[pre-commit.ci lite] apply automatic fixes

Author: pre-commit-ci-lite[bot]

apps/predbat/unit_test.py

@@ -9133,6 +9133,7 @@ def run_test_units(my_predbat):
 
     return failed
 
+
 def add_incrementing_sensor_total(data):
     max_entry = max(data.keys()) if data else 0
     total = 0
@@ -9141,13 +9142,14 @@ def add_incrementing_sensor_total(data):
         total += increment
     return total
 
+
 def test_previous_days_modal_filter(my_predbat):
     """
     Test the previous_days_modal_filter function
     """
     print("**** Running previous_days_modal_filter tests ****")
     failed = False
-    
+
     # Set up test environment
     my_predbat.load_minutes_age = 7  # 7 days of data
     my_predbat.days_previous = [1, 2]  # Test with 3 days
@@ -9159,19 +9161,21 @@ def test_previous_days_modal_filter(my_predbat):
     my_predbat.iboost_energy_subtract = False
     my_predbat.iboost_energy_today = None
     my_predbat.base_load = 0.0
-    
+
     # Mock the get_arg method
     original_get_arg = my_predbat.get_arg
+
     def mock_get_arg(key, default=None):
         if key == "load_filter_threshold":
             return 30
         return original_get_arg(key, default)
+
     my_predbat.get_arg = mock_get_arg
-    
+
     # Test 1: Empty data set - should be filled with gap filling logic
     print("Test 1: Empty data set")
     test_data = {}
-    
+
     # Call the function
     my_predbat.previous_days_modal_filter(test_data)
     data_length = len(test_data)
@@ -9182,7 +9186,7 @@ def mock_get_arg(key, default=None):
     # Check that data has been filled for at least some minutes
     total_filled_data = dp2(add_incrementing_sensor_total(test_data))
     expected_total_per_day = 24.0  # 24 kWh per day as default when no data
-    
+
     print("Total filled data: {} kWh".format(dp2(total_filled_data)))
 
     # With 2 days and complete gaps, should use 24kWh default for each day
@@ -9195,81 +9199,81 @@ def mock_get_arg(key, default=None):
     # Test 2: Data with gaps - should be filled to create 1 kWh per hour pattern
     print("Test 2: Data with some gaps")
     test_data = {}
-    
+
     # Create partial data for 1 day (1 kWh per hour = incrementing total)
     # PREDICT_STEP is 5 minutes, so 12 steps per hour
     # Each step should increment by 1/12 kWh to get 1 kWh per hour
     step_increment = 1.0 / 60
     running_total = 0
-    
+
     # Fill first half of day with proper incremental data
     for minute in range(0, 12 * 60):  # 12 hours worth
         running_total += step_increment
         test_data[24 * 60 - minute] = running_total  # Backwards indexing as used in function
-    
+
     # Leave second half empty to test gap filling
-    
+
     # Set up days_previous for this test
     my_predbat.days_previous = [1]  # Only test with 1 day
     my_predbat.days_previous_weight = [1.0]
-    
+
     initial_data_sum = dp2(add_incrementing_sensor_total(test_data))
     print("Initial partial data sum: {} kWh".format(dp2(initial_data_sum)))
-    
+
     # Call the function
     my_predbat.previous_days_modal_filter(test_data)
-    
+
     final_data_sum = dp2(add_incrementing_sensor_total(test_data))
     print("Final data sum after gap filling: {} kWh".format(dp2(final_data_sum)))
-    
+
     # Should now have approximately 24 kWh total (1 kWh per hour for 24 hours)
     expected_final_total = 24.0
     if abs(final_data_sum - expected_final_total) > 2.0:  # Allow 2 kWh tolerance
         print("ERROR: Expected final total around {} kWh, got {} kWh".format(expected_final_total, final_data_sum))
         failed = True
     else:
         print("Gap filling successful: filled from {} kWh to {} kWh".format(dp2(initial_data_sum), dp2(final_data_sum)))
-    
+
     # Test 3: Modal filtering - remove lowest consumption day
     print("Test 3: Modal filtering removes lowest day")
-    
+
     # Reset for modal filter test
     my_predbat.days_previous = [1, 2, 3]
     my_predbat.days_previous_weight = [1.0, 1.0, 1.0]
     original_days_count = len(my_predbat.days_previous)
-    
+
     # Create test data with different consumption per day
     test_data = {}
-    
+
     # Day 1: Low consumption (10 kWh total)
     day1_total = 10.0
     step_increment_day1 = day1_total / (24 * 60)
     running_total = 0
     for minute in range(0, 24 * 60):
         running_total += step_increment_day1
         test_data[24 * 60 - minute] = running_total
-    
-    # Day 2: Medium consumption (20 kWh total) 
+
+    # Day 2: Medium consumption (20 kWh total)
     day2_total = 20.0
     step_increment_day2 = day2_total / (24 * 60)
     running_total = 0
     for minute in range(0, 24 * 60):
         running_total += step_increment_day2
         test_data[2 * 24 * 60 - minute] = running_total
-    
+
     # Day 3: High consumption (30 kWh total)
     day3_total = 30.0
     step_increment_day3 = day3_total / (24 * 60)
     running_total = 0
     for minute in range(0, 24 * 60):
         running_total += step_increment_day3
         test_data[3 * 24 * 60 - minute] = running_total
-    
+
     print("Created test data with day totals: {} kWh, {} kWh, {} kWh".format(day1_total, day2_total, day3_total))
-    
+
     # Call the function - should remove day 1 (lowest consumption)
     my_predbat.previous_days_modal_filter(test_data)
-    
+
     # Check that one day was removed
     final_days_count = len(my_predbat.days_previous)
     if final_days_count != original_days_count - 1:
@@ -9280,10 +9284,10 @@ def mock_get_arg(key, default=None):
         failed = True
     else:
         print("Modal filter correctly removed lowest consumption day")
-    
+
     # Restore original get_arg method
     my_predbat.get_arg = original_get_arg
-    
+
     return failed