mirror of
https://github.com/triqs/dft_tools
synced 2024-12-22 12:23:41 +01:00
[feat] improved standard behavior of block struct (#248)
* previously the default gf_struct_solver had keys up / down, inconsistent with the default behavior after analyse_block_structure was run: up_0 / down_0. Now the default solver structure always has the _0 in the key. * old behavior resulted in error when analyse_block_structure was called twice * fixed analyse block structure tests with new changes * to correctly use analyse_block_structure use now extract_G_loc(transform_to_solver_blocks=False) * changed density_matrix function to use directly extract_G_loc() if using_gf is selected. * print deprecation warning in density_matrix, same can be achieved via extract_G_loc and [G.density() for G in Gloc] * new function density_matrix_using_point_integration() * enforce in analyse block structure that input dm or G is list with length of n_corr_shells * correct doc string for how include_shells are given * fixed other tests accordingly * fixed small bug in initial block structure regarding length of lists
This commit is contained in:
Alexander Hampel
GitHub
parent
d4d231786e
commit
b355173cf1
@ -168,8 +168,8 @@ class SumkDFT(object):
|
||||
# blocks possible
|
||||
self.gf_struct_sumk = [[(sp, self.corr_shells[icrsh]['dim']) for sp in self.spin_block_names[self.corr_shells[icrsh]['SO']]]
|
||||
for icrsh in range(self.n_corr_shells)]
|
||||
# First set a standard gf_struct solver:
|
||||
self.gf_struct_solver = [dict([(sp, self.corr_shells[self.inequiv_to_corr[ish]]['dim'])
|
||||
# First set a standard gf_struct solver (add _0 here for consistency with analyse_block_structure):
|
||||
self.gf_struct_solver = [dict([(sp+'_0', self.corr_shells[self.inequiv_to_corr[ish]]['dim'])
|
||||
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]])
|
||||
for ish in range(self.n_inequiv_shells)]
|
||||
# Set standard (identity) maps from gf_struct_sumk <->
|
||||
@ -180,12 +180,12 @@ class SumkDFT(object):
|
||||
for ish in range(self.n_inequiv_shells)]
|
||||
for ish in range(self.n_inequiv_shells):
|
||||
for block, inner_dim in self.gf_struct_sumk[self.inequiv_to_corr[ish]]:
|
||||
self.solver_to_sumk_block[ish][block] = block
|
||||
self.solver_to_sumk_block[ish][block+'_0'] = block
|
||||
for inner in range(inner_dim):
|
||||
self.sumk_to_solver[ish][
|
||||
(block, inner)] = (block, inner)
|
||||
(block, inner)] = (block+'_0', inner)
|
||||
self.solver_to_sumk[ish][
|
||||
(block, inner)] = (block, inner)
|
||||
(block+'_0', inner)] = (block, inner)
|
||||
# assume no shells are degenerate
|
||||
self.deg_shells = [[] for ish in range(self.n_inequiv_shells)]
|
||||
|
||||
@ -862,8 +862,8 @@ class SumkDFT(object):
|
||||
If the difference between density matrix / hloc elements is below threshold,
|
||||
they are considered to be equal.
|
||||
include_shells : list of integers, optional
|
||||
List of correlated shells to be analysed.
|
||||
If include_shells is not provided all correlated shells will be analysed.
|
||||
List of inequivalent shells to be analysed.
|
||||
If include_shells is not provided all inequivalent shells will be analysed.
|
||||
dm : list of dict, optional
|
||||
List of density matrices from which block stuctures are to be analysed.
|
||||
Each density matrix is a dict {block names: 2d numpy arrays} for each correlated shell.
|
||||
@ -874,14 +874,11 @@ class SumkDFT(object):
|
||||
If not provided, it will be calculated using eff_atomic_levels.
|
||||
"""
|
||||
|
||||
self.gf_struct_solver = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.sumk_to_solver = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.solver_to_sumk = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.solver_to_sumk_block = [{}
|
||||
for ish in range(self.n_inequiv_shells)]
|
||||
|
||||
if dm is None:
|
||||
dm = self.density_matrix(method='using_point_integration')
|
||||
warn("WARNING: No density matrix given. Calculating density matrix with default parameters. This will be deprecated in future releases.")
|
||||
dm = self.density_matrix(method='using_gf', transform_to_solver_blocks=False)
|
||||
|
||||
assert len(dm) == self.n_corr_shells, "The number of density matrices must be equal to the number of correlated shells."
|
||||
dens_mat = [dm[self.inequiv_to_corr[ish]]
|
||||
for ish in range(self.n_inequiv_shells)]
|
||||
if hloc is None:
|
||||
@ -890,8 +887,13 @@ class SumkDFT(object):
|
||||
if include_shells is None:
|
||||
include_shells = list(range(self.n_inequiv_shells))
|
||||
for ish in include_shells:
|
||||
self.gf_struct_solver[ish] = {}
|
||||
self.sumk_to_solver[ish] = {}
|
||||
self.solver_to_sumk[ish] = {}
|
||||
self.solver_to_sumk_block[ish] = {}
|
||||
|
||||
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
|
||||
assert sp in dens_mat[ish], f"The density matrix does not contain the block {sp}. Is the input dm given in sumk block structure?"
|
||||
n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim']
|
||||
# gives an index list of entries larger that threshold
|
||||
dmbool = (abs(dens_mat[ish][sp]) > threshold)
|
||||
@ -1049,8 +1051,8 @@ class SumkDFT(object):
|
||||
If the difference between matrix elements is below threshold,
|
||||
they are considered to be equal.
|
||||
include_shells : list of integers, optional
|
||||
List of correlated shells to be analysed.
|
||||
If include_shells is not provided all correlated shells will be analysed.
|
||||
List of inequivalent shells to be analysed.
|
||||
If include_shells is not provided all inequivalent shells will be analysed.
|
||||
analyse_deg_shells : bool
|
||||
Whether to call the analyse_deg_shells function
|
||||
after having finished the block structure analysis
|
||||
@ -1062,29 +1064,26 @@ class SumkDFT(object):
|
||||
"""
|
||||
|
||||
assert isinstance(G, list), "G must be a list (with elements for each correlated shell)"
|
||||
assert len(G) == self.n_corr_shells, "G must have one element for each correlated shell, run extract_G_loc with transform_to_solver_blocks=False to get the correct G"
|
||||
|
||||
gf = self._get_hermitian_quantity_from_gf(G)
|
||||
|
||||
# initialize the variables
|
||||
self.gf_struct_solver = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.sumk_to_solver = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.solver_to_sumk = [{} for ish in range(self.n_inequiv_shells)]
|
||||
self.solver_to_sumk_block = [{}
|
||||
for ish in range(self.n_inequiv_shells)]
|
||||
|
||||
# the maximum value of each matrix element of each block and shell
|
||||
max_gf = [{name:np.max(np.abs(g.data),0) for name, g in gf[ish]} for ish in range(self.n_inequiv_shells)]
|
||||
|
||||
if include_shells is None:
|
||||
# include all shells
|
||||
include_shells = list(range(self.n_inequiv_shells))
|
||||
|
||||
for ish in include_shells:
|
||||
self.gf_struct_solver[ish] = {}
|
||||
self.sumk_to_solver[ish] = {}
|
||||
self.solver_to_sumk[ish] = {}
|
||||
self.solver_to_sumk_block[ish] = {}
|
||||
for sp in self.spin_block_names[self.corr_shells[self.inequiv_to_corr[ish]]['SO']]:
|
||||
assert sp in gf[self.inequiv_to_corr[ish]].indices, f"The Green's function does not contain the block {sp}. Is the input G given in sumk block structure?"
|
||||
n_orb = self.corr_shells[self.inequiv_to_corr[ish]]['dim']
|
||||
|
||||
# gives an index list of entries larger that threshold
|
||||
maxgf_bool = (abs(max_gf[ish][sp]) > threshold)
|
||||
max_gf = np.max(np.abs(gf[self.inequiv_to_corr[ish]][sp].data),0)
|
||||
maxgf_bool = (max_gf > threshold)
|
||||
|
||||
# Determine off-diagonal entries in upper triangular part of the
|
||||
# Green's function
|
||||
@ -1455,21 +1454,12 @@ class SumkDFT(object):
|
||||
|
||||
return trans
|
||||
|
||||
|
||||
def density_matrix(self, method='using_gf'):
|
||||
"""Calculate density matrices in one of two ways.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
method : string, optional
|
||||
|
||||
- if 'using_gf': First get lattice gf (g_loc is not set up), then density matrix.
|
||||
It is useful for Hubbard I, and very quick.
|
||||
No assumption on the hopping structure is made (ie diagonal or not).
|
||||
- if 'using_point_integration': Only works for diagonal hopping matrix (true in wien2k).
|
||||
|
||||
beta : float, optional
|
||||
Inverse temperature.
|
||||
def density_matrix_using_point_integration(self):
|
||||
"""
|
||||
Calculate density matrices using point integration: Only works for
|
||||
diagonal hopping matrix (true in wien2k). Consider using extract_G_loc
|
||||
together with [G.density() for G in Gloc] instead. Returned density
|
||||
matrix is always given in SumK block structure.
|
||||
|
||||
Returns
|
||||
-------
|
||||
@ -1483,34 +1473,21 @@ class SumkDFT(object):
|
||||
[self.corr_shells[icrsh]['dim'], self.corr_shells[icrsh]['dim']], complex)
|
||||
|
||||
ikarray = np.array(list(range(self.n_k)))
|
||||
ntoi = self.spin_names_to_ind[self.SO]
|
||||
spn = self.spin_block_names[self.SO]
|
||||
for ik in mpi.slice_array(ikarray):
|
||||
dims = {sp:self.n_orbitals[ik, ntoi[sp]] for sp in spn}
|
||||
MMat = [np.zeros([dims[sp], dims[sp]], complex) for sp in spn]
|
||||
|
||||
if method == "using_gf":
|
||||
|
||||
G_latt_iw = self.lattice_gf(ik=ik, mu=self.chemical_potential)
|
||||
G_latt_iw *= self.bz_weights[ik]
|
||||
dm = G_latt_iw.density()
|
||||
MMat = [dm[sp] for sp in self.spin_block_names[self.SO]]
|
||||
|
||||
elif method == "using_point_integration":
|
||||
|
||||
ntoi = self.spin_names_to_ind[self.SO]
|
||||
spn = self.spin_block_names[self.SO]
|
||||
dims = {sp:self.n_orbitals[ik, ntoi[sp]] for sp in spn}
|
||||
MMat = [np.zeros([dims[sp], dims[sp]], complex) for sp in spn]
|
||||
|
||||
for isp, sp in enumerate(spn):
|
||||
ind = ntoi[sp]
|
||||
for inu in range(self.n_orbitals[ik, ind]):
|
||||
# only works for diagonal hopping matrix (true in
|
||||
# wien2k)
|
||||
if (self.hopping[ik, ind, inu, inu] - self.h_field * (1 - 2 * isp)) < 0.0:
|
||||
MMat[isp][inu, inu] = 1.0
|
||||
else:
|
||||
MMat[isp][inu, inu] = 0.0
|
||||
|
||||
else:
|
||||
raise ValueError("density_matrix: the method '%s' is not supported." % method)
|
||||
for isp, sp in enumerate(spn):
|
||||
ind = ntoi[sp]
|
||||
for inu in range(self.n_orbitals[ik, ind]):
|
||||
# only works for diagonal hopping matrix (true in
|
||||
# wien2k)
|
||||
if (self.hopping[ik, ind, inu, inu] - self.h_field * (1 - 2 * isp)) < 0.0:
|
||||
MMat[isp][inu, inu] = 1.0
|
||||
else:
|
||||
MMat[isp][inu, inu] = 0.0
|
||||
|
||||
for icrsh in range(self.n_corr_shells):
|
||||
for isp, sp in enumerate(self.spin_block_names[self.corr_shells[icrsh]['SO']]):
|
||||
@ -1519,11 +1496,7 @@ class SumkDFT(object):
|
||||
dim = self.corr_shells[icrsh]['dim']
|
||||
n_orb = self.n_orbitals[ik, ind]
|
||||
projmat = self.proj_mat[ik, ind, icrsh, 0:dim, 0:n_orb]
|
||||
if method == "using_gf":
|
||||
dens_mat[icrsh][sp] += np.dot(np.dot(projmat, MMat[isp]),
|
||||
projmat.transpose().conjugate())
|
||||
elif method == "using_point_integration":
|
||||
dens_mat[icrsh][sp] += self.bz_weights[ik] * np.dot(np.dot(projmat, MMat[isp]),
|
||||
dens_mat[icrsh][sp] += self.bz_weights[ik] * np.dot(np.dot(projmat, MMat[isp]),
|
||||
projmat.transpose().conjugate())
|
||||
|
||||
# get data from nodes:
|
||||
@ -1546,6 +1519,55 @@ class SumkDFT(object):
|
||||
|
||||
return dens_mat
|
||||
|
||||
|
||||
def density_matrix(self, method='using_gf', mu=None, with_Sigma=True, with_dc=True, broadening=None,
|
||||
transform_to_solver_blocks=True, show_warnings=True):
|
||||
"""Calculate density matrices in one of two ways.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
method : string, optional
|
||||
|
||||
- if 'using_gf': First get lattice gf (g_loc is not set up), then density matrix.
|
||||
It is useful for Hubbard I, and very quick.
|
||||
No assumption on the hopping structure is made (ie diagonal or not).
|
||||
- if 'using_point_integration': Only works for diagonal hopping matrix (true in wien2k).
|
||||
mu : real, optional
|
||||
Input chemical potential. If not provided the value of self.chemical_potential is used as mu.
|
||||
with_Sigma : boolean, optional
|
||||
If True then the local GF is calculated with the self-energy self.Sigma_imp.
|
||||
with_dc : boolean, optional
|
||||
If True then the double-counting correction is subtracted from the self-energy in calculating the GF.
|
||||
broadening : float, optional
|
||||
Imaginary shift for the axis along which the real-axis GF is calculated.
|
||||
If not provided, broadening will be set to double of the distance between mesh points in 'mesh'.
|
||||
Only relevant for real-frequency GF.
|
||||
transform_to_solver_blocks : bool, optional
|
||||
If True (default), the returned G_loc will be transformed to the block structure ``gf_struct_solver``,
|
||||
else it will be in ``gf_struct_sumk``.
|
||||
show_warnings : bool, optional
|
||||
Displays warning messages during transformation
|
||||
(Only effective if transform_to_solver_blocks = True
|
||||
|
||||
Returns
|
||||
-------
|
||||
dens_mat : list of dicts
|
||||
Density matrix for each spin in each correlated shell.
|
||||
"""
|
||||
|
||||
if method == "using_gf":
|
||||
warn("WARNING: density_matrix: method 'using_gf' is deprecated. Use 'extract_G_loc' instead.")
|
||||
Gloc = self.extract_G_loc(mu, with_Sigma, with_dc, broadening,
|
||||
transform_to_solver_blocks, show_warnings)
|
||||
dens_mat = [G.density() for G in Gloc]
|
||||
elif method == "using_point_integration":
|
||||
warn("WARNING: density_matrix: method 'using_point_integration' is deprecated. Use 'density_matrix_using_point_integration' instead. All additionally provided arguments are ignored.")
|
||||
dens_mat = self.density_matrix_using_point_integration()
|
||||
else:
|
||||
raise ValueError("density_matrix: the method '%s' is not supported." % method)
|
||||
|
||||
return dens_mat
|
||||
|
||||
# For simple dft input, get crystal field splittings.
|
||||
def eff_atomic_levels(self):
|
||||
r"""
|
||||
|
||||
@ -1,9 +1,9 @@
|
||||
from triqs.gf import *
|
||||
from triqs.gf import MeshImFreq, inverse, Omega
|
||||
from triqs_dft_tools.sumk_dft import SumkDFT
|
||||
from scipy.linalg import expm
|
||||
import numpy as np
|
||||
from triqs.utility.comparison_tests import assert_gfs_are_close, assert_arrays_are_close, assert_block_gfs_are_close
|
||||
from h5 import *
|
||||
from h5 import HDFArchive
|
||||
import itertools
|
||||
|
||||
# The full test checks all different possible combinations of conjugated
|
||||
@ -19,11 +19,10 @@ full_test = False
|
||||
#######################################################################
|
||||
|
||||
mesh = MeshImFreq(40, 'Fermion', 1025)
|
||||
SK = SumkDFT(hdf_file = 'SrIrO3_rot.h5', mesh=mesh)
|
||||
SK = SumkDFT(hdf_file='SrIrO3_rot.h5', mesh=mesh)
|
||||
Sigma = SK.block_structure.create_gf(mesh=mesh)
|
||||
SK.put_Sigma([Sigma])
|
||||
G = SK.extract_G_loc()
|
||||
|
||||
G = SK.extract_G_loc(transform_to_solver_blocks=False)
|
||||
# the original block structure
|
||||
block_structure1 = SK.block_structure.copy()
|
||||
G_new = SK.analyse_block_structure_from_gf(G)
|
||||
@ -31,30 +30,29 @@ G_new = SK.analyse_block_structure_from_gf(G)
|
||||
# the new block structure
|
||||
block_structure2 = SK.block_structure.copy()
|
||||
|
||||
with HDFArchive('analyse_block_structure_from_gf.out.h5','w') as ar:
|
||||
with HDFArchive('analyse_block_structure_from_gf.out.h5', 'w') as ar:
|
||||
ar['bs1'] = block_structure1
|
||||
ar['bs2'] = block_structure2
|
||||
|
||||
# check whether the block structure is the same as in the reference
|
||||
with HDFArchive('analyse_block_structure_from_gf.out.h5','r') as ar,\
|
||||
HDFArchive('analyse_block_structure_from_gf.ref.h5','r') as ar2:
|
||||
with HDFArchive('analyse_block_structure_from_gf.out.h5', 'r') as ar, HDFArchive('analyse_block_structure_from_gf.ref.h5', 'r') as ar2:
|
||||
assert ar['bs1'] == ar2['bs1'], 'bs1 not equal'
|
||||
a1 = ar['bs2']
|
||||
a2 = ar2['bs2']
|
||||
assert a1==block_structure2, "writing/reading block structure incorrect"
|
||||
assert a1 == block_structure2, 'writing/reading block structure incorrect'
|
||||
# we set the deg_shells to None because the transformation matrices
|
||||
# have a phase freedom and will, therefore, not be equal in general
|
||||
a1.deg_shells = None
|
||||
a2.deg_shells = None
|
||||
assert a1==a2, 'bs2 not equal'
|
||||
assert a1 == a2, 'bs2 not equal'
|
||||
|
||||
# check if deg shells are correct
|
||||
assert len(SK.deg_shells[0])==1, "wrong number of equivalent groups"
|
||||
assert len(SK.deg_shells[0]) == 1, 'wrong number of equivalent groups'
|
||||
|
||||
# check if the Green's functions that are found to be equal in the
|
||||
# routine are indeed equal
|
||||
for d in SK.deg_shells[0]:
|
||||
assert len(d)==2, "wrong number of shells in equivalent group"
|
||||
assert len(d) == 2, 'wrong number of shells in equivalent group'
|
||||
# the convention is that for every degenerate shell, the transformation
|
||||
# matrix v and the conjugate bool is saved
|
||||
# then,
|
||||
@ -70,8 +68,8 @@ for d in SK.deg_shells[0]:
|
||||
normalized_gf << normalized_gf.transpose()
|
||||
normalized_gfs.append(normalized_gf)
|
||||
for i in range(len(normalized_gfs)):
|
||||
for j in range(i+1,len(normalized_gfs)):
|
||||
assert_arrays_are_close(normalized_gfs[i].data, normalized_gfs[j].data, 1.e-5)
|
||||
for j in range(i + 1, len(normalized_gfs)):
|
||||
assert_arrays_are_close(normalized_gfs[i].data, normalized_gfs[j].data, 1.0e-5)
|
||||
|
||||
#######################################################################
|
||||
# Second test #
|
||||
@ -80,18 +78,21 @@ for d in SK.deg_shells[0]:
|
||||
# model #
|
||||
#######################################################################
|
||||
|
||||
|
||||
# helper function to get random Hermitian matrix
|
||||
def get_random_hermitian(dim):
|
||||
herm = np.random.rand(dim,dim)+1.0j*np.random.rand(dim,dim)
|
||||
herm = np.random.rand(dim, dim) + 1.0j * np.random.rand(dim, dim)
|
||||
herm = herm + herm.conjugate().transpose()
|
||||
return herm
|
||||
|
||||
|
||||
# helper function to get random unitary matrix
|
||||
def get_random_transformation(dim):
|
||||
herm = get_random_hermitian(dim)
|
||||
T = expm(1.0j*herm)
|
||||
T = expm(1.0j * herm)
|
||||
return T
|
||||
|
||||
|
||||
# we will conjugate the Green's function blocks according to the entries
|
||||
# of conjugate_values
|
||||
# for each of the 5 blocks that will be constructed, there is an entry
|
||||
@ -101,34 +102,34 @@ if full_test:
|
||||
conjugate_values = list(itertools.product([False, True], repeat=5))
|
||||
else:
|
||||
# in the quick test we check a random combination
|
||||
conjugate_values = [np.random.rand(5)>0.5]
|
||||
conjugate_values = [np.random.rand(5) > 0.5]
|
||||
|
||||
for conjugate in conjugate_values:
|
||||
# construct a random block-diagonal Hloc
|
||||
Hloc = np.zeros((10,10), dtype=complex)
|
||||
Hloc = np.zeros((10, 10), dtype=complex)
|
||||
# the Hloc of the first three 2x2 blocks is equal
|
||||
Hloc0 = get_random_hermitian(2)
|
||||
Hloc[:2,:2] = Hloc0
|
||||
Hloc[2:4,2:4] = Hloc0
|
||||
Hloc[4:6,4:6] = Hloc0
|
||||
Hloc[:2, :2] = Hloc0
|
||||
Hloc[2:4, 2:4] = Hloc0
|
||||
Hloc[4:6, 4:6] = Hloc0
|
||||
# the Hloc of the last two 2x2 blocks is equal
|
||||
Hloc1 = get_random_hermitian(2)
|
||||
Hloc[6:8,6:8] = Hloc1
|
||||
Hloc[8:,8:] = Hloc1
|
||||
Hloc[6:8, 6:8] = Hloc1
|
||||
Hloc[8:, 8:] = Hloc1
|
||||
# construct the hybridization delta
|
||||
# this is equal for all 2x2 blocks
|
||||
V = get_random_hermitian(2) # the hopping elements from impurity to bath
|
||||
b1 = np.random.rand() # the bath energy of the first bath level
|
||||
b2 = np.random.rand() # the bath energy of the second bath level
|
||||
delta = G[0]['ud'][:2,:2].copy()
|
||||
delta[0,0] << (V[0,0]*V[0,0].conjugate()*inverse(Omega-b1)+V[0,1]*V[0,1].conjugate()*inverse(Omega-b2))/2.0
|
||||
delta[0,1] << (V[0,0]*V[1,0].conjugate()*inverse(Omega-b1)+V[0,1]*V[1,1].conjugate()*inverse(Omega-b2))/2.0
|
||||
delta[1,0] << (V[1,0]*V[0,0].conjugate()*inverse(Omega-b1)+V[1,1]*V[0,1].conjugate()*inverse(Omega-b2))/2.0
|
||||
delta[1,1] << (V[1,0]*V[1,0].conjugate()*inverse(Omega-b1)+V[1,1]*V[1,1].conjugate()*inverse(Omega-b2))/2.0
|
||||
V = get_random_hermitian(2) # the hopping elements from impurity to bath
|
||||
b1 = np.random.rand() # the bath energy of the first bath level
|
||||
b2 = np.random.rand() # the bath energy of the second bath level
|
||||
delta = G[0]['ud'][:2, :2].copy()
|
||||
delta[0, 0] << (V[0, 0] * V[0, 0].conjugate() * inverse(Omega - b1) + V[0, 1] * V[0, 1].conjugate() * inverse(Omega - b2)) / 2.0
|
||||
delta[0, 1] << (V[0, 0] * V[1, 0].conjugate() * inverse(Omega - b1) + V[0, 1] * V[1, 1].conjugate() * inverse(Omega - b2)) / 2.0
|
||||
delta[1, 0] << (V[1, 0] * V[0, 0].conjugate() * inverse(Omega - b1) + V[1, 1] * V[0, 1].conjugate() * inverse(Omega - b2)) / 2.0
|
||||
delta[1, 1] << (V[1, 0] * V[1, 0].conjugate() * inverse(Omega - b1) + V[1, 1] * V[1, 1].conjugate() * inverse(Omega - b2)) / 2.0
|
||||
# construct G
|
||||
G[0].zero()
|
||||
for i in range(0,10,2):
|
||||
G[0]['ud'][i:i+2,i:i+2] << inverse(Omega-delta)
|
||||
for i in range(0, 10, 2):
|
||||
G[0]['ud'][i : i + 2, i : i + 2] << inverse(Omega - delta)
|
||||
G[0]['ud'] << inverse(inverse(G[0]['ud']) - Hloc)
|
||||
|
||||
# for testing symm_deg_gf below, we need this
|
||||
@ -136,12 +137,12 @@ for conjugate in conjugate_values:
|
||||
# mean of the blocks of G_noisy is equal to G[0]
|
||||
G_noisy = G[0].copy()
|
||||
noise1 = np.random.randn(*delta.target_shape)
|
||||
G_noisy['ud'][:2,:2].data[:,:,:] += noise1
|
||||
G_noisy['ud'][2:4,2:4].data[:,:,:] -= noise1/2.0
|
||||
G_noisy['ud'][4:6,4:6].data[:,:,:] -= noise1/2.0
|
||||
G_noisy['ud'][:2, :2].data[:, :, :] += noise1
|
||||
G_noisy['ud'][2:4, 2:4].data[:, :, :] -= noise1 / 2.0
|
||||
G_noisy['ud'][4:6, 4:6].data[:, :, :] -= noise1 / 2.0
|
||||
noise2 = np.random.randn(*delta.target_shape)
|
||||
G_noisy['ud'][6:8,6:8].data[:,:,:] += noise2
|
||||
G_noisy['ud'][8:,8:].data[:,:,:] -= noise2
|
||||
G_noisy['ud'][6:8, 6:8].data[:, :, :] += noise2
|
||||
G_noisy['ud'][8:, 8:].data[:, :, :] -= noise2
|
||||
|
||||
# for testing backward-compatibility in symm_deg_gf, we need the
|
||||
# un-transformed Green's functions
|
||||
@ -149,33 +150,35 @@ for conjugate in conjugate_values:
|
||||
G_noisy_pre_transform = G_noisy.copy()
|
||||
|
||||
# transform each block using a random transformation matrix
|
||||
for i in range(0,10,2):
|
||||
for i in range(0, 10, 2):
|
||||
T = get_random_transformation(2)
|
||||
G[0]['ud'][i:i+2,i:i+2].from_L_G_R(T, G[0]['ud'][i:i+2,i:i+2], T.conjugate().transpose())
|
||||
G_noisy['ud'][i:i+2,i:i+2].from_L_G_R(T, G_noisy['ud'][i:i+2,i:i+2], T.conjugate().transpose())
|
||||
G[0]['ud'][i : i + 2, i : i + 2].from_L_G_R(T, G[0]['ud'][i : i + 2, i : i + 2], T.conjugate().transpose())
|
||||
G_noisy['ud'][i : i + 2, i : i + 2].from_L_G_R(T, G_noisy['ud'][i : i + 2, i : i + 2], T.conjugate().transpose())
|
||||
# if that block shall be conjugated, go ahead and do it
|
||||
if conjugate[i//2]:
|
||||
G[0]['ud'][i:i+2,i:i+2] << G[0]['ud'][i:i+2,i:i+2].transpose()
|
||||
G_noisy['ud'][i:i+2,i:i+2] << G_noisy['ud'][i:i+2,i:i+2].transpose()
|
||||
if conjugate[i // 2]:
|
||||
G[0]['ud'][i : i + 2, i : i + 2] << G[0]['ud'][i : i + 2, i : i + 2].transpose()
|
||||
G_noisy['ud'][i : i + 2, i : i + 2] << G_noisy['ud'][i : i + 2, i : i + 2].transpose()
|
||||
|
||||
# analyse the block structure
|
||||
G_new = SK.analyse_block_structure_from_gf(G, 1.e-7)
|
||||
G_new = SK.analyse_block_structure_from_gf(G, 1.0e-7)
|
||||
|
||||
# transform G_noisy etc. to the new block structure
|
||||
G_noisy = SK.block_structure.convert_gf(G_noisy, block_structure1, beta = G_noisy.mesh.beta, space_from='sumk')
|
||||
G_pre_transform = SK.block_structure.convert_gf(G_pre_transform, block_structure1, beta = G_noisy.mesh.beta, space_from='sumk')
|
||||
G_noisy_pre_transform = SK.block_structure.convert_gf(G_noisy_pre_transform, block_structure1, beta = G_noisy.mesh.beta, space_from='sumk')
|
||||
G_noisy = SK.block_structure.convert_gf(G_noisy, block_structure1, beta=G_noisy.mesh.beta, space_from='sumk')
|
||||
G_pre_transform = SK.block_structure.convert_gf(G_pre_transform, block_structure1, beta=G_noisy.mesh.beta, space_from='sumk')
|
||||
G_noisy_pre_transform = SK.block_structure.convert_gf(
|
||||
G_noisy_pre_transform, block_structure1, beta=G_noisy.mesh.beta, space_from='sumk'
|
||||
)
|
||||
|
||||
assert len(SK.deg_shells[0]) == 2, "wrong number of equivalent groups found"
|
||||
assert sorted([len(d) for d in SK.deg_shells[0]]) == [2,3], "wrong number of members in the equivalent groups found"
|
||||
assert len(SK.deg_shells[0]) == 2, 'wrong number of equivalent groups found'
|
||||
assert sorted([len(d) for d in SK.deg_shells[0]]) == [2, 3], 'wrong number of members in the equivalent groups found'
|
||||
for d in SK.deg_shells[0]:
|
||||
if len(d)==2:
|
||||
assert 'ud_3' in d, "shell ud_3 missing"
|
||||
assert 'ud_4' in d, "shell ud_4 missing"
|
||||
if len(d)==3:
|
||||
assert 'ud_0' in d, "shell ud_0 missing"
|
||||
assert 'ud_1' in d, "shell ud_1 missing"
|
||||
assert 'ud_2' in d, "shell ud_2 missing"
|
||||
if len(d) == 2:
|
||||
assert 'ud_3' in d, 'shell ud_3 missing'
|
||||
assert 'ud_4' in d, 'shell ud_4 missing'
|
||||
if len(d) == 3:
|
||||
assert 'ud_0' in d, 'shell ud_0 missing'
|
||||
assert 'ud_1' in d, 'shell ud_1 missing'
|
||||
assert 'ud_2' in d, 'shell ud_2 missing'
|
||||
|
||||
# the convention is that for every degenerate shell, the transformation
|
||||
# matrix v and the conjugate bool is saved
|
||||
@ -192,21 +195,21 @@ for conjugate in conjugate_values:
|
||||
normalized_gf << normalized_gf.transpose()
|
||||
normalized_gfs.append(normalized_gf)
|
||||
for i in range(len(normalized_gfs)):
|
||||
for j in range(i+1,len(normalized_gfs)):
|
||||
for j in range(i + 1, len(normalized_gfs)):
|
||||
# here, we use a threshold that is 1 order of magnitude less strict
|
||||
# because of numerics
|
||||
assert_gfs_are_close(normalized_gfs[i], normalized_gfs[j], 1.e-6)
|
||||
assert_gfs_are_close(normalized_gfs[i], normalized_gfs[j], 1.0e-6)
|
||||
|
||||
# now we check symm_deg_gf
|
||||
# symmetrizing the GF has is has to leave it unchanged
|
||||
G_new_symm = G_new[0].copy()
|
||||
SK.symm_deg_gf(G_new_symm, 0)
|
||||
assert_block_gfs_are_close(G_new[0], G_new_symm, 1.e-6)
|
||||
assert_block_gfs_are_close(G_new[0], G_new_symm, 1.0e-6)
|
||||
|
||||
# symmetrizing the noisy GF, which was carefully constructed,
|
||||
# has to give the same result as G_new[0]
|
||||
SK.symm_deg_gf(G_noisy, 0)
|
||||
assert_block_gfs_are_close(G_new[0], G_noisy, 1.e-6)
|
||||
assert_block_gfs_are_close(G_new[0], G_noisy, 1.0e-6)
|
||||
|
||||
# check backward compatibility of symm_deg_gf
|
||||
# first, construct the old format of the deg shells
|
||||
@ -217,9 +220,9 @@ for conjugate in conjugate_values:
|
||||
# symmetrizing the GF as is has to leave it unchanged
|
||||
G_new_symm << G_pre_transform
|
||||
SK.symm_deg_gf(G_new_symm, 0)
|
||||
assert_block_gfs_are_close(G_new_symm, G_pre_transform, 1.e-6)
|
||||
assert_block_gfs_are_close(G_new_symm, G_pre_transform, 1.0e-6)
|
||||
|
||||
# symmetrizing the noisy GF pre transform, which was carefully constructed,
|
||||
# has to give the same result as G_pre_transform
|
||||
SK.symm_deg_gf(G_noisy_pre_transform, 0)
|
||||
assert_block_gfs_are_close(G_noisy_pre_transform, G_pre_transform, 1.e-6)
|
||||
assert_block_gfs_are_close(G_noisy_pre_transform, G_pre_transform, 1.0e-6)
|
||||
|
||||
Binary file not shown.
@ -48,7 +48,7 @@ G['ud'] << inverse(inverse(G['ud']) - Hloc)
|
||||
|
||||
|
||||
SK = SumkDFT(hdf_file = 'SrIrO3_rot.h5', use_dft_blocks=False)
|
||||
G_new = SK.analyse_block_structure_from_gf([G])
|
||||
G_new = SK.analyse_block_structure_from_gf([G]*2)
|
||||
G_new_symm = G_new[0].copy()
|
||||
SK.symm_deg_gf(G_new_symm, 0)
|
||||
assert_block_gfs_are_close(G_new[0], G_new_symm)
|
||||
@ -93,7 +93,7 @@ known_moments[1,:] = np.eye(10)
|
||||
tail, err = fit_tail(G['ud'], known_moments)
|
||||
Gt['ud'].set_from_fourier(G['ud'], tail)
|
||||
|
||||
G_new = SK.analyse_block_structure_from_gf([Gt])
|
||||
G_new = SK.analyse_block_structure_from_gf([Gt] * 2)
|
||||
G_new_symm = G_new[0].copy()
|
||||
SK.symm_deg_gf(G_new_symm, 0)
|
||||
assert_block_gfs_are_close(G_new[0], G_new_symm)
|
||||
|
||||
@ -55,7 +55,7 @@ def test_dc(SK_compat, SK_new, method, method_dict, dens, Uval, Jval, filename):
|
||||
|
||||
dc_no = method_dict[method]["numbering_convention"]
|
||||
dc_string = method_dict[method]["new_convention"]
|
||||
|
||||
|
||||
mpi.report("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
|
||||
mpi.report(f"\n Testing interface {method} \n")
|
||||
mpi.report("XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX")
|
||||
@ -71,8 +71,8 @@ def test_dc(SK_compat, SK_new, method, method_dict, dens, Uval, Jval, filename):
|
||||
mpi.report("Up DC matrix:")
|
||||
mpi.report(SK_new.dc_imp[0]['up'])
|
||||
mpi.report(f"Double counting energy = {SK_new.dc_energ} ")
|
||||
|
||||
# Load previously computed DC values from h5 archive
|
||||
|
||||
# Load previously computed DC values from h5 archive
|
||||
R = HDFArchive(f'./{filename}', 'r')
|
||||
dc_comp = R[f'DC_{method}_benchmark']['dc_imp']
|
||||
en_comp = R[f'DC_{method}_benchmark']['dc_energ']
|
||||
@ -84,7 +84,7 @@ def test_dc(SK_compat, SK_new, method, method_dict, dens, Uval, Jval, filename):
|
||||
assert np.allclose(SK_new.dc_imp[0]['up'], dc_comp, atol=1e-12), f"Assertion failed comparing Vdc to reference, method: {method} "
|
||||
assert np.allclose(SK_new.dc_energ, en_comp, atol=1e-12), f"Assertion failed comparing energy to reference, method: {method} "
|
||||
mpi.report("Comparison with stored DC values successfull!\n")
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@ -104,20 +104,20 @@ SK_new.set_mu(13.9)
|
||||
|
||||
|
||||
icrsh = 0
|
||||
dens = SK_compat.density_matrix()
|
||||
dens = SK_compat.density_matrix(transform_to_solver_blocks=True)
|
||||
|
||||
with np.printoptions(precision=5):
|
||||
for key in dens[0].keys():
|
||||
mpi.report(f"{key} channel")
|
||||
mpi.report(dens[0][key].real)
|
||||
|
||||
N_up = np.trace(dens[0]['up'].real)
|
||||
N_down = np.trace(dens[0]['down'].real)
|
||||
N_up = np.trace(dens[0]['up_0'].real)
|
||||
N_down = np.trace(dens[0]['down_0'].real)
|
||||
N_tot = N_up + N_down
|
||||
|
||||
mpi.report(f"{N_up=} ,{N_down=}, {N_tot=}\n")
|
||||
|
||||
for method in ["FLL", "AMF", "Held"]:
|
||||
for method in ["FLL", "AMF", "Held"]:
|
||||
test_dc(SK_compat, SK_new, method, method_dict, dens, Uval, Jval, filename = f"{dft_filename}.h5")
|
||||
|
||||
#in case implementation changes, to write new testing data into archive
|
||||
@ -141,16 +141,15 @@ SK_compat = SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks)
|
||||
SK_new = SumkDFT(hdf_file=dft_filename+'.h5',use_dft_blocks=use_blocks)
|
||||
|
||||
icrsh = 0
|
||||
dens = SK_compat.density_matrix()
|
||||
|
||||
dens = SK_compat.density_matrix(transform_to_solver_blocks=True)
|
||||
|
||||
with np.printoptions(precision=5):
|
||||
for key in dens[0].keys():
|
||||
mpi.report(f"{key} channel")
|
||||
mpi.report(dens[0][key].real)
|
||||
|
||||
N_up = np.trace(dens[0]['up'].real)
|
||||
N_down = np.trace(dens[0]['down'].real)
|
||||
N_up = np.trace(dens[0]['up_0'].real)
|
||||
N_down = np.trace(dens[0]['down_0'].real)
|
||||
N_tot = N_up + N_down
|
||||
|
||||
mpi.report(f"{N_up=} ,{N_down=}, {N_tot=}\n")
|
||||
@ -158,9 +157,9 @@ mpi.report(f"{N_up=} ,{N_down=}, {N_tot=}\n")
|
||||
Uval = 5
|
||||
Jval = 0.3
|
||||
|
||||
for method in ["FLL", "AMF", "Held"]:
|
||||
for method in ["FLL", "AMF", "Held"]:
|
||||
test_dc(SK_compat, SK_new, method, method_dict, dens, Uval, Jval, filename = f"{dft_filename}.h5" )
|
||||
|
||||
|
||||
#in case implementation changes, to write new testing data into archive
|
||||
#R = HDFArchive(f'./{dft_filename}.h5', 'a')
|
||||
#R.create_group(f'DC_{method}_benchmark')
|
||||
|
||||
@ -20,16 +20,20 @@
|
||||
#
|
||||
################################################################################
|
||||
|
||||
from h5 import *
|
||||
from h5 import HDFArchive
|
||||
from triqs_dft_tools.sumk_dft_tools import SumkDFTTools
|
||||
import triqs.utility.mpi as mpi
|
||||
from triqs.utility.comparison_tests import *
|
||||
from triqs.utility.h5diff import h5diff
|
||||
|
||||
import numpy as np
|
||||
SK = SumkDFTTools(hdf_file = 'SrVO3.ref.h5')
|
||||
|
||||
dm = SK.density_matrix(method = 'using_gf')
|
||||
dm = SK.density_matrix(method = 'using_gf', transform_to_solver_blocks=False, with_Sigma=False)
|
||||
dm_pc = SK.partial_charges(with_Sigma=False, with_dc=False)
|
||||
dm_pi = SK.density_matrix_using_point_integration()
|
||||
|
||||
for key, value in dm[0].items():
|
||||
assert (np.allclose(value, dm_pi[0][key], atol=1e-6, rtol=1e-6))
|
||||
|
||||
with HDFArchive('sumkdft_basic.out.h5','w') as ar:
|
||||
ar['dm'] = dm
|
||||
|
||||
Loading…
Reference in New Issue
Block a user