CODEWITHSUNDEEP
pythoncurve-fittinggaussiandata-fitting
boson
boson

Reputation: 894

Not sure how to fit data with a gaussian python

I've tried multiple ways of fitting a gaussian to this scatterplot, but nothing has worked for me. Any help would be greatly appreciated. Thanks!

from numpy import *
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit

data = loadtxt("/home/***/****/***")

t,q = data[:,2], data[:,3]                         
t,q = loadtxt("/home/***/****/***", usecols = (2,3), unpack=True)

plt.scatter(t,q, marker='.', s=20)
plt.show()

Upvotes: 1

Views: 1883

Answers (2)

Otto
Otto

Reputation: 9

This is coming a bit late! But the error message about too many values to to unpack can be fixed by including the option unpack=True when loading data i.e.

data = np.loadtxt("/home/***/****/***", unpack=True, usecols=(2, 3))

Upvotes: 0

lmjohns3
lmjohns3

Reputation: 7602

It seems like you can just use the definitions of the mean and covariance parameters to fit them using maximum likelihood estimates from your data, right ?

import numpy as np

data = np.loadtxt("/home/***/****/***", usecols=(2, 3))
mu = data.mean(axis=0)
sigma = np.cov(data, rowvar=0)

In my understanding, this is what "fitting" means. However, it sounds like you're then looking to show these quantities on a plot. This can be a little trickier, but only because you need to use some linear algebra to find the eigenvectors of the estimated covariance matrix, and then use those to draw the covariance ellipse.

import matplotlib.pyplot as plt
from matplotlib.patches import Ellipse

# compute eigenvalues and associated eigenvectors
vals, vecs = np.linalg.eigh(sigma)

# compute "tilt" of ellipse using first eigenvector
x, y = vecs[:, 0]
theta = np.degrees(np.arctan2(y, x))

ax = plt.subplot(111)

# eigenvalues give length of ellipse along each eigenvector
w, h = 2 * np.sqrt(vals)
ax.add_artist(Ellipse(mu, w, h, theta))

# show data on top of ellipse
ax.scatter(data[:, 0], data[:, 1])

plt.show()

Upvotes: 1

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