def write_search_results(outfile, result_files):
result_dict = {}
for result in result_files:
threads = result["threads"]
result_file = result["file"]
print result
if not os.path.exists(result_file):
continue
fastest = get_fastest(result_file)
fastest_throughput = float(fastest["txns"]) / float(fastest["time"])
if not threads in result_dict:
result_dict[threads] = []
result_dict[threads].append(fastest_throughput)
print result_dict
median_dict = {}
for thread in result_dict:
median_dict[thread] = clean.confidence_interval(result_dict[thread])
write_search_inner(outfile, median_dict)
def write_search_results(outfile, result_files):
result_dict = {}
for result in result_files:
threads = result["threads"]
result_file = result["file"]
print result
if not os.path.exists(result_file):
continue
fastest = get_fastest(result_file)
fastest_throughput = float(fastest["txns"])/float(fastest["time"])
if not threads in result_dict:
result_dict[threads] = []
result_dict[threads].append(fastest_throughput)
print result_dict
median_dict = {}
for thread in result_dict:
median_dict[thread] = clean.confidence_interval(result_dict[thread])
write_search_inner(outfile, median_dict)
def write_mv_searches_theta(outfile, toplevel, dirs):
theta_list = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
results = {}
confidences = {}
for t in theta_list:
leaf_name = str(int(10 * t)) + ".txt"
for d in dirs:
dirname = os.path.join(toplevel, d)
fname = os.path.join(dirname, leaf_name)
val = get_fastest(fname)
throughput = float(val["txns"]) / val["time"]
if not t in results:
results[t] = []
results[t].append(throughput)
print results
for key in results:
confidences[key] = clean.confidence_interval(results[key])
print confidences
write_search_inner(outfile, confidences)
def write_mv_searches_theta(outfile, toplevel, dirs):
theta_list = [0.0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9]
results = {}
confidences = {}
for t in theta_list:
leaf_name = str(int(10*t)) + ".txt"
for d in dirs:
dirname = os.path.join(toplevel, d)
fname = os.path.join(dirname, leaf_name)
val = get_fastest(fname)
throughput = float(val["txns"]) / val["time"]
if not t in results:
results[t] = []
results[t].append(throughput)
print results
for key in results:
confidences[key] = clean.confidence_interval(results[key])
print confidences
write_search_inner(outfile, confidences)
