calibration-statistics-tool 1.0.5

Statistics and antenna pattern analysis tool for radar systems

StatisticToolPython

A comprehensive statistics and antenna pattern analysis tool for radar systems, built with Python and PySide6.

Features

• Statistics Analysis: Process and analyze measurement data with statistical limits
• Antenna Pattern Processing: Handle antenna diagrams and patterns for various radar types
• GUI Interface: User-friendly Qt-based interface for data visualization
• Report Generation: Automated PowerPoint and PDF report creation
• Data Processing: Support for multiple data formats (JSON, YAML, protocol files)

Requirements

• Python 3.8+
• PySide6 for GUI
• matplotlib for plotting
• numpy for numerical operations
• PyYAML for configuration
• python-pptx for PowerPoint generation

Installation

1. Clone the repository:

git clone <repository-url>
cd statistictoolpython

2. Create virtual environment:

python -m venv .venv
.venv\Scripts\activate  # Windows
# or
source .venv/bin/activate  # Linux/Mac

3. Install dependencies:

pip install -e .

Usage

Run the main application:

python main.py

Or use the command-line entry point:

statistictool

Project Structure

The application follows an MVC architecture split across several sub-packages:

main.py                         # Thin wiring file -- startup, DI, handler wiring
shared/                         # Domain logic (controllers, parsers, models)
    models.py                   # Data model classes
    parse_controller.py         # ParseController -- protocol/PTU2 business logic
    plot_controller.py          # PlotController -- diagram/plot state management
    calcdiagram_functions.py    # Vectorised diagram calculations (NumPy)
    parsePTU2data.py            # PTU2 file parser and data structures
    parse_helpers.py            # Protocol parsing helpers
    loaddata.py                 # Data loading utilities
ui/
    main_view.py                # MainView -- owns QApplication and all widgets
    pyside_qtgui.py             # Backward-compat shim (delegates to MainView)
    ui_constants.py             # Pure-Python layout/size/style constants
    widgets.py                  # Reusable Qt widget classes (PlotFrame, etc.)
    handlers/                   # Signal handler modules (one per tab)
        app_context.py          # AppContext -- dependency-injection container
        parse_handlers.py       # Parse Protocols tab handlers
        sys_diag_handlers.py    # System Diagrams tab handlers
        tx_ant_diag_handlers.py # Tx-Antenna Diagrams tab handlers
        ant_pat_handlers.py     # Antenna Patterns tab handlers
    tabs/                       # Tab widget declarations (one file per tab)
        parse_prot_tab.py
        sys_diag_tab.py
        tx_ant_diag_tab.py
        ant_pat_tab.py
system_diagrams/                # AntennaSystemDiagrams computation module
antenna_patterns/               # Antenna pattern analysis (antpat_v5)
tx_diagrams/                    # Tx-Antenna Diagrams computation module
config/                         # YAML configuration files
tests/                          # Pytest test suite (613 tests)
    conftest.py                 # Shared fixtures and Qt session setup
    integration/                # Real-Qt integration tests
docs/                           # Architecture documentation

Development

Install development dependencies:

pip install -e ".[dev]"

Run tests:

pytest

Run benchmarks (local only, informational):

pytest --benchmark-only

Lint code:

ruff check .

Architecture

Detailed architecture documentation is in docs/:

• docs/mvc_architecture.md — MVC design, dependency injection, layer boundaries

Test Writer Agent (Jira Zephyr)

The agents/zephyr package analyses the Python source tree and generates manual test cases as a CSV file that can be imported directly into Jira Zephyr Scale (Test Cases → Import → CSV).

Quick start

# Generate Unit + Integration test cases for the shared/ module
python -m agents.zephyr.mcp_server \
    --source shared/ \
    --level Unit \
    --level Integration \
    --output zephyr_tests.csv

Or from Python:

from test_writer import TestWriterAgent

agent = TestWriterAgent(
    source_paths=["shared/", "ui/handlers/"],
    levels=["Unit", "Integration", "System"],
    output_csv="zephyr_tests.csv",
)
path = agent.run()
print(f"Generated: {path}")

CLI reference

Option	Default	Description
--source PATH	.	Directory or .py file to scan. Repeatable.
--level LEVEL	Unit, Integration	Testing level: Unit, Integration, System, Smoke, Regression, Acceptance. Repeatable.
--output FILE	zephyr_tests.csv	Output CSV path.
--root DIR	cwd	Repo root for module-name resolution.
--prefix PREFIX	Calibration-Statistics-Tool	Jira component prefix.
--skip-dir NAME	—	Directory name to skip. Repeatable.

CSV format

The generated file follows the Zephyr Scale CSV import schema:

Column	Description
Name	Test case name (e.g. [Unit] shared.models — MyClass)
Status	Draft / Approved / Deprecated
Priority	High / Medium / Low
Component	Jira component (<prefix>/<top-level-package>)
Labels	Comma-separated: level, symbol kind, package
Precondition	Environment prerequisites
Objective	One-sentence description of what is verified
Folder	Zephyr folder path (/<Level>/<module/path>)
Test Script (Step)	Step action text
Test Script (Step Data)	Input data for the step
Test Script (Expected Result)	Expected outcome

---

License

MIT License - see LICENSE file for details.

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request