Python-将2D高斯拟合到数据集

Question

我在.txt文件中有数据点（分隔符=空格），第一列是x轴，第二列是y轴。我想使用Python将2D高斯拟合到这些数据点。事实是，我不理解高斯拟合（一维或二维）背后的理论。我已经在stackoverflow上阅读了类似的帖子并得到了一个代码，但它并不合适。请有人帮忙。感谢

以下是.txt文件中的内容：

3.369016418457e+02 3.761813938618e-01
3.369006652832e+02 4.078308343887e-01
3.368996887207e+02 4.220226705074e-01
3.368987121582e+02 4.200653433800e-01
3.368977355957e+02 4.454285204411e-01
3.368967590332e+02 4.156131148338e-01
3.368957824707e+02 3.989491164684e-01
3.368948059082e+02 4.512043893337e-01
3.368938293457e+02 4.565380811691e-01
3.368928527832e+02 4.095999598503e-01
3.368918762207e+02 4.196371734142e-01
3.368908996582e+02 4.002234041691e-01
3.368899230957e+02 4.133881926537e-01
3.368889465332e+02 4.394644796848e-01
3.368879699707e+02 4.504477381706e-01
3.368869934082e+02 3.946847021580e-01
3.368860168457e+02 4.214486181736e-01
3.368850402832e+02 3.753573596478e-01
3.368840637207e+02 3.673824667931e-01
3.368830871582e+02 4.088735878468e-01
3.368821105957e+02 4.351278841496e-01
3.368811340332e+02 4.393630325794e-01
3.368801574707e+02 4.210205972195e-01
3.368791809082e+02 4.322172403336e-01
3.368782043457e+02 4.652716219425e-01
3.368772277832e+02 5.251595377922e-01
3.368762512207e+02 5.873318314552e-01
3.368752746582e+02 6.823697686195e-01
3.368742980957e+02 8.375824093819e-01
3.368733215332e+02 9.335057735443e-01
3.368723449707e+02 1.083636641502e+00
3.368713684082e+02 1.170072913170e+00
3.368703918457e+02 1.224770784378e+00
3.368694152832e+02 1.158735513687e+00
3.368684387207e+02 1.131350398064e+00
3.368674621582e+02 1.073648810387e+00
3.368664855957e+02 9.659162163734e-01
3.368655090332e+02 8.495713472366e-01
3.368645324707e+02 7.637447714806e-01
3.368635559082e+02 6.956064105034e-01
3.368625793457e+02 6.713893413544e-01
3.368616027832e+02 5.285132527351e-01
3.368606262207e+02 4.968771338463e-01
3.368596496582e+02 5.077748298645e-01
3.368586730957e+02 4.686309695244e-01
3.368576965332e+02 4.693206846714e-01
3.368567199707e+02 4.462305009365e-01
3.368557434082e+02 3.872672021389e-01
3.368547668457e+02 4.243377447128e-01
3.368537902832e+02 3.918920457363e-01
3.368528137207e+02 3.848327994347e-01
3.368518371582e+02 4.093343317509e-01
3.368508605957e+02 4.321203231812e-01

以下是我尝试的代码：

%pylab inline
import matplotlib.pyplot as plt
import numpy as np
import astropy
import scipy.optimize as opt
import pylab as plb
from scipy.optimize import curve_fit
from scipy import asarray as ar,exp
x,y=np.loadtxt('taper2reduced.txt', unpack= True, delimiter=' ')

mean = sum(x * y) / sum(y)
sigma = np.sqrt(sum(y * (x - mean)**2) / sum(y))

def Gauss(x, a, x0, sigma):
    <pre>return a * np.exp(-(x - x0)\**2 / (2 * sigma**2))<code>

popt,pcov = curve_fit(Gauss, x, y, p0=[max(y), mean, sigma])

plt.plot(x, y, 'b+:', label='data')
plt.plot(x, Gauss(x, *popt), 'r-', label='fit')
plt.legend()
plt.title('Fig. 1 - Fit for Frequency')
plt.xlabel('Frequecy (GHz)')
plt.ylabel('Flux Density (mJy)')
plt.show()

Answer 1

您的问题是您的功能无法很好地反映您的数据集。您可以定义"serviceBus": { // The maximum number of concurrent calls to the callback the message // pump should initiate. The default is 16. "maxConcurrentCalls": 10 } 和0之间的分布，而实际上您的数据介于max_y和min_y之间。像这样改变你的功能：

max_y

输出：

你可能已经注意到我改变了两件事 我解除了进口的混乱。 It is not a good idea to load a lot of different unused functions and modules into your name space.
我改变了起始参数估计。我们在这里不一定正确，通常会做出近似。一些不需要太多代码且速度很快的东西。

Answer 2

是的，您想将数据拟合为二维分布，但代码意味着 了解如何拟合一维分布。