diff --git a/kats/data/utils.py b/kats/data/utils.py
index 4332eb1e..4514e64b 100644
--- a/kats/data/utils.py
+++ b/kats/data/utils.py
@@ -51,13 +51,11 @@ def load_data(file_name: str, reset_columns: bool = False) -> pd.DataFrame:
 
 
 @overload
-def load_air_passengers(return_ts: Literal[True]) -> TimeSeriesData:
-    ...
+def load_air_passengers(return_ts: Literal[True]) -> TimeSeriesData: ...
 
 
 @overload
-def load_air_passengers(return_ts: Literal[False] = ...) -> pd.DataFrame:
-    ...
+def load_air_passengers(return_ts: Literal[False] = ...) -> pd.DataFrame: ...
 
 
 def load_air_passengers(return_ts: bool = True) -> Union[pd.DataFrame, TimeSeriesData]:
diff --git a/kats/detectors/cusum_detection.py b/kats/detectors/cusum_detection.py
index eb510856..fbbb6216 100644
--- a/kats/detectors/cusum_detection.py
+++ b/kats/detectors/cusum_detection.py
@@ -1224,10 +1224,12 @@ def detector_(self, **kwargs: Any) -> List[List[CUSUMChangePoint]]:
                 if not list(change_meta_["changepoint"]):
                     continue
                 change_meta = {
-                    k: change_meta_[k][col_idx]
-                    if isinstance(change_meta_[k], np.ndarray)
-                    or isinstance(change_meta_[k], list)
-                    else change_meta_[k]
+                    k: (
+                        change_meta_[k][col_idx]
+                        if isinstance(change_meta_[k], np.ndarray)
+                        or isinstance(change_meta_[k], list)
+                        else change_meta_[k]
+                    )
                     for k in change_meta_
                 }
                 change_meta["llr"] = llr = self._get_llr(
diff --git a/kats/detectors/cusum_model.py b/kats/detectors/cusum_model.py
index 08b9a1c6..7752f6cc 100644
--- a/kats/detectors/cusum_model.py
+++ b/kats/detectors/cusum_model.py
@@ -1276,9 +1276,11 @@ def _fit(
             )
 
             cps = [
-                sorted(x, key=lambda x: x.start_time)[0]
-                if x and alert_set_on_mask[i]
-                else None
+                (
+                    sorted(x, key=lambda x: x.start_time)[0]
+                    if x and alert_set_on_mask[i]
+                    else None
+                )
                 for i, x in enumerate(changepoints)
             ]
 
diff --git a/kats/detectors/detector_consts.py b/kats/detectors/detector_consts.py
index db5667f1..41014b66 100644
--- a/kats/detectors/detector_consts.py
+++ b/kats/detectors/detector_consts.py
@@ -260,10 +260,12 @@ def stat_sig(self) -> Union[bool, ArrayLike]:
         if self.num_series > 1:
             return np.array(
                 [
-                    False
-                    if cast(np.ndarray, self.upper)[i] > 1.0
-                    and cast(np.ndarray, self.lower)[i] < 1
-                    else True
+                    (
+                        False
+                        if cast(np.ndarray, self.upper)[i] > 1.0
+                        and cast(np.ndarray, self.lower)[i] < 1
+                        else True
+                    )
                     for i in range(self.current.num_series)
                 ]
             )
@@ -649,11 +651,13 @@ def get_last_n(self, N: int) -> AnomalyResponse:
 
         return AnomalyResponse(
             scores=self.scores[-N:],
-            confidence_band=None
-            if cb is None
-            else ConfidenceBand(
-                upper=cb.upper[-N:],
-                lower=cb.lower[-N:],
+            confidence_band=(
+                None
+                if cb is None
+                else ConfidenceBand(
+                    upper=cb.upper[-N:],
+                    lower=cb.lower[-N:],
+                )
             ),
             predicted_ts=None if pts is None else pts[-N:],
             anomaly_magnitude_ts=self.anomaly_magnitude_ts[-N:],
diff --git a/kats/detectors/distribution_distance_model.py b/kats/detectors/distribution_distance_model.py
index 3b8b8c0d..473be4e9 100644
--- a/kats/detectors/distribution_distance_model.py
+++ b/kats/detectors/distribution_distance_model.py
@@ -260,10 +260,12 @@ def fit_predict(
             window=str(self.window_size_sec) + "s",
             closed="both",
         ).agg(
-            lambda rows: rows[0]
-            if (rows.index[-1] - rows.index[0]).total_seconds()
-            > 0.9 * self.window_size_sec  # tolerance
-            else np.nan
+            lambda rows: (
+                rows[0]
+                if (rows.index[-1] - rows.index[0]).total_seconds()
+                > 0.9 * self.window_size_sec  # tolerance
+                else np.nan
+            )
         )
 
         # exclude the beginning part of NANs
diff --git a/kats/detectors/meta_learning/synth_metadata_reader.py b/kats/detectors/meta_learning/synth_metadata_reader.py
index 80f0b17a..276ad7d1 100644
--- a/kats/detectors/meta_learning/synth_metadata_reader.py
+++ b/kats/detectors/meta_learning/synth_metadata_reader.py
@@ -70,9 +70,11 @@ def get_metadata(self, algorithm_name: str) -> Dict[str, pd.DataFrame]:
                     .map(lambda kv: kv[a][0])
                     .map(
                         lambda kv: {
-                            k: v
-                            if k not in self.PARAMS_TO_SCALE_DOWN
-                            else v / SynthMetadataReader.NUM_SECS_IN_DAY
+                            k: (
+                                v
+                                if k not in self.PARAMS_TO_SCALE_DOWN
+                                else v / SynthMetadataReader.NUM_SECS_IN_DAY
+                            )
                             for k, v in kv.items()
                         }
                     )
diff --git a/kats/detectors/outlier.py b/kats/detectors/outlier.py
index ce901602..87b03265 100644
--- a/kats/detectors/outlier.py
+++ b/kats/detectors/outlier.py
@@ -62,7 +62,7 @@ def __init__(
 
     def __clean_ts__(
         self,
-        original: Union[pd.Series, pd.DataFrame]
+        original: Union[pd.Series, pd.DataFrame],
         # pyre-fixme[11]: Annotation `Timestamp` is not defined as a type.
     ) -> Tuple[List[int], List[float], List[pd.Timestamp]]:
         """
diff --git a/kats/detectors/prophet_detector.py b/kats/detectors/prophet_detector.py
index 130f9132..b87c2a35 100644
--- a/kats/detectors/prophet_detector.py
+++ b/kats/detectors/prophet_detector.py
@@ -41,6 +41,8 @@
 import sys
 
 NOT_SUPPRESS_PROPHET_FIT_LOGS_VAR_NAME = "NOT_SUPPRESS_PROPHET_FIT_LOGS"
+
+
 # this is a bug in prophet which was discussed in open source thread
 # issues was also suggested
 # details https://github.com/facebook/prophet/issues/223#issuecomment-326455744
diff --git a/kats/detectors/stat_sig_detector.py b/kats/detectors/stat_sig_detector.py
index 3116f4f6..98483024 100644
--- a/kats/detectors/stat_sig_detector.py
+++ b/kats/detectors/stat_sig_detector.py
@@ -922,7 +922,6 @@ def predict(
 
 
 class MultiStatSigDetectorModel(StatSigDetectorModel):
-
     """
     MultiStatSigDetectorModel is a multivariate version of the StatSigDetector.  It applies a univariate
     t-test to each of the components of the multivariate time series to see if the means between the control
diff --git a/kats/metrics/metrics.py b/kats/metrics/metrics.py
index c75c184d..588436a6 100644
--- a/kats/metrics/metrics.py
+++ b/kats/metrics/metrics.py
@@ -24,6 +24,7 @@
 # from numpy.typing import ArrayLike
 ArrayLike = Union[np.ndarray, Sequence[float]]
 
+
 # Type aliases
 #
 # Most metrics have the shape:
@@ -47,13 +48,13 @@ def __call__(
         y_true: ArrayLike,
         y_pred: ArrayLike,
         sample_weight: Optional[ArrayLike] = ...,
-    ) -> np.ndarray:
-        ...  # pragma: no cover
+    ) -> np.ndarray: ...  # pragma: no cover
 
 
 class Metric(Protocol):
-    def __call__(self, y_true: ArrayLike, y_pred: ArrayLike) -> float:
-        ...  # pragma: no cover
+    def __call__(
+        self, y_true: ArrayLike, y_pred: ArrayLike
+    ) -> float: ...  # pragma: no cover
 
 
 class WeightedMetric(Protocol):
@@ -62,8 +63,7 @@ def __call__(
         y_true: ArrayLike,
         y_pred: ArrayLike,
         sample_weight: Optional[ArrayLike] = ...,
-    ) -> float:
-        ...  # pragma: no cover
+    ) -> float: ...  # pragma: no cover
 
 
 class MultiOutputMetric(Protocol):
@@ -73,8 +73,7 @@ def __call__(
         y_pred: ArrayLike,
         sample_weight: Optional[ArrayLike] = ...,
         multioutput: Union[str, ArrayLike] = ...,
-    ) -> float:
-        ...  # pragma: no cover
+    ) -> float: ...  # pragma: no cover
 
 
 class ThresholdMetric(Protocol):
@@ -83,8 +82,7 @@ def __call__(
         y_true: ArrayLike,
         y_pred: ArrayLike,
         threshold: float,
-    ) -> float:
-        ...  # pragma: no cover
+    ) -> float: ...  # pragma: no cover
 
 
 class MultiThresholdMetric(Protocol):
@@ -93,8 +91,7 @@ def __call__(
         y_true: ArrayLike,
         y_pred: ArrayLike,
         threshold: ArrayLike,
-    ) -> np.ndarray:
-        ...  # pragma: no cover
+    ) -> np.ndarray: ...  # pragma: no cover
 
 
 KatsMetric = Union[
diff --git a/kats/models/ensemble/kats_ensemble.py b/kats/models/ensemble/kats_ensemble.py
index e9ae7932..371b537b 100644
--- a/kats/models/ensemble/kats_ensemble.py
+++ b/kats/models/ensemble/kats_ensemble.py
@@ -747,7 +747,7 @@ def _fit_single(
         data: TimeSeriesData,
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
         model_func: Callable,
-        model_param: Params
+        model_param: Params,
         # pyre-fixme[24]: Generic type `Model` expects 1 type parameter.
     ) -> Model:
         """Private method to fit individual model
diff --git a/kats/models/globalmodel/data_processor.py b/kats/models/globalmodel/data_processor.py
index 71279758..64d8b34f 100644
--- a/kats/models/globalmodel/data_processor.py
+++ b/kats/models/globalmodel/data_processor.py
@@ -263,12 +263,12 @@ def __init__(
         if params.model_type == "rnn" and params.seasonality > 1:
             init_seasonality = self._get_seasonality(train_x, params.seasonality)
             # bound initial seasonalities
-            init_seasonality[
-                init_seasonality < params.init_seasonality[0]
-            ] = params.init_seasonality[0]
-            init_seasonality[
-                init_seasonality > params.init_seasonality[1]
-            ] = params.init_seasonality[1]
+            init_seasonality[init_seasonality < params.init_seasonality[0]] = (
+                params.init_seasonality[0]
+            )
+            init_seasonality[init_seasonality > params.init_seasonality[1]] = (
+                params.init_seasonality[1]
+            )
             # pyre-fixme[4]: Attribute must be annotated.
             self.init_seasonality = torch.tensor(init_seasonality, dtype=tdtype)
         else:
@@ -451,7 +451,6 @@ def _get_array(
         Optional[np.ndarray],
         Optional[np.ndarray],
     ]:
-
         """
 
         Helper function for transforming TS to arrays, including truncating/padding values,
diff --git a/kats/models/globalmodel/model.py b/kats/models/globalmodel/model.py
index 4011f11f..d98e077f 100644
--- a/kats/models/globalmodel/model.py
+++ b/kats/models/globalmodel/model.py
@@ -545,9 +545,7 @@ def _format_fcst(
         for i, idx in enumerate(ids):
 
             df = pd.DataFrame(
-                fcst[i].transpose()[
-                    :steps,
-                ],
+                fcst[i].transpose()[:steps,],
                 columns=cols,
             )
             df["time"] = pd.date_range(
@@ -651,12 +649,12 @@ def save_model(self, file_name: str) -> None:
         info = {
             "gmparam_string": self.params.to_string(),
             "state_dict": self.rnn.state_dict() if self.rnn is not None else None,
-            "encoder_state_dict": self.encoder.state_dict()
-            if self.encoder is not None
-            else None,
-            "decoder_state_dict": self.decoder.state_dict()
-            if self.decoder is not None
-            else None,
+            "encoder_state_dict": (
+                self.encoder.state_dict() if self.encoder is not None else None
+            ),
+            "decoder_state_dict": (
+                self.decoder.state_dict() if self.decoder is not None else None
+            ),
         }
         with open(file_name, "wb") as f:
             joblib.dump(info, f)
@@ -1099,7 +1097,11 @@ def _single_pass_s2s(
                 cur_step + 1
             )
 
-            (x_t, anchor_level, x_lt,) = self._process_s2s(
+            (
+                x_t,
+                anchor_level,
+                x_lt,
+            ) = self._process_s2s(
                 prev_idx, cur_idx, batch.x, x_lt, period, params.input_window
             )
 
diff --git a/kats/models/globalmodel/stdmodel.py b/kats/models/globalmodel/stdmodel.py
index 48390036..858932ef 100644
--- a/kats/models/globalmodel/stdmodel.py
+++ b/kats/models/globalmodel/stdmodel.py
@@ -195,7 +195,6 @@ def _deseasonal(
     def _predict_seasonality(
         self, steps: int, tsd_model: Union[ProphetModel, np.ndarray]
     ) -> np.ndarray:
-
         """Predict the future seasonality.
 
         Args:
diff --git a/kats/models/globalmodel/utils.py b/kats/models/globalmodel/utils.py
index 188471d1..cae8154e 100644
--- a/kats/models/globalmodel/utils.py
+++ b/kats/models/globalmodel/utils.py
@@ -73,7 +73,7 @@ def get_filters(isna_idx, seasonality) -> np.ndarray:
         else:
             i += 1
     filters = np.array([True] * n)
-    for (i, j) in flips:
+    for i, j in flips:
         filters[i:j] = False
     return filters
 
@@ -188,9 +188,11 @@ def split(
         split_data = [
             (
                 {t: train_TSs[t] for t in keys[~index[i]]},
-                {t: valid_TSs[t] for t in keys[~index[i]]}
-                if valid_TSs is not None
-                else None,
+                (
+                    {t: valid_TSs[t] for t in keys[~index[i]]}
+                    if valid_TSs is not None
+                    else None
+                ),
             )
             for i in range(splits)
         ]
@@ -198,9 +200,11 @@ def split(
         split_data = [
             (
                 {t: train_TSs[t] for t in keys[index[i]]},
-                {t: valid_TSs[t] for t in keys[index[i]]}
-                if valid_TSs is not None
-                else None,
+                (
+                    {t: valid_TSs[t] for t in keys[index[i]]}
+                    if valid_TSs is not None
+                    else None
+                ),
             )
             for i in range(splits)
         ]
diff --git a/kats/models/metalearner/get_metadata.py b/kats/models/metalearner/get_metadata.py
index 3d34c762..9cc14af3 100644
--- a/kats/models/metalearner/get_metadata.py
+++ b/kats/models/metalearner/get_metadata.py
@@ -220,7 +220,7 @@ def _tune_single(
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
         single_model: Callable,
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
-        single_params: Callable
+        single_params: Callable,
         # pyre-fixme[24]: Generic type `dict` expects 2 type parameters, use
         #  `typing.Dict` to avoid runtime subscripting errors.
     ) -> Tuple[Dict, float]:
diff --git a/kats/models/metalearner/metalearner_hpt.py b/kats/models/metalearner/metalearner_hpt.py
index a8dec3d2..58b24410 100644
--- a/kats/models/metalearner/metalearner_hpt.py
+++ b/kats/models/metalearner/metalearner_hpt.py
@@ -456,9 +456,7 @@ def build_network(
         print("Multi-task neural network structure:")
         print(self.model)
 
-    def _prepare_data(
-        self, val_size: float
-    ) -> Tuple[
+    def _prepare_data(self, val_size: float) -> Tuple[
         torch.FloatTensor,
         Optional[torch.LongTensor],
         Optional[torch.FloatTensor],
diff --git a/kats/models/ml_ar.py b/kats/models/ml_ar.py
index 268757fa..9323f196 100644
--- a/kats/models/ml_ar.py
+++ b/kats/models/ml_ar.py
@@ -934,9 +934,9 @@ def _merge_past_and_future_reg(
                 num_rows_dat = norm_in_data[target_var].shape[0]
                 num_cols_dat = norm_in_data[target_var].shape[1]
 
-                full_mat[
-                    tv_idx : (tv_idx + num_rows_dat), 0:num_cols_dat
-                ] = norm_in_data[target_var]
+                full_mat[tv_idx : (tv_idx + num_rows_dat), 0:num_cols_dat] = (
+                    norm_in_data[target_var]
+                )
 
                 full_mat[
                     tv_idx : (tv_idx + num_rows_dat),
diff --git a/kats/tests/test_ensemble.py b/kats/tests/test_ensemble.py
index 79533b25..7da27d53 100644
--- a/kats/tests/test_ensemble.py
+++ b/kats/tests/test_ensemble.py
@@ -73,35 +73,47 @@ def get_predict_model(m: Model, model_name: str, steps: int, freq: str) -> np.nd
 def get_ensemble_param(ts_param: Dict[str, bool]) -> EnsembleParams:
     """Returns EnsembleParams based on which base_models are included."""
     base_model_list = [
-        BaseModelParams("arima", arima.ARIMAParams(p=1, d=1, q=1))
-        if ts_param["arima"]
-        else "",
-        BaseModelParams("holtwinters", holtwinters.HoltWintersParams())
-        if ts_param["holtwinters"]
-        else "",
-        BaseModelParams(
-            "sarima",
-            sarima.SARIMAParams(
-                p=2,
-                d=1,
-                q=1,
-                trend="ct",
-                seasonal_order=(1, 0, 1, 12),
-                enforce_invertibility=False,
-                enforce_stationarity=False,
-            ),
-        )
-        if ts_param["sarima"]
-        else "",
-        BaseModelParams("prophet", prophet.ProphetParams())
-        if ts_param["prophet"]
-        else "",
-        BaseModelParams("linear", linear_model.LinearModelParams())
-        if ts_param["linear"]
-        else "",
-        BaseModelParams("quadratic", quadratic_model.QuadraticModelParams())
-        if ts_param["quadratic"]
-        else "",
+        (
+            BaseModelParams("arima", arima.ARIMAParams(p=1, d=1, q=1))
+            if ts_param["arima"]
+            else ""
+        ),
+        (
+            BaseModelParams("holtwinters", holtwinters.HoltWintersParams())
+            if ts_param["holtwinters"]
+            else ""
+        ),
+        (
+            BaseModelParams(
+                "sarima",
+                sarima.SARIMAParams(
+                    p=2,
+                    d=1,
+                    q=1,
+                    trend="ct",
+                    seasonal_order=(1, 0, 1, 12),
+                    enforce_invertibility=False,
+                    enforce_stationarity=False,
+                ),
+            )
+            if ts_param["sarima"]
+            else ""
+        ),
+        (
+            BaseModelParams("prophet", prophet.ProphetParams())
+            if ts_param["prophet"]
+            else ""
+        ),
+        (
+            BaseModelParams("linear", linear_model.LinearModelParams())
+            if ts_param["linear"]
+            else ""
+        ),
+        (
+            BaseModelParams("quadratic", quadratic_model.QuadraticModelParams())
+            if ts_param["quadratic"]
+            else ""
+        ),
         BaseModelParams("theta", theta.ThetaParams(m=12)) if ts_param["theta"] else "",
     ]
     return EnsembleParams(
diff --git a/kats/tests/tsfeatures/test_tsfeatures.py b/kats/tests/tsfeatures/test_tsfeatures.py
index 12c0facd..d75d2ebf 100644
--- a/kats/tests/tsfeatures/test_tsfeatures.py
+++ b/kats/tests/tsfeatures/test_tsfeatures.py
@@ -180,7 +180,7 @@ def test_tsfeatures_basic(self, test_name: str, ts_name: str) -> None:
             "firstzero_ac": 4.0,
             # holt_params
             "holt_alpha": 0.0,
-            "holt_beta": 0.0
+            "holt_beta": 0.0,
             # hw_params
             # cusum_detector
             # robust_stat_detector
diff --git a/kats/tests/utils/test_backtest.py b/kats/tests/utils/test_backtest.py
index 2ddd6673..d3b89769 100644
--- a/kats/tests/utils/test_backtest.py
+++ b/kats/tests/utils/test_backtest.py
@@ -727,7 +727,7 @@ def test_forbidden_initialization_parameters(self) -> None:
             (50, 50, 110, "Invalid window percentage"),
         ]
 
-        for (train_p, test_p, window_p, expected_msg) in forbidden_init_params:
+        for train_p, test_p, window_p, expected_msg in forbidden_init_params:
 
             # Create backtester with forbidden initialization parameters
             with self.assertRaises(ValueError) as e:
@@ -1048,7 +1048,7 @@ def test_forbidden_initialization_parameters(self) -> None:
             (50, 50, -10, "Invalid number of folds"),
         ]
 
-        for (train_p, test_p, num_folds, expected_msg) in forbidden_init_params:
+        for train_p, test_p, num_folds, expected_msg in forbidden_init_params:
 
             # Create cross validation object with forbidden initialization parameters
             with self.assertRaises(ValueError) as e:
diff --git a/kats/tests/utils/test_decomposition.py b/kats/tests/utils/test_decomposition.py
index 26ba5514..4976d5d1 100644
--- a/kats/tests/utils/test_decomposition.py
+++ b/kats/tests/utils/test_decomposition.py
@@ -434,9 +434,11 @@ def test_10_minutes_level_sparse_data(self) -> None:
         # dates are sparse, there are some gaps between dates
         sparse_dates_df = dense_dates_ts.to_dataframe().copy()
         sparse_dates_df["time"] = sparse_dates_df["time"].map(
-            lambda x: x + pd.Timedelta(365, "D")
-            if (x >= pd.Timestamp(2021, 1, 2)) & (x < pd.Timestamp(2021, 1, 3))
-            else x
+            lambda x: (
+                x + pd.Timedelta(365, "D")
+                if (x >= pd.Timestamp(2021, 1, 2)) & (x < pd.Timestamp(2021, 1, 3))
+                else x
+            )
         )
         sparse_dates_ts = TimeSeriesData(sparse_dates_df)
 
diff --git a/kats/tests/utils/test_time_series_parameter_tuning.py b/kats/tests/utils/test_time_series_parameter_tuning.py
index 87566587..5b55d66b 100644
--- a/kats/tests/utils/test_time_series_parameter_tuning.py
+++ b/kats/tests/utils/test_time_series_parameter_tuning.py
@@ -349,6 +349,7 @@ def test_compute_search_cardinality(self) -> None:
 
     def test_time_series_parameter_tuning_prophet_bayes_opt_scheduler(self) -> None:
         random_state: RandomState = RandomState(seed=0)
+
         #     # pyre-fixme[2]: Parameter must be annotated.
         def prophet_evaluation_function(params) -> Tuple[float, float]:
             error: float = random_state.random()
@@ -423,6 +424,7 @@ def prophet_evaluation_function(params) -> Tuple[float, float]:
 
     def test_time_series_parameter_tuning_NeverGrad(self) -> None:
         random_state: RandomState = RandomState(seed=0)
+
         # pyre-fixme[2]: Parameter must be annotated.
         def prophet_evaluation_function(**kwargs) -> float:
             error: float = random_state.random()
@@ -549,6 +551,7 @@ def prophet_evaluation_function(**kwargs) -> float:
 
     def test_time_series_parameter_tuning_NeverGrad_multi(self) -> None:
         random_state: RandomState = RandomState(seed=0)
+
         #     # pyre-fixme[2]: Parameter must be annotated.
         def prophet_evaluation_function(**kwargs) -> float:
             error: float = random_state.random()
diff --git a/kats/tsfeatures/tsfeatures.py b/kats/tsfeatures/tsfeatures.py
index 246b71bf..66be0af8 100644
--- a/kats/tsfeatures/tsfeatures.py
+++ b/kats/tsfeatures/tsfeatures.py
@@ -2122,7 +2122,10 @@ def __init__(
 
     def get_features(
         self, data: Union[TimeSeriesData, pd.Series], raw: bool = False
-    ) -> Union[pd.DataFrame, np.ndarray,]:
+    ) -> Union[
+        pd.DataFrame,
+        np.ndarray,
+    ]:
 
         if isinstance(data, TimeSeriesData):
             timestamps = data.time.dt
diff --git a/kats/utils/backtesters.py b/kats/utils/backtesters.py
index ab335e06..10c1f7a0 100644
--- a/kats/utils/backtesters.py
+++ b/kats/utils/backtesters.py
@@ -76,8 +76,9 @@ class BacktesterResult:
 
 
 class Forecaster(Protocol):
-    def __call__(self, train: DataPartition, test: DataPartition) -> pd.DataFrame:
-        ...  # pragma: no cover
+    def __call__(
+        self, train: DataPartition, test: DataPartition
+    ) -> pd.DataFrame: ...  # pragma: no cover
 
     """
     Function of fitting a forecasting model with `train` and evaluate the fitted model on `test`.
@@ -86,8 +87,9 @@ def __call__(self, train: DataPartition, test: DataPartition) -> pd.DataFrame:
 
 
 class Scorer(Protocol):
-    def __call__(self, result: pd.DataFrame) -> Dict[str, float]:
-        ...  # pragma: no cover
+    def __call__(
+        self, result: pd.DataFrame
+    ) -> Dict[str, float]: ...  # pragma: no cover
 
     """Function for calculating evaluation metrics based on `result`.
     """
@@ -122,6 +124,7 @@ def _get_scorer(
             except Exception as e:
                 msg = f"Unsupported error function {error} with error message {e}."
                 _log_error(msg)
+
         # define scorer function
         # pyre-fixme Incompatible return type [7]: Expected `Optional[typing.Callable[[DataFrame], Dict[str, float]]]` but got `Union[Metric, MultiOutputMetric, WeightedMetric]`.
         def calc_error(result: pd.DataFrame) -> Dict[str, float]:
diff --git a/kats/utils/cupik.py b/kats/utils/cupik.py
index 31cce322..4453a319 100644
--- a/kats/utils/cupik.py
+++ b/kats/utils/cupik.py
@@ -23,19 +23,16 @@ class Step(Protocol):
     __type__: str
     data: TimeSeriesData
 
-    def remover(self, interpolate: bool) -> TimeSeriesData:
-        ...
+    def remover(self, interpolate: bool) -> TimeSeriesData: ...
 
-    def transform(self, data: TimeSeriesData) -> object:
-        ...
+    def transform(self, data: TimeSeriesData) -> object: ...
 
     def fit(
         self,
         x: Union[pd.DataFrame, np.ndarray],
         y: Optional[Union[pd.Series, np.ndarray]],
         **kwargs: Any,
-    ) -> List[TimeSeriesData]:
-        ...
+    ) -> List[TimeSeriesData]: ...
 
 
 PipelineStep = Tuple[str, Step]
diff --git a/kats/utils/simulator.py b/kats/utils/simulator.py
index 0579f353..3ced15a7 100644
--- a/kats/utils/simulator.py
+++ b/kats/utils/simulator.py
@@ -207,7 +207,6 @@ def add_noise(
         magnitude: float = 1.0,
         multiply: bool = False,
     ) -> Simulator:
-
         """Add noise to the generated time series for STL-based simulator.
 
         Noise type is normal - noise will be generated from iid normal distribution;
diff --git a/kats/utils/time_series_parameter_tuning.py b/kats/utils/time_series_parameter_tuning.py
index 08262fa9..674283aa 100644
--- a/kats/utils/time_series_parameter_tuning.py
+++ b/kats/utils/time_series_parameter_tuning.py
@@ -481,7 +481,7 @@ def generate_evaluate_new_parameter_values(
         self,
         # pyre-fixme[24]: Generic type `Callable` expects 2 type parameters.
         evaluation_function: Callable,
-        arm_count: int = -1  # -1 means
+        arm_count: int = -1,  # -1 means
         # create all arms (i.e. all combinations of parameter values)
     ) -> None:
         """A place holder method for users that are still using it.
@@ -894,7 +894,9 @@ class BayesMethodOptions(SearchMethodOptions):
     window_global_stop_size: int = 3
     experiment: Optional[Experiment] = None
     timeout_hours: Optional[int] = None  # timeout in hours for optimization
-    improvement_bar: float = 0.02  # imporvement step for gloabl stop strategy, imporvement bar default value sets for f_score func
+    improvement_bar: float = (
+        0.02  # imporvement step for gloabl stop strategy, imporvement bar default value sets for f_score func
+    )
     max_initialization_trials: int = 5
     seed: Optional[int] = None
 
@@ -1106,9 +1108,11 @@ def generate_evaluate_new_parameter_values_with_options(
             search_space=self._exp.search_space,
             max_parallelism_cap=min(
                 cpu_count(),
-                options.multiprocessing
-                if options.multiprocessing > 0
-                else options.max_trials,
+                (
+                    options.multiprocessing
+                    if options.multiprocessing > 0
+                    else options.max_trials
+                ),
             ),
             # use_batch_trials option somehow on parallel run limits initial number ob trials to 1 ¯\_(ツ)_/¯
             # use_batch_trials=bool(self.multiprocessing),