semvec/pypi-packages huggingface dataset:
robotpy-rev-digit 1.1.0
A module for controlling the REV Digit Board from RobotPy.
Requires Python: >=3.12

# RobotPy Rev Digit

[![RobotPy - 2024](https://img.shields.io/badge/RobotPy-2024-2ea44f)](https://github.com/robotpy)
[![python](https://img.shields.io/badge/Python-3.11-3776AB.svg?style=flat&logo=python&logoColor=white)](https://www.python.org)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)

A RobotPy module for control of the REV Robotics Digit Board.
Ported from the Java repository: https://github.com/vampjaz/REVDigitBoard

-----

**Table of Contents**

- [Installation](#installation)
- [License](#license)

## Installation

```console
pip install robotpy-rev-digit
```

## License

`robotpy-rev-digit` is distributed under the terms of the [GPL-3.0-only](https://spdx.org/licenses/MIT.html) license.
qifeaturex 0.1.1
Quantum Information Feature Engineering Library
Requires Python: >=3.9

# QIFeatureX 🔮  
### Quantum Information Feature Engineering Library

[![PyPI version](https://badge.fury.io/py/qifeaturex.svg)](https://badge.fury.io/py/qifeaturex)
[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
![Python](https://img.shields.io/badge/python-3.9%2B-blue)
![Status](https://img.shields.io/badge/status-early--alpha-purple)

QIFeatureX is an open-source **Quantum Information Feature Engineering** library that converts **quantum states** (pure vectors or density matrices) into **machine-learning-ready numerical feature vectors**. It enables ML-driven analysis of entanglement, coherence, entropy, nonlocality, and quantum similarity without heavy symbolic calculations.

QIFeatureX is designed for research in **quantum computing**, **quantum communication**, **quantum sensing**, **quantum machine learning**, and **condensed matter physics**.

---

## ✨ Key Features
- 📌 Convert quantum states → structured ML feature tables
- 📌 Support for pure states (`|ψ⟩`) and density matrices (`ρ`)
- 📌 Entanglement metrics: concurrence, negativity, log-negativity, tangle
- 📌 Entropy metrics: von Neumann, Rényi-2, linear entropy
- 📌 Coherence measures: ℓ₁-coherence, relative entropy of coherence
- 📌 Mutual information & bipartite correlations
- 📌 Bell-CHSH violation measurement
- 📌 Quantum similarity distances: trace distance, fidelity, Bures, Hilbert-Schmidt
- 📌 Fully compatible with **scikit-learn pipelines**

---

## 🚀 Installation

```bash
pip install qifeaturex

### 2. Right below that section, paste the Basic Usage example block:

```markdown
---

## 🧠 Basic Usage Example

```python
import numpy as np
from qifeaturex import extract_features
from qifeaturex.ml import QIFeatureExtractor
from sklearn.ensemble import RandomForestClassifier
from sklearn.pipeline import Pipeline

def bell_state_phi_plus():
    psi = np.zeros(4, dtype=complex)
    psi[0] = psi[3] = 1/np.sqrt(2)
    return psi

# Create a Bell state and convert to density matrix
psi = bell_state_phi_plus()
rho = np.outer(psi, psi.conj())

# Extract features
df = extract_features([rho], dims=(2,2))
print(df)
testAUC 1.1.0
testAUC is a library of tools to evaluate the true performance of ML/AI models
Requires Python: >=3.7

# testAUC
Official implementation of the tools discussed in *The curious case of the test set AUROC*

It is a set of tools designed for better evaluation of **Binary Classification** tasks in ML and AI.
Specifically dealing with the expected performance of the model on **new data**.
## Quick start
    pip install testAUC

For and all-in-one view of the toolset, use the dashboad() function:
```python
from testAUC import faux_normal_predictions, dashboard

# Simulate a model, evaluated on a Validation set and a Test set:
y_true_val, y_score_val = faux_normal_predictions(neg_mu=0.3, pos_mu=0.8, seed=2023)
y_true_tst, y_score_tst = faux_normal_predictions(std=0.5, neg_mu=0.4, pos_mu=0.9, seed=2023)

# All in one Dashboard to evaluate the Validation vs. Test sets performance
dashboard(y_true_val, y_score_val, y_true_tst, y_score_tst)
```
![Demo](https://github.com/alonhzn/testAUC/blob/main/images/demo1.png?raw=true)

roc_drift, val_tst_colored_roc_curve, colored_roc_curve,dashboard
noise_robustness, bias_robustness, plot_noise_robustness, plot_bias_robustness
plot_wasserstein_distance_matrix, plot_predictions_hist

### Mini-documentation of functions:
* dashboard() -> An All-In-One dashboard to evaluate the test performance (good place to start!)
* roc_drift() -> Calculate the ROC drift from validation to test sets  
* noise_robustness() -> Calculate the robustness of the predictions to normal noise
* plot_noise_robustness() -> uses the noise_robustness to generate a plot
* bias_robustness() -> Calculate the robustness of the predictions to bias between the classes
* plot_bias_robustness() -> uses the bias_robustness to generate a plot
* plot_wasserstein_distance_matrix() -> See paper to understand the importance of the matrix
* colored_roc_curve() ->  Plot an ROC curve that is color coded by threshold
* val_tst_colored_roc_curve() -> Same colored ROC curve but for both val&tst sets (sharing color limits!)
* faux_normal_predictions() -> A small utility function to create fake model predictions
* plot_predictions_hist() -> Plot a histogram of predictions for the Positive and Negative classes