asebytes 0.3.0a3

Fast binary serialization and storage for ASE Atoms.

asebytes

Storage-agnostic, lazy-loading data layer with pluggable backends (LMDB, Zarr, HDF5/H5MD, HuggingFace Datasets, ASE file formats). Three IO tiers — raw bytes, structured dicts, and ASE Atoms — each with full sync and async APIs plus pandas-style column views.

pip install asebytes[lmdb]      # LMDB backend (recommended)
pip install asebytes[zarr]      # Zarr backend (fast compression)
pip install asebytes[h5md]      # HDF5/H5MD backend
pip install asebytes[hf]        # HuggingFace Datasets backend
pip install asebytes[mongodb]   # MongoDB backend (shared remote storage)
# In-memory backend (MemoryObjectBackend) is built-in — no extras needed

Quick Start

from asebytes import ASEIO

# Sync
db = ASEIO("data.lmdb")
db.extend(atoms_list)
db[0] = new_atoms
atoms = db[0]

# Async
import asyncio
from asebytes import AsyncASEIO

async def main():
    db = AsyncASEIO("data.lmdb")
    await db.extend(atoms_list)
    atoms = await db[0]
    async for atoms in db:
        process(atoms)

asyncio.run(main())

String paths auto-detect the backend from the file extension. Pass a backend instance directly for full control.

Three IO Layers

Class	Async class	Row type	Use case
ASEIO	AsyncASEIO	ase.Atoms	Atomistic simulations
ObjectIO	AsyncObjectIO	dict[str, Any]	Structured data without ASE
BlobIO	AsyncBlobIO	dict[bytes, bytes]	Raw bytes, zero deserialization

ASEIO — Atoms objects

from asebytes import ASEIO, AsyncASEIO

# Sync
db = ASEIO("atoms.lmdb")
db.extend(atoms_list)
db.update(0, calc={"energy": -10.5})
atoms = db[0]                  # ase.Atoms

# Async
db = AsyncASEIO("atoms.lmdb")
await db.extend(atoms_list)
atoms = await db[0]            # ase.Atoms
await db.update(0, calc={"energy": -10.5})

ObjectIO — plain dicts

from asebytes import ObjectIO, AsyncObjectIO

# Sync
db = ObjectIO("records.lmdb")
db.extend([
    {"arrays.numbers": [29], "calc.energy": -3.5},
    {"arrays.numbers": [26], "calc.energy": -8.3},
])
row = db[0]  # {"arrays.numbers": [29], "calc.energy": -3.5}

# Async
db = AsyncObjectIO("records.lmdb")
await db.extend([{"arrays.numbers": [29], "calc.energy": -3.5}])
row = await db[0]

BlobIO — raw bytes

from asebytes import BlobIO, AsyncBlobIO

# Sync
db = BlobIO("blobs.lmdb")
db.extend([{b"key": b"value"}, {b"key": b"other"}])
row = db[0]                    # {b"key": b"value"}

# Async
db = AsyncBlobIO("blobs.lmdb")
await db.extend([{b"key": b"value"}])
row = await db[0]

Lazy Views

Indexing with slices, lists, or strings returns lazy views — nothing is loaded until you iterate or materialize.

Row views

# Sync
view = db[5:100]               # RowView (lazy)
view = db[[0, 42, 99]]         # RowView from index list
for row in view:
    process(row)

# Async
view = db[5:100]               # AsyncRowView (lazy)
async for row in view:
    process(row)
rows = await view.to_list()    # materialize to list

Column views

# Sync
energies = db["calc.energy"].to_list()
cols = db[["calc.energy", "calc.forces"]].to_dict()
# → {"calc.energy": [...], "calc.forces": [...]}

# Async
energies = await db["calc.energy"].to_list()
cols = await db[["calc.energy", "calc.forces"]].to_dict()

Chaining rows + columns

# Sync
db[0:500]["calc.energy"].to_list()

# Async
await db[0:500]["calc.energy"].to_list()

Materialization

# Sync
view.to_list()                 # load all into memory
view.to_dict()                 # column-oriented dict (ColumnView only)
for batch in view.chunked(1000):  # iterate in chunks
    process(batch)

# Async
await view.to_list()
await view.to_dict()
async for batch in view.chunked(1000):
    process(batch)

Write-back

Views support in-place mutations when backed by a writable backend.

# Sync
db[0:10].set(new_rows)         # overwrite rows
db[0:10].update({"info.tag": "train"})  # partial update (applies to all rows)
db[0:10].delete()              # delete rows (contiguous only)

# Async
await db[0:10].set(new_rows)
await db[0:10].update({"info.tag": "train"})
await db[0:10].delete()

Backends

Backend is auto-detected from the file extension:

Extension	Backend	Install extra
*.lmdb	LMDBObjectBackend / LMDBBlobBackend	asebytes[lmdb]
*.zarr	ZarrBackend	asebytes[zarr]
*.h5 / *.h5md	H5MDBackend	asebytes[h5md]
*.xyz / *.extxyz / *.traj	ASEReadOnlyBackend	(none)

URI schemes for remote/streaming sources:

Scheme	Source	Example
memory://	In-memory (no persistence)	ObjectIO("memory://")
mongodb://	MongoDB	ObjectIO("mongodb://host:port/db")
redis://	Redis	ObjectIO("redis://host:port")
hf://	HuggingFace Datasets	ASEIO("hf://user/dataset", ...)
colabfit://	ColabFit datasets	ASEIO("colabfit://mlearn_Cu_train", ...)
optimade://	OPTIMADE datasets	ASEIO("optimade://LeMaterial/LeMat-Bulk", ...)

Groups

All backends support a unified group parameter to organize data into independent collections within the same storage location. Groups are useful for storing multiple datasets, splits, or configurations in a single file/database.

# LMDB: separate subdirectories per group
db1 = ASEIO("data.lmdb", group="train")
db2 = ASEIO("data.lmdb", group="test")

# H5MD: /particles/{group}/ in the HDF5 structure
db = ASEIO("multi.h5", group="solvent")

# MongoDB: each group = a collection in the database
db = ObjectIO("mongodb://host:port/mydb", group="train")

# Zarr: separate subdirectories per group
db = ASEIO("data.zarr", group="conformers")

# Redis: key prefix = group
db = ObjectIO("redis://host:port", group="mydata")

# Memory: independent storage per group
db = ObjectIO("memory://", group="temp")

List available groups with list_groups():

from asebytes import ASEIO, H5MDBackend, LMDBObjectBackend

# Static method on backends
groups = H5MDBackend.list_groups("multi.h5")
groups = LMDBObjectBackend.list_groups("data.lmdb")

# Or via facades
groups = ASEIO.list_groups("data.lmdb")

Default group is backend-specific when not specified ("default" for most backends; H5MD defaults to "atoms"). Backends store groups using native strategies:

Backend	Group storage
LMDB	Subdirectory: {path}/{group}/
H5MD	HDF5 group: /particles/{group}/
Zarr	Subdirectory: {path}/{group}/
MongoDB	Collection: group in database
Redis	Key prefix: {group}:
Memory	Internal dict keyed by group

Read-Through Cache

For slow or remote sources, cache_to creates a persistent local cache. First pass reads from source and fills the cache; subsequent reads are served from cache.

db = ASEIO("colabfit://dataset", split="train", cache_to="cache.lmdb")
for atoms in db:    # epoch 1: reads source, populates cache
    train(atoms)
for atoms in db:    # epoch 2+: reads from local cache
    train(atoms)

cache_to is available on ASEIO only. Accepts a file path (auto-creates backend) or any ReadWriteBackend instance. No invalidation — delete the cache file to reset.

HuggingFace Datasets

Stream or download datasets from the HuggingFace Hub via URI schemes.

# ColabFit (auto-selects column mapping, streams by default)
db = ASEIO("colabfit://mlearn_Cu_train", split="train")

# OPTIMADE (e.g. LeMaterial)
db = ASEIO("optimade://LeMaterial/LeMat-Bulk", split="train", name="compatible_pbe")

# Generic HuggingFace (requires explicit column mapping)
from asebytes import ColumnMapping
mapping = ColumnMapping(
    positions="pos", numbers="nums",
    calc={"energy": "total_energy"},
)
db = ASEIO("hf://user/dataset", mapping=mapping, split="train")

# Downloaded mode for faster access
db = ASEIO("colabfit://dataset", split="train", streaming=False)

Zarr / HDF5 / H5MD

Zarr

Flat layout with Blosc/LZ4 compression. Compact files and fast reads. Supports variable particle counts via NaN padding.

db = ASEIO("trajectory.zarr")
db.extend(atoms_list)

# Custom compression
from asebytes import ZarrBackend
db = ASEIO(ZarrBackend("data.zarr", compressor="zstd", clevel=9))

HDF5 / H5MD

H5MD-standard files with variable particle counts, per-frame PBC, and bond connectivity.

db = ASEIO("trajectory.h5", author_name="Jane Doe", compression="gzip")
db.extend(atoms_list)

# Multi-group files
from asebytes import H5MDBackend
groups = H5MDBackend.list_groups("multi.h5")
db = ASEIO("multi.h5", group="solvent")

MongoDB

Shared remote storage for multi-client access. Requires a running MongoDB instance (>= 4.4).

# Sync
db = ObjectIO("mongodb://user:pass@host:27017/mydb", group="train")
db.extend([{"energy": -3.5, "positions": [[0, 0, 0]]}])
row = db[0]

# Async — auto-dispatches to native AsyncMongoObjectBackend
db = AsyncObjectIO("mongodb://user:pass@host:27017/mydb", group="test")
row = await db[0]

Uses a sort-key array for O(1) positional access, with server-side field filtering via MongoDB projections — requesting specific keys (e.g. db.get(0, keys=["energy"])) only transfers those fields over the network.

In-Memory Backend

MemoryObjectBackend stores data in a plain Python list — no persistence, no dependencies. Useful for testing, ephemeral storage, and prototyping.

from asebytes import ObjectIO, ASEIO

db = ObjectIO("memory://")
db.extend([{"a": 1}, {"a": 2}])
assert len(db) == 2

# Works with all facades
db = ASEIO("memory://")
db.extend(atoms_list)

Key Convention

All data follows a flat namespace:

Prefix	Content	Examples
arrays.*	Per-atom arrays	arrays.positions, arrays.numbers, arrays.forces
calc.*	Calculator results	calc.energy, calc.stress
info.*	Frame metadata	info.smiles, info.label
(top-level)	cell, pbc, constraints

from asebytes import atoms_to_dict, dict_to_atoms

d = atoms_to_dict(atoms)   # Atoms → flat dict
atoms = dict_to_atoms(d)   # flat dict → Atoms

Facade API Reference

All three tiers share the same method names. Async facades use await instead of direct calls.

Method	BlobIO / ObjectIO / ASEIO	AsyncBlobIO / AsyncObjectIO / AsyncASEIO
Read one row	db[i]	await db[i]
Read with key filter	db.get(i, keys=[...])	await db.get(i, keys=[...])
List keys at index	db.keys(i)	await db.keys(i)
Append rows	n = db.extend([...])	n = await db.extend([...])
Insert at position	db.insert(i, row)	await db.insert(i, row)
Overwrite row	db[i] = row	await db[i].set(row)
Partial update	db.update(i, {...})	await db.update(i, {...})
Delete row	del db[i]	await db[i].delete()
Drop columns	db.drop(keys=[...])	await db.drop(keys=[...])
Pre-allocate slots	db.reserve(n)	await db.reserve(n)
Clear all rows	db.clear()	await db.clear()
Remove container	db.remove()	await db.remove()
Length	len(db)	await db.len()
Iterate	for row in db:	async for row in db:
Context manager	with db:	async with db:

ASEIO / AsyncASEIO additionally support keyword-style updates:

db.update(i, info={"tag": "done"}, calc={"energy": -10.5})

Backend Adapters

Adapters convert between blob-level (dict[bytes, bytes]) and object-level (dict[str, Any]) backends:

Adapter	Wraps	Exposes
BlobToObjectReadAdapter	ReadBackend[bytes, bytes]	ReadBackend[str, Any]
BlobToObjectReadWriteAdapter	ReadWriteBackend[bytes, bytes]	ReadWriteBackend[str, Any]
ObjectToBlobReadAdapter	ReadBackend[str, Any]	ReadBackend[bytes, bytes]
ObjectToBlobReadWriteAdapter	ReadWriteBackend[str, Any]	ReadWriteBackend[bytes, bytes]

Async variants (AsyncBlobToObjectReadAdapter, etc.) mirror the same pattern for async backends.

from asebytes import BlobToObjectReadWriteAdapter, ObjectIO
from asebytes import LMDBBlobBackend

# Use a blob backend through the ObjectIO facade
blob_backend = LMDBBlobBackend("data.lmdb")
object_backend = BlobToObjectReadWriteAdapter(blob_backend)
db = ObjectIO(object_backend)

The registry uses these adapters automatically — e.g., BlobIO("data.lmdb") wraps the object backend as a blob backend via ObjectToBlobReadWriteAdapter when no native blob backend is registered.

Custom Backends

Implement ReadBackend[K, V] for read-only access or ReadWriteBackend[K, V] for full read-write:

from asebytes import ReadBackend

class MyBackend(ReadBackend[str, object]):
    def __len__(self) -> int: ...
    def get(self, index: int, keys: list[str] | None = None) -> dict[str, object] | None: ...

db = ObjectIO(MyBackend())

For async backends, subclass AsyncReadBackend[K, V] / AsyncReadWriteBackend[K, V], or wrap an existing sync backend:

from asebytes import SyncToAsyncAdapter, AsyncObjectIO

async_backend = SyncToAsyncAdapter(MyBackend())
db = AsyncObjectIO(async_backend)

Benchmarks

1000 frames each on two datasets — ethanol conformers (small molecules, fixed size) and LeMat-Traj (periodic structures, variable atom counts). All frames include energy, forces, and stress. Compared against aselmdb, znh5md, extxyz, and SQLite. Log scale — lower is better.

# LeMat-Traj benchmark data
lemat = list(ASEIO("optimade://LeMaterial/LeMat-Traj", split="train", name="compatible_pbe")[:1000])

Note: HDF5 performance is heavily influenced by compression and chunking settings. Both asebytes H5MD and znh5md use gzip compression by default, which reduces file size at the cost of read/write speed. The Zarr backend uses Blosc/LZ4 compression, which achieves compact file sizes with faster decompression than gzip.

Write

Read

Random Access

Property Access

Column Access

Update