Remove intermediate version

tools/plotting/fastnnlo_runtime_ah.py

-#!/usr/bin/env python3
-#-*- coding:utf-8 -*-
-
-import glob
-import argparse
-import glob
-import sys
-import matplotlib as mpl
-import matplotlib.gridspec as gridspec
-import matplotlib.lines as mlines
-import matplotlib.patches as mpatches
-import matplotlib.ticker as mticker
-from matplotlib.ticker import (FormatStrFormatter, LogFormatter, NullFormatter, ScalarFormatter, AutoMinorLocator, MultipleLocator)
-from matplotlib import cm
-# We do not want any interactive plotting! Figures are saved to files instead.
-# This also avoids the ANNOYANCE of frequently missing Tkinter/tkinter (python2/3) GUI backends!
-# To produce scalable graphics for publication use eps, pdf, or svg as file format.
-# For this to work we try the Cairo backend, which can do all of these plus the raster format png.
-# If this is not usable, we fall back to the Agg backend capable only of png for nice web plots.
-#ngbackends = mpl.rcsetup.non_interactive_bk
-#print('[fastnnlo_pdfunc]: Non GUI backends are: ', ngbackends)
-# 1st try cairo
-backend = 'cairo'
-usecairo = True
-try:
-    import cairocffi as cairo
-except ImportError:
-    try:
-        import cairo
-    except ImportError:
-        usecairo = False
-#        print('[fastnnlo_pdfunc]: Can not use cairo backend :-(')
-#        print('                   cairocffi or pycairo are required to be installed')
-    else:
-        if cairo.version_info < (1, 11, 0):
-            # Introduced create_for_data for Py3.
-            usecairo = False
-#            print('[fastnnlo_pdfunc]: Can not use cairo backend :-(')
-#            print('                   cairo {} is installed; cairo>=1.11.0 is required'.format(cairo.version))
-if usecairo:
-    mpl.use('cairo')
-else:
-    backend = 'agg'
-    useagg = True
-    try:
-        mpl.use(backend, force=True)
-    except:
-        useagg = False
-        raise ImportError('[PlotRuntime]: Neither cairo nor agg backend found :-( Cannot produce any plots. Good bye!')
-    mpl.use('agg')
-import matplotlib.pyplot as plt
-# numpy
-import numpy as np
-
-
-
-def main():
-
-    # Parse arguments
-    args = arguments()
-
-    # extract correct paths for input and outputfiles
-    logfiles = get_files(args['logfiles'])
-    outputpath = args['output']
-    all_plot = args['All']
-    fmt = args['format']
-
-    # get all the information from logfiles as dict
-    # dict contains: runtime, runtime_unit, channel, events
-    loginformation = get_loginformation(logfiles)
-
-    # plot all the information
-    if args['CPUtime']:
-        plot_elapsed_time(loginformation, outputpath, all_plot, fmt)
-    if args['Events']:
-        plot_events_per_hour(loginformation, outputpath, all_plot, fmt)
-    if not args['CPUtime'] and not args['Events']:
-        plot_elapsed_time(loginformation, outputpath, all_plot, fmt)
-        plot_events_per_hour(loginformation, outputpath, all_plot, fmt)
-
-    exit(0)
-
-def arguments():
-
-    # Define arguments and options
-    parser = argparse.ArgumentParser(epilog='Skript to plot elapsed time of fastNLO channels', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-
-    # positional argument
-    parser.add_argument('logfiles', nargs='+', type=str, help='Either input is a single string from LAW Task or it is a list')
-
-    # optional arguments
-    parser.add_argument('-o', '--output', nargs=1, type=str, default='./',
-                        help='Set here the outputpath')
-    parser.add_argument('--CPUtime', dest='CPUtime', action='store_true',
-                        help='Plot only the elapsed time')
-    parser.add_argument('--Events', dest='Events', action='store_true',
-                        help='Plot only the events per hour')
-    parser.add_argument('--All', dest='All', action='store_true',
-                        help='Set plotoptions according to plot for all channels')
-    parser.add_argument('--format', required=False, nargs=1, type=str, default='png',
-                        help='Comma-separated list of plot formats to use: eps, pdf, png, svg. If nothing is chosen, png is used.')
-
-    return vars(parser.parse_args())
-
-
-def get_files(files):
-    # check if logfiles argument is one filename glob or a list of files
-    if len(files)==1:
-        files = glob.glob(files[0])
-        if len(files)==1:
-            print('fastnnlo_runtime: ERROR! Aborted, only one log file found: {}'.format(files[0]))
-            exit(3)
-
-    return files
-
-def get_loginformation(files):
-
-    run_time = []
-    number_events = []
-    channel = None
-
-    for file in files:
-        event = False
-        run_time_temp = []
-
-        with open(file) as origin:
-            for line in origin:
-                # extract elapsed time with time unit
-                if 'Time elapsed' in line:
-                    line = line.split(':')
-                    hours = float(line[1])
-                    minutes = float(line[2])
-                    seconds = float(line[3])
-
-                    if hours == True:
-                        run_time_temp.append(hours + minutes/60 + seconds/360)
-                        unit = 'hours'
-                    else:
-                        run_time_temp.append(minutes + seconds/60)
-                        unit = 'minutes'
-
-                # extract channel name
-                if 'Tablename' in line and not channel:
-                    line = line.split()
-                    tablename = line[2].split('.')
-                    channel = tablename[0] + '.' + tablename[1]
-                # extract total events
-                if 'ncalltot=' in line and not event:
-                    line = line.split(',')
-                    number_events.append(float(line[4][10:]))
-                    event = True
-
-        run_time.append(run_time_temp[-1])
-
-
-    run_time = np.array(run_time)
-    number_events = np.array(number_events)
-
-    information = {
-        'runtime': run_time,
-        'runtime_unit': unit,
-        'channel': channel,
-        'events': number_events
-    }
-
-    return information
-
-
-def plot_elapsed_time(informationdict, out_path, plot_all=False, format='png'):
-
-    time = informationdict['runtime']
-    unit = informationdict['runtime_unit']
-    channel = informationdict['channel']
-
-    if plot_all:
-        channel = channel.split('.')
-        channel = channel[0]
-
-    # get relevant values
-    mean = np.mean(time)
-    std = np.std(time)
-    median = np.median(time)
-    iqd = np.subtract(*np.percentile(time, [75, 25], interpolation='linear'))/2.
-
-    CPUtime = np.sum(time) / (1 if unit == 'hours' else 60)
-
-    # set saving location
-    filename = out_path[0] + ('' if out_path[0][-1] == '/' else '/')
-    filename += channel + '.Hist_Elapsed_time.' + format
-
-    # set figure
-    fig = plt.figure(figsize=(16, 12))
-    ax = fig.gca()
-
-    # plot histogram
-    n, batches, _ = ax.hist(time, bins=20, color='deepskyblue', edgecolor='black', label='Total CPU time: {0:0.0f} hours'.format(CPUtime))
-
-    if not plot_all:
-        # plot mean and median
-        ax.vlines(mean, 0, max(n), colors='red', linestyles='dashed', label=r'Mean: {0:0.1f}$\pm${2:0.1f} {1}'.format(mean, unit, std))
-        ax.vlines(median, 0, max(n), colors='green', linestyles='dashed', label=r'Median: {0:0.1f}$\pm${2:0.1f} {1}'.format(median, unit, iqd))
-
-    # finish and save figure
-    ax.set_title('Elapsed time of ' + channel + ' production', fontsize=20)
-    ax.set_xlabel('CPU time [' + unit + ']', horizontalalignment='right', x=1.0, verticalalignment='top', y=1.0, fontsize=20, labelpad=15)
-    ax.set_ylabel('frequency', horizontalalignment='right', x=1.0, verticalalignment='top', y=1.0, fontsize=20, labelpad=15)
-    ax.set_yscale('log')
-    ax.tick_params(axis='both', which='major', labelsize=20)
-
-    ax.legend(loc='best', fontsize=20)
-    ax.grid()
-    ax.set_axisbelow(True)
-
-    fig.savefig(filename)
-
-def plot_events_per_hour(informationdict, out_path, plot_all=False, format='png'):
-
-    time = informationdict['runtime']
-    unit = informationdict['runtime_unit']
-    channel = informationdict['channel']
-    events = informationdict['events']
-
-    if unit == 'hours':
-        eph = events/time
-    else:
-        eph = events/(time/60)
-
-    if plot_all:
-        channel = channel.split('.')
-        channel = channel[0]
-
-    # get relevant values
-    mean = np.mean(eph)
-    std = np.std(eph)
-    median = np.median(eph)
-    iqd = np.subtract(*np.percentile(eph, [75, 25], interpolation='linear'))/2.
-
-    CPUtime = np.sum(time) / (1 if unit == 'hours' else 60)
-
-
-    # set saving location
-    filename = out_path[0] + ('' if out_path[0][-1] == '/' else '/')
-    filename += channel + '.Hist_Events_per_hour.' + format
-
-    # set figure
-    fig = plt.figure(figsize=(16, 12))
-    ax = fig.gca()
-
-    # plot histogram
-    n, batches, _ = ax.hist(eph, bins=20, color='deepskyblue', edgecolor='black', label='Total CPU time: {0:0.0f} hours'.format(CPUtime))
-
-    if not plot_all:
-        # scientific format
-        f = mticker.ScalarFormatter(useOffset=False, useMathText=True)
-        g = lambda x,pos : "${}$".format(f._formatSciNotation('%0.2e' % x))
-        sci = mticker.FuncFormatter(g)
-
-        # plot mean and median
-        ax.vlines(mean, 0, max(n), colors='red', linestyles='dashed', label=r'Mean: {0}$\pm${1} events/hour'.format(sci(mean), sci(std)))
-        ax.vlines(median, 0, max(n), colors='green', linestyles='dashed', label=r'Median: {0}$\pm${1} events/hour'.format(sci(median), sci(iqd)))
-
-    # finish and save figure
-    ax.set_title('Events per hour of ' + channel + ' production', fontsize=20)
-    ax.set_xlabel('events/hour', horizontalalignment='right', x=1.0, verticalalignment='top', y=1.0, fontsize=20, labelpad=15)
-    ax.set_ylabel('frequency', horizontalalignment='right', x=1.0, verticalalignment='top', y=1.0, fontsize=20, labelpad=15)
-    ax.set_yscale('log')
-    ax.tick_params(axis='both', which='major', labelsize=20)
-
-    ax.legend(loc='best', fontsize=20)
-    ax.grid()
-    ax.set_axisbelow(True)
-
-    fig.savefig(filename)
-
-
-if __name__ == "__main__":
-    main()