Add multi_sorcha.py and multi_sorcha.sh example files.

demo/multi_sorcha.py

@@ -0,0 +1,63 @@
+import os
+import astropy.table as tb
+from multiprocessing import Pool
+import pandas as pd
+import sqlite3
+
+def run_sorcha(i, args, path_inputs, pointings, instance, config):
+    print(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob  {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv", flush=True)
+    os.system(f"sorcha run -c {config} -pd {pointings} -o {args.path}{instance}/ -t {instance}_{i} -ob  {args.path}{instance}/orbits_{i}.csv -p {args.path}{instance}/physical_{i}.csv")
+
+if __name__ == '__main__':
+        import argparse
+
+        parser = argparse.ArgumentParser()
+        parser.add_argument('--input_orbits', type=str)
+        parser.add_argument('--input_physical', type=str)
+        parser.add_argument('--path', type=str)
+        parser.add_argument('--chunksize', type=int)
+        parser.add_argument('--norbits', type=int)
+        parser.add_argument('--cores', type=int)
+        parser.add_argument('--instance', type=int)
+        parser.add_argument('--cleanup',  action='store_true')
+        parser.add_argument('--copy_inputs', action='store_true')
+        parser.add_argument('--pointings', type=str)
+        parser.add_argument('--config', type=str)
+        args = parser.parse_args()
+        chunk = args.chunksize
+        instance = args.instance
+        norbits = args.norbits
+        pointings = args.pointings
+        path = args.path
+        config = args.config
+
+        orbits = tb.Table.read(args.input_orbits)
+        orbits = orbits[instance*chunk:(instance+1)*chunk]
+        physical = tb.Table.read(args.input_physical)
+        physical = physical[instance*chunk:(instance+1)*chunk]
+
+        os.system(f'mkdir {instance}')
+
+
+        if args.copy_inputs:
+                os.system(f'cp {pointings} {instance}/')
+                path_inputs = f'{instance}'
+
+        for i in range(args.cores):
+                sub_orb = orbits[i*norbits:(i+1)*norbits]
+                sub_phys = physical[i*norbits:(i+1)*norbits]
+                sub_orb.write(f"{args.path}{instance}/orbits_{i}.csv", overwrite=True)
+                sub_phys.write(f"{args.path}{instance}/physical_{i}.csv", overwrite=True)
+
+        with Pool(processes=args.cores) as pool:
+            pool.starmap(run_sorcha, [(i, args, path_inputs, pointings, instance, config) for i in range(args.cores)])
+
+        data = [] 
+        for i in range(args.cores):
+                data.append(pd.read_sql("select * from sorcha_results", sqlite3.connect(f"{args.path}{instance}/{instance}_{i}.db")))
+        data = pd.concat(data)
+        data.to_sql("sorcha_results", sqlite3.connect(f"{args.path}output_{instance}.db"))
+        if args.cleanup:
+               os.system(f"rm {args.path}{instance}/*")
+               os.system(f"rmdir {args.path}{instance}")
+

demo/multi_sorcha.sh

@@ -0,0 +1,10 @@
+#!/bin/bash
+#SBATCH --job-name=my_job
+#SBATCH --account=my_account
+#SBATCH --partition=my_partition
+#SBATCH --ntasks=10                  # Run a single task
+#SBATCH --mem=40gb                   # Job Memory
+#SBATCH --time=2:00:00             # Time limit hrs:min:sec
+#SBATCH --output=log-%a.log    # Standard output and error log
+
+python3 multi_sorcha.py --config my_config.ini --input_orbits my_orbits.csv --input_physical my_colors.csv --pointings my_pointings.db --path ./ --chunksize $(($1 * $2)) --norbits $1 --cores $2 --instance ${SLURM_ARRAY_TASK_ID} --cleanup --copy_inputs