def test_nauty(verbose=0):
if verbose:
print('test_nauty: test reduction to normal form')
al = oapackage.exampleArray(0, verbose)
alr = al.randomperm()
tr = oapackage.reduceOAnauty(alr)
alx = tr.apply(alr)
assert (alx == al)
def selectIsomorphismClasses(sols, verbose=1):
""" Select isomorphism classes from a list of designs
Args:
sols (list of arrays)
verbose (int)
Return:
indices (list of integers): indices of the isomorphism classes
mm (list of arrays): the arrays in normal form
Example:
>>> sols=[oapackage.exampleArray(i) for i in [26,26,27,26]]
>>> idx, mm = selectIsomorphismClasses(sols)
To select one representative array from each isomorphism class one can use:
>>> _, ui = np.unique(idx, return_index=True)
>>> representatives = [sols[i] for i in ui]
"""
# perform check on array data type
mm = []
for ii, al in enumerate(sols):
if verbose:
oapackage.tprint('selectIsomorphismClasses: process aray %d/%d' %
(ii, len(sols)))
al = oapackage.makearraylink(al)
tt = oapackage.reduceOAnauty(al, verbose >= 2)
#pp, tt = reduceBliss(al, arrayclass, verbose >= 2)
#tt = graph2arrayTransformation(pp, arrayclass)
alx = tt.apply(al)
mm.append(np.array(alx))
pass
# perform uniqueness check
nn = len(mm)
qq = np.array([None] * nn, dtype=object)
for ii in range(nn):
qq[ii] = mm[ii].flatten()
# Trick to make unique work...
nx = qq[0].size
dt = qq[0].dtype.descr * nx
qqq = np.array(qq, dtype=dt)
a, indices = np.unique(qqq, return_inverse=True)
if verbose >= 1:
print('selectIsomorphismClasses: reduce %d to %d' %
(len(sols), np.unique(indices).size))
return indices, mm
def selectIsomorphismClasses(sols, verbose=1):
""" Select isomorphism classes from a list of designs
Args:
sols (list of arrays)
verbose (int)
Return:
indices (list of integers): indices of the isomorphism classes
mm (list of arrays): the arrays in normal form
"""
# perform check on array data type
mm = []
for ii, al in enumerate(sols):
if verbose:
oapackage.tprint('selectIsomorphismClasses: process aray %d/%d' %
(ii, len(sols)),
dt=4)
al = oapackage.makearraylink(al)
tt = oapackage.reduceOAnauty(al, verbose >= 2)
#pp, tt = reduceBliss(al, arrayclass, verbose >= 2)
#tt = graph2arrayTransformation(pp, arrayclass)
alx = tt.apply(al)
mm.append(np.array(alx))
pass
# perform uniqueness check
nn = len(mm)
qq = np.array([None] * nn, dtype=object)
for ii in range(nn):
qq[ii] = mm[ii].flatten()
if 0:
# Trick to make unique work...
# old code
nx = qq[0].size
dt = qq[0].dtype.descr * nx
qqq = [np.array(q, dtype=dt) for q in qq]
qqq = np.array(qq, dtype=dt)
a, indices = np.unique(qqq, return_inverse=True)
# Trick to make unique work...
a, indices = np.unique(np.vstack(qq), axis=0, return_inverse=True)
if verbose >= 1:
print('selectIsomorphismClasses: %d reduced to %d' %
(len(sols), np.unique(indices).size))
return indices, mm
def selectIsomorphismClasses(sols, verbose=1):
""" Select isomorphism classes from a list of designs
Args:
sols (list of arrays): list of arrays from which to determine the unique ones
verbose (int): verbosity level
Return:
indices (list of integers): indices of the isomorphism classes
mm (list of arrays): the arrays in normal form
Example:
>>> import oapackage.graphtools; import numpy as np
>>> sols=[oapackage.exampleArray(idx) for idx in [26,26,27,26]]
>>> idx, mm = oapackage.graphtools.selectIsomorphismClasses(sols, verbose=0)
>>> _, unique_indices = np.unique(idx, return_index=True)
>>> print('found %d isomorphism classes from %d arrays' % (len(unique_indices), len(sols)) )
found 2 isomorphism classes from 4 arrays
>>> representatives = [sols[idx] for idx in unique_indices]
"""
mm = []
for ii, al in enumerate(sols):
if verbose:
oapackage.tprint('selectIsomorphismClasses: process aray %d/%d' %
(ii, len(sols)))
al = oapackage.makearraylink(al)
tt = oapackage.reduceOAnauty(al, verbose >= 2)
alx = tt.apply(al)
mm.append(np.array(alx))
# perform uniqueness check
nn = len(mm)
qq = np.array([None] * nn, dtype=object)
for ii in range(nn):
qq[ii] = mm[ii].flatten()
# Trick to make unique work...
_, indices = np.unique(np.vstack(qq), axis=0, return_inverse=True)
if verbose >= 1:
print('selectIsomorphismClasses: reduce %d to %d' %
(len(sols), np.unique(indices).size))
return indices, mm
