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About

readcon-core is the reference implementation of versioned .con / .convel. This stack puts CON everywhere: every optimizer, potential driver, analysis tool, campaign store, and ML hand-off that needs a durable atomic configuration with constraints, forces, and identity.

CON is human-readable and complete on one frame: cell, type-grouped coordinates, per-direction fixed masks, column-5 atom_id, optional per-atom sections (velocities, forces, energies, charges, spins, magmoms), and JSON metadata (spec v2–v3, docs/orgmode/spec.org). That payload is why saddle, dimer, and NEB codes already work on CON; the library exists so the rest of the atomistic stack adopts the same file.

Layer	Role in spreading CON
Spec + hot path	Spec v3 parse/write, `validate`, units, `sections`, SoA, Cachegrind CI
Hourglass ABI	C / C++ / Python / Julia / Fortran (`rkr_*`): link CON into any language
Device / ML hand-off	DLPack (optional CUDA); optional metatensor `TensorBlock` without leaving CON authority
Ingress	Chemfiles import/selection: foreign structures into CON
Campaigns	`index_proj` + readcon-db (`cargo add` / `pip install`; docs · docs.rs)

Already on that path: rare-event clients, rgpot, rgpycrumbs, ASE adapters, amsel, campaign stores, and anything that takes DLPack or metatensor blocks.

Rust rewrite of readCon. Chemfiles owns format diversity at the edge; this crate owns CON fidelity on the wire and in memory.

Measurements: Cachegrind I-refs (examples/cachegrind_harness.rs); Python ASV + spyglass on PRs (benchmarks/); CON peers via benches/compare_readers.py (and other scripts under benches/). See docs/orgmode/benchmarks.org.

Features

CON and convel: Coordinates; optional sections declared in sections (velocities, forces, energies, charges, spins, magmoms). Velocities also auto-detect on legacy .convel without a sections key.
Lazy iteration: ConFrameIterator; next_with_raw_span keeps the on-disk blob for corpus ingest.
Hot path: fast-float2, memmap2, Cachegrind-tracked scenarios.
Parallel frames: Rayon behind the parallel Cargo feature.
Bindings: Python (PyO3), Julia (ccall), C (shipped header), C++ (RAII header), Fortran (fpm); hourglass ABI patterned on metatensor.
Metadata helpers: Typed energy, frame_index, time, timestep, neb_bead, neb_band across bindings; raw JSON still available.
Validation: validate=true enforces finiteness, reserved keys, geometry, labels, symbols, section presence, identity columns.
Fidelity: atom_id, per-direction fixed masks, and declared optional sections round-trip through the core reader/writer.
Campaigns: Pair with readcon-db (CON-text indexes, dedup, multi-reader; docs · docs.rs).
RPC: Cap'n Proto behind the rpc feature.

Migrate onto CON

Why switch: use a real frame API and multi-language library instead of hand-rolling XYZ and a private atoms object.

API: parse/write, builders, metadata, validation, compression, lazy multi-frame iteration
Payload: constraints, atom_id, optional sections, versioned JSON on one frame
Selection: select_atoms / rkr_frame_select (name H, bonds/angles when topology is present)
Languages: hourglass rkr_* in Fortran / C / C++ / Python / Julia / Rust (same semantics when you add a language)
Campaigns: readcon-db on CON text (energy / formula / sections, dedup, multi-reader; docs · docs.rs)
Plotting: chemparseplot (+ rgpycrumbs) on the same files
Measurements: Cachegrind I-refs; PR ASV + spyglass; peer scripts in benches/ — benchmarks.org

foreign → CON (needs --features chemfiles)

cargo run --release --features chemfiles -- convert structure.xyz structure.con

Python (readcon-chemfiles or maturin --features python,chemfiles)

python -c "import readcon; readcon.convert_to_con('structure.xyz','structure.con')"

How-to: docs/orgmode/migrate.org. Chemfiles path (CI-run): chemfiles-notebook. Campaigns: readcon-db docs · docs.rs/readcon-db. Plotting: chemparseplot.

Install

Language	Install	Destination
Rust	`cargo add readcon-core`	docs.rs
Python	`pip install readcon`	PyPI
Python + chemfiles	`pip install readcon-chemfiles`	PyPI
Campaign store	`cargo add readcon-db` / `pip install readcon-db`	docs · docs.rs
Julia	`julia --project=julia/ReadCon -e 'using Pkg; Pkg.instantiate()'`	bindings
C / C++ CMake	`FetchContent` / `find_package(readcon-core)` (cxx tarball)	headers + `libreadcon_core` + `readcon-core.pc`
C / C++ Meson	`dependency('readcon-core')` (wrapdb / wrap-file)	same
C / C++ cargo-c	`cargo cinstall --release --prefix /usr/local`	same

The C/C++ headers are shipped (include/readcon-core.h). cbindgen is a maintainer tool, not a consumer dependency. C99 (readcon-core.h) or C++17 (readcon-core.hpp) compiler. FetchContent URL: readcon-core-cxx-$VERSION.tar.gz on the GitHub Release. Full matrix: getting-started.

Tutorial

One Good Tutorial (Diátaxis): install, read a multi-frame fixture, inspect atom_id, write a round-trip, build a frame with energy. Full steps: docs/orgmode/tutorial.org (or the published HTML tutorial page).

Short Python path from the repository root:

import readcon

for frame in readcon.iter_con("resources/test/tiny_multi_cuh2.con"):
    print(frame.cell, len(frame), frame.energy)

frames = readcon.read_con("resources/test/tiny_multi_cuh2.con")
readcon.write_con("out.con", frames)

atoms = [readcon.Atom("Cu", 0.0, 0.0, 0.0, atom_id=0, mass=63.546)]
frame = readcon.ConFrame(cell=[10.0, 10.0, 10.0], angles=[90.0, 90.0, 90.0], atoms=atoms)
frame.set_energy(-42.5)
frame.write_con("built.con")

Rust smoke (same fixture):

cargo run --example rust_usage -- resources/test/tiny_multi_cuh2.con

Other languages and task recipes: docs/orgmode/howto.org. Conversion from XYZ/PDB/GRO: chemfiles-tutorial.

Design Decisions

Lazy parsing: ConFrameIterator parses one frame at a time for large trajectories.
Hourglass FFI: shipped C header plus a hand-written C++ RAII wrapper, same pattern as metatensor. CMake FetchContent, Meson wrap, and readcon-core.pc do not run cbindgen.

FFI Layer

Two exposure modes:

Opaque handles (RKRConFrame*): client calls Rust accessors (rkr_frame_get_header_line, …). Hides layout; ABI can evolve behind the handle.
Transparent #[repr(C)] extract (rkr_frame_to_c_frame → CFrame): client owns a flat atom table for hot loops and frees it with free_c_frame.

Specification

See docs/orgmode/spec.org (or the published HTML build) for the full specification. A summary follows.

CON format

A 9-line header (comments, cell dimensions, cell angles, atom type/count/mass metadata)
Line 2 is reserved for spec-v2 JSON metadata
Per-type coordinate blocks (symbol, label, atom lines with x y z fixed atomID)
Optional spec-v2 sections and validate metadata for declared per-atom sections and strict validation
Multiple frames are concatenated directly with no separator

convel format

Same as CON, with an additional velocity section after each frame's coordinates:

A blank separator line
Per-type velocity blocks (symbol, label, atom lines with vx vy vz fixed atomID)

Capabilities

Area	Surface
Payload	Constraints, `atom_id`; optional velocities / forces / energies / charges / spins / magmoms; versioned JSON
Languages	One `rkr_*` surface for Fortran / C / C++ / Python / Julia
Spec	v2–v3, `validate=true`, declared sections (including optional physics blocks above), units (v3)
Tensors	DLPack; optional metatensor `TensorBlock`
Campaigns	`index_proj` + readcon-db (docs · docs.rs)
Import	Optional chemfiles → CON
Measurements	Cachegrind I-refs; PR ASV + spyglass; `benches/compare_readers.py`

Predecessor: readCon.

Citation

If you use readcon-core in academic work, please cite it via the metadata in CITATION.cff. The Zenodo DOI tracks the latest release.

License

MIT.

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readcon 0.14.7

Table of Contents

About

Features

Migrate onto CON

foreign → CON (needs --features chemfiles)

Python (readcon-chemfiles or maturin --features python,chemfiles)

python -c "import readcon; readcon.convert_to_con('structure.xyz','structure.con')"

Install

Tutorial

Design Decisions

FFI Layer

Specification

CON format

convel format

Capabilities

Citation

License

Project links

Key dates

1 maintainer

GitHub Statistics

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BLAKE2b-256	`45a631c709b8fce27e4a1065e6a9c12f4f7641a61221d216a35659aba9480c3b`

Algorithm	Hash digest
SHA256	`86abdd361c7bd68a2f1a19b1d8eedac02a048372f1300ec479639f1289fd0216`
MD5	`2e714628ca2b68f9e90255fe36f9f7b6`
BLAKE2b-256	`bcc48a2efea21c16248895662a2c7c1cf0df7f798cbb396c8937c44b8bda536e`

Algorithm	Hash digest
SHA256	`fd5ecd0bf594003d0f51c7155cef123e7cf532968fad1f988e25537a6c2261bc`
MD5	`6bb3c9d698511c25035a3213de881d89`
BLAKE2b-256	`072c19c182ae7498b713d582bb3e58897eb318d04bcef11da9d4635036842d9e`

Algorithm	Hash digest
SHA256	`2783fa00647755782ebdb2bd2fd79c5165421a3e72d2619b6374fd5ea38477e1`
MD5	`e0ea042fd89f022199e7701dafba5404`
BLAKE2b-256	`c5d646595944b02160f3bacbf22bd131085db0c1c134321ec525324ca01bcce1`

Algorithm	Hash digest
SHA256	`4e0748943019598f60b336aa9d0fae81ae5cccc5c5d9315a8784add501430b9f`
MD5	`2881c9996de9958976eb42d847a1c6fc`
BLAKE2b-256	`3d17604969a017ddb7bd66f67dcefe865c1d474d044d9ff91990a48703e405ab`

Algorithm	Hash digest
SHA256	`ccce6492826d285b02ffb52bcd78c4d5c00c20e0a7c92e21c8da7626bd15a418`
MD5	`d2fa9854f250d9229029ee65a9098b29`
BLAKE2b-256	`12410003ddcbf63c5be96efe59df14dfd15c26b9bd2c446ad14634495915e7a4`

Algorithm	Hash digest
SHA256	`1bf718d49d13976cd07490b124494f35dddc93cf7f69991f0fbba2f1111b1c85`
MD5	`3fc5000a84f19267d2b66362a1f1dfdf`
BLAKE2b-256	`d4ee72f9c32ae3d61306fb99a1b526f285cf13495d982159c2933654d4e183dc`

Algorithm	Hash digest
SHA256	`edd774275ae66b83cd9bef4af3ae3ea5a56796a887ac2547af4c6f2107a7579c`
MD5	`f63274d7004b47690bb279b40988f891`
BLAKE2b-256	`c9866250b7fb0af8554ef59d7ce2ada2e61007613246326f17b8f2e1f6dc82c3`

Algorithm	Hash digest
SHA256	`bf581d3162b2a7ea2c83c3813a9abfddff37d8173c2bf70831f1599b79bafc27`
MD5	`10215ebecbe5492b7a334adce981b9f3`
BLAKE2b-256	`7f5b3a8bbc8333dadb2811e90a1665bf6781e70c4917241e9185e38eea6f10a8`