def bar(x: tc.all(tc.re("abcdef"), tc.re("defghi"), tc.re("^abc"))):
pass
def foo_all(arg: tc.all(tc.any(bytes,bytearray), complete_blocks)): pass foo_all(b"x" * 512) # OK
def foo(x: tc.all()):
pass
def rips_filtration(max_dim: tc.all(int, gte_zero), max_scale: tc.all(
tc.any(int, float), gt_zero), dist_mat: array_like_2d):
"""
Builds a boundary matrix for the boundary-Rips filtration up to dimension `max_dim`.
Also builds the corresponding list of bigrades follows closely
the "incremental algorithm" in the paper on fast Vietoris-Rips comptuation
by Zomorodian, with some modification to store boundary matrix and
filtration info. That in turn is based on a version of Bron-Kerbosch algorithm.
Parameters
----------
max_dim: int >= 0
the highest dimension to compute
max_scale: float
the highest scale (distance) to consider
dist_mat: 2D array
an n x n distance matrix, which may be lower-triangular.
Returns
-------
pairs: list of (column, grade) pairs
The barcodes up to dimension max_dim, for the truncated Vietoris-Rips
filtration, including only simplices whose index of appearance is <= max_scale
The barcodes are output as a list of three-element lists. Each three-element
lists represents one interval of in barcode and has the form
[birth,death,dimension]
"""
sorted_simplices = _rips_simplices(max_dim, max_scale, dist_mat)
len_minus_one = len(sorted_simplices) - 1
cobdy_matrix_pre = _create_coboundary_matrix(sorted_simplices, max_dim)
# print(cobdy_matrix_pre);
# print(sorted_simplices)
# print(bdy_matrix_pre)
cobdy_matrix = phat.boundary_matrix(
representation=phat.representations.bit_tree_pivot_column)
cobdy_matrix.columns = cobdy_matrix_pre
# call Bryn's PHAT wrapper for the persistence computation
pairs = cobdy_matrix.compute_persistence_pairs()
# next, rescale the pairs to their original filtration values, eliminating pairs with the same birth and death time.
# In keeping with our chosen output format, we also add the dimension to the pair.
scaled_pairs = []
for i in range(len(pairs)):
cobirth = sorted_simplices[len_minus_one - pairs[i][0]][1]
codeath = sorted_simplices[len_minus_one - pairs[i][1]][1]
if codeath < cobirth:
dimension = len(
sorted_simplices[len_minus_one - pairs[i][1]][0]) - 1
scaled_pairs.append((codeath, cobirth, dimension))
# add in the intervals with endpoint inf
# To do this, we first construct an array paired_indices such that
# if the j^th simplex (in the coboundary order) appears in a pair, paired_incides[j] = 1
# otherwise paired_incides[j] = 0.
paired_indices = np.zeros(len(sorted_simplices))
for i in range(len(pairs)):
paired_indices[pairs[i][0]] = 1
paired_indices[pairs[i][1]] = 1
for i in range(len(paired_indices)):
if paired_indices[i] == 0:
birth = sorted_simplices[len_minus_one - i][1]
dimension = len(sorted_simplices[len_minus_one - i][0]) - 1
scaled_pairs.append((birth, float("inf"), dimension))
return scaled_pairs
def foo_all(arg: tc.all(tc.any(bytes, bytearray), complete_blocks)):
pass
def bar(x: tc.all(tc.re("abcdef"), tc.re("defghi"), tc.re("^abc"))):
pass
def foo(x: tc.all()):
pass
