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 optimize_arrays(sols, alpha=None, verbose=1, niter=400):
""" Optimize set of designs """
vv2 = []
ll = oapackage.arraylist_t()
for ii, A in enumerate(sols):
Dinit = A.Defficiency()
Df, Ax = oapackage.optimDeffPython(
A, niter=niter, verbose=0, alpha=alpha)
D, Ds, D1 = Ax.Defficiencies()
ll.push_back(Ax)
oapackage.tprint('optimize array %d: %f -> D %f Ds %f D1 %f' % (ii, Dinit, D, Ds, D1))
vv2.append(np.array([D, Ds, D1]))
return np.array(vv2).T, ll
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
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
def optimize_arrays(sols, alpha=None, verbose=1, niter=400):
""" Optimize set of designs """
vv2 = []
ll = oapackage.arraylist_t()
for ii, A in enumerate(sols):
Dinit = A.Defficiency()
Df, Ax = oapackage.optimDeffPython(A,
niter=niter,
verbose=0,
alpha=alpha)
D, Ds, D1 = Ax.Defficiencies()
ll.push_back(Ax)
oapackage.tprint('optimize array %d: %f -> D %f Ds %f D1 %f' %
(ii, Dinit, D, Ds, D1))
vv2.append(np.array([D, Ds, D1]))
return np.array(vv2).T, ll
def read_hadamard(f, narrays=1e9, N=32, k=32):
""" Read all designs in a file with hex encoding """
if isinstance(f, str):
fid = open(f, 'rt')
else:
fid = f
ll = []
for ii in range(int(narrays)):
oapackage.tprint('reading array %d' % ii, dt=.5)
ii = _get_integer(fid)
if ii == -1:
break
al = _get_array(fid, N, k)
if al is not None:
ll += [al]
if isinstance(f, str):
fid.close()
print('read_hadamard: %d designs' % len(ll))
return ll
infile = oapackage.arrayfile_t(inputfile)
narrays=infile.narrays
#outfile = oapackage.arrayfile_t(outputfile, infile.nrows, infile.ncols, -1, fmode )
if not infile.isopen():
raise Exception('could not open file %s' % inputfile)
print('oaselect: reading %d arrays from %s' % (infile.narrays, inputfile) )
#%% Loop over the arrays
outlist=[]
if narrays==-1:
narrays=oapackage.arrayfile_t.NARRAYS_MAX
for ii in range(narrays):
oapackage.tprint('oaselect: parse array %d/%d' % (ii, narrays) )
al=infile.readnext()
if al.index==-1:
break
if verbose>=3:
print(' index %d' % al.index)
if fname=='subset':
if ii%args.nsubset==0:
outlist.append(al)
else:
if filterArray(al, fname, verbose=0):
outlist.append(al)
if narrays>0:
oapackage.tprint('oaselect: parse array %d/%d' % (ii, narrays), dt=-1 )
oapackage.writearrayfile(outputfile, outlist, fmode, infile.nrows, infile.ncols)