diagnostics-framework 0.1.1

A pluggable diagnostics framework for running system-specific tests, plots, and reports.

Diagnostics Framework

A pluggable Python framework for running diagnostic tests, generating plots, and viewing reports for any system — all from a Streamlit dashboard.

Overview

The framework uses a plugin architecture: each "system" is a self-contained module that registers its own diagnostic tests, plots, and reports using simple decorators. Adding a new system is as easy as dropping a new Python file into the systems/ folder.

Built-in example systems:

System	Description
generic_example	Basic tabular data checks (nulls, ranges, emptiness)
sensor_monitoring	IoT sensor diagnostics with battery health, temperature validation, and status tracking

Quick Start

Install from PyPI

pip install diagnostics-framework

Install from source (for development)

git clone https://github.com/ppwadhwa/diagnostics-framework.git
cd diagnostics-framework
python -m venv .venv
source .venv/bin/activate
pip install -e .

Run the Dashboard

# If installed via pip:
diagnostics

# Or run directly from the repo:
streamlit run diagnostics_framework/app.py

Open http://localhost:8501, select a system, upload a data file, and click Run Diagnostics.

A sample dataset is included in the repo at sample_data.csv for testing the sensor_monitoring system.

Project Structure

diagnostics_framework/
├── models.py           # Dataclasses: DiagnosticResult, DiagnosticStatus, etc.
├── registry.py         # Singleton registry + decorator API
├── runner.py           # Test execution engine with error isolation
├── app.py              # Streamlit dashboard
└── systems/
    ├── __init__.py             # Auto-discovers all system modules
    ├── generic_example.py      # Example: generic tabular data checks
    └── sensor_monitoring.py    # Example: IoT sensor diagnostics

Adding a New System

Create a new file in diagnostics_framework/systems/ — it will be auto-discovered on import.

# diagnostics_framework/systems/my_system.py
from diagnostics_framework import (
    register_system, register_test, register_plot, register_report,
    DiagnosticResult, DiagnosticStatus,
)

SYSTEM_NAME = "my_system"

@register_system(SYSTEM_NAME, description="My custom system")
class MySystem:
    pass

@register_test(SYSTEM_NAME, name="my_check", description="Validates something important")
def my_check(data):
    # Your test logic here
    return DiagnosticResult(
        test_name="my_check",
        status=DiagnosticStatus.PASS,
        message="Everything looks good.",
    )

@register_plot(SYSTEM_NAME, name="my_plot", description="Visualizes the data")
def my_plot(data):
    import matplotlib.pyplot as plt
    fig, ax = plt.subplots()
    # Your plotting logic here
    return fig

@register_report(SYSTEM_NAME, name="my_report", description="Summary of findings")
def my_report(data):
    return "# My Report\n\nEverything is fine."

That's it — the new system will appear in the dashboard dropdown automatically.

Decorator API

Decorator	Purpose
@register_system(name, description, version)	Register a new system
@register_test(system, name, description)	Add a diagnostic test
@register_plot(system, name, description)	Add a plot generator
@register_report(system, name, description)	Add a report generator

Diagnostic Result Statuses

Status	Meaning
PASS	Test passed successfully
FAIL	Test found a problem
WARNING	Test found something worth noting
ERROR	Test itself crashed (caught automatically by the runner)

Programmatic Usage

You can also use the framework without the dashboard:

import pandas as pd
from diagnostics_framework import run_diagnostics

data = pd.read_csv("sample_data.csv")
summary = run_diagnostics("sensor_monitoring", data)

for result in summary.results:
    print(f"[{result.status.value}] {result.test_name}: {result.message}")

Dependencies

• Python >= 3.10
• streamlit
• pandas
• matplotlib
• seaborn