<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Introduction :: Unfolding Documentation</title><link>https://mailhexu.github.io/unfolding/index.html</link><description>unfolding maps band structures computed in a supercell back onto the Brillouin zone of the primitive cell — the standard “band unfolding” analysis for defect, distorted, or commensurately-modulated calculations.&#10;It works for electrons and phonons, and reads the output of several popular DFT and force-constant codes:&#10;SIESTA Hamiltonians and wavefunctions (LCAO, spinors supported) ABINIT planewave wavefunctions (ETSF netCDF WFK) and phonon DDB files phonopy force constants Wannier90 tight-binding Hamiltonians Weights follow the Popescu–Zunger / Lee et al. formulation; every adapter returns a matplotlib Axes with weight-coded (alpha) bands.</description><generator>Hugo</generator><language>en-us</language><atom:link href="https://mailhexu.github.io/unfolding/index.xml" rel="self" type="application/rss+xml"/><item><title>Guide</title><link>https://mailhexu.github.io/unfolding/guide/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mailhexu.github.io/unfolding/guide/index.html</guid><description/></item><item><title>Installation</title><link>https://mailhexu.github.io/unfolding/install/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mailhexu.github.io/unfolding/install/index.html</guid><description>Requires Python &gt;= 3.9. Core dependencies: numpy, scipy, matplotlib, ase.&#10;pip install unfolding Optional extras Install the extra matching the code you want to read output from:&#10;Extra Enables Dependencies phonopy phonopy_unfold (phonon supercell unfolding) phonopy siesta unfold_siesta (SIESTA LCAO unfolding) HamiltonIO &gt;= 0.3.8, sisl abinit unfold_abinit (ABINIT WFK unfolding); HamiltonIO.abinit.read_wfk parses the WFK HamiltonIO &gt;= 0.3.8, netCDF4 abinit-paw (manual) PAW WFK + JTH XML spectral weights HamiltonIO, pypao, abinao vasp-paw (manual) PAW WAVECAR + licensed POTCAR spectral weights HamiltonIO, pymatgen, pypao abipy DDB_unfolder (ABINIT DDB phonon unfolding) abipy + a working anaddb dev test suite pytest, sympy pip install unfolding[siesta] # SIESTA pip install unfolding[abinit] # ABINIT WFK pip install unfolding[abipy] # ABINIT DDB (needs anaddb) pip install unfolding[phonopy] # phonopy force constants PAW backends also need pypao and abinao (ABINIT route), or pymatgen and pypao (VASP route). Install those packages in the same environment as unfolding; no VASP POTCAR is shipped or fetched.</description></item><item><title>Examples</title><link>https://mailhexu.github.io/unfolding/examples/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mailhexu.github.io/unfolding/examples/index.html</guid><description>Each card links to a walkthrough with input settings and runnable reproduction scripts. Some fixtures are generated locally instead of bundled (large GPAW restarts and VASP’s licensed POTCAR/WAVECAR).&#10;Phonons Phonopy Cu phonons Unfold a 3×3×3 fcc Cu phonon calculation from FORCE_CONSTANTS.&#10;ABINIT DDB phonons Phonon unfolding from ABINIT DDB files (Cu and CaTiO₃).&#10;Magnons TB2J magnons SrMnO3 Downfold TB2J magnon bands from the G-AFM cell onto the pseudo-cubic primitive cell.</description></item><item><title>API Reference</title><link>https://mailhexu.github.io/unfolding/api/index.html</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>https://mailhexu.github.io/unfolding/api/index.html</guid><description>Curated reference for the public interfaces (see unfolding/__init__.py).&#10;CLI and TOML configuration The unfolding console script exposes the same routes as the Python API. From an unpacked example bundle, run either the complete set of route flags or a TOML file as the only argument:&#10;unfolding siesta --fdf data/si_sc.fdf --primitive data/si_prim.vasp \ --unfold-mat -1 1 1 1 -1 1 1 1 -1 --special-points GXWGLX \ --npts 300 --method ideal --output si_unfolded.png unfolding --config unfold.toml For the Python equivalent, pass explicit parameters to the adapter (shown on each example page), or load the same TOML file:</description></item></channel></rss>