analytics_data_processing.py

import pandas as pd
from datetime import datetime, timedelta, time
import logging

# Configure logging for this module
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s')

def filter_dataframe_by_date(df, date_column, start_date, end_date):
    """Filters a DataFrame by a date column within a given date range."""
    if df is None or df.empty or not date_column:
        logging.warning(f"Filter by date: DataFrame is None, empty, or no date_column provided. DF: {df is not None}, empty: {df.empty if df is not None else 'N/A'}, date_column: {date_column}")
        return pd.DataFrame()
    if date_column not in df.columns:
        logging.warning(f"Filter by date: Date column '{date_column}' not found in DataFrame columns: {df.columns.tolist()}.")
        return pd.DataFrame()
    
    df_copy = df.copy() # Work on a copy to avoid SettingWithCopyWarning
    try:
        # Ensure the date column is pandas datetime objects
        if not pd.api.types.is_datetime64_any_dtype(df_copy[date_column]):
            df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')

        # Drop rows where date conversion might have failed (NaT) or was originally NaT
        df_copy.dropna(subset=[date_column], inplace=True)
        if df_copy.empty:
            logging.info(f"Filter by date: DataFrame empty after to_datetime and dropna for column '{date_column}'.")
            return pd.DataFrame()

        # Normalize to midnight. This preserves timezone information if present.
        df_copy[date_column] = df_copy[date_column].dt.normalize()

        # If the column is timezone-aware, convert its values to naive UTC equivalent.
        # This allows comparison with naive filter dates.
        if hasattr(df_copy[date_column].dt, 'tz') and df_copy[date_column].dt.tz is not None:
            logging.info(f"Column '{date_column}' is timezone-aware ({df_copy[date_column].dt.tz}). Converting to naive (from UTC) for comparison.")
            df_copy[date_column] = df_copy[date_column].dt.tz_convert('UTC').dt.tz_localize(None)

    except Exception as e:
        logging.error(f"Error processing date column '{date_column}': {e}", exc_info=True)
        return pd.DataFrame() 

    # Convert start_date and end_date (which are naive Python datetime or naive Pandas Timestamp)
    # to naive pandas Timestamps and normalize them.
    start_dt_obj = pd.to_datetime(start_date, errors='coerce').normalize() if start_date else None
    end_dt_obj = pd.to_datetime(end_date, errors='coerce').normalize() if end_date else None

    # Perform the filtering
    # df_filtered is already df_copy which has NaNs dropped and dates processed
    if start_dt_obj and end_dt_obj:
        df_filtered_final = df_copy[(df_copy[date_column] >= start_dt_obj) & (df_copy[date_column] <= end_dt_obj)]
    elif start_dt_obj:
        df_filtered_final = df_copy[df_copy[date_column] >= start_dt_obj]
    elif end_dt_obj:
        df_filtered_final = df_copy[df_copy[date_column] <= end_dt_obj]
    else:
        df_filtered_final = df_copy # No date filtering if neither start_date nor end_date is provided
    
    if df_filtered_final.empty:
        logging.info(f"Filter by date: DataFrame became empty after applying date range to column '{date_column}'.")
    
    return df_filtered_final


def prepare_filtered_analytics_data(token_state_value, date_filter_option, custom_start_date, custom_end_date):
    """
    Retrieves data from token_state, determines date range, filters posts, mentions, and follower time-series data.
    Merges posts with post stats.
    Returns:
        - filtered_merged_posts_df: Posts merged with stats, filtered by date.
        - filtered_mentions_df: Mentions filtered by date.
        - date_filtered_follower_stats_df: Follower stats filtered by date (for time-series plots).
        - raw_follower_stats_df: Unfiltered follower stats (for demographic plots).
        - start_dt_filter: Determined start date for filtering.
        - end_dt_filter: Determined end date for filtering.
    """
    logging.info(f"Preparing filtered analytics data. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")

    posts_df = token_state_value.get("bubble_posts_df", pd.DataFrame()).copy()
    mentions_df = token_state_value.get("bubble_mentions_df", pd.DataFrame()).copy()
    follower_stats_df = token_state_value.get("bubble_follower_stats_df", pd.DataFrame()).copy()
    post_stats_df = token_state_value.get("bubble_post_stats_df", pd.DataFrame()).copy() # Fetch post_stats_df

    date_column_posts = token_state_value.get("config_date_col_posts", "published_at")
    date_column_mentions = token_state_value.get("config_date_col_mentions", "date")
    # Assuming follower_stats_df has a 'date' column for time-series data
    date_column_followers = token_state_value.get("config_date_col_followers", "date") 

    # Determine date range for filtering
    current_datetime_obj = datetime.now()
    current_time_normalized = current_datetime_obj.replace(hour=0, minute=0, second=0, microsecond=0) 
    
    end_dt_filter = current_time_normalized 
    start_dt_filter = None

    if date_filter_option == "Last 7 Days":
        start_dt_filter = current_time_normalized - timedelta(days=6) 
    elif date_filter_option == "Last 30 Days":
        start_dt_filter = current_time_normalized - timedelta(days=29) 
    elif date_filter_option == "Custom Range":
        start_dt_filter_temp = pd.to_datetime(custom_start_date, errors='coerce')
        start_dt_filter = start_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(start_dt_filter_temp) else None 
        
        end_dt_filter_temp = pd.to_datetime(custom_end_date, errors='coerce')
        end_dt_filter = end_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(end_dt_filter_temp) else current_time_normalized
    
    logging.info(f"Date range for filtering: Start: {start_dt_filter}, End: {end_dt_filter}")

    # Merge posts_df and post_stats_df
    merged_posts_df = pd.DataFrame()
    if not posts_df.empty and not post_stats_df.empty:
        # Assuming posts_df has 'id' and post_stats_df has 'post_id' for merging
        if 'id' in posts_df.columns and 'post_id' in post_stats_df.columns:
            merged_posts_df = pd.merge(posts_df, post_stats_df, left_on='id', right_on='post_id', how='left')
            logging.info(f"Merged posts_df ({len(posts_df)} rows) and post_stats_df ({len(post_stats_df)} rows) into merged_posts_df ({len(merged_posts_df)} rows).")
        else:
            logging.warning("Cannot merge posts_df and post_stats_df due to missing 'id' or 'post_id' columns.")
            # Fallback to using posts_df if merge fails but provide an empty df for stats-dependent plots
            merged_posts_df = posts_df # Or handle as an error / empty DF for those plots
    elif not posts_df.empty:
        logging.warning("post_stats_df is empty. Proceeding with posts_df only for plots that don't require stats.")
        merged_posts_df = posts_df # Create necessary columns with NaN if they are expected by plots
        # For columns expected from post_stats_df, add them with NaNs if not present
        expected_stat_cols = ['engagement', 'impressionCount', 'clickCount', 'likeCount', 'commentCount', 'shareCount']
        for col in expected_stat_cols:
            if col not in merged_posts_df.columns:
                merged_posts_df[col] = pd.NA


    # Filter DataFrames by date
    filtered_merged_posts_data = pd.DataFrame()
    if not merged_posts_df.empty and date_column_posts in merged_posts_df.columns:
        filtered_merged_posts_data = filter_dataframe_by_date(merged_posts_df, date_column_posts, start_dt_filter, end_dt_filter)
    elif not merged_posts_df.empty:
        logging.warning(f"Date column '{date_column_posts}' not found in merged_posts_df. Returning unfiltered merged posts data.")
        filtered_merged_posts_data = merged_posts_df # Or apply other logic
    
    filtered_mentions_data = pd.DataFrame()
    if not mentions_df.empty and date_column_mentions in mentions_df.columns:
        filtered_mentions_data = filter_dataframe_by_date(mentions_df, date_column_mentions, start_dt_filter, end_dt_filter)
    elif not mentions_df.empty:
        logging.warning(f"Date column '{date_column_mentions}' not found in mentions_df. Returning unfiltered mentions data.")
        filtered_mentions_data = mentions_df

    date_filtered_follower_stats_df = pd.DataFrame()
    raw_follower_stats_df = follower_stats_df.copy() # For demographic plots, use raw (or latest snapshot logic)

    if not follower_stats_df.empty and date_column_followers in follower_stats_df.columns:
        date_filtered_follower_stats_df = filter_dataframe_by_date(follower_stats_df, date_column_followers, start_dt_filter, end_dt_filter)
    elif not follower_stats_df.empty:
        logging.warning(f"Date column '{date_column_followers}' not found in follower_stats_df. Time-series follower plots might be empty or use unfiltered data.")
        # Decide if date_filtered_follower_stats_df should be raw_follower_stats_df or empty
        date_filtered_follower_stats_df = follower_stats_df # Or pd.DataFrame() if strict filtering is required

    logging.info(f"Processed - Filtered Merged Posts: {len(filtered_merged_posts_data)} rows, Filtered Mentions: {len(filtered_mentions_data)} rows, Date-Filtered Follower Stats: {len(date_filtered_follower_stats_df)} rows.")
    
    return filtered_merged_posts_data, filtered_mentions_data, date_filtered_follower_stats_df, raw_follower_stats_df, start_dt_filter, end_dt_filter