https://github.com/certara/pydarwin
Python solution for the application of machine learning to Pop PK model selection.
Science Score: 26.0%
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○CITATION.cff file
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○Scientific vocabulary similarity
Low similarity (13.9%) to scientific vocabulary
Repository
Python solution for the application of machine learning to Pop PK model selection.
Basic Info
Statistics
- Stars: 27
- Watchers: 2
- Forks: 5
- Open Issues: 2
- Releases: 13
Metadata Files
README.md
pyDarwin
Python solution for using several machine learning methods to search a candidate solution space for the optimal population models in NONMEM.
Visit pyDarwin Documentation to learn more.
System Requirements
- Windows 10
- Windows Server 2018, 2019
- CentOS8/RHEL8
- Ubuntu >= 18.04
Grid Computing Support
- Sun Grid Engine (SGE)
Installation Prerequisites
- Python >= 3.10
- NONMEM >= 7.4.3
- R >= 4.0.0 (optional)
Note: Requirements are Python and NONMEM installation with nmfe.bat available. R is required if using post-run R penalty function.
Installation
First, create a new virtual environment:
python -m venv .venv
This will create a virtual environment in the folder .venv
Next, use pip to install the pyDarwin package from the Certara managed PyPi repo:
Released Version
pip install pyDarwin-Certara --index-url https://certara.jfrog.io/artifactory/api/pypi/certara-pypi-release-public/simple --extra-index-url https://pypi.python.org/simple/
Development Version
pip install pyDarwin-Certara --pre --upgrade --force-reinstall --index-url https://certara.jfrog.io/artifactory/api/pypi/certara-pypi-develop-local/simple --extra-index-url https://pypi.python.org/simple/
Usage
python -m darwin.run_search <template_path> <tokens_path> <options_path>
To execute, call the run_search function from the darwin module and provide the following file paths as arguments:
- Template file (e.g., template.txt) - basic shell for NONMEM control files
- Tokens file (e.g., tokens.json) - json file describing the dimensions of the search space and the options in each dimension
- Options file (e.g., options.json) - json file describing algorithm, run options, and post-run penalty code configurations.
Example
After cloning https://github.com/certara/pyDarwin from GitHub, navigate to one of the example folders e.g.,
cd .\pyDarwin\examples\user\Example1
Then execute:
python -m darwin.run_search template.txt tokens.json options.json
Note: Both absolute and relative file paths are supported.
Owner
- Name: Certara USA, Inc.
- Login: certara
- Kind: organization
- Email: github-admins@certara.com
- Website: https://www.certara.com/
- Repositories: 8
- Profile: https://github.com/certara
GitHub Events
Total
- Watch event: 3
- Delete event: 6
- Member event: 2
- Push event: 94
- Pull request event: 15
- Fork event: 1
- Create event: 9
Last Year
- Watch event: 3
- Delete event: 6
- Member event: 2
- Push event: 94
- Pull request event: 15
- Fork event: 1
- Create event: 9
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 0
- Total pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: 1 minute
- Total issue authors: 0
- Total pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 9
- Average time to close issues: N/A
- Average time to close pull requests: 1 minute
- Issue authors: 0
- Pull request authors: 2
- Average comments per issue: 0
- Average comments per pull request: 0.0
- Merged pull requests: 8
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- certara-jcraig (1)
- samjrrr (1)
- shihao94 (1)
Pull Request Authors
- certara-amazur (7)
- certara-jcraig (3)
- YFY-21 (1)