refactor: Update date range generation with gaps for better visibility

app_trans_new.py

@@ -239,6 +239,21 @@ def fetch_transactions():
     df_transactions_new.to_csv(csv_filename, index=False)
     return df_transactions_new
 
+def date_range_with_gaps(start_date, end_date):
+    """Generates a range of dates from start_date to end_date inclusive, with extra days between weeks."""
+    start_dt = datetime.strptime(start_date, "%Y-%m-%d")
+    end_dt = datetime.strptime(end_date, "%Y-%m-%d")
+    delta = timedelta(days=1)
+    current_dt = start_dt
+    date_list = []
+    while current_dt <= end_dt:
+        date_list.append(current_dt.date())
+        if current_dt.weekday() == 6:  # If it's Sunday, add gaps for better visibility
+            for _ in range(2):
+                current_dt += delta
+                date_list.append(current_dt.date())
+        current_dt += delta
+    return date_list
 
 def create_transcation_visualizations():
     df_transactions_new = fetch_transactions()
@@ -248,21 +263,12 @@ def create_transcation_visualizations():
     daily_counts = df_transactions_new.groupby([df_transactions_new['timestamp'].dt.date, 'chain']).size().unstack(fill_value=0)
 
     daily_counts = daily_counts[['optimism', 'base', 'ethereum']]
-    
+
     # Create a complete date range with gaps between weeks
-    full_date_range = pd.date_range(start=daily_counts.index.min(), end=daily_counts.index.max(), freq='D').to_list()
-    for d in list(full_date_range):
-        if d.weekday() == 6:  # If it's Sunday, add gaps for better visibility
-            full_date_range.extend([d + timedelta(days=1), d + timedelta(days=2)])
-    full_date_range = sorted(set(full_date_range))
-    
-    full_date_range = pd.date_range(start=daily_counts.index.min(), end=daily_counts.index.max(), freq='D').to_list()
-    for d in full_date_range:
-        if d.weekday() == 6:
-            full_date_range.extend([d + timedelta(days=1), d + timedelta(days=2)])
-    full_date_range = sorted(set(full_date_range))
+    full_date_range = date_range_with_gaps(daily_counts.index.min().strftime("%Y-%m-%d"), daily_counts.index.max().strftime("%Y-%m-%d"))
     
     # Reindex the DataFrame to include the new date range
+    full_date_range = pd.to_datetime(full_date_range)
     daily_counts = daily_counts.reindex(full_date_range, fill_value=0)
     # Set up the plot
     fig_tx_chain = px.bar(