Question

我正在使用here

中评分最高的代码

并将其修改为适合3个光谱的4高斯给出。当我只是一次处理一个光谱但是我想自动化代码以便在多个光谱上进行曲线拟合时，代码可以工作。这是我到目前为止只有3个光谱的代码，但我打算做更多。请注意，我的y_real是3种不同通量的列表，而我的xfit指出了所有3种光谱的相同范围的波长。我只是为这个例子使用了一些数据点。我的问题出现了res语句（ValueError：操作数不能与形状一起广播），在开始绘图之前我不知道如何修复。

import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import leastsq


xfit = np.array([6520.0,  6545.0,  6570.0,  6595.0,  6620.0])
y_real = np.array([[0.0, 8.9013813844724581, 33.114149624958607, 13.297472921668451,3.8540928722192049], [0.0, 11.491419225489725, 18.866478352891686, 9.0782365151426738, 1.8757625220616632], [0.0, 10.680394752562883, 21.394692831684814, 11.661265465293802, 0.9362924799953376]])


Lamda = np.array([ 6564.61389433,  6564.61389433,  6564.61389433])
delLamda1 = np.array([ 14.75496508,  14.75496508,  14.75496508])
delLamda2 = np.array([ 20.65455091,  20.65455091,  20.65455091])


def norm(xfit, mean, sd, a):
  norm = []
  for i in range(xfit.size):
    norm += [a*np.exp(-(xfit[i] - mean)**2/(2*sd**2))]
  return np.array(norm)

mean1, mean2 = 0, -2
std1, std2 = 0.5, 1

m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab= [Lamda, -delLamda1, delLamda2, 1.0,1.0, 1.0, 2.0, 20.0, 20.0, 20.0, 10.0]
p = [m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab] # Initial guesses for leastsq                                                                                       
y_init = norm(xfit, m, sd1, a) + norm(xfit, m + dm1, sd2, a2) + norm(xfit, m+dm2, sd3, a3) +  norm(xfit, m, sd4, ab) # For final comparison plot                        \


def res(p, y_real, xfit):
  m, dm1, dm2, sd1, sd2, sd3, sd4, a, a2, a3, ab= p
  m1 = m
  m2 = m1 + dm1
  m3 = m1 +dm2
  y_fit = norm(xfit, m1, sd1, a) + norm(xfit, m2, sd2, a2) + norm(xfit, m3, sd3, a3) +   norm(xfit, m1, sd4, ab)
  err = y_real - y_fit
  return err

plsq = leastsq(res, p, args = (y_real, xfit), ftol=1.0e-09, gtol=1.0e-09, xtol=1.0e-09, maxfev=2000)

Python：4曲线高斯拟合，具有多个光谱的非线性最小二乘法

0 个答案: