semvec/pypi-packages huggingface dataset:
xaj 0.1.8
ODE integrator for JAX
Requires Python: >=3.7,<3.12

[![Lint & test](https://github.com/adxsrc/xaj/actions/workflows/python-package.yml/badge.svg)](https://github.com/adxsrc/xaj/actions/workflows/python-package.yml)
[![PyPI](https://github.com/adxsrc/xaj/actions/workflows/python-publish.yml/badge.svg)](https://pypi.org/project/xaj/)

.. warning::
    This version of `XAJ` works only with `jax` and `jaxlib` 0.3.
    But `jaxlib` 0.3 is not available on PyPI.
    To install this version of `XAJ`, please first install `jaxlib`
    with:

    .. code-block:: bash
        pip install jaxlib==0.3.25 -f https://storage.googleapis.com/jax-releases/jax_releases.html


# `XAJ`

Ordinary differential equation (ODE) integrator compatible with
[Google's JAX](https://github.com/google/jax).

`XAJ` implements the Runge-Kutta Dormand-Prince 4/5 pair (DP5) with
adaptive step control and dense output according to the
[Numerical Recipes](http://numerical.recipes/).
It provides a fast and efficient way to solve non-stiff ODEs.
The programming interface is designed so it feels similar to the
derivative functions in `JAX`.

Specifically, `XAJ` provides a single function `odeint()`.
Applying it to another function `rhs()` and the initial conditions
returns the numerical solution as a callable `ns`, which interpolates
the dense output.

    from xaj import odeint
    from jax import numpy as np

    rhs = lambda x, y: y
    x0  = 0
    y0  = 1

    ns  = odeint(rhs, x0, y0, 1)

    x   = np.linspace(0, 5)
    y   = ns(x)

The numerical integration happens in a "lazy" way, which is triggered
by the extreme values of the argument of `ns`.
Alternatively, it is possible to obtain the numerical solutions at the
full steps by

    xs = ns.xs
    ys = ns.ys

Demos on how to use `XAJ` can be found in the [`demos`](demos/)
directory.
indycarpy-TMCabrera 0.0.1
A Python package to scrape Indycar session data
Requires Python: >=3.8

# IndyCarPy

IndyCarPy is a Python package designed to scrape IndyCar session data from
the official IndyCar website. It provides functionalities to retrieve
information about races, practices, qualifications, and warm-up sessions,
as well as the records associated with these sessions.

## Features

- **Session Data Retrieval**: Fetch data about IndyCar sessions including
  races, practices, qualifications, and warm-ups.
- **Data Cleaning and Preparation**: Clean and prepare the retrieved data
  for further analysis or storage.
- **Export to CSV**: Option to export the data to CSV format for easy
  integration with other tools or databases.

## Installation

You can install IndyCarPy using pip:

```bash
pip install indycarpy
```

Make sure you have the required dependencies installed: pandas, requests,
tqdm, and numpy.

## Usage

```bash
import indycarpy

# Retrieve session data between specific years
sessions_df = indycarpy.get_sessions_record(from_year=1996, to_year=2024, session_type="R", data_format="df")

# Clean session records data
cleaned_data = indycarpy.clean_sessions_records(sessions_df)
```

## Data Files

This package includes a `data/race_track.csv` file containing information
about the track names for each event, which is utilized during data
retrieval.

## Contributing

Contributions to IndyCarPy are welcome! If you encounter any issues or have
suggestions for improvements, feel free to open an issue or submit a pull
request on GitHub.

## License

IndyCarPy is licensed under the GNU General Public License v3.0. See the
LICENSE file for more details.