Interactive Plot Large Files Module

interactive_plot_large_files.py

This module is designed to facilitate the creation of interactive visualizations for large datasets, specifically tailored for the analysis of periodic phenomena in astronomical observations. It leverages the HoloViews and Bokeh libraries to render dynamic, interactive plots that support efficient exploration of vast amounts of data through decimation, rasterization, and dynamic spreading techniques.

The core functionality is encapsulated in the create_interactive_plot_large function, which processes a CSV file containing period analysis results and generates an interactive plot. This visualization aids in the identification of patterns, outliers, and correlations within the data, offering insights into the underlying astronomical phenomena.

Requirements: - pandas: For loading and processing the CSV data. - HoloViews: For constructing interactive visualizations. - datashader: For rasterizing large datasets to enhance performance. - bokeh: For backend rendering of interactive plots.

Example usage can be found at the bottom of this module, illustrating how to generate and display an interactive plot from a CSV file containing large dataset period analysis results.

QhX.interactive_plot_large_files.create_interactive_plot_large(file_path)

Generates an interactive plot from a specified CSV file containing large datasets of period analysis results. It employs decimation, rasterization, and dynamic spreading to manage large data volumes efficiently, ensuring interactive performance and clarity in data visualization.

Parameters:

file_pathstr

Path to the CSV file containing the dataset for visualization. Expected columns include detected periods, error bounds, significance, band identifiers, period differences, IoU scores, and classifications.

Returns:

hv.DynamicMap

An interactive HoloViews plot object that can be displayed in Jupyter Notebooks or exported as an HTML file for broader analysis and exploration.

Example:

>>> interactive_plot = create_interactive_plot_large('path_to_large_dataset.csv')
>>> hv.save(interactive_plot, 'interactive_visualization.html', backend='bokeh')

Note:

This function is specifically optimized for large datasets, applying techniques such as decimation and rasterization to maintain performance without compromising the ability to discern patterns and outliers in the data.