Read FEBio results with XPLT

Purpose

The pyfebiopt.xplt module reads FEBio .xplt files and exposes result data as sliceable views. Use it directly for postprocessing, or inside optimization adapters when an objective needs values from a FEBio run.

Reader pipeline

Parse the file header and dictionary to discover variable names, storage formats, and whether each variable is nodal, elemental, facial, or regional.
Parse mesh data: nodes, domains, surfaces, node sets, connectivity, and name lookup tables.
Load all states with readAllStates() or selected states with readSteps([...]).
Build Results views at xp.results so variables can be sliced by time, region, item, node, and component.

Basic read

import numpy as np

from pyfebiopt.xplt import xplt

xp = xplt("run.xplt")
xp.readAllStates()

displacement = xp.results["displacement"]
tip_z = displacement.time(-1).nodes(-1).comp("z")
tip_z = np.asarray(tip_z, dtype=float)

Selectors

time(sel): Selects one state, several states, or all states with ":".
nodes(sel) and nodeset(name): Select nodal values by node index or named FEBio node set.
domain(name) and surface(name): Select element or face results by named domain/surface.
items(sel), elems(sel), faces(sel), and enodes(sel): Select element, face, or element-node axes depending on the variable storage.
comp(sel): Select result components. Vector components use "x", "y", "z"; symmetric tensor components use tokens such as "xx", "yy", "zz", "xy", "yz", and "xz".

Element result example

xp = xplt("run.xplt")
xp.readAllStates()

stress = xp.results["stress"].domain("sample")
sigma_xx = stress.time(":").items(0).comp("xx")
time = np.asarray(xp._time, dtype=float)

The view API keeps slicing lazy until values are evaluated, which makes it usable for large outputs. Missing variables on selected states are represented with NaN arrays of the expected shape so downstream array code can stay vectorized.