def plot_and_test(args, xarr=np.linspace(-25,25,1000), moments=moments, nsigcut=None):
arrargs = np.array([0] + list(args))
gg = gaussian(xarr, *args)
noise = np.random.randn(1000)
moments_nonoise = np.array(moments(xarr,gg,veryverbose=True))
fmtstring = "h: %6.2f a: %6.2f x: %6.2f w: %6.2f "
print("No noise")
print("Moments: "+fmtstring % tuple(moments_nonoise) )
print("Difference: "+fmtstring % tuple(moments_nonoise-arrargs))
pylab.subplot(2,2,1)
pylab.plot(xarr,gg,'k')
pylab.plot(xarr,gaussian(xarr,*moments_nonoise[1:]),'r')
print("diff^2: %0.2f" % (((gg-gaussian(xarr,*moments_nonoise[1:]))**2).sum()))
def plottest_withnoise(noiseamp, spnum, xarr=xarr, gg=gg, noise=noise):
fitspec = gg+noiseamp*noise
moments_noise1 = np.array(moments(xarr,fitspec,veryverbose=True,nsigcut=nsigcut))
print("Noise = %i%% of peak" % (round(noiseamp/args[0]*100)))
print("Moments: "+fmtstring % tuple(moments_noise1) )
print("Difference: "+fmtstring % tuple(moments_noise1-arrargs))
pylab.subplot(2,2,spnum)
pylab.plot(xarr,fitspec,'k')
pylab.plot(xarr,gaussian(xarr,*moments_noise1[1:]),'r')
print("chi^2: %0.2f" % ( (((fitspec-gaussian(xarr,*moments_noise1[1:]))**2) / ((noise*noiseamp)**2)).sum() / len(noise) ),)
print("correct chi^2: %0.2f" % ( (((fitspec-gg)**2) / ((noise*noiseamp)**2)).sum() / len(noise) ))
for noiseamp,spnum in zip((0.5,2,5),(2,3,4)):
plottest_withnoise(noiseamp, spnum)
def voigt_moments(self, *args, **kwargs):
"""
Get the spectral moments from the moments package. Use the gaussian width
for the lorentzian width (not a great guess!)
"""
m = moments(*args,**kwargs)
return list(m) + [m[-1]]
def plottest_withnoise(noiseamp, spnum, xarr=xarr, gg=gg, noise=noise):
fitspec = gg+noiseamp*noise
moments_noise1 = np.array(moments(xarr,fitspec,veryverbose=True,nsigcut=nsigcut))
print("Noise = %i%% of peak" % (round(noiseamp/args[0]*100)))
print("Moments: "+fmtstring % tuple(moments_noise1) )
print("Difference: "+fmtstring % tuple(moments_noise1-arrargs))
pylab.subplot(2,2,spnum)
pylab.plot(xarr,fitspec,'k')
pylab.plot(xarr,gaussian(xarr,*moments_noise1[1:]),'r')
print("chi^2: %0.2f" % ( (((fitspec-gaussian(xarr,*moments_noise1[1:]))**2) / ((noise*noiseamp)**2)).sum() / len(noise) ),)
print("correct chi^2: %0.2f" % ( (((fitspec-gg)**2) / ((noise*noiseamp)**2)).sum() / len(noise) ))
def plottest_withnoise(noiseamp, spnum, xarr=xarr, gg=gg, noise=noise):
fitspec = gg + noiseamp * noise
moments_noise1 = np.array(
moments(xarr, fitspec, veryverbose=True, nsigcut=nsigcut))
print("Noise = %i%% of peak" % (round(noiseamp / args[0] * 100)))
print("Moments: " + fmtstring % tuple(moments_noise1))
print("Difference: " + fmtstring % tuple(moments_noise1 - arrargs))
pylab.subplot(2, 2, spnum)
pylab.plot(xarr, fitspec, 'k')
pylab.plot(xarr, gaussian(xarr, *moments_noise1[1:]), 'r')
print(
"chi^2: %0.2f" %
((((fitspec - gaussian(xarr, *moments_noise1[1:]))**2) /
((noise * noiseamp)**2)).sum() / len(noise)), )
print("correct chi^2: %0.2f" %
((((fitspec - gg)**2) /
((noise * noiseamp)**2)).sum() / len(noise)))
def plot_and_test(args,
xarr=np.linspace(-25, 25, 1000),
moments=moments,
nsigcut=None):
arrargs = np.array([0] + list(args))
gg = gaussian(xarr, *args)
noise = np.random.randn(1000)
moments_nonoise = np.array(moments(xarr, gg, veryverbose=True))
fmtstring = "h: %6.2f a: %6.2f x: %6.2f w: %6.2f "
print("No noise")
print("Moments: " + fmtstring % tuple(moments_nonoise))
print("Difference: " + fmtstring % tuple(moments_nonoise - arrargs))
pylab.subplot(2, 2, 1)
pylab.plot(xarr, gg, 'k')
pylab.plot(xarr, gaussian(xarr, *moments_nonoise[1:]), 'r')
print("diff^2: %0.2f" %
(((gg - gaussian(xarr, *moments_nonoise[1:]))**2).sum()))
def plottest_withnoise(noiseamp, spnum, xarr=xarr, gg=gg, noise=noise):
fitspec = gg + noiseamp * noise
moments_noise1 = np.array(
moments(xarr, fitspec, veryverbose=True, nsigcut=nsigcut))
print("Noise = %i%% of peak" % (round(noiseamp / args[0] * 100)))
print("Moments: " + fmtstring % tuple(moments_noise1))
print("Difference: " + fmtstring % tuple(moments_noise1 - arrargs))
pylab.subplot(2, 2, spnum)
pylab.plot(xarr, fitspec, 'k')
pylab.plot(xarr, gaussian(xarr, *moments_noise1[1:]), 'r')
print(
"chi^2: %0.2f" %
((((fitspec - gaussian(xarr, *moments_noise1[1:]))**2) /
((noise * noiseamp)**2)).sum() / len(noise)), )
print("correct chi^2: %0.2f" %
((((fitspec - gg)**2) /
((noise * noiseamp)**2)).sum() / len(noise)))
for noiseamp, spnum in zip((0.5, 2, 5), (2, 3, 4)):
plottest_withnoise(noiseamp, spnum)
def moments(self, *args, **kwargs):
"""
Get the spectral moments from the moments package
"""
return moments(*args,**kwargs)
def moments(self, *args, **kwargs):
"""
Get the spectral moments from the moments package
"""
return moments(*args, **kwargs)
