feat: add CLI script to convert SP2XR zip/CSV files to parquet using Dask

scripts/sp2xr_csv2parquet.py

@@ -0,0 +1,83 @@
+import argparse
+import time
+import gc
+import dask
+from dask.distributed import Client
+from dask_jobqueue import SLURMCluster
+from dask import delayed
+from sp2xr.io import process_sp2xr_file
+from sp2xr.helpers import find_files, chunks
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Batch convert SP2XR zip to parquet using Dask + SLURM"
+    )
+    parser.add_argument(
+        "--source", required=True, help="Directory containing input zip/CSV files"
+    )
+    parser.add_argument(
+        "--target", required=True, help="Output directory for parquet files"
+    )
+    parser.add_argument(
+        "--config", required=True, help="Path to YAML schema config file"
+    )
+    parser.add_argument(
+        "--filter", default="PbP", help="Pattern to filter files (default: PbP)"
+    )
+    parser.add_argument(
+        "--chunk",
+        type=int,
+        default=100,
+        help="Number of files per batch (default: 100)",
+    )
+    parser.add_argument(
+        "--cores", type=int, default=64, help="Cores per job (default: 64)"
+    )
+    parser.add_argument(
+        "--memory", default="128GB", help="Memory per job (default: 128GB)"
+    )
+    parser.add_argument(
+        "--walltime", default="05:59:00", help="Walltime (default: 5h59m)"
+    )
+    parser.add_argument("--partition", default="daily", help="SLURM partition to use")
+    args = parser.parse_args()
+
+    # --- Setup SLURM cluster ---
+    start_time = time.time()
+    cluster = SLURMCluster(
+        cores=args.cores,
+        processes=args.cores,
+        memory=args.memory,
+        walltime=args.walltime,
+        job_extra_directives=[f"--partition={args.partition}"],
+    )
+    cluster.scale(1)
+    client = Client(cluster)
+    print(f"Dask dashboard: {client.dashboard_link}")
+
+    # --- Find files ---
+    files = find_files(args.source, args.filter)
+    print(f"Found {len(files)} files matching pattern '{args.filter}'")
+
+    # --- Process in chunks ---
+    i = 0
+    for chunk in chunks(files, args.chunk):
+        print(f"Processing chunk {i+1} / {len(files)//args.chunk + 1}")
+
+        tasks = [
+            delayed(process_sp2xr_file)(f, args.config, args.target) for f in chunk
+        ]
+        dask.compute(*tasks)
+
+        gc.collect()
+        i += 1
+
+    # --- Cleanup ---
+    client.close()
+    cluster.close()
+    print(f"✅ Finished in {(time.time() - start_time)/60:.2f} minutes")
+
+
+if __name__ == "__main__":
+    main()