(self, mock):
             # is error raised when name of sample column is missing
             path = self.metadata_path
             self.obj.sample = "wrong_sample_column"
-            self.obj.load_metadata(file_path=path)
+            self.obj._load_metadata(file_path=path)
             mock.assert_called_once()
 
         @patch("logging.Logger.warning")
         def test_load_metadata_warning(self, mock):
             # is dataframe None and is warning produced
             file_path = "wrong/file.xxx"
-            self.obj.load_metadata(file_path=file_path)
+            self.obj._load_metadata(file_path=file_path)
             mock.assert_called_once()
 
         def test_create_matrix(self):
@@ -221,15 +221,15 @@ def test_dataset_without_metadata(self):
     def test_load_metadata_fileformats(self):
         # test if different fileformats get loaded correctly
         metadata_path = "testfiles/alphapept/metadata.txt"
-        self.obj.load_metadata(file_path=metadata_path)
+        self.obj._load_metadata(file_path=metadata_path)
         self.assertEqual(self.obj.metadata.shape, (2, 2))
 
         metadata_path = "testfiles/alphapept/metadata.tsv"
-        self.obj.load_metadata(file_path=metadata_path)
+        self.obj._load_metadata(file_path=metadata_path)
         self.assertEqual(self.obj.metadata.shape, (2, 2))
 
         metadata_path = "testfiles/alphapept/metadata.csv"
-        self.obj.load_metadata(file_path=metadata_path)
+        self.obj._load_metadata(file_path=metadata_path)
         self.assertEqual(self.obj.metadata.shape, (2, 2))
 
     @patch("logging.Logger.warning")