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 Sep 3, 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

Credits

Author: Kentaro Tanabe (kent-tokyo)

GitHub Statistics

Data verified by PyPI on Sep 3, 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

Provides Extra

test

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-1.0.2.tar.gz (4.1 MB view details)

Uploaded Source

Built Distributions

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

chematic-1.0.2-cp313-cp313-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.13Windows x86-64

chematic-1.0.2-cp313-cp313-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.13macOS 11.0+ ARM64

chematic-1.0.2-cp313-cp313-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.13macOS 10.12+ x86-64

chematic-1.0.2-cp312-cp312-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.12Windows x86-64

chematic-1.0.2-cp312-cp312-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.12macOS 11.0+ ARM64

chematic-1.0.2-cp312-cp312-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.12macOS 10.12+ x86-64

chematic-1.0.2-cp311-cp311-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.11Windows x86-64

chematic-1.0.2-cp311-cp311-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.11macOS 11.0+ ARM64

chematic-1.0.2-cp311-cp311-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.11macOS 10.12+ x86-64

chematic-1.0.2-cp310-cp310-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.10Windows x86-64

chematic-1.0.2-cp310-cp310-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.10macOS 11.0+ ARM64

chematic-1.0.2-cp310-cp310-macosx_10_12_x86_64.whl (4.9 MB view details)

Uploaded CPython 3.10macOS 10.12+ x86-64

chematic-1.0.2-cp39-cp39-win_amd64.whl (4.9 MB view details)

Uploaded CPython 3.9Windows x86-64

chematic-1.0.2-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-1.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl (4.9 MB view details)

Uploaded CPython 3.9manylinux: glibc 2.17+ ARM64

chematic-1.0.2-cp39-cp39-macosx_11_0_arm64.whl (4.7 MB view details)

Uploaded CPython 3.9macOS 11.0+ ARM64

chematic-1.0.2-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-1.0.2.tar.gz.

File metadata

  • Download URL: chematic-1.0.2.tar.gz
  • Upload date:
  • Size: 4.1 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for chematic-1.0.2.tar.gz
Algorithm Hash digest
SHA256 f8fdda2c2460a78adc55e4ae087a48e835478140ac88c4e3c86bf53f25a81139
MD5 3cfdb5fbb00929c93ea5ce1eabf46a11
BLAKE2b-256 e186b2e6d9d130e8c9e2f32f666d232a4ff65590cbbe2e67ed760006acec4110

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2.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-1.0.2-cp313-cp313-win_amd64.whl.

File metadata

  • Download URL: chematic-1.0.2-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-1.0.2-cp313-cp313-win_amd64.whl
Algorithm Hash digest
SHA256 460d2c17191ff6dbcf858fefd63b3bab1a9973abaef6a8357b974f5c1ae24a69
MD5 32827915679061d402c4f56e589e2722
BLAKE2b-256 84dc51c9c9e15e8ed8b08ce3ab5839f7e29bf7707afb4433e82d6018bc8890ce

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp313-cp313-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp313-cp313-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 64a132082a57bf858fb51aa035167677e03aec68651635d95a110ca9c4329076
MD5 d73ad414e4ae8758c3c4292b5b685dfd
BLAKE2b-256 6b6bb1d83de4a32943f95a4aab7d7939b483d714006d724bb5209b96e27bfb6c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp313-cp313-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp313-cp313-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 87a86e2ef1a1f53e5032a52f8928f0b7d0a65a01fecd8ba94f4a73c29a149024
MD5 be76a31628f8c277e3ee5f11189f14d0
BLAKE2b-256 66d0bc0994ff7476c0916b117ddd0b1379a8d127a264046e66876b4672197364

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp312-cp312-win_amd64.whl.

File metadata

  • Download URL: chematic-1.0.2-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-1.0.2-cp312-cp312-win_amd64.whl
Algorithm Hash digest
SHA256 c7c41cc248da8b2958fc51dafce850d11139b8e282aefa61e67e7b8f2350d3b0
MD5 01237ef80e84b4f3e4024040b5bb8e24
BLAKE2b-256 43ddd284634eb3b6c2029eaef401a11bbd27e162d4f6783a2b989f4c0377e7fb

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp312-cp312-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp312-cp312-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 348bbea7db676fcbe4ff37fb13c69ef4ee90cddfdff629ce56f983863bc7cc4f
MD5 bdea4ddddb022a444941e9e87d268ff1
BLAKE2b-256 958e62e1140ec79256eeeadcf0c046e91a8f4782a5fdd670a6c1d03264d4d408

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp312-cp312-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp312-cp312-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 9969005c4292189c4d5ac512cdfb263f7ce9298ad05c659ea99ca6336b980377
MD5 6eae55e8f19755f0324c8b7206e08804
BLAKE2b-256 e7644e2cbbe61a1d27f041dd2cc31ae6e78afa4fb1ef27f0636a3bc2b056d95b

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp311-cp311-win_amd64.whl.

File metadata

  • Download URL: chematic-1.0.2-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-1.0.2-cp311-cp311-win_amd64.whl
Algorithm Hash digest
SHA256 19ca7d5806e3f31e4abb065367635d78670f109dc590d9069bc58ab9a0d5d777
MD5 5712a3cc49df8fb07392f9956e2babbe
BLAKE2b-256 ab867fc395bf8289bf0c94e3838316d547287d721c651ab16b6c1d1633d83f8d

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp311-cp311-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp311-cp311-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 34f03f0157c5f43910ab8d66fbb2b4d9335f9058271de3607d2e7fa94b0ebc1e
MD5 41f06f945e2d49e9cfacfb8060fbb92a
BLAKE2b-256 2a3dc89e6574c3318c25c7f5b6c1d01167c7ea3b37278283f2d3c765ae5f74f5

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp311-cp311-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp311-cp311-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 faa04284daebb4ac442ec1cdb38b88401b67b61ef70f82afdac45259ef1bf749
MD5 d0c08f3faa5e239ee6d8f59288a18d0c
BLAKE2b-256 ebccb09c545d5cdaaabe23f3110c68888e9e4c4874992f45665850df7cd65504

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp310-cp310-win_amd64.whl.

File metadata

  • Download URL: chematic-1.0.2-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-1.0.2-cp310-cp310-win_amd64.whl
Algorithm Hash digest
SHA256 3b47b6df323217a8ddd6f574e908240761491f62f7e9e60e87a29f5e3e9d00e5
MD5 900e86bdc64a0fa67fda98e43bebda3e
BLAKE2b-256 e29f2f3c8ddd38b076a396107f027fd925c9fa083d5f5fce6f4098d14a0a5114

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp310-cp310-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp310-cp310-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 f94da4dd85baefd4ab329ca40868acf974d59d445eb6cd2a84516bfcf1e6656f
MD5 5c3f487f41024831d41da80bfbc1ea4d
BLAKE2b-256 d3b045f1355a75f573c8362929700885c635c0161d1bcefa2b08e0db5ec7d20e

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp310-cp310-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp310-cp310-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 9826b671e775d0a07f79852560147ee894bf856853eef65a454e0548516cee55
MD5 ff2193f685e57f492d354f3f4bae71d0
BLAKE2b-256 8135e07136c276011249f3a75e9f8a014e6c45f27140f5941c6f0db55e403b0a

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp39-cp39-win_amd64.whl.

File metadata

  • Download URL: chematic-1.0.2-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-1.0.2-cp39-cp39-win_amd64.whl
Algorithm Hash digest
SHA256 b183d48048387aa016e726e485bf53ea5f838a1607b793939c5bce1a8a4209cc
MD5 3c57cfce8f6d34e3614d5826019ca7a7
BLAKE2b-256 3761fc688fedfc78567165d5ff703cddbb1490a54c6a47bb8497e399c36fe94c

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl
Algorithm Hash digest
SHA256 6ebb76c46a99457221aa3678203dc8b18e1add408a731713bb96d2e44261f938
MD5 d6b2f5571aeec4fa0137333e910d54d4
BLAKE2b-256 460d09a0e1b1d11e007060aa395d9d7f7397bebc34e72049b47967c0340cf6cc

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
Algorithm Hash digest
SHA256 94f668f0497364410296fd1aa2965589a01fded6f8b37e739e510f4425041b00
MD5 2e42dd0993b101e0f37aec46caf7d793
BLAKE2b-256 b1b6f5e4b7a1fd9faef54b0269a08e122c0d0d7519554862505779d047f49b29

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp39-cp39-macosx_11_0_arm64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp39-cp39-macosx_11_0_arm64.whl
Algorithm Hash digest
SHA256 68160ccab6df5bc2b34c263ab32889f0c0b386eb717fc494fdd8c18aba1c733c
MD5 7f09399dbce12e804004ff23515f122d
BLAKE2b-256 4dcc4815e0ab253beb6fe76f58ae268122e0e11038d2cc533745fe03a9deffa3

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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-1.0.2-cp39-cp39-macosx_10_12_x86_64.whl.

File metadata

File hashes

Hashes for chematic-1.0.2-cp39-cp39-macosx_10_12_x86_64.whl
Algorithm Hash digest
SHA256 a661b425eabadc119b5d122becc5b7edda9936f334d8fa5cf574df10f9129959
MD5 2f3df66298a9c4f1f6170c826dd382ad
BLAKE2b-256 612d74d2737517572de190ce3622204b8262b2ffbeca42a37f38c8d1701a8fdc

See more details on using hashes here.

Provenance

The following attestation bundles were made for chematic-1.0.2-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

This release

1.0.2 This release

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

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