def customReporting(sciBTest, mResults):
# This gets called immediately post the models being run.
# First set up overall analysis images
modelplots.plotOverAllRuns(mResults, 'Vrms', depName='Time',
path=sciBTest.outputPathBase)
sciBTest.mSuite.analysisImages = ["Vrms-multiRunTimeSeries.png"]
# Now specific per-run images
fStep = mResults[0].freqOutput.finalStep()
dEvery = sciBTest.mSuite.runs[0].simParams.dumpevery
lastImgStep = fStep / dEvery * dEvery
vrmsPeakTime = sciBTest.testComps[0]['VRMS of first diapir'].actualTime
vrmsPeakStep = mResults[0].freqOutput.getClosest('Time', vrmsPeakTime)[1]
vrmsPeakImgStep = int(round(vrmsPeakStep / float(dEvery))) * dEvery
vrmsPeakImgStep = min([vrmsPeakImgStep, lastImgStep])
# Create an empty list of images to display
sciBTest.mSuite.modelImagesToDisplay = [[] for runI in \
range(len(sciBTest.mSuite.runs))]
# Choose which model timestep images to display:- note that here we're
# programmatically choosing to show the Peak VRMS timestep.
sciBTest.mSuite.modelImagesToDisplay[0] = [
(10, "initial state"),
(vrmsPeakImgStep, "near first VRMS peak at t=%f" % vrmsPeakTime),
(lastImgStep, "")]
# Here we just ask the CREDO reporting API to get Report Generators for
# PDF (ReportLab) and RST (Restructured Text) output, and create a
# standard science benchmark report.
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 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 customReport_VRMS(sciBTest, mResults):
for mRes in mResults: mRes.readFrequentOutput()
# Some custom output generation and analysis
fSteps = [mResult.freqOutput.finalStep() for mResult in mResults]
vrmsTCs, vrmsResults = sciBTest.getTCRes("VRMS Max")
vrmsActuals = [tc.actualVal for tc in vrmsTCs]
# Now specific per-run images
dEvery = sciBTest.mSuite.runs[0].simParams.dumpevery
lastImgSteps = [fStep / dEvery * dEvery for fStep in fSteps]
# find the timestep
vrmsPeakTimes = [tc.actualTime for tc in vrmsTCs]
vrmsPeakSteps = [mRes.freqOutput.getClosest('Time', peakTime)[1] for mRes, peakTime in\
zip(mResults, vrmsPeakTimes) ]
# TODO: useful if below values available on modelResults automatically.
# Plotting
modelplots.plotOverAllRuns(mResults, 'Vrms', depName='Time',
path=sciBTest.outputPathBase)
modelplots.plotOverAllRuns(mResults, 'Entrainment',
path=sciBTest.outputPathBase, depName='Time')
#TODO: modularise the below
import plotCpuTimesAllRuns as plotCpus
plotCpus.plotAllRuns(sciBTest.outputPathBase)
sciBTest.mSuite.analysisImages = [
'Vrms-multiRunTimeSeries.png',
'Entrainment-multiRunTimeSeries.png',
'cpuTimePerStep.png']
sciBTest.mSuite.modelImagesToDisplay = [[] for runI in \
range(len(sciBTest.mSuite.runs))]
lastImgSteps = []
vrmsPeakImgSteps = []
for runI, mRun in enumerate(sciBTest.mSuite.runs):
finalStep = fSteps[runI]
simParams = mRun.getSimParams()
lastImgSteps.append(simParams.nearestDumpStep(finalStep, finalStep))
vrmsPeakImgSteps.append(simParams.nearestDumpStep(vrmsPeakSteps[runI],
finalStep))
for resI, mResult in enumerate(mResults):
simParams = sciBTest.mSuite.runs[resI].getSimParams()
qtrStep = simParams.nearestDumpStep(fSteps[resI]//4, fSteps[resI])
halfStep = simParams.nearestDumpStep(fSteps[resI]//2, fSteps[resI])
qtrTime = mResult.freqOutput.getValueAtStep("Time", qtrStep)
halfTime = mResult.freqOutput.getValueAtStep("Time", halfStep)
sciBTest.mSuite.modelImagesToDisplay[resI] = [
(0, ""),
(vrmsPeakImgSteps[resI], "VRMS Peak, t=%f" % vrmsPeakTimes[resI]),
(qtrStep, "t=%f" % qtrTime),
(halfStep, "t=%f" % halfTime),
(lastImgSteps[resI], "Final, t=%f" % (stopTime))]
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)
plt.plot(r, var / scale[field_name], 'k--', label=sim, zorder=1)
plt.xlabel('radius (m)')
plt.ylabel(field_name + ' (' + unit[field_name] + ')')
plt.title(' '.join((model_name, 'comparison with', tc_name)))
img_filename_base = '_'.join(
(model_name, tc_name, 'comparison', field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(problem1_test.mSuite.runs[run_index].basePath,
problem1_test.mSuite.outputPathBase,
img_filename_base)
plt.legend(loc='upper left')
plt.tight_layout(pad=3.)
plt.savefig(img_filename + '.png', dpi=300)
plt.savefig(img_filename + '.pdf')
plt.clf()
problem1_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], problem1_test.outputPathBase):
report_filename = os.path.join(
problem1_test.outputPathBase,
"%s-report.%s" % (problem1_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(problem1_test, mResults, rGen, report_filename)
html_filename = os.path.join(
problem1_test.outputPathBase,
"%s-report.%s" % (problem1_test.testName, 'html'))
html = publish_file(source_path=report_filename,
destination_path=html_filename,
writer_name="html")
title = run_name.replace('single', 'single porosity').replace('minc', 'MINC')
plt.plot(var[:n], z, '-', label = 'Waiwera ' + title)
var = AUTOUGH2_result[run_name].getFieldAtOutputIndex(field_name, -1) / scale
plt.plot(var[:n], z, 's', label = 'AUTOUGH2 ' + title)
plt.xlabel(field_name + ' (' + unit + ')')
plt.ylabel('z (m)')
plt.title(' '. join(['Final', field_name.lower(), 'profile']))
img_filename_base = '_'.join((model_name, field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(minc_column_test.mSuite.runs[0].basePath,
minc_column_test.mSuite.outputPathBase,
img_filename_base)
plt.legend(loc = 'best')
plt.tight_layout(pad = 3.)
plt.savefig(img_filename + '.png', dpi = 300)
plt.savefig(img_filename + '.pdf')
plt.clf()
minc_column_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], minc_column_test.outputPathBase):
report_filename = os.path.join(minc_column_test.outputPathBase,
"%s-report.%s" % (minc_column_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(minc_column_test, mResults, rGen, report_filename)
html_filename = os.path.join(minc_column_test.outputPathBase,
"%s-report.%s" % (minc_column_test.testName, 'html'))
html = publish_file(source_path = report_filename,
destination_path = html_filename,
writer_name = "html")
plt.xlabel('time (years)')
plt.ylabel(field_name + ' (' + unit + ')')
plt.legend(loc='best')
plt.title(' '.join((field_name, 'history at production well')))
img_filename_base = '_'.join(
(model_name, run_name, 'history', field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(
minc_production_test.mSuite.runs[run_index].basePath,
minc_production_test.mSuite.outputPathBase, img_filename_base)
plt.tight_layout(pad=3.)
plt.savefig(img_filename + '.png', dpi=300)
plt.savefig(img_filename + '.pdf')
plt.clf()
minc_production_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], minc_production_test.outputPathBase):
report_filename = os.path.join(
minc_production_test.outputPathBase,
"%s-report.%s" % (minc_production_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(minc_production_test, mResults, rGen,
report_filename)
html_filename = os.path.join(
minc_production_test.outputPathBase,
"%s-report.%s" % (minc_production_test.testName, 'html'))
html = publish_file(source_path=report_filename,
destination_path=html_filename,
writer_name="html")
if labels[i] in labels[:i]:
del (labels[i])
del (handles[i])
else:
i += 1
plt.legend(handles, labels, loc='best')
# time labels:
plt.text(0.013, 0.5, "0.01 days")
plt.text(0.03, 0.65, "0.06 days")
plt.text(0.06, 0.73, "0.11 days")
plt.tight_layout(pad=3.)
plt.savefig(img_filename + '.png', dpi=300)
plt.savefig(img_filename + '.pdf')
plt.clf()
infiltration_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], infiltration_test.outputPathBase):
report_filename = os.path.join(
infiltration_test.outputPathBase,
"%s-report.%s" % (infiltration_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(infiltration_test, mResults, rGen,
report_filename)
html_filename = os.path.join(
infiltration_test.outputPathBase,
"%s-report.%s" % (infiltration_test.testName, 'html'))
html = publish_file(source_path=report_filename,
destination_path=html_filename,
writer_name="html")
r = result.getCoordinates()
var = result.getFieldAtOutputIndex(field_name, outputIndex)
plt.semilogx(r, var / field_scale[field_name], symbol[sim], label = sim)
plt.xlabel('radius (m)')
plt.ylabel(field_name + ' (' + field_unit[field_name] + ')')
plt.title(field_name)
img_filename_base = '_'.join((model_name, tc_name, 'comparison', field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(heat_pipe_test.mSuite.runs[run_index].basePath,
heat_pipe_test.mSuite.outputPathBase,
img_filename_base)
plt.xlim([min_radius, max_radius])
plt.legend(loc = 'center right')
plt.tight_layout(pad = 3.)
plt.savefig(img_filename + '.png', dpi = 300)
plt.savefig(img_filename + '.pdf')
plt.clf()
heat_pipe_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], heat_pipe_test.outputPathBase):
report_filename = os.path.join(heat_pipe_test.outputPathBase,
"%s-report.%s" % (heat_pipe_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(heat_pipe_test, mResults, rGen, report_filename)
html_filename = os.path.join(heat_pipe_test.outputPathBase,
"%s-report.%s" % (heat_pipe_test.testName, 'html'))
html = publish_file(source_path = report_filename,
destination_path = html_filename,
writer_name = "html")
"temp": ("Temperature", "Temperature relative error", "$^\circ$C"),
"vapsat": ("Vapour saturation", "Vapour saturation relative error", ""),
}
for i, tc_name in enumerate(names.keys()):
field_name, title, unit = names[tc_name]
var = np.array(sciBTest.testComps[0][tc_name].fieldErrors[field_name])
var = np.hstack([atmvals, var])
geo.slice_plot(slc, var, title, unit, plt=plt)
img_filename = os.path.join(sciBTest.mSuite.runs[0].basePath,
sciBTest.mSuite.outputPathBase, ("%i.png" % i))
plt.savefig(img_filename,
dpi=None,
facecolor='w',
edgecolor='w',
orientation='portrait',
papertype=None,
format=None,
transparent=False)
plt.clf()
sciBTest.mSuite.analysisImages.append(img_filename)
# ---------------------------------------------------------------------------
# report generation
for rGen in getGenerators(["RST"], sciBTest.outputPathBase):
sReps.makeSciBenchReport(
sciBTest, mResults, rGen,
os.path.join(sciBTest.outputPathBase,
"%s-report.%s" % (sciBTest.testName, rGen.stdExt)))
print("NOTE: use 'rst2html xxx-report.rst > xxx-report.html' to generate html")
plt.xlabel('time (s)')
plt.ylabel(field_name + ' (' + unit[field_name] + ')')
plt.legend(loc='best')
plt.title(' '.join((run_name, field_name.lower())))
img_filename_base = '_'.join(
(model_name, run_name, tc_name, field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(
deliverability_test.mSuite.runs[run_index].basePath,
deliverability_test.mSuite.outputPathBase,
img_filename_base + '.png')
plt.tight_layout(pad=3.)
plt.savefig(img_filename)
plt.clf()
deliverability_test.mSuite.analysisImages.append(img_filename)
# generate report:
for rGen in getGenerators(["RST"], deliverability_test.outputPathBase):
report_filename = os.path.join(
deliverability_test.outputPathBase,
"%s-report.%s" % (deliverability_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(deliverability_test, mResults, rGen,
report_filename)
html_filename = os.path.join(
deliverability_test.outputPathBase,
"%s-report.%s" % (deliverability_test.testName, 'html'))
html = publish_file(source_path=report_filename,
destination_path=html_filename,
writer_name="html")
Example created:- 2011/04/12, PatrickSunter"""
import os
import credo.jobrunner
import credo.reporting as rep
import credo.reporting.standardReports as sReps
import testAll_lowres
ts = testAll_lowres.suite()
sysTest = ts.sysTests[0]
# TODO: really should post-proc from existing, but this isn't working perfectly
# yet for basic sys tests.
jobRunner = credo.jobrunner.defaultRunner()
sysTest.setupTest()
testResult, mResults = sysTest.runTest(jobRunner,
postProcFromExisting=False,
createReports=True)
# Dodgy test report configuration
sysTest.mSuite.analysisImages = []
sysTest.mSuite.modelImagesToDisplay = None
for rGen in rep.getGenerators(["RST", "ReportLab"], sysTest.outputPathBase):
sReps.makeSciBenchReport(sysTest, mResults, rGen,
os.path.join(sysTest.outputPathBase, "%s-report.%s" %\
(sysTest.testName, rGen.stdExt)), imgPerRow=2)
suite to run a report and generate images.
Example created:- 2011/04/12, PatrickSunter"""
import os
import credo.jobrunner
import credo.reporting as rep
import credo.reporting.standardReports as sReps
import testAll_lowres
ts = testAll_lowres.suite()
sysTest = ts.sysTests[0]
# TODO: really should post-proc from existing, but this isn't working perfectly
# yet for basic sys tests.
jobRunner = credo.jobrunner.defaultRunner()
sysTest.setupTest()
testResult, mResults = sysTest.runTest(jobRunner,
postProcFromExisting=False, createReports=True)
# Dodgy test report configuration
sysTest.mSuite.analysisImages = []
sysTest.mSuite.modelImagesToDisplay = None
for rGen in rep.getGenerators(["RST", "ReportLab"], sysTest.outputPathBase):
sReps.makeSciBenchReport(sysTest, mResults, rGen,
os.path.join(sysTest.outputPathBase, "%s-report.%s" %\
(sysTest.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)))
plt.plot(t, var / field_scale[field_name], 's', label='AUTOUGH2', zorder=2)
plt.xlabel('time (s)')
plt.ylabel(field_name + ' (' + field_unit[field_name] + ')')
plt.legend(loc='best')
img_filename_base = '_'.join((model_name, field_name))
img_filename_base = img_filename_base.replace(' ', '_')
img_filename = os.path.join(
co2_one_cell_test.mSuite.runs[run_index].basePath,
co2_one_cell_test.mSuite.outputPathBase, img_filename_base)
plt.tight_layout(pad=3.)
plt.savefig(img_filename + '.png', dpi=300)
plt.savefig(img_filename + '.pdf')
plt.clf()
co2_one_cell_test.mSuite.analysisImages.append(img_filename + '.png')
# generate report:
for rGen in getGenerators(["RST"], co2_one_cell_test.outputPathBase):
report_filename = os.path.join(
co2_one_cell_test.outputPathBase,
"%s-report.%s" % (co2_one_cell_test.testName, rGen.stdExt))
sReps.makeSciBenchReport(co2_one_cell_test, mResults, rGen,
report_filename)
html_filename = os.path.join(
co2_one_cell_test.outputPathBase,
"%s-report.%s" % (co2_one_cell_test.testName, 'html'))
html = publish_file(source_path=report_filename,
destination_path=html_filename,
writer_name="html")