我有这7条准洛伦兹曲线,它们符合我的数据。
我想和他们一起,画出一条相连的曲线。你有什么想法要怎么做吗?我在lmfit
文档上读到过有关ComposingModel
的内容,但不清楚如何做到这一点。
下面是我的两条拟合曲线的代码示例。
for dataset in [Bxfft]:
dataset = np.asarray(dataset)
freqs, psd = signal.welch(dataset, fs=266336/300, window='hamming', nperseg=16192, scaling='spectrum')
plt.semilogy(freqs[0:-7000], psd[0:-7000]/dataset.size**0, color='r', label='Bx')
x = freqs[100:-7900]
y = psd[100:-7900]
# 8 Hz
model = Model(lorentzian)
params = model.make_params(amp=6, cen=5, sig=1, e=0)
result = model.fit(y, params, x=x)
final_fit = result.best_fit
print "8 Hz mode"
print(result.fit_report(min_correl=0.25))
plt.plot(x, final_fit, 'k-', linewidth=2)
# 14 Hz
x2 = freqs[220:-7780]
y2 = psd[220:-7780]
model2 = Model(lorentzian)
pars2 = model2.make_params(amp=6, cen=10, sig=3, e=0)
pars2['amp'].value = 6
result2 = model2.fit(y2, pars2, x=x2)
final_fit2 = result2.best_fit
print "14 Hz mode"
print(result2.fit_report(min_correl=0.25))
plt.plot(x2, final_fit2, 'k-', linewidth=2)
更新!
我使用了用户@MNewville的一些提示,他发布了一个答案,并使用他的代码得到了以下结果:
因此,我的代码与他的类似,但随着峰值的增加而扩展。我现在正在苦苦挣扎的是用我自己的LorentzModel
替换ready。
问题是,当我这样做的时候,代码会给我一个这样的错误。
C:\Python27\lib\site-packages\lmfit\printfuncs.py:153:
RuntimeWarning:在模型[ double_scalars ] spercent =‘({0:2.2%})’.format(abs(par.stderr/par.value))中遇到无效值
关于我自己的模型:
def lorentzian(x, amp, cen, sig, e):
return (amp*(1-e)) / ((pow((1.0 * x - cen), 2)) + (pow(sig, 2)))
peak1 = Model(lorentzian, prefix='p1_')
peak2 = Model(lorentzian, prefix='p2_')
peak3 = Model(lorentzian, prefix='p3_')
# make composite by adding (or multiplying, etc) components
model = peak1 + peak2 + peak3
# make parameters for the full model, setting initial values
# using the prefixes
params = model.make_params(p1_amp=6, p1_cen=8, p1_sig=1, p1_e=0,
p2_ampe=16, p2_cen=14, p2_sig=3, p2_e=0,
p3_amp=16, p3_cen=21, p3_sig=3, p3_e=0,)
其余的代码类似于@MNewville
发布于 2018-06-02 10:21:05
3个洛伦兹人的复合模型如下所示:
from lmfit import Model, LorentzianModel
peak1 = LorentzianModel(prefix='p1_')
peak2 = LorentzianModel(prefix='p2_')
peak3 = LorentzianModel(prefix='p3_')
# make composite by adding (or multiplying, etc) components
model = peak1 + peaks2 + peak3
# make parameters for the full model, setting initial values
# using the prefixes
params = model.make_params(p1_amplitude=10, p1_center=8, p1_sigma=3,
p2_amplitude=10, p2_center=15, p2_sigma=3,
p3_amplitude=10, p3_center=20, p3_sigma=3)
# perhaps set bounds to prevent peaks from swapping or crazy values
params['p1_amplitude'].min = 0
params['p2_amplitude'].min = 0
params['p3_amplitude'].min = 0
params['p1_sigma'].min = 0
params['p2_sigma'].min = 0
params['p3_sigma'].min = 0
params['p1_center'].min = 2
params['p1_center'].max = 11
params['p2_center'].min = 10
params['p2_center'].max = 18
params['p3_center'].min = 17
params['p3_center'].max = 25
# then do a fit over the full data range
result = model.fit(y, params, x=x)
我认为您遗漏的关键部分是: a)只是将模型添加到一起,以及b)使用前缀来避免参数的名称冲突。
我希望这足以让你开始...
https://stackoverflow.com/questions/50643564
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