# Research Findings

## Decision: Python 3.11

**Rationale**: Modern Python version offering performance improvements and new features.

**Alternatives considered**: Python 3.9, 3.10 (older, less performant).

## Decision: FastAPI for API Development

**Rationale**: High performance, easy to use, automatic OpenAPI documentation, and strong typing with Pydantic. Well-suited for building robust APIs quickly.

**Alternatives considered**: Flask (less batteries-included, requires more setup for features like validation and documentation), Django (more heavyweight, better for full-stack web applications with ORM and admin panel).

## Decision: Pandas, NumPy, SciPy for Data Analysis

**Rationale**: Industry-standard libraries for numerical operations, data manipulation, and scientific computing in Python. Essential for processing workout data efficiently.

**Alternatives considered**: Custom implementations (time-consuming, error-prone), R (different ecosystem).

## Decision: fitparse, tcxparser, gpxpy for File Parsing

**Rationale**: Dedicated and well-maintained libraries for parsing common workout file formats (FIT, TCX, GPX).

**Alternatives considered**: Manual parsing (complex, error-prone), other less mature libraries.

## Decision: Matplotlib for Chart Generation

**Rationale**: Powerful and flexible library for creating static, interactive, and animated visualizations in Python. Can generate various chart types required by the specification.

**Alternatives considered**: Plotly (more interactive, but potentially more complex for basic static charts), Seaborn (built on Matplotlib, good for statistical plots).

## Decision: PostgreSQL for Data Storage

**Rationale**: Robust, open-source relational database with strong support for complex queries, data integrity, and scalability. Suitable for storing user configurations (FTP) and analysis results.

**Alternatives considered**: SQLite (simpler, file-based, less suitable for concurrent access or larger deployments), MySQL (similar to PostgreSQL, but PostgreSQL often preferred for its advanced features and open-source nature).

## Decision: Pytest for Testing

**Rationale**: Popular, easy-to-use, and powerful testing framework for Python. Supports various testing styles (unit, integration, functional) and has a rich plugin ecosystem.

**Alternatives considered**: unittest (built-in, but often considered less ergonomic than pytest).

## Decision: Linux Server as Target Platform

**Rationale**: Standard and cost-effective platform for deploying Python web applications.

**Alternatives considered**: Windows Server (less common for Python web apps), cloud-specific platforms (can be used on top of Linux).