moved method (#138)

Author: MartinBuessemeyer

wkcuber/cubing.py

@@ -6,7 +6,7 @@
 from natsort import natsorted
 
 from .mag import Mag
-from .downsampling import downsample_cube, parse_interpolation_mode
+from .downsampling import parse_interpolation_mode, downsample_unpadded_data
 from .utils import (
     get_chunks,
     find_files,
@@ -193,29 +193,6 @@ def cubing_job(args):
                 raise exc
 
 
-def downsample_unpadded_data(buffer, target_mag, interpolation_mode):
-    logging.info(
-        f"Downsampling buffer of size {buffer.shape} to mag {target_mag.to_layer_name()}"
-    )
-    target_mag_np = np.array(target_mag.to_array())
-    current_dimension_size = np.array(buffer.shape[1:])
-    padding_size_for_downsampling = (
-        target_mag_np - (current_dimension_size % target_mag_np) % target_mag_np
-    )
-    padding_size_for_downsampling = list(zip([0, 0, 0], padding_size_for_downsampling))
-    buffer = np.pad(
-        buffer, pad_width=[(0, 0)] + padding_size_for_downsampling, mode="constant"
-    )
-    dimension_decrease = np.array([1] + target_mag.to_array())
-    downsampled_buffer_shape = np.array(buffer.shape) // dimension_decrease
-    downsampled_buffer = np.empty(dtype=buffer.dtype, shape=downsampled_buffer_shape)
-    for channel in range(buffer.shape[0]):
-        downsampled_buffer[channel] = downsample_cube(
-            buffer[channel], target_mag.to_array(), interpolation_mode
-        )
-    return downsampled_buffer
-
-
 def cubing(source_path, target_path, layer_name, dtype, batch_size, args=None) -> dict:
 
     target_mag = Mag(args.target_mag)

wkcuber/downsampling.py

@@ -412,6 +412,29 @@ def downsample_cube(cube_buffer, factors, interpolation_mode):
         raise Exception("Invalid interpolation mode: {}".format(interpolation_mode))
 
 
+def downsample_unpadded_data(buffer, target_mag, interpolation_mode):
+    logging.info(
+        f"Downsampling buffer of size {buffer.shape} to mag {target_mag.to_layer_name()}"
+    )
+    target_mag_np = np.array(target_mag.to_array())
+    current_dimension_size = np.array(buffer.shape[1:])
+    padding_size_for_downsampling = (
+        target_mag_np - (current_dimension_size % target_mag_np) % target_mag_np
+    )
+    padding_size_for_downsampling = list(zip([0, 0, 0], padding_size_for_downsampling))
+    buffer = np.pad(
+        buffer, pad_width=[(0, 0)] + padding_size_for_downsampling, mode="constant"
+    )
+    dimension_decrease = np.array([1] + target_mag.to_array())
+    downsampled_buffer_shape = np.array(buffer.shape) // dimension_decrease
+    downsampled_buffer = np.empty(dtype=buffer.dtype, shape=downsampled_buffer_shape)
+    for channel in range(buffer.shape[0]):
+        downsampled_buffer[channel] = downsample_cube(
+            buffer[channel], target_mag.to_array(), interpolation_mode
+        )
+    return downsampled_buffer
+
+
 def downsample_mag(
     path,
     layer_name,