def plotAllTSForInitalPopulationType(initPopType):
title = None # 'Fitness time series for the two types of sensors attachment'
dataDict = {attachments[x]: -1.*np.loadtxt(fitnessFileName(x, initPopType)) for x in attachments.keys()}
tplt.plotAverageTimeSeries(dataDict, 'Error', 'errorComparison_GENS50_IP' + initPopType + '.png', title=title, legendLocation=None, xlabel=xlabel, xlimit=50, ylimit=ylimit, figsize=(2.5,4), xscale=xscale, yscale=yscale, margins=margins)
tplt.plotAllTimeSeries(dataDict, 'Error', 'errorAllTrajectories_GEN50_IP' + initPopType + '.png', title=title, legendLocation=None, xlabel=xlabel, xlimit=50, ylimit=ylimit, figsize=(2.5,4), xscale=xscale, yscale=yscale, margins=margins, alpha=alpha)
tplt.plotAverageTimeSeries(dataDict, 'Error', 'errorComparison_IP' + initPopType + '.png', title=title, legendLocation=1, xlabel=xlabel, xlimit=500, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins)
tplt.plotAllTimeSeries(dataDict, 'Error', 'errorAllTrajectories_IP' + initPopType + '.png', title=title, legendLocation=1, xlabel=xlabel, xlimit=500, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha)
def plotAll():
os.chdir('results')
xlabel = 'Generations'
ylimit = None
yscale = 'lin'
xscale = 'lin'
margins = 0.5
xlimit = 6000
alpha = 0.3
title = None
# figsize = (2.5,4)
figsize = None
striptype = 'conf95'
filenames = [ 'gid{}'.format(x) for x in range(1,9) ]
dataDict = { fn: np.loadtxt(fn) for fn in filenames }
comp3 = { tsn: dataDict[fn] for tsn,fn in {'no turning': 'gid3', 'smooth turning': 'gid7', 'abrupt turning': 'gid8'}.items() }
comparisons = { 'comp3': comp3 }
for e,d in comparisons.items():
tplt.plotAverageTimeSeries(d, 'Fitness', e + '-avg.png',
title=title, legendLocation=4, xlabel=xlabel,
xlimit=xlimit, ylimit=ylimit, figsize=figsize, xscale=xscale, yscale=yscale, margins=margins, strips=striptype)
tplt.plotAllTimeSeries(d, 'Fitness', e + '-all.png', alpha=alpha,
title=title, legendLocation=4, xlabel=xlabel,
xlimit=xlimit, ylimit=ylimit, figsize=figsize, xscale=xscale, yscale=yscale, margins=margins)
os.chdir('..')
def plotMinMMMDistTSs(experiment, prefixFun):
'''Extracting and plotting time series for distance to the maximally modular morphology (MMM)'''
xlabel = r'$T$'
figureDims = [7, 4]
#figureDims = None
xlimit = evsDefaults['genStopAfter']
margins = 0.5
strips = 'conf95'
title = None
legendLocation = 1
gridFileNamePrefix = prefixFun
def minMMMDistFileName(gridPoint):
return '../results/' + gridFileNamePrefix(gridPoint) + '_minMMMDist'
def generateMinMMMDistTimeSeries(gridPoint):
minMMMDistTS = [
gctools.minParetoFrontHammingDistanceToMMM(gen)
for gen in range(1, evsDefaults['genStopAfter'] + 1)
]
filename = minMMMDistFileName(gridPoint)
with open(filename, 'a') as file:
file.write(' '.join(map(str, minMMMDistTS)) + '\n')
experiment.executeAtEveryGridPointDir(generateMinMMMDistTimeSeries)
os.chdir('results')
ylabel = r'$\mu$'
ylimit = None
title = None
dataDict = {
gridFileNamePrefix(p): np.loadtxt(minMMMDistFileName(p))
for p in nonRSGrid
}
# Plotting averages in linear time scales on y
yscale = 'lin'
xscale = 'lin'
tplt.plotAverageTimeSeries(dataDict,
ylabel,
'minMMMDistTS.png',
title=title,
legendLocation=legendLocation,
xlabel=xlabel,
xlimit=xlimit,
ylimit=ylimit,
xscale=xscale,
yscale=yscale,
margins=margins,
strips=strips,
figureDims=figureDims)
plt.clf()
os.chdir('..')
def plotErrorTSs(experiment, prefixFun):
import shutil
gridFileNamePrefix = prefixFun
##### Extracting and plotting fitness time series #####
xlabel = r'$T$'
#figureDims = [7,4]
figureDims = None
xlimit = evsDefaults['genStopAfter']
margins = 0.5
strips = 'conf95'
title = None
legendLocation = 1
def fitnessFileName(gp):
return gridFileNamePrefix(gp) + '_fitness'
def columnExtractor(gp):
outFile = fitnessFileName(gp)
subprocess.call('cut -d \' \' -f 2 bestIndividual*.log | tail -n +4 | tr \'\n\' \' \' >> ../results/' + outFile, shell=True)
subprocess.call('echo >> ../results/' + outFile, shell=True)
experiment.executeAtEveryGridPointDir(columnExtractor)
os.chdir('results')
ylabel = r'$\log_{10} E$'
forcedYLabelPos = [0.05, 1.]
ylimit = None
title = None
dataDict = {gridFileNamePrefix(p): -1.*np.loadtxt(fitnessFileName(p)) for p in nonRSGrid}
# Plotting averages
yscale = 'lin'
xscale = 'lin'
tplt.plotAverageTimeSeries(dataDict, ylabel, 'errorComparisonLinLin.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, strips=strips, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
xscale = 'log'
tplt.plotAverageTimeSeries(dataDict, ylabel, 'errorComparisonLogLin.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, strips=strips, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
# Plotting the trajectory scatter
alpha = 0.3
yscale = 'lin'
xscale = 'lin'
tplt.plotAllTimeSeries(dataDict, ylabel, 'errorAllTrajectoriesLinLog.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
xscale = 'log'
tplt.plotAllTimeSeries(dataDict, ylabel, 'errorAllTrajectoriesLogLog.png', title=title, legendLocation=legendLocation, xlabel=xlabel, xlimit=xlimit, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha, figureDims=figureDims, forcedYLabelPos=forcedYLabelPos)
plt.clf()
os.chdir('..')
def processResults(experiment):
import os
import shutil
import numpy as np
import pbsGridWalker.tools.plotutils as tplt
# tfs.makeDirCarefully('results', maxBackups=100)
def fitnessFileName(gp):
return 'EV' + str(gp['evolver']) + '_NI' + str(
gp['newIndividualsPerGeneration']
) + '_IP' + gp['initialPopulationType'] + '_fitness'
def columnExtractor(gp):
outFile = fitnessFileName(gp)
subprocess.call(
'cut -d \' \' -f 2 bestIndividual*.log | tail -n +4 | tr \'\n\' \' \' >> ../results/'
+ outFile,
shell=True)
subprocess.call('echo >> ../results/' + outFile, shell=True)
# experiment.executeAtEveryGridPointDir(columnExtractor)
os.chdir('results')
xlabel = 'Generations'
ylimit = None
yscale = 'lin'
xscale = 'lin'
margins = 0.5
xlimit = None
alpha = 0.3
title = None
def robustLoadTxt(ffn):
arr0 = []
with open(ffn, 'r') as ff:
for line in ff:
vals = map(float, line.split())
arr0.append(vals)
minlen = min([len(vs) for vs in arr0])
arr1 = []
for vs in arr0:
arr1.append(vs[:minlen])
arr1 = np.array(arr1)
return arr1
# dataDict = { str(mg): np.loadtxt(fitnessFileName(mg)) for mg in [0.1, 0.3, 0.5, 0.7, 0.9] }
# dataDict = { str(mg): robustLoadTxt(fitnessFileName(mg)) for mg in [0.1, 0.3, 0.5, 0.7, 0.9] }
# tplt.plotAverageTimeSeries(dataDict, 'Error', 'mg.png',
# title=title, legendLocation=None, xlabel=xlabel,
# xlimit=xlimit, ylimit=ylimit, figsize=(2.5,4), xscale=xscale, yscale=yscale, margins=margins)
ni = 0
for ip in ['sparse', 'random']:
dataDict = {
'with SRS' if ev == 'temporalUnrolling' else 'without SRS':
robustLoadTxt(
fitnessFileName({
'initialPopulationType': ip,
'newIndividualsPerGeneration': ni,
'evolver': ev
}))
for ev in ['temporalUnrolling', 'temporalUnrollingVer0']
}
tplt.plotAllTimeSeries(dataDict,
'Fitness',
'ev_IP' + ip + '.png',
title=title,
legendLocation=4,
xlabel=xlabel,
xlimit=xlimit,
ylimit=ylimit,
xscale=xscale,
yscale=yscale,
margins=margins,
alpha=alpha)
tplt.plotAverageTimeSeries(dataDict,
'Fitness',
'ev_IP' + ip + '_NI' + str(ni) + '_avg.png',
title=title,
legendLocation=4,
xlabel=xlabel,
xlimit=xlimit,
ylimit=ylimit,
xscale=xscale,
yscale=yscale,
margins=margins)
ni = 5
for ip in ['sparse', 'random']:
dataDict = {
'with SRS' if ev == 'temporalUnrolling' else 'without SRS':
robustLoadTxt(
fitnessFileName({
'initialPopulationType': ip,
'newIndividualsPerGeneration': ni,
'evolver': ev
}))
for ev in ['temporalUnrolling', 'temporalUnrollingVer0']
}
tplt.plotAllTimeSeries(dataDict,
'Fitness',
'ev_IP' + ip + '_NI' + str(ni) + '.png',
title=title,
legendLocation=4,
xlabel=xlabel,
xlimit=xlimit,
ylimit=ylimit,
xscale=xscale,
yscale=yscale,
margins=margins,
alpha=alpha)
tplt.plotAverageTimeSeries(dataDict,
'Fitness',
'ev_IP' + ip + '_NI' + str(ni) + '_avg.png',
title=title,
legendLocation=4,
xlabel=xlabel,
xlimit=xlimit,
ylimit=ylimit,
xscale=xscale,
yscale=yscale,
margins=margins)
# def plotAllTSForInitalPopulationType(initPopType):
# title = None
# dataDict = {attachments[x]: -1.*np.loadtxt(fitnessFileName(x, initPopType)) for x in attachments.keys()}
# tplt.plotAverageTimeSeries(dataDict, 'Error', 'errorComparison_GENS50_IP' + initPopType + '.png', title=title, legendLocation=None, xlabel=xlabel, xlimit=50, ylimit=ylimit, figsize=(2.5,4), xscale=xscale, yscale=yscale, margins=margins)
# tplt.plotAllTimeSeries(dataDict, 'Error', 'errorAllTrajectories_GEN50_IP' + initPopType + '.png', title=title, legendLocation=None, xlabel=xlabel, xlimit=50, ylimit=ylimit, figsize=(2.5,4), xscale=xscale, yscale=yscale, margins=margins, alpha=alpha)
# tplt.plotAverageTimeSeries(dataDict, 'Error', 'errorComparison_IP' + initPopType + '.png', title=title, legendLocation=1, xlabel=xlabel, xlimit=500, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins)
# tplt.plotAllTimeSeries(dataDict, 'Error', 'errorAllTrajectories_IP' + initPopType + '.png', title=title, legendLocation=1, xlabel=xlabel, xlimit=500, ylimit=ylimit, xscale=xscale, yscale=yscale, margins=margins, alpha=alpha)
# for ip in initialPopulationTypes:
# plotAllTSForNew(ip)
os.chdir('..')