def test_Lorentzian(self):
truevals = [4.2,0.3,10000,1,0,150]
x,y = createpeak(truevals)
yfit,result = quick_fit_peak(x,y)
saveplot(x,y,yfit,'lorentzian.png')
self.assertAlmostEqual(result['centre'].nominal_value,truevals[0],2)
self.assertAlmostEqual(result['width'].nominal_value, truevals[1],2)
self.assertAlmostEqual(result['area'].nominal_value,truevals[2],0)
self.assertAlmostEqual(result['eta'].nominal_value,truevals[3],2)
self.assertEqual(result['asymmetry'].nominal_value,truevals[4])
def test_PseudovoigtAsymmetric(self):
truevals = [4.2,0.75,10000,0.1,0.8,150]
x,y = createpeak(truevals)
yfit,result = quick_fit_peak(x,y,shape='apv')
saveplot(x,y,yfit,'aysymmetric_pseudovoigt.png')
self.assertAlmostEqual(result['centre'].nominal_value,truevals[0],2)
self.assertAlmostEqual(result['width'].nominal_value, truevals[1],2)
self.assertAlmostEqual(result['area'].nominal_value,truevals[2],0)
self.assertAlmostEqual(result['eta'].nominal_value,truevals[3],3)
self.assertAlmostEqual(result['asymmetry'].nominal_value,truevals[4],4)
def test_Gaussian_unumpy(self):
truevals = [4.2,0.75,10000,0,0,150]
x,y = createpeak(truevals)
u = np.sqrt(y)
y = unumpy.uarray((y,u))
yfit,result = quick_fit_peak(x,y)
saveplot(x,unumpy.nominal_values(y),yfit,'ugaussian.png')
self.assertAlmostEqual(result['centre'].nominal_value,truevals[0],2)
self.assertAlmostEqual(result['width'].nominal_value, truevals[1],2)
self.assertAlmostEqual(result['area'].nominal_value,truevals[2],0)
self.assertAlmostEqual(result['eta'].nominal_value,truevals[3],3)
self.assertEqual(result['asymmetry'].nominal_value,truevals[4])
import numpy as np
import matplotlib.pyplot as plt
from edxrd.peakfitting import Peak,quick_fit_peak,fpeak #only needed for making fake data
from edxrd.peaksearch import estimate_peak
truevals = [4.2,0.2,500.,0.25,0.0,150.]
x = np.linspace(2,6,100)
peak = Peak(*truevals)
y = peak.profile(x,add_background=True)
y += np.sqrt(y)*(np.random.rand(y.size)-0.5)*0.95
if 0:
f,ax = plt.subplots(1,1,figsize=(7,4))
fit, res = quick_fit_peak(x,y)
ax.plot(x,y,'b.')
ax.plot(x,fit,'r-',lw=1)
f.savefig('quick_fit_example.png',dpi=100)
if 0:
f,axs = plt.subplots(2,1,figsize=(7,7))
fit, res = quick_fit_peak(x,y,shape='pv',ymin_on_ymax=0.001)
axs[0].plot(x,y,'b.')
axs[0].plot(x,fit,'r-',lw=1)
axs[0].text(0.05, 0.8, 'ymin_on_ymax = 0.001',transform=axs[0].transAxes,fontsize=15)
fit, res = quick_fit_peak(x,y,shape='pv',ymin_on_ymax=0.03)
axs[1].plot(x,y,'b.')
axs[1].plot(x,fit,'r-',lw=1)
axs[1].text(0.05, 0.8, 'ymin_on_ymax = 0.03',transform=axs[1].transAxes,fontsize=15)