f3-nation-data 0.0.3

library to extract, parse, and analyze F3 Nation SQL database data

f3-nation-data

A Python library for extracting, parsing, and analyzing F3 Nation SQL database data.

Overview

This library provides tools for working with F3 Nation database data, focusing on:

• Users: PAX (participants) information
• AOs: Areas of Operations (workout locations)
• Beatdowns: Workout sessions and backblast data

Setup

Environment Variables

Before using this library, you must set the following environment variables to connect to your F3 Nation database:

export F3_NATION_USER="your_database_username"
export F3_NATION_PASSWORD="your_database_password"
export F3_NATION_HOST="your-database-host.com"
export F3_NATION_DATABASE="your_database_name"
export F3_NATION_PORT="3306"  # Optional, defaults to 3306

Security Note: Never commit database credentials to version control. Always use environment variables.

Usage

Example: Gather All Beatdowns for a Given Week

from f3_nation_data.database import get_sql_engine, create_session
from f3_nation_data.fetch import fetch_sql_beatdowns
from f3_nation_data.analytics import get_week_range

# Create database connection
engine = get_sql_engine()
session = create_session(engine)

# Get the current week's date range
week_start, week_end = get_week_range()

# Fetch all beatdowns for the week
beatdowns = fetch_sql_beatdowns(
    session,
    start_date=week_start.date(),
    end_date=week_end.date(),
)

for bd in beatdowns:
    print(f"AO: {bd.ao_id}, Date: {bd.bd_date}, Q: {bd.q_user_id}")

session.close()

Using Context Manager

from f3_nation_data.database import db_session
from f3_nation_data.models.sql import SqlUserModel

with db_session() as session:
    users = session.query(SqlUserModel).all()
    for user in users:
        print(user.user_id, user.user_name)
    # Session automatically commits and closes

Command-Line Interface (CLI)

This project provides a CLI tool for generating weekly F3 Nation beatdown reports directly from your database.

Features

• Generate a weekly report for any week (default: current week)
• Region-agnostic analytics and reporting
• Robust error handling and clear output
• Options for specifying week, showing version, and more

Usage

To run the CLI and generate a weekly report:

uvx --from f3-nation-data f3-weekly-report [DATE]

• DATE (optional): Any date within the target week (format: YYYY-MM-DD). If omitted, the current week is used.

Example

uvx --from f3-nation-data f3-weekly-report 2024-03-09

Options

• --date YYYY-MM-DD : Specify the week by date
• --version : Show CLI version and exit

Output

The CLI prints a formatted weekly report to stdout, including:

• Week summary (beatdowns, attendance, unique PAX)
• Highest attended workout at each AO
• Top HIMs who posted
• Leaders in Q counts
• AO rankings by unique PAX

Error Handling

• Invalid date format: clear error message and exit
• Missing environment variable: clear error message and exit
• No beatdowns found: clear message and exit

See the tests for examples of CLI output and error handling.

Development

Generating SQL Models

This library includes a script to automatically generate SQLAlchemy models from your database schema:

python dev_utilities/generate_models.py

This ensures models stay in sync with any database schema changes.

Code Quality

# Run all code quality checks (formatting, linting, types, complexity, coverage, prettier)
poe ci-checks

You can also run individual tasks (format, lint, test, etc.) if needed, but poe ci-checks is recommended for full validation.

Architecture

• SQL Models (f3_nation_data/models/sql/): Direct database mappings using SQLAlchemy
• App Models (f3_nation_data/models/app/): Business logic models using Pydantic
• Database (f3_nation_data/database.py): Connection utilities
• Parsing (f3_nation_data/parsing.py): Backblast and data parsing functions
• Analytics (f3_nation_data/analytics.py): Data analysis utilities

Contributing

See CONTRIBUTING.md for development setup and guidelines.