def find_spectrum(fname, adj=None):
''' '''
cells, spacing, zones, J, feedback = parse_qca_file(fname, one_zone=True)
h, J = qca_to_coef(cells, spacing, J, adj=adj)
h, J = normalized_coefs(h, J)
# get clocking schedule
s, eps, gamma = clock_schedule(scheme='linear')
spectrum = Spectrum()
spectrum.solve(h, J, eps, gamma, show=True, exact=False)
def find_spectrum(fname, adj=None):
''' '''
cells, spacing, zones, J, feedback = parse_qca_file(fname, one_zone=True)
h, J = qca_to_coef(cells, spacing, J, adj=adj)
h, J = normalized_coefs(h,J)
# get clocking schedule
s, eps, gamma = clock_schedule(scheme='linear')
spectrum = Spectrum()
spectrum.solve(h, J, eps, gamma, show=True, exact=False)
def main(fname):
''' '''
try:
cells, spacing, zones, J, fb = parse_qca_file(fname, one_zone=True)
except:
print('Failed to load qca file')
return
h, J = qca_to_coef(cells, spacing, J, adj='full')
h /= np.max(np.abs(J))
J /= np.max(np.abs(J))
for _ in range(100):
h_, J_, K, hp, inds = mix_seriation(h, J)
hval, K_, hp_, inds_ = hash_problem(h_, J_)
if False:
print('K: {0:.4f}\t hp: {1}\ninds: {2}'.format(K, hp, inds))
print('K: {0:.4f}\t hp: {1}\ninds: {2}'.format(K_, hp_, inds_))
print('hash val: {0}'.format(hval))
def main(fname):
''' '''
try:
cells, spacing, zones, J, fb = parse_qca_file(fname, one_zone=True)
except:
print('Failed to load qca file')
return
h, J = qca_to_coef(cells, spacing, J, adj='full')
h /= np.max(np.abs(J))
J /= np.max(np.abs(J))
for _ in range(100):
h_, J_, K, hp, inds = mix_seriation(h, J)
hval, K_, hp_, inds_ = hash_problem(h_, J_)
if False:
print('K: {0:.4f}\t hp: {1}\ninds: {2}'.format(K, hp, inds))
print('K: {0:.4f}\t hp: {1}\ninds: {2}'.format(K_, hp_, inds_))
print('hash val: {0}'.format(hval))
