def shape_divide(arr, scale, reduction='mean'):
'''Scale down an array (shape N x M x ...) by the specified scale
in each dimension (n x m x ...)
Each dimension in arr must be divisible by its scale
(throws an error otherwise)
This is reduces each sub-array (n x m x ...) to a single element
according to the reduction parameter, which is one of:
* mean (default): mean of each sub-array
* median: median of each sub-array
* first: the [0,0,0, ...] element of the sub-array
* all: all the possible (N x M x ...) sub-arrays;
returns an array of shape (n, m, ..., N, M, ...)
This is a downsampling operation, similar to
scipy.misc.imresize and scipy.ndimage.interpolate'''
arr = np.asanyarray(arr)
reduction_options = ['mean', 'median', 'first', 'all']
assert reduction in reduction_options, \
'reduction must be one of: ' + ' '.join(reduction_options)
scale = coerce_to_target_length(scale, arr.ndim)
assert all([sh % sc == 0 for sh, sc in zip(arr.shape,scale)]), \
'all dimensions must be divisible by their respective scale!'
new_shape = flatten([sh//sc, sc] for sh, sc in zip(arr.shape, scale))
# group pixes into smaller sub-arrays that can then be modified by standard operations
subarrays = _transpose_interleaved(arr.reshape(new_shape))
flat_subarrays = subarrays.reshape([np.product(scale)] + new_shape[::2])
return (np.mean(flat_subarrays, axis=0) if reduction == 'mean' else
np.median(flat_subarrays, axis=0) if reduction == 'median' else
flat_subarrays[0] if reduction == 'first' else
subarrays if reduction == 'all' else
None)
def zeroFill(t, a, sc):
t *= 0
middle_slice = flatten([slice(None), i//2] for i in sc)
t[middle_slice] = a
return t
def GetTriangleBorderPoints(p0,p1,p2):
'''Collect all the border points for a raster triangle and remove any duplicates'''
allPts = [BresenhamFunction(pa,pb)
for pa,pb in ((p0,p1),(p1,p2),(p2,p0))]
return list(set(totuple(flatten(allPts))))
