我如何使用scipy.optimize.curve_fit在python上拟合一个好的Lorentzian? [英] How can I fit a good Lorentzian on python using scipy.optimize.curve_fit?
问题描述
我试图用一个以上吸收峰(莫斯鲍尔光谱)拟合一个洛伦兹函数,但是curve_fit函数不能正常工作,只拟合了几个峰.我怎么适应呢?
I'm trying to fit a Lorentzian function with more than one absorption peak (Mössbauer spectra), but the curve_fit function it not working properly, fitting just few peaks. How can I fit it?
Figure: Trying to adjusting multi-Lorentzian
Below I show my code. Please, help me.
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def mymodel_hema(x,a1,b1,c1,a2,b2,c2,a3,b3,c3,a4,b4,c4,a5,b5,c5,a6,b6,c6):
f = 160000 - (c1*a1)/(c1+(x-b1)**2) - (c2*a2)/(c2+(x-b2)**2) - (c3*a3)/(c3+(x-b3)**2) - (c4*a4)/(c4+(x-b4)**2) - (c5*a5)/(c5+(x-b5)**2) - (c6*a6)/(c6+(x-b6)**2)
return f
def main():
abre = np.loadtxt('HEMAT_1.dat')
x = np.zeros(len(abre))
y = np.zeros(len(abre))
for i in range(len(abre)):
x[i] = abre[i,0]
y[i] = abre[i,1]
popt,pcov = curve_fit(mymodel_hema, x, y,maxfev=1000000000)
My data --> https://drive.google.com/file/d/1LvCKNdv0oBza_TDwuyNwd29PgQv22VPA/view?usp=sharing
This code uses leastsq
instead of curve_fit
as the latter one requires a fixed number of parameters. Here I do not want this as I let the code "decide" how many peaks are there. Note that I scaled the data to simplify the fit. The true fitting parameters are calculated easily be scaling back ( and standard error propagation )
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import leastsq
def lorentzian( x, x0, a, gam ):
return a * gam**2 / ( gam**2 + ( x - x0 )**2)
def multi_lorentz( x, params ):
off = params[0]
paramsRest = params[1:]
assert not ( len( paramsRest ) % 3 )
return off + sum( [ lorentzian( x, *paramsRest[ i : i+3 ] ) for i in range( 0, len( paramsRest ), 3 ) ] )
def res_multi_lorentz( params, xData, yData ):
diff = [ multi_lorentz( x, params ) - y for x, y in zip( xData, yData ) ]
return diff
xData, yData = np.loadtxt('HEMAT_1.dat', unpack=True )
yData = yData / max(yData)
generalWidth = 1
yDataLoc = yData
startValues = [ max( yData ) ]
counter = 0
while max( yDataLoc ) - min( yDataLoc ) > .1:
counter += 1
if counter > 20: ### max 20 peak...emergency break to avoid infinite loop
break
minP = np.argmin( yDataLoc )
minY = yData[ minP ]
x0 = xData[ minP ]
startValues += [ x0, minY - max( yDataLoc ), generalWidth ]
popt, ier = leastsq( res_multi_lorentz, startValues, args=( xData, yData ) )
yDataLoc = [ y - multi_lorentz( x, popt ) for x,y in zip( xData, yData ) ]
print popt
testData = [ multi_lorentz(x, popt ) for x in xData ]
fig = plt.figure()
ax = fig.add_subplot( 1, 1, 1 )
ax.plot( xData, yData )
ax.plot( xData, testData )
plt.show()
Providing
[ 9.96855817e-01 4.94106598e+02 -2.82103813e-01 4.66272773e+00
2.80688160e+01 -2.72449246e-01 4.71728295e+00 1.31577189e+02
-2.29698620e-01 4.20685229e+00 4.01421993e+02 -1.85917255e-01
5.57859380e+00 2.29704607e+02 -1.47193792e-01 3.91112196e+00
3.03387957e+02 -1.37127711e-01 4.39571905e+00]
and
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