feat: update target version from python38 to python39

darts/ad/aggregators/aggregators.py

@@ -23,7 +23,8 @@
     from typing_extensions import Literal
 
 from abc import ABC, abstractmethod
-from typing import Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Optional, Union
 
 from darts import TimeSeries
 from darts.ad.utils import (

darts/ad/aggregators/and_aggregator.py

@@ -3,7 +3,7 @@
 --------------
 """
 
-from typing import Sequence
+from collections.abc import Sequence
 
 from darts import TimeSeries
 from darts.ad.aggregators.aggregators import Aggregator

darts/ad/aggregators/ensemble_sklearn_aggregator.py

@@ -3,7 +3,7 @@
 --------------------------------
 """
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 from sklearn.ensemble import BaseEnsemble

darts/ad/aggregators/or_aggregator.py

@@ -3,7 +3,7 @@
 -------------
 """
 
-from typing import Sequence
+from collections.abc import Sequence
 
 from darts import TimeSeries
 from darts.ad.aggregators.aggregators import Aggregator

darts/ad/anomaly_model/anomaly_model.py

@@ -4,16 +4,13 @@
 
 import sys
 from abc import ABC, abstractmethod
-from typing import Dict, Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Literal, Optional, Union
 
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
     from typing_extensions import Self
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
 
 from darts.ad.scorers.scorers import AnomalyScorer
 from darts.ad.utils import (
@@ -130,10 +127,10 @@ def eval_metric(
         metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC",
         **kwargs,
     ) -> Union[
-        Dict[str, float],
-        Dict[str, Sequence[float]],
-        Sequence[Dict[str, float]],
-        Sequence[Dict[str, Sequence[float]]],
+        dict[str, float],
+        dict[str, Sequence[float]],
+        Sequence[dict[str, float]],
+        Sequence[dict[str, Sequence[float]]],
     ]:
         """Compute the accuracy of the anomaly scores computed by the model.
 
@@ -246,7 +243,7 @@ def show_anomalies(
         self,
         series: TimeSeries,
         anomalies: TimeSeries = None,
-        predict_kwargs: Optional[Dict] = None,
+        predict_kwargs: Optional[dict] = None,
         names_of_scorers: Union[str, Sequence[str]] = None,
         title: str = None,
         metric: Optional[Literal["AUC_ROC", "AUC_PR"]] = None,

darts/ad/anomaly_model/filtering_am.py

@@ -8,16 +8,13 @@
 """
 
 import sys
-from typing import Dict, Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Literal, Optional, Union
 
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
     from typing_extensions import Self
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
 
 from darts.ad.anomaly_model.anomaly_model import AnomalyModel
 from darts.ad.scorers.scorers import AnomalyScorer
@@ -158,10 +155,10 @@ def eval_metric(
         metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC",
         **filter_kwargs,
     ) -> Union[
-        Dict[str, float],
-        Dict[str, Sequence[float]],
-        Sequence[Dict[str, float]],
-        Sequence[Dict[str, Sequence[float]]],
+        dict[str, float],
+        dict[str, Sequence[float]],
+        Sequence[dict[str, float]],
+        Sequence[dict[str, Sequence[float]]],
     ]:
         """Compute a metric for the anomaly scores computed by the model.
 

darts/ad/anomaly_model/forecasting_am.py

@@ -9,16 +9,13 @@
 # TODO:
 #     - put start default value to its minimal value (wait for the release of historical_forecast)
 import sys
-from typing import Dict, Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Literal, Optional, Union
 
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
     from typing_extensions import Self
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
 
 import pandas as pd
 
@@ -346,10 +343,10 @@ def eval_metric(
         enable_optimization: bool = True,
         metric: Literal["AUC_ROC", "AUC_PR"] = "AUC_ROC",
     ) -> Union[
-        Dict[str, float],
-        Dict[str, Sequence[float]],
-        Sequence[Dict[str, float]],
-        Sequence[Dict[str, Sequence[float]]],
+        dict[str, float],
+        dict[str, Sequence[float]],
+        Sequence[dict[str, float]],
+        Sequence[dict[str, Sequence[float]]],
     ]:
         """Compute the accuracy of the anomaly scores computed by the model.
 

darts/ad/detectors/detectors.py

@@ -10,12 +10,9 @@
 
 import sys
 from abc import ABC, abstractmethod
-from typing import Any, List, Optional, Sequence, Tuple, Union
+from collections.abc import Sequence
+from typing import Any, Literal, Optional, Union
 
-try:
-    from typing import Literal
-except ImportError:
-    from typing_extensions import Literal
 if sys.version_info >= (3, 11):
     from typing import Self
 else:
@@ -187,7 +184,7 @@ def _prepare_boundaries(
         upper_bound_name: str,
         lower_bound: Optional[Union[Sequence[float], float]] = None,
         upper_bound: Optional[Union[Sequence[float], float]] = None,
-    ) -> Tuple[List[Optional[float]], List[Optional[float]]]:
+    ) -> tuple[list[Optional[float]], list[Optional[float]]]:
         """
         Process the boundaries argument and perform some sanity checks
 
@@ -221,7 +218,7 @@ def _prepare_boundaries(
                 logger=logger,
             )
 
-        def _prep_boundaries(boundaries) -> List[Optional[float]]:
+        def _prep_boundaries(boundaries) -> list[Optional[float]]:
             """Convert boundaries to List"""
             return (
                 boundaries.tolist()
@@ -275,7 +272,7 @@ def _prep_boundaries(boundaries) -> List[Optional[float]]:
         return lower_bound, upper_bound
 
     @staticmethod
-    def _expand_threshold(series: TimeSeries, threshold: List[float]) -> List[float]:
+    def _expand_threshold(series: TimeSeries, threshold: list[float]) -> list[float]:
         return threshold * series[0].width if len(threshold) == 1 else threshold
 
     @property

darts/ad/detectors/iqr_detector.py

@@ -8,7 +8,8 @@
 computed as distances from the IQR of historical data when the detector is fitted.
 """
 
-from typing import Sequence, Union
+from collections.abc import Sequence
+from typing import Union
 
 import numpy as np
 

darts/ad/detectors/quantile_detector.py

@@ -7,7 +7,8 @@
 computed as quantiles of historical data when the detector is fitted.
 """
 
-from typing import Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Optional, Union
 
 import numpy as np
 

darts/ad/detectors/threshold_detector.py

@@ -7,7 +7,8 @@
 identifies time points as anomalous when values are beyond the thresholds.
 """
 
-from typing import Sequence, Union
+from collections.abc import Sequence
+from typing import Union
 
 import numpy as np
 

darts/ad/scorers/scorers.py

@@ -9,7 +9,8 @@
 import copy
 import sys
 from abc import ABC, abstractmethod
-from typing import Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Optional, Union
 
 if sys.version_info >= (3, 11):
     from typing import Self

darts/ad/scorers/wasserstein_scorer.py

@@ -11,7 +11,7 @@
 .. [1] https://en.wikipedia.org/wiki/Wasserstein_metric
 """
 
-from typing import Sequence
+from collections.abc import Sequence
 
 import numpy as np
 from scipy.stats import wasserstein_distance

darts/ad/utils.py

@@ -12,7 +12,8 @@
 #     - add an option to visualize: "by window", "unique", "together"
 #     - create a normalize option in plot function (norm every anomaly score btw 1 and 0) -> to be seen on the same plot
 
-from typing import Callable, Optional, Sequence, Union
+from collections.abc import Sequence
+from typing import Callable, Optional, Union
 
 try:
     from typing import Literal

darts/dataprocessing/dtw/_plot.py

@@ -1,4 +1,4 @@
-from typing import Tuple, Union
+from typing import Union
 
 import numpy as np
 import xarray as xr
@@ -102,7 +102,7 @@ def plot_alignment(
     new_plot: bool = False,
     series1_y_offset: float = 0,
     series2_y_offset: float = 0,
-    components: Union[Tuple[Union[str, int], Union[str, int]]] = (0, 0),
+    components: Union[tuple[Union[str, int], Union[str, int]]] = (0, 0),
     args_line: dict = {},
     args_series1: dict = {},
     args_series2: dict = {},

darts/dataprocessing/dtw/cost_matrix.py

@@ -1,13 +1,12 @@
 import array
 from abc import ABC, abstractmethod
 from itertools import repeat
-from typing import Tuple
 
 import numpy as np
 
 from darts.dataprocessing.dtw.window import CRWindow, Window
 
-Elem = Tuple[int, int]
+Elem = tuple[int, int]
 
 
 class CostMatrix(ABC):

darts/dataprocessing/dtw/window.py

@@ -6,7 +6,6 @@
 import array
 from abc import ABC, abstractmethod
 from math import atan, tan
-from typing import Tuple
 
 import numpy as np
 
@@ -36,7 +35,7 @@ def __len__(self) -> int:
         pass
 
     @abstractmethod
-    def column_index(self, elem: Tuple[int, int]) -> int:
+    def column_index(self, elem: tuple[int, int]) -> int:
         """Gives the number of active grid cells before row element j, in column i.
 
         Parameters
@@ -101,7 +100,7 @@ class NoWindow(Window):
     def __len__(self):
         return self.n * self.m + 1  # include (0,0) element
 
-    def column_index(self, elem: Tuple[int, int]):
+    def column_index(self, elem: tuple[int, int]):
         return elem[1] - 1
 
     def column_length(self, column: int) -> int:
@@ -215,7 +214,7 @@ def add_range(self, column: int, start: int, end: int):
         self.column_ranges[start_idx] = start
         self.column_ranges[end_idx] = end
 
-    def add(self, elem: Tuple[int, int]):
+    def add(self, elem: tuple[int, int]):
         """Marks a grid cell as active.
 
         Parameters
@@ -230,7 +229,7 @@ def column_length(self, column: int) -> int:
         start, end = self.column_ranges[column]
         return gtz(end - start)
 
-    def column_index(self, elem: Tuple[int, int]) -> int:
+    def column_index(self, elem: tuple[int, int]) -> int:
         i, j = elem
 
         start, end = self.column_ranges[i]
@@ -239,7 +238,7 @@ def column_index(self, elem: Tuple[int, int]) -> int:
         else:
             return j - start
 
-    def __contains__(self, elem: Tuple[int, int]) -> bool:
+    def __contains__(self, elem: tuple[int, int]) -> bool:
         i, j = elem
         start, end = self.column_ranges[i]
         return start <= j < end