Input and output formats

This page documents the exact HDF5 layouts produced by TDSEZ and consumed by zkit.

static/EigenData_<input>.h5

Created by TDSEZCore::Output().

Top-level datasets:

• /spectrum — 1-D float vector of converged eigenvalues in Hartree.

• /psi_0, /psi_1, … — 1-D or 2-D arrays holding IGA coefficients. When 2-D, shape is (n_dof, 2) with columns [Re, Im].

• /knots_x, /knots_y, /knots_z — 1-D compact knot vectors from PetIGA.

Top-level group:

• /run_metadata — attributes:

  • code_version

  • input_file

  • units

  • Dimension

  • SplineDegree

  • Nelements

  • LMinX, LMaxX, LMinY, LMaxY, LMinZ, LMaxZ

  • Hbar, Charge

  • TargetEigenvalue

  • NBoundStatesSave

  • NBoundStates

  • state_format

• /run_metadata/eig_residual — 1-D relative error per saved state.

Reader:

from zkit.io.eigen import read_eigen

eigen = read_eigen("static/EigenData_h2p.inp.h5")
print(eigen.values)
print(eigen.vectors.shape)
print(eigen.dimension)

Dimension is inferred from:

1. /run_metadata/Dimension

2. number of /knots_* datasets

3. dipole column width in time-evolution files

td/TimeEvolutionData_<input>.h5

Created by TDSEZManager::WriteHDF5().

Top-level datasets:

• dipoles

• populations

• energies

• currents

• autocorrelation

All datasets are 2-D with first dimension = recorded steps.

Column layouts by dimension:

Column layouts by dimension

Dataset	1D width	2D width	3D width
dipoles	4	7	10
populations	1 + N_pop	1 + N_pop	1 + N_pop
energies	7	7	7
currents	11	11	15
autocorrelation	3	3	3

Reader:

from zkit.io.evolution import read_evolution

evolution = read_evolution("td/TimeEvolutionData_h2p.inp.h5")
print(evolution.dimension)
print(evolution.time.shape)
print(evolution.dipoles.shape)
print(evolution.energies.shape)
print(evolution.currents.shape)
print(evolution.autocorrelation.shape)

td/wfs_<input>.h5

Created by the TS monitor via a collective VecView into an HDF5 viewer.

Layouts:

• Group wavefunction with integer datasets 0, 1, …, each with trailing Re/Im dimension.

• Flat top-level datasets with shape (n_snapshots, <spatial...>, 2).

Reader:

from zkit.io.wavefunction import read_wfs

wfs = read_wfs("td/wfs_h2p.inp.h5")
print(wfs.n_snapshots)
print(wfs.data.shape)

ts_<input>.h5

Created by TSView.

Layout:

• Group timestepper with integer datasets, or integer datasets at top level.

Reader:

from zkit.io.ts import read_timeseries

ts = read_timeseries("ts_h2p.inp.h5")
print(ts.times)

TDM outputs

TDSEZ emits:

• static/TDM_Dx_<stem>.npy

• static/EigenEnergies_<stem>.npy

• static/States_<stem>.npy

Reader:

from zkit.io.tdm import read_tdm

tdm = read_tdm("static/EigenData_h2p.inp.h5")
print(tdm.keys())