semvec/pypi-packages huggingface dataset:
juice-coverage-reporter 0.0.9
Python package for reporting Juice Coverage Metrics
Keywords: esa, Juice, planetary, space, metrics, trajectory, coverage, assessment, crema

# Juice Coverage Reporter 

## 1. Getting Started

This Python package provides libs and tools to assess the Juice Coverage reporting metrics, using 
the outputs provided from different SOC tools (e.g. geopipeline, moon coverage) as inputs.

This tool allows to generate a Coverage report for a given Trajectory (crema)

    juice_coverage_reporter -c <path to configuration file>
 
The output include the report in rst, html and docx format.


### 1.1 Installation

#### 1.1.1 Prerequisites
 
* Python 3.7 or higher.

#### 1.1.2 Setup

##### 1.1.2.1 Package installation 

From command line install the following packages using pip:

    pip install juice_coverage_reporter
quantum-ml-system 0.1.2
A Flexible Quantum Machine Learning Framework
Requires Python: >=3.8
Keywords: quantum machine learning quantum-computing ml ai

# QuantumML: A Flexible Quantum Machine Learning Framework

## Overview

QuantumML is an advanced quantum machine learning framework built on top of PennyLane, designed to provide researchers and practitioners with a comprehensive toolkit for quantum machine learning experiments.

## Key Features

- Modular quantum machine learning architectures
- Multiple feature embedding techniques
- Variational quantum circuit implementations
- Advanced training and evaluation utilities
- Quantum-classical model comparisons
- Circuit complexity and barren plateau analysis

## Installation

```bash
pip install quantum-ml-system
```

## Project Structure

```
quantum-ml/
│
├── quantumml/
│   ├── __init__.py
│   ├── core/
│   │   ├── __init__.py
│   │   ├── base_model.py
│   │   └── quantum_circuit.py
│   │
│   ├── models/
│   │   ├── __init__.py
│   │   ├── classifier.py
│   │   ├── regressor.py
│   │   └── generative.py
│   │
│   ├── feature_maps/
│   │   ├── __init__.py
│   │   ├── zz_map.py
│   │   ├── amplitude_map.py
│   │   └── angle_map.py
│   │
│   ├── ansatzes/
│   │   ├── __init__.py
│   │   ├── strongly_entangling.py
│   │   ├── basic_entangler.py
│   │   └── custom_ansatz.py
│   │
│   ├── utils/
│   │   ├── __init__.py
│   │   ├── visualization.py
│   │   ├── complexity_analysis.py
│   │   └── gradient_analysis.py
│   │
│   └── metrics/
│       ├── __init__.py
│       ├── quantum_metrics.py
│       └── comparison_metrics.py
│
├── examples/
│   ├── classification_demo.py
│   ├── regression_demo.py
│   └── generative_model_demo.py
│
├── tests/
│   ├── test_core.py
│   ├── test_models.py
│   └── test_feature_maps.py
│
├── setup.py
├── requirements.txt
└── README.md
```

## Basic Usage Example

```python
from quantumml.models import QuantumClassifier
from quantumml.feature_maps import ZZFeatureMap
from quantumml.ansatzes import StronglyEntanglingAnsatz

# Create a quantum classifier
model = QuantumClassifier(
    n_qubits=4,
    feature_map=ZZFeatureMap(),
    ansatz=StronglyEntanglingAnsatz(n_layers=2)
)

# Train the model
model.fit(X_train, y_train)

# Make predictions
predictions = model.predict(X_test)
```

## Advanced Features

- Multiple Feature Embedding Techniques
- Flexible Variational Circuit Architectures
- Quantum-Classical Model Comparisons
- Circuit Complexity Analysis
- Barren Plateau Detection
- Model Serialization and Loading

## Contributing

Contributions are welcome! Please read our contributing guidelines and code of conduct.

## License

MIT License

## Citation

If you use QuantumML in your research, please cite our framework.
```
@software{quantum_ml_system,
  author = {Nikolaos Roufas},
  title = {Quantum Machine Learning System},
  year = {2025},
  url = {https://github.com/nikolasroufas/quantum-ml-system
/}
}
```