for pid, vals in unit_info.iteritems():
if "Terminated" in vals.keys():
line = "{0}, {1}, {2:0.5f}, {3:0.5f}, None\n".format(pid, vals["Interrupt"], vals["Started"],vals["Terminated"])
f1.write(line)
elif "Done" in vals.keys():
line = "{0}, None, {1:0.5f}, None, {2:0.5f}\n".format(pid, vals["Started"],vals["Done"])
f1.write(line)
f1.close()
import radical.pilot.utils as rpu
# we have a session
sid = session.uid
profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/')
profile = rpu.combine_profiles (profiles)
frame = rpu.prof2frame(profile)
sf, pf, uf = rpu.split_frame(frame)
rpu.add_info(uf)
rpu.add_states(uf)
adv = uf[uf['event'].isin(['advance'])]
rpu.add_frequency(adv, 'f_exe', 0.5, {'state' : 'Executing', 'event' : 'advance'})
s_frame, p_frame, u_frame = rpu.get_session_frames(sid)
print str(u_frame)
def profile_analysis(sid):
import radical.pilot.utils as rpu
report.header('profile analysis')
# fetch profiles for all pilots
profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/')
print(profiles)
# combine into a single profile
profile = rpu.combine_profiles(profiles)
# derive a global data frame
frame = rpu.prof2frame(profile)
# split into session / pilot / unit frames
sf, pf, uf = rpu.split_frame(frame)
print(len(sf))
print(len(pf))
print(len(uf))
print(sf[0:10])
print(pf[0:10])
print(uf[0:10])
# derive some additional 'info' columns, which contains some commonly used
# tags
rpu.add_info(uf)
for index, row in uf.iterrows():
if str(row['info']) != 'nan':
print("%-20s : %-10s : %-25s : %-20s" %
(row['time'], row['uid'], row['state'], row['info']))
# add a 'state_from' columns which signals a state transition
rpu.add_states(uf)
adv = uf[uf['event'].isin(['advance'])]
print('---------------')
print(len(adv))
print(uf[uf['uid'] == 'unit.000001'])
print(list(pf['event']))
tmp = uf[uf['uid'] == 'unit.000001'].dropna()
print(tmp[['time', 'uid', 'state', 'state_from']])
# add a columns 'rate_out' which contains the rate (1/s) of the event
# 'advance to state STAGING_OUTPUT'
print('---------------')
rpu.add_frequency(adv, 'rate_out', 0.5, {
'state': 'StagingOutput',
'event': 'advance'
})
print(adv[['time', 'rate_out']].dropna(subset=['rate_out']))
print('---------------')
fig, plot = rpu.create_plot()
plot.set_title('rate of ouput staging transitions', y=1.05, fontsize=18)
plot.set_xlabel('time (s)', fontsize=14)
plot.set_ylabel('rate (1/s)', fontsize=14)
plot.set_frame_on(True)
adv[['time', 'rate_out']].dropna().plot(ax=plot,
logx=False,
logy=False,
x='time',
y='rate_out',
drawstyle='steps',
label='output rate',
legend=False)
plot.legend(labels=['output rate'], loc='best', fontsize=14, frameon=True)
fig.savefig('profile.png', bbox_inches='tight')
import radical.pilot.utils as rpu
# we have a session
sid = session.uid
profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/')
profile = rpu.combine_profiles(profiles)
frame = rpu.prof2frame(profile)
sf, pf, uf = rpu.split_frame(frame)
rpu.add_info(uf)
rpu.add_states(uf)
adv = uf[uf['event'].isin(['advance'])]
rpu.add_frequency(adv, 'f_exe', 0.5, {
'state': 'Executing',
'event': 'advance'
})
s_frame, p_frame, u_frame = rpu.get_session_frames(sid)
#print str(u_frame)
info = [
"uid", "Unscheduled", "StagingInput", "AgentStagingInputPending",
"AgentStagingInput", "AllocatingPending", "Allocating",
"ExecutingPending", "Executing", "AgentStagingOutputPending",
"AgentStagingOutput", "StagingOutput", "Canceled", "Done"
]
u_frame.to_csv(
"execution_profile_nsims_{0}_simdur_{3}_anamin_{1}_anamax{5}_anatotdur_{2}_max_{4}.csv"
.format(tot_sim_tasks, interrupt_gap_min, interrupt_total_duration,
sim_arg, interrupt_max_tasks, interrupt_gap_max),
def profile_analysis(sid):
import radical.pilot.utils as rpu
report.header('profile analysis')
# fetch profiles for all pilots
profiles = rpu.fetch_profiles(sid=sid, tgt='/tmp/')
print profiles
# combine into a single profile
profile = rpu.combine_profiles(profiles)
# derive a global data frame
frame = rpu.prof2frame(profile)
# split into session / pilot / unit frames
sf, pf, uf = rpu.split_frame(frame)
print len(sf)
print len(pf)
print len(uf)
print sf[0:10]
print pf[0:10]
print uf[0:10]
# derive some additional 'info' columns, which contains some commonly used
# tags
rpu.add_info(uf)
for index, row in uf.iterrows():
if str(row['info']) != 'nan':
print "%-20s : %-10s : %-25s : %-20s" % \
(row['time'], row['uid'], row['state'], row['info'])
# add a 'state_from' columns which signals a state transition
rpu.add_states(uf)
adv = uf[uf['event'].isin(['advance'])]
print '---------------'
print len(adv)
print uf[uf['uid'] == 'unit.000001']
print list(pf['event'])
tmp = uf[uf['uid'] == 'unit.000001'].dropna()
print tmp[['time', 'uid', 'state', 'state_from']]
# add a columns 'rate_out' which contains the rate (1/s) of the event
# 'advance to state STAGING_OUTPUT'
print '---------------'
rpu.add_frequency(adv, 'rate_out', 0.5, {'state' : 'StagingOutput', 'event' : 'advance'})
print adv[['time', 'rate_out']].dropna(subset=['rate_out'])
print '---------------'
fig, plot = rpu.create_plot()
plot.set_title('rate of ouput staging transitions', y=1.05, fontsize=18)
plot.set_xlabel('time (s)', fontsize=14)
plot.set_ylabel('rate (1/s)', fontsize=14)
plot.set_frame_on(True)
adv[['time', 'rate_out']].dropna().plot(ax=plot, logx=False, logy=False,
x='time', y='rate_out',
drawstyle='steps',
label='output rate', legend=False)
plot.legend(labels=['output rate'], loc='best', fontsize=14, frameon=True)
fig.savefig('profile.png', bbox_inches='tight')
