semvec/pypi-packages huggingface dataset:
Package-Mehdi 0.0.4
Un package minimal
Requires Python: >=3.7

# package_mehdi

Un package Python simple avec des fonctions basiques pour afficher un message et effectuer des opérations arithmétiques.

---

## Fonctions

### `Mehdi_print()`

Affiche un message simple.

**Usage :**
```python
Mehdi_print()
# Sortie :
# hello I am Mehdi
```

---

### `Mehdi_add(a, b)`

Calcule la somme de deux nombres.

- **Arguments :**
  - `a` (int ou float) : premier nombre
  - `b` (int ou float) : deuxième nombre

- **Retourne :**  
  La somme de `a` et `b`.

**Exemple :**
```python
result = Mehdi_add(4, 5)
print(result)  # Affiche 9
```

---

### `Mehdi_subtract(a, b)`

Calcule la différence entre deux nombres.

- **Arguments :**
  - `a` (int ou float) : premier nombre
  - `b` (int ou float) : deuxième nombre

- **Retourne :**  
  La différence `a - b`.

**Exemple :**
```python
result = Mehdi_subtract(10, 3)
print(result)  # Affiche 7
```

---

### `Mehdi_multiply(a, b)`

Calcule le produit de deux nombres.

- **Arguments :**
  - `a` (int ou float) : premier nombre
  - `b` (int ou float) : deuxième nombre

- **Retourne :**  
  Le produit `a * b`.

**Exemple :**
```python
result = Mehdi_multiply(6, 7)
print(result)  # Affiche 42
```

---

## Installation

Pour installer ce package depuis PyPI (ou localement), utilisez :

```bash
pip install package_mehdi
```

---

## Utilisation

Importez les fonctions et utilisez-les comme suit :

```python
from package_mehdi import Mehdi_print, Mehdi_add, Mehdi_subtract, Mehdi_multiply

Mehdi_print()
print(Mehdi_add(2, 3))
print(Mehdi_subtract(5, 2))
print(Mehdi_multiply(4, 3))
```
scorecard_mcp 0.0.3
Basic Model Context Protocol (MCP) server for OpenSSF Scorecard
Requires Python: >=3.10

# scorecard_mcp

This is an example [MCP server](https://github.com/modelcontextprotocol/servers/) for [OpenSSF Scorecard](https://scorecard.dev/).

You can use it to ask questions like:

> Is urllib3/urllib3 secure?

Or:

> What security best practices does node-semver follow?

### Installation

There are several ways to install, depending on what editor you're using; see the [installation instructions on the example `fetch` MCP server](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch).

I recommend using:

```
...
    "command": "uxv",
    "args": ["scorecard_mcp"]
...
```

So if you're using Visual Studio Code you'd create a `.vscode/` directory in your project and add a `mcp.json` file that looks like this:

```
{
    "servers": {
        "scorecard": {
            "type": "stdio",
            "command": "uvx",
            "args": ["scorecard_mcp"]
        }
    }
}
```
immerschema 1.3.2
JSON-Schema & taxonomy for Fulldome pipelines
Requires Python: >=3.9

# Immersive Projects CG Pipeline Schema Suite

A JSON Schema designed to ensure consistent data structure across production pipelines.

## 🌟 Features

* **Core shot schema** – validates every scene or sub-scene with detailed metadata
* **Project schema** – wraps all shots for whole-show validation and global settings
* **Enum packs** – single source of truth for techniques, risk flags, screen zones, and more
* **Taxonomy packs** – human-friendly categorization for UI components
* **LLM I/O schema** – guarantees safe, auditable AI edits
* **Comprehensive examples** – real-world usage patterns and edge cases
* **Automated testing** – full validation suite with edge case coverage
* **ESM/CommonJS Support** – dual module format support for modern Node.js environments

## 📦 Installation

```bash
# npm
npm install immerschema@1.3.2

# pip
pip install immerschema==1.3.2
```

## 📦 Project Structure

```
immerschema/
├── schemas/                    # Core JSON Schema files
│   ├── enum/                  # Enumeration schemas
│   ├── ext/                   # Extension schemas
│   ├── io/                    # Input/Output schemas
│   └── taxonomy/              # Taxonomy definitions
├── examples/                   # Comprehensive examples
├── test/                      # Test files and validation
└── scripts/
    └── test_runner.js         # Automated test runner
```

## 🚀 Quick Start

### Installation

```bash
# Install dependencies
npm install

# Or install AJV globally for command-line validation
npm run install-global-ajv
```

### Basic Validation

```bash
# Validate a shot against core schema
ajv validate -s schemas/core.schema.json -d examples/simple_shot.json

# Validate whole project
ajv validate -s schemas/project.schema.json -d examples/simple_project.json

# Run all automated tests
npm test
```

## 🧪 Testing & Validation

### Automated Test Suite

Run the comprehensive test suite:

```bash
npm test
```

This validates:
- All example files against their schemas
- Edge cases and boundary conditions
- Invalid input detection
- Enum value validation
- Required field enforcement

### Manual Testing

```bash
# Test individual files
ajv validate -s schemas/core.schema.json -d test/edge_cases.json

# Test with verbose output
ajv validate -s schemas/project.schema.json -d examples/comprehensive_project.json --verbose
```


```bash
# Install dependencies
npm install

# Run tests during development
npm test

# Validate specific examples
npm run validate-shot examples/ai_generated_shot.json
npm run validate-project examples/comprehensive_project.json
```

## 📄 License

MIT License - see LICENSE file for details.

---
cfspopcon 7.2.0
Empirically-derived scoping of tokamak operational space.
Requires Python: >=3.10

cfspopcon: 0D Plasma Calculations & Plasma OPerating CONtours
--------------------------------------------------------------

[![Build Status](https://github.com/cfs-energy/cfspopcon/actions/workflows/workflow_actions.yml/badge.svg)](https://github.com/cfs-energy/cfspopcon/actions)
[![Checked with mypy](http://www.mypy-lang.org/static/mypy_badge.svg)](http://mypy-lang.org/)
[![Documentation Status](https://readthedocs.org/projects/cfspopcon/badge/?version=latest)](https://cfspopcon.readthedocs.io/en/latest/?badge=latest)
[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/cfs-energy/cfspopcon/HEAD)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.10054879.svg)](https://doi.org/10.5281/zenodo.10054879)

POPCONs (Plasma OPerating CONtours) is a tool developed to explore the performance and constraints of tokamak designs based on 0D scaling laws, model plasma kinetic profiles, and physics assumptions on the properties and behavior of the core plasma.

All of our documentation is available at [cfspopcon.readthedocs.io](https://cfspopcon.readthedocs.io/en/latest/). There, you can find installation instructions, instructions for how to run `cfsPOPCON` via the command-line-interface and also explanations of the example Jupyter notebooks in [docs/doc_sources](docs/doc_sources).