def customReport_VRMS_Nusselt(sciBTest, mResults):
# Some custom output generation and analysis
vrmsTCs, vrmsResults = sciBTest.getTCRes("VRMS vs Blankenbach")
nusseltTCs, nusseltResults = sciBTest.getTCRes("Nusselt vs Blankenbach")
vrmsActuals = [tc.actualVal for tc in vrmsTCs]
nusseltActuals = [tc.actualVal for tc in nusseltTCs]
# TODO: useful if below values available on modelResults automatically.
for mRes in mResults: mRes.readFrequentOutput()
nSteps = [mRes.freqOutput.finalStep() for mRes in mResults]
# Plotting / CSV writing
thermalConvPostProc.plotResultsVsBlankenbach(BBRa,
BBVrmsMin, BBVrmsMax,
BBNusseltMin, BBNusseltMax,
{"UW Actual":vrmsActuals}, {"UW Actual":nusseltActuals},
os.path.join(sciBTest.outputPathBase, "VrmsAndNusseltValues.png"))
observables = {'Vrms':vrmsActuals, 'Vrms Passed':vrmsResults,
'Nusselt':nusseltActuals, 'Nusselt Passed':nusseltResults,
'nSteps':nSteps}
msuite.writeInputsOutputsToCSV(sciBTest.mSuite, observables,
"thermalDimBMResults.csv")
modelplots.plotOverAllRuns(mResults, 'Nusselt',
path=sciBTest.outputPathBase)
modelplots.plotOverAllRuns(mResults, 'Vrms',
path=sciBTest.outputPathBase)
#TODO: modularise the below
import plotCpuTimesAllRuns as plotCpus
plotCpus.plotAllRuns(sciBTest.outputPathBase)
sciBTest.mSuite.analysisImages = [
'VrmsAndNusseltValues.png',
'Nusselt-multiRunTimeSeries.png',
'Vrms-multiRunTimeSeries.png',
'cpuTimePerStep.png']
sciBTest.mSuite.modelImagesToDisplay = [[] for runI in \
range(len(sciBTest.mSuite.runs))]
lastImgSteps = []
for finalStep, mRun in zip(nSteps, sciBTest.mSuite.runs):
simParams = mRun.getSimParams()
lastImgSteps.append(simParams.nearestDumpStep(finalStep, finalStep))
sciBTest.mSuite.modelImagesToDisplay[0] = [
(0, ""),
(700, ""),
(lastImgSteps[0], "")]
sciBTest.mSuite.modelImagesToDisplay[1] = [
(0, ""),
(800, ""),
(lastImgSteps[1], "")]
sciBTest.mSuite.modelImagesToDisplay[2] = [
(0, ""),
(400, ""),
(lastImgSteps[2], "")]
for rGen in getGenerators(["RST", "ReportLab"], sciBTest.outputPathBase):
sReps.makeSciBenchReport(sciBTest, mResults, rGen,
os.path.join(sciBTest.outputPathBase, "%s-report.%s" %\
(sciBTest.testName, rGen.stdExt)), imgPerRow=3)
def customReporting(sciBTest, mResults):
#Plotting/CSV writing
for mRes in mResults: mRes.readFrequentOutput()
vrmsTCs, vrmsResults = sciBTest.getTCRes("Scaled VRMS")
recSteps = [mRes.freqOutput.finalStep() for mRes in mResults]
vrmsActuals = [mRes.freqOutput.getValueAtStep("Nusselt", ns) \
for mRes, ns in zip(mResults, recSteps)]
nusseltActuals = [mRes.freqOutput.getValueAtStep("Nusselt", ns) \
for mRes, ns in zip(mResults, recSteps)]
for mRes in mResults:
mRes.freqOutput.plotOverTime('Vrms', depName='Time', show=False,
path=mRes.outputPath)
mRes.freqOutput.plotOverTime('Nusselt', depName='Time', show=False,
path=mRes.outputPath)
observables = {'Vrms':vrmsActuals, 'Vrms Pass':vrmsResults,
'Nusselt':nusseltActuals, 'nSteps':recSteps,
'ScalingFac':vrmsScalingFactors}
msuite.writeInputsOutputsToCSV(sciBTest.mSuite, observables,
"scalingResults.csv")
# Actually for this benchmark, we want to show the VRMS and Nusselt
# images generated in _each run_
sciBTest.mSuite.analysisImages = None
#sciBTest.mSuite.analysisImages = [
# 'VrmsAndNusseltValues.png',
# 'Nusselt-multiRunTimeSeries.png',
# 'Vrms-multiRunTimeSeries.png']
sciBTest.mSuite.modelImagesToDisplay = [[] for runI in \
range(len(sciBTest.mSuite.runs))]
lastImgSteps = []
for finalStep, mRun in zip(recSteps, sciBTest.mSuite.runs):
simParams = mRun.getSimParams()
lastImgSteps.append(simParams.nearestDumpStep(finalStep, finalStep))
for runI in range(len(mResults)):
sciBTest.mSuite.modelImagesToDisplay[runI] = [
(0, "initial"),
(lastImgSteps[runI], "final")]
for rGen in getGenerators(["RST", "ReportLab"], sciBTest.outputPathBase):
sReps.makeSciBenchReport(sciBTest, mResults, rGen,
os.path.join(sciBTest.outputPathBase, "%s-report.%s" %\
(sciBTest.testName, rGen.stdExt)), imgPerRow=2)
def customReporting(sciBTest, mResults):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
stress = {}
recDeviatoricStress = {}
recDeviatoricStressTCs = {}
recDeviatoricStressRes = {}
stressTCs = {}
stressRes = {}
for fComp in ["XX", "XY", "YY"]:
recDeviatoricStressTCs[fComp], recDeviatoricStressRes[fComp] = sciBTest.getTCRes(\
"recoveredDeviatoricStress-%s" % fComp)
stressTCs[fComp], stressRes[fComp] = sciBTest.getTCRes(\
"Stress-%s" % fComp)
recDeviatoricStress[fComp] = [tc.actualVal for tc in recDeviatoricStressTCs[fComp]]
stress[fComp] = [tc.actualVal for tc in stressTCs[fComp]]
inIter = msuite.getVariantIndicesIter(sciBTest.mSuite.modelVariants,
iterStrategy)
varDicts = msuite.getVariantNameDicts(sciBTest.mSuite.modelVariants,
inIter)
for resI, mRes in enumerate(mResults):
print "Post-process result %d" % (resI),
print "with variants applied of:"
print varDicts[resI]
print "Value of recDeviatoricStress(%s) is (%s)\n stress(%s) is (%s)." %\
(", ".join(fCompMap),
", ".join(["%g" % recDeviatoricStress[c][resI] for c in fCompMap]),
", ".join(fCompMap),
", ".join(["%g" % stress[c][resI] for c in fCompMap]))
# TO DO:
#plotDeviatoricStressVsAnalytic(maxStressXX, maxStressYY, maxStressXY,
# minStressXX, minStressYY, minStressXY,
# {"DeviatoricStress_XX":recDeviatoricStress['XX']}, {"DeviatoricStress_YY":recDeviatoricStress['YY']},
# {"DeviatoricStress_XY":recDeviatoricStress['XY']},
# {"Stress_XX":stressXX}, {"Stress_YY":stressYY}, {"Stress_XY":stressXY})
#plt.savefig(os.path.join(mSuite.outputPathBase,
# "RecoveredDeviatoricStressAndStressValues.png"),
# dpi=None, facecolor='w', edgecolor='w',
# orientation='portrait', papertype=None, format=None,
# transparent=False)
# Save to a CSV file.
observables = {'recovered DeviatoricStress XX': recDeviatoricStress['XX'],
'recovered DeviatoricStress XX Passed': recDeviatoricStressRes['XX'],
'recovered DeviatoricStress YY': recDeviatoricStress['YY'],
'recovered DeviatoricStress YY Passed': recDeviatoricStressRes['YY'],
'recovered DeviatoricStress XY': recDeviatoricStress['XY'],
'recovered DeviatoricStress XY Passed': recDeviatoricStressRes['XY'],
'stress XX': stress['XX'],
'stress XX Passed': stressRes['XX'],
'stress YY': stress['YY'],
'stress YY Passed': stressRes['YY'],
'stress XY': stress['XY'],
'stress XY Passed': stressRes['XY']}
msuite.writeInputsOutputsToCSV(sciBTest.mSuite, observables,
"OrthotropicTestStressValues.csv")
sciBTest.mSuite.analysisImages = None
sciBTest.mSuite.modelImagesToDisplay = None
for rGen in getGenerators(["RST", "ReportLab"], sciBTest.outputPathBase):
sReps.makeSciBenchReport(sciBTest, mResults, rGen,
os.path.join(sciBTest.outputPathBase, "%s-report.%s" %\
(sciBTest.testName, rGen.stdExt)))
