Deployment Guide

This guide explains how to deploy the timeseries-sparklines package on other machines.

Package Files

After building, the following distribution files are created in the dist/ directory:

Installation on Target Machine

  1. Transfer the wheel file to the target machine: 
    # From your local machine
scp dist/timeseries_svg-0.1.0-py3-none-any.whl user@target-machine:/path/to/deploy/
  2. Install the package on the target machine: 
    pip install timeseries_svg-0.1.0-py3-none-any.whl

Option 2: Install from Source Distribution

  1. Transfer the tar.gz file to the target machine: 
    scp dist/timeseries_svg-0.1.0.tar.gz user@target-machine:/path/to/deploy/
  2. Install the package on the target machine: 
    pip install timeseries_svg-0.1.0.tar.gz

Option 3: Install from Git Repository (if available)

If you have a git repository:

pip install git+https://github.com/vrraj/timeseries-sparklines.git

Installation with API Dependencies

If you want to use the FastAPI server, install with API dependencies:

pip install timeseries_svg-0.1.0-py3-none-any.whl[api]

Running the API Server

After installation, you can run the API server using the provided CLI command:

timeseries-server

This will start a FastAPI server on http://0.0.0.0:9300

API Endpoints

Using as a Library

Basic Library Usage

from timeseries_svg import SparklineRenderer, TimeSeriesChartRenderer

# Sparkline example
data = [
    {"d": "2024-01-01", "v": 100.0},
    {"d": "2024-01-02", "v": 102.5},
    {"d": "2024-01-03", "v": 101.2},
]

renderer = SparklineRenderer(width=96, height=32)
svg = renderer.render(data)
print(svg)

Integration with Your Application

from fastapi import FastAPI, Response
from timeseries_svg import SparklineRenderer

app = FastAPI()
renderer = SparklineRenderer()

@app.post("/api/sparkline")
async def get_sparkline(request: dict):
    # Your data fetching logic here
    data = fetch_your_data(request)
    
    # Render SVG
    svg = renderer.render(data, color_by_open=True)
    
    return Response(content=svg, media_type="image/svg+xml")

System Requirements

For API Server:

Verification

After installation, verify the package is working:

python -c "from timeseries_svg import SparklineRenderer; print('Installation successful')"

Or test the API server:

timeseries-server
# Then in another terminal:
curl http://localhost:9300/health

Rebuilding the Package

If you make changes to the source code, rebuild the package:

# Clean previous builds
rm -rf dist/ build/ src/*.egg-info

# Build new package
python -m build

Troubleshooting

Import Errors

If you get import errors, ensure you’re using the correct Python environment where the package was installed.

API Server Won’t Start

Make sure you installed with API dependencies:

pip install timeseries_svg-0.1.0-py3-none-any.whl[api]

Permission Errors

Use a virtual environment to avoid permission issues:

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate
pip install timeseries_svg-0.1.0-py3-none-any.whl

Production Deployment

For production deployment, consider:

  1. Use a process manager (systemd, supervisor) to keep the API server running
  2. Use a reverse proxy (nginx) in front of the API server
  3. Configure proper logging and monitoring
  4. Use environment variables for configuration

Example systemd Service

Create /etc/systemd/system/timeseries-server.service:

[Unit]
Description=Timeseries SVG API Server
After=network.target

[Service]
Type=simple
User=www-data
WorkingDirectory=/path/to/your/app
Environment="PATH=/path/to/venv/bin"
ExecStart=/path/to/venv/bin/timeseries-server
Restart=always

[Install]
WantedBy=multi-user.target

Enable and start:

sudo systemctl enable timeseries-server
sudo systemctl start timeseries-server

License

MIT License - see LICENSE file for details.