bluebell.plot

bluebell.plot.cov_ellipse(mu, C, ax=None, **kwargs)

Plot an ellipse at point mu given its covariance matrix cov.

Parameters:

• mu (array (2,)) – Center of the ellipse

• C (array (2,2)) – The covariance matrix for the point

• ax (matplotlib.Axes, optional) – The axis to overplot on

• **kwargs (dict) – These keywords are passed to matplotlib.patches.Ellipse

Returns:

ellipse

Return type:

matplotlib Ellipse object

Example

import matplotlib.pyplot as pl
import bluebell.plot as bbplot

A = np.random.rand(2,2)
C = A.T.dot(A)
mu = np.random.rand(2)
std = np.sqrt(np.diag(C))

bbplot.cov_ellipse(mu, C, ec='C0', fc='none')
pl.axis((mu + np.array([[-1.1],[1.1]])*std).T.flatten())

(Source code, png, hires.png, pdf)

bluebell.plot.chi2_corner(x, chi2, vmin=None, vmax=None, names=None, cmap='inferno', scale='sqrt')

Create a corner plot showing the 2-d projections of the sample x in with each point coloured by its relative value of chi2, as well as a top row showing 1-d plots of chi2 for each dimension.

Parameters:

• x (2-d NumPy array of shape (N, D)) – Points at which function has been evaluated.

• chi2 (1-d NumPy array of shape (N,)) – Values of chi-squared for the points in x.

Returns:

• fig (matplotlib figure)

• ax (2-d array of shape (D, D) of matplotlib.axes) – Array of the matplotlib axes containing the individual scatter plots.

Example

import matplotlib.pyplot as pl
import bluebell as bb
import bluebell.plot as bbplot

D = 3
C = np.eye(D)
mu = np.zeros(D)
std = np.sqrt(np.diag(C))

x = np.random.uniform(low=mu-std, high=mu+std, size=(100,D))
x[0] = mu
chi2 = np.sum(x.dot(np.linalg.inv(C))*x, axis=1)

fig, ax = bbplot.chi2_corner(x, chi2)

(Source code, png, hires.png, pdf)