semvec/pypi-packages huggingface dataset:
repen 0.1.0
Automation engine for report generation
License: MIT
Requires Python: >=3.12

# Repen

Automation Engine for Report Generation in Python

Repen is a Python library for automated report generation that follows the same philosophy as [Manim](https://github.com/3b1b/manim) but for reports. It provides a declarative way to create beautiful, reproducible reports from various data sources.

## ✨ Features

- Declarative API - Build reports using a clean, chainable API
- Smart Adapters - Automatically convert common Python objects to report components
- Multiple Outputs - HTML with themes, debug output, extensible renderers
- Rich Components - Text with markdown-like syntax, tables, metrics, figures, images
- Extensible - Easy to add new adapters, components, and renderers

## 🚀 Quick Start

Installation

``` shell
pip install repen
```

Basic Usage

``` python
import matplotlib.pyplot as plt
import pandas as pd
from repen import Report

# Create a report
report = Report(title="Sales Analysis Q4 2023")

# Add content - Repen automatically adapts different data types
report.add("# Executive Summary")
report.add("Monthly sales performance with **key metrics** and trends.")

# Add metrics
report.add(
    {
        "Revenue": ("$1.2M", "USD", "highlight"),
        "Growth": ("15.2%", None, "success"),
        "Customers": (1250, "active", "default"),
    }
)

# Add a pandas DataFrame
df = pd.DataFrame(
    {
        "Month": ["Oct", "Nov", "Dec"],
        "Sales": [350000, 420000, 430000],
        "Growth": ["12%", "20%", "2.4%"],
    }
)
report.add(df)

# Add a matplotlib figure
fig, ax = plt.subplots()
ax.plot([1, 2, 3], [350, 420, 430])
ax.set_xlabel("Month")
ax.set_ylabel("Sales (K)")
ax.set_title("Sales Trend")
report.add(fig)

# Render and save
report.save("sales_report.html")
```
timepix 0.1.1
A lightweight Python utility for measuring execution time between named checkpoints
License: MIT
Requires Python: >=3.10
Keywords: timer, benchmark, profiling, performance, time

# TimePix

A lightweight Python utility for measuring execution time between named checkpoints.

## Installation

```bash
$ pip install timepix
```
## Quick start
```python
from timepix import timepix as tp
import time

tp.set_point("start")
time.sleep(0.5)

tp.set_point("middle")
time.sleep(0.3)

tp.from_point("start") # stdout >> Time from point "start": 0.7988662s.
tp.from_last_point() # stdout >> Time from point "middle": 0.3008336s.
tp.between_points("start", "middle") # stdout >> Time between "start" and "middle" is 0.4981095s.
```
mcp-test-kits 0.0.6
A comprehensive MCP testing server for debugging and testing MCP clients
License: MIT
Requires Python: >=3.13
Keywords: ai, llm, mcp, model-context-protocol, testing

# MCP Test Kits - Python

Build from source using FastMCP.

## Build

```bash
cd python
uv sync
```

---

## stdio

```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "command": "uv",
      "args": ["run", "mcp-test-kits"],
      "cwd": "/path/to/mcp-test-kits/python",
      "transport": "stdio"
    }
  }
}
```

---

## HTTP

Start server:
```bash
uv run mcp-test-kits --transport http --port 3000
```

MCP client config:
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/mcp",
      "transport": "http"
    }
  }
}
```

### With OAuth

Start server:
```bash
uv run mcp-test-kits --transport http --port 3000 --enable-oauth
# or auto-approve for testing
uv run mcp-test-kits --transport http --port 3000 --enable-oauth --oauth-auto-approve
```

MCP client config (OAuth discovery automatic):
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/mcp",
      "transport": "http"
    }
  }
}
```

Or with pre-obtained token:
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/mcp",
      "transport": "http",
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_TOKEN"
      }
    }
  }
}
```

---

## SSE

Start server:
```bash
uv run mcp-test-kits --transport sse --port 3000
```

MCP client config:
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/sse",
      "transport": "sse"
    }
  }
}
```

### With OAuth

Start server:
```bash
uv run mcp-test-kits --transport sse --port 3000 --enable-oauth
# or auto-approve for testing
uv run mcp-test-kits --transport sse --port 3000 --enable-oauth --oauth-auto-approve
```

MCP client config (OAuth discovery automatic):
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/sse",
      "transport": "sse"
    }
  }
}
```

Or with pre-obtained token:
```json
{
  "mcpServers": {
    "mcp-test-kits": {
      "url": "http://localhost:3000/sse",
      "transport": "sse",
      "headers": {
        "Authorization": "Bearer YOUR_ACCESS_TOKEN"
      }
    }
  }
}
```

---

## Test with MCP Inspector

```bash
# stdio
npx @modelcontextprotocol/inspector uv run mcp-test-kits

# HTTP (start server first: uv run mcp-test-kits --transport http --port 3000)
npx @modelcontextprotocol/inspector --transport http --server-url http://localhost:3000/mcp

# SSE (start server first: uv run mcp-test-kits --transport sse --port 3000)
npx @modelcontextprotocol/inspector --transport sse --server-url http://localhost:3000/sse
```

---

## Development

```bash
uv sync --all-extras
uv run pytest
uv run mypy src --check-untyped-defs
uv run ruff check src
```
pyxelist 0.3.0
A small Python library for drawing grid-based graphics using turtle.
License: MIT
Requires Python: >=3.10
Keywords: turtle, grid, graphics, pyxel, 2D

# PyxelList

**PyxelList** is a small Python library for creating and drawing grid-based graphics using the standard `turtle` module.  
It allows you to display 2D grids, move them around, and even handle simple frame-based animations—all in pure Python with no external dependencies.

---

## Features

- Draw solid or animated grids using turtle.
- Easily position grids anywhere on the screen.
- Set or move grids along `x`, `y`, or both axes.
- Handle simple transparency using `"#======"` in grid data.
- Works with Python’s built-in `turtle`—no additional libraries required.

---

## Installation

You can install PyxelList (once it’s published on PyPI) using:

```bash
pip install pyxelist