semvec/pypi-packages huggingface dataset:
filtertree 0.2.0
Apply a filter to the directory tree
License: MIT
Requires Python: <4.0,>=3.10

# filtertree

Apply a filter to the directory tree.

## Install

filtertree can be installed with pip:

    $ pip install filtertree

## Usage

filtertree module provides a function `filtertree`.

```python
from filtertree import filtertree

filtertree(func, src_dir_path, dest_dir_path, overwrite=overwrite)
```

Arguments are:

- `func` is a filter function that takes 2 arguments, source and destination file paths.
- `src_dir_path` is a base directory path for source.
- `dest_dir_path` is a base directory path for destination.
- `overwrite` is a flag to overwrite destination files.

`filtertree` function applies `func` to each file under `src_dir_path` directory tree and the same location under `dest_dir_path`.

If `overwrite` is `True`, overwrite the destination files, Or not. Default to `False`.

## CLI tool `filtre`

`filtre` applies command to `src_dir` directory tree as follows:

    $ filtre src_dir dest_dir cp

Or you can give one-line-script with `-c` option.

    $ filtre -c "cp $1 $2" src_dir dest_dir

`$1` and `$2` in one-lien-script are replaced by src and dest file paths.

If `-o` / `--overwite` option is passed, overwrite the destination files. Otherwise, skip.

## License

MIT license.
DaeFinder 0.2.1
A Python package to discover Differential Algebraic Equations from data.
License: MIT
Requires Python: >=3.7

# **DaeFinder**

DaeFinder is a Python package designed to discover Differential Algebraic Equations (DAEs) from noisy data using sparse optimization framework. It is based on the **SODAs algorithm** developed by the researchers in the **Mangan Group** at Northwestern University. The associated research paper will soon be available on arXiv, and a link will be provided here once published.

If you use DaeFinder for your development or research, please cite the SODAs paper once it is available.

---

## **Author and Contributors**
- Manu Jayadharan (Primary Developer)
- Christina Catlett
- Arthur Montanari
- Niall Mangan
---

## **Features**
- Decoupling of Algebraic and Dynamic Equations
- Smoothening noisy data and calculating derivatives.
- Generate polynomial features for regression models.
- Support for sparse feature coupling.
- SVD Analysis.
- Example notebooks for practical demonstrations including chemical reaction networks, power grid networks, etc. 

---

## **Dependencies**

The following Python packages are required to use `DaeFinder`:

- `numpy`
- `scipy`
- `pandas`
- `sympy`
- `scikit-learn`
- `matplotlib`
- `joblib`

## **Installation**

To install the `DaeFinder` package, follow these steps:

1. Ensure you have Python 3.7 or higher installed.
2. Install the package using pip:
   ```bash
   pip install DaeFinder

## Examples

Walkthrough notebooks are available in the `Examples/` folder of the repository. These notebooks include:

- A step-by-step guide to using DaeFinder.
- Application to chemical reaction network, non-linear pendulum, power grid, etc. 

For examples that require additional data (e.g., the power grid example), the data files are included in the GitHub repository. Be sure to download the required datasets from the relevant folders in the repository.

## Known Issues

- The parallel function currently has some bugs that need fixing.
- If you encounter issues with the installation or the package itself, please feel free to contact the authors or contributors.

## Contributing

We welcome contributions to improve DaeFinder! If you are interested in contributing to the package or working on related research, please reach out to the author or the Mangan Group.

## Contact

For any questions, issues, or collaboration inquiries, please contact:

- Manu Jayadharan [manu.jayadharan@gmail.com](mailto:manu.jayadharan@gmail.com)
- Niall Mangan [niall.mangan@northwestern.edu](mailto:niall.mangan@northwestern.edu)
- Christina Catlett
- Arthur Montanari