Update analytics_plot_generator.py

analytics_plot_generator.py

@@ -243,8 +243,15 @@ def generate_mention_sentiment_plot(df, sentiment_column='sentiment_label'):
         # Define a list of distinct colors for the pie slices
         pie_slice_colors = plt.cm.get_cmap('Pastel2', len(sentiment_counts))
         
-        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, 
-               colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))], zorder=1)
+        # Removed zorder from ax.pie
+        wedges, texts, autotexts = ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, 
+                                          colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))])
+        # Set zorder for pie elements if needed, though usually not necessary as they draw on top of the background patch
+        for wedge in wedges:
+            wedge.set_zorder(1)
+        for text_item in texts + autotexts:
+            text_item.set_zorder(2)
+
         ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
         # fig.tight_layout(pad=0.5) # tight_layout can sometimes mess with pie charts if labels are long
         fig.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.95) # Give pie chart space
@@ -294,7 +301,8 @@ def generate_followers_count_over_time_plot(df, date_info_column='category_name'
         ax.plot(df_filtered['datetime_obj'], df_filtered[paid_count_col], marker='x', linestyle='--', color='seagreen', label='Paid Followers', zorder=1)
         ax.set_xlabel('Date')
         ax.set_ylabel('Follower Count')
-        ax.legend(zorder=2)
+        legend = ax.legend() # Removed zorder from legend call
+        if legend: legend.set_zorder(2) # Set zorder on the legend object itself
         ax.grid(True, linestyle='--', alpha=0.6, zorder=0)
         plt.xticks(rotation=45)
         fig.tight_layout(pad=0.5)
@@ -359,7 +367,8 @@ def generate_followers_growth_rate_plot(df, date_info_column='category_name',
         ax.set_xlabel('Date')
         ax.set_ylabel('Growth Rate (%)')
         ax.yaxis.set_major_formatter(mticker.PercentFormatter())
-        ax.legend(zorder=2)
+        legend = ax.legend() # Removed zorder from legend call
+        if legend: legend.set_zorder(2) # Set zorder on the legend object itself
         ax.grid(True, linestyle='--', alpha=0.6, zorder=0)
         plt.xticks(rotation=45)
         fig.tight_layout(pad=0.5)
@@ -415,9 +424,6 @@ def generate_followers_by_demographics_plot(df, category_col='category_name',
         bar_width = 0.35
         index = np.arange(len(demographics_data.index))
         
-        # Using tab10 colormap for distinct colors for organic and paid
-        # If you wanted each CATEGORY (e.g., Italy, UK) to have different colors for its pair of bars,
-        # that would require a more complex color mapping. Current setup is distinct for Organic vs Paid.
         color_organic = plt.cm.get_cmap('tab10')(0) 
         color_paid = plt.cm.get_cmap('tab10')(1)
 
@@ -428,7 +434,8 @@ def generate_followers_by_demographics_plot(df, category_col='category_name',
         ax.set_ylabel('Number of Followers')
         ax.set_xticks(index)
         ax.set_xticklabels(demographics_data.index, rotation=45, ha="right")
-        ax.legend(zorder=2)
+        legend = ax.legend() # Removed zorder from legend call
+        if legend: legend.set_zorder(2) # Set zorder on the legend object itself
         ax.grid(axis='y', linestyle='--', alpha=0.6, zorder=0)
 
         for bar_group in [bars1, bars2]:
@@ -694,11 +701,14 @@ def generate_comments_sentiment_breakdown_plot(df, sentiment_column='comment_sen
     if df is None or df.empty:
         return create_placeholder_plot(title=title, message="No data for comment sentiment.")
     if sentiment_column not in df.columns:
-        if 'sentiment' in df.columns and sentiment_column != 'sentiment':
+        if 'sentiment' in df.columns and sentiment_column != 'sentiment': # Check for a common alternative name
             logging.warning(f"Sentiment column '{sentiment_column}' not found, attempting to use 'sentiment' column as fallback for comment sentiment plot.")
-            sentiment_column = 'sentiment'
-        else:
-            return create_placeholder_plot(title=title, message=f"Sentiment column '{sentiment_column}' (and fallback 'sentiment') not found. Available: {df.columns.tolist()}")
+            sentiment_column = 'sentiment' 
+            if sentiment_column not in df.columns: # If fallback also not found
+                 return create_placeholder_plot(title=title, message=f"Fallback sentiment column 'sentiment' also not found. Available: {df.columns.tolist()}")
+        else: # If original and 'sentiment' fallback are not found
+            return create_placeholder_plot(title=title, message=f"Sentiment column '{sentiment_column}' not found. Available: {df.columns.tolist()}")
+
 
     if df[sentiment_column].isnull().all():
         return create_placeholder_plot(title=title, message=f"Sentiment column '{sentiment_column}' contains no valid data.")
@@ -716,8 +726,14 @@ def generate_comments_sentiment_breakdown_plot(df, sentiment_column='comment_sen
         _apply_rounded_corners_and_transparent_bg(fig, ax)
         
         pie_slice_colors = plt.cm.get_cmap('coolwarm', len(sentiment_counts))
-        ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, 
-               colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))], zorder=1)
+        # Removed zorder from ax.pie
+        wedges, texts, autotexts = ax.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=90, 
+                                          colors=[pie_slice_colors(i) for i in range(len(sentiment_counts))])
+        for wedge in wedges:
+            wedge.set_zorder(1)
+        for text_item in texts + autotexts:
+            text_item.set_zorder(2)
+            
         ax.axis('equal') 
         # fig.tight_layout(pad=0.5)
         fig.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.95)
@@ -907,9 +923,16 @@ if __name__ == '__main__':
             "['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': [
@@ -942,37 +965,45 @@ if __name__ == '__main__':
         ]
     }
     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()]),
-        "Mention Sentiment": (generate_mention_sentiment_plot, [sample_mentions_df.copy()]),
+        "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()]),
+        "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 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"
+    # output_dir = "test_plots_updated" # Changed dir name
     # os.makedirs(output_dir, exist_ok=True)
 
-    for name, (func, args, kwargs) in plot_functions_to_test.items():
+    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 if kwargs else {})
+        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"))
@@ -981,4 +1012,3 @@ if __name__ == '__main__':
             logging.warning(f"{name} plot generation failed or returned None.")
     
     logging.info("Test script finished. Review plots if saved locally.")
-