def main():
# parse command line
parser = argparse.ArgumentParser(
description='Display the load of each processor.'
)
parser.add_argument(
'jobsCSV',
help='The name of the CSV file which contains jobs information'
)
parser.add_argument(
'--output', '-o',
help='The output file (format depending on the given extension, pdf'
'is RECOMMENDED). For example: figure.pdf'
)
args = parser.parse_args()
j = JobSet.from_csv(args.jobsCSV) # create data structure from input args
fig = plt.figure() # create figure
ax = fig.gca() # extract axes
plot_load(j, ax, str(args.jobsCSV)) # build visualization
# show figure
if args.output is not None:
plt.savefig(args.output)
else:
plt.show()
def main():
print(" >>> Analyzing the jobs")
schedulers = [
'qarnotNodeSched', 'qarnotNodeSchedAndrei',
'qarnotNodeSchedReplicate3LeastLoaded',
'qarnotNodeSchedReplicate10LeastLoaded', 'qarnotNodeSchedFullReplicate'
]
#schedulers2 = ["standard_scheduler", "locationBased_scheduler", "replicate3LeastLoaded_scheduler", "replicate10LeastLoaded_scheduler"]
#schedulers = ['qarnotNodeSched', 'qarnotNodeSchedStatic']
max_time = 200000
jobsets = {}
jobsets_time_interval = {}
current_dir = os.getcwd()
workload_name = current_dir.split('/')[-1]
print(" >>> Workload: " + workload_name)
for scheduler in schedulers:
max_time = 200000
# To remove the burn and staging jobs
prepare_file(scheduler, 'jobs')
# To keep only the staging jobs
#prepare_file(scheduler, 'dyn-staging')
# To plot several gantt charts together
jobs = JobSet.from_csv(scheduler + "/out_jobs_processed.csv")
jobsets[scheduler] = jobs
# To remove the burn and staging jobs and filter by max_time
#prepare_file_by_interval_time(scheduler, 200000)
#jobs_time_interval = JobSet.from_csv(scheduler + "/out_jobs_processed_by_time.csv")
#jobsets_time_interval[scheduler] = jobs_time_interval
#To plot the Gantt charts
# To plot the full Gantt Chart (3 graphs)
#plot_all_charts(scheduler, jobs)
# It plots the simple GanttChart
#plot_gantt_chart(scheduler, jobs, workload_name, 0, max_time)
# It plots the simple GanttChart looking for jobs with finish_time reduced by max_time
#plot_gantt_chart(scheduler, jobs_time_interval, workload_name, 1, max_time)
# To compute the waiting_time and slowdown for the real jobs
prepare_file_metrics(scheduler, 1)
prepare_file_metrics(scheduler, 2)
prepare_file_metrics(scheduler, 3)
# To plot the data sets dependencies
data_sets_analyzes(workload_name) # Bar chart
def compare_jobsets_fragmentation(files):
width = 10
height = 10
fig, axe = plt.subplots(nrows=3, figsize=(width, height))
frag_serie = pd.Series()
for f in files:
js = JobSet.from_csv(f, resource_bounds=(0, 239))
frag = js.fragmentation()
label = f.split("/")[-1:][0]
mean_frag = frag.mean()
frag_serie.set_value(label, round(mean_frag, 2))
label = label + '(mean frag: {0:.2f})'.format(mean_frag)
plot_fragmentation(frag, axe, label)
axe[0].legend()
return frag_serie
def main():
# parse command line
parser = argparse.ArgumentParser(description="Display the load of each processor.")
parser.add_argument("jobsCSV", help="The name of the CSV file which contains jobs information")
parser.add_argument(
"--output",
"-o",
help="The output file (format depending on the given extension, pdf" "is RECOMMENDED). For example: figure.pdf",
)
args = parser.parse_args()
j = JobSet.from_csv(args.jobsCSV) # create data structure from input args
fig = plt.figure() # create figure
ax = fig.gca() # extract axes
plot_load(j, ax, str(args.jobsCSV)) # build visualization
# show figure
if args.output is not None:
plt.savefig(args.output)
else:
plt.show()
# coding: utf-8
import matplotlib.pyplot as plt
from evalys.jobset import JobSet
#matplotlib.use('WX')
js = JobSet.from_csv('jobs.csv')
print(js.df.describe())
js.df.hist()
fig, axe = plt.subplots()
js.gantt(axe, "test")
plt.show()
def test_jobset_import_export(self):
from evalys.jobset import JobSet
js = JobSet.from_csv("./examples/jobs.csv")
js.to_csv("/tmp/jobs.csv")
js0 = JobSet.from_csv("/tmp/jobs.csv")
assert js.df.equals(js0.df)
from evalys.jobset import JobSet
import matplotlib.pyplot as plt
import argparse
parser = argparse.ArgumentParser(description="Visualisation tool for Batsim\
out_jobs.csv file")
parser.add_argument('file', metavar='file', type=str,
help='path to the csv file.')
args = parser.parse_args()
js = JobSet.from_csv(args.file)
js.plot(with_details=True)
plt.show()
def main():
parser = argparse.ArgumentParser(description='visualisation tool for scheduling trace and Batsim output file.')
parser.add_argument('inputCSV', nargs='+', help='The input CSV file(s)')
parser.add_argument('--gantt', '-g',
action='store_true',
default=False, help='Generate Gantt charts')
parser.add_argument('--series', '-s',
nargs='?',
default=None,
const='all',
help='Generate timeseries on cumulative metrics. Available metrics are: {}'.format(available_series))
parser.add_argument('--output', '-o',
nargs='?',
help='The output Gantt chart file depending on the extension (PDF format is RECOMMENDED). For example: figure.pdf')
parser.add_argument('--gantt_diff', '-d',
action='store_true',
default=False,
help='Generate a gantt diff comparison between inputs (no more than 3 recommended')
args = parser.parse_args()
if NO_GRAPHICS and not args.output:
print("No available display: please provide an output using the -o,--output option")
exit(1)
# generate subplot
nb_subplot = 0
if args.gantt:
nb_subplot += len(args.inputCSV)
if args.gantt_diff:
nb_subplot += 1
if args.series:
nb_subplot += 1
if not args.gantt and not args.gantt_diff and not args.series:
print("You must select at least one option (use -h to see available options)")
exit(1)
fig, ax_list = plt.subplots(nb_subplot, sharex=True,)
#sharey=True)
# manage unique ax probleme
try:
iter(ax_list)
except:
ax_list = [ax_list]
else:
ax_list = list(ax_list)
# backup all ax
all_ax = list(ax_list)
# reserve last plot for series
if args.series:
ax_series = ax_list[-1:][0]
ax_list = ax_list[:-1]
# reserve last remaining plot for gantt diff
if args.gantt_diff:
ax_shape = ax_list[-1:][0]
ax_list = ax_list[:-1]
# generate josets from CSV inputs
jobsets = {}
index = 0
for inputCSV in sorted(args.inputCSV):
js = JobSet.from_csv(inputCSV)
file_name = os.path.basename(inputCSV)
file_name = unique_file_name(jobsets, file_name)
jobsets[file_name] = js
if args.gantt:
js.gantt(ax_list[index], file_name)
index += 1
if args.gantt_diff:
plot_gantt_general_shape(jobsets, ax_shape)
if args.series:
plot_series(args.series, jobsets, ax_series)
# set axes and resources
x_axes_min_value = min({m.df.submission_time.min()
for m in jobsets.values()})
x_axes_max_value = max({m.df.finish_time.max() for m in jobsets.values()})
y_axes_min_value = min([js.res_bounds[0] for js in jobsets.values()])
y_axes_max_value = max([js.res_bounds[1] for js in jobsets.values()])
x_size = x_axes_max_value - x_axes_min_value
y_size = y_axes_max_value - y_axes_min_value
print("x = ({},{})".format(x_axes_min_value, x_axes_max_value))
print("y = ({},{})".format(y_axes_min_value, y_axes_max_value))
print("x size = {}".format(x_size))
print("y size = {}".format(y_size))
for ax in all_ax:
ax.set_xlim((x_axes_min_value, x_axes_max_value))
#ax.set_ylim((y_axes_min_value, y_axes_max_value))
# Layout and cosmetic changes
# plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)
fig.set_tight_layout(True)
y_inches = max(y_size * len(all_ax) * 0.15, 8)
if args.gantt_diff:
y_inches += y_size * 0.15
fig.set_size_inches(y_inches * 1.7,
y_inches,
forward=True)
if args.output is not None:
plt.savefig(args.output)
else:
plt.show()
from evalys.jobset import JobSet
import matplotlib.pyplot as plt
import json
import numpy as np
js = JobSet.from_csv("../visualization/output/imt_jobs.csv")
js.plot(with_details=True)
#Loading spaces load log.
with open('../visualization/output/imt_storages_load.json') as f:
loads = json.load(f)
x_axis = loads["time"]
index = np.arange(len(x_axis))
del loads["time"]
y_axis = loads["qb0_disk"]
fig, subplots = plt.subplots(3, 1, sharex=True)
current_plot = 0
min_size = 0
max_size = 0
#Compute max storage size
for storageSpace, load in loads.items():
if max(load) > max_size:
max_size = max(load)
# Generating graph for each storage space
for storageSpace, load in loads.items():
subplots[current_plot].set_title(storageSpace)
subplots[current_plot].set_ylabel("load in bytes")
subplots[current_plot].set_ylim(min_size, max_size)
def main():
parser = argparse.ArgumentParser(
description=
'visualisation tool for scheduling trace and Batsim output file.')
parser.add_argument('inputCSV', nargs='+', help='The input CSV file(s)')
parser.add_argument('--gantt',
'-g',
action='store_true',
default=False,
help='Generate Gantt charts')
parser.add_argument(
'--series',
'-s',
nargs='?',
default=None,
const='all',
help=
'Generate timeseries on cumulative metrics. Available metrics are: {}'
.format(available_series))
parser.add_argument(
'--output',
'-o',
nargs='?',
help=
'The output Gantt chart file depending on the extension (PDF format is RECOMMENDED). For example: figure.pdf'
)
parser.add_argument(
'--gantt_diff',
'-d',
action='store_true',
default=False,
help=
'Generate a gantt diff comparison between inputs (no more than 3 recommended'
)
args = parser.parse_args()
if NO_GRAPHICS and not args.output:
print(
"No available display: please provide an output using the -o,--output option"
)
exit(1)
# generate subplot
nb_subplot = 0
if args.gantt:
nb_subplot += len(args.inputCSV)
if args.gantt_diff:
nb_subplot += 1
if args.series:
nb_subplot += 1
if not args.gantt and not args.gantt_diff and not args.series:
print(
"You must select at least one option (use -h to see available options)"
)
exit(1)
fig, ax_list = plt.subplots(
nb_subplot,
sharex=True,
)
#sharey=True)
# manage unique ax probleme
try:
iter(ax_list)
except:
ax_list = [ax_list]
else:
ax_list = list(ax_list)
# backup all ax
all_ax = list(ax_list)
# reserve last plot for series
if args.series:
ax_series = ax_list[-1:][0]
ax_list = ax_list[:-1]
# reserve last remaining plot for gantt diff
if args.gantt_diff:
ax_shape = ax_list[-1:][0]
ax_list = ax_list[:-1]
# generate josets from CSV inputs
jobsets = {}
index = 0
for inputCSV in sorted(args.inputCSV):
js = JobSet.from_csv(inputCSV)
file_name = os.path.basename(inputCSV)
file_name = unique_file_name(jobsets, file_name)
jobsets[file_name] = js
if args.gantt:
js.gantt(ax=ax_list[index], title=file_name)
index += 1
if args.gantt_diff:
plot_gantt_general_shape(jobsets, ax_shape)
if args.series:
plot_series(args.series, jobsets, ax_series)
# set axes and resources
x_axes_min_value = min(
{m.df.submission_time.min()
for m in jobsets.values()})
x_axes_max_value = max({m.df.finish_time.max() for m in jobsets.values()})
y_axes_min_value = min([js.res_bounds[0] for js in jobsets.values()])
y_axes_max_value = max([js.res_bounds[1] for js in jobsets.values()])
x_size = x_axes_max_value - x_axes_min_value
y_size = y_axes_max_value - y_axes_min_value
print("x = ({},{})".format(x_axes_min_value, x_axes_max_value))
print("y = ({},{})".format(y_axes_min_value, y_axes_max_value))
print("x size = {}".format(x_size))
print("y size = {}".format(y_size))
for ax in all_ax:
ax.set_xlim((x_axes_min_value, x_axes_max_value))
#ax.set_ylim((y_axes_min_value, y_axes_max_value))
# Layout and cosmetic changes
# plt.subplots_adjust(left=0.0, right=1.0, bottom=0.0, top=1.0)
fig.set_tight_layout(True)
y_inches = max(y_size * len(all_ax) * 0.15, 8)
if args.gantt_diff:
y_inches += y_size * 0.15
fig.set_size_inches(y_inches * 1.7, y_inches, forward=True)
if args.output is not None:
plt.savefig(args.output)
else:
plt.show()
import tkinter as tk
from evalys.jobset import JobSet
from evalys import visu
js = JobSet.from_csv("./expe-out/out_jobs.csv")
visu.gantt.plot_gantt(js)
tk.mainloop()
def test_jobset_import_export(self):
from evalys.jobset import JobSet
js = JobSet.from_csv("./examples/jobs.csv")
js.to_csv("/tmp/jobs.csv")
js0 = JobSet.from_csv("/tmp/jobs.csv")
assert js.df.equals(js0.df)