Move transforms to transforms file

Author: golmschenk

src/qusi/internal/light_curve_dataset.py

@@ -32,7 +32,7 @@
 )
 from qusi.internal.light_curve_transforms import (
     from_light_curve_observation_to_fluxes_array_and_label_array,
-    pair_array_to_tensor,
+    pair_array_to_tensor, normalize_tensor_by_modified_z_score, make_uniform_length,
 )
 
 if TYPE_CHECKING:
@@ -337,56 +337,6 @@ def default_light_curve_post_injection_transform(
     return x
 
 
-def normalize_tensor_by_modified_z_score(tensor: Tensor) -> Tensor:
-    """
-    Normalizes a tensor by a modified z-score. That is, normalizes the values of the tensor based on the median
-    absolute deviation.
-
-    :param tensor: The tensor to normalize.
-    :return: The normalized tensor.
-    """
-    median = torch.median(tensor)
-    deviation_from_median = tensor - median
-    absolute_deviation_from_median = torch.abs(deviation_from_median)
-    median_absolute_deviation_from_median = torch.median(absolute_deviation_from_median)
-    if median_absolute_deviation_from_median != 0:
-        modified_z_score = (
-                0.6745 * deviation_from_median / median_absolute_deviation_from_median
-        )
-    else:
-        modified_z_score = torch.zeros_like(tensor)
-    return modified_z_score
-
-
-def make_uniform_length(
-        example: np.ndarray, length: int, *, randomize: bool = True
-) -> np.ndarray:
-    """Makes the example a specific length, by clipping those too large and repeating those too small."""
-    if len(example.shape) not in [1, 2]:  # Only tested for 1D and 2D cases.
-        raise ValueError(
-            f"Light curve dimensions expected to be in [1, 2], but found {len(example.shape)}"
-        )
-    if randomize:
-        example = randomly_roll_elements(example)
-    if example.shape[0] == length:
-        pass
-    elif example.shape[0] > length:
-        example = example[:length]
-    else:
-        elements_to_repeat = length - example.shape[0]
-        if len(example.shape) == 1:
-            example = np.pad(example, (0, elements_to_repeat), mode="wrap")
-        else:
-            example = np.pad(example, ((0, elements_to_repeat), (0, 0)), mode="wrap")
-    return example
-
-
-def randomly_roll_elements(example: np.ndarray) -> np.ndarray:
-    """Randomly rolls the elements."""
-    example = np.roll(example, np.random.randint(example.shape[0]), axis=0)
-    return example
-
-
 class OutOfBoundsInjectionHandlingMethod(Enum):
     """
     An enum of approaches for handling cases where the injectable signal is shorter than the injectee signal.

src/qusi/internal/light_curve_transforms.py

@@ -1,3 +1,5 @@
+from __future__ import annotations
+
 import numpy as np
 import numpy.typing as npt
 import torch
@@ -38,3 +40,47 @@ def randomly_roll_elements(example: np.ndarray) -> np.ndarray:
     """Randomly rolls the elements."""
     example = np.roll(example, np.random.randint(example.shape[0]), axis=0)
     return example
+
+
+def normalize_tensor_by_modified_z_score(tensor: Tensor) -> Tensor:
+    """
+    Normalizes a tensor by a modified z-score. That is, normalizes the values of the tensor based on the median
+    absolute deviation.
+
+    :param tensor: The tensor to normalize.
+    :return: The normalized tensor.
+    """
+    median = torch.median(tensor)
+    deviation_from_median = tensor - median
+    absolute_deviation_from_median = torch.abs(deviation_from_median)
+    median_absolute_deviation_from_median = torch.median(absolute_deviation_from_median)
+    if median_absolute_deviation_from_median != 0:
+        modified_z_score = (
+                0.6745 * deviation_from_median / median_absolute_deviation_from_median
+        )
+    else:
+        modified_z_score = torch.zeros_like(tensor)
+    return modified_z_score
+
+
+def make_uniform_length(
+        example: np.ndarray, length: int, *, randomize: bool = True
+) -> np.ndarray:
+    """Makes the example a specific length, by clipping those too large and repeating those too small."""
+    if len(example.shape) not in [1, 2]:  # Only tested for 1D and 2D cases.
+        raise ValueError(
+            f"Light curve dimensions expected to be in [1, 2], but found {len(example.shape)}"
+        )
+    if randomize:
+        example = randomly_roll_elements(example)
+    if example.shape[0] == length:
+        pass
+    elif example.shape[0] > length:
+        example = example[:length]
+    else:
+        elements_to_repeat = length - example.shape[0]
+        if len(example.shape) == 1:
+            example = np.pad(example, (0, elements_to_repeat), mode="wrap")
+        else:
+            example = np.pad(example, ((0, elements_to_repeat), (0, 0)), mode="wrap")
+    return example

src/qusi/transform.py

@@ -3,17 +3,18 @@
 """
 from qusi.internal.light_curve import randomly_roll_light_curve, remove_nan_flux_data_points_from_light_curve
 from qusi.internal.light_curve_dataset import default_light_curve_post_injection_transform, \
-    default_light_curve_observation_post_injection_transform, make_uniform_length
+    default_light_curve_observation_post_injection_transform
 from qusi.internal.light_curve_observation import remove_nan_flux_data_points_from_light_curve_observation, \
     randomly_roll_light_curve_observation
 from qusi.internal.light_curve_transforms import from_light_curve_observation_to_fluxes_array_and_label_array, \
-    pair_array_to_tensor
+    pair_array_to_tensor, make_uniform_length, normalize_tensor_by_modified_z_score
 
 __all__ = [
     'default_light_curve_post_injection_transform',
     'default_light_curve_observation_post_injection_transform',
     'from_light_curve_observation_to_fluxes_array_and_label_array',
     'make_uniform_length',
+    'normalize_tensor_by_modified_z_score',
     'pair_array_to_tensor',
     'randomly_roll_light_curve',
     'randomly_roll_light_curve_observation',