def create(x,strains,phis,material):
qs = material.get_peaks(x.min(),x.max())[0]
for i,s in enumerate(strains):
y = np.zeros(x.shape)
e1,e2,e12 = s
#print e1,e2,e12
for i in range(len(phis)):
sqs = qs - qs*(e1*np.cos(phis[i])**2 + e2*np.sin(phis[i])**2 + e12*np.sin(2*phis[i]))
for q in sqs:
y[i,:] += fpeak(x[i,:],q, 0.013+q*0.001,rand(11500,12500)/q**3,0)
y[i,:] += fpeak(x[i,:],1.3,0.5,200,0,2)
y[i,:] += fpeak(x[i,:],2.8,2,1500,0,-0.5)
y[i,:] += fpeak(x[i,:],4.8,4,80,0,0)
y[i,:] += rand(-20,20,y[i,:].size)
yield y,{'x':i}
def test_Estimate(self):
truevals = [4.2,0.5,10000,0.0,0.0,150]
x = np.linspace(2,6,50)
y = fpeak(x,*truevals)
p = estimate_peak(x,y)
p.shape = 'g'
f,ax = plt.subplots(figsize=(7,7))
ax.plot(x,y,'b+')
ax.plot(x,p.profile(x)+p.background.nominal_value,'r',lw=1)
f.savefig('estimate.png',dpi=200)
self.assertAlmostEqual(p.centre.nominal_value,truevals[0],1)
self.assertAlmostEqual(p.width.nominal_value, truevals[1],1)
self.assertAlmostEqual(p.area.nominal_value,truevals[2],-2)
self.assertEqual(p.eta.nominal_value,truevals[3])
self.assertEqual(p.asymmetry.nominal_value,truevals[4])
self.assertAlmostEqual(p.background,truevals[5],1)
def createpeak(truevals):
x = np.linspace(2,6,800)
y = fpeak(x,*truevals)
#y += np.sqrt(y)*(np.random.rand(x.size)-0.5)*0.0025
return x,y
x = np.arange(4000)
y = np.zeros_like(x)
vals = [(1000,15.3,1200,0.25),
(1300,15.5,12000,0.25),
(1700,15.8,22000,0.25),
(1750,16.3,32000,0.25),
(1800,17.3,22000,0.25),
(2300,18.3,11000,0.25),
(2600,70.3,8000,0.25),
(3200,22.3,6000,0.25),
(3700,25.3,4000,0.25),
]
for v in vals:
y += fpeak(x,*v)
back = np.poly1d((-1e-12,3e-9,-1e-7,0.01,100))(x)
back += fpeak(x,1700,800,100000)
y+=back
ax1.plot(x,back,'k--',lw=1)
#ax2.plot(x,back,'k--',lw=1)
y+=np.random.uniform(-10,10,y.size)
reduction=1
xb = x[::reduction]
ax1.plot(x,y,'b',lw=1)
#line1, = ax1.plot(xb,np.zeros_like(xb),'r.',markersize=2)
#ax2.plot(x,y,'b',lw=1)
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)
f.savefig('effect_of_ymin_on_ymax.png',dpi=100)
if 0:
lx = np.linspace(3.4,5.,100)
f,axs = plt.subplots(1,5,figsize=(7,4))
ax1,ax2,ax3,ax4,ax5 = axs
truevals[3] = 0
y = fpeak(lx,*truevals)
fit, res = quick_fit_peak(lx,y,shape='g')
ax1.plot(lx,y,'b.')
ax1.plot(lx,fit,'r-',lw=1)
truevals[3] = 1
y = fpeak(lx,*truevals)
fit, res = quick_fit_peak(lx,y,shape='l')
ax2.plot(lx,y,'b.')
ax2.plot(lx,fit,'r-',lw=1)
truevals[3] = 0.5
y = fpeak(lx,*truevals)
fit, res = quick_fit_peak(lx,y,shape='pv')
ax3.plot(lx,y,'b.')
ax3.plot(lx,fit,'r-',lw=1)
