Framalytics
Framalytics is a Python package to work with the Functional Resonance Analysis Method (FRAM).
Our goal is to bring FRAM to Python, enabling the interaction of FRAM models with data science
and machine learning tools. Framalytics can load FRAM models from .xfmv files created through
the FRAM Model Visualizer.
Components of the FRAM models can be extracted, such as list of functions, or which functions
are connected together via which aspect.
Models can be visualized directly inside a Jupyter notebook environment, reproducing the visual style of the FMV.
Real data can also be integrated with the FRAM model visualizations.
Documentation
Framalytics documentation is hosted on https://framalytics.readthedocs.io.
Metadata
Release files for framalytics 1.0.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| framalytics-1.0.0.tar.gz | 293.2 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| framalytics-1.0.0-py2.py3-none-any.whl | Python 3, Python 2 | none | any | Details |
Total release size: 319.6 kB
Release files / framalytics-1.0.0.tar.gz
| Download URL | framalytics-1.0.0.tar.gz |
|---|---|
| Size | 293.2 kB |
| Tags | Source |
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SHA-256 checksum How to use checksums |
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python-requests/2.31.0
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Release files / framalytics-1.0.0-py2.py3-none-any.whl
| Download URL | framalytics-1.0.0-py2.py3-none-any.whl |
|---|---|
| Size | 26.4 kB |
| Tags | Python 2 Python 3 |
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SHA-256 checksum How to use checksums |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
python-requests/2.31.0
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