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 23, 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 23, 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-0.19.0.tar.gz (3.8 MB view details)

Uploaded Source

Built Distributions

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

chematic-0.19.0-cp313-cp313-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.13Windows x86-64

chematic-0.19.0-cp313-cp313-macosx_11_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chematic-0.19.0-cp313-cp313-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

chematic-0.19.0-cp312-cp312-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.12Windows x86-64

chematic-0.19.0-cp312-cp312-macosx_11_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chematic-0.19.0-cp312-cp312-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

chematic-0.19.0-cp311-cp311-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.11Windows x86-64

chematic-0.19.0-cp311-cp311-macosx_11_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

chematic-0.19.0-cp311-cp311-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

chematic-0.19.0-cp310-cp310-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.10Windows x86-64

chematic-0.19.0-cp310-cp310-macosx_11_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

chematic-0.19.0-cp310-cp310-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

chematic-0.19.0-cp39-cp39-win_amd64.whl (4.7 MB view details)

Uploaded CPython 3.9Windows x86-64

chematic-0.19.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ x86-64

chematic-0.19.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.7 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

chematic-0.19.0-cp39-cp39-macosx_11_0_arm64.whl (4.5 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

chematic-0.19.0-cp39-cp39-macosx_10_12_x86_64.whl (4.7 MB view details)

Uploaded CPython 3.9macOS 10.12+ x86-64

File details

Details for the file chematic-0.19.0.tar.gz.

File metadata

  • Download URL: chematic-0.19.0.tar.gz
  • Upload date:
  • Size: 3.8 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-0.19.0.tar.gz
Algorithm Hash digest
SHA256 308ae1be9a52ea49dadef48ce3cd726cbd829814ad39bdba42c07ff451ed4661
MD5 c1cfd724ef52b0d027267d61874f9d0b
BLAKE2b-256 bce941a72eebd6a883294480de2a0a5418f85ce405dc949ac44be955670e4455

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0.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-0.19.0-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: chematic-0.19.0-cp313-cp313-win_amd64.whl
  • Upload date:
  • Size: 4.7 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-0.19.0-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 f11d97e6282bb6710786891bb58eb34c9dfa38f7e36aafdbc78155497ad91cbf
MD5 7b5f7582cac6d32dd371349121edf71d
BLAKE2b-256 10a4b21a3a2911b8b397e0dcb47e3992cba5f16053a26f7156b891eafcce21c4

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 9fa50b9042058573608b66d55d2dcbb8f5ab3ec6325073fd24b65061400a6495
MD5 ef7ce734026b0cebdc85b8d0ff27c744
BLAKE2b-256 9ef368722b9be9a819cd5bfa82911e43c100461cffee238ace43e7a063e64ef0

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 15b78a30cc5752561090c442e2e21decc178869197913887394fa91452de49b5
MD5 de681ebdc76edbc6f7e99012c730c58a
BLAKE2b-256 939977f114e478f91c76e37ea6386135fa6821dca4e4b3f3e31b5fca2abca130

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: chematic-0.19.0-cp312-cp312-win_amd64.whl
  • Upload date:
  • Size: 4.7 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-0.19.0-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 44372fbbc1fa0eef4acc5255f49c471532fe68283f459d12fff92627ab8f1b16
MD5 04adc679cf0ec547cd79ee3ff690444c
BLAKE2b-256 269eed4c31be81810d024927a070e9bf1f2efdc41b47376784b334c0c2c3f454

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 60cfc706133ab625f93e3a3ff0a6a103adeedfeaa719452b58619a329a09c2b4
MD5 42c1cb4ce5872ef01ad77c70f92f0713
BLAKE2b-256 0852b5a12e6dc68b1bdf8e6c8a9bde100589a8a0a8f3f4ed23306fb9b8105406

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 5810dc10037549a8e3ca58caa1f5c5ae47eb494912db2b09b807ddd4866b6f65
MD5 df9536ecc8806f4bc792da162abe8007
BLAKE2b-256 97c726b860f6e7b1cbf4794df6fe0c5df3878c5448d0bee6b3e9d62c011c28a2

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: chematic-0.19.0-cp311-cp311-win_amd64.whl
  • Upload date:
  • Size: 4.7 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-0.19.0-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 e675e8fddab5c081aecee2df4544f0a07009921e075033fe709827be3bd3888a
MD5 a045de226e8e005f361f9822af4c3dd5
BLAKE2b-256 ffd6efe8f12f0edf912363ed54bccf6550663b3c54a7c1c15cf02f4b6dee9cac

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 397507dcc17dfe3a43b423392d752e165f4cc75cfb38a8f430660a02af31ff21
MD5 ee1f93dcf67cb5e28831e3420d419753
BLAKE2b-256 8d93616bc037c0bb3fe32b716216eed03d97069553637c9ed413a649e2805d0b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 ebada2ebd4a929d9fdecece55c43fd89f27d4d231de6f168aeb6da6927b069df
MD5 803fbb8bf9b2e4c7df867bfdf512c9f9
BLAKE2b-256 ce3a9d61f92f1555a8f645e3b802a9f98fd2f2b550dea521f9ea13e1e1de4f2d

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: chematic-0.19.0-cp310-cp310-win_amd64.whl
  • Upload date:
  • Size: 4.7 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-0.19.0-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 ffaf4ceb2938078fa412bf77d6018af214d28e7577abb077e9019c940f767536
MD5 f65ce5545249a39ed74f28f03d6dec41
BLAKE2b-256 46087d731ea25a6bb2e2e73a096e402117faa2d898f06cfa4033eca30754acf3

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 1cd0a50700c60d05e23df9e0041ff11fc8b14092310257a86a0295b0f828d290
MD5 8abd02ec78a7ed3cba66ff7915002c13
BLAKE2b-256 bbf51e3436c0486751dceef0d6bd5e026ee80eefcd250cf9365d4961c0bb0701

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 ac3ef74f6e8956fca6804ba8911695fa54e9bf68f5306475ca9f4c04b5820111
MD5 7e3dcf5345e936ccbfbfe928b361156a
BLAKE2b-256 665e21561d00299a0fede4efa8879c2352354da3221ff772c79285cfabdaecee

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: chematic-0.19.0-cp39-cp39-win_amd64.whl
  • Upload date:
  • Size: 4.7 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-0.19.0-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 54244840e049ff3b7e3c25d108901d5a3667c5093fe5bbb316abb4b0af1a6a9a
MD5 415c4c9e5fd7fbc9e3cf270c5200de3c
BLAKE2b-256 1cc37328007d2868010de696004bbf96dbf7baed73a6056908070aaa72ec57ab

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 2b2b4acdf169ecc6a71a1f607d9ee734465a3bfe6431f90b16ec5ab70fada4f3
MD5 5048a1337a774f46bff9a897b46f55ce
BLAKE2b-256 94fc77575e504c45babff9b644de979cdbea2375ba2b3a4a10d89f3699f8ecb4

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 fbc153090e637fd6947ac092db52491ab17e210e70f9fcce43c00b76ea6659cd
MD5 ea4f7cac1c8988d005f4c449f43636ae
BLAKE2b-256 c68368403e3017799989e640aea1239881dd96ef4b044a5e2313961645b595e2

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 3aa2e132d9bf21703247cf909bd0892b92099dc6d1c9e45866dcd655e55f1f97
MD5 db42e9285af1b99a37e67daaad0389b5
BLAKE2b-256 b453d00373ba6d007e33b31c07b363fe3f5a6b24381bc95536c7ddafd2e920f6

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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-0.19.0-cp39-cp39-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-0.19.0-cp39-cp39-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 ffae208163e8a27bc56bdabc51215749bbbc0d7ded786669f5288e2b0e772988
MD5 534633463bc687f500c13ed3e7e7e54d
BLAKE2b-256 adbd03f5fac19ad5733849d89f063f99cf47904475cd6fc28431670c438ac7d0

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-0.19.0-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

This release

0.19.0 This release

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