@@ -392,7 +392,6 @@ def translate_z_stack(_image, _shift, transform_type='z'):
392392def _translate_z_stack_cpu (_image , _shift , transform_type ):
393393 """CPU implementation using scipy.ndimage."""
394394 _image_out = []
395- _shift = - _shift # Negate shift for correct direction
396395 return_numpy = isinstance (_image , np .ndarray ) and _image .ndim == 3
397396 image_iter = [_image ] if return_numpy else _image
398397
@@ -458,7 +457,6 @@ def _translate_z_stack_cpu(_image, _shift, transform_type):
458457def _translate_z_stack_gpu (_image , _shift , transform_type ):
459458 """GPU implementation using pyclesperanto."""
460459 _image_out = []
461- _shift = - _shift # Negate shift for correct direction
462460
463461 def _apply_gpu_transform (img_data , shift_params , ttype ):
464462 """Helper to apply GPU transformation to numpy array."""
@@ -835,4 +833,4 @@ def read_tmp_data(_path, _new_shape=None):
835833# - This allows independent, efficient access to individual channels and timepoints
836834# - Built-in compression (Blosc/LZ4) reduces disk usage by ~2-3x
837835# - Better random access patterns compared to NPY stacks
838- # - Particularly efficient for multi-channel images
836+ # - Particularly efficient for multi-channel images
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