如何在python中拟合高斯曲线？
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/44480137/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
How can I fit a gaussian curve in python?
提问by P. Kaur
I'm given an array and when I plot it I get a gaussian shape with some noise. I want to fit the gaussian. This is what I already have but when I plot this I do not get a fitted gaussian, instead I just get a straight line. I've tried this many different ways and I just can't figure it out.
我得到一个数组，当我绘制它时，我得到一个带有一些噪音的高斯形状。我想拟合高斯。这是我已经拥有的，但是当我绘制此图时，我没有得到拟合的高斯分布，而是得到一条直线。我已经尝试了很多不同的方法，但我无法弄清楚。
random_sample=norm.rvs(h)
parameters = norm.fit(h)
fitted_pdf = norm.pdf(f, loc = parameters[0], scale = parameters[1])
normal_pdf = norm.pdf(f)
plt.plot(f,fitted_pdf,"green")
plt.plot(f, normal_pdf, "red")
plt.plot(f,h)
plt.show()
回答by Miriam Farber
You can use fitfrom scipy.stats.normas follows:
您可以fit从scipy.stats.norm如下使用：
import numpy as np
from scipy.stats import norm
import matplotlib.pyplot as plt
data = np.random.normal(loc=5.0, scale=2.0, size=1000)
mean,std=norm.fit(data)
norm.fittries to fit the parameters of a normal distribution based on the data. And indeed in the example above meanis approximately 2 and stdis approximately 5.
norm.fit尝试根据数据拟合正态分布的参数。事实上，在上面的例子mean中大约是 2，std大约是 5。
In order to plot it, you can do:
为了绘制它，您可以执行以下操作：
plt.hist(data, bins=30, normed=True)
xmin, xmax = plt.xlim()
x = np.linspace(xmin, xmax, 100)
y = norm.pdf(x, mean, std)
plt.plot(x, y)
plt.show()
The blue boxes are the histogram of your data, and the green line is the Gaussian with the fitted parameters.
蓝色框是数据的直方图，绿线是具有拟合参数的高斯图。
回答by MSeifert
There are many ways to fit a gaussian function to a data set. I often use astropy when fitting data, that's why I wanted to add this as additional answer.
有很多方法可以将高斯函数拟合到数据集。我在拟合数据时经常使用 astropy，这就是我想将其添加为附加答案的原因。
I use some data set that should simulate a gaussian with some noise:
我使用了一些应该模拟带有一些噪声的高斯的数据集：
import numpy as np
from astropy import modeling
m = modeling.models.Gaussian1D(amplitude=10, mean=30, stddev=5)
x = np.linspace(0, 100, 2000)
data = m(x)
data = data + np.sqrt(data) * np.random.random(x.size) - 0.5
data -= data.min()
plt.plot(x, data)
Then fitting it is actually quite simple, you specify a model that you want to fit to the data and a fitter:
然后拟合它实际上非常简单，您指定一个要拟合数据的模型和一个拟合器：
fitter = modeling.fitting.LevMarLSQFitter()
model = modeling.models.Gaussian1D() # depending on the data you need to give some initial values
fitted_model = fitter(model, x, data)
And plotted:
并绘制：
plt.plot(x, data)
plt.plot(x, fitted_model(x))
However you can also use just Scipy but you have to define the function yourself:
但是，您也可以只使用 Scipy，但您必须自己定义函数：
from scipy import optimize
def gaussian(x, amplitude, mean, stddev):
return amplitude * np.exp(-((x - mean) / 4 / stddev)**2)
popt, _ = optimize.curve_fit(gaussian, x, data)
This returns the optimal arguments for the fit and you can plot it like this:
这将返回拟合的最佳参数，您可以像这样绘制它：
plt.plot(x, data)
plt.plot(x, gaussian(x, *popt))
