Update analytics_plot_generator.py

analytics_plot_generator.py

@@ -839,115 +839,3 @@ def generate_content_topic_breakdown_plot(df, topics_col='li_eb_labels', top_n=1
         logging.error(f"Error generating {title}: {e}", exc_info=True)
         if fig: plt.close(fig)
         return create_placeholder_plot(title=f"{title} Error", message=str(e))
-
-
-if __name__ == '__main__':
-    # Create dummy data for testing
-    posts_data = {
-        'id': [f'post{i}' for i in range(1, 8)],
-        'published_at': pd.to_datetime(['2023-01-01', '2023-01-01', '2023-01-02', '2023-01-03', '2023-01-03', '2023-01-03', '2023-01-04']),
-        'likeCount': [10, 5, 12, 8, 15, 3, 20],
-        'commentCount': [2, 1, 3, 1, 4, 0, 5],
-        'shareCount': [1, 0, 1, 1, 2, 0, 1],
-        'clickCount': [20, 15, 30, 22, 40, 10, 50],
-        'impressionCount': [200, 150, 300, 220, 400, 100, 500],
-        'engagement': [0.05, 0.04, 0.06, 0.055, 0.07, 0.03, 0.08],
-        'media_type': ['TEXT', 'IMAGE', 'TEXT', 'VIDEO', 'IMAGE', 'TEXT', 'IMAGE'],
-        'eb_labels': [
-            "['AI', 'Tech']", # String representation of a list
-            ['Innovation'],    # Actual list
-            'General',         # Single string
-            None,
-            ['Tech', 'Future'],
-            "['AI', 'Development']",
-            ['Tech']
-        ],
-        # Corrected 'comment_sentiment' to match a potential fallback name 'sentiment' if the primary is missing.
-        # For testing, let's assume 'comment_sentiment' is the primary and 'sentiment' is a fallback.
-        # To test the fallback, you could rename 'comment_sentiment' to something else in one run.
-        'comment_sentiment': ['Positive', 'Neutral', 'Positive', 'Negative', 'Positive', 'Neutral', 'Positive']
-    }
-    sample_merged_posts_df = pd.DataFrame(posts_data)
-    # Example: To test fallback for comment sentiment, you could do:
-    # sample_merged_posts_df_test_fallback = sample_merged_posts_df.rename(columns={'comment_sentiment': 'actual_comment_sentiment'})
-    # sample_merged_posts_df_test_fallback['sentiment'] = sample_merged_posts_df_test_fallback['actual_comment_sentiment']
-
-
-    follower_data = {
-        'follower_count_type': [
-            'follower_gains_monthly', 'follower_gains_monthly', 'follower_gains_monthly',
-            'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 'follower_geo', 
-            'follower_function', 'follower_function',
-            'follower_industry', 'follower_industry',
-            'follower_seniority', 'follower_seniority'
-        ],
-        'category_name': [
-            '2024-01-01', '2024-02-01', '2024-03-01',
-            'Italy', 'United Kingdom', 'Spain', 'Germany', 'Switzerland', 'France', 'United States', 'Netherlands', 'Brazil', 'Belgium', 
-            'Engineering', 'Sales',
-            'Tech', 'Finance',
-            'Senior', 'Junior'
-        ],
-        'follower_count_organic': [
-            100, 110, 125,
-            4500, 187, 106, 83, 68, 63, 55, 41, 22, 22, 
-            400, 200,
-            250, 180,
-            300, 220
-        ],
-        'follower_count_paid': [
-            20, 30, 25,
-            200, 10, 5, 10, 5, 8, 2, 5, 3, 1, 
-            30, 20,
-            45, 35,
-            60, 40
-        ]
-    }
-    sample_follower_stats_df = pd.DataFrame(follower_data)
-    
-    mentions_data = { # Added for mentions plot testing
-        'date': pd.to_datetime(['2023-01-01', '2023-01-02', '2023-01-02', '2023-01-03']),
-        'sentiment_label': ['Positive', 'Negative', 'Positive', 'Neutral']
-    }
-    sample_mentions_df = pd.DataFrame(mentions_data)
-
-
-    # --- Test individual plots ---
-    # (Using a dictionary for easier management and selective testing)
-    plot_functions_to_test = {
-        "Posts Activity": (generate_posts_activity_plot, [sample_merged_posts_df.copy()], {}),
-        "Engagement Type": (generate_engagement_type_plot, [sample_merged_posts_df.copy()], {}),
-        "Mentions Activity": (generate_mentions_activity_plot, [sample_mentions_df.copy()], {}), # Added sample_mentions_df
-        "Mention Sentiment": (generate_mention_sentiment_plot, [sample_mentions_df.copy()], {}), # Added sample_mentions_df
-        "Followers Count": (generate_followers_count_over_time_plot, [sample_follower_stats_df.copy()], {'type_value':'follower_gains_monthly'}),
-        "Followers Growth": (generate_followers_growth_rate_plot, [sample_follower_stats_df.copy()], {'type_value':'follower_gains_monthly'}),
-        "Followers Geo": (generate_followers_by_demographics_plot, [sample_follower_stats_df.copy()], {'type_value':'follower_geo', 'plot_title':"Followers by Location"}),
-        "Engagement Rate": (generate_engagement_rate_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Reach (Clicks)": (generate_reach_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Impressions": (generate_impressions_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Likes Over Time": (generate_likes_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Shares Over Time": (generate_shares_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Comments Over Time": (generate_comments_over_time_plot, [sample_merged_posts_df.copy()], {}),
-        "Comments Sentiment": (generate_comments_sentiment_breakdown_plot, [sample_merged_posts_df.copy()], {'sentiment_column':'comment_sentiment'}),
-        "Post Frequency Daily": (generate_post_frequency_plot, [sample_merged_posts_df.copy()], {'resample_period':'D'}),
-        "Post Frequency Weekly": (generate_post_frequency_plot, [sample_merged_posts_df.copy()], {'resample_period':'W'}),
-        "Content Format": (generate_content_format_breakdown_plot, [sample_merged_posts_df.copy()], {}),
-        "Content Topics": (generate_content_topic_breakdown_plot, [sample_merged_posts_df.copy()], {'top_n':5}),
-    }
-
-    # Create a directory to save plots if it doesn't exist
-    # import os
-    # output_dir = "test_plots_updated" # Changed dir name
-    # os.makedirs(output_dir, exist_ok=True)
-
-    for name, (func, args, kwargs_dict) in plot_functions_to_test.items(): # Renamed kwargs to kwargs_dict
-        logging.info(f"--- Testing: {name} ---")
-        fig = func(*args, **kwargs_dict) # Use kwargs_dict
-        if fig:
-            logging.info(f"{name} plot generated.")
-            # fig.savefig(os.path.join(output_dir, f"{name.lower().replace(' ', '_')}_test.png"))
-            # plt.close(fig) # Close the figure after saving to free memory
-        else:
-            logging.warning(f"{name} plot generation failed or returned None.")
-    
-    logging.info("Test script finished. Review plots if saved locally.")