我认为您就在那儿。您需要将数据集放到一个数组或结构中，以供您使用一个单独的全局目标函数，该函数提供给minimum（）并使用所有数据集的单个参数集来适合所有数据集。您可以根据需要在数据集之间共享此集。稍微扩展一下示例，下面的代码确实可以对5种不同的高斯函数进行一次拟合。对于跨数据集绑定参数的示例，我对5个数据集的sigma使用了几乎相同的值。我创建了5个不同的sigma参数（“ sig_1”，“ sig_2”，…，“ sig_5”），然后使用数学约束将其设置为相同的值。因此，问题中有11个变量，而不是15个。
import numpy as np
import matplotlib.pyplot as plt
from lmfit import minimize, Parameters, report_fit
def gauss(x, amp, cen, sigma):
"basic gaussian"
return amP*np.exp(-(x-cen)**2/(2.*sigma**2))
def gauss_dataset(params, i, x):
"""calc gaussian from params for data set i
using simple, hardwired naming convention"""
amp = params['amp_%i' % (i+1)].value
cen = params['cen_%i' % (i+1)].value
sig = params['sig_%i' % (i+1)].value
return gauss(x, amp, cen, sig)
def objective(params, x, data):
""" calculate total residual for fits to several data sets held
in a 2-D array, and modeled by Gaussian functions"""
ndata, nx = data.shape
resid = 0.0*data[:]
# make residual per data set
for i in range(ndata):
resid[i, :] = data[i, :] - gauss_dataset(params, i, x)
# Now flatten this to a 1D array, as minimize() needs
return resid.flatten()
# create 5 datasets
x = np.linspace( -1, 2, 151)
data = []
for i in np.arange(5):
params = Parameters()
amp = 0.60 + 9.50*np.random.rand()
cen = -0.20 + 1.20*np.random.rand()
sig = 0.25 + 0.03*np.random.rand()
dat = gauss(x, amp, cen, sig) + np.random.normal(size=len(x), scale=0.1)
data.append(dat)
# data has shape (5, 151)
data = np.array(data)
assert(data.shape) == (5, 151)
# create 5 sets of parameters, one per data set
fit_params = Parameters()
for iy, y in enumerate(data):
fit_params.add( 'amp_%i' % (iy+1), value=0.5, min=0.0, max=200)
fit_params.add( 'cen_%i' % (iy+1), value=0.4, min=-2.0, max=2.0)
fit_params.add( 'sig_%i' % (iy+1), value=0.3, min=0.01, max=3.0)
# but Now constrain all values of sigma to have the same value
# by assigning sig_2, sig_3, .. sig_5 to be equal to sig_1
for iy in (2, 3, 4, 5):
fit_params['sig_%i' % iy].expr='sig_1'
# run the global fit to all the data sets
result = minimize(objective, fit_params, args=(x, data))
report_fit(result.fit_params)
# plot the data sets and fits
plt.figure()
for i in range(5):
y_fit = gauss_dataset(result.fit_params, i, x)
plt.plot(x, data[i, :], 'o', x, y_fit, '-')
plt.show()
对于它的价值，我会考虑将多个数据集保存在字典或DataSet类列表中，而不是多维数组。无论如何，我希望这能帮助您进入真正需要做的事情。
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