"""

try:

# Get the function from the global namespace using its name

func = globals().get(function_name)

print(f"Attempting to run function: {function_name}") # Debugging line

if func is None:

raise ValueError( f"Function '{function_name}' not found." )

if not callable( func ):

raise ValueError( f"'{function_name}' is not callable" )

# Redirect standard output to capture it

captured_output = io.StringIO()

original_stdout = sys.stdout

sys.stdout = captured_output

try:

# Call the function with the provided arguments

result = func( *args, **kwargs )

# Check if the function returns a tuple with two elements

if isinstance( result, tuple ) and len( result ) == 2:

result, func_logs = result

else:

result, func_logs = False, "Function did not return (boolean, logs)."

# Combine captured output with returned logs

captured_logs = captured_output.getvalue()

logs = f"{func_logs}\n{captured_logs}".strip() if captured_output else func_logs

finally:

sys.stdout = original_stdout # Restore stdout

return result, logs

except Exception as e:

print( "Error while trying to run the dynamic function." )

"""

try:

return pd.read_csv(file_name)

except Exception as e:

print(f"Error reading file {file_name}: {e}")

"""

for index, row in operational_df.iterrows():

if row.get("is_active") == 1:

print(f"Processing TCID: {row.get('TCID')} with function {row.get('test')}") # Debugging line

# Read the corresponding data file

data_file_path = "data/" + row.get( "file_name" )

print( f"Loading data from {data_file_path}" )

try:

file_data = pd.read_parquet( data_file_path )

except Exception as e:

print( f"Error loading file {data_file_path}: {e}" )

continue # Skip this iteration if there's an error loading the data

print( f"Data preview from {data_file_path}:" )

print( file_data.head() )

print( f"Columns in loaded data: {file_data.columns}" )

# Get the function name to execute

function_name = row.get("test")

TCID = row.get("TCID")

# Initialize variables for success and logs

success, logs = False, ""

# Dynamically match the function name and execute the corresponding function

match function_name:

case 'count_records':

print( "Executing count_records with parameters:" )

success, logs = run_dynamic_function( 'count_records', file_data, range_between=[0, 1048500] )

case 'validate_sum':

column = row.get( 'tested_field' )

expected_sum = row.get( 'expected_value' )

success, logs = run_dynamic_function( 'validate_sum', file_data, column=column,

expected_sum=expected_sum )

case 'validate_numeric_range':

success, logs = run_dynamic_function('validate_numeric_range', df=file_data,

column_name='CustAccountBalance', min_value=0, max_value=8000)

case 'check_nulls':

success, logs = run_dynamic_function( 'check_nulls', df=file_data, column='file_date' )

case 'file_timestamp':

success, logs = run_dynamic_function( 'file_timestamp', file_data,

timestamp_column='TransactionTime' )

case _:

print( f"Unknown function {function_name}" )

continue

print( f"Result of function {function_name}: Success: {success}, Logs: {logs}" ) # debugging line

# Explicitly convert the success boolean to an integer (1 for True, 0 for False)

status = int(success) # Convert to integer here

data = {'TCID': TCID, 'status': status, 'logs': logs}

print( f"Writing to status table: {data}" )

write_to_status_table( data )

# Save the updated operational DataFrame back to the CSV file

"""

status_table_path = 'data/status_table.csv'

operational_table_path = 'data/updated_operational_table.csv'

initialize_status_table( status_table_path )

try:

operational_df = read_csv_to_df( operational_table_path )

print( f"Operational table loaded successfully:\n{operational_df.head()}" ) # Debugging line

except Exception as e:

print( f"Failed to load operational table: {e}" )

return # Exit if loading fails

# Step 3: Execute functions based on the configuration

execute_functions( operational_df )