def runHighDimAnalysis(data,
objHeaders=None,
saveFigsPrepend=None,
freqsToKeep=None,
displayFigs=True):
"""
standard set of plots generated for 2-objective problems
:param data: designs to plot. each row is a design and each column is an objective
:param saveFigsPrepend: a prepend name for saving figures generated. None (default) prevents automatic saving.
"""
if objHeaders is None:
objHeaders = list(map(lambda n: 'obj: ' + str(n),
range(data.shape[1])))
mp = lowDimMeanPlane(data) # create the mean plane
# runShowSaveClose(ft.partial(plotLogTradeRatios,mp,objHeaders),saveFigsPrepend+'_tradeRatios.png',displayFig=displayFigs)
if freqsToKeep is None:
freqsToKeep = 2**data.shape[1]
fa = rbfSummarizerAnalyzer.fromMeanPlane(mp, freqsToKeep)
if displayFigs:
fa.report()
if saveFigsPrepend is not None:
fa.report(saveFigsPrepend + '_report.csv')
runShowSaveClose(fa.rbfWeightPlot,
saveFigsPrepend + '_powerDeclinePlot.png',
displayFig=displayFigs)
def run2danalysis(data,
objHeaders=None,
saveFigsPrepend=None,
freqsToKeep=None,
displayFigs=True,
isMaxObj=None):
"""
standard set of plots generated for 2-objective problems
:param data: designs to plot. each row is a design and each column is an objective
:param saveFigsPrepend: a prepend name for saving figures generated. None (default) prevents automatic saving.
"""
if objHeaders is None:
objHeaders = list(map(lambda n: 'obj: ' + str(n),
range(data.shape[1])))
mp = lowDimMeanPlane(data) # create the mean plane
if saveFigsPrepend is not None:
mps = saveFigsPrepend + '_meanPlane.png'
trs = saveFigsPrepend + '_tradeRatios.png'
spts = saveFigsPrepend + '_spectralPower_legendre.png'
rts = saveFigsPrepend + '_reverseTransform.png'
pdr = saveFigsPrepend + '_powerDeclineReport.png'
else:
mps = None
trs = None
spts = None
rts = None
pdr = None
runShowSaveClose(mp.draw2dMeanPlane, mps, displayFig=displayFigs)
fa = rbfSummarizerAnalyzer.fromMeanPlane(mp, freqsToKeep)
if displayFigs:
fa.report()
if saveFigsPrepend is not None:
fa.report(saveFigsPrepend + '_report.csv')
# maybe use a scatterplot?
# runShowSaveClose(ft.partial(spectral1dPowerPlot_nonFFT,fa),spts,displayFig=displayFigs)
runShowSaveClose(fa.rbfWeightPlot, pdr, displayFig=displayFigs)
runShowSaveClose(ft.partial(rbfApproximationPlot2d, mp, fa, objHeaders),
saveFigsPrepend + '_reverseTransform.png',
displayFig=displayFigs)
# runShowSaveClose(ft.partial(plotTradeRatios,mp,fa,objHeaders),saveFigsPrepend+'_tradeoffPlot.png',displayFig=displayFigs)
return (mp, fa)
def run2danalysis(data,objHeaders=None,saveFigsPrepend=None,freqsToKeep=2, displayFigs=True, isMaxObj=None):
"""
standard set of plots generated for 2-objective problems
:param data: designs to plot. each row is a design and each column is an objective
:param saveFigsPrepend: a prepend name for saving figures generated. None (default) prevents automatic saving.
"""
if objHeaders is None:
objHeaders=list(map(lambda n: 'obj: '+str(n),range(data.shape[1])))
mp=lmp.lowDimMeanPlane(data) # create the mean plane
runShowSaveClose(mp.draw2dMeanPlane,saveFigsPrepend+'_meanPlane.png',displayFig=displayFigs)
runShowSaveClose(ft.partial(plotLogTradeRatios,mp,objHeaders),saveFigsPrepend+'_tradeRatios.png',displayFig=displayFigs)
fa=FourierSummarizerAnalyzer.fromMeanPlane(mp,freqsToKeep)
if displayFigs:
fa.report()
if saveFigsPrepend is not None:
fa.report(saveFigsPrepend+'_report.csv')
runShowSaveClose(ft.partial(spectral1dPowerPlot,fa),saveFigsPrepend+'_spectralPower.png',displayFig=displayFigs)
runShowSaveClose(ft.partial(spectral1dPhasePlot,fa),saveFigsPrepend+'_spectralPhase.png',displayFig=displayFigs)
plt.figure()
spectralPower=np.abs(np.fft.fftshift(fa.trueSpectrum()))**2
plt.plot(np.fft.fftshift(fa.fftFreqs),spectralPower,'k.-')
# axis_font={'size':'28'}
# plt.xlabel('frequency',**axis_font)
# plt.ylabel('square power',**axis_font)
plt.xlabel('frequency')
plt.ylabel('square power')
if saveFigsPrepend is not None:
plt.savefig(saveFigsPrepend+'_trueSpectralPower.png',bbox_inches='tight')
plt.show()
if not displayFigs:
plt.close('all')
runShowSaveClose(ft.partial(approximationPlot2d,mp,fa,objHeaders),saveFigsPrepend+'_reverseTransform.png',displayFig=displayFigs)
runShowSaveClose(ft.partial(plotTradeRatios,mp,fa,objHeaders),saveFigsPrepend+'_tradeoffPlot.png',displayFig=displayFigs)
return (mp,fa)
def run3danalysis(data,objHeaders=None,saveFigsPrepend=None,freqsToKeep=3**2,displayFigs=True):
"""
standard set of plots generated for 2-objective problems
:param data: designs to plot. each row is a design and each column is an objective
:param saveFigsPrepend: a prepend name for saving figures generated. None (default) prevents automatic saving.
"""
if objHeaders is None:
objHeaders=list(map(lambda n: 'obj: '+str(n),range(data.shape[1])))
mp=lmp.lowDimMeanPlane(data) # create the mean plane
runShowSaveClose(mp.draw3dMeanPlane,noneSafeConcat(saveFigsPrepend,'_meanPlane.png'),displayFig=displayFigs)
runShowSaveClose(ft.partial(plotLogTradeRatios,mp,objHeaders),noneSafeConcat(saveFigsPrepend,'_tradeRatios.png'),displayFig=displayFigs)
fa=FourierSummarizerAnalyzer.fromMeanPlane(mp,freqsToKeep)
if displayFigs:
fa.report()
if saveFigsPrepend is not None:
fa.report(saveFigsPrepend+'_report.csv')
runShowSaveClose(ft.partial(spectral2dPowerImage,fa),noneSafeConcat(saveFigsPrepend,'_spectralPower.png'),displayFig=displayFigs)
runShowSaveClose(ft.partial(spectral2dPowerPlot,fa),noneSafeConcat(saveFigsPrepend,'_spectralPower3d.png'),displayFig=displayFigs)
runShowSaveClose(ft.partial(approximationPlot3d,mp,fa),noneSafeConcat(saveFigsPrepend,'_reverseTransform.png'),displayFig=displayFigs)
runShowSaveClose(ft.partial(plot3dErr,mp.inputInPlane,mp.inputResidual),noneSafeConcat(saveFigsPrepend,'_errorPlot.png'),displayFig=displayFigs)
runShowSaveClose(fa.powerDeclineReport,noneSafeConcat(saveFigsPrepend,'_powerDeclineReport.png'),displayFig=displayFigs)
runShowSaveClose(ft.partial(plotTradeRatios,mp,fa,objHeaders),noneSafeConcat(saveFigsPrepend,'_tradeoffPlot.png'),displayFig=displayFigs)