mirror of
https://github.com/PlaneQuery/OpenAirframes.git
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210 lines
6.4 KiB
Python
210 lines
6.4 KiB
Python
#!/usr/bin/env python3
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"""
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Script to trigger historical-adsb workflow runs in 15-day chunks.
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Usage:
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python scripts/run_historical_adsb_action.py --start-date 2025-01-01 --end-date 2025-06-01
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"""
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import argparse
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import subprocess
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import sys
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from datetime import datetime, timedelta
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def generate_date_chunks(start_date_str, end_date_str, chunk_days=15):
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"""Generate date ranges in fixed-day chunks from start to end date."""
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start_date = datetime.strptime(start_date_str, '%Y-%m-%d')
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end_date = datetime.strptime(end_date_str, '%Y-%m-%d')
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chunks = []
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current = start_date
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while current < end_date:
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# Calculate end of current chunk
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chunk_end = current + timedelta(days=chunk_days)
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# Don't go past the global end date
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if chunk_end > end_date:
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chunk_end = end_date
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chunks.append({
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'start': current.strftime('%Y-%m-%d'),
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'end': chunk_end.strftime('%Y-%m-%d')
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})
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current = chunk_end
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return chunks
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def trigger_workflow(start_date, end_date, chunk_days=1, branch='main', dry_run=False):
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"""Trigger the historical-adsb workflow via GitHub CLI."""
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cmd = [
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'gh', 'workflow', 'run', 'historical-adsb.yaml',
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'--ref', branch,
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'-f', f'start_date={start_date}',
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'-f', f'end_date={end_date}',
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'-f', f'chunk_days={chunk_days}'
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]
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if dry_run:
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print(f"[DRY RUN] Would run: {' '.join(cmd)}")
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return True, None
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print(f"Triggering workflow: {start_date} to {end_date} (on {branch})")
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result = subprocess.run(cmd, capture_output=True, text=True)
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if result.returncode == 0:
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print(f"✓ Successfully triggered workflow for {start_date} to {end_date}")
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# Get the run ID of the workflow we just triggered
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# Wait a moment for it to appear
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import time
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time.sleep(2)
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# Get the most recent run (should be the one we just triggered)
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list_cmd = [
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'gh', 'run', 'list',
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'--workflow', 'historical-adsb.yaml',
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'--branch', branch,
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'--limit', '1',
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'--json', 'databaseId',
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'--jq', '.[0].databaseId'
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]
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list_result = subprocess.run(list_cmd, capture_output=True, text=True)
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run_id = list_result.stdout.strip() if list_result.returncode == 0 else None
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return True, run_id
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else:
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print(f"✗ Failed to trigger workflow for {start_date} to {end_date}")
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print(f"Error: {result.stderr}")
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return False, None
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def main():
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parser = argparse.ArgumentParser(
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description='Trigger historical-adsb workflow runs in monthly chunks'
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)
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parser.add_argument(
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'--start-date',
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required=True,
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help='Start date in YYYY-MM-DD format (inclusive)'
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)
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parser.add_argument(
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'--end-date',
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required=True,
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help='End date in YYYY-MM-DD format (exclusive)'
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)
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parser.add_argument(
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'--chunk-days',
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type=int,
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default=1,
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help='Days per job chunk within each workflow run (default: 1)'
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)
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parser.add_argument(
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'--workflow-chunk-days',
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type=int,
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default=15,
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help='Days per workflow run (default: 15)'
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)
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parser.add_argument(
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'--branch',
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type=str,
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default='main',
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help='Branch to run the workflow on (default: main)'
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)
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parser.add_argument(
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'--dry-run',
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action='store_true',
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help='Print commands without executing them'
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)
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parser.add_argument(
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'--delay',
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type=int,
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default=5,
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help='Delay in seconds between workflow triggers (default: 5)'
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)
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args = parser.parse_args()
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# Validate dates
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try:
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start = datetime.strptime(args.start_date, '%Y-%m-%d')
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end = datetime.strptime(args.end_date, '%Y-%m-%d')
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if start >= end:
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print("Error: start_date must be before end_date")
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sys.exit(1)
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except ValueError as e:
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print(f"Error: Invalid date format - {e}")
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sys.exit(1)
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# Generate date chunks
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chunks = generate_date_chunks(args.start_date, args.end_date, chunk_days=args.workflow_chunk_days)
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print(f"\nGenerating {len(chunks)} workflow runs ({args.workflow_chunk_days} days each) on branch '{args.branch}':")
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for i, chunk in enumerate(chunks, 1):
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print(f" {i}. {chunk['start']} to {chunk['end']}")
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if not args.dry_run:
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response = input(f"\nProceed with triggering {len(chunks)} workflows on '{args.branch}'? [y/N]: ")
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if response.lower() != 'y':
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print("Cancelled.")
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sys.exit(0)
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print()
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# Trigger workflows
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import time
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success_count = 0
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triggered_runs = []
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for i, chunk in enumerate(chunks, 1):
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print(f"\n[{i}/{len(chunks)}] ", end='')
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success, run_id = trigger_workflow(
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chunk['start'],
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chunk['end'],
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chunk_days=args.chunk_days,
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branch=args.branch,
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dry_run=args.dry_run
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)
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if success:
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success_count += 1
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if run_id:
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triggered_runs.append({
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'run_id': run_id,
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'start': chunk['start'],
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'end': chunk['end']
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})
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# Add delay between triggers (except for last one)
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if i < len(chunks) and not args.dry_run:
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time.sleep(args.delay)
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print(f"\n\nSummary: {success_count}/{len(chunks)} workflows triggered successfully")
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# Save triggered run IDs to a file
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if triggered_runs and not args.dry_run:
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import json
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timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
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runs_file = f"./triggered_runs_{timestamp}.json"
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with open(runs_file, 'w') as f:
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json.dump({
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'start_date': args.start_date,
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'end_date': args.end_date,
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'branch': args.branch,
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'runs': triggered_runs
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}, f, indent=2)
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print(f"\nRun IDs saved to: {runs_file}")
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print(f"\nTo download and concatenate these artifacts, run:")
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print(f" python scripts/download_and_concat_runs.py {runs_file}")
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if success_count < len(chunks):
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sys.exit(1)
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if __name__ == '__main__':
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main()
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