diff --git a/tools/plotting/fastnnlo_runtime_ah.py b/tools/plotting/fastnnlo_runtime_ah.py
deleted file mode 100755
index 8a16a4d9bee6812415addab058625959fe74e65e..0000000000000000000000000000000000000000
--- a/tools/plotting/fastnnlo_runtime_ah.py
+++ /dev/null
@@ -1,277 +0,0 @@
-#!/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()