get_average.py

"""
/* Modification of Masstree
 * VLSC Laboratory
 * Copyright (c) 2018-2019 Ecole Polytechnique Federale de Lausanne
 *
 * Permission is hereby granted, free of charge, to any person obtaining a
 * copy of this software and associated documentation files (the "Software"),
 * to deal in the Software without restriction, subject to the conditions
 * listed in the Masstree LICENSE file. These conditions include: you must
 * preserve this copyright notice, and you cannot mention the copyright
 * holders in advertising related to the Software without their permission.
 * The Software is provided WITHOUT ANY WARRANTY, EXPRESS OR IMPLIED. This
 * notice is a summary of the Masstree LICENSE file; the license in that file
 * is legally binding.
 */
"""

import json
import sys
import numpy as np

def get_notebook():
    text = "notebook-mttest.json"
    with open(text, 'r') as f:
        json_obj = json.load(f)
        data = json_obj["data"]
        return data


class Trial:
    def __init__(self):
        self.values = []

    def add_val(self, val):
        self.values.append(val)

    def get_avg(self):
        return np.mean(self.values)

    def get_sum(self):
        return sum(self.values)

    def get_max(self):
        return max(self.values)


class Stats:
    def __init__(self):
        self.trials = []

    def add(self, i, val):
        if len(self.trials) == i:
            self.trials.append(Trial())
        self.trials[i].add_val(val)

    def get_sum(self):
        return np.mean([t.get_sum() for t in self.trials])

    def get_sum_std(self):
        return np.std([t.get_sum() for t in self.trials])

    def get_avg(self):
        return np.mean([t.get_avg() for t in self.trials])

    def get_max(self):
        return np.mean([t.get_max() for t in self.trials])


def get_stats(data):
    op_stats = Stats()

    for i, run in enumerate(data):
        for thread_run in data[run]:
            ops = thread_run["ops"]
            op_stats.add(i, ops)

    return op_stats


def analyze_ops_stats():
    params = sys.argv[1] if len(sys.argv) > 1 else None
    sf = "{},{},{},{},{}" if params else "{},{},{},{}"

    data = get_notebook()
    all_stats = get_stats(data)
    op_stats = all_stats

    op_stat_sum = op_stats.get_sum()
    op_stat_avg = op_stats.get_avg()
    op_stat_sum_std = op_stats.get_sum_std()
    op_stat_stdp = op_stat_sum_std / op_stat_sum

    s = sf.format(op_stat_sum, op_stat_avg, op_stat_sum_std, op_stat_stdp, 
        params)
    print(s)


if __name__ == '__main__':
    analyze_ops_stats()