def test_truncate():
S = np.exp(-np.arange(15) - 0.1 * np.random.rand(15))
np.random.shuffle(S)
# default arguments
pars = dict(verbose=1)
mask, norm_new, TE = truncation.truncate(S, pars)
print(S[mask])
assert np.all(mask) # don't truncate by default
pars['chi_max'] = 18
pars['chi_min'] = 5
mask, norm_new, TE = truncation.truncate(S, pars)
print(S[mask])
assert (pars['chi_min'] <= np.sum(mask) <= pars['chi_max'])
pars['trunc_cut'] = 0.0005**2
mask, norm_new, TE = truncation.truncate(S, pars)
print(S[mask])
assert (pars['chi_min'] <= np.sum(mask) <= pars['chi_max'])
assert (TE.eps <= pars['trunc_cut'])
pars[
'svd_min'] = 0.005 # 10 times as large as trunc_cut -> allows to discard more
mask, norm_new, TE = truncation.truncate(S, pars)
print(S[mask])
print(S[~mask])
assert (pars['chi_min'] <= np.sum(mask) <= pars['chi_max'])
assert (np.all(S[~mask] < pars['svd_min']))
assert (np.all(S[mask] >= pars['svd_min']))
def test_truncate():
# generate a test-S
S = 10**(-np.arange(15.)) # exponentially decaying
assert len(S) == 15 # 15 values
# make artificial degeneracy
S[5] = S[6] * (1. + 1.e-11)
S[4] = S[5] * (1. + 1.e-9)
# for degeneracy_tol = 1.e-10, S[4] != S[5] = S[6]
# S is not normalized, but shouldn't matter for `truncate`
S_shuffled = S.copy()
np.random.shuffle(S_shuffled)
# default arguments
pars = dict(verbose=1, svd_min=None, trunc_cut=None)
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S, S_shuffled[mask]) # don't truncate by default
pars['chi_max'] = 12
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:12], S_shuffled[mask]) # chi_max dominates
pars[
'svd_min'] = 1.e-13 # smaller than S[11], so chi_max should still dominate
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:12], S_shuffled[mask])
pars[
'svd_min'] = 2.e-10 # between S[9] and S[10], should keep S[9], exclude S[10]
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:10], S_shuffled[mask])
# now with degeneracy: decrease chi_max to 7 to expect keeping S[6]
pars['chi_max'] = 6 # not allowed to keep S[6]
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:6], S_shuffled[mask])
pars['degeneracy_tol'] = 1.e-10 # degeneracy: then also can't keep S[5]
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:5], S_shuffled[mask])
pars['degeneracy_tol'] = 1.e-8 # more degernacy: also can't keep S[4]
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:4], S_shuffled[mask])
pars['chi_min'] = 5 # want to keep S[4] nevertheless
with pytest.warns(UserWarning):
# can't satisfy the degeneracy criteria: S[4] == S[5] == S[6],
# but chi_min requires S[4] to be kept, and chi_max excludes S[6]
# so the degeneracy criteria shoulb be ignored and we should just keep 6 states
mask, norm_new, TE = truncation.truncate(S_shuffled, pars.copy())
is_expected_S(S[:6], S_shuffled[mask])