T3Frame.unstack

t3toolbox.frame_variations_format.T3Frame.unstack()

def unstack(self):

Unstack into an array-like tree.

Examples

>>> import numpy as np
>>> import t3toolbox.frame_variations_format as bvf
>>> import t3toolbox.corewise as cw
>>> np.random.seed(0)
>>> rnd = lambda x: np.random.randn(*x)
>>> ss = (2, 3)                                   # stack C: unstack splits these leading axes
>>> up_tucker_cores = (rnd(ss+(10, 14)), rnd(ss+(11, 15)), rnd(ss+(12, 16)))
>>> down_tt_cores = (rnd(ss+(1, 9, 4)), rnd(ss+(2, 8, 5)), rnd(ss+(3, 7, 1)))
>>> left_tt_cores = (rnd(ss+(1, 10, 2)), rnd(ss+(2, 11, 3)), rnd(ss+(3, 12, 5)))
>>> right_tt_cores = (rnd(ss+(2, 10, 4)), rnd(ss+(4, 11, 5)), rnd(ss+(5, 12, 1)))
>>> frame = bvf.T3Frame(up_tucker_cores, down_tt_cores, left_tt_cores, right_tt_cores)
>>> S = frame.unstack()
>>> print(len(S), len(S[0]))                      # nested tree shaped like the stack (2, 3)
2 3
>>> ii, jj = 1, 2                                 # the [ii][jj] leaf is just the cores sliced at [ii, jj]
>>> Sij = S[ii][jj]
>>> sliced = bvf.T3Frame(
...     tuple(c[ii, jj] for c in up_tucker_cores), tuple(c[ii, jj] for c in down_tt_cores),
...     tuple(c[ii, jj] for c in left_tt_cores), tuple(c[ii, jj] for c in right_tt_cores))
>>> print(np.allclose(cw.corewise_norm(cw.corewise_sub(Sij.data, sliced.data)), 0.0))
True