format

src/ontime/core/time_series/time_series.py

@@ -50,7 +50,7 @@ def from_darts(ts: DartsTimeSeries) -> TimeSeries:
         :return: OnTime TimeSeries
         """
         return TimeSeries(ts.data_array())
-    
+
     @staticmethod
     def from_pandas(df: pd.DataFrame, freq=None) -> TimeSeries:
         """
@@ -75,20 +75,20 @@ def from_csv(file: str, index_col=None) -> TimeSeries:
         """
         df = pd.read_csv(file)
         if index_col is not None:
-            df.index = df[index_col].astype('datetime64[ns]')
+            df.index = df[index_col].astype("datetime64[ns]")
             del df[index_col]
         return TimeSeries.from_pandas(df)
-    
+
     @staticmethod
     def from_data(data, index=None, columns=None) -> TimeSeries:
         """
         Converts data to a TimeSeries. Takes data as a dict, ndarray, Iterable or pandas DataFrame.
         Assumes the index is compliant with TimeEval's canonical format.
-        
+
         :param data: a dict, ndarray, Iterable or pandas DataFrame
         :param index: the array-like series to be used as index (will default to a range if none is specified)
         :param columns: will be used for column names if there are none in the data parameter (will default to a range if nothing is specified)
         :return: OnTime TimeSeries
         """
-        df = pd.DataFrame(data, index, columns, copy = True)
-        return TimeSeries.from_pandas(df)
+        df = pd.DataFrame(data, index, columns, copy=True)
+        return TimeSeries.from_pandas(df)

src/ontime/module/__init__.py

@@ -1,2 +1,2 @@
 from . import processing
-from . import data
+from . import data

src/ontime/module/data/dataset.py

@@ -12,23 +12,26 @@ class Dataset:
 
     class ImportedDataset:
         """Class for dataset loaders"""
-        def __init__(self, dataset, args = {}):
+
+        def __init__(self, dataset, args={}):
             self.dataset = dataset
             self.args = args
 
         @abstractmethod
         def load(self) -> TimeSeries:
             pass
-    
+
     class DartsDataset(ImportedDataset):
         """Darts dataset loader"""
+
         def load(self) -> TimeSeries:
             return TimeSeries.from_darts(self.dataset.load(*self.args))
-    
+
     class OpenMLDataset(ImportedDataset):
         """OpenML dataset loader"""
+
         def load(self) -> TimeSeries:
-            self.dataset.index = self.dataset[self.args['time_col']]
+            self.dataset.index = self.dataset[self.args["time_col"]]
             dts = dTimeSeries.from_dataframe(self.dataset, **self.args)
             return TimeSeries.from_darts(dts)
 
@@ -62,6 +65,12 @@ def load(self) -> TimeSeries:
     WoolyDataset = DartsDataset(dd.WoolyDataset())
 
     # OpenML datasets
-    AMDStockPrices = OpenMLDataset(fetch_openml(
-        "AMD-Stock-Prices-Historical-Data", version=1, as_frame=True, parser="pandas").frame,
-        args = {'time_col': 'Date', 'fill_missing_dates':True, 'freq':'D'})
+    AMDStockPrices = OpenMLDataset(
+        fetch_openml(
+            "AMD-Stock-Prices-Historical-Data",
+            version=1,
+            as_frame=True,
+            parser="pandas",
+        ).frame,
+        args={"time_col": "Date", "fill_missing_dates": True, "freq": "D"},
+    )