ETAP Automated PDF Report Generator

This repository contains a modular Python automation pipeline designed to parse operational parameter logs (from ETAP simulation run routines, exported as .xlsx or .csv files) and dynamically generate publication-quality, classic academic conference-style PDF reports.

Features

• Lightweight & Fast: Built using pure-Python libraries (ReportLab & Matplotlib). Runs in milliseconds without heavy system dependencies (no headless browsers like Playwright/Chromium or HTML-to-PDF compilers like WeasyPrint/wkhtmltopdf).
• Classic Academic Styling: Adheres to a monochrome greyscale IEEE-style format including title sections, authors, dates, structured tables, mathematical limits status (PASS/FAIL/WARNING highlight), and running headers/footers with dynamic page numbers (Page X of Y).
• Data Visualizations: Matplotlib plots are auto-generated from incoming time-series datasets:
  1. Line Chart: Trend runs of parameters across the operational period.
  2. Bar Chart: Direct comparative analysis of final measured parameter values against safety limits.
• Dual Execution Modes:
  1. CLI Mode: Run a command to compile a single report from a path.
  2. Watcher Mode: Run a lightweight background monitoring service that automatically processes any new spreadsheets dropped into the input folder.

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Directory Structure

├── data/
│   ├── input/             # Place incoming spreadsheets here (.xlsx / .csv)
│   └── output/            # Contains completed PDF reports and temporary charts
├── logs/
│   └── automation.log     # Detailed pipeline logs and execution traces
├── src/
│   ├── parser.py          # Handles Excel & CSV reading, metadata, and tables
│   ├── charts.py          # Builds Matplotlib monochrome visualizations
│   ├── generator.py       # Renders the ReportLab PDF pages and custom canvas layouts
│   └── main.py            # Orchestrator for CLI arguments, logging, and Watchdog daemon
├── requirements.txt       # Python package requirements
└── README.md              # Project documentation

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Setup & Installation

1. Create a Python virtual environment:

   python3 -m venv .venv

2. Activate and install requirements:

   .venv/bin/pip install -r requirements.txt

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Usage

Ensure you run commands with PYTHONPATH=src set (or add src/ to your environment path) to resolve local module imports correctly.

1. Compile a Specific Report (CLI Mode)

To compile a spreadsheet into a PDF report immediately:

PYTHONPATH=src .venv/bin/python src/main.py --input data/input/sample_run_01.xlsx

You can also explicitly define the destination PDF path:

PYTHONPATH=src .venv/bin/python src/main.py --input data/input/sample_run_02.csv --output data/output/turbine_compliance_report.pdf

2. Auto-Process New Files (Watcher Mode)

To launch the background folder watcher monitoring the data/input/ directory:

PYTHONPATH=src .venv/bin/python src/main.py --watch

Drop any new .csv or .xlsx file into data/input/, and the PDF report will automatically be created in data/output/ within seconds. Press Ctrl+C in the terminal to stop the watcher daemon.

3. Live Demo Portal (Web Portal Mode)

To launch a self-hosted Flask portal where anyone can upload spreadsheets and download generated PDFs directly in their browser:

PYTHONPATH=src .venv/bin/python src/app.py

By default, the server runs on port 5000 (e.g., http://localhost:5000/ or http://<your-homelab-ip>:5000/).

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Input Spreadsheet Layout Formats

Format A: Multi-sheet Excel (.xlsx)

Create an Excel file with exactly three sheets:

1. Metadata: Key-value rows (A: parameter key, B: text value) for Title, Author, Date, Abstract, and Methodology.
2. Summary: Tabular columns: Parameter, Measured, Units, Limit, Status, Conclusion.
3. TimeSeries: Row-column table with a time index in the first column, and measurements for the parameters in remaining columns.

Format B: Marked Block CSV (.csv or single-sheet Excel)

Provide a flat file separated into blocks using section markers [Metadata], [Summary], and [TimeSeries]:

[Metadata]
Title,Primary Utility Inverter Run
Author,Vijay Kumar Singh
Date,2026-06-18
Abstract,Brief summary of the test.
Methodology,Step load testing.

[Summary]
Parameter,Measured,Units,Limit,Status,Conclusion
Active Power,45.2,kW,>40,PASS,Normal feed-in.

[TimeSeries]
Time,Active Power
0,40.1
10,42.5
20,44.8

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Running Regression Tests

The project includes a regression test suite that verifies parsing logic (Excel & CSV), chart generation, PDF compiling, and the end-to-end processing pipeline:

.venv/bin/python -m unittest discover -s tests