AOS-Project/utils/plot/analyze.py

#!/usr/bin/env python
import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
import sets
import math
import csv

# List containing all the platform names
platforms = ['cpu', 'gpu-primary', 'gpu-secondary']

# Function that takes as parameter a platform and computes averages and variance of the results, and saves them in two dedicated files (averages.dat and variances.dat)
def computeAverages( platform ):

    # Import raw data from file
    data = np.genfromtxt('results/' + platform + '/total.dat', dtype=None, delimiter=',', names=['name', 'time', 'power'])

    # We construct a set in which we insert each benchmark name once
    nameset = set()
    names = data['name']
    for name in names:
        nameset.add(name)

    # Helper dicts to store the results computed in the next loop
    timesAverages, powerAverages, timesVariances, powerVariances  = {}, {}, {}, {}

    # For each benchmark we iterate over the results, take into account the values and save the average and the variance in a corresponding record in the dicts
    for benchmarkName in nameset:

        # Temporary variables to store the results of the currently analyzed benchmark
        times, power = [], []

        # For each record we copy the values if the name is equal to the currently analyzed
        for record in data:
            if benchmarkName == record['name']:
                times.append(record['time'])

                # Before writing the value we convert it from Watt/hour to mWatt/hour
                power.append(record['power']*1000)

        # Since the power measurement utility sometimes is not able to get the results from the measurement device, we cleanse the list containing the power measurement values from the invalid (nan) values
        cleanedPower = [x for x in power if math.isnan(x) == False]

        # We compute the average of time and consumption and store it in the corresponding entries in the dict
        timesAverages[benchmarkName] = np.average(times)
        powerAverages[benchmarkName] = np.average(cleanedPower)
        timesVariances[benchmarkName] = np.std(times)
        powerVariances[benchmarkName] = np.std(cleanedPower)

    # Write on file the averages
    with open('results/' + platform + '/average.csv', 'wb') as csvfile:
        filewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)

        for benchmarkName in nameset:
            filewriter.writerow([benchmarkName, timesAverages[benchmarkName], powerAverages[benchmarkName]])

    # Write on file the variance
    with open('results/' + platform + '/stderr.csv', 'wb') as csvfile:
        filewriter = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)

        for benchmarkName in nameset:
            filewriter.writerow([benchmarkName, timesVariances[benchmarkName], powerVariances[benchmarkName]])

# Execute the average and variance computation for each platform specified in the platforms list
for platform in platforms:
    computeAverages(platform)