使用lmfit.Model或scipy.optimize.curve_fit我必须优化一个函数，该函数的输出需要与一些实验数据进行卷积，然后才能拟合到其他一些实验数据。总结一下，工作流程如下：
(1)定义函数A（例如，高斯函数）。（2）函数A的输出与称为数据B的实验信号卷积。（3）函数A的参数针对（2）中提到的卷积进行优化，以完全匹配称为数据C的某些其他实验数据。
我使用傅立叶变换将函数A的输出与数据B卷积，如下所示：

from scipy.fftpack import fft, ifft

    def convolve(data_B, function_A):
        convolved = ifft(fft(IRF) * fft(model)).real
        return convolved

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如何使用lmfit.Model或scipy.optimize.curve_fit来拟合数据C的“卷积”？
编辑：为了回应提交的答案，我已经将我的卷积步骤以以下方式纳入用于拟合的方程中：

#1 component exponential distribution:
def ExpDecay_1(x, ampl1, tau1, y0, x0, args=(new_y_irf)): # new_y_irf is a list. 
    h = np.zeros(x.size) 
    lengthVec = len(new_y_decay)
    
    shift_1 = np.remainder(np.remainder(x-np.floor(x0)-1, lengthVec) + lengthVec, lengthVec)
    shift_Incr1 = (1 - x0 + np.floor(x0))*new_y_irf[shift_1.astype(int)]
    
    shift_2 = np.remainder(np.remainder(x-np.ceil(x0)-1, lengthVec) + lengthVec, lengthVec)
    shift_Incr2 = (x0 - np.floor(x0))*new_y_irf[shift_2.astype(int)]
    
    irf_shifted = (shift_Incr1 + shift_Incr2)
    irf_norm = irf_shifted/sum(irf_shifted)
    h = ampl1*np.exp(-(x)/tau1)
    conv = ifft(fft(h) * fft(irf_norm)).real # This is the convolution step.
    return conv

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然而，当我尝试这个：

gmodel = Model(ExpDecay_1)

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我得到了这个：
gmodel = Model（ExpDecay_1）追溯（最近的调用）：
文件“"，第1行，gmodel = Model（ExpDecay_1）
文件“C：\Users\洛佩斯\Anaconda 3\lib\site-packages\lmfit\model.py”，第273行，在initself._parse_params（）中
文件“C：\Users\洛佩斯\Anaconda3\lib\site-packages\lmfit\model.py”，第477行，in _parse_params if fpar.default == fpar.empty：
ValueError：包含多个元素的数组的真值是不明确的。使用.any（）或.all（）
编辑2：我设法使它工作如下：

import pandas as pd
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
import numpy as np
from lmfit import Model
from scipy.fftpack import fft, ifft

def Test_fit2(x, arg=new_y_irf, data=new_y_decay, num_decay=1):
    IRF = arg
    DATA = data
    def Exp(x, ampl1=1.0, tau1=3.0): # This generates an exponential model.
        res = ampl1*np.exp(-x/tau1)
        return res
    def Conv(IRF,decay): # This convolves a model with the data (data = Instrument Response Function, IRF).
        conv = ifft(fft(decay) * fft(IRF)).real
        return conv
    if num_decay == 1: # If the user chooses to use a model equation with one exponential term.
        def fitting(x, ampl1=1.0, tau1=3.0): 
            exponential = Exp(x,ampl1,tau1)
            convolved = Conv(IRF,exponential)
            return convolved
        modelling = Model(fitting)
        res = modelling.fit(DATA,x=new_x_decay,ampl1=1.0,tau1=2.0)
    if num_decay == 2: # If the user chooses to use a model equation with two exponential terms.
        def fitting(x, ampl1=1.0, tau1=3.0, ampl2=1.0, tau2=1.0): 
            exponential = Exp(x,ampl1,tau1)+Exp(x,ampl2,tau2)
            convolved = Conv(IRF,exponential)
            return convolved
        modelling = Model(fitting)
        res = modelling.fit(DATA,x=new_x_decay,ampl1=1.0,tau1=2.0)
    if num_decay == 3: # If the user chooses to use a model equation with three exponential terms.
        def fitting(x, ampl1=1.0, tau1=3.0, ampl2=2.0, tau2=1.0, ampl3=3.0, tau3=5.0): 
            exponential = Exp(x,ampl1,tau1)+Exp(x,ampl2,tau2)+Exp(x,ampl3,tau3)
            convolved = Conv(IRF,exponential)
            return convolved
        modelling = Model(fitting)
        res = modelling.fit(DATA,x=new_x_decay,ampl1=1.0,tau1=2.0)
    if num_decay == 4: # If the user chooses to use a model equation with four exponential terms.
        def fitting(x, ampl1=1.0, tau1=0.1, ampl2=2.0, tau2=1.0, ampl3=3.0, tau3=5.0, ampl4=1.0, tau4=10.0): 
            exponential = Exp(x,ampl1,tau1)+Exp(x,ampl2,tau2)+Exp(x,ampl3,tau3)+Exp(x,ampl4,tau4)
            convolved = Conv(IRF,exponential)
            return convolved
        modelling = Model(fitting)
        res = modelling.fit(DATA,x=new_x_decay,ampl1=1.0,tau1=2.0)
    return res

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