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c14af1c ec2b4e7 c14af1c ec2b4e7 c14af1c ec2b4e7 c14af1c ec2b4e7 c14af1c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 | #!/usr/bin/env python3
"""
Data Processing Pipeline Orchestrator
This script orchestrates the three main data processing steps:
1. EUV data cleaning (euv_data_cleaning.py) - removes bad AIA files
2. ITI data processing (iti_data_processing.py) - processes the good data
3. Data alignment (align_data.py) - concatenates GOES data and checks for missing data
Each step can be skipped if it's already completed.
Configuration:
- Use --config to specify a custom configuration file (YAML or Python)
- Use --show-config to display current configuration
- Use --create-template to create a YAML configuration template
"""
import os
import sys
import subprocess
import time
import logging
from datetime import datetime
from pathlib import Path
from pipeline_config import PipelineConfig
# Configure logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(levelname)s - %(message)s',
handlers=[
logging.FileHandler('data_processing_pipeline.log'),
logging.StreamHandler(sys.stdout)
]
)
logger = logging.getLogger(__name__)
class DataProcessingPipeline:
def __init__(self, base_dir=None, config=None):
"""
Initialize the data processing pipeline.
Args:
base_dir: Base directory for the project. If None, uses current script's directory.
config: PipelineConfig instance. If None, uses default configuration.
"""
if base_dir is None:
self.base_dir = Path(__file__).parent
else:
self.base_dir = Path(base_dir)
# Load configuration
self.config = config if config is not None else PipelineConfig()
# Define script paths
self.scripts = {
'euv_cleaning': self.base_dir / 'euv_data_cleaning.py',
'iti_processing': self.base_dir / 'iti_data_processing.py',
'align_data': self.base_dir / 'align_data.py'
}
# Define step names and descriptions
self.steps = {
'euv_cleaning': {
'name': 'EUV Data Cleaning',
'description': 'Remove bad AIA files based on timestamp validation',
'output_check': self._check_euv_cleaning_complete
},
'iti_processing': {
'name': 'ITI Data Processing',
'description': 'Process good AIA data using ITI methods',
'output_check': self._check_iti_processing_complete
},
'align_data': {
'name': 'Data Alignment',
'description': 'Concatenate GOES data and check for missing data',
'output_check': self._check_align_data_complete
}
}
def _check_euv_cleaning_complete(self):
"""
Check if EUV data cleaning is complete by looking for the bad files directory.
"""
bad_files_dir = Path(self.config.get_path('euv', 'bad_files_dir'))
if bad_files_dir.exists():
# Check if any files were moved (indicating cleaning was done)
wavelengths = self.config.get_path('euv', 'wavelengths')
wavelength_dirs = [bad_files_dir / str(wl) for wl in wavelengths]
return any(d.exists() and any(d.iterdir()) for d in wavelength_dirs)
return False
def _check_iti_processing_complete(self):
"""
Check if ITI data processing is complete by looking for processed files.
"""
output_dir = Path(self.config.get_path('iti', 'output_folder'))
if output_dir.exists():
# Check if there are processed .npy files
npy_files = list(output_dir.glob('*.npy'))
return len(npy_files) > 0
return False
def _check_align_data_complete(self):
"""
Check if data alignment is complete by looking for output directories.
"""
output_dir = Path(self.config.get_path('alignment', 'output_sxr_dir'))
return output_dir.exists() and any(output_dir.iterdir())
def run_script(self, script_name, step_info):
"""
Run a single processing script.
Args:
script_name: Name of the script to run
step_info: Dictionary containing step information
Returns:
bool: True if successful, False otherwise
"""
script_path = self.scripts[script_name]
if not script_path.exists():
logger.error(f"Script not found: {script_path}")
return False
logger.info(f"Starting {step_info['name']}...")
logger.info(f"Description: {step_info['description']}")
logger.info(f"Running: {script_path}")
# Create environment variables for configuration
env = os.environ.copy()
env.update({
'PIPELINE_CONFIG': self.config.to_json(),
'BASE_DATA_DIR': self.config.get_path('base_data_dir', 'base_data_dir')
})
start_time = time.time()
try:
# Run the script
result = subprocess.run(
[sys.executable, str(script_path)],
cwd=self.base_dir,
env=env
)
end_time = time.time()
duration = end_time - start_time
if result.returncode == 0:
logger.info(f"✓ {step_info['name']} completed successfully in {duration:.2f} seconds")
return True
else:
logger.error(f"✗ {step_info['name']} failed with return code {result.returncode}")
return False
except Exception as e:
end_time = time.time()
duration = end_time - start_time
logger.error(f"✗ {step_info['name']} failed with exception: {e}")
logger.error(f"Duration: {duration:.2f} seconds")
return False
def run_pipeline(self, force_rerun=False):
"""
Run the complete data processing pipeline.
Args:
force_rerun: If True, run all steps regardless of completion status
"""
logger.info("=" * 80)
logger.info("Starting Data Processing Pipeline")
logger.info("=" * 80)
logger.info(f"Base directory: {self.base_dir}")
logger.info(f"Force rerun: {force_rerun}")
logger.info("=" * 80)
pipeline_start_time = time.time()
successful_steps = 0
failed_steps = 0
for step_name, step_info in self.steps.items():
logger.info(f"\n--- Step: {step_info['name']} ---")
# Check if step is already complete
if not force_rerun and step_info['output_check']():
logger.info(f"✓ {step_info['name']} already completed - skipping")
successful_steps += 1
continue
# Run the step
if self.run_script(step_name, step_info):
successful_steps += 1
else:
failed_steps += 1
logger.error(f"Pipeline stopped due to failure in {step_info['name']}")
break
pipeline_end_time = time.time()
total_duration = pipeline_end_time - pipeline_start_time
# Summary
logger.info("\n" + "=" * 80)
logger.info("PIPELINE SUMMARY")
logger.info("=" * 80)
logger.info(f"Total duration: {total_duration:.2f} seconds")
logger.info(f"Successful steps: {successful_steps}")
logger.info(f"Failed steps: {failed_steps}")
if failed_steps == 0:
logger.info("✓ All steps completed successfully!")
else:
logger.error("✗ Pipeline completed with errors")
logger.info("=" * 80)
return failed_steps == 0
def main():
"""Main function to run the pipeline."""
import argparse
parser = argparse.ArgumentParser(description='Data Processing Pipeline Orchestrator')
parser.add_argument('--force', action='store_true',
help='Force rerun all steps regardless of completion status')
parser.add_argument('--base-dir', type=str,
help='Base directory for the project (default: script directory)')
parser.add_argument('--config', type=str,
help='Path to custom configuration file (YAML or Python)')
parser.add_argument('--show-config', action='store_true',
help='Display current configuration and exit')
parser.add_argument('--create-template', action='store_true',
help='Create a YAML configuration template file and exit')
parser.add_argument('--validate', action='store_true',
help='Validate configuration paths and exit')
args = parser.parse_args()
# Handle special commands
if args.create_template:
config = PipelineConfig()
config.save_config_template()
return
# Load configuration
config = PipelineConfig(args.config)
if args.show_config:
config.print_config()
return
if args.validate:
is_valid, missing_paths = config.validate_paths()
if is_valid:
print("✓ All required paths exist")
else:
print("✗ Missing required paths:")
for path in missing_paths:
print(f" - {path}")
return
# Create pipeline instance
pipeline = DataProcessingPipeline(args.base_dir, config)
# Validate paths before running
is_valid, missing_paths = config.validate_paths()
if not is_valid:
logger.error("Configuration validation failed. Missing required paths:")
for path in missing_paths:
logger.error(f" - {path}")
logger.error("Use --validate to check configuration")
sys.exit(1)
# Create necessary directories
config.create_directories()
# Run the pipeline
success = pipeline.run_pipeline(force_rerun=args.force)
# Exit with appropriate code
sys.exit(0 if success else 1)
if __name__ == "__main__":
main()
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