Data Loading, Visualization, and Results Access
This module provides essential tools for interacting with input data, visualizing trend detection results, and accessing structured outputs. These utilities support both exploratory analysis and integration into larger workflows.
Included Modules
1. data_loader
Loads built-in datasets packaged with PyTrendy. These include:
'series_synthetic': A synthetic time series with embedded uptrends, downtrends, and flat regions.'classes_signals': Reference signals used internally for classifying segments as gradual or abrupt.
Useful for testing, demos, and validating detection logic.
2. plot_pytrendy
- Generates annotated matplotlib plots of detected trend segments over the original signal.
- Highlights Up, Down, Flat, and Noise regions with shaded overlays and ranks significant trends.
- Supports visual debugging and presentation-ready output.
3. results_pytrendy
Wraps the output of detect_trends into a structured PyTrendyResults object. It provides:
- Summary statistics (counts, rankings, best segment)
- Filtering by direction and ranking
- Tabular access to segment metadata
Use this module to prepare input data, visualize detection output, and interact with results in a clean, modular way.