def test_next_delta(g0_wk, gamma, expected_next_delta):
Gamma_pp_dyn_tr, Gamma_pp_const_r = preprocess_gamma_for_fft(gamma)
next_delta = eliashberg_product_fft(Gamma_pp_dyn_tr, Gamma_pp_const_r,
g0_wk, g0_wk)
np.testing.assert_allclose(next_delta.data,
expected_next_delta.data,
atol=10e-12)
def test_preprocess_gamma_for_fft_types(gamma):
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
assert type(gamma_dyn_tr.mesh) == MeshProduct
assert type(gamma_dyn_tr.mesh[0]) == MeshImTime
assert type(gamma_dyn_tr.mesh[1]) == MeshCycLat
assert type(gamma_const_r.mesh) == MeshCycLat
def save_new_preprocess_gamma_for_fft_benchmark(filename, p):
eliashberg_ingredients = create_eliashberg_ingredients(p)
gamma = eliashberg_ingredients.gamma
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
p.gamma = gamma
p.gamma_dyn_tr = gamma_dyn_tr
p.gamma_const_r = gamma_const_r
write_TarGZ_HDFArchive(filename, p=p)
def save_new_benchmarks(filename, p):
eliashberg_ingredients = create_eliashberg_ingredients(p)
g0_wk = eliashberg_ingredients.g0_wk
gamma = eliashberg_ingredients.gamma
Gamma_pp_dyn_tr, Gamma_pp_const_r = preprocess_gamma_for_fft(gamma)
next_delta = eliashberg_product_fft(Gamma_pp_dyn_tr, Gamma_pp_const_r, g0_wk, g0_wk)
Es, eigen_modes = solve_eliashberg(gamma, g0_wk, product='FFT', solver='IRAM')
p.next_delta = next_delta
p.E = Es[0]
p.eigen_mode = eigen_modes[0]
write_TarGZ_HDFArchive(filename, p=p)
def test_preprocess_gamma_for_fft_benchmark(gamma, p):
p_benchmark = read_TarGZ_HDFArchive(p.benchmark_filename)['p']
model_parameters_to_test = ['dim', 'norb', 't', 'mu', 'beta', 'U']
assert_parameter_collection_not_equal_model_parameters(
p, p_benchmark, model_parameters_to_test)
np.testing.assert_allclose(gamma.data, p_benchmark.gamma.data, atol=1e-10)
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
np.testing.assert_allclose(gamma_dyn_tr.data,
p_benchmark.gamma_dyn_tr.data,
atol=1e-10)
np.testing.assert_allclose(gamma_const_r.data,
p_benchmark.gamma_const_r.data,
atol=1e-10)
def test_eliashberg_product_for_same_initital_delta(g0_wk, gamma, gamma_big):
initial_delta = semi_random_initial_delta(g0_wk, seed=1337)
next_delta_summation = eliashberg_product(gamma_big, g0_wk, initial_delta)
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
next_delta_fft = eliashberg_product_fft(gamma_dyn_tr, gamma_const_r, g0_wk,
initial_delta)
diff = compare_deltas([next_delta_summation, next_delta_fft])
print_diff(diff)
np.testing.assert_allclose(diff, 0, atol=p.atol)
print('The summation and FFT implementation of the eliashberg product'
' both yield the same result.')
def plot_output(g0_wk, gamma):
from triqs.plot.mpl_interface import oplot, plt
initial_delta = semi_random_initial_delta(g0_wk)
next_delta_summation = eliashberg_product(gamma_big, g0_wk, initial_delta)
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
next_delta_fft = eliashberg_product_fft(gamma_dyn_tr, gamma_const_r, g0_wk,
initial_delta)
deltas = [initial_delta, next_delta_summation, next_delta_fft]
warnings.filterwarnings("ignore") #ignore some matplotlib warnings
subp = [4, 3, 1]
fig = plt.figure(figsize=(18, 15))
titles = ['Input', 'Summation', 'FFT']
for k_point in [Idx(0, 0, 0), Idx(1, 0, 0)]:
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(g0_wk[:, k_point])
plt.title('GF')
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(gamma[:, k_point])
plt.title('Gamma')
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(gamma_dyn_tr[:, k_point])
plt.title('Gamma dyn tr')
for delta, title in zip(deltas, titles):
ax = plt.subplot(*subp)
subp[-1] += 1
oplot(delta[:, k_point])
plt.title(title)
ax.legend_ = None
plt.show()
def test_eliashberg_product_fft_constant(g0_wk, gamma):
gamma.data[:] = np.random.rand(*gamma.data.shape[1:])
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
initial_delta = semi_random_initial_delta(g0_wk)
delta_1 = eliashberg_product_fft_constant(gamma_const_r, g0_wk,
initial_delta)
delta_2 = eliashberg_product_fft(gamma_dyn_tr, gamma_const_r, g0_wk,
initial_delta)
np.testing.assert_allclose(delta_1.data, delta_2.data)
print(
'The functions eliashberg_product_fft and eliashberg_product_fft_constant'
' yield the same result for a Gamma that is only constant in momentum space.'
'\nThe function split_into_dynamic_wk_and_constant_k therefore also worked correcty.'
)
def test_eliashberg_product_for_different_initital_delta(
g0_wk, gamma, gamma_big):
initial_delta = semi_random_initial_delta(g0_wk, seed=1337)
next_delta_summation = eliashberg_product(gamma_big, g0_wk, initial_delta)
gamma_dyn_tr, gamma_const_r = preprocess_gamma_for_fft(gamma)
initial_delta = semi_random_initial_delta(g0_wk, seed=1338)
next_delta_fft = eliashberg_product_fft(gamma_dyn_tr, gamma_const_r, g0_wk,
initial_delta)
diff = compare_deltas([next_delta_summation, next_delta_fft])
print_diff(diff)
try:
np.testing.assert_allclose(diff, 0, atol=p.atol)
raise ValueError
except AssertionError:
print(
'The summation and FFT implementation of the eliashberg product'
' both yield DIFFERENT results, as expected when using a different inital delta.'
)
