Pixel KPI

Create beautiful pixel-art dashboards for your KPIs! Connect to Notion, GitHub, or PyPi and display your data on Pixoo devices or other displays.

Features

• 🎨 Pixel-art style components (text, charts, progress bars, etc.)
• 🔌 Connect to multiple data sources:
  • GitHub stars statistics
  • Notion sprint progress
  • PyPi download counts
  • ... (more to come)
• 📺 Multiple display options:
  • Divoom Pixoo devices
  • Terminal output
  • Matplotlib visualization
  • ... (more to come)

Quick Start

1. Install the package:

pip install pixel_kpi

2. Create a simple PyPi Download dashboard:

from pixel_kpi.displays.matplotlib_display import MatplotlibDisplay
from pixel_kpi.connectors.pypi_connector import PyPiConnector
from pixel_kpi.views.package_download_view import PackageDownloadView

# Create display
display = MatplotlibDisplay(
    display_name="MatplotlibDisplay",
    display_id=0,
    width=64,
    height=64
)

# Set up PyPi connector
pypi_connector = PyPiConnector()

# Create and run view 
view = PackageDownloadView(display=display, connector=pypi_connector, processor=None, refresh_rate=60*15, main_color=(80, 255, 255), package_name=args.package_name)
view.run()

Examples

Check out the example scripts in the examples folder:

• github_star_stats.py - Display GitHub repository stars
• notion_sprint.py - Show Notion sprint progress
• pypi_downloads.py - Track PyPi package downloads
• notion_github_rotate.py - Rotate between multiple views

Example usage:

python examples/pypi_downloads.py --display_type matplot --package_name pi_optimal

python examples/github_star_stats.py \
  --display_type matplot \
  --github_api_key "xxx" \
  --github_repo "owner/repo"

python examples/notion_sprint.py \
  --display_type terminal \
  --notion_api_key "your_notion_api_key" \
  --notion_database_id "your_database_id" \
  --refresh_rate 900 

Learn More

Browse the Jupyter notebooks in notebooks to:

• See component examples and usage
• Learn how to create custom views
• Explore different display options
• Understand the data pipeline

Contributing

Setup

Here's how to get started:

1. Fork & clone
2. Setup dev environment:

poetry install

3. Create feature branch:

git checkout -b feature/name

Structure

Here is the project structure:

📦 pixel_kpi
 ┣ 📂 pixel_kpi/      # Core package
 ┃ ┣ 📂 components/   # UI elements
 ┃ ┣ 📂 connectors/   # Data sources
 ┃ ┣ 📂 displays/     # Output targets
 ┃ ┣ 📂 processors/   # Data transforms
 ┃ ┗ 📂 views/        # Dashboards
 ┣ 📂 examples/       # Sample scripts
 ┗ 📂 notebooks/      # Tutorials

Core Concepts

• Components: Pixel-art UI elements (text, charts, progress bars, etc.)
• Connectors: Data sources (GitHub, Notion, PyPi, etc.)
• Displays: Output targets (Pixoo, terminal, Matplotlib, etc.)
• Processors: Data transforms (aggregations, filters, etc.)
• Views: Dashboards that combine components, connectors, displays, and processors

License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.