grogu/test.py

# 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.


import os

os.environ["OMP_NUM_THREADS"] = "1"  # export OMP_NUM_THREADS=1
os.environ["OPENBLAS_NUM_THREADS"] = "1"  # export OPENBLAS_NUM_THREADS=1
os.environ["MKL_NUM_THREADS"] = "1"  # export MKL_NUM_THREADS=1
os.environ["VECLIB_MAXIMUM_THREADS"] = "1"  # export VECLIB_MAXIMUM_THREADS=1
os.environ["NUMEXPR_NUM_THREADS"] = "1"  # export NUMEXPR_NUM_THREADS=1


from timeit import default_timer as timer

# runtime information
times = dict()
times["start_time"] = timer()


import warnings
from sys import getsizeof

import sisl
from mpi4py import MPI

from src.grogu_magn import *

# input output stuff
######################################################################
######################################################################
######################################################################

infile = (
    "/Users/danielpozsar/Downloads/nojij/Fe3GeTe2/monolayer/soc/lat3_791/Fe3GeTe2.fdf"
)
outfile = "./Fe3GeTe2_notebook"

magnetic_entities = [
    dict(atom=3, l=2),
    dict(atom=4, l=2),
    dict(atom=5, l=2),
]
pairs = [
    dict(ai=0, aj=1, Ruc=np.array([0, 0, 0])),
    dict(ai=0, aj=2, Ruc=np.array([0, 0, 0])),
    dict(ai=1, aj=2, Ruc=np.array([0, 0, 0])),
    dict(ai=0, aj=2, Ruc=np.array([-1, -1, 0])),
    dict(ai=1, aj=2, Ruc=np.array([-1, -1, 0])),
    dict(ai=0, aj=2, Ruc=np.array([-1, 0, 0])),
    dict(ai=1, aj=2, Ruc=np.array([-1, 0, 0])),
    dict(ai=1, aj=2, Ruc=np.array([-2, 0, 0])),
    dict(ai=1, aj=2, Ruc=np.array([-3, 0, 0])),
]
simulation_parameters = default_args
simulation_parameters["infile"] = infile
simulation_parameters["outfile"] = outfile
simulation_parameters["kset"] = 20
simulation_parameters["kdirs"] = "xy"
simulation_parameters["eset"] = 600
simulation_parameters["esetp"] = 10000

fdf = sisl.io.fdfSileSiesta("input.fdf")
fdf.get("XCF_Rotation")

######################################################################
######################################################################
######################################################################


# MPI parameters
comm = MPI.COMM_WORLD
size = comm.Get_size()
rank = comm.Get_rank()
root_node = 0

# include parallel size in simulation parameters
simulation_parameters["parallel_size"] = size

# check versions for debugging
if rank == root_node:
    try:
        print("sisl version: ", sisl.__version__)
    except:
        print("sisl version unknown.")
    try:
        print("numpy version: ", np.__version__)
    except:
        print("numpy version unknown.")

# rename outfile
if not simulation_parameters["outfile"].endswith(".pickle"):
    simulation_parameters["outfile"] += ".pickle"

# if ebot is not given put it 0.1 eV under the smallest energy
if simulation_parameters["ebot"] is None:
    try:
        eigfile = simulation_parameters["infile"][:-3] + "EIG"
        simulation_parameters["ebot"] = read_siesta_emin(eigfile) - 0.1
        simulation_parameters["automatic_ebot"] = True
    except:
        print("Could not determine ebot.")
        print("Parameter was not given and .EIG file was not found.")

# read sile
fdf = sisl.get_sile(simulation_parameters["infile"])

# read in hamiltonian
dh = fdf.read_hamiltonian()

# read unit cell vectors
simulation_parameters["cell"] = fdf.read_geometry().cell

# unit cell index
uc_in_sc_idx = dh.lattice.sc_index([0, 0, 0])

if rank == root_node:
    print("\n\n\n\n\n")
    print(
        "#################################################################### JOB INFORMATION ###########################################################################"
    )
    print_job_description(simulation_parameters)
    print(
        "################################################################################################################################################################"
    )
    print("\n\n\n\n\n")

    times["setup_time"] = timer()
    print(f"Setup done. Elapsed time: {times['setup_time']} s")
    print(
        "================================================================================================================================================================"
    )


# reformat Hamltonian and Overlap matrix for manipulations
hh, ss, NO = build_hh_ss(dh)

# symmetrizing Hamiltonian and Overlap matrix to make them hermitian
for i in range(dh.lattice.sc_off.shape[0]):
    j = dh.lattice.sc_index(-dh.lattice.sc_off[i])
    h1, h1d = hh[i], hh[j]
    hh[i], hh[j] = (h1 + h1d.T.conj()) / 2, (h1d + h1.T.conj()) / 2
    s1, s1d = ss[i], ss[j]
    ss[i], ss[j] = (s1 + s1d.T.conj()) / 2, (s1d + s1.T.conj()) / 2

# identifying TRS and TRB parts of the Hamiltonian
TAUY = np.kron(np.eye(NO), tau_y)
hTR = np.array([TAUY @ hh[i].conj() @ TAUY for i in range(dh.lattice.nsc.prod())])
hTRS = (hh + hTR) / 2
hTRB = (hh - hTR) / 2

# extracting the exchange field
traced = [spin_tracer(hTRB[i]) for i in range(dh.lattice.nsc.prod())]  # equation 77
XCF = np.array(
    [
        np.array([f["x"] / 2 for f in traced]),
        np.array([f["y"] / 2 for f in traced]),
        np.array([f["z"] / 2 for f in traced]),
    ]
)

# check if exchange field has scalar part
max_xcfs = abs(np.array(np.array([f["c"] / 2 for f in traced]))).max()
if max_xcfs > 1e-12:
    warnings.warn(
        f"Exchange field has non negligible scalar part. Largest value is {max_xcfs}"
    )

if rank == root_node:
    times["H_and_XCF_time"] = timer()
    print(
        f"Hamiltonian and exchange field rotated. Elapsed time: {times['H_and_XCF_time']} s"
    )
    print(
        "================================================================================================================================================================"
    )

# initialize pairs and magnetic entities based on input information
pairs, magnetic_entities = setup_pairs_and_magnetic_entities(
    magnetic_entities, pairs, dh, simulation_parameters
)

if rank == root_node:
    times["site_and_pair_dictionaries_time"] = timer()
    print(
        f"Site and pair dictionaries created. Elapsed time: {times['site_and_pair_dictionaries_time']} s"
    )
    print(
        "================================================================================================================================================================"
    )

# generate k space sampling
kset = make_kset(
    dirs=simulation_parameters["kdirs"], NUMK=simulation_parameters["kset"]
)

# generate weights for k points
wkset = np.ones(len(kset)) / len(kset)

# split the k points based on MPI size
kpcs = np.array_split(kset, size)

# use progress bar if available
if rank == root_node and tqdm_imported:
    kpcs[root_node] = tqdm(kpcs[root_node], desc="k loop")

if rank == root_node:
    times["k_set_time"] = timer()
    print(f"k set created. Elapsed time: {times['k_set_time']} s")
    print(
        "================================================================================================================================================================"
    )

# this will contain the three Hamiltonian in the
# reference directions needed to calculate the energy
# variations upon rotation
hamiltonians = []

# iterate over the reference directions (quantization axes)
for i, orient in enumerate(simulation_parameters["ref_xcf_orientations"]):
    # obtain rotated exchange field and Hamiltonian
    R = RotMa2b(simulation_parameters["scf_xcf_orientation"], orient["o"])
    rot_XCF = np.einsum("ij,jklm->iklm", R, XCF)
    rot_H_XCF = sum(
        [np.kron(rot_XCF[i], tau) for i, tau in enumerate([tau_x, tau_y, tau_z])]
    )
    rot_H_XCF_uc = rot_H_XCF[uc_in_sc_idx]

    # obtain total Hamiltonian with the rotated exchange field
    rot_H = hTRS + rot_H_XCF  # equation 76

    # store the relevant information of the Hamiltonian
    hamiltonians.append(dict(orient=orient["o"], H=rot_H))

    if simulation_parameters["calculate_charge"]:
        hamiltonians[-1]["GS"] = np.zeros(
            (simulation_parameters["eset"], rot_H.shape[1], rot_H.shape[2]),
            dtype="complex128",
        )
        hamiltonians[-1]["GS_tmp"] = np.zeros(
            (simulation_parameters["eset"], rot_H.shape[1], rot_H.shape[2]),
            dtype="complex128",
        )

    # these are the rotations (for now) perpendicular to the quantization axis
    for u in orient["vw"]:
        # section 2.H
        Tu = np.kron(np.eye(NO, dtype=int), tau_u(u))
        Vu1, Vu2 = calc_Vu(rot_H_XCF_uc, Tu)

        for mag_ent in magnetic_entities:
            idx = mag_ent["spin_box_indices"]
            # fill up the perturbed potentials (for now) based on the on-site projections
            mag_ent["Vu1"][i].append(onsite_projection(Vu1, idx, idx))
            mag_ent["Vu2"][i].append(onsite_projection(Vu2, idx, idx))

if rank == root_node:
    times["reference_rotations_time"] = timer()
    print(
        f"Rotations done perpendicular to quantization axis. Elapsed time: {times['reference_rotations_time']} s"
    )
    print(
        "================================================================================================================================================================"
    )

# provide helpful information to estimate the runtime and memory
# requirements of the Greens function calculations
if rank == root_node:
    print("Starting matrix inversions.")
    if simulation_parameters["padawan_mode"]:
        print("Padawan mode: ")
        print(f"Total number of k points: {kset.shape[0]}")
        print(f"Number of energy samples per k point: {simulation_parameters['eset']}")
        print(f"Total number of directions: {len(hamiltonians)}")
        print(
            f"Total number of matrix inversions: {kset.shape[0] * len(hamiltonians) * simulation_parameters['eset']}"
        )
        print(
            f"The shape of the Hamiltonian and the Greens function is {NO}x{NO}={NO*NO}"
        )
        # https://stackoverflow.com/questions/70746660/how-to-predict-memory-requirement-for-np-linalg-inv
        # memory is O(64 n**2) for complex matrices
        memory_size = getsizeof(hamiltonians[0]["H"].base) / 1024
        print(
            f"Memory taken by a single Hamiltonian is: {getsizeof(hamiltonians[0]['H'].base) / 1024} KB"
        )
        print(f"Expected memory usage per matrix inversion: {memory_size * 32} KB")
        print(
            f"Expected memory usage per k point for parallel inversion: {memory_size * len(hamiltonians) * simulation_parameters['eset'] * 32} KB"
        )
        print(
            f"Expected memory usage on root node: {len(np.array_split(kset, size)[0]) * memory_size * len(hamiltonians) * simulation_parameters['eset'] * 32 / 1024} MB"
        )
    print(
        "================================================================================================================================================================"
    )

# MPI barrier
comm.Barrier()

# make energy contour
cont = make_contour(
    emin=simulation_parameters["ebot"],
    enum=simulation_parameters["eset"],
    p=simulation_parameters["esetp"],
)
eran = cont.ze

# sampling the integrand on the contour and the BZ
for k in kpcs[rank]:
    # weight of k point in BZ integral
    wk = wkset[rank]

    # iterate over reference directions
    for i, hamiltonian_orientation in enumerate(hamiltonians):
        # calculate Hamiltonian and Overlap matrix in a given k point
        H = hamiltonian_orientation["H"]
        HK, SK = hsk(H, ss, dh.sc_off, k)

        if simulation_parameters["parallel_solver_for_Gk"]:
            Gk = parallel_Gk(HK, SK, eran, simulation_parameters["eset"])
        else:
            # solve Greens function sequentially for the energies, because of memory bound
            Gk = sequential_GK(HK, SK, eran, simulation_parameters["eset"])

        # saving this for total charge
        if simulation_parameters["calculate_charge"]:
            hamiltonian_orientation["GS_tmp"] += Gk @ SK * wk

        # store the Greens function slice of the magnetic entities
        for mag_ent in magnetic_entities:
            idx = mag_ent["spin_box_indices"]
            mag_ent["Gii_tmp"][i] += onsite_projection(Gk, idx, idx) * wk

        for pair in pairs:
            # add phase shift based on the cell difference
            phase = np.exp(1j * 2 * np.pi * k @ pair["Ruc"].T)

            # get the pair orbital sizes from the magnetic entities
            ai = magnetic_entities[pair["ai"]]["spin_box_indices"]
            aj = magnetic_entities[pair["aj"]]["spin_box_indices"]

            # store the Greens function slice of the magnetic entities
            pair["Gij_tmp"][i] += onsite_projection(Gk, ai, aj) * phase * wk
            pair["Gji_tmp"][i] += onsite_projection(Gk, aj, ai) / phase * wk

# summ reduce partial results of mpi nodes
for i in range(len(hamiltonians)):
    # for total charge calculation
    if simulation_parameters["calculate_charge"]:
        comm.Reduce(hamiltonians[i]["GS_tmp"], hamiltonians[i]["GS"], root=root_node)

    for mag_ent in magnetic_entities:
        comm.Reduce(mag_ent["Gii_tmp"][i], mag_ent["Gii"][i], root=root_node)

    for pair in pairs:
        comm.Reduce(pair["Gij_tmp"][i], pair["Gij"][i], root=root_node)
        comm.Reduce(pair["Gji_tmp"][i], pair["Gji"][i], root=root_node)

if rank == root_node:
    times["green_function_inversion_time"] = timer()
    print(
        f"Calculated Greens functions. Elapsed time: {times['green_function_inversion_time']} s"
    )
    print(
        "================================================================================================================================================================"
    )


if rank == root_node:
    # Calculate total charge
    if simulation_parameters["calculate_charge"]:
        for hamiltonian in hamiltonians:
            GS = hamiltonian["GS"]
            traced = np.trace((GS), axis1=1, axis2=2)
            simulation_parameters["charges"].append(int_de_ke(traced, cont.we))

    # iterate over the magnetic entities
    for tracker, mag_ent in enumerate(magnetic_entities):
        # iterate over the quantization axes
        for i, Gii in enumerate(mag_ent["Gii"]):
            storage = []
            # iterate over the first and second order local perturbations
            for Vu1, Vu2 in zip(mag_ent["Vu1"][i], mag_ent["Vu2"][i]):
                # The Szunyogh-Lichtenstein formula
                traced = np.trace(
                    (Vu2 @ Gii + 0.5 * Gii @ Vu1 @ Gii), axis1=1, axis2=2
                )  # this is the on site projection
                # evaluation of the contour integral
                storage.append(int_de_ke(traced, cont.we))
            # fill up the magnetic entities dictionary with the energies
            magnetic_entities[tracker]["energies"].append(storage)
        # convert to np array
        magnetic_entities[tracker]["energies"] = np.array(
            magnetic_entities[tracker]["energies"]
        )

    # iterate over the pairs
    for tracker, pair in enumerate(pairs):
        # iterate over the quantization axes
        for i, (Gij, Gji) in enumerate(zip(pair["Gij"], pair["Gji"])):
            site_i = magnetic_entities[pair["ai"]]
            site_j = magnetic_entities[pair["aj"]]
            storage = []
            # iterate over the first order local perturbations in all possible orientations for the two sites
            for Vui in site_i["Vu1"][i]:
                for Vuj in site_j["Vu1"][i]:
                    # The Szunyogh-Lichtenstein formula
                    traced = np.trace(
                        (Vui @ Gij @ Vuj @ Gji), axis1=1, axis2=2
                    )  # this is the on site projection
                    # evaluation of the contour integral
                    storage.append(int_de_ke(traced, cont.we))
            # fill up the pairs dictionary with the energies
            pairs[tracker]["energies"].append(storage)
        # convert to np array
        pairs[tracker]["energies"] = np.array(pairs[tracker]["energies"])

    # calculate magnetic parameters
    for mag_ent in magnetic_entities:
        Kxx, Kyy, Kzz, consistency = calculate_anisotropy_tensor(mag_ent)
        mag_ent["K"] = np.array([Kxx, Kyy, Kzz]) * sisl.unit_convert("eV", "meV")
        mag_ent["K_consistency"] = consistency

    for pair in pairs:
        J_iso, J_S, D, J = calculate_exchange_tensor(pair)
        pair["J_iso"] = J_iso * sisl.unit_convert("eV", "meV")
        pair["J_S"] = J_S * sisl.unit_convert("eV", "meV")
        pair["D"] = D * sisl.unit_convert("eV", "meV")
        pair["J"] = J * sisl.unit_convert("eV", "meV")

    times["end_time"] = timer()
    print("\n\n\n\n\n")
    print(
        "##################################################################### GROGU OUTPUT #############################################################################"
    )

    print_parameters(simulation_parameters)
    print_atoms_and_pairs(magnetic_entities, pairs)
    print(
        "################################################################################################################################################################"
    )
    print_runtime_information(times)
    print("")

    # remove unwanted stuff before saving
    pairs, magnetic_entities = remove_clutter_for_save(pairs, magnetic_entities)
    # create output dictionary with all the relevant data
    results = dict(
        parameters=simulation_parameters,
        magnetic_entities=magnetic_entities,
        pairs=pairs,
        runtime=times,
    )

    # save results
    save_pickle(simulation_parameters["outfile"], results)

    print("\n\n\n\n\n")