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 migration 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 30, 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 30, 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-0.35.0.tar.gz (4.0 MB view details)

Uploaded Source

Built Distributions

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

chematic-0.35.0-cp313-cp313-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.13Windows x86-64

chematic-0.35.0-cp313-cp313-macosx_11_0_arm64.whl (4.6 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chematic-0.35.0-cp313-cp313-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

chematic-0.35.0-cp312-cp312-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.12Windows x86-64

chematic-0.35.0-cp312-cp312-macosx_11_0_arm64.whl (4.6 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chematic-0.35.0-cp312-cp312-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

chematic-0.35.0-cp311-cp311-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.11Windows x86-64

chematic-0.35.0-cp311-cp311-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

chematic-0.35.0-cp311-cp311-macosx_10_12_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

chematic-0.35.0-cp310-cp310-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.10Windows x86-64

chematic-0.35.0-cp310-cp310-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

chematic-0.35.0-cp310-cp310-macosx_10_12_x86_64.whl (4.8 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

chematic-0.35.0-cp39-cp39-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.9Windows x86-64

chematic-0.35.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (5.1 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

chematic-0.35.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.8 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

chematic-0.35.0-cp39-cp39-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

chematic-0.35.0-cp39-cp39-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.9macOS 10.12+ x86-64

File details

Details for the file chematic-0.35.0.tar.gz.

File metadata

  • Download URL: chematic-0.35.0.tar.gz
  • Upload date:
  • Size: 4.0 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.35.0.tar.gz
Algorithm Hash digest
SHA256 5628ca660062208bf89dd333d0a352a5a42cbc6e255b47fd3d5dd942bde70c39
MD5 221806bc7dcf2c25ae474ef14562fecd
BLAKE2b-256 d30db78212bdad354dff0fe1dc1ae6f523a259d30dd6a664cbb0aeee10cee06c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0.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-0.35.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: chematic-0.35.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 4.9 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-0.35.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 03e4477dd0a716609442ee34580bbcb33799004618be1f28d2909f6bee637443
MD5 093babe5e01b016803c79441782ea372
BLAKE2b-256 fff92eb99565e1353de855b531e9b33fcca5cd9b48c15ea5153e0af9ec617a91

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 89b48567358dc0c57ca3e4f65dec9924786e131b19ab1ee66da5df6cd836ee50
MD5 4304525c8a9ae2f5c7ee40f32bf32828
BLAKE2b-256 bdc9a060d38430898b5e12e4a54e0231ae3769fd7e4f1260d7e624869765e62e

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 cb91ecb3f2667a80dfa08e6dbf6746ef9983b8bcf28af2ff61eee686ea8bb1eb
MD5 05d1fc11e340754f64d28b1475391753
BLAKE2b-256 38d817d087a2902abc0aafa809abbc8794f6762611478f926b87b78b112e1e05

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: chematic-0.35.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 4.9 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-0.35.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 00dd0b43f469618b1babceb6f501bbca122ac50db97549d64a72dcb6992eee45
MD5 d5193a17061ea6fac6b382c27a0011f8
BLAKE2b-256 6d872a089877ee10024418530c1acdd7f260689a4a0b903bd41fc63d09ccf470

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 58abe48dc64f12292616f740c54f7cf4f2713cb432a1e28b4a9395d39d10ff22
MD5 9fa1d1409f93f4cd7ddf2a1aa45dd567
BLAKE2b-256 ea31918eb3de0963d7c4b344c072d2cb454e174bfc3da6879c60ffa0aeeee356

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 f670980adfca25c8132e1f99a712ec0d6c522c57586668d613a7cb45826733b6
MD5 0bae89bfead05d8e649798e896e4c9cf
BLAKE2b-256 460512f040203e94543b3acd15f817d74a7165cfd6c1c1941669e1b74e662f7f

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: chematic-0.35.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 4.9 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-0.35.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 264787a96bcf3cb8fd53fa04b1c0f5b289e3d948c9ccd11cd07bfa3f554b6459
MD5 38ae6f5e5967eb87e78a5695659ca752
BLAKE2b-256 057e1c889c72934ef0de81794fe76f05f40b781e400fc802560dee68328076d9

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 cdfe592cafd43c3266aca6cbb5edb4812f04fa35e0db5e1859d79ada58f87f35
MD5 79fb2b499ad0d0b30ff4e69acf5f88f9
BLAKE2b-256 06996c078b44bb1b5424968e59ad5c973683901b8e43fceb15e2899c1d8040b9

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 c3ad351211f1b5f1f096fe7780031aab3f2790aad1e706f10a69f949ead427c6
MD5 fce49f1086a76575d0c28378486b6365
BLAKE2b-256 2bba1e7ceb43ab35163973a0b3b26d070c32a01252b16c8311c3ead8c45bc149

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: chematic-0.35.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.9 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-0.35.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 10449a9fc484951af066c4cef9c933c10ad0256b6d0c7a8add39c96cf318e5a5
MD5 7c51e76a97d55385a983606a370a950b
BLAKE2b-256 93d61d075f7a082899c9204e55d85800c50826fd987c9ab60095a9aabb57845e

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 6d82079d74b4e2fb701cca8c8dd4ee749e5b6c077c651affcbbaa4e0e9cfd103
MD5 a590860462042114a33fe85d398fc469
BLAKE2b-256 2ffb967faf4ce879cb5cb81133aac9e29a7f2d02349e00f280a840d7a3f3ab1b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 817862fffb5a95d8ab710dacb6deca317a1c620fd3d1dc8d82a49e798741fdb0
MD5 45e66d9feedf9eb549ccb08a859fe2b3
BLAKE2b-256 75e8115a7a4f5aa49f1447f80e9da2c6f43f3410789c5677c4cd0b75ebb52970

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: chematic-0.35.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 4.9 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-0.35.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 719ba5d5cdabdc2342fb0c5cd3c21b5493de3c55b609f0922568148827bd51c9
MD5 3ca37227a61cc3b1db4e5236ea523248
BLAKE2b-256 47bb10437acaf36c1c21bb7e75b540b13af8075992053b193b25a0a93acd818b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 3b90f6a2ddd6d311d5cd78d16bcf7e2080a4f6a07925981323061af6d8751033
MD5 ede22b0e8aa3891d4b257fb48d644868
BLAKE2b-256 55c508cef4183be2cd1264ec26be6f307a98d095011fb6707dd1b9fd78b7491a

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 558d4c9f4ab0fdfab891ac1f7bee89283224ff3ad00fe3a632b96db16e9a258d
MD5 fe073ecc4bf08d8b64e25a02601efe72
BLAKE2b-256 ad46fe791849b8e52a255e7d5232f4c40af59381b2063d06b34e517b55942255

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9767752db094becb4ef53f088287068eb9a3e4061c9b499ac87ddc43b5a7d5a2
MD5 57ae85937bcde16a220b0890de5eb6e7
BLAKE2b-256 e20b419825e1c385adaad70f8565540216390e4da6bb1e57ad046fa8b92a80c1

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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-0.35.0-cp39-cp39-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.35.0-cp39-cp39-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 56979661426d667703210486754759a317f8d2151b734fdf7b5300a867a2d212
MD5 91e86c77e1ff407ff103a32c54a65db6
BLAKE2b-256 4dcd6893574b9b3ff2c1a30ddc2fb94d44638bdb61446fb6b5d4aedca69f1fc3

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.35.0-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

This release

0.35.0 This release

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

0.12.0

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