chematic

Pure-Rust cheminformatics library for Python — SMILES parsing, 190+ descriptor values (71 functions), fingerprints, pKa prediction, ADMET profiling, and template-based retrosynthesis.

Installation

pip install chematic

Quick Start

import chematic

mol = chematic.from_smiles("CC(=O)Oc1ccccc1C(=O)O")  # aspirin

print(mol.mw)      # 180.16
print(mol.logp)    # 1.31
print(mol.tpsa)    # 63.6
print(mol.qed)     # 0.55

# New descriptors
print(mol.vabc)              # van der Waals volume (no 3D needed)
print(mol.schultz_mti)       # Schultz MTI
print(mol.gutman_mti)        # Gutman MTI*
print(mol.gravitational_index)  # gravitational index

# pKa prediction
print(mol.pka())   # {"most_acidic": 3.49, "most_basic": None}

# ADMET profile
print(mol.admet())
# {"bbb": False, "bbb_score": ..., "caco2": ..., "herg_risk": ..., "cyp3a4_risk": ...}

# Fingerprints (bytes, 2048-bit ECFP4)
fp = mol.ecfp4()

# Tanimoto similarity
mol2 = chematic.from_smiles("c1ccccc1")
sim = chematic.tanimoto(mol.ecfp4(), mol2.ecfp4())

# Natural-language property summary (for LLM / MCP agents)
print(mol.describe())

# Structural diff between two molecules
ibuprofen = chematic.from_smiles("CC(C)Cc1ccc(CC(C)C(=O)O)cc1")
d = mol.diff(ibuprofen)  # {"summary": "...", "delta_mw": 66.1, "delta_logp": 2.75, ...}

# SVG / PDF / EPS depiction
svg = mol.to_svg()
pdf_bytes = mol.to_pdf()   # bytes; requires pdf feature
eps_str   = mol.to_eps()   # PostScript string

# ChemicalJSON (Avogadro 2 / MolSSI)
cjson_str = mol.to_cjson(coords=[])   # coords: list of (x,y,z) tuples, optional
mol2, coords = chematic.from_cjson(cjson_str)

# Template-based retrosynthesis (60 retro-SMIRKS templates)
mol3 = chematic.from_smiles("CC(=O)Nc1ccccc1")  # acetanilide
results = mol3.retro_disconnect(max_results=5)
for r in results:
    print(r["template"], "→", r["precursors"])
# amide_secondary → ['CC(=O)O', 'Nc1ccccc1']

# Filter by reaction class
amides = mol3.retro_disconnect(reaction_class="AmideBond")

# Bulk substructure match against a pre-parsed Mol list (returns indices)
mols = [chematic.from_smiles(s) for s in ["CCO", "c1ccccc1O", "CC(=O)O"]]
hits = chematic.bulk.substructure_match("[OH]", mols)  # → [0, 1, 2]

# All descriptors as a dict (for Pandas)
import pandas as pd
smiles = ["CCO", "c1ccccc1", "CC(=O)O"]
df = pd.DataFrame([chematic.from_smiles(s).descriptors() for s in smiles])

# Opt-in v2 embedding pipeline: torsion-knowledge-aware distance geometry +
# stereo verification/repair + policy-gated force field, with full per-stage
# evidence (never just final coordinates)
config = chematic.PipelineV2Config.safe(
    force_field="mmff94_with_uff_fallback",
    stereo_policy="repair_and_verify",
    ring_torsion_policy="fail_closed",
)
try:
    result = mol.embed_pipeline_v2(config)
    coords = result["coords"]                       # same atom order as mol
    print(result["force_field"]["actual_force_field_used"])  # fallback if MMFF94 lacked params
    print(result["final_validation"]["sound"])
except chematic.PipelineV2Error as e:
    print(e.diagnostics["stage"], e.diagnostics["cause"])   # structured, not just a message

Features

  • Zero C/C++ dependencies — pure Rust, no RDKit or OpenBabel required
  • SMILES / MOL / SDF / ChemicalJSON parsing and writing
  • 190+ descriptor values (71 functions; MQN returns 42 values, BCUT2D / autocorr2d / geary / moran return multi-value arrays): MW, LogP (±0.01, 96.5% of 4,999-mol ChEMBL subset), TPSA (±0.1 Ų, 98.1%), QED, Fsp3, SA Score, HBD (100% vs RDKit, incl. S-H), vabc, schultz_mti, gutman_mti, gravitational_index
  • 14 fingerprint algorithms: ECFP2/4/6, FCFP4/6, MACCS, AtomPair, Torsion, …
  • pKa prediction (15 SMARTS rules — unique to chematic)
  • ADMET profile: BBB, Caco-2, hERG, CYP3A4
  • Template-based retrosynthesis: mol.retro_disconnect() — 60 retro-SMIRKS templates, SA Score ranked
  • SMARTS substructure search — chematic.smarts_match() and bulk.substructure_match(smarts, mols) (pre-parsed Mol list, returns indices)
  • SVG / PDF / EPS depiction: mol.to_svg(), mol.to_pdf(), mol.to_eps()
  • ChemicalJSON: mol.to_cjson(coords=[]), chematic.from_cjson(s) — Avogadro 2 / MolSSI compatible
  • Opt-in v2 embedding pipeline: mol.embed_pipeline_v2(config) — torsion-knowledge-aware distance geometry, stereo verify/repair, and policy-gated force field (PipelineV2Config), returning full per-stage evidence (embed stats, torsion knowledge/optimization reports, stereo before/after, force-field actual policy and fallback, final geometry validation, stage timings) instead of just coordinates; raises chematic.PipelineV2Error with structured .diagnostics on failure

RDKit compatibility

chematic.rdkit_compat provides a lightweight RDKit-compatible subset for environments where RDKit is unavailable (WASM, serverless, conda-free CI):

from chematic import rdkit_compat as Chem
from chematic.rdkit_compat import Descriptors, rdMolDescriptors, DataStructs

mol = Chem.MolFromSmiles("CC(=O)Oc1ccccc1C(=O)O")

# Descriptors
Descriptors.MolWt(mol)          # 180.16
rdMolDescriptors.CalcTPSA(mol)  # 63.6

# Fingerprint (ExplicitBitVect) with bitInfo
bitInfo = {}
fp = rdMolDescriptors.GetMorganFingerprintAsBitVect(mol, 2, nBits=2048, bitInfo=bitInfo)
fp.GetNumBits()                         # 2048
bitInfo                                 # {bit: ((atom_idx, radius), ...)}
DataStructs.TanimotoSimilarity(fp, fp)  # 1.0
DataStructs.BulkTanimotoSimilarity(fp, [fp])  # [1.0]

import numpy as np
arr = DataStructs.ConvertToNumpyArray(fp)  # (2048,) int8 for sklearn / PyTorch

# Atom / Bond traversal
for atom in mol.GetAtoms():
    atom.GetSymbol(), atom.GetAtomicNum(), atom.IsInRing()
for bond in mol.GetBonds():
    bond.GetBondType(), bond.GetBondTypeAsDouble(), bond.IsInRing()

# Ring information
ri = mol.GetRingInfo()
ri.NumRings()       # 1
ri.AtomRings()      # tuple of tuples of atom indices
ri.NumAtomRings(0)  # rings containing atom 0

# SDF I/O with SD properties
with Chem.SDWriter("out.sdf") as w:
    mol.SetProp("ID", "aspirin")
    w.write(mol)
for m in Chem.SDMolSupplier("out.sdf"):
    print(m.GetProp("ID"))

Unsupported options raise NotImplementedError or TypeError — they are never silently ignored.

Compatibility matrix

Area Status Notes
SMILES I/O ✅ Supported MolFromSmiles (aromaticity perceived when sanitize=True) / MolToSmiles
SDF I/O ✅ Supported SDMolSupplier / SDWriter + SD properties
Mol properties ✅ Supported Get/Set/Has/ClearProp, typed setters, GetPropsAsDict
Mol / Atom / Bond ✅ Supported read-only traversal (GetAtoms/GetBonds/GetAtomWithIdx/…)
RingInfo ✅ Supported SSSR-based; NumRings/AtomRings/BondRings/NumAtomRings/NumBondRings
Substructure 🟡 Partial SMARTS via chematic; match order may differ from RDKit (use set comparison)
Descriptors ✅ Supported MW/HBA/HBD exact, TPSA ±1.0, LogP ±0.5 vs RDKit (differential-tested)
Morgan fingerprint 🟡 Partial nBits folding + bitInfo shape-/origin-consistent, not RDKit bit-identical (FNV-1a vs MurmurHash)
DataStructs ✅ Supported TanimotoSimilarity/DiceSimilarity/BulkTanimotoSimilarity/ConvertToNumpyArray
RWMol / editing ❌ Unsupported read-only layer
useFeatures, useBondTypes=False 🔊 Fails loudly raise NotImplementedError instead of silently ignoring

A live differential suite (tests/test_rdkit_diff.py, auto-skipped when RDKit is absent) compares chematic against RDKit across descriptors, ring counts, SMARTS match counts, SDF round-trips, and Morgan self-similarity, writing an explainable diff to validation/results/rdkit_diff.jsonl.

chematic.rdkit_compat is not a full RDKit clone — it is a lightweight RDKit-compatible subset for common 2D cheminformatics workflows. See the full RDKit compatibility guide (compatibility matrix, differential-validation results, known divergences, and runnable examples).

License

MIT OR Apache-2.0

Project links

Data verified by PyPI on Aug 9, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.0.5)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for kent.tokyo from gravatar.com kent.tokyo

GitHub Statistics

Data verified by PyPI on Aug 9, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License, MIT License (MIT OR Apache-2.0)

Requires

Python >=3.9

Tags

cheminformatics smiles chemistry rdkit fingerprints drug-discovery molecular-descriptors wasm admet qed

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chematic-0.12.0.tar.gz (3.3 MB view details)

Uploaded Source

Built Distributions

If you're not sure about the file name format, learn more about wheel file names.

chematic-0.12.0-cp313-cp313-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.13Windows x86-64

chematic-0.12.0-cp313-cp313-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chematic-0.12.0-cp313-cp313-macosx_10_12_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

chematic-0.12.0-cp312-cp312-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.12Windows x86-64

chematic-0.12.0-cp312-cp312-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chematic-0.12.0-cp312-cp312-macosx_10_12_x86_64.whl (4.3 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

chematic-0.12.0-cp311-cp311-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.11Windows x86-64

chematic-0.12.0-cp311-cp311-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

chematic-0.12.0-cp311-cp311-macosx_10_12_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

chematic-0.12.0-cp310-cp310-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.10Windows x86-64

chematic-0.12.0-cp310-cp310-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

chematic-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

chematic-0.12.0-cp39-cp39-win_amd64.whl (4.2 MB view details)

Uploaded CPython 3.9Windows x86-64

chematic-0.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.4 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

chematic-0.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.3 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

chematic-0.12.0-cp39-cp39-macosx_11_0_arm64.whl (4.1 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

chematic-0.12.0-cp39-cp39-macosx_10_12_x86_64.whl (4.2 MB view details)

Uploaded CPython 3.9macOS 10.12+ x86-64

File details

Details for the file chematic-0.12.0.tar.gz.

File metadata

  • Download URL: chematic-0.12.0.tar.gz
  • Upload date:
  • Size: 3.3 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0.tar.gz
Algorithm Hash digest
SHA256 ec41bb572d67d6cbf9cd2a5288f2dfeef3bad5bccb8b957c241bceb279422636
MD5 472b6d8850b03ad6bccd8863ed265fc9
BLAKE2b-256 ff7230df0ef7804be3a26bbdff57fce1eb14be6ec34c7de15c024c0751077fe2

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0.tar.gz:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: chematic-0.12.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.13, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 468c681dca5f34992f3726439e2a4d1361d9442c6dc6d88b8504823c18cdf09c
MD5 a163725a9045f47da5907af8028c6e82
BLAKE2b-256 c160bdc0051c3a38c9b8c677ca01708b4ce212a457c94441a796256eebd6ed95

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp313-cp313-win_amd64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 0bc0e72e724844ac34fe06c3ed4df759f2ef04fb99842dd1a198fb345220f0c5
MD5 74d2f9d7f314c4458a5da277ab6d5ad3
BLAKE2b-256 3bc8b58d9151c7ee85163be30dbff6b96243721c9a30db1d6d298fd4d2cf53c6

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp313-cp313-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 dc6da3226b30744a28e7ce8a2dd515ad720799b5967b57907b15f07c5479503e
MD5 ec62a2a116ee84e5fe3703412885a42c
BLAKE2b-256 b5ff4b23c3e9c1eb657caf240c68fa23041a3f0c980ff08be78b3d835109a66b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp313-cp313-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: chematic-0.12.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.12, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 906cf81274537e59e1b1990b9e95c4bce336a9825262d356d86e00c838f11515
MD5 550831b2dbb9360b1f9b2e1a17f0f1ee
BLAKE2b-256 a4f411762d888a93621ac02854bb3cec79d4d2306fe54799a60eacb6632f0bc5

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp312-cp312-win_amd64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 08bcb7166b5be0ccfcef375df01e20cd8d27df5f1b78bccbd1ac18a14e2d6948
MD5 757376c145b728e74d4c7d85b4d0546e
BLAKE2b-256 998572a893ad978ec3139350e29c2f9e4a1fca6d212038c71afa0b064fe5cefb

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp312-cp312-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 bbd0fc2f18eba286f062e5f1eda3fba30768bef80d2c2c072e3e9080e4fd152b
MD5 d1e0f265b672fa8b89fb919098f9b2b9
BLAKE2b-256 f2651732593c80e0fd3467b44a938b205333b17b6cd490ac56b8d2c41503a99b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp312-cp312-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: chematic-0.12.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.11, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 3a48ee42166b83cbc5b770f091ddabfcc2e8fd44bfeeada12afb3d9caefedbc3
MD5 57c1a77b6a0551e41760b6ffb70dc647
BLAKE2b-256 5ff414a0034ba371ae66bd9dbd3b76e5457d83b1c32610a017e0b5c8a63c718c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp311-cp311-win_amd64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 ce42dd6f2e3668959c81fe687b569c0c9c293a61ff1ddac7c1fa7d5c20d71231
MD5 0d997a453649e43dfbd8fb9482cc37da
BLAKE2b-256 c54f227400dfec27afbde442816f04ce234a368f81b8d6d87f3611e6671980d1

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp311-cp311-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 9b1519e4d7887971a345934323c9ff728b7319ce61fff64d6e3c2375da47cf08
MD5 45c05d99888a688ae06fd0e510de0d54
BLAKE2b-256 e6992d84b9cba7f456fbef4e71ce14a1a6d51fc80eb6d7c32654e776ed2d00f4

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp311-cp311-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: chematic-0.12.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.10, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 2be74b0c47fa1f87b66f2e85709b79f8cb6d31e665f17e2000b4d83f249942f5
MD5 3fdc01becb892cf9d24f2841f7e22b0f
BLAKE2b-256 4c2d6e76853bf56aa70d0d578e3c6b3a5174dce062f1640345effe9115e03eff

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp310-cp310-win_amd64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 22f2df494e3618034b7966881b9c88a4b373de8fa711161d6bdfe3a09839e3c9
MD5 5f6d1f5a6000ad5bd69da23946b3fb5f
BLAKE2b-256 fdb0ecdb85d1175418c25abcad1d17f49505e881845de58cd3b79ca14de93c0c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp310-cp310-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 d8d9a3231a929c73b0e98ccc3863037c850394e414effed1efbc60217c1f4784
MD5 1a5d2a58f69bcb617806469f9b2886ee
BLAKE2b-256 0d58d6fafa0c7ccfd85c29acdebefec80490cae1cbc4be1418fa7355fa96189e

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp310-cp310-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: chematic-0.12.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 4.2 MB
  • Tags: CPython 3.9, Windows x86-64
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.12.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 3b0226cddb584eb1a23e7195fb5bfe49a6541c9fc319bd0bf06ea41a96b48b0e
MD5 9bf2140172e42450cf994776e781cff7
BLAKE2b-256 6fcb5bbdc5bbfcda8ebed584ac5ac3aa4210e9c1cff111c7d0dca98d29732ff3

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp39-cp39-win_amd64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 1f0e5c0675fea8af5d09ae266fecc3f36395d6238a74b8fea49534e226e89e18
MD5 d38fa8ada72bed45c6d0576fe5224b04
BLAKE2b-256 1b97aa364ab9564f82f63d1e2a5b93f865a29f65c13c577f11dfc86b54f63653

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 a952ce675c3f16a7f6f007c872157d2d66fc6b29ce461d42b3a0c796eed41382
MD5 f523116b4fa099cc87e82d0c9b1d3a69
BLAKE2b-256 eca52896c996c9b9a6911530d5aef53e30b81067a4567d88516c0de186149e2d

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 a91c7497a3cdaa634f8169fae7a72a8d6bf454f9011bdb0f2f291e21e8130cd8
MD5 d9a778f4ee6598e195dbc30f9beacb0b
BLAKE2b-256 153acc28224f623d8a06a801db07008b69db423dcaa4fe49f322d4550836672b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp39-cp39-macosx_11_0_arm64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file chematic-0.12.0-cp39-cp39-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.12.0-cp39-cp39-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 99d21546eef9927a359d924107f7d5b440fb4509b10f9ce8f52edf6045df3b05
MD5 7fc1ae9d0914af95d7aac651f6ecee62
BLAKE2b-256 181f85d3767360e52b3206be62c1e4d30bdfc85f6c9caf8b83cbbfd3a8ebb471

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.12.0-cp39-cp39-macosx_10_12_x86_64.whl:

Publisher: publish-pypi.yml on kent-tokyo/chematic

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

1.0.5

18 files

1.0.4

18 files

1.0.3

18 files

1.0.2

18 files

1.0.1

18 files

1.0.0

18 files

0.89.0

18 files

0.49.0

18 files

0.48.0

18 files

0.47.0

18 files

0.46.0

18 files

0.45.0

18 files

0.44.0

18 files

0.43.0

18 files

0.42.0

18 files

0.41.0

18 files

0.40.0

18 files

0.39.0

18 files

0.38.0

18 files

0.37.0

18 files

0.36.0

18 files

0.35.0

18 files

0.34.0

18 files

0.33.0

18 files

0.31.0

18 files

0.30.0

18 files

0.29.0

18 files

0.28.0

18 files

0.27.0

18 files

0.26.0

18 files

0.25.0

18 files

0.24.0

18 files

0.23.0

18 files

0.22.0

18 files

0.21.0

18 files

0.20.1

18 files

0.20.0

18 files

0.19.0

18 files

0.18.0

18 files

0.17.0

18 files

0.16.0

18 files

0.15.0

18 files

0.14.1

18 files

0.14.0

18 files

0.13.0

18 files

This release

0.12.0 This release

18 files

0.11.0

18 files

0.10.0

18 files

0.9.0

18 files

0.8.1

18 files

0.8.0

18 files

0.7.0

18 files

0.6.0

18 files

0.5.0

18 files

0.4.30

18 files

0.4.29

18 files

0.4.28

18 files

0.4.22

18 files

0.4.21

18 files

0.4.20

18 files

0.4.19

18 files

0.4.18

18 files

0.4.17

18 files

0.4.16

18 files

0.4.15

18 files

0.4.14

18 files

0.4.9

18 files

0.4.8

18 files

0.4.7

18 files

0.4.6

6 files

0.4.5

6 files

0.4.4

6 files

0.4.0

6 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page