def _run_py(self):
for gp in self._grid_points:
self._set_phonon_py(gp)
if self._sigma is None:
self._set_integration_weights()
t_inv = []
for bi in self._band_indices:
vec0 = self._eigenvectors[self._grid_point][:, bi].conj()
f0 = self._frequencies[self._grid_point][bi]
ti_sum = 0.0
for i, gp in enumerate(self._grid_points):
for j, (f, vec) in enumerate(
zip(self._frequencies[i], self._eigenvectors[i].T)):
if f < self._cutoff_frequency:
continue
ti_sum_band = np.sum(
np.abs((vec * vec0).reshape(-1, 3).sum(axis=1)) ** 2
* self._mass_variances)
if self._sigma is None:
ti_sum += ti_sum_band * self._integration_weights[
i, bi, j]
else:
ti_sum += ti_sum_band * gaussian(f0 - f, self._sigma)
t_inv.append(np.pi / 2 / np.prod(self._mesh) * f0 ** 2 * ti_sum)
self._gamma = np.array(t_inv, dtype='double') / 2
def _ise_at_bands(self, i, bi, freqs, interaction, weight):
sum_g = 0
for (j, k) in list(np.ndindex(interaction.shape[1:])):
if (freqs[1][j] > self._cutoff_frequency and
freqs[2][k] > self._cutoff_frequency):
n2 = occupation(freqs[1][j], self._temperature)
n3 = occupation(freqs[2][k], self._temperature)
g1 = gaussian(freqs[0, bi] - freqs[1, j] - freqs[2, k],
self._sigma)
g2 = gaussian(freqs[0, bi] + freqs[1, j] - freqs[2, k],
self._sigma)
g3 = gaussian(freqs[0, bi] - freqs[1, j] + freqs[2, k],
self._sigma)
sum_g += ((n2 + n3 + 1) * g1 +
(n2 - n3) * (g2 - g3)) * interaction[i, j, k] * weight
return sum_g
def _run_py(self):
for gp in self._grid_points:
self._set_phonon_py(gp)
if self._sigma is None:
self._set_integration_weights()
t_inv = []
for bi in self._band_indices:
vec0 = self._eigenvectors[self._grid_point][:, bi].conj()
f0 = self._frequencies[self._grid_point][bi]
ti_sum = 0.0
for i, gp in enumerate(self._grid_points):
for j, (f, vec) in enumerate(
zip(self._frequencies[i], self._eigenvectors[i].T)):
if f < self._cutoff_frequency:
continue
ti_sum_band = np.sum(
np.abs((vec * vec0).reshape(-1, 3).sum(axis=1))**2 *
self._mass_variances)
if self._sigma is None:
ti_sum += ti_sum_band * self._integration_weights[i,
bi,
j]
else:
ti_sum += ti_sum_band * gaussian(f0 - f, self._sigma)
t_inv.append(np.pi / 2 / np.prod(self._mesh) * f0**2 * ti_sum)
self._gamma = np.array(t_inv, dtype='double') / 2
def _ise_at_bands(self, i, bi, freqs, interaction, weight):
sum_g = 0
for (j, k) in list(np.ndindex(interaction.shape[1:])):
if (freqs[1][j] > self._cutoff_frequency
and freqs[2][k] > self._cutoff_frequency):
n2 = occupation(freqs[1][j], self._temperature)
n3 = occupation(freqs[2][k], self._temperature)
g1 = gaussian(freqs[0, bi] - freqs[1, j] - freqs[2, k],
self._sigma)
g2 = gaussian(freqs[0, bi] + freqs[1, j] - freqs[2, k],
self._sigma)
g3 = gaussian(freqs[0, bi] - freqs[1, j] + freqs[2, k],
self._sigma)
sum_g += ((n2 + n3 + 1) * g1 + (n2 - n3) *
(g2 - g3)) * interaction[i, j, k] * weight
return sum_g
def _ise_at_bands_0K(self, i, bi, freqs, interaction, weight):
sum_g = 0
for (j, k) in list(np.ndindex(interaction.shape[1:])):
g1 = gaussian(freqs[0, bi] - freqs[1, j] - freqs[2, k],
self._sigma)
sum_g += g1 * interaction[i, j, k] * weight
return sum_g
def _ise_at_bands_0K(self, i, bi, freqs, interaction, weight):
sum_g = 0
for (j, k) in list(np.ndindex(interaction.shape[1:])):
g1 = gaussian(freqs[0, bi] - freqs[1, j] - freqs[2, k],
self._sigma)
sum_g += g1 * interaction[i, j, k] * weight
return sum_g
def _ise_with_frequency_points(self, freqs, interaction, weight):
for j, k in list(np.ndindex(interaction.shape[1:])):
if (freqs[0][j] > self._cutoff_frequency and
freqs[1][k] > self._cutoff_frequency):
n2 = occupation(freqs[0][j], self._temperature)
n3 = occupation(freqs[1][k], self._temperature)
g1 = gaussian(self._frequency_points
- freqs[0][j] - freqs[1][k], self._sigma)
g2 = gaussian(self._frequency_points
+ freqs[0][j] - freqs[1][k], self._sigma)
g3 = gaussian(self._frequency_points
- freqs[0][j] + freqs[1][k], self._sigma)
else:
continue
for i in range(len(interaction)):
self._imag_self_energy[:, i] += (
(n2 + n3 + 1) * g1 +
(n2 - n3) * (g2 - g3)) * interaction[i, j, k] * weight
def _ise_with_frequency_points(self, freqs, interaction, weight):
for j, k in list(np.ndindex(interaction.shape[1:])):
if (freqs[0][j] > self._cutoff_frequency and
freqs[1][k] > self._cutoff_frequency):
n2 = occupation(freqs[0][j], self._temperature)
n3 = occupation(freqs[1][k], self._temperature)
g1 = gaussian(self._frequency_points
- freqs[0][j] - freqs[1][k], self._sigma)
g2 = gaussian(self._frequency_points
+ freqs[0][j] - freqs[1][k], self._sigma)
g3 = gaussian(self._frequency_points
- freqs[0][j] + freqs[1][k], self._sigma)
else:
continue
for i in range(len(interaction)):
self._imag_self_energy[:, i] += (
(n2 + n3 + 1) * g1 +
(n2 - n3) * (g2 - g3)) * interaction[i, j, k] * weight
def _ise_with_frequency_points_0K(self, freqs, interaction, weight):
for (i, j, k) in list(np.ndindex(interaction.shape)):
g1 = gaussian(self._frequency_points - freqs[0][j] - freqs[1][k],
self._sigma)
self._imag_self_energy[:, i] += g1 * interaction[i, j, k] * weight
def _ise_with_frequency_points_0K(self, freqs, interaction, weight):
for (i, j, k) in list(np.ndindex(interaction.shape)):
g1 = gaussian(self._frequency_points - freqs[0][j] - freqs[1][k],
self._sigma)
self._imag_self_energy[:, i] += g1 * interaction[i, j, k] * weight
