Source code for rhodent.density_matrices.time

from __future__ import annotations

from typing import Generator, Collection

from pathlib import Path
import numpy as np
from numpy.typing import NDArray

from gpaw.lcaotddft.ksdecomposition import KohnShamDecomposition
from gpaw.tddft.units import as_to_au, au_to_as, eV_to_au, au_to_eV
from gpaw.mpi import world

from .buffer import DensityMatrixBuffer
from .distributed.pulse import PulseConvolver
from .readers.gpaw import KohnShamRhoWfsReader
from .readers.numpy import FrequencyDensityMatrixReader, TimeDensityMatrixReader
from .density_matrix import DensityMatrix
from .base import BaseDensityMatrices, WorkMetadata
from ..perturbation import create_perturbation, Perturbation, DeltaKick, PerturbationLike, PulsePerturbation
from ..utils import Logger, two_communicator_sizes, get_gaussian_pulse_values
from ..utils.logging import format_times
from ..utils.memory import MemoryEstimate
from ..typing import Array1D, Communicator


[docs] class ConvolutionDensityMatrixMetadata(WorkMetadata): """ Metadata to the density matrices. Properties ---------- density_matrices Parent of this object. globalt Time index. localt Time index on this rank. globalp Pulse index. localp Pulse index on this rank. """ density_matrices: ConvolutionDensityMatrices globalt: int globalp: int localt: int localp: int def __new__(cls, density_matrices: ConvolutionDensityMatrices, globalt: int, globalp: int, localt: int, localp: int): self = WorkMetadata.__new__(cls, density_matrices=density_matrices) self.globalt = globalt self.globalp = globalp self.localt = localt self.localp = localp return self @property def global_indices(self): return (self.globalp, self.globalt) @property def time(self) -> float: """ Simulation time in units of as. """ return self.density_matrices.times[self.globalt] @property def pulse(self) -> Perturbation: """ Pulse. """ return self.density_matrices.pulses[self.globalp] @property def desc(self) -> str: timestr = f'{self.time:.1f}as' if len(self.density_matrices.pulses) == 1: return timestr d = self.pulse.todict() if d['name'] == 'GaussianPulse': pulsestr = f'{d["frequency"]:.1f}eV' else: pulsestr = f'#{self.globalp}' return f'{timestr} @ Pulse {pulsestr}'
class ConvolutionDensityMatrices(BaseDensityMatrices[ConvolutionDensityMatrixMetadata]): """ Collection of density matrices in the Kohn-Sham basis for different times, after convolution with various pulses. Plain density matrices and/or derivatives thereof may be represented. Parameters ---------- ksd KohnShamDecomposition object or file name. pulses Convolute the density matrices with these pulses. times Compute density matrices for these times (or as close to them as possible). In units of as. derivative_order_s Compute density matrix derivatives of the following orders. 0 for plain density matrix and positive integers for derivatives. real Calculate the real part of density matrices. imag Calculate the imaginary part of density matrices. calc_size Size of the calculation communicator. """ _derivative_order_s: list[int] def __init__(self, ksd: KohnShamDecomposition | str, pulses: Collection[PerturbationLike], times: list[float] | NDArray[np.float64], derivative_order_s: list[int] = [0], real: bool = True, imag: bool = True, calc_size: int = 1, log: Logger | None = None): self._time_t = np.array(times) self._pulses = [create_perturbation(pulse) for pulse in pulses] self._derivative_order_s = derivative_order_s super().__init__(ksd=ksd, real=real, imag=imag, calc_size=calc_size, log=log) tag_s = ['', '-Iomega', '-omega2'] return_keys_by_order = {0: 'pulserho', 1: 'pulsedrho', 2: 'pulseddrho'} self.keys_by_order = {0: 'rho_ia', 1: 'drho_ia', 2: 'ddrho_ia'} if 1 not in derivative_order_s: tag_s.remove('-Iomega') return_keys_by_order.pop(1) if 2 not in derivative_order_s: tag_s.remove('-omega2') return_keys_by_order.pop(2) self.tag_s = tag_s self.return_keys_by_order = return_keys_by_order @property def times(self) -> Array1D[np.float64]: """ Simulation time in units of as. """ return self._time_t # type: ignore @property def nt(self) -> int: """ Number of times. """ return len(self.times) @property def pulses(self) -> list[Perturbation]: """ Pulses with which density matrices are convoluted. """ return self._pulses @property def derivative_order_s(self) -> list[int]: """ List of orders of the density matrix derivatives to be computed. ``0`` for plain density matrix and positive integers for derivatives. """ return self._derivative_order_s def work_loop(self, rank: int) -> Generator[ConvolutionDensityMatrixMetadata | None, None, None]: nt = len(self.times) ntperrank = (nt + self.loop_comm.size - 1) // self.loop_comm.size # Do convolution pulse-by-pulse, time-by-time for p, pulse in enumerate(self.pulses): # Determine which times to compute on this loop_comm rank for good load balancing shift = (p * nt + rank) % self.loop_comm.size for localt in range(ntperrank): globalt = shift + localt * self.loop_comm.size if globalt < nt: yield ConvolutionDensityMatrixMetadata(density_matrices=self, globalt=globalt, localt=localt, globalp=p, localp=p) else: yield None def write_to_disk(self, pulserho_fmt: str): """ Calculate the density matrices and save to disk. Parameters ---------- pulserho_fmt Formatting string for the density matrices saved to disk. The formatting string should be a plain string containing variable placeholders within curly brackets ``{}``. It should not be confused with a formatted string literal (f-string). Example: * pulserho_fmt = ``pulserho/t{time:09.1f}{tag}.npy``. Accepts variables * ``{time}`` - Time in units of as. * ``{tag}`` - Derivative tag, ``''``, ``'-Iomega'``, or ``'-omega2'``. * ``{pulsefreq}`` - Pulse frequency in units of eV. * ``{pulsefwhm}`` - Pulse FWHM in units of fs. """ nlocaltot = len(self.local_work_plan) tags_keys = [(tag, key) for s, (tag, key) in enumerate( [('', 'rho_ia'), ('-Iomega', 'drho_ia'), ('-omega2', 'ddrho_ia')]) if s in self.derivative_order_s] # Iterate over density matrices on all ranks for ndone, (work, dm) in enumerate(self, 1): avg = self.log.elapsed('read')/ndone estrem = avg * (nlocaltot - ndone) self.log(f'Obtained density matrix {ndone:4.0f}/{nlocaltot:4.0f} on this rank. ' f'Avg: {avg:10.3f}s, estimated remaining: {estrem:10.3f}s', who='Writer', flush=True) for tag, key in tags_keys: fname_kw = dict(time=work.time, tag=tag, **get_gaussian_pulse_values(work.pulse)) fpath = Path(pulserho_fmt.format(**fname_kw)) rho_ia = getattr(dm, key) if self.calc_comm.rank == 0: assert isinstance(rho_ia, np.ndarray) fpath.parent.mkdir(parents=True, exist_ok=True) if fpath.suffix == '.npz': # Write numpy archive np.savez(str(fpath), rho_ia=rho_ia) else: # Save numpy binary file np.save(str(fpath), rho_ia) self.log(f'Written {fpath}', who='Writer', flush=True) world.barrier()
[docs] class TimeDensityMatricesFromWaveFunctions(ConvolutionDensityMatrices): """ Collection of density matrices in the Kohn-Sham basis for different times, obtained by reading the time-dependent wave functions file. Parameters ---------- ksd KohnShamDecomposition object or file name. wfs_fname File name of the time-dependent wave functions file. times Compute density matrices for these times (or as close to them as possible). In units of as. real Calculate the real part of density matrices. imag Calculate the imaginary part of density matrices. calc_size Size of the calculation communicator. stridet Skip this many steps when reading the time-dependent wave functions file. """ def __init__(self, ksd: KohnShamDecomposition | str, wfs_fname: str, times: list[float] | Array1D[np.float64], real: bool = True, imag: bool = True, calc_size: int = 1, log: Logger | None = None, stridet: int = 1): rho_nn_direct = KohnShamRhoWfsReader(wfs_fname=wfs_fname, ksd=ksd, yield_re=real, yield_im=imag, filter_times=np.array(times) * as_to_au, # type: ignore stridet=stridet, log=log) self.rho_nn_direct = rho_nn_direct super().__init__(ksd=rho_nn_direct.ksd, times=rho_nn_direct.time_t * au_to_as, real=real, imag=imag, pulses=[None], calc_size=1, log=rho_nn_direct.log) imin, imax, amin, amax = self.ksd.ialims() # Read density matrices corresponding to ksd ialims self._n1slice = slice(imin, imax + 1) self._n2slice = slice(amin, amax + 1) def __str__(self) -> str: lines = ['Response from time-dependent wave functions'] lines.append('') lines += [' ' + line for line in str(self.rho_nn_direct).split('\n')] shape = (self._n1slice.stop - self._n1slice.start, self._n2slice.stop - self._n2slice.start,) if 'Re' in self.reim and 'Im' in self.reim: reim_desc = 'real and imaginary parts' elif 'Re' in self.reim: reim_desc = 'real parts' else: reim_desc = 'imaginary parts' lines.append('') lines.append(f'Density matrix shape {shape}') lines.append(f'reading {reim_desc}') return '\n'.join(lines) def get_memory_estimate(self) -> MemoryEstimate: shape = (self._n1slice.stop - self._n1slice.start, self._n2slice.stop - self._n2slice.start,) narrays = 2 if 'Re' in self.reim and 'Im' in self.reim else 1 for t_r in self.rho_nn_direct.work_loop_by_ranks(): nreading = len(t_r) break memory_estimate = MemoryEstimate() memory_estimate.add_child('Time-dependent wave functions reader', self.rho_nn_direct.get_memory_estimate()) memory_estimate.add_key('Density matrix', (shape) + (narrays, ), on_num_ranks=nreading) return memory_estimate def __iter__(self) -> Generator[tuple[ConvolutionDensityMatrixMetadata, DensityMatrix], None, None]: assert self.calc_comm.size == 1 # TODO for work, dm_buffer in zip(self.work_loop(self.loop_comm.rank), self.rho_nn_direct.iread(0, 0, self._n1slice, self._n2slice)): assert work is not None if 'Re' in self.reim: Rerho_ia = dm_buffer._get_real(0) if 'Im' in self.reim: Imrho_ia = dm_buffer._get_imag(0) if 'Re' in self.reim and 'Im' in self.reim: # Complex result # Compared to numpy, we use another convention, hence the minus sign rho_ia = Rerho_ia - 1j * Imrho_ia elif 'Re' in self.reim: # Real result rho_ia = Rerho_ia else: rho_ia = -1j * Imrho_ia matrices: dict[int, NDArray[np.complex128] | None] = {0: rho_ia} dm = DensityMatrix(ksd=self.ksd, matrices=matrices, comm=self.calc_comm) yield work, dm
[docs] def parallel_prepare(self): self.rho_nn_direct.parallel_prepare()
[docs] class ConvolutionDensityMatricesFromDisk(ConvolutionDensityMatrices): """ Collection of density matrices in the Kohn-Sham basis after convolution with one or several pulses, for different times. Read from disk. Parameters ---------- ksd KohnShamDecomposition object or file name. pulserho_fmt Formatting string for the density matrices saved to disk. The formatting string should be a plain string containing variable placeholders within curly brackets ``{}``. It should not be confused with a formatted string literal (f-string). Example: * pulserho_fmt = ``pulserho/t{time:09.1f}{tag}.npy``. Accepts variables * ``{time}`` - Time in units of as. * ``{tag}`` - Derivative tag, ``''``, ``'-Iomega'``, or ``'-omega2'``. * ``{pulsefreq}`` - Pulse frequency in units of eV. * ``{pulsefwhm}`` - Pulse FWHM in units of fs. pulses Convolute the density matrices with these pulses. times Compute density matrices for these times (or as close to them as possible). In units of as. derivative_order_s Compute density matrix derivatives of the following orders. ``0`` for plain density matrix and positive integers for derivatives. real Calculate real part of density matrices. imag Calculate imaginary part of density matrices. calc_size Size of the calculation communicator. """ def __init__(self, ksd: KohnShamDecomposition | str, pulserho_fmt: str, pulses: Collection[PerturbationLike], times: list[float] | Array1D[np.float64], derivative_order_s: list[int] = [0], log: Logger | None = None, real: bool = True, imag: bool = True, calc_size: int = 1): reader = TimeDensityMatrixReader(pulserho_fmt=pulserho_fmt, ksd=ksd, pulses=pulses, filter_times=times, derivative_order_s=derivative_order_s, log=log) self.reader = reader super().__init__(ksd=reader.ksd, pulses=reader.pulses, times=reader.times, real=real, imag=imag, derivative_order_s=derivative_order_s, calc_size=calc_size, log=reader.log) self.ksd.distribute(self.calc_comm) self.pulserho_fmt = pulserho_fmt def __str__(self) -> str: return str(self.reader) def __iter__(self) -> Generator[tuple[ConvolutionDensityMatrixMetadata, DensityMatrix], None, None]: for work in self.local_work_plan: self.log.start('read') matrices: dict[int, NDArray[np.complex128] | None] = dict() for derivative in self.derivative_order_s: if self.calc_comm.rank > 0: # Don't read on non calc comm root ranks matrices[derivative] = None continue matrices[derivative] = self.reader.read(work.time, work.pulse, derivative) dm = DensityMatrix(ksd=self.ksd, matrices=matrices, comm=self.calc_comm) yield work, dm
[docs] class ConvolutionDensityMatricesFromFrequency(ConvolutionDensityMatrices): """ Collection of density matrices in the Kohn-Sham basis after convolution with one or several pulses, for different times. Obtained from the the density matrices in frequency space, which are read from disk. Parameters ---------- ksd KohnShamDecomposition object or file name. frho_fmt Formatting string for the density matrices in frequency space saved to disk. The formatting string should be a plain string containing variable placeholders within curly brackets ``{}``. It should not be confused with a formatted string literal (f-string). Example: * frho_fmt = ``frho/w{freq:05.2f}-{reim}.npy'``. Accepts variables: * ``{freq}`` - Frequency in units of eV. * ``{reim}`` - ``'Re'`` or ``'Im'`` for Fourier transform of real/imaginary part of density matrix. perturbation Perturbation that was present during time propagation. pulses Convolute the density matrices with these pulses. times Compute density matrices for these times (or as close to them as possible). In units of as. derivative_order_s Compute density matrix derivatives of the following orders. `0` for plain density matrix and positive integers for derivatives. real Calculate real part of density matrices. imag Calculate imaginary part of density matrices. calc_size Size of the calculation communicator. """ def __init__(self, ksd: KohnShamDecomposition | str, frho_fmt: str, perturbation: PerturbationLike, pulses: Collection[PerturbationLike], times: list[float] | NDArray[np.float64], derivative_order_s: list[int] = [0], log: Logger | None = None, real: bool = True, imag: bool = True, calc_size: int = 1): super().__init__(ksd=ksd, pulses=pulses, times=times, real=real, imag=imag, derivative_order_s=derivative_order_s, calc_size=calc_size, log=log) # Frequency grid for convolution if not all(pulse.todict()['name'] == 'GaussianPulse' for pulse in self.pulses): raise NotImplementedError('Only Gaussian pulses implemented for ResponseFromFourierTransform') freq_spacing = 0.05 # 50meV freq_min = min((pulse.pulse.omega0 - 4 * pulse.pulse.sigma) * au_to_eV # type: ignore for pulse in self.pulses) freq_min = max(freq_min, freq_spacing) freq_max = min((pulse.pulse.omega0 + 4 * pulse.pulse.sigma) * au_to_eV # type: ignore for pulse in self.pulses) transformer = ExactFourierTransformer.from_file( frho_fmt=frho_fmt, ksd=ksd, perturbation=perturbation, real=real, imag=imag, log=self.log, comm=self.calc_comm, freq_spacing=freq_spacing, freq_min=freq_min, freq_max=freq_max) self.transformer = transformer def __str__(self) -> str: lines = ['Response from Fourier transform of density matrices on disk'] lines.append('') lines.append(f'Calculating response for {self.nt} times and {len(self.pulses)} pulses') lines.append(f' times: {format_times(self.times)}') return '\n'.join(lines) def __iter__(self) -> Generator[tuple[ConvolutionDensityMatrixMetadata, DensityMatrix], None, None]: self.transformer.parallel_prepare() for work in self.local_work_plan: matrices: dict[int, NDArray[np.complex128] | None] = dict() for derivative in self.derivative_order_s: buffer = self.transformer.convolve(work.time, work.pulse, derivative) if self.calc_comm.rank > 0: matrices[derivative] = None continue assert buffer is not None # Buffer shape is i, a, pulses, times if 'Re' in self.reim: Rerho_ia = buffer._get_real(derivative) if 'Im' in self.reim: Imrho_ia = buffer._get_imag(derivative) if 'Re' in self.reim and 'Im' in self.reim: # Complex result rho_ia = Rerho_ia + 1j * Imrho_ia elif 'Re' in self.reim: # Real result rho_ia = Rerho_ia else: rho_ia = 1j * Imrho_ia matrices[derivative] = rho_ia dm = DensityMatrix(ksd=self.ksd, matrices=matrices, comm=self.calc_comm) yield work, dm
[docs] class ConvolutionDensityMatricesFromWaveFunctions(ConvolutionDensityMatrices): """ Collection of density matrices in the Kohn-Sham basis after convolution with one or several pulses, for different times. Obtained from the time-dependent wave functions file, which is read from disk. Parameters ---------- ksd KohnShamDecomposition object or file name. wfs_fname File name of the time-dependent wave functions file. perturbation Perturbation that was present during time propagation. pulses Convolute the density matrices with these pulses. times Compute density matrices for these times (or as close to them as possible). In units of as. derivative_order_s Compute density matrix derivatives of the following orders. ``0`` for plain density matrix and positive integers for derivatives. real Calculate real part of density matrices. imag Calculate imaginary part of density matrices. calc_size Size of the calculation communicator. stridet Skip this many steps when reading the time-dependent wave functions file. """ def __init__(self, ksd: KohnShamDecomposition | str, wfs_fname: str, perturbation: PerturbationLike, pulses: Collection[PerturbationLike], times: list[float] | Array1D[np.float64], derivative_order_s: list[int] = [0], real: bool = True, imag: bool = True, calc_size: int = 1, log: Logger | None = None, stridet: int = 1): _, calc_size = two_communicator_sizes(-1, calc_size) # The calc_comm rank 0's are world ranks 0, with a spacing of calc_size result_on_ranks = list(range(0, world.size, calc_size)) rho_nn_conv = PulseConvolver.from_parameters( wfs_fname, ksd, pulses=pulses, perturbation=perturbation, yield_re=real, yield_im=imag, filter_times=np.array(times) * as_to_au, derivative_order_s=list(sorted(derivative_order_s)), stridet=stridet, result_on_ranks=result_on_ranks, log=log) self.rho_nn_conv = rho_nn_conv super().__init__(ksd=rho_nn_conv.ksd, pulses=pulses, times=rho_nn_conv.time_t * au_to_as, real=real, imag=imag, derivative_order_s=derivative_order_s, calc_size=calc_size, log=rho_nn_conv.log) def __str__(self) -> str: lines = [] lines.append('Response from time-dependent wave functions ') lines.append('') lines += ['' + line for line in str(self.rho_nn_conv.rho_nn_reader.rho_wfs_reader).split('\n')] lines.append('') lines += ['' + line for line in str(self.rho_nn_conv.rho_nn_reader).split('\n')] lines.append('') lines += ['' + line for line in str(self.rho_nn_conv).split('\n')] return '\n'.join(lines) def get_memory_estimate(self) -> MemoryEstimate: return self.rho_nn_conv.get_memory_estimate() @property def myt(self) -> list[int]: """ List of indices corresponding to the time indices on held on this rank. """ return self.rho_nn_conv.my_work()
[docs] def work_loop(self, rank: int) -> Generator[ConvolutionDensityMatrixMetadata | None, None, None]: nt = len(self.times) ntperrank = (nt + self.loop_comm.size - 1) // self.loop_comm.size for p, pulse in enumerate(self.pulses): for localt in range(ntperrank): globalt = rank + localt * self.loop_comm.size if globalt < nt: yield ConvolutionDensityMatrixMetadata(density_matrices=self, globalt=globalt, localt=localt, globalp=p, localp=p) else: yield None
def __iter__(self) -> Generator[tuple[ConvolutionDensityMatrixMetadata, DensityMatrix], None, None]: parameters = self.rho_nn_conv.rho_nn_reader._parameters flt = (slice(parameters.n1size), slice(parameters.n2size)) dist_buffer = self.rho_nn_conv.dist_buffer # Perform the redistribution self.ksd.distribute(self.calc_comm) if self.calc_comm.rank != 0: assert len(self.myt) == 0, self.myt for work in self.local_work_plan: if self.calc_comm.rank == 0: assert self.myt[work.localt] == work.globalt localflt = flt + (work.localp, work.localt) matrices: dict[int, NDArray[np.complex128] | None] = dict() for derivative in self.derivative_order_s: if self.calc_comm.rank > 0: matrices[derivative] = None continue # Buffer shape is i, a, pulses, times if 'Re' in self.reim: Rerho_ia = dist_buffer._get_real(derivative)[localflt] if 'Im' in self.reim: Imrho_ia = dist_buffer._get_imag(derivative)[localflt] if 'Re' in self.reim and 'Im' in self.reim: # Complex result # Compared to numpy, we use another convention, hence the minus sign rho_ia = Rerho_ia - 1j * Imrho_ia elif 'Re' in self.reim: # Real result rho_ia = Rerho_ia else: rho_ia = -1j * Imrho_ia matrices[derivative] = rho_ia dm = DensityMatrix(ksd=self.ksd, matrices=matrices, comm=self.calc_comm) yield work, dm
[docs] def parallel_prepare(self): self.rho_nn_conv.dist_buffer # Perform the redistribution
class ExactFourierTransformer: def __init__(self, reader: FrequencyDensityMatrixReader, perturbation: PerturbationLike, real: bool = True, imag: bool = True, comm: Communicator | None = None, log: Logger | None = None): if comm is None: comm = world self._comm = comm self.reader = reader if log is None: log = Logger() self.log = log self.real = real self.imag = imag self.frequency_buffer = DensityMatrixBuffer(self.nnshape, (self.reader.nw, ), np.complex128) self._ready = False omega_w = self.reader.frequencies * eV_to_au self.perturbation = create_perturbation(perturbation) assert isinstance(self.perturbation, DeltaKick) # Need a uniform spacing for Simpson's integration rule dw = omega_w[1] - omega_w[0] nw = len(omega_w) if not np.allclose(omega_w[1:] - dw, omega_w[:-1]): raise ValueError('Variable frequency spacing.') # integration weights from Simpson's integration rule self._weight_w = dw / 3 * np.array([1] + [4, 2] * int((nw - 2) / 2) + [4] * (nw % 2) + [1]) self._weight_w *= 2 / (2 * np.pi) self._weight_w /= self.perturbation.strength @property def comm(self) -> Communicator: """ MPI commonicator. During convolution, the density matrices are distributed with different chunks on different ranks of this communicator. """ return self._comm @property def full_nnshape(self) -> tuple[int, int]: imin, imax, amin, amax = [int(i) for i in self.reader.ksd.ialims()] return (imax - imin + 1, amax - amin + 1) @property def nnshape(self) -> tuple[int, int]: stridei = (self.full_nnshape[0] + self.comm.size - 1) // self.comm.size return (stridei, self.full_nnshape[1]) @property def myslice(self) -> slice: return self.slice_of_rank(self.comm.rank) def slice_of_rank(self, rank: int) -> slice: stridei = (self.full_nnshape[0] + self.comm.size - 1) // self.comm.size return slice(rank * stridei, (rank + 1) * stridei) def parallel_prepare(self): # Read all the Fourier transform of density matrices self.frequency_buffer.zero_buffers(real=self.real, imag=self.imag, derivative_order_s=[0]) for w, freq in enumerate(self.reader.frequencies): if self.real: rho_ia = self.reader.read(frequency=freq, real=True)[self.myslice] self.frequency_buffer[w].real[:len(rho_ia)] = rho_ia if self.imag: rho_ia = self.reader.read(frequency=freq, real=False)[self.myslice] self.frequency_buffer[w].imag[:len(rho_ia)] = rho_ia self._ready = True def convolve(self, time: float, pulse: Perturbation, derivative: int) -> DensityMatrixBuffer | None: if not self._ready: self.parallel_prepare() assert isinstance(pulse, PulsePerturbation) omega_w = self.reader.frequencies * eV_to_au pulse_w = pulse.pulse.fourier(omega_w) buffer = DensityMatrixBuffer(self.frequency_buffer.nnshape, (), dtype=np.float64) self.log.start('convolve') exp_w = np.exp(-1j * omega_w * time * as_to_au) exp_w *= self._weight_w * pulse_w * (-1j * omega_w) ** derivative if self.real: # Real part rho_iaw = self.frequency_buffer.real[:] conv_rho_ia = rho_iaw.real @ exp_w.real conv_rho_ia -= rho_iaw.imag @ exp_w.imag buffer.store(True, derivative, conv_rho_ia) if self.imag: # Imaginary part rho_iaw = self.frequency_buffer.imag[:] conv_rho_ia = rho_iaw.real @ exp_w.real conv_rho_ia -= rho_iaw.imag @ exp_w.imag buffer.store(False, derivative, conv_rho_ia) if self.comm.rank == 0: full_buffer = DensityMatrixBuffer(self.frequency_buffer.nnshape, (), dtype=np.float64) full_buffer.zero_buffers(real=self.real, imag=self.imag, derivative_order_s=[derivative]) # Send to root for rank in range(self.comm.size): if rank == self.comm.rank: # Send own work to root buffer.send_arrays(self.comm, 0) if self.comm.rank == 0: # Receive on root, fill buffer buffer.recv_arrays(self.comm, rank) for full_rho_ia, part_rho_ia in zip(full_buffer._iter_buffers(), buffer._iter_buffers()): full_slice_ia = full_rho_ia[self.slice_of_rank(rank)] full_slice_ia[:] = part_rho_ia[:len(full_slice_ia)] if self.comm.rank == 0: self.log(f'Convolved density matrices in {self.log.elapsed("convolve"):.1f} s', who='Response', flush=True) if self.comm.rank == 0: return full_buffer return None @classmethod def from_file(cls, frho_fmt: str, ksd: KohnShamDecomposition | str, perturbation: PerturbationLike, freq_min: float, freq_spacing: float, freq_max: float, real: bool = True, imag: bool = True, comm: Communicator | None = None, log: Logger | None = None) -> ExactFourierTransformer: frequencies = np.arange(freq_min, freq_max + freq_spacing * 0.1, freq_spacing) reader = FrequencyDensityMatrixReader(frho_fmt=frho_fmt, ksd=ksd, filter_frequencies=frequencies, real=real, imag=imag, comm=None, # Look for files on world log=log) if reader.frequencies[0] > freq_min: raise ValueError(f'Could not satisfy lower bound for frequency grid with files matching {frho_fmt}. ' f'Found {reader.frequencies[0]:.2f}eV, reqested {freq_min:.2f}eV.') if reader.frequencies[-1] > freq_max: raise ValueError(f'Could not satisfy upper bound for frequency grid with files matching {frho_fmt}. ' f'Found {reader.frequencies[-1]:.2f}eV, reqested {freq_max:.2f}eV.') dw = reader.frequencies[1] - reader.frequencies[0] if dw > freq_spacing + 1e-8: raise ValueError(f'Could not construct sufficiently dense frequency grid with files matching {frho_fmt}. ' f'Found spacing of {dw:.4f}eV, reqested {freq_spacing:.4f}eV.') return cls(reader=reader, perturbation=perturbation, real=real, imag=imag, comm=comm, log=log)