{"id":13869692,"url":"https://github.com/statbiophys/MINIMALIST","last_synced_at":"2025-07-15T18:31:45.565Z","repository":{"id":102526228,"uuid":"370301718","full_name":"statbiophys/MINIMALIST","owner":"statbiophys","description":"Companion Github of the MINIMALIST preprint.","archived":false,"fork":false,"pushed_at":"2021-06-03T10:34:21.000Z","size":107,"stargazers_count":1,"open_issues_count":0,"forks_count":1,"subscribers_count":2,"default_branch":"main","last_synced_at":"2024-11-23T15:35:34.480Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/statbiophys.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null}},"created_at":"2021-05-24T09:44:23.000Z","updated_at":"2022-06-08T20:44:07.000Z","dependencies_parsed_at":"2023-04-17T12:31:06.871Z","dependency_job_id":null,"html_url":"https://github.com/statbiophys/MINIMALIST","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/statbiophys/MINIMALIST","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/statbiophys%2FMINIMALIST","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/statbiophys%2FMINIMALIST/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/statbiophys%2FMINIMALIST/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/statbiophys%2FMINIMALIST/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/statbiophys","download_url":"https://codeload.github.com/statbiophys/MINIMALIST/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/statbiophys%2FMINIMALIST/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":265451443,"owners_count":23767768,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-08-05T20:01:12.137Z","updated_at":"2025-07-15T18:31:45.279Z","avatar_url":"https://github.com/statbiophys.png","language":"Python","funding_links":[],"categories":["Python"],"sub_categories":[],"readme":"## MINIMALIST\n\nWritten by Giulio Isacchini, MPIDS Göttingen - ENS Paris and Natanael Spisak, ENS Paris\n\nThe code is written in Python3. Last updated on 24-05-2021\n\nReference: MINIMALIST: Mutual INformatIon Maximization for Amortized Likelihood Inference from Sampled Trajectories, Giulio Isacchini, Natanael Spisak, Armita Nourmohammad, Thierry Mora and Aleksandra M. Walczak\n\n### To reproduce the figures\n\nIn order to reproduce the plots you need to run the following commands.\n\n1) Install the `mimsbi` package\n\nEnter the mimsbi folder and run the command `python setup.py install`\n\n2) Run analysis\n\nTo run the analysis for the the 4 task run the script `run_analysis.py` with the options: `ou`,`bd`,`sir` and/or `lorenz`. \n\n3) Plot the results.\n\nFor Figure 2, run `fig2.py`\n\nFor Figure 3 run `fig3.py`\n\nFor Figure 4, run `fig4.py`\n\n\n### Extended usage\n\nThis directory includes a stable version of the `mimsbi` package.  \u003c!-- The full package is available in ... --\u003e\n\nThe package allows to infer the likelihood-to-evidence ratio model using one of three objective functions: MINE, FDIV or BCE. The package has implemented simulators for the processes studied in the MINIMALIST paper: Ornstein-Uhlenbeck, birth-death, SIR and Lorenz processes. To add another functionality one needs to add a new `Simulator` class to `mimsbi/models`. Then, inference can be performed using the `DensityRatioEstimator` class. For example of usage go to the `scripts` directory where separate files can be used to \n1) simulate the data  `scripts/simulate.py`\n2) tune network hyperparameters  `scripts/infer_hyperpars.py`\n3) likelihood-to-evidence ratio inference  `scripts/infer_estimators.py`\n4) posterior evaluation  `scripts/compare_estimators.py`\n\nTo use the above scripts with a new model, its specifications need to be added in `scripts/utils.py` `return_pars` function. A simple data generation to posterior evaluation protocol is also available in the `mimsbi/tutorial.ipynb` Jupyter notebook.\n\n### Requisites\n\n- `tensorflow\u003e2.1`\n- `numpy`\n- `pandas`\n- `scipy`\n- `matplotlib`\n- `tqdm`\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstatbiophys%2FMINIMALIST","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fstatbiophys%2FMINIMALIST","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fstatbiophys%2FMINIMALIST/lists"}