Commit 10a02d87 authored by Martin Reinecke's avatar Martin Reinecke
Browse files

remove unused code

parent 2154c904
Pipeline #19445 passed with stage
in 4 minutes and 20 seconds
...@@ -8,66 +8,3 @@ from functools import reduce ...@@ -8,66 +8,3 @@ from functools import reduce
def from_object(object, dtype=None, copy=True): def from_object(object, dtype=None, copy=True):
return np.array(object, dtype=dtype, copy=copy) return np.array(object, dtype=dtype, copy=copy)
def bincount_axis(obj, minlength=None, weights=None, axis=None):
if minlength is not None:
length = max(np.amax(obj) + 1, minlength)
length = np.amax(obj) + 1
if obj.shape == ():
raise ValueError("object of too small depth for desired array")
data = obj
# if present, parse the axis keyword and transpose/reorder
# such that all affected axes follow each other. Only if they are in a
# sequence flattening will be possible
if axis is not None:
# do the reordering
ndim = len(obj.shape)
axis = sorted(cast_iseq_to_tuple(axis))
reordering = [x for x in range(ndim) if x not in axis]
reordering += axis
data = np.transpose(data, reordering)
if weights is not None:
weights = np.transpose(weights, reordering)
reord_axis = list(range(ndim-len(axis), ndim))
# semi-flatten the dimensions in `axis`, i.e. after reordering
# the last ones.
semi_flat_dim = reduce(lambda x, y: x*y,
flat_shape = data.shape[:ndim-len(reord_axis)] + (semi_flat_dim, )
flat_shape = (reduce(lambda x, y: x*y, data.shape), )
data = np.ascontiguousarray(data.reshape(flat_shape))
if weights is not None:
weights = np.ascontiguousarray(weights.reshape(flat_shape))
# compute the local bincount results
# -> prepare the local result array
result_dtype = if weights is None else np.float
local_counts = np.empty(flat_shape[:-1] + (length, ), dtype=result_dtype)
# iterate over all entries in the surviving axes and compute the local
# bincounts
for slice_list in get_slice_list(flat_shape, axes=(len(flat_shape)-1,)):
current_weights = None if weights is None else weights[slice_list]
local_counts[slice_list] = np.bincount(data[slice_list],
# restore the original ordering
# place the bincount stuff at the location of the first `axis` entry
if axis is not None:
# axis has been sorted above
insert_position = axis[0]
new_ndim = len(local_counts.shape)
return_order = (list(range(0, insert_position)) +
[new_ndim-1, ] +
list(range(insert_position, new_ndim-1)))
local_counts = np.ascontiguousarray(
return local_counts
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