Remove randomization from make_uniform_length and leave that to a sep…

…arate randomly roll call

docs/source/tutorials/crafting_standard_datasets.md

@@ -39,7 +39,7 @@ def default_light_curve_observation_post_injection_transform(x: LightCurveObserv
     if randomize:
         x = randomly_roll_light_curve_observation(x)
     x = from_light_curve_observation_to_fluxes_array_and_label_array(x)
-    x = (make_uniform_length(x[0], length=length, randomize=randomize), x[1])  # Make the fluxes a uniform length.
+    x = (make_uniform_length(x[0], length=length), x[1])
     x = pair_array_to_tensor(x)
     x = (normalize_tensor_by_modified_z_score(x[0]), x[1])
     return x

src/qusi/internal/light_curve_dataset.py

@@ -306,7 +306,7 @@ def default_light_curve_observation_post_injection_transform(
     if randomize:
         x = randomly_roll_light_curve_observation(x)
     x = from_light_curve_observation_to_fluxes_array_and_label_array(x)
-    x = (make_uniform_length(x[0], length=length, randomize=randomize), x[1])  # Make the fluxes a uniform length.
+    x = (make_uniform_length(x[0], length=length), x[1])  # Make the fluxes a uniform length.
     x = pair_array_to_tensor(x)
     x = (normalize_tensor_by_modified_z_score(x[0]), x[1])
     return x
@@ -331,7 +331,7 @@ def default_light_curve_post_injection_transform(
     if randomize:
         x = randomly_roll_light_curve(x)
     x = x.fluxes
-    x = make_uniform_length(x, length=length, randomize=randomize)
+    x = make_uniform_length(x, length=length)
     x = torch.tensor(x, dtype=torch.float32)
     x = normalize_tensor_by_modified_z_score(x)
     return x

src/qusi/internal/light_curve_transforms.py

@@ -63,16 +63,12 @@ def normalize_tensor_by_modified_z_score(tensor: Tensor) -> Tensor:
     return modified_z_score
 
 
-def make_uniform_length(
-        example: np.ndarray, length: int, *, randomize: bool = True
-) -> np.ndarray:
+def make_uniform_length(example: np.ndarray, length: int) -> 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: