Source code for ysights.algorithms.topics

"""
Topic Analysis Algorithms
==========================

This module provides functions for analyzing topic-related dynamics in YSocial simulations.
These functions help understand how topics spread, how quickly they are adopted,
and when engagement peaks occur.

The module is currently under development and contains placeholder functions for:
- Topic spread analysis
- Adoption rate calculations
- Peak engagement time detection

Example:
    Basic usage of topic analysis functions::

        from ysights import YDataHandler
        from ysights.algorithms import topics

        # Initialize data handler
        ydh = YDataHandler('path/to/database.db')

        # Analyze topic spread (to be implemented)
        # spread = topics.topic_spread(ydh)

        # Calculate adoption rates (to be implemented)
        # rates = topics.adoption_rate(ydh)
"""

from ysights import YDataHandler




def topic_spread(YDH: YDataHandler):
    """
    Analyze the spread of topics across the social network.

    This function will analyze how topics diffuse through the network over time,
    identifying patterns of information spread and influence.

    :param YDH: YDataHandler instance for database operations
    :type YDH: YDataHandler
    :return: Topic spread analysis results (to be implemented)
    :rtype: None

    Example::

        from ysights import YDataHandler
        from ysights.algorithms.topics import topic_spread

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

        # Analyze topic spread (function to be implemented)
        # results = topic_spread(ydh)

    Note:
        This function is currently a placeholder and needs to be implemented.

    See Also:
        :func:`adoption_rate`: Calculate topic adoption rates
        :func:`peak_engagement_time`: Find peak engagement times
    """
    pass





def adoption_rate(YDH: YDataHandler):
    """
    Calculate the adoption rate of topics over time.

    This function will measure how quickly agents adopt and engage with
    different topics in the simulation, providing insights into topic
    popularity and virality.

    :param YDH: YDataHandler instance for database operations
    :type YDH: YDataHandler
    :return: Topic adoption rate metrics (to be implemented)
    :rtype: None

    Example::

        from ysights import YDataHandler
        from ysights.algorithms.topics import adoption_rate

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

        # Calculate adoption rates (function to be implemented)
        # rates = adoption_rate(ydh)
        # print(f"Average adoption rate: {rates}")

    Note:
        This function is currently a placeholder and needs to be implemented.

    See Also:
        :func:`topic_spread`: Analyze topic spread patterns
        :func:`peak_engagement_time`: Find peak engagement times
    """
    pass





def peak_engagement_time(YDH: YDataHandler):
    """
    Identify peak engagement times for topics.

    This function will determine when topics receive the most attention
    and engagement from agents, helping to understand temporal patterns
    in topic dynamics.

    :param YDH: YDataHandler instance for database operations
    :type YDH: YDataHandler
    :return: Peak engagement time analysis (to be implemented)
    :rtype: None

    Example::

        from ysights import YDataHandler
        from ysights.algorithms.topics import peak_engagement_time

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

        # Find peak engagement times (function to be implemented)
        # peaks = peak_engagement_time(ydh)
        # for topic, peak_time in peaks.items():
        #     print(f"Topic {topic} peaked at {peak_time}")

    Note:
        This function is currently a placeholder and needs to be implemented.

    See Also:
        :func:`topic_spread`: Analyze topic spread patterns
        :func:`adoption_rate`: Calculate topic adoption rates
    """
    pass