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gplot_cpu
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#!/usr/bin/python
'''
Extract and plot info contained in cpu.txt files
Yves Revaz
jeu avr 6 15:58:19 CEST 2006
'''
import os, sys, string
from optparse import OptionParser
from Gtools import *
from Gtools import io
from optparse import OptionParser
import Ptools as pt
def parse_options():
usage = "usage: %prog [options] file"
parser = OptionParser(usage=usage)
parser = pt.add_postscript_options(parser)
parser = pt.add_limits_options(parser)
parser = pt.add_log_options(parser)
parser = pt.add_cmd_options(parser)
parser.add_option("--mode",
action="store",
dest="mode",
type="string",
default = 'total',
help="mode : cpu, cpu/step",
metavar=" NAME")
parser.add_option("--legend",
action="store_true",
dest="legend",
default = False,
help="add a legend")
parser.add_option("-t",
action="store",
dest="time",
type="string",
default = 'hour',
help="time : day, hour, minute, second",
metavar=" TYPE")
(options, args) = parser.parse_args()
if len(args) == 0:
print "you must specify a filename"
sys.exit(0)
files = args
return files,options
#######################################
# MakePlot
#######################################
def MakePlot(dirs,opt):
# some inits
colors = pt.Colors()
linestyles = pt.LineStyles(n=len(files))
datas = []
# read files
for file in files:
linestyle = linestyles.get()
try:
vals = pt.io.read_ascii(file)
except:
Step,Time,CPUs,CPU_Total,CPU_Gravity,CPU_Hydro,CPU_Domain,CPU_Potential,CPU_Predict,CPU_TimeLine,CPU_Snapshot,CPU_TreeWalk,CPU_TreeConstruction,CPU_CommSum,CPU_Imbalance,CPU_HydCompWalk,CPU_HydCommSumm,CPU_HydImbalance,CPU_EnsureNgb,CPU_PM,CPU_Peano=io.read_cpu(file)
vals = {}
vals["Step"] = Step
vals["Time"] = Time
vals["nCPUs"] = CPUs
vals["CPU_Total"] = CPU_Total
vals["CPU_Gravity"] = CPU_Gravity
vals["CPU_Hydro"] = CPU_Hydro
vals["CPU_Domain"] = CPU_Domain
vals["CPU_Potential"] = CPU_Potential
vals["CPU_Predict"] = CPU_Predict
vals["CPU_TimeLine"] = CPU_TimeLine
vals["CPU_Snapshot"] = CPU_Snapshot
vals["CPU_TreeWalk"] = CPU_TreeWalk
vals["CPU_TreeConstruction"] = CPU_TreeConstruction
vals["CPU_CommSum"] = CPU_CommSum
vals["CPU_Imbalance"] = CPU_Imbalance
vals["CPU_HydCompWalk"] = CPU_HydCompWalk
vals["CPU_HydCommSumm"] = CPU_HydCommSumm
vals["CPU_HydImbalance"] = CPU_HydImbalance
vals["CPU_EnsureNgb"] = CPU_EnsureNgb
vals["CPU_PM"] = CPU_PM
vals["CPU_Peano"] = CPU_Peano
Time = vals["Time"]
nCPUs = vals["nCPUs"]
##############
# global bilan
##############
if opt.mode == 'total':
Gravity = vals["CPU_Gravity"] / vals["CPU_Total"]
Hydro = vals["CPU_Hydro"] / vals["CPU_Total"]
Domain = vals["CPU_Domain"] / vals["CPU_Total"]
Potential = vals["CPU_Potential"] / vals["CPU_Total"]
Predict = vals["CPU_Predict"] / vals["CPU_Total"]
TimeLine = vals["CPU_TimeLine"] / vals["CPU_Total"]
Snapshot = vals["CPU_Snapshot"] / vals["CPU_Total"]
Peano = vals["CPU_Peano"] / vals["CPU_Total"]
datas.append( pt.DataPoints(Time,Gravity,color='r' ,linestyle=linestyle,label='Gravity') )
datas.append( pt.DataPoints(Time,Hydro,color='g' ,linestyle=linestyle,label='Hydro') )
datas.append( pt.DataPoints(Time,Domain,color='b' ,linestyle=linestyle,label='Domain') )
datas.append( pt.DataPoints(Time,Potential,color='c',linestyle=linestyle,label='Potential') )
datas.append( pt.DataPoints(Time,Predict,color='m' ,linestyle=linestyle,label='Predict') )
datas.append( pt.DataPoints(Time,TimeLine,color='y' ,linestyle=linestyle,label='TimeLine') )
datas.append( pt.DataPoints(Time,Snapshot,color='k' ,linestyle=linestyle,label='Snapshot') )
datas.append( pt.DataPoints(Time,Peano,color='k' ,linestyle=linestyle,label='Peano') )
if vals.has_key('CPU_Chimie'):
Chimie = vals["CPU_Chimie"] / vals["CPU_Total"]
datas.append( pt.DataPoints(Time,Chimie,color='y' ,linestyle=linestyle,label='Chimie') )
if vals.has_key('CPU_StarFormation'):
StarFormation = vals["CPU_StarFormation"] / vals["CPU_Total"]
datas.append( pt.DataPoints(Time,StarFormation,color='b' ,linestyle=linestyle,label='StarFormation') )
##############
# gravity bilan
##############
elif opt.mode == 'gravity':
TreeWalk = vals["CPU_TreeWalk"] / vals["CPU_Gravity"]
TreeConstruction = vals["CPU_TreeConstruction"] / vals["CPU_Gravity"]
CommSum = vals["CPU_CommSum"] / vals["CPU_Gravity"]
Imbalance = vals["CPU_Imbalance"] / vals["CPU_Gravity"]
datas.append( pt.DataPoints(Time,TreeWalk, color='r',linestyle=linestyle,label='TreeWalk') )
datas.append( pt.DataPoints(Time,TreeConstruction,color='g',linestyle=linestyle,label='TreeConstruction') )
datas.append( pt.DataPoints(Time,CommSum, color='b',linestyle=linestyle,label='CommSum') )
datas.append( pt.DataPoints(Time,Imbalance, color='k',linestyle=linestyle,label='Imbalance') )
##############
# gas bilan
##############
elif opt.mode == 'hydro':
print "warning, CPU_HydCompWalk,CPU_HydCommSumm,CPU_HydImbalance, CPU_EnsureNgb "
print "are means of total time"
HydCompWalk = vals["CPU_HydCompWalk"] / vals["CPU_Hydro"]
HydCommSumm = vals["CPU_HydCommSumm"] / vals["CPU_Hydro"]
HydImbalance = vals["CPU_HydImbalance"] / vals["CPU_Hydro"]
EnsureNgb = vals["CPU_EnsureNgb"] / vals["CPU_Hydro"]
datas.append( pt.DataPoints(Time,HydCompWalk, color='r',linestyle=linestyle,label='HydCompWalk') )
datas.append( pt.DataPoints(Time,HydCommSumm, color='g',linestyle=linestyle,label='HydCommSumm') )
datas.append( pt.DataPoints(Time,HydImbalance, color='b',linestyle=linestyle,label='HydImbalance') )
datas.append( pt.DataPoints(Time,EnsureNgb, color='k',linestyle=linestyle,label='EnsureNgb') )
pt.legend(['HydCompWalk','HydCommSumm','HydImbalance','EnsureNgb'])
# now, plot
for d in datas:
pt.plot(d.x,d.y,color=d.color,linestyle=d.linestyle)
# set limits and draw axis
xmin,xmax,ymin,ymax = pt.SetLimitsFromDataPoints(opt.xmin,opt.xmax,opt.ymin,opt.ymax,datas,opt.log)
pt.SetAxis(xmin,xmax,ymin,ymax,log=opt.log)
if opt.legend:
pt.LegendFromDataPoints(datas)
pt.xlabel('Time')
pt.ylabel('Fraction')
pt.title('Runned on %d CPUs'%(nCPUs[0]))
del vals
if __name__ == '__main__':
files,opt = parse_options()
pt.InitPlot(files,opt)
#pt.figure(figsize=(8*2,6*2))
#pt.figure(dpi=10)
#fig = pt.gcf()
#fig.subplots_adjust(left=0.1)
#fig.subplots_adjust(right=1)
#fig.subplots_adjust(bottom=0.12)
#fig.subplots_adjust(top=0.95)
#fig.subplots_adjust(wspace=0.25)
#fig.subplots_adjust(hspace=0.02)
MakePlot(files,opt)
pt.EndPlot(files,opt)
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