B-FADE: Bayesian FAtigue moDel Estimator
The package implements Maximum a Posteriori Estimation (MAP) to accomplish the estimation of fatigue models' parameters. Currently the package is designed to identify the El Haddad (EH) curve given a fatigue & defectivity characterisation dataset. Other curves are foreseen in future developments.
Features
- Maximum a Posteriori Estimation and computation of the predictive posterior.
- Monte Carlo Estimation of the prediction interval for the considered curve.
- Data pre-processing & visualisation.
Quick Setup
B-FADE is available at PyPI, so it can be installed using common package managers, such as pip or conda:
pip install --user b-fade
conda install b-fade
For further instructions, please take a look at the documentation.
Documentation
Please refer to ReadTheDocs for detailed instructions about installing, utilising B-FADE and working examples.
Metadata
Release files for b-fade 0.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| b_fade-0.0.0.tar.gz | 33.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| b_fade-0.0.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 70.3 kB
Release files / b_fade-0.0.0.tar.gz
| Download URL | b_fade-0.0.0.tar.gz |
|---|---|
| Size | 33.3 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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Release files / b_fade-0.0.0-py3-none-any.whl
| Download URL | b_fade-0.0.0-py3-none-any.whl |
|---|---|
| Size | 37.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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