def pw_transform(self, pwfn, verbose=False, **kwargs):
"""Spawn another frame sequence, pixelwise applying a user-provided
function.
Parameters:
- `pwfn`: (`func`) -- a ``f(vector) -> vector`` function
- `verbose`: (`Bool`) -- whether to be verbose while going through
the pixels
- `**kwargs``: keyword arguments to be passed to `self.pix_iter`
"""
#nrows, ncols = self.shape()[:2]
if 'dtype' in kwargs:
dtype = kwargs['dtype']
else:
dtype = _dtype_
L = len(pwfn(np.random.randn(len(self))))
#testv = pwfn(self.pix_iter(rand=True,**kwargs).next()[0])
#L = len(testv)
out = lib.memsafe_arr((L,) + self.shape(), dtype)
for v, row, col in self.pix_iter(**kwargs):
if verbose:
sys.stderr.write('\rworking on pixel (%03d,%03d)'%(row, col))
out[:,row,col] = pwfn(v)
if hasattr(out, 'flush'):
out.flush()
## assuming the dz is not changed. if it *is*, provide new meta
## in kwargs
if 'meta' in kwargs:
newmeta = kwargs['meta'].copy()
else:
newmeta = self.meta.copy()
return FSeq_arr(out, meta=newmeta)
def supp_from_obj(object,
min_level=0,
max_level=10,
verbose=0,
mode=0,
weights=None):
"""Return support arrays from object"""
sh = object.labels.shape
new_shape = [object.level + 1] + list(sh)
out = lib.memsafe_arr(new_shape, _dtype_)
flat = flat_tree(object)
nfl = len(flat)
if weights is None:
weights = [1] * (object.level + 1)
for j, n in enumerate(flat):
if verbose:
sys.stderr.write('\rnode %d out of %d' % (j + 1, nfl))
if n.level > min_level and n.level <= max_level:
val = weights[n.level]
if mode == 1:
out[n.level][n.labels == n.ind] = val
elif mode == 2:
out[n.level][n.labels == n.ind] = n.ind
else:
out[n.level][n.slice] = np.where(n.labels[n.slice] == n.ind,
val, 0)
return out
def as3darray(self, fslice=None, crop=None,
dtype = _dtype_):
"""Return the frames as a `3D` array.
//An alternative way is to use the __getitem__ interface:
//``data = np.asarray(fs[10:100])``
Parameters:
- `fslice`: (`int`, `tuple-like` or `None`) --
[start,] stop [, step] to go through frames
- `crop`: (`slice` or `None`) -- a crop (tuple of slices) to take from each frame
- `dtype`: (`type`) -- data type to use. Default, ``np.float64``
Returns:
`3D` array `d`, where frames are stored in higher dimensions, such
that ``d[0]`` is the first frame, etc.
"""
if fslice is None or isinstance(fslice, int):
fslice = (fslice, )
shape = self.shape(crop)
newshape = (len(self),) + shape
out = lib.memsafe_arr(newshape, dtype)
for k,frame in enumerate(itt.islice(self.frames(), *fslice)):
out[k,:,:] = frame[crop]
out = out[:k+1]
if hasattr (out, 'flush'):
out.flush()
return out
def supp_from_connectivity(graph, nlevels):
nodes = lib.flatten(graph)
sh = nodes[0].labels.shape
new_shape = [nlevels] + list(sh)
out = lib.memsafe_arr(new_shape, _dtype_) * 0.0
for n in nodes:
out[n.level][n.labels > 0] = 1.0
return out
