Better border removal for array maxima detection

pynkowski/data/array.py

@@ -2,30 +2,6 @@
 from .base_da import DataField
 
 
-def _remove_borders(array, last_axis=None):
- """Remove the borders of an array in all dimensions up to `last_axis`.
- 
- Arguments
- ---------
- array : np.array
- Array to remove the borders.
- 
- last_axis : int, optional
- Last axis to remove the borders. If `None`, all the axes are considered.
- 
- Returns
- -------
- new_array : np.array
- Array with the borders removed.
- 
- """
- if last_axis is None:
- last_axis = len(array.shape)
- new_array = np.delete(array, [0,-1], axis=last_axis)
- if last_axis>0:
- new_array = _remove_borders(new_array, last_axis-1)
- return new_array
-
 def _hotspot_array(field):
  """Find the local maxima of the input field.
 
@@ -48,7 +24,13 @@ def _hotspot_array(field):
  max_mask = np.all(field > np.array([np.roll(field, shift, axis=ii) for shift in [-1,1] for ii in range(ndim)]), axis=0)
 
  # We then remove all the pixels in the border to remove the edge effects.
- max_mask = _remove_borders(max_mask)
+ for dim in range(max_mask.ndim):
+ # Make current dimension the first dimension
+ array_moved = np.moveaxis(max_mask, dim, 0)
+ # Set border values to `False` in the current dimension
+ array_moved[0] = False
+ array_moved[-1] = False
+ # No need to reorder the dimensions as moveaxis returns a view
  max_mask = np.pad(max_mask, pad_width=1, mode='constant', constant_values=False)
 
  pixels = np.argwhere(max_mask)