starting to implement callbacks

visuals/components.py

@@ -4,8 +4,7 @@ import pandas as pd
 import logging
 import sys
 import config.exit_codes as ec
-
-
+import datetime
 
 try:
     accounts = pl.read_parquet('data/warehouse/accounts.parquet')
@@ -31,80 +30,98 @@ except Exception as e:
     logging.error(f'Error joining DataFrames: {e}')
     sys.exit(ec.BAD_JOIN)
 
-# Create aggregations
-spend_per_day = master_transactions.sql('''
-    SELECT 
-        date,
-        year,
-        month,
-        day,
-        ABS(SUM(transaction_amount)) as total
-    FROM self
-    WHERE category_name != 'Inflow: Ready to Assign'
-    GROUP BY date, year, month, day
-    ORDER BY date DESC
-    '''
-)
+def update_dates(start_date, end_date):
+    start_year = int(start_date[:4])
+    start_month = int(start_date[5:7])
+    start_day = int(start_date[8:10])
 
-spend_per_category = master_transactions.sql('''
-    SELECT
-        category_name,
-        ABS(SUM(transaction_amount)) as total
-    FROM self
-    WHERE category_name != 'Inflow: Ready to Assign'
-    GROUP BY category_name
-    ORDER BY total DESC
-    '''
-)
+    end_year = int(end_date[:4])
+    end_month = int(end_date[5:7])
+    end_day = int(end_date[8:10])
 
-spend_per_payee = master_transactions.sql('''
-    SELECT
-        payee_name,
-        ABS(SUM(transaction_amount)) as total
-    FROM self
-    WHERE payee_name != 'Starting Balance'
-        AND transaction_amount < 0
-    GROUP BY payee_name
-    ORDER BY total DESC
-    '''
-)
-total_spend = master_transactions.sql('''
-    SELECT ABS(SUM(transaction_amount)) AS total
-    FROM self
-    WHERE payee_name != 'Starting Balance'
-        AND transaction_amount < 0
-''').item()
+    master_data = master_transactions.filter(
+        (pl.col('year_date') >= start_year ) & (pl.col('year_date') <= end_year) & 
+        (pl.col('month_date') >= start_month ) & (pl.col('month_date') <= end_month) &
+        (pl.col('day_date') >= start_day ) & (pl.col('day_date') <= end_day)
+    )
+    return master_data
+
+def update_data(master_data):
+    # Create aggregations
+    spend_per_day = master_data.sql('''
+        SELECT 
+            date,
+            year,
+            month,
+            day,
+            ABS(SUM(transaction_amount)) as total
+        FROM self
+        WHERE category_name != 'Inflow: Ready to Assign'
+        GROUP BY date, year, month, day
+        ORDER BY date DESC
+        '''
+    )
+
+    spend_per_category = master_data.sql('''
+        SELECT
+            category_name,
+            ABS(SUM(transaction_amount)) as total
+        FROM self
+        WHERE category_name != 'Inflow: Ready to Assign'
+        GROUP BY category_name
+        ORDER BY total DESC
+        '''
+    )
+
+    spend_per_payee = master_data.sql('''
+        SELECT
+            payee_name,
+            ABS(SUM(transaction_amount)) as total
+        FROM self
+        WHERE payee_name != 'Starting Balance'
+            AND transaction_amount < 0
+        GROUP BY payee_name
+        ORDER BY total DESC
+        '''
+    )
+    total_spend = master_data.sql('''
+        SELECT ABS(SUM(transaction_amount)) AS total
+        FROM self
+        WHERE payee_name != 'Starting Balance'
+            AND transaction_amount < 0
+    ''').item()
 
 
-# Convert DataFrame to list of dictionaries
-spend_per_day_data = spend_per_day.to_dicts()
-spend_per_category_data = spend_per_category.to_dicts()
-spend_per_payee_data = spend_per_payee.to_dicts()
+    # Convert DataFrame to list of dictionaries
+    spend_per_day_data = spend_per_day.to_dicts()
+    spend_per_category_data = spend_per_category.to_dicts()
+    spend_per_payee_data = spend_per_payee.to_dicts()
 
 
-# Convert list of dictionaries to Pandas DataFrame
-spend_per_day_df = pd.DataFrame(spend_per_day_data)
-spend_per_category_df = pd.DataFrame(spend_per_category_data)
-spend_per_payee_df = pd.DataFrame(spend_per_payee_data)
+    # Convert list of dictionaries to Pandas DataFrame
+    spend_per_day_df = pd.DataFrame(spend_per_day_data)
+    spend_per_category_df = pd.DataFrame(spend_per_category_data)
+    spend_per_payee_df = pd.DataFrame(spend_per_payee_data)
 
 
-spend_per_day_line = px.line(spend_per_day_df, x="date", y="total")
-spend_per_day_line.update_layout(
-    plot_bgcolor='black',
-    paper_bgcolor='black',
-    font_color='white'
-)
+    spend_per_day_line = px.line(spend_per_day_df, x="date", y="total")
+    spend_per_day_line.update_layout(
+        plot_bgcolor='black',
+        paper_bgcolor='black',
+        font_color='white'
+    )
 
-spend_per_category_bar = px.bar(spend_per_category_df, x="category_name", y="total")
-spend_per_category_bar.update_layout(
-    plot_bgcolor='black',
-    paper_bgcolor='black',
-    font_color='white'
-)
+    spend_per_category_bar = px.bar(spend_per_category_df, x="category_name", y="total")
+    spend_per_category_bar.update_layout(
+        plot_bgcolor='black',
+        paper_bgcolor='black',
+        font_color='white'
+    )
 
-spend_per_payee_bar = px.bar(spend_per_payee_df, x="payee_name", y="total")
-spend_per_payee_bar.update_layout(
-    plot_bgcolor='black',
-    paper_bgcolor='black',
-    font_color='white'
-)
+    spend_per_payee_bar = px.bar(spend_per_payee_df, x="payee_name", y="total")
+    spend_per_payee_bar.update_layout(
+        plot_bgcolor='black',
+        paper_bgcolor='black',
+        font_color='white'
+    )
+    return spend_per_day_line, spend_per_category_bar, spend_per_payee_bar, total_spend