本文整理汇总了Python中lsst.pex.logging.Log.logdebug方法的典型用法代码示例。如果您正苦于以下问题：Python Log.logdebug方法的具体用法？Python Log.logdebug怎么用？Python Log.logdebug使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类lsst.pex.logging.Log的用法示例。
在下文中一共展示了Log.logdebug方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test1
# 需要导入模块: from lsst.pex.logging import Log [as 别名]
# 或者: from lsst.pex.logging.Log import logdebug [as 别名]
def test1(self):
res = self.getAstrometrySolution()
matches = res.matches
print 'Test1'
logLevel = Log.DEBUG
log = Log(Log.getDefaultLog(),
'testPhotoCal',
logLevel)
schema = matches[0].second.schema
config = PhotoCalConfig()
# The test and associated data have been prepared on the basis that we
# use the PsfFlux to perform photometry.
config.fluxField = "base_PsfFlux_flux"
config.doWriteOutput = False # schema is fixed because we already loaded the data
task = PhotoCalTask(config=config, schema=schema)
pCal = task.run(exposure=self.exposure, matches=matches)
print "Ref flux fields list =", pCal.arrays.refFluxFieldList
refFluxField = pCal.arrays.refFluxFieldList[0]
# These are *all* the matches; we don't really expect to do that well.
diff=[]
for m in matches:
refFlux = m[0].get(refFluxField) # reference catalog flux
if refFlux <= 0:
continue
refMag = afwImage.abMagFromFlux(refFlux) # reference catalog mag
instFlux = m[1].getPsfFlux() #Instrumental Flux
if instFlux <= 0:
continue
instMag = pCal.calib.getMagnitude(instFlux) #Instrumental mag
diff.append(instMag - refMag)
diff = np.array(diff)
self.assertGreater(len(diff), 50)
log.info('%i magnitude differences; mean difference %g; mean abs diff %g' %
(len(diff), np.mean(diff), np.mean(np.abs(diff))))
self.assertLess(np.mean(diff), 0.6)
# Differences of matched objects that were used in the fit.
zp = pCal.calib.getMagnitude(1.)
log.logdebug('zeropoint: %g' % zp)
fitdiff = pCal.arrays.srcMag + zp - pCal.arrays.refMag
log.logdebug('number of sources used in fit: %i' % len(fitdiff))
log.logdebug('rms diff: %g' % np.mean(fitdiff**2)**0.5)
log.logdebug('median abs(diff): %g' % np.median(np.abs(fitdiff)))
# zeropoint: 31.3145
# number of sources used in fit: 65
# median diff: -0.009681
# mean diff: 0.00331871
# median abs(diff): 0.0368904
# mean abs(diff): 0.0516589
self.assertLess(abs(zp - 31.3145), 0.05)
self.assertGreater(len(fitdiff), 50)
# Tolerances are somewhat arbitrary; they're set simply to avoid regressions, and
# are not based on we'd expect to get given the data quality.
self.assertLess(np.mean(fitdiff**2)**0.5, 0.07) # rms difference
self.assertLess(np.median(np.abs(fitdiff)), 0.06) # median absolution difference