本文整理匯總了Python中ramanTools.RamanSpectrum.reindex方法的典型用法代碼示例。如果您正苦於以下問題：Python RamanSpectrum.reindex方法的具體用法？Python RamanSpectrum.reindex怎麽用？Python RamanSpectrum.reindex使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類ramanTools.RamanSpectrum的用法示例。
在下文中一共展示了RamanSpectrum.reindex方法的1個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: calibration3
# 需要導入模塊: from ramanTools import RamanSpectrum [as 別名]
# 或者: from ramanTools.RamanSpectrum import reindex [as 別名]
def calibration3(save_it= False):
global correctionVis1
ax1=subplot(221)
ax2=subplot(222)
ax3=subplot(223)
os.chdir('/home/chris/Documents/DataWeiss/150228')
spec1=RamanSpectrum('RhB 500sec full power_filter.SPE')-1320
spec1.plot(axes =ax3)
names = ['/home/chris/Documents/DataWeiss/150227/1 Rhb.SPE',
'/home/chris/Documents/DataWeiss/150227/10 Rhb 1100 grating.SPE',
'dark 50 s.SPE',
'RhB 500sec 0_01_filter.SPE',#,
'RhB 500sec 0_1_filter.SPE',
'RhB 500sec full power_filter.SPE',
'/home/chris/Documents/DataWeiss/150227/1 Rhb.SPE']
clf()
#spec1 = NA.remove_dust(spec1,blind=True)
sum_array = zeros((1024,1))
ax1=subplot(221)
ax2=subplot(222)
ax3=subplot(223)
r = spec1.size-1
darksignal = 500*12
spec1-=darksignal-1
xs = array(spec1.index)
ys= spec1.values
average = SGsmooth(xs,ys)
fit = polyfit(xs,ys,6)
dust = polyeval(fit,xs)
noise1=transpose([(average)/(ys)])
dustnoise = dust/average
if save_it == True:
savetxt('/home/chris/Documents/DataWeiss/CCD Pixel-to-Pixel Correction Factor.txt', noise1)
fullnoise = dustnoise*noise1.flatten()
sumnoise = sum((noise1-1)**2)
for name in names:
spectrum = RamanSpectrum(name)-1320
spectrum = spectrum.reindex(spec1.index,fill='backfill')
xs = array(spectrum.index)
ys= spectrum.values
average = SGsmooth(xs, ys)
noise = transpose([(average)/(ys)])
fullnoise = (noise.flatten())*(dustnoise)
print name,correlate(noise[:,0]-1, noise1[:,0]-1)/sqrt(sum((noise-1)**2)*sumnoise)
sum_array = append(sum_array,noise,axis=1)
ax2.plot(noise)
ax3.plot(xs, ys)
#ax3.plot(xs,average)
ax3.legend(['a','b','c','d','e','f'])
ax2.legend(list(x[-14:-9] for x in names))
ax1.legend(list(x[-14:-9] for x in names))
subplot(224)
sum_array = sum_array[:,1:]
CCDcorrectionfactor = 1+mean(sum_array,axis=1)
errorbar(range(1024), CCDcorrectionfactor, yerr=std(sum_array,axis=1)/sqrt(len(names)))
return 0