fixes

pts/__init__.py

@@ -0,0 +1 @@
+from .exception import assert_pts

pts/dataset/list_dataset.py

@@ -6,7 +6,7 @@ from .process import ProcessDataEntry
 
 class ListDataset(Dataset):
     def __init__(
-        self, data_iter: Iterable[DataEntry], freq: str, one_dim_target: bool = True
+            self, data_iter: Iterable[DataEntry], freq: str, one_dim_target: bool = True
     ) -> None:
         process = ProcessDataEntry(freq, one_dim_target)
         self.list_data = [process(data) for data in data_iter]

pts/dataset/sampler.py

@@ -1,11 +1,11 @@
+from abc import ABC, abstractmethod
 from typing import Union, List
 
 import numpy as np
 
-from abc import ABC, abstractmethod
-
 from .stat import ScaleHistogram
 
+
 class InstanceSampler(ABC):
     @abstractmethod
     def __call__(self, ts: np.ndarray, a: int, b: int) -> Union[np.ndarray, List[int]]:
@@ -30,7 +30,7 @@ class UniformSplitSampler(InstanceSampler):
         while ts.shape[-1] >= len(self.lookup):
             self.lookup = np.arange(2 * len(self.lookup))
         mask = np.random.uniform(low=0.0, high=1.0, size=b - a + 1) < self.p
-        return self.lookup[a : a + len(mask)][mask]
+        return self.lookup[a: a + len(mask)][mask]
 
 
 class TestSplitSampler(InstanceSampler):
@@ -69,7 +69,7 @@ class ExpectedNumInstanceSampler(InstanceSampler):
         p = self.num_instances / self.avg_length
 
         mask = np.random.uniform(low=0.0, high=1.0, size=b - a + 1) < p
-        indices = self.lookup[a : a + len(mask)][mask]
+        indices = self.lookup[a: a + len(mask)][mask]
         return indices
 
 
@@ -97,5 +97,5 @@ class BucketInstanceSampler(InstanceSampler):
             self.lookup = np.arange(2 * len(self.lookup))
         p = 1.0 / self.scale_histogram.count(ts)
         mask = np.random.uniform(low=0.0, high=1.0, size=b - a + 1) < p
-        indices = self.lookup[a : a + len(mask)][mask]
+        indices = self.lookup[a: a + len(mask)][mask]
         return indices

pts/exception.py

@@ -0,0 +1,4 @@
+
+def assert_pts(condition: bool, message: str, *args, **kwargs) -> None:
+    if not condition:
+        raise Exception(message.format(*args, **kwargs))

pts/feature/transform.py

@@ -7,6 +7,7 @@ import numpy as np
 import pandas as pd
 
 from pts.dataset import DataEntry, InstanceSampler
+from pts import assert_pts
 from .time_feature import TimeFeature
 
 MAX_IDLE_TRANSFORMS = 100
@@ -237,7 +238,7 @@ class AsNumpyArray(SimpleTransformation):
             # ugly: required as list conversion will fail in the case of a
             # float
             value = np.asarray(value, dtype=self.dtype)
-        assert_data_error(
+        assert_pts(
             value.ndim >= self.expected_ndim,
             'Input for field "{self.field}" does not have the required'
             "dimension (field: {self.field}, ndim observed: {value.ndim}, "