ysights.viz.topics_viz

Topic Visualization Functions

This module provides visualization functions for analyzing topic dynamics and evolution in YSocial simulations. It focuses on temporal patterns of interest and topic density across different user groups.

Functions:

topic_density_temporal_evolution: Visualize topic density over time as a heatmap

Example

Visualizing topic evolution:

from ysights import YDataHandler
from ysights.viz import topic_density_temporal_evolution

ydh = YDataHandler('path/to/database.db')

# Visualize all topics over time
fig = topic_density_temporal_evolution(ydh, min_days=15)
fig.show()

# Filter by user leaning
fig_left = topic_density_temporal_evolution(ydh, min_days=10, leaning='left')
fig_left.update_layout(title="Topics: Left-leaning Users")
fig_left.show()

See also

ysights.algorithms.topics: Topic analysis algorithms
ysights.viz.global_trends: Aggregate trend visualizations

Functions

topic_density_temporal_evolution(YDH[, ...])

Visualize the temporal evolution of topic density in a heatmap format.

ysights.viz.topics_viz.topic_density_temporal_evolution(YDH, min_days=15, leaning=None)[source]

Visualize the temporal evolution of topic density in a heatmap format. This function retrieves user interest data, aggregates it by interest and day, and generates a heatmap

Parameters:

YDH (YDataHandler) – YDataHandler instance for database operations
min_days – Minimum number of days an interest must be present to be included in the heatmap
leaning (str) – Optional parameter to filter interests by user leaning (e.g., ‘democrats’, ‘republican’, etc.)

Returns:

A Plotly heatmap figure showing the density of topics over time