script for time intensive compute on cluster

Author: teaguetomesh

Diff for: multiple_trials.py

@@ -0,0 +1,53 @@
+from term_grouping import *
+from collections import Counter
+import time
+import numpy as np
+import argparse
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-s','--savepath',type=str,default=None,help='Path to save data')
+    args = parser.parse_args()
+    return args
+
+
+def many_trials(filename, commutativity_type, numtrials):
+    H = parseHamiltonian(filename)
+    ops = [term[1] for term in H]
+    Nq = max([int(op[-1][1:]) for op in ops]) + 1
+    print('--------------')
+    print(filename)
+    results = []
+    runtimes = []
+    for i in range(numtrials):
+        start_time = time.time()
+        cliques = genMeasureCircuit(H, Nq, commutativity_type)
+        end_time = time.time()
+        results += [len(cliques)]
+        runtimes += [end_time - start_time]
+    return Counter(results), runtimes
+
+
+def main():
+
+    args = parse_args()
+
+    Hfiles = ['hamiltonians/H2_6-31g_{}_0.7_AS4.txt'.format(e) for e in ['JW','BK','BKSF','BKT','PC']]
+
+    results = []
+    for f in Hfiles:
+        for comm_type, comm_str in zip([QWCCommutativity, FullCommutativity],['QWC','FULL']):
+            ret, times = many_trials(f, comm_type, 100)
+            results += [(f,comm_str,ret,times)]
+
+    print('\n\n--------------')
+    print('All trials finished')
+    with open(args.savepath, 'w') as savefile:
+        for r in results:
+            print(r[0],r[1],r[2],'{:.3f}'.format(np.mean(r[3])))
+            savefile.write('{0} {1} {2} {3:.3f}\n'.format(r[0],r[1],r[2],np.mean(r[3])))
+
+
+if __name__ == '__main__':
+    main()