# pyright: reportUnusedExpression=false
import numpy as np
from triqs.utility import mpi
from h5 import HDFArchive
from triqs.gf import Gf, MeshReFreq, BlockGf
from solid_dmft.postprocessing.maxent_sigma import _read_h5
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def _write_sigma_omega_to_h5(sigma_w, external_path, iteration):
""" Writes real-frequency self energy to h5 archive. """
h5_internal_path = 'DMFT_results/' + ('last_iter' if iteration is None
else f'it_{iteration}')
with HDFArchive(external_path, 'a') as archive:
for i, sigma_imp in enumerate(sigma_w):
archive[h5_internal_path][f'Sigma_Refreq_{i}'] = sigma_imp
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def _run_pade(sigma_iw_list, n_w, w_min, w_max, n_iw, eta):
"""
Run pade in parallel. Call via main function.
"""
mpi.report('Continuing impurities with blocks:')
imps_blocks = []
sigma_iw_flat_list = []
# create flattened list of self-energies
for i, sigma_iw in enumerate(sigma_iw_list):
blocks = list(sigma_iw.indices)
mpi.report('- Imp {}: {}'.format(i, blocks))
for block in blocks:
imps_blocks.append((i, block))
sigma_iw_flat_list.append(sigma_iw[block])
sigma_w_flat_list = []
wmesh = MeshReFreq(w_min=w_min,w_max=w_max,n_w=n_w)
imps_blocks_indices = np.arange(len(imps_blocks))
for i in imps_blocks_indices:
sigma_w_flat_list.append(Gf(mesh=wmesh, target_shape=sigma_iw_flat_list[i].target_shape))
# Runs Pade while parallelizing over impurities and blocks
for i in mpi.slice_array(imps_blocks_indices):
print(f'Rank {mpi.rank} continuing Σ {i}/{len(imps_blocks)}')
sigma_w_flat_list[i].set_from_pade(sigma_iw_flat_list[i],n_points=n_iw, freq_offset=eta)
# sync Pade data
for i in imps_blocks_indices:
sigma_w_flat_list[i] = mpi.all_reduce(sigma_w_flat_list[i])
# Create list of BlockGf
sigma_w_list = []
for i, sigma_iw in enumerate(sigma_iw_list):
block_list = []
for block in sigma_iw.indices:
block_list.append(sigma_w_flat_list.pop(0))
sigma_w_list.append(BlockGf(name_list=list(sigma_iw.indices), block_list=block_list, make_copies=True))
return sigma_w_list
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def main(external_path, n_w, w_min, w_max, n_iw, iteration=None, eta=0.0):
"""
Main function that reads the Matsubara self-energy from h5, analytically continues it,
writes the results back to the h5 archive and also returns the results.
Function parallelizes using MPI over impurities and blocks.
Parameters
----------
external_path : string
Path to the h5 archive to read from and write to
n_w : int
number of real frequencies of the final self-energies returned
w_min : float
Lower end of range where Sigma is being continued.
w_max : float
Upper end of range where Sigma is being continued.
n_iw : int
number of Matsubara frequencies to consider for the Pade approximant
iteration : int/string
Iteration to read from and write to. Default to last_iter
eta : float
frequency offset within Pade
Returns
-------
sigma_w : list of triqs.gf.BlockGf
Sigma(omega) per inequivalent shell
"""
sigma_iw = None
if mpi.is_master_node():
sigma_iw, _, _, _ = _read_h5(external_path, iteration)
sigma_iw = mpi.bcast(sigma_iw)
# run pade in parallel
sigma_w = _run_pade(sigma_iw, n_w, w_min, w_max, n_iw, eta)
mpi.report('Writing results to h5 archive now.')
if mpi.is_master_node():
_write_sigma_omega_to_h5(sigma_w, external_path, iteration)
mpi.report('Finished writing Σ(ω) to archive.')
return sigma_w