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https://github.com/cadet/cadet-hd-chromoo

https://github.com/cadet/cadet-hd-chromoo

Science Score: 46.0%

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    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
    Found 2 DOI reference(s) in README
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    Links to: zenodo.org
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    1 of 2 committers (50.0%) from academic institutions
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    Low similarity (13.9%) to scientific vocabulary
Last synced: 6 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: cadet
  • License: gpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 11.2 MB
Statistics
  • Stars: 0
  • Watchers: 5
  • Forks: 0
  • Open Issues: 0
  • Releases: 2
Created almost 3 years ago · Last pushed 9 months ago
Metadata Files
Readme License

README.md

chromoo

DOI

chromoo is a chromatography multi-objective optimization tool built on Cadet-Core and pymoo==0.5.

Installation

```

Install cadet. This can be done via conda as below

or directly from source https://github.com/modsim/CADET

conda config --add channels conda-forge conda config --set channel_priority strict conda install cadet

Install python dependencies

pip install -r requirements.txt

Install this package. Use -e for an editable install.

pip install [-e] . ```

Usage

Chromoo requires a YAML config file of the following form.

yaml filename: 10k-mono.mono1d.h5 load_checkpoint: checkpoint.npy force_checkpoint_continue: false nproc: 4 store_temp: false transforms: parameters: lognorm objectives: geometric parameters: - name: axial length: 1 path: input.model.unit_002.col_dispersion min_value: 1.0e-9 max_value: 1.0e-4 objectives: - name: outlet filename: chromatogram-from-xns.csv # times: timesteps.txt score: sse path: output.solution.unit_003.solution_outlet_comp_000 algorithm: name: nsga3 pop_size: 10 termination: x_tol: 1e-8 cv_tol: 1e-6 f_tol: 1e-9 nth_gen: 2 n_last: 10 n_max_gen: 10 n_max_evals: 100000

Notes

  • It runs multiple cadet simulations from a pool size of nproc for every evaluation of a population.
  • parameters and objectives are lists
  • Objective targets can be provided as an (times,values) csv file in objectives.filename or with the times separately specified in objectives.times
    • chromatograms already contain times, so it's easier to just provide the filename
  • The solution_times section of the provided cadet simulation will be changed to match those of objectives[0] exactly.
  • Recommended population sizes for n-dimensional problems is 100*n
  • Don't fit porosity and velocity together. You can fit porosity and flowrate instead
  • Provided examples, while valid, are NOT guaranteed to be correct as the software is not guaranteed to be stable in terms of development and backwards compatibility.
  • Checkpoints are saved at every generation by default.
  • Use force_checkpoint_continue to force the algorithm to continue from a terminated checkpoint. Helpful if you made the termination criteria stricter than required.
  • Be careful when resuming from a checkpoint. Any changes to problem parameters might not be reflected because the algorithm/problem is fully restored from the checkpoint

Known Issues

  • Reading inputs from YAML loads strings as str and from h5 files we get numpy.bytes_. CADET-Python runload() uses loadresults() instead of full load(). So if we check for input string values after simulation, the type of it depends on whether we use full load() or loadresults() since we deal with YAML files as well. So we have to consider whether we deal with strings or bytestrings. Simple solution: Don't use runload in scripts.
  • Loading checkpoints also loads the previous values for all/most parameters. So if nproc is updated before loading, the new value isn't used.

Owner

  • Name: CADET
  • Login: cadet
  • Kind: organization
  • Email: cadet@fz-juelich.de

GitHub Events

Total
  • Release event: 2
  • Create event: 2
Last Year
  • Release event: 2
  • Create event: 2

Committers

Last synced: 10 months ago

All Time
  • Total Commits: 304
  • Total Committers: 2
  • Avg Commits per committer: 152.0
  • Development Distribution Score (DDS): 0.089
Past Year
  • Commits: 27
  • Committers: 1
  • Avg Commits per committer: 27.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jayghosh Rao j****o@f****e 277
Jayghosh Rao j****r@g****m 27
Committer Domains (Top 20 + Academic)
fz-juelich.de: 1

Issues and Pull Requests

Last synced: 10 months ago

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  • Total issues: 0
  • Total pull requests: 0
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  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
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  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
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  • Average comments per pull request: 0
  • Merged pull requests: 0
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Dependencies

requirements.txt pypi
  • GitPython *
  • SALib *
  • addict ==2.3
  • cadet-python ==0.11
  • corner *
  • matplotlib >=3.4
  • numpy <2.0
  • pandas *
  • pymoo ==0.5
  • rich >10.15
  • ruamel.yaml ==0.17
setup.py pypi