Update ui/analytics_plot_generator.py

ui/analytics_plot_generator.py

@@ -86,9 +86,11 @@ def create_placeholder_plot(title="No Data or Plot Error", message="Data might b
 # --- Generic and Reusable Plotting Functions ---
 
 def generate_generic_time_series_plot(df, date_column, value_column, title, ylabel, color='cyan'):
-    """Generic function to create a theme-aware time series plot."""
+    """
+    Generic function to create a theme-aware time series plot with explicit date formatting.
+    """
     if df is None or df.empty or date_column not in df.columns or value_column not in df.columns:
-        logging.info(f"len df {len(df) if df else 0}, dat col {date_column}, value_column {value_column} ")
+        logging.info(f"DataFrame is empty or columns are missing. len df: {len(df) if df is not None else 0}, date_column: {date_column}, value_column: {value_column}")
         return create_placeholder_plot(title=title, message="No data available.")
 
     fig = None
@@ -97,10 +99,13 @@ def generate_generic_time_series_plot(df, date_column, value_column, title, ylab
         df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
         df_copy[value_column] = pd.to_numeric(df_copy[value_column], errors='coerce')
         df_copy = df_copy.dropna(subset=[date_column, value_column]).set_index(date_column)
+
         if df_copy.empty:
-            return create_placeholder_plot(title=title, message="No valid data.")
+            return create_placeholder_plot(title=title, message="No valid data after cleaning.")
 
+        # Resample data by day. This correctly aggregates your data.
         data_over_time = df_copy.resample('D')[value_column].sum()
+
         if data_over_time.empty:
             return create_placeholder_plot(title=title, message="No data in the selected period.")
 
@@ -109,16 +114,30 @@ def generate_generic_time_series_plot(df, date_column, value_column, title, ylab
 
         ax.plot(data_over_time.index, data_over_time.values, marker='o', linestyle='-', color=color, zorder=1, markersize=5, alpha=0.8)
         ax.fill_between(data_over_time.index, data_over_time.values, color=color, alpha=0.1, zorder=1)
+
         ax.set_title(title, fontsize=14, weight='bold')
         ax.set_xlabel('Date', fontsize=10)
         ax.set_ylabel(ylabel, fontsize=10)
+
+        # --- FIX STARTS HERE ---
+        # Use an automatic date locator and a concise formatter for smart,
+        # adaptive date ticks that work well for any time range. This avoids
+        # cluttering the axis when the date range is large.
+        locator = mdates.AutoDateLocator()
+        formatter = mdates.ConciseDateFormatter(locator)
+        ax.xaxis.set_major_locator(locator)
+        ax.xaxis.set_major_formatter(formatter)
+        # --- FIX ENDS HERE ---
+
         plt.xticks(rotation=30, ha="right")
         fig.tight_layout(pad=1.5)
         return fig
+
     except Exception as e:
         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 fig:
+            plt.close(fig)
+        return create_placeholder_plot(title=f"Error in {title}", message=str(e))
 
 
 def generate_generic_bar_plot(data_series, title, xlabel, ylabel, color_map='viridis'):