fp_csv_metrics)
if individualImages:
if kpiimage != "None" and kpiimage != "" and kpiimage != "plot":
if not (os.path.exists(outputDir)):
os.makedirs(outputDir)
SaveScreenshot(outputDir + "/out_" + kpi + ".png",
magnification=magnification,
quality=100)
if 'animation' in metrichash:
makeAnim = data_IO.str2bool(metrichash['animation'])
else:
makeAnim = False
if makeAnim:
pvutils.makeAnimation(outputDir, kpi, magnification)
if 'blender' in metrichash:
export2Blender = data_IO.str2bool(metrichash['blender'])
else:
export2Blender = False
if export2Blender:
try:
blenderContext = metrichash['blendercontext'].split(",")
except:
blenderContext = []
try:
renderBody = metrichash['blenderbody'].split(",")
except:
renderBody = False
pvutils.exportx3d(outputDir, kpi, d, dataReader, renderBody,
d = pvutils.createBasic(metrichash, dataReader, readerDisplay)
if extractStats:
pvutils.extractStats(d, kpi, kpifield, kpiComp, kpitype,
fp_csv_metrics)
if kpiimage != "None" and kpiimage != "plot":
if not (os.path.exists(outputDir)):
os.makedirs(outputDir)
if caseNumber:
metrichash['imageName'] = metrichash['imageName'].format(
int(caseNumber))
SaveScreenshot(outputDir + '/' + metrichash['imageName'],
magnification=magnification,
quality=100)
if makeAnim:
animationName = metrichash['animationName']
if caseNumber:
animationName = animationName.format(int(caseNumber))
pvutils.makeAnimation(outputDir, kpi, magnification, animationName)
if export2Blender:
blenderContext = metrichash['blendercontext']
renderBody = metrichash['blenderbody']
pvutils.exportx3d(outputDir, kpi, d, dataReader, renderBody,
blenderContext)
fp_csv_metrics.close()