Python

AsciiChem or SMILES — try something your colleagues useCC(=O)OC1=CC=CC=C1C(=O)O

·Ruby

asciichem

gem install asciichemReference implementation.

·TypeScript

asciichem-ts

npm install asciichemPowers this page.

·Pythonyou are here

asciichem-py

pip install asciichem-pyZero runtime deps.

pip install asciichem-py

Quick start

from asciichem import parse_text, ParseError
from asciichem.text import render
from asciichem.wire import to_model_json, from_model_json
from asciichem.smiles import parse_smiles, write_smiles
from asciichem.mathml import render_mathml

formula = parse_text("2H_2 + O_2 ->[heat] 2H_2O")
print(render(formula))        # canonical AsciiChem text (round-trip)
print(render_mathml(formula))  # presentation MathML (L2 goldens)

aspirin = parse_smiles("CC(=O)OC1=CC=CC=C1C(=O)O")
print(write_smiles(aspirin))  # deterministic — round-trips exactly

# Canonical wire form (asciichem-model v1) with lossless round-trip
wire = to_model_json(formula)
back = from_model_json(wire)
assert render(back) == render(formula)

Claimed levels

Parse / reject · L0 emission · L1 text · L2 MathML · SMILES · molfile — 100% of the shared corpus. See the Implementations matrix for the full table.

What you get here, not elsewhere

The Python implementation is built for data and science workflows where dependencies are liabilities:

• Zero runtime dependencies — jsonschema and pytest are dev-only for the conformance suite. The shipped package is plain stdlib + the model dataclasses.
• Plain dataclasses — the model is @dataclass-shaped, introspectable with standard tools (dataclasses.asdict, pydantic if you must). No framework magic.
• Wire form as the canonical I/O — round-trips via to_model_json/from_model_json for pipelines that already speak JSON.
• Optional asciichem[inchi] extra — RDKit-backed InChI engine when you need to derive identity locally; off by default.
• Three-pane live diff (above) — the parity is provable client-side.

Repository

github.com/asciichem/asciichem-py — zero runtime deps.