我想对两个数据集进行全局拟合并绘制结果。 我找到了@M Newville回答的“Python和lmfit:如何使用共享参数拟合多个数据集”的答案。修改后的代码为:
params = Parameters()
params.add('center_1', 5.0, vary=True)
params.add('amplitude_1', 10.0, vary=True)
params.add('sigma_1', 1.0, vary=True)
params.add('center_2', 8.0, vary=True)
params.add('amplitude_2', 3.0, vary=True)
params.add('sigma_2', 2.0, vary=True)
x = linspace(-10, 10, 101)
data1 = gaussian(x, 5.33, 3.21, 1.51) + random.normal(0, 0.2, x.size)
data2 = gaussian(x, 3.1, 1.21, 1.51) + random.normal(0, 0.2, x.size)
datasets = [data1, data2]
def residual(params, x, datasets):
model1 = gaussian(x, params['amplitude_1'], params['center_1'], params['sigma_1'])
model2 = gaussian(x, params['amplitude_2'], params['center_2'], params['sigma_2'])
resid1 = datasets[0] - model1
resid2 = datasets[1] - model2
return np.concatenate((resid1, resid2))
fit = minimize(residual, params, args=(x,), kws={'datasets': datasets})
print(fit_report(fit))
Só,我的问题是,我想把数据和拟合结果显示在一个图表中,但我不知道怎么做? 拜托,有人能给我一个线索吗
编辑:
我的新代码是:
params = Parameters()
params.add('cen', 5.0, vary=True)
params.add('amp', 10.0, vary=True)
params.add('sig', 1.0, vary=True)
params.add('pen', 8.0, vary=True)
params.add('inter', 3.0, vary=True)
def reta(x, a, c):
return a * x + c
x = linspace(-10, 10, 101)
data1 = gaussian(x, 5.33, 3.21, 1.51) + random.normal(0, 0.2, x.size)
data2 = reta(x, 3.1, 1) + random.normal(0, 0.2, x.size)
datasets = [data1, data2]
def residual(params, x, datasets):
model1 = gaussian(x, params['amp'], params['cen'], params['sig'])
model2 = reta(x, params['pen'], params['inter'])
resid1 = datasets[0] - model1
resid2 = datasets[1] - model2
return np.concatenate((resid1, resid2))
fit = minimize(residual, params, args=(x,), kws={'datasets': datasets})
print(fit_report(fit))
plt.figure()
plt.plot(x, data1)
plt.plot(x, data2)
如果我只有一个函数,我可以绘制如下图:
plt.plot(x, residual(fit.params) + data1, 'r', label='best fit')
但对于两款车型,我遇到了问题
目前没有回答
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