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restructuring code

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class-solution
Daniel Pozsar 2 months ago
parent 933a50fddf
commit 49d2be4982
6 changed files with 607 additions and 227 deletions
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13
src/grogu_magn/__init__.py
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@ -17,3 +17,16 @@
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE. # SOFTWARE.
from grogu_magn.core import *
from grogu_magn.io import *
from grogu_magn.magnetism import *
from grogu_magn.utils import *
try:
from tqdm import tqdm
tqdm_imported = True
except:
print("Please install tqdm for nice progress bar.")
tqdm_imported = False

263
src/grogu_magn/core.py
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import numpy as np
from numpy.linalg import inv
from grogu_magn.utils import blow_up_orbindx, commutator, parse_magnetic_entity
def parallel_Gk(HK, SK, eran, eset):
return inv(SK * eran.reshape(eset, 1, 1) - HK)
def sequential_GK(HK, SK, eran, eset):
Gk = np.zeros(shape=(eset, HK.shape[0], HK.shape[1]), dtype="complex128")
for j in range(eset):
Gk[j] = inv(SK * eran[j] - HK)
return Gk
def calc_Vu(H, Tu):
"""_summary_
Args:
H (_type_): _description_
Tu (_type_): _description_
Returns:
_type_: _description_
"""
Vu1 = 1j / 2 * commutator(H, Tu) # equation 100
Vu2 = 1 / 8 * commutator(commutator(Tu, H), Tu) # equation 100
return Vu1, Vu2
def remove_clutter_for_save(pairs, magnetic_entities):
"""_summary_
Args:
pairs (_type_): _description_
magnetic_entities (_type_): _description_
Returns:
_type_: _description_
"""
# remove clutter from magnetic entities and pair information
for pair in pairs:
del pair["Gij"]
del pair["Gij_tmp"]
del pair["Gji"]
del pair["Gji_tmp"]
for mag_ent in magnetic_entities:
del mag_ent["Gii"]
del mag_ent["Gii_tmp"]
del mag_ent["Vu1"]
del mag_ent["Vu2"]
return pairs, magnetic_entities
def build_hh_ss(dh):
"""_summary_
Args:
dh (_type_): _description_
Returns:
_type_: _description_
"""
NO = dh.no # shorthand for number of orbitals in the unit cell
# preprocessing Hamiltonian and overlap matrix elements
h11 = dh.tocsr(dh.M11r)
h11 += dh.tocsr(dh.M11i) * 1.0j
h11 = h11.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype("complex128")
h22 = dh.tocsr(dh.M22r)
h22 += dh.tocsr(dh.M22i) * 1.0j
h22 = h22.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype("complex128")
h12 = dh.tocsr(dh.M12r)
h12 += dh.tocsr(dh.M12i) * 1.0j
h12 = h12.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype("complex128")
h21 = dh.tocsr(dh.M21r)
h21 += dh.tocsr(dh.M21i) * 1.0j
h21 = h21.toarray().reshape(NO, dh.n_s, NO).transpose(0, 2, 1).astype("complex128")
sov = (
dh.tocsr(dh.S_idx)
.toarray()
.reshape(NO, dh.n_s, NO)
.transpose(0, 2, 1)
.astype("complex128")
)
# Reorganization of Hamiltonian and overlap matrix elements to SPIN BOX representation
U = np.vstack(
[np.kron(np.eye(NO, dtype=int), [1, 0]), np.kron(np.eye(NO, dtype=int), [0, 1])]
)
# This is the permutation that transforms ud1ud2 to u12d12
# That is this transforms FROM SPIN BOX to ORBITAL BOX => U
# the inverse transformation is U.T u12d12 to ud1ud2
# That is FROM ORBITAL BOX to SPIN BOX => U.T
# From now on everything is in SPIN BOX!!
hh, ss = np.array(
[
U.T
@ np.block([[h11[:, :, i], h12[:, :, i]], [h21[:, :, i], h22[:, :, i]]])
@ U
for i in range(dh.lattice.nsc.prod())
]
), np.array(
[
U.T
@ np.block(
[[sov[:, :, i], sov[:, :, i] * 0], [sov[:, :, i] * 0, sov[:, :, i]]]
)
@ U
for i in range(dh.lattice.nsc.prod())
]
)
return hh, ss, NO
def setup_pairs_and_magnetic_entities(
magnetic_entities, pairs, dh, simulation_parameters
):
"""_summary_
Args:
magnetic_entities (_type_): _description_
pairs (_type_): _description_
dh (_type_): _description_
simulation_parameters (_type_): _description_
Returns:
_type_: _description_
"""
# for every site we have to store 3 Greens function (and the associated _tmp-s) in the 3 reference directions
for mag_ent in magnetic_entities:
parsed = parse_magnetic_entity(dh, **mag_ent) # parse orbital indexes
mag_ent["orbital_indeces"] = parsed
mag_ent["spin_box_indeces"] = blow_up_orbindx(
parsed
) # calculate spin box indexes
# if orbital is not set use all
if "l" not in mag_ent.keys():
mag_ent["l"] = "all"
if isinstance(mag_ent["atom"], int):
mag_ent["tags"] = [
f"[{mag_ent['atom']}]{dh.atoms[mag_ent['atom']].tag}({mag_ent['l']})"
]
mag_ent["xyz"] = [dh.xyz[mag_ent["atom"]]]
if isinstance(mag_ent["atom"], list):
mag_ent["tags"] = []
mag_ent["xyz"] = []
# iterate over atoms
for atom_idx in mag_ent["atom"]:
mag_ent["tags"].append(
f"[{atom_idx}]{dh.atoms[atom_idx].tag}({mag_ent['l']})"
)
mag_ent["xyz"].append(dh.xyz[atom_idx])
# calculate size for Greens function generation
spin_box_shape = len(mag_ent["spin_box_indeces"])
mag_ent["energies"] = (
[]
) # we will store the second order energy derivations here
# These will be the perturbed potentials from eq. 100
mag_ent["Vu1"] = [] # so they are independent in memory
mag_ent["Vu2"] = []
mag_ent["Gii"] = [] # Greens function
mag_ent["Gii_tmp"] = [] # Greens function for parallelization
for i in simulation_parameters["ref_xcf_orientations"]:
# Rotations for every quantization axis
mag_ent["Vu1"].append([])
mag_ent["Vu2"].append([])
# Greens functions for every quantization axis
mag_ent["Gii"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape, spin_box_shape),
dtype="complex128",
)
)
mag_ent["Gii_tmp"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape, spin_box_shape),
dtype="complex128",
)
)
# for every site we have to store 2x3 Greens function (and the associated _tmp-s)
# in the 3 reference directions, because G_ij and G_ji are both needed
for pair in pairs:
# calculate distance
xyz_ai = magnetic_entities[pair["ai"]]["xyz"]
xyz_aj = magnetic_entities[pair["aj"]]["xyz"]
xyz_aj = xyz_aj + pair["Ruc"] @ simulation_parameters["cell"]
pair["dist"] = np.linalg.norm(xyz_ai - xyz_aj)
# calculate size for Greens function generation
spin_box_shape_i = len(magnetic_entities[pair["ai"]]["spin_box_indeces"])
spin_box_shape_j = len(magnetic_entities[pair["aj"]]["spin_box_indeces"])
pair["tags"] = []
for mag_ent in [magnetic_entities[pair["ai"]], magnetic_entities[pair["aj"]]]:
tag = ""
# get atoms of magnetic entity
atoms_idx = mag_ent["atom"]
orbitals = mag_ent["l"]
# if magnetic entity contains one atoms
if isinstance(atoms_idx, int):
tag += f"[{atoms_idx}]{dh.atoms[atoms_idx].tag}({orbitals})"
# if magnetic entity contains more than one atoms
if isinstance(atoms_idx, list):
# iterate over atoms
atom_group = "{"
for atom_idx in atoms_idx:
atom_group += f"[{atom_idx}]{dh.atoms[atom_idx].tag}({orbitals})--"
# end {} of the atoms in the magnetic entity
tag += atom_group[:-2] + "}"
pair["tags"].append(tag)
pair["energies"] = [] # we will store the second order energy derivations here
pair["Gij"] = [] # Greens function
pair["Gji"] = []
pair["Gij_tmp"] = [] # Greens function for parallelization
pair["Gji_tmp"] = []
for i in simulation_parameters["ref_xcf_orientations"]:
# Greens functions for every quantization axis
pair["Gij"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape_i, spin_box_shape_j),
dtype="complex128",
)
)
pair["Gij_tmp"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape_i, spin_box_shape_j),
dtype="complex128",
)
)
pair["Gji"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape_j, spin_box_shape_i),
dtype="complex128",
)
)
pair["Gji_tmp"].append(
np.zeros(
(simulation_parameters["eset"], spin_box_shape_j, spin_box_shape_i),
dtype="complex128",
)
)
return pairs, magnetic_entities

19
src/grogu_magn/grogu.py
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# Copyright (c) [2024] [Daniel Pozsar]
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE.

219
src/grogu_magn/io.py
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from argparse import ArgumentParser
from pickle import dump, load
import numpy as np
default_args = dict(
infile=None,
outfile=None,
scf_xcf_orientation=np.array([0, 0, 1]),
ref_xcf_orientations=[
dict(o=np.array([1, 0, 0]), vw=[np.array([0, 1, 0]), np.array([0, 0, 1])]),
dict(o=np.array([0, 1, 0]), vw=[np.array([1, 0, 0]), np.array([0, 0, 1])]),
dict(o=np.array([0, 0, 1]), vw=[np.array([1, 0, 0]), np.array([0, 1, 0])]),
],
kset=2,
kdirs="xyz",
ebot=None,
eset=42,
esetp=1000,
)
# parser = ArgumentParser()
# parser.add_argument('--input' , dest = 'infile' , default=None , help = 'Input file name')
# parser.add_argument('--output' , dest = 'outfile', default=None , help = 'Output file name')
# parser.add_argument('--kset' , dest = 'kset' , default = 2 , type=int , help = 'k-space resolution of Jij calculation')
# parser.add_argument('--kdirs' , dest = 'kdirs' , default = 'xyz' , help = 'Definition of k-space dimensionality')
# parser.add_argument('--ebot' , dest = 'ebot' , default = None , type=float, help = 'Bottom energy of the contour')
# parser.add_argument('--eset' , dest = 'eset' , default = 42 , type=int , help = 'Number of energy points on the contour')
# parser.add_argument('--eset-p' , dest = 'esetp' , default = 1000 , type=int , help = 'Parameter tuning the distribution on the contour')
# cmd_line_args = parser.parse_args()
def save_pickle(outfile, data):
"""_summary_
Args:
outfile (_type_): _description_
data (_type_): _description_
"""
# save dictionary
with open(outfile, "wb") as output_file:
dump(data, output_file)
def load_pickle(infile, data):
"""_summary_
Args:
infile (_type_): _description_
data (_type_): _description_
Returns:
_type_: _description_
"""
with open(infile, "wb") as input_file:
data = load(data, input_file)
return data
def print_parameters(simulation_parameters):
"""_summary_
Args:
simulation_parameters (_type_): _description_
"""
print(
"================================================================================================================================================================"
)
print("Input file: ")
print(simulation_parameters["infile"])
print("Output file: ")
print(simulation_parameters["outfile"])
print(
"Number of nodes in the parallel cluster: ",
simulation_parameters["parallel_size"],
)
print(
"================================================================================================================================================================"
)
print("Cell [Ang]: ")
print(simulation_parameters["cell"])
print(
"================================================================================================================================================================"
)
print("DFT axis: ")
print(simulation_parameters["scf_xcf_orientation"])
print("Quantization axis and perpendicular rotation directions:")
for ref in simulation_parameters["ref_xcf_orientations"]:
print(ref["o"], " --» ", ref["vw"])
print(
"================================================================================================================================================================"
)
print("Parameters for the contour integral:")
print("Number of k points: ", simulation_parameters["kset"])
print("k point directions: ", simulation_parameters["kdirs"])
print("Ebot: ", simulation_parameters["ebot"])
print("Eset: ", simulation_parameters["eset"])
print("Esetp: ", simulation_parameters["esetp"])
print(
"================================================================================================================================================================"
)
def print_atoms_and_pairs(magnetic_entities, pairs):
"""_summary_
Args:
magnetic_entities (_type_): _description_
pairs (_type_): _description_
"""
print("Atomic information: ")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(
"[atom index]Element(orbitals) x [Ang] y [Ang] z [Ang] Sx Sy Sz Q Lx Ly Lz Jx Jy Jz"
)
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over magnetic entities
for mag_ent in magnetic_entities:
# iterate over atoms
for tag, xyz in zip(mag_ent["tags"], mag_ent["xyz"]):
# coordinates and tag
print(f"{tag} {xyz[0]} {xyz[1]} {xyz[2]}")
print("")
print(
"================================================================================================================================================================"
)
print("Anisotropy [meV]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print("Magnetic entity x [Ang] y [Ang] z [Ang]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over magnetic entities
for mag_ent in magnetic_entities:
# iterate over atoms
for tag, xyz in zip(mag_ent["tags"], mag_ent["xyz"]):
# coordinates and tag
print(f"{tag} {xyz[0]} {xyz[1]} {xyz[2]}")
print("Consistency check: ", mag_ent["K_consistency"])
print("Anisotropy diag: ", mag_ent["K"])
print("")
print(
"================================================================================================================================================================"
)
print("Exchange [meV]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print("Magnetic entity1 Magnetic entity2 [i j k] d [Ang]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over pairs
for pair in pairs:
# print pair parameters
print(
f"{pair['tags'][0]} {pair['tags'][1]} {pair['Ruc']} d [Ang] {pair['dist']}"
)
# print magnetic parameters
print("Isotropic: ", pair["J_iso"])
print("DMI: ", pair["D"])
print("Symmetric-anisotropy: ", pair["J_S"])
print("J: ", pair["J"].flatten())
print("Energies for debugging: ")
print(np.array(pair["energies"]))
print(
"J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)"
)
o1, o2, o3 = pair["energies"]
print(np.array([o2[-1], o3[0], o1[0]]))
print("Test J_xx = E(y,z) = E(z,y)")
print(o2[-1], o3[-1])
print("")
print(
"================================================================================================================================================================"
)
def print_runtime_information(times):
"""_summary_
Args:
times (_type_): _description_
"""
print("Runtime information: ")
print(f"Total runtime: {times['end_time'] - times['start_time']} s")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(f"Initial setup: {times['setup_time'] - times['start_time']} s")
print(
f"Hamiltonian conversion and XC field extraction: {times['H_and_XCF_time'] - times['setup_time']:.3f} s"
)
print(
f"Pair and site datastructure creatrions: {times['site_and_pair_dictionaries_time'] - times['H_and_XCF_time']:.3f} s"
)
print(
f"k set cration and distribution: {times['k_set_time'] - times['site_and_pair_dictionaries_time']:.3f} s"
)
print(
f"Rotating XC potential: {times['reference_rotations_time'] - times['k_set_time']:.3f} s"
)
print(
f"Greens function inversion: {times['green_function_inversion_time'] - times['reference_rotations_time']:.3f} s"
)
print(
f"Calculate energies and magnetic components: {times['end_time'] - times['green_function_inversion_time']:.3f} s"
)

88
src/grogu_magn/magnetism.py
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import numpy as np
def calculate_anisotropy_tensor(mag_ent):
"""_summary_
Args:
mag_ent (_type_): _description_
Returns:
_type_: _description_
"""
energies = mag_ent["energies"]
Kxx = energies[1, 1] - energies[1, 0]
Kyy = energies[0, 1] - energies[0, 0]
Kzz = 0
calculated_diff = Kyy - Kxx
expected_diff = energies[2, 0] - energies[2, 1]
consistency_check = abs(calculated_diff - expected_diff)
return Kxx, Kyy, Kzz, consistency_check
def calculate_exchange_tensor(pair):
"""_summary_
Args:
pair (_type_): _description_
Returns:
_type_: _description_
"""
energies = pair["energies"]
# Initialize output arrays
J = np.zeros((3, 3))
D = np.zeros(3)
# J matrix calculations
# J(1,1) = mean([DEij(2,2,2), DEij(2,2,3)])
J[0, 0] = np.mean([energies[1, 3], energies[2, 3]])
# J(1,2) = -mean([DEij(1,2,3), DEij(2,1,3)])
J[0, 1] = -np.mean([energies[2, 1], energies[2, 2]])
J[1, 0] = J[0, 1]
# J(1,3) = -mean([DEij(1,2,2), DEij(2,1,2)])
J[0, 2] = -np.mean([energies[1, 1], energies[1, 2]])
J[2, 0] = J[0, 2]
# J(2,2) = mean([DEij(2,2,1), DEij(1,1,3)])
J[1, 1] = np.mean([energies[0, 3], energies[2, 0]])
# J(2,3) = -mean([DEij(1,2,1), DEij(2,1,1)])
J[1, 2] = -np.mean([energies[0, 1], energies[0, 2]])
J[2, 1] = J[1, 2]
# J(3,3) = mean([DEij(1,1,1), DEij(1,1,2)])
J[2, 2] = np.mean([energies[0, 0], energies[1, 0]])
# D vector calculations
# D(1) = mean([DEij(1,2,1), -DEij(2,1,1)])
D[0] = np.mean([energies[0, 1], -energies[0, 2]])
# D(2) = mean([DEij(2,1,2), -DEij(1,2,2)])
D[1] = np.mean([energies[1, 2], -energies[1, 1]])
# D(3) = mean([DEij(1,2,3), -DEij(2,1,3)])
D[2] = np.mean([energies[2, 1], -energies[2, 2]])
J_iso = np.trace(J) / 3
J_S = (J - J_iso * np.eye(3)).flatten()
return J_iso, J_S, D, J
def int_de_ke(traced, we):
"""_summary_
Args:
traced (_type_): _description_
we (_type_): _description_
Returns:
_type_: _description_
"""
return np.trapz(-1 / np.pi * np.imag(traced * we))

232
src/grogu_magn/utils.py
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@ -18,11 +18,9 @@
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
# SOFTWARE. # SOFTWARE.
from itertools import permutations, product
from pprint import pprint
import numpy as np import numpy as np
from scipy.special import roots_legendre from scipy.special import roots_legendre
from sisl.io.siesta import eigSileSiesta
# Pauli matrices # Pauli matrices
tau_x = np.array([[0, 1], [1, 0]]) tau_x = np.array([[0, 1], [1, 0]])
@ -306,235 +304,15 @@ def blow_up_orbindx(orb_indices):
return np.array([[2 * o, 2 * o + 1] for o in orb_indices]).flatten() return np.array([[2 * o, 2 * o + 1] for o in orb_indices]).flatten()
def calculate_anisotropy_tensor(mag_ent): def read_siesta_emin(eigfile):
"""_summary_ """_summary_
Args: Args:
mag_ent (_type_): _description_ eigfile (_type_): _description_
Returns: Returns:
_type_: _description_ _type_: _description_
""" """
eigs = eigSileSiesta(eigfile).read_data()
energies = mag_ent["energies"] return eigs.min()
Kxx = energies[1, 1] - energies[1, 0]
Kyy = energies[0, 1] - energies[0, 0]
Kzz = 0
calculated_diff = Kyy - Kxx
expected_diff = energies[2, 0] - energies[2, 1]
consistency_check = abs(calculated_diff - expected_diff)
return Kxx, Kyy, Kzz, consistency_check
def calculate_exchange_tensor(pair):
"""_summary_
Args:
pair (_type_): _description_
Returns:
_type_: _description_
"""
energies = pair["energies"]
# Initialize output arrays
J = np.zeros((3, 3))
D = np.zeros(3)
# J matrix calculations
# J(1,1) = mean([DEij(2,2,2), DEij(2,2,3)])
J[0, 0] = np.mean([energies[1, 3], energies[2, 3]])
# J(1,2) = -mean([DEij(1,2,3), DEij(2,1,3)])
J[0, 1] = -np.mean([energies[2, 1], energies[2, 2]])
J[1, 0] = J[0, 1]
# J(1,3) = -mean([DEij(1,2,2), DEij(2,1,2)])
J[0, 2] = -np.mean([energies[1, 1], energies[1, 2]])
J[2, 0] = J[0, 2]
# J(2,2) = mean([DEij(2,2,1), DEij(1,1,3)])
J[1, 1] = np.mean([energies[0, 3], energies[2, 0]])
# J(2,3) = -mean([DEij(1,2,1), DEij(2,1,1)])
J[1, 2] = -np.mean([energies[0, 1], energies[0, 2]])
J[2, 1] = J[1, 2]
# J(3,3) = mean([DEij(1,1,1), DEij(1,1,2)])
J[2, 2] = np.mean([energies[0, 0], energies[1, 0]])
# D vector calculations
# D(1) = mean([DEij(1,2,1), -DEij(2,1,1)])
D[0] = np.mean([energies[0, 1], -energies[0, 2]])
# D(2) = mean([DEij(2,1,2), -DEij(1,2,2)])
D[1] = np.mean([energies[1, 2], -energies[1, 1]])
# D(3) = mean([DEij(1,2,3), -DEij(2,1,3)])
D[2] = np.mean([energies[2, 1], -energies[2, 2]])
J_iso = np.trace(J) / 3
J_S = (J - J_iso * np.eye(3)).flatten()
return J_iso, J_S, D, J
def print_parameters(simulation_parameters):
"""_summary_
Args:
simulation_parameters (_type_): _description_
"""
print(
"================================================================================================================================================================"
)
print("Input file: ")
print(simulation_parameters["path"])
print("Output file: ")
print(simulation_parameters["outpath"])
print(
"Number of nodes in the parallel cluster: ",
simulation_parameters["parallel_size"],
)
print(
"================================================================================================================================================================"
)
print("Cell [Ang]: ")
print(simulation_parameters["cell"])
print(
"================================================================================================================================================================"
)
print("DFT axis: ")
print(simulation_parameters["scf_xcf_orientation"])
print("Quantization axis and perpendicular rotation directions:")
for ref in simulation_parameters["ref_xcf_orientations"]:
print(ref["o"], " --» ", ref["vw"])
print(
"================================================================================================================================================================"
)
print("Parameters for the contour integral:")
print("Number of k points: ", simulation_parameters["kset"])
print("k point directions: ", simulation_parameters["kdirs"])
print("Ebot: ", simulation_parameters["ebot"])
print("Eset: ", simulation_parameters["eset"])
print("Esetp: ", simulation_parameters["esetp"])
print(
"================================================================================================================================================================"
)
def print_atoms_and_pairs(magnetic_entities, pairs):
"""_summary_
Args:
magnetic_entities (_type_): _description_
pairs (_type_): _description_
"""
print("Atomic information: ")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(
"[atom index]Element(orbitals) x [Ang] y [Ang] z [Ang] Sx Sy Sz Q Lx Ly Lz Jx Jy Jz"
)
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over magnetic entities
for mag_ent in magnetic_entities:
# iterate over atoms
for tag, xyz in zip(mag_ent["tags"], mag_ent["xyz"]):
# coordinates and tag
print(f"{tag} {xyz[0]} {xyz[1]} {xyz[2]}")
print("")
print(
"================================================================================================================================================================"
)
print("Anisotropy [meV]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print("Magnetic entity x [Ang] y [Ang] z [Ang]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over magnetic entities
for mag_ent in magnetic_entities:
# iterate over atoms
for tag, xyz in zip(mag_ent["tags"], mag_ent["xyz"]):
# coordinates and tag
print(f"{tag} {xyz[0]} {xyz[1]} {xyz[2]}")
print("Consistency check: ", mag_ent["K_consistency"])
print("Anisotropy diag: ", mag_ent["K"])
print("")
print(
"================================================================================================================================================================"
)
print("Exchange [meV]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print("Magnetic entity1 Magnetic entity2 [i j k] d [Ang]")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
# iterate over pairs
for pair in pairs:
# print pair parameters
print(
f"{pair['tags'][0]} {pair['tags'][1]} {pair['Ruc']} d [Ang] {pair['dist']}"
)
# print magnetic parameters
print("Isotropic: ", pair["J_iso"])
print("DMI: ", pair["D"])
print("Symmetric-anisotropy: ", pair["J_S"])
print("J: ", pair["J"].flatten())
print("Energies for debugging: ")
pprint(np.array(pair["energies"]))
print(
"J_ii for debugging: (check if this is the same as in calculate_exchange_tensor)"
)
o1, o2, o3 = pair["energies"]
pprint(np.array([o2[-1], o3[0], o1[0]]))
print("Test J_xx = E(y,z) = E(z,y)")
print(o2[-1], o3[-1])
print("")
print(
"================================================================================================================================================================"
)
def print_runtime_information(times):
"""_summary_
Args:
times (_type_): _description_
"""
print("Runtime information: ")
print(f"Total runtime: {times['end_time'] - times['start_time']} s")
print(
"----------------------------------------------------------------------------------------------------------------------------------------------------------------"
)
print(f"Initial setup: {times['setup_time'] - times['start_time']} s")
print(
f"Hamiltonian conversion and XC field extraction: {times['H_and_XCF_time'] - times['setup_time']:.3f} s"
)
print(
f"Pair and site datastructure creatrions: {times['site_and_pair_dictionaries_time'] - times['H_and_XCF_time']:.3f} s"
)
print(
f"k set cration and distribution: {times['k_set_time'] - times['site_and_pair_dictionaries_time']:.3f} s"
)
print(
f"Rotating XC potential: {times['reference_rotations_time'] - times['k_set_time']:.3f} s"
)
print(
f"Greens function inversion: {times['green_function_inversion_time'] - times['reference_rotations_time']:.3f} s"
)
print(
f"Calculate energies and magnetic components: {times['end_time'] - times['green_function_inversion_time']:.3f} s"
)

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