Example 1: Run Analysis (+3)
Example 1: Run Analysis
# Direct execution
python -m workflow_executor config/workflows/analysis.yaml
# With overrides
python -m workflow_executor config/workflows/analysis.yaml \
--override filter_value=completed \
--override date_range.start=2024-06-01
# Via bash script
./scripts/run_workflow.sh config/workflows/analysis.yaml -v
Example 2: Batch Processing
from pathlib import Path
# Process multiple configs
config_dir = Path('config/workflows/')
for config_file in config_dir.glob('*.yaml'):
print(f"Processing: {config_file}")
result = execute_workflow(str(config_file))
print(f"Result: {result}")
Example 3: Programmatic Use
# Load and modify config programmatically
config = WorkflowConfig.from_yaml('config/base.yaml')
config.parameters['custom_param'] = 'value'
config.input['data_path'] = 'data/custom_input.csv'
result = router.route(config)
Example 4: Dynamic Workflow Generation
import yaml
def generate_workflow_config(data_files: list, output_dir: str) -> str:
"""Generate workflow config for multiple data files."""
config = {
'task': 'pipeline',
'steps': []
}
for i, data_file in enumerate(data_files):
config['steps'].append({
'name': f'process_{i}',
'task': 'analyze_data',
'input': {'data_path': data_file},
'output': {'results_path': f'{output_dir}/result_{i}.json'}
})
config_path = 'config/generated_workflow.yaml'
with open(config_path, 'w') as f:
yaml.dump(config, f)
return config_path
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