utils.py

#!/usr/bin/python
# -*- coding: UTF8 -*-
import csv
import json

from objectpath import *
from os import path
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import multiprocessing as mp


class ExperimentResults:
    def __init__(self, configs, stats, props):
        self.configs = configs
        self.stats = stats
        self.props = props


class ExperimentConfigs:
    def __init__(self, raw_configs):
        self.raw_configs = raw_configs

    def __getitem__(self, index):
        return self.raw_configs.execute('$.' + index)


class ExperimentStats:
    def __init__(self, raw_stats):
        self.raw_stats = raw_stats

    def __getitem__(self, index):
        return self.raw_stats.execute('$.' + index)


def read_configs(result_dir, config_json_file_name):
    try:
        with open(path.join(result_dir, config_json_file_name)) as config_json_file:
            configs = Tree(json.load(config_json_file))
    except Exception as e:
        print(e)
        return None
    else:
        return configs


def read_stats(result_dir, stats_file_name):
    try:
        with open(path.join(result_dir, stats_file_name)) as stats_json_file:
            configs = Tree(json.load(stats_json_file))
    except Exception as e:
        print(e)
        return None
    else:
        return configs


def parse_result(result_dir, config_json_file_name='config.noc.json', stats_json_file_name='stats.json', **props):
    return ExperimentResults(ExperimentConfigs(read_configs(result_dir, config_json_file_name)),
                             ExperimentStats(read_stats(result_dir, stats_json_file_name)), props)


def to_csv(output_file_name, results, fields):
    with open(output_file_name, 'w') as output_file:
        writer = csv.writer(output_file, delimiter=',', quotechar='"', quoting=csv.QUOTE_ALL)

        writer.writerow([field[0] for field in fields])

        for result in results:
            writer.writerow([field[1](result) for field in fields])


def generate_plot(csv_file_name, plot_file_name, x, y, hue, y_title, xticklabels_rotation=90):
    sns.set(font_scale=1.5)

    sns.set_style("white", {"legend.frameon": True})

    df = pd.read_csv(csv_file_name)

    ax = sns.barplot(data=df, x=x, y=y, hue=hue, palette=sns.color_palette("Paired"))
    ax.set_xlabel('')
    ax.set_ylabel(y_title)

    labels = ax.get_xticklabels()
    ax.set_xticklabels(labels, rotation=xticklabels_rotation)

    fig = ax.get_figure()

    if hue:
        legend = ax.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0.)
        legend.set_label('')

        fig.savefig(plot_file_name + '.pdf', bbox_extra_artists=(legend,), bbox_inches='tight')
        fig.savefig(plot_file_name + '.jpg', bbox_extra_artists=(legend,), bbox_inches='tight')
    else:
        fig.tight_layout()

        fig.savefig(plot_file_name) + '.pdf'
        fig.savefig(plot_file_name + '.jpg')

    plt.clf()
    plt.close('all')


def run_experiments(experiments, run_experiment):
    num_processes = (mp.cpu_count() - 1) if mp.cpu_count() > 1 else 1
    pool = mp.Pool(num_processes)
    pool.map(run_experiment, experiments)

    pool.close()
    pool.join()


def add_experiment(experiments, *args):
    experiments.append(args)