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ugropy

A Python library designed to swiftly and effortlessly obtain the UNIFAC-like groups from molecules by their names and subsequently integrate them into inputs for thermodynamic libraries. UNIFAC, PSRK, Joback, and Abdulelah-Gani models are implemented.

https://github.com/ipqa-research/ugropy

Science Score: 62.0%

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Keywords

chemical chemical-engineering compound contribution engineering excess fragmentation functional gibbs group groups grupal joback molecule properties pure python thermodynamics unifac
Last synced: 6 months ago · JSON representation ·

Repository

A Python library designed to swiftly and effortlessly obtain the UNIFAC-like groups from molecules by their names and subsequently integrate them into inputs for thermodynamic libraries. UNIFAC, PSRK, Joback, and Abdulelah-Gani models are implemented.

Basic Info
Statistics
  • Stars: 22
  • Watchers: 3
  • Forks: 2
  • Open Issues: 3
  • Releases: 8
Topics
chemical chemical-engineering compound contribution engineering excess fragmentation functional gibbs group groups grupal joback molecule properties pure python thermodynamics unifac
Created over 2 years ago · Last pushed 7 months ago
Metadata Files
Readme Contributing License Citation

README.md

logo

Open In Colab License Python 3.10+ Docs PyPI version Powered by RDKit

ugropy is a Python library to obtain subgroups from different thermodynamic group contribution models using both the name or the SMILES representation of a molecule. If the name is given, the library uses the PubChemPy library to obtain the SMILES representation from PubChem. In both cases, ugropy uses the RDKit library to search the functional groups in the molecule.

ugropy is tested for Python 3.10, 3.11, 3.12, and 3.13 on Linux, Windows and Mac OS.

You can access the documentation here: https://ipqa-research.github.io/ugropy/

Try ugropy now

You can try ugropy without installing it by clicking on the Colab badge.

You can install ugropy by:

shell pip install ugropy

Models implemented

Gibbs / EoS models

  • Classic liquid-vapor UNIFAC
  • Predictive Soave-Redlich-Kwong (PSRK)
  • Dortmund (modified UNIFAC)

Property estimators

  • Joback
  • Abdulelah-Gani (beta)

Writers

ugropy allows you to convert the obtained functional groups or estimated properties to the input format required by the following thermodynamic libraries:

Example of use

Here is a little taste of ugropy, please, check the full tutorial here to see all it has to offer!

Get groups from the molecule's name:

```python from ugropy import Groups

hexane = Groups("hexane")

print(hexane.unifac.subgroups) print(hexane.psrk.subgroups) print(hexane.dortmund.subgroups) print(hexane.joback.subgroups) print(hexane.agani.primary.subgroups) ```

{'CH3': 2, 'CH2': 4}
{'CH3': 2, 'CH2': 4}
{'CH3': 2, 'CH2': 4}
{'-CH3': 2, '-CH2-': 4}
{'CH3': 2, 'CH2': 4}

Get groups from molecule's SMILES:

```python propanol = Groups("CCCO", "smiles")

print(propanol.unifac.subgroups) print(propanol.psrk.subgroups) print(propanol.dortmund.subgroups) print(propanol.joback.subgroups) print(propanol.agani.primary.subgroups) ```

{'CH3': 1, 'CH2': 2, 'OH': 1}
{'CH3': 1, 'CH2': 2, 'OH': 1}
{'CH3': 1, 'CH2': 2, 'OH (P)': 1}
{'-CH3': 1, '-CH2-': 2, '-OH (alcohol)': 1}
{'CH3': 1, 'CH2': 2, 'OH': 1}

Estimate properties with the Joback and Abdulelah-Gani models!

```python limonene = Groups("limonene")

print(limonene.joback.subgroups) print(f"{limonene.joback.criticaltemperature} K") print(f"{limonene.joback.vaporpressure(176 + 273.15)} bar") ```

{'-CH3': 2, '=CH2': 1, '=C<': 1, 'ring-CH2-': 3, 'ring>CH-': 1, 'ring=CH-': 1, 'ring=C<': 1}
657.4486692170663 kelvin
1.0254019428522743 bar

python print(limonene.agani.primary.subgroups) print(limonene.agani.secondary.subgroups) print(limonene.agani.tertiary.subgroups) print(f"{limonene.agani.critical_temperature}") print(limonene.agani.molecular_weight / limonene.agani.liquid_molar_volume) 

{'CH3': 2, 'CH2=C': 1, 'CH2 (cyclic)': 3, 'CH (cyclic)': 1, 'CH=C (cyclic)': 1}
{'CH3-CHm=CHn (m,n in 0..2)': 1, '(CHn=C)cyc-CH3 (n in 0..2)': 1, 'CHcyc-C=CHn (n in 1..2)': 1}
{}
640.1457030826214 kelvin
834.8700605718585 gram / liter

Visualize your results! (The next code creates the ugropy logo)

```Python mol = Groups("CCCC1=C(COC(C)(C)COC(=O)OCC)C=C(CC2=CC=CC=C2)C=C1", "smiles")

mol.unifac.draw( title="ugropy", width=800, height=450, titlefontsize=50, legendfontsize=14 ) ```

ugropy can obtain multiple solutions, even nonoptimal ones if desired. For example:

```python from ugropy import unifac

solutions = unifac.getgroups( "9,10-dihydroanthracene", searchmultiplesolutions=True, searchnonoptimal=True )

for sol in solutions: print(sol.subgroups) ```

{'ACH': 8, 'AC': 2, 'ACCH2': 2} {'CH2': 1, 'ACH': 8, 'AC': 3, 'ACCH2': 1} {'CH2': 2, 'ACH': 8, 'AC': 4}

Write down the Clapeyron.jl .csv input files.

```python from ugropy import writers

names = ["limonene", "adrenaline", "Trinitrotoluene"]

grps = [Groups(n) for n in names]

Write the csv files into a database directory

writers.toclapeyron( moleculesnames=names, unifacgroups=[g.unifac.subgroups for g in grps], psrkgroups=[g.psrk.subgroups for g in grps], joback_objects=[g.joback for g in grps], path="database" ) ``` Obtain the Caleb Bell's Thermo subgroups

```python from ugropy import unifac

names = ["hexane", "ethanol"]

grps = [unifac.get_groups(n) for n in names]

[writers.to_thermo(g.subgroups, unifac) for g in grps] ```

[{1: 2, 2: 4}, {1: 1, 2: 1, 14: 1}]

Owner

  • Name: IPQA research
  • Login: ipqa-research
  • Kind: organization
  • Location: Córdoba, Argentina

Citation (CITATION.cff) 

cff-version: 1.2.0
title: "Ugropy: An Extensible Python Package for Thermodynamic Model’s Functional Groups Identification via ILP Minimization"
message: "If you use this software, please cite it as below."
authors:
  - family-names: Brandolín
    given-names: Salvador Eduardo
    orcid: https://orcid.org/0000-0002-8255-6322
  - family-names: Benelli
    given-names: Federico Ezequiel
    orcid: https://orcid.org/0009-0002-0072-815X
  - family-names: Magario
    given-names: Ivana
    orcid: https://orcid.org/0000-0001-7909-4131
  - family-names: Scilipoti
    given-names: José Antonio
version: 3.1.0
repository-code: 'https://github.com/ipqa-research/ugropy'
url: 'https://ipqa-research.github.io/ugropy/'
license: MIT

GitHub Events

Total
  • Create event: 10
  • Release event: 3
  • Issues event: 4
  • Watch event: 5
  • Delete event: 6
  • Issue comment event: 5
  • Push event: 66
  • Pull request review comment event: 1
  • Pull request review event: 5
  • Pull request event: 16
Last Year
  • Create event: 10
  • Release event: 3
  • Issues event: 4
  • Watch event: 5
  • Delete event: 6
  • Issue comment event: 5
  • Push event: 66
  • Pull request review comment event: 1
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  • Pull request event: 16

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 183
  • Total Committers: 4
  • Avg Commits per committer: 45.75
  • Development Distribution Score (DDS): 0.366
Past Year
  • Commits: 122
  • Committers: 4
  • Avg Commits per committer: 30.5
  • Development Distribution Score (DDS): 0.328
Top Committers
Name Email Commits
salvador s****n@m****r 116
salvadorbrandolin s****n@g****m 58
Salvador Brandolin 8****n 6
Federico E. Benelli f****i@o****m 3
Committer Domains (Top 20 + Academic)
mi.unc.edu.ar: 1

Issues and Pull Requests

Last synced: 6 months ago

All Time
  • Total issues: 7
  • Total pull requests: 30
  • Average time to close issues: about 2 months
  • Average time to close pull requests: 10 minutes
  • Total issue authors: 2
  • Total pull request authors: 2
  • Average comments per issue: 0.71
  • Average comments per pull request: 0.17
  • Merged pull requests: 30
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 12
  • Average time to close issues: about 13 hours
  • Average time to close pull requests: 21 minutes
  • Issue authors: 1
  • Pull request authors: 1
  • Average comments per issue: 2.0
  • Average comments per pull request: 0.33
  • Merged pull requests: 12
  • Bot issues: 0
  • Bot pull requests: 0
View more stats
Top Authors
Issue Authors
  • SalvadorBrandolin (6)
  • fedebenelli (2)
Pull Request Authors
  • SalvadorBrandolin (40)
  • fedebenelli (3)
Top Labels
Issue Labels
enhancement (7) help wanted (1) good first issue (1) bug (1)
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 433 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 10
  • Total maintainers: 1
pypi.org: ugropy

Get UNIFAC functional groups of PubChem compounds or SMILES representation.

  • Versions: 10
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 433 Last month
Rankings
Dependent packages count: 10.1%
Average: 38.4%
Dependent repos count: 66.6%
Maintainers (1)
SalvadorBrandolin
Last synced: 6 months ago

Dependencies

.github/workflows/ci.yml actions
  • actions/checkout master composite
  • actions/setup-python v2 composite
pyproject.toml pypi
  • numpy >= 1.25.1
  • pandas >= 2.0.3
  • pubchempy == 1.0.4
  • rdkit == 2023.3.2
requirements-dev.txt pypi
  • check-manifest * development
  • chemspipy ==2.0.0 development
  • commitizen * development
  • flake8-black * development
  • flake8-builtins * development
  • flake8-import-order * development
  • flake8-nb * development
  • ipdb * development
  • ipython * development
  • jupyter * development
  • matplotlib * development
  • nbsphinx * development
  • numpy >=1.25.1 development
  • pandas >=2.0.3 development
  • pep8-naming * development
  • pubchempy ==1.0.4 development
  • pydocstyle * development
  • pytest * development
  • pytest-cov * development
  • rdkit ==2023.3.2 development
  • sphinx * development
  • sphinx_copybutton * development
  • sphinx_rtd_theme * development
  • tox * development
requirements.txt pypi
  • numpy >=1.25.1
  • pandas >=2.0.3
  • pubchempy ==1.0.4
  • rdkit ==2023.3.2
docs/requirements.txt pypi
  • Sphinx *
  • ipykernel *
  • ipython *
  • matplotlib *
  • nbsphinx *
  • sphinx_copybutton *
  • sphinx_rtd_theme *
  • sphinxcontrib-bibtex *