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)