from __future__ import absolute_import, division, print_function, unicode_literals
import os
import socket
import argparse
import matplotlib
import numpy as np
from builtins import range
import matplotlib.pyplot as plt
from AdaptivePELE.utilities import utilities
machine = socket.gethostname()
if machine == "bsccv03":
    matplotlib.use('wxagg')
elif 'login' in machine:
    matplotlib.use('TkAgg')
try:
    # This might fail for older versions of matplotlib (e.g in life cluster)
    plt.style.use("ggplot")
except NameError:
    pass


def printHelp():
    """
        Create command line interface

        :returns: str -- Output filename ( if specified )
    """
    desc = "Program that prints the number of clusters of each threshold that"\
           "have been selected by the spwaning throughout an adaptive sampling simulation. "\
           "It must be run in the root folder. "
    parser = argparse.ArgumentParser(description=desc)
    parser.add_argument("-filename", type=str, default="", help="Output filename")
    parser.add_argument("-o", "--output", type=str, default="", help="Output folder")
    args = parser.parse_args()
    return args.filename, args.output


def main(filename, output_folder):
    print("FILENAME", filename)
    templateSummary = "%d/clustering/summary.txt"
    allFolders = os.listdir(".")
    numberOfEpochs = len([epoch for epoch in allFolders if epoch.isdigit() and
                          os.path.isfile(templateSummary % int(epoch))])

    clustering = utilities.loadtxtfile(templateSummary % (numberOfEpochs-1))
    clustersThres = list(set(clustering[:, 4]))
    clustersThres.sort(reverse=True)
    spawningPerThres = np.zeros((numberOfEpochs, len(clustersThres)))
    for i in range(numberOfEpochs):
        clustering = utilities.loadtxtfile(templateSummary % i)
        for j, threshold in enumerate(clustersThres):
            spawningPerThres[i, j] = clustering[clustering[:, 4] == threshold, 2].sum()
    line_objects = plt.plot(spawningPerThres)
    plt.legend(line_objects, tuple(["Cluster size %d" % x for x in clustersThres]),
               loc="best")
    plt.title("Processors spawned per epoch and cluster size")
    plt.xlabel("Epoch")
    plt.ylabel("Number of spawned processors")
    if output_folder and not os.path.exists(output_folder):
        os.makedirs(output_folder)
    if filename != "":
        plt.savefig(os.path.join(output_folder, "%s_spawning.png" % filename))
    plt.show()

if __name__ == "__main__":
    filename, out_folder = printHelp()
    main(filename, out_folder)

from __future__ import absolute_import, division, print_function, unicode_literals
import os
import argparse
import numpy as np
from builtins import range
import matplotlib.pyplot as plt
from AdaptivePELE.utilities import utilities
avail_backend = utilities.get_available_backend()
if avail_backend is not None:
    plt.switch_backend(avail_backend)
try:
    # This might fail for older versions of matplotlib (e.g in life cluster)
    plt.style.use("ggplot")
except NameError:
    pass


def printHelp():
    """
        Create command line interface

        :returns: str -- Output filename ( if specified )
    """
    desc = "Program that prints the number of clusters of each threshold that"\
           "have been selected by the spwaning throughout an adaptive sampling simulation. "\
           "It must be run in the root folder. "
    parser = argparse.ArgumentParser(description=desc)
    parser.add_argument("-filename", type=str, default="", help="Output filename")
    parser.add_argument("-o", "--output", type=str, default="", help="Output folder")
    args = parser.parse_args()
    return args.filename, args.output


def main(filename, output_folder):
    print("FILENAME", filename)
    templateSummary = "%d/clustering/summary.txt"
    allFolders = os.listdir(".")
    numberOfEpochs = len([epoch for epoch in allFolders if epoch.isdigit() and
                          os.path.isfile(templateSummary % int(epoch))])

    clustering = utilities.loadtxtfile(templateSummary % (numberOfEpochs-1))
    clustersThres = list(set(clustering[:, 4]))
    clustersThres.sort(reverse=True)
    spawningPerThres = np.zeros((numberOfEpochs, len(clustersThres)))
    for i in range(numberOfEpochs):
        clustering = utilities.loadtxtfile(templateSummary % i)
        for j, threshold in enumerate(clustersThres):
            spawningPerThres[i, j] = clustering[clustering[:, 4] == threshold, 2].sum()
    line_objects = plt.plot(spawningPerThres)
    plt.legend(line_objects, tuple(["Cluster size %d" % x for x in clustersThres]),
               loc="best")
    plt.title("Processors spawned per epoch and cluster size")
    plt.xlabel("Epoch")
    plt.ylabel("Number of spawned processors")
    if output_folder and not os.path.exists(output_folder):
        os.makedirs(output_folder)
    if filename != "":
        plt.savefig(os.path.join(output_folder, "%s_spawning.png" % filename))
    plt.show()

if __name__ == "__main__":
    filename, out_folder = printHelp()
    main(filename, out_folder)

Use get_available_backend to select proper backend