oxyz 0.3.0

Fast, schema-aware extxyz reading for atomistic machine-learning workflows

oxyz

Fast, schema-aware extxyz reading for atomistic machine learning. A Rust parser behind a small, typed Python API: numpy arrays out, ase.Atoms on request, and a one-pass schema report that tells you whether a training file is what you think it is.

import oxyz

frames = oxyz.read_frames("train.extxyz")        # all cores, one pass
frames[0].columns["pos"]                         # float64 ndarray, shape (n_atoms, 3)
frames[0].metadata["energy"]                     # float

schema = oxyz.infer_schema("train.extxyz")
schema.is_consistent                             # False — now you know before training
print(schema)                                    # which keys drift, and in how many frames

oxyz exists for the gap between "extxyz is the lingua franca of atomistic ML datasets" and "every Python extxyz reader is slow enough to matter". Reading a dataset into numpy is 16–27× faster than ase.io.read on the benchmarks below; reading it into ase.Atoms objects is 3.5–6× faster. The same single pass can also tell you the dataset's schema — which columns and metadata keys appear, with what types and shapes, and how consistently — which is the part of dataset ingestion that usually goes unchecked.

Pre-1.0: minor versions may change the API.

Install

pip install oxyz                # numpy is the only dependency
pip install "oxyz[ase]"         # adds ASE conversion (ase >=3.23,<4)
pip install "oxyz[metatomic]"   # adds metatomic.torch.System reading (torch >=2)
pip install "oxyz[torch-sim]"   # adds torch_sim.SimState reading (torch >=2)

Wheels cover CPython ≥3.12 on Linux (x86_64, aarch64), macOS (arm64, x86_64), and Windows (x64).

oxyz follows SPEC 0 for its support window: Python versions are dropped three years after release, so the current minimum is 3.12. Older interpreters can pin an earlier oxyz (3.11 is supported up to 0.2.0).

Installing puts an oxyz command on the path; oxyz scan train.extxyz summarises a file without writing any Python. It also runs without installing, via uvx oxyz scan train.extxyz.

In place of ASE

oxyz.ase.read and oxyz.ase.iread are drop-ins for ase.io.read / ase.io.iread on extxyz files, including ASE's full index grammar (-1, "::2", slices):

import oxyz.ase

atoms = oxyz.ase.read("train.extxyz")             # last frame, like ase.io.read
images = oxyz.ase.read("train.extxyz", ":")       # every frame
for atoms in oxyz.ase.iread("train.extxyz", "::10"):
    ...

The conversion reuses ase.io.extxyz's own routing tables and set_calc_and_arrays, so key handling (which results go to the calculator, which to arrays) agrees with ASE by construction; golden tests hold the two readers equal on the test corpus apart from the divergences below. Reads are lazy: read(path, 3) parses four frames and stops, and negative or reverse selections resolve through a structural scan and seek rather than a full parse — read(path) on a long trajectory does not parse the whole file to return the last frame.

Divergences from ASE

oxyz.ase.read matches ase.io.read field for field on the test corpus except for the cases below — two deliberate, two that follow from honouring the extxyz grammar and oxyz's typed model where ASE's parser does not.

Deliberate — an error or an acceptance, never a silently different value:

• Voigt stress. 6-component stress is accepted and routed to the calculator; ASE's comment parser rejects the file.
• Non-symbol species. A species that is not a chemical symbol raises an error; ASE builds a nonsense Atoms.

Grammar and typing — a different value, no error:

• New-style string arrays. tags=["a","b"] is typed as list[str]; ASE keeps the one raw string '"a","b"'.
• Single-quoted values. The grammar makes " the only quote character, so label='hello' keeps its quotes and note=it's keeps its apostrophe; ASE strips the single quotes (and reads it's as its).

To PyTorch (metatomic)

oxyz.metatomic.read and iread read extxyz straight into metatomic.torch.Systems, reproducing metatomic.torch.systems_to_torch(ase.io.read(...)) without the ASE round-trip — species map to atomic numbers through oxyz's own element table, and the cell follows the same Fortran-order Lattice reshape and pbc-masked zeroing. Needs pip install "oxyz[metatomic]".

import torch
import oxyz.metatomic

systems = oxyz.metatomic.read("train.extxyz", dtype=torch.float64)   # list[System]
for system in oxyz.metatomic.iread("train.extxyz"):                  # constant memory
    ...

read/iread mirror oxyz.ase: the same index grammar, plus dtype/device/positions_requires_grad/cell_requires_grad matching systems_to_torch (dtype=None follows torch.get_default_dtype()).

For pipelines that also need targets, SystemSource parses a file once and serves both the structures and array-native target extraction:

source = oxyz.metatomic.SystemSource("train.extxyz")
systems = source.systems(dtype=torch.float64)
energy = source.per_config("energy", dtype=torch.float64)         # (n_frames, ...)
forces, offsets = source.per_atom("forces", dtype=torch.float64)  # (total_atoms, 3) + offsets

These are the pieces a downstream reader would build on — for example a metatrain readers/oxyz.py, where SystemSource.systems() backs read_systems and per_config/per_atom back the energy/forces/stress readers, with the gradient-sign, volume, and TensorMap conventions staying on the metatrain side. oxyz depends only on torch and metatomic-torch, never on metatrain or metatensor; that integration is left to metatrain deliberately.

To PyTorch (torch_sim)

oxyz.torch_sim reads extxyz into torch_sim.SimState, reproducing torch_sim.io.atoms_to_state(ase.io.read(...)). SimState is natively batched — one state holds many systems with their atoms concatenated — so the reader maps onto oxyz's batched parse rather than the per-frame path: read returns a single batched state, iread streams the file as a sequence of batched states. Needs pip install "oxyz[torch-sim]".

import torch
import oxyz.torch_sim

state = oxyz.torch_sim.read("train.extxyz")              # one batched SimState
substate = oxyz.torch_sim.read("train.extxyz", "0:64")   # a slice, still one state

With a model and a GPU, hand the whole-file state to torch_sim's BinningAutoBatcher, which sizes memory-aware batches by probing the model:

from torch_sim.autobatching import BinningAutoBatcher

batcher = BinningAutoBatcher(model, memory_scales_with="n_atoms_x_density")
batcher.load_states(oxyz.torch_sim.read("train.extxyz"))

For files too large to materialise, iread streams batches itself, with the same binning knobs as oxyz.iter_batches (frames_per_batch / atoms_per_batch / memory_scales_with + max_scaler):

for batch in oxyz.torch_sim.iread("huge.extxyz", memory_scales_with="n_atoms_x_density",
                                  max_scaler=50_000):
    ...

Cells follow torch_sim's column-vector convention (ASE's cell transposed), every system shares one pbc (frames that disagree are an error), and masses come from a masses column or, failing that, the ASE-parity atomic-weight table. dtype=None infers from the data (float64), matching atoms_to_state; pass torch.float32 for ML use. SimStateSource parses once and serves the state plus array-native per_config / per_atom extraction.

What you get beyond ASE

Array-native frames. A Frame is a frozen dataclass holding the file's columns as numpy arrays and its comment-line metadata as typed Python values — no per-atom Python objects, no calculator indirection. Names and values are kept exactly as written: no force/forces aliasing, no reordering, Lattice stays the flat 9-value array from the file. Normalisation is the ASE layer's job (or yours).

Batches in the PyG layout. Batch concatenates frames atom-major, CSR-style: every per-atom column is one dense array of total_atoms rows, frame i occupying rows offsets[i]:offsets[i+1]; per-frame metadata stacks into arrays of n_frames rows. batch.ptr and batch.batch carry their PyTorch Geometric names, and torch.from_numpy(batch.columns["pos"]) is zero-copy, so the path into a training loop is short.

for batch in oxyz.iter_batches("bulk.extxyz", atoms_per_batch=4096,
                               shuffle=True, seed=0):
    batch.columns["forces"]        # (total_atoms, 3)
    batch.metadata["energy"]       # (n_frames,)
    batch.frame_indices            # which file frames these are — provenance

iter_batches packs by frame count or by a total-atom budget, in file order or seeded-shuffled. Batch composition depends only on the file, the knobs, and the seed — never on threads.

Schema inference. infer_schema folds the whole file into a Schema: per-column and per-metadata-key observed variants (kind, width or shape, and how many frames used each), presence counts, a strict is_consistent, and per-entry unified — the single type an Int/Real drift can be promoted to, or None when the conflict is genuine. The classic failure it catches: a generator script that writes isolated-atom frames with integer forces and no Lattice into an otherwise uniform bulk dataset. The same pass keeps the per-frame atom counts, so a Schema also reports the atom-count distribution (mean_atoms, median_atoms, std_atoms, alongside the min/max above) without a second read of the file.

>>> print(oxyz.infer_schema("train.extxyz"))
1000 frames, 63841 atoms (min 1, max 96)

per-atom columns:
  species: S:1 (1000/1000 frames)
  pos: R:3 (1000/1000 frames)
  forces: I:3 (5/1000 frames), R:3 (995/1000 frames) (unifies to R:3)

metadata:
  energy: Real (1000/1000 frames)
  Lattice: RealArray[9] (995/1000 frames)

Structural scanning. oxyz.scan reads only the frame skeleton — byte offsets and declared atom counts — without parsing any contents. It is the cheap first question to ask of an unfamiliar file (5 ms for a 22 MiB file below) and the machinery behind random access, shuffled batching, and lazy negative indexing. The same statistics, alongside the inferred schema, are a terminal away with oxyz scan (see Command line).

Parallelism as a knob, not a mode. Readers take threads: None parses on every core, 1 is the exact serial streaming path. Results and errors are identical either way — the parallel path is held to the serial path's behaviour by parity tests, not by intention.

Command line

Installing oxyz provides an oxyz command for inspecting files from the shell; uvx oxyz runs it without installing anything.

oxyz scan train.extxyz

scan prints per-frame atom-count statistics followed by the inferred schema. Unlike the oxyz.scan primitive, which parses nothing, the command reads the whole file to infer the schema; --no-schema drops back to the cheap structural pass and reports only the statistics. --json emits a single {"stats": ..., "schema": ...} object for piping into other tools.

$ oxyz scan train.extxyz
frames:      3
atoms total: 6
atoms/frame: min 1  max 3  mean 2.00  median 2.00  std 0.82

3 frames, 6 atoms (min 1, max 3)

per-atom columns:
  species: S:1 (3/3 frames)
  pos: R:3 (3/3 frames)
  forces: R:3 (3/3 frames)

metadata:
  Lattice: IntArray[9] (3/3 frames)
  energy: Real (3/3 frames)

Performance

Timings below are means over repeated rounds — each case gets a one-second budget over at least five rounds — on an Apple M3 Pro under CPython 3.13. Full tables with standard deviations, the environment, and the fixture definitions are in benchmarks/RESULTS.md; benchmarks/run.py reproduces them.

Whole-file reads to numpy (oxyz.read_frames vs cextxyz, the libAtoms C parser, via its read_dicts):

workload	oxyz	oxyz threads=1	cextxyz
2 000 small frames	9.2 ms	18.6 ms	215 ms
4 × 100 000 atoms	26.6 ms	60.2 ms	92.7 ms
2 000 frames, heavy metadata	12.7 ms	25.6 ms	356 ms
MACE-style mixed file	6.4 ms	13.1 ms	135 ms

Whole-file reads to ase.Atoms (oxyz.ase.read vs the ase-extxyz plugin wrapping the same C parser, vs ase.io.read):

workload	oxyz.ase	ase-extxyz	ase
2 000 small frames	60 ms	101 ms	209 ms
4 × 100 000 atoms	71 ms	90 ms	426 ms
2 000 frames, heavy metadata	74 ms	241 ms	339 ms
MACE-style mixed file	37 ms	75 ms	152 ms

Beyond whole-file reads: on selective reads (every 20th frame of the small-frames file) oxyz.read_batch takes 1.6 ms against 21 ms for ASE; on peak memory, streaming iter_frames through the small-frames file grows RSS by 12 MiB where ase.io.iread grows it by 56 MiB (benchmarks/MEMORY.md). The one place a text parser is predictably slower is against binary stores (LMDB, SQLite, mmap-backed formats); see benchmarks/RESULTS.md for those comparisons.

API

oxyz.read_frames(path, *, threads=None)      -> list[Frame]
oxyz.iter_frames(path)                       -> Iterator[Frame]   # constant memory
oxyz.read_first(path)                        -> Frame
oxyz.read_batch(path, indices=None, *, threads=None) -> Batch    # indices=None: whole file
oxyz.iter_batches(path, *, frames_per_batch=None, atoms_per_batch=None,
                  shuffle=False, seed=None, threads=None) -> Iterator[Batch]
oxyz.scan(path)                              -> FrameIndex
oxyz.infer_schema(path)                      -> Schema

oxyz.ase.read(path, index=None, *, format=None)  -> Atoms | list[Atoms]  # index=None: last frame
oxyz.ase.iread(path, index=":", *, format=None)  -> Iterator[Atoms]
oxyz.ase.to_atoms(frame)                     -> Atoms              # also Frame.to_ase()

oxyz.metatomic.read(path, index=":", *, dtype=None, device=None,
                    positions_requires_grad=False, cell_requires_grad=False,
                    threads=None)            -> System | list[System]
oxyz.metatomic.iread(path, index=":", *, dtype=None, ...)  -> Iterator[System]
oxyz.metatomic.SystemSource(path, *, threads=None)         # .systems() / .per_config() / .per_atom()

Frame, Batch, FrameIndex, Schema and its parts (ColumnSchema, MetadataSchema, the variant records, the Kind enum) are frozen dataclasses; everything ships with type stubs.

The command line mirrors a subset:

oxyz scan <path> [--no-schema] [--json]   # stats + inferred schema

The fine print

Contracts worth knowing before relying on them:

• Mixed-schema files read per-frame, but do not batch. read_frames and iter_frames handle files whose frames disagree (the MACE isolated-atom-plus-bulk pattern) without complaint — each Frame stands alone. Batch assembly currently requires every gathered frame to share a schema; infer_schema tells you in advance whether a file qualifies. A missing-key policy (NaN-fill plus presence mask) is planned.
• Duplicate metadata keys collapse. Frame.metadata is a dict; if a comment line repeats a key, the last occurrence wins.
• Batch.batch is computed per access (np.repeat over the atom counts); hoist it out of a hot loop.
• Errors carry frame context. Malformed input raises oxyz.ParseError (a ValueError subclass) with the frame index and the offending line or value in the message, and the same location on the exception as attributes — frame_index, line_number, column, each None where the parser cannot pin it down — so you can find the bad frame without parsing the message. Out-of-range frame requests raise IndexError; I/O problems raise OSError. After a parse error, streaming iterators stop rather than guess at a resynchronisation point.
• Partial reads only promise the prefix. read_batch and indexed reads inspect the file no further than the last requested frame; damage past that point goes unreported. Whole-file validation is infer_schema's job.

Supported extxyz

The parser accepts and preserves; it does not interpret. Accepted: the count line; a comment line of key=value pairs with bare or double-quoted values, [1, 2.0, 3]-style or quoted whitespace-separated arrays, T/TRUE/True/true booleans (a bare 1 stays an integer in metadata, but is a boolean in an L-kind atom column, following the spec); a Properties descriptor with S/R/I/L columns of any name and width; any species strings. Metadata values are typed by shape, and anything that fits no narrower type falls back to a string rather than rejecting the file. Not supported: writing (reading only, for now), compressed input, comment lines that are not key=value metadata, and single-quoted values.

How it is put together

Three layers, with the boundary chosen so that each is testable on its own:

• crates/oxyz-core — the Rust core: parser, the columnar lossless Frame model, the structural scanner and byte-offset index, batch assembly, and the schema fold. No Python anywhere in the crate; it builds and tests standalone. Errors are structured (thiserror) and wrapped with the frame they occurred in.
• crates/oxyz-py — the PyO3 binding, a cdylib named oxyz._rust. Parsing runs with the interpreter detached (the GIL released), so threads parse in parallel; conversion to numpy happens once at the boundary, column buffers passing across as whole arrays rather than element-wise. Built as a single abi3 wheel per platform covering CPython ≥3.11.
• src/oxyz — thin typed Python: frozen dataclasses over the binding's dicts, batch planning (the pure-Python part of iter_batches), and the index grammar (shared by the conversion layers via oxyz._select). The conversion layers stay last-moment and optional: ASE knowledge lives in oxyz.ase, torch/metatomic knowledge in oxyz.metatomic, each importing its extra lazily; the core depends on neither.

Testing follows the shape of the promises: Rust unit and corpus tests for the parser; parity tests holding parallel reads byte-identical to serial, including which error wins when several frames are bad; golden tests holding oxyz.ase.read equal to ase.io.read and oxyz.metatomic.read equal to systems_to_torch(ase.io.read(...)) frame-by-frame; and malformed-file tests asserting the frame index in the error message, not just that an error occurred.

Roadmap

In rough order of intent, shaped by what removes the most reasons to fall back to other tools:

• Write support — lossless Frame round-tripping, removing the most common reason to keep ASE in a read → filter → write workflow.
• Field selection and a missing-key batching policy — request only the columns and metadata you need; NaN-fill or error on absent keys, so mixed-schema training files batch directly.
• Normalisation accessors — positions, cell, numbers, pbc, forces, energy as conventional views over the untouched raw data, for training loops that want neither ASE nor the raw spelling.
• Additional inputs and outputs — compressed input (.xyz.gz, .xz), torch.Tensor output, and a public lazy dataset object (len, indexing, slicing over an open file).

Licence

MIT or Apache-2.0, at your option.