如何在python上使用scipy.optimize.curve U拟合？

Question

我试图拟合一个有多个吸收峰的洛伦兹函数（穆斯堡尔谱），但曲线拟合函数不能正常工作，只拟合了几个峰。我怎么才能装呢？在

Figure: Trying to adjusting multi-Lorentzian

下面我展示我的代码。请帮帮我。在

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)

我的数据-->；https://drive.google.com/file/d/1LvCKNdv0oBza_TDwuyNwd29PgQv22VPA/view?usp=sharing

Answer 1

这段代码使用leastsq，而不是{}，因为后者需要固定数量的参数。这里我不想这样，因为我让代码“决定”有多少个峰值。请注意，我简化了数据的比例。真正的拟合参数很容易按比例缩小（和标准误差传播）

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()

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