semvec/pypi-packages huggingface dataset:
pfpy 0.0.2
Power Factory with Python
Requires Python: >=3.7

# PFPy - PowerFactory with Python

[![Documentation Status](https://readthedocs.org/projects/pfpy/badge/?version=latest)](https://pfpy.readthedocs.io/en/latest/?badge=latest)

PFPy provides a wrapper around PowerFactory's Python API with additional features in  `pfpy.pf`. 
Furthermore,  `pfpy.models` provides a high-level access to PowerFactory models, e.g. via:
- `model.simulate()`
- `model.linearize()`
- `model.set_params()`

It has been developed at the Division of Electric Power and Energy Systems, KTH Royal Institute of Technology.

The code has been tested with PowerFactory 2019 and PowerFactory 2020. Note that $PYTHONPATH needs to point to the PowerFactory Python API.

## Examples
The PowerFactory model used in all of the examples is located in [./examples/data/models/PFPY_DEMO.pfd](https://github.com/tinrabuzin/PFPy/blob/master/examples/data/models/PFPY_DEMO.pfd)

### Simulation Example

The simulation of the aforementioned model is demonstrated in the [simulation example](https://github.com/tinrabuzin/PFPy/blob/master/examples/simulation_with_inputs.py).
The model contains a controllable voltage source at the PCC. CSV files are generated that are replayed at the PCC, simulation is executed and the results are plotted.

### Linearization Examples

The same model is linearized as seen from the PCC in the following [Jupyter notebook](https://github.com/tinrabuzin/PFPy/blob/master/examples/linearization.ipynb).
When compared to the linearization in PowerFacory, this code, in addition to the A matrix, provides also B,C, and D matrices.

## Documentation

Automatically generated documentation from docstrings can be found [here](https://pfpy.readthedocs.io/en/latest/).

### Acknowledgments
Parts of this code were inspired by the following book:
*Francisco Gonzalez-Longatt, JosÃ© Luis Rueda Torres, Advanced Smart Grid Functionalities Based on PowerFactory, Springer, 2017*
xplainer 0.0.2
xplainer.ai toolkit enables developers to explore predictions of their deep learning models. Focused on image classification and TensorFlow 2+.
Requires Python: >=3.6

The ultimate goal of this toolkit is to promote explainable AI. 
It should offer developers varied ways to examine predictions of their models.

We are just at the beginning of a long road. For a start, we decided to aim at:
- Image classification.
- Tensorflow 2+ models.

There are already a couple of existing libraries. We do not want to duplicate them.
This project rather tries to make them available with as little effort as possible,
at one place.

For more information, please visit the [GitHub repository](https://github.com/liborvaneksw/xplainer/).