Fix flake8 and black errors

Author: judithspd

pycanon/report/pdf_utility_report.py

@@ -31,11 +31,11 @@
 
 
 def get_utility_report_values(
-        data_raw: pd.DataFrame,
-        data_anon: pd.DataFrame,
-        quasi_ident: typing.Union[typing.List, np.ndarray],
-        sens_att: typing.Union[typing.List, np.ndarray],
-        sup=True,
+    data_raw: pd.DataFrame,
+    data_anon: pd.DataFrame,
+    quasi_ident: typing.Union[typing.List, np.ndarray],
+    sens_att: typing.Union[typing.List, np.ndarray],
+    sup=True,
 ) -> typing.Tuple[float, float, float, dict]:
     """Generate a report with the parameters obtained for each utility metric.
 
@@ -67,13 +67,13 @@ def get_utility_report_values(
 
 
 def get_pdf_utility_report(
-        data_raw: pd.DataFrame,
-        data_anon: pd.DataFrame,
-        quasi_ident: typing.Union[typing.List, np.ndarray],
-        sens_att: typing.Union[typing.List, np.ndarray],
-        sup=True,
-        gen=True,
-        file_pdf="utility_report.pdf",
+    data_raw: pd.DataFrame,
+    data_anon: pd.DataFrame,
+    quasi_ident: typing.Union[typing.List, np.ndarray],
+    sens_att: typing.Union[typing.List, np.ndarray],
+    sup=True,
+    gen=True,
+    file_pdf="utility_report.pdf",
 ) -> None:
     """Generate the PDF report both with the utility metrics obtained and the
     anonymity parameters.

pycanon/utility/attribute_statistics.py

@@ -24,8 +24,8 @@ def sizes_ec(
     data: pd.DataFrame, quasi_ident: typing.Union[typing.List, np.ndarray]
 ) -> dict:
     """Calculate statistics associated to the equivalence classes.
-    :param data: dataframe with the data anonymized.
 
+    :param data: dataframe with the data anonymized.
     :type data: pandas dataframe
 
     :param quasi_ident: list with the name of the columns of the dataframe

pycanon/utility/utility_metrics.py

@@ -27,7 +27,7 @@ def average_ecsize(
     quasi_ident: typing.Union[typing.List, np.ndarray],
     sup=True,
 ) -> float:
-    """Calculate the metric "average equivalence class size ". Two scenarios are allowed
+    """Calculate the metric average equivalence class size. Two scenarios are allowed
     depending on whether any records from the original database have been deleted.
 
     :param data_raw: dataframe with the data raw under study.
@@ -57,7 +57,7 @@ def classification_metric(
     quasi_ident: typing.Union[typing.List, np.ndarray],
     sens_att: typing.Union[typing.List, np.ndarray],
 ) -> float:
-    """Calculate the metric "classification metric" according to the original (raw)
+    """Calculate the classification metric according to the original (raw)
     and anonymized datasets.
 
     :param data_raw: dataframe with the data raw under study.
@@ -90,7 +90,7 @@ def discernability_metric(
     data_anon: pd.DataFrame,
     quasi_ident: typing.Union[typing.List, np.ndarray],
 ) -> float:
-    """Calculate the discernability metric, assuming that all thw equivalence classes
+    """Calculate the discernability metric, assuming that all the equivalence classes
     have more than k records, and given each suppressed record a penalty of the size
     of the input dataset.