WikiPulse Analytics & Advanced PDF Report Generator

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📌 General Info

WikiPulse is a powerful analytics pipeline and automation tool designed for deep processing of historical Wikipedia pageviews data. The project transforms raw traffic figures into professional, executive-ready PDF reports tailored for research and business insights.

Why it's useful:

• Market & Trend Analysis: Evaluates audience interest across various concepts and language sections.
• Deep Statistics: Automatically computes a comprehensive range of metrics beyond simple averages (median, quartiles, P90/P95 percentiles, volatility CV%, and growth momentum).
• Cyclicity & Seasonality: Groups traffic data by meteorological seasons to detect cyclical interest peaks.
• Automated Archiving: Organizes generated reports into a clean, date-based hierarchical directory structure (var/reports/YYYY/MM/DD/), preventing file overwrites.

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🚀 Key Features

• Advanced Statistical Indices: Volatility (CV%), Growth Momentum, Median, and P90/P95 percentiles.
• Seasonality Breakdown: Cyclical traffic analysis by calendar seasons.
• Dual-Panel Visualizations: Combines actual trends, smoothed trendline overlays, and seasonal average bar charts.
• Context-Aware Insights: Automated detection of traffic anomalies and spikes.

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📂 Project Structure

    wikipulse/
    ├── scripts/
    │   ├── generate_report.py    # Main analytics and PDF report generation pipeline
    │   └── ...                   # Additional helper scripts
    ├── var/
    │   ├── cache/                # Temporary charts and processed cache
    │   ├── logs/                 # Execution audit logs
    │   └── reports/              # Hierarchical PDF report archives (YYYY/MM/DD/)
    ├── Makefile                  # Automation shortcuts & build targets
    ├── LICENSE                   # MIT License
    └── README.md                 # Project documentation

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🛠️ Installation & Setup

1. Clone the repository:

    git clone https://github.com/your-username/wikipulse.git
    cd wikipulse

2. Install dependencies:

    pip install pandas matplotlib reportlab

3. Configure & Setup Makefile: The project uses a Makefile to streamline routine tasks. Ensure that make is installed on your system (standard on Linux/macOS; for Windows, use environments like Git Bash or WSL).

   Verify the command configurations in your Makefile, allowing you to run the pipeline with a single command:

    make report

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📊 Usage

Run the report generator directly via Python or using the Makefile:

    python scripts/generate_report.py --input path/to/data.json

Or via the shortcut:

    make report

The output will be automatically generated and stored in a path such as: var/reports/YYYY/MM/DD/report_HHMMSS.pdf

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📄 License

Distributed under the MIT License. See LICENSE for more information.