semvec/pypi-packages huggingface dataset:
rapidpe-rift-rota-tools 0.0.3
Rota tools for  RapidPE-RIFT
Requires Python: >=3.0

# RapidPE-RIFT ROTA Tools

Generates status page for RapidPE-RIFT runs


# Credit

Copyright (c) 2024 Vinaya Valsan, Daniel Wysocki, Caitlin Rose

This software may be freely distributed under the terms of the GNU General Public License v2.  See [`COPYING`](./COPYING) for details.
fairness-datasets 0.4.0
PyTorch dataset wrapper for the
Requires Python: >=3.9
Keywords: PyTorch, Dataset, Fairness, Adult, Census Income, Law School

# fairness-datasets
[![PyPI version](https://badge.fury.io/py/fairness-datasets.svg)](https://badge.fury.io/py/fairness-datasets)

PyTorch dataset wrappers for the several popular datasets from 
fair machine learning research.

The following datasets are wrapped:
 - [Adult (Census Income)](https://archive.ics.uci.edu/dataset/2/adult).
 - [Default](https://archive.ics.uci.edu/dataset/350/default+of+credit+card+clients)
 - [Law School](https://eric.ed.gov/?id=ED469370) (data from [here](https://www.tensorflow.org/responsible_ai/fairness_indicators/tutorials/Fairness_Indicators_Pandas_Case_Study))
 - [SouthGerman](https://archive.ics.uci.edu/dataset/573/south+german+credit+update)

## Installation
```shell
pip install fairness-datasets
```

## Basic Usage
```python
from fairnessdatasets import Adult

# load (if necessary, download) the Adult training dataset 
train_set = Adult(root="datasets", download=True)
# load the test set
test_set = Adult(root="datasets", train=False, download=True)

inputs, target = train_set[0]  # retrieve the first sample of the training set

# iterate over the training set
for inputs, target in iter(train_set):
    ...  # Do something with a single sample

# use a PyTorch data loader
from torch.utils.data import DataLoader

loader = DataLoader(test_set, batch_size=32, shuffle=True)
for epoch in range(100):
    for inputs, targets in iter(loader):
        ...  # Do something with a batch of samples
```
You can use `Adult(..., raw=True)` to turn off the one-hot encoding
and z-score normalization applied by the `Adult` class by default.

The remaining dataset classes can be used in the same way as `Adult`.
However, these datasets don't come with a fixed train/test split, 
so that the dataset instances always contain all data.
To create a train/test split, use
```python
from fairnessdatasets import Default
from torch.utils.data import random_split

dataset = Default(root="datasets", download=True)

rng = torch.Generator().manual_seed(42)  # for reproducible results
train_set, test_set = random_split(dataset, [0.7, 0.3], generator=rng)
```

## Advanced Usage

Turn off status messages while downloading the dataset:
```python
Adult(root=..., output_fn=None)
```

Use the `logging` module for logging status messages while downloading the
dataset instead of placing the status messages on `sys.stdout`.
```python
import logging

Adult(root=..., output_fn=logging.info)
```
exp-hurst 0.0.2
It computes the Hurst exponent of a time series.
Requires Python: >=3.9

# Hurst Exponent Package

## Description
The function hurst takes a np.array of numbers and returns the Hurst exponent of the time series. The Hurst exponent is a measure of randomness of a time series. It is used in the study of long-term memory of time series. The value of the Hurst exponent is between 0 and 1. A value of 0.5 indicates that the time series is random. A value greater than 0.5 indicates that the time series is trending. A value less than 0.5 indicates that the time series is mean reverting.

## Installation
```bash
pip install exp_hurst
```

## Requirements
- numpy
- mmq

## Usage
```python
from coef_hurst import hurst
hurst(time_series)
```

## Example
```python
from exp_hurst import hurst
import numpy as np

# Create a time series of random numbers
rs = np.random.normal(0, 1, 100000)

# Evaluate the Hurst exponent
h = hurst(rs)
```

## License
[MIT](https://choosealicense.com/licenses/mit/)

## Author
[Igor Matheus Jasenovski]

## Version
0.0.1

## References
[Hurst Exponent](https://en.wikipedia.org/wiki/Hurst_exponent#:~:text=The%20Hurst%20exponent%20is%20used,between%20pairs%20of%20values%20increases.)
jcmath 0.0.1
JenCat custom math library with implemented math functions
Requires Python: >=3.6

# Custom JCMath Library

This is a Python library that provides custom implementations of mathematical functions such as fmod, atan, sin, cos, tan, and atan2 without relying on external libraries.

## Installation

You can install the library using pip:

```bash
python3 -m pip install jcmath
```

## Usage

```python
from jcmath import JCMath

# Create an instance of JCMath
jcmath = JCMath()

# Compute and print values
custom_math.test()
```

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.