Plotting Ellipses in Python

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Nicholas Kern February, 2016

Ellipses in Python[edit]

To plot ellipses in Python we will use the matplotlib.pyplot module. One can plot Ellipses using the matplotlib.patches.Ellipse function, but this is not ideal because if we do not choose to set our aspect ratio to 'equal', the ellipse is "frozen" into the image and does not scale with the x and y axes, which is problematic (try it yourself). We have come up with a relatively fast and simple way to plot Ellipses in Python that will scale with the axes if stretch or squeezed. The function is posted below, and we will run through some examples using it.

import numpy as np
from scipy.stats import chi2
#import pylab as mp

def plot_ellipse(semimaj=1,semimin=1,phi=0,x_cent=0,y_cent=0,theta_num=1e3,ax=None,plot_kwargs=None,\
        An easy to use function for plotting ellipses in Python 2.7!

        The function creates a 2D ellipse in polar coordinates then transforms to cartesian coordinates.
        It can take a covariance matrix and plot contours from it.
        semimaj : float
            length of semimajor axis (always taken to be some phi (-90<phi<90 deg) from positive x-axis!)

        semimin : float
            length of semiminor axis

        phi : float
            angle in radians of semimajor axis above positive x axis

        x_cent : float
            X coordinate center

        y_cent : float
            Y coordinate center

        theta_num : int
            Number of points to sample along ellipse from 0-2pi

        ax : matplotlib axis property
            A pre-created matplotlib axis

        plot_kwargs : dictionary
            matplotlib.plot() keyword arguments

        fill : bool
            A flag to fill the inside of the ellipse 

        fill_kwargs : dictionary
            Keyword arguments for matplotlib.fill()

        data_out : bool
            A flag to return the ellipse samples without plotting

        cov : ndarray of shape (2,2)
            A 2x2 covariance matrix, if given this will overwrite semimaj, semimin and phi

        mass_level : float
            if supplied cov, mass_level is the contour defining fractional probability mass enclosed
            for example: mass_level = 0.68 is the standard 68% mass

    # Get Ellipse Properties from cov matrix
    if cov is not None:
        eig_vec,eig_val,u = np.linalg.svd(cov)
        # Make sure 0th eigenvector has positive x-coordinate
        if eig_vec[0][0] < 0:
            eig_vec[0] *= -1
        semimaj = np.sqrt(eig_val[0])
        semimin = np.sqrt(eig_val[1])
        if mass_level is None:
            multiplier = np.sqrt(2.279)
            distances = np.linspace(0,20,20001)
            chi2_cdf = chi2.cdf(distances,df=2)
            multiplier = np.sqrt(distances[np.where(np.abs(chi2_cdf-mass_level)==np.abs(chi2_cdf-mass_level).min())[0][0]])
        semimaj *= multiplier
        semimin *= multiplier
        phi = np.arccos([0],np.array([1,0])))
        if eig_vec[0][1] < 0 and phi > 0:
            phi *= -1

    # Generate data for ellipse structure
    theta = np.linspace(0,2*np.pi,theta_num)
    r = 1 / np.sqrt((np.cos(theta))**2 + (np.sin(theta))**2)
    x = r*np.cos(theta)
    y = r*np.sin(theta)
    data = np.array([x,y])
    S = np.array([[semimaj,0],[0,semimin]])
    R = np.array([[np.cos(phi),-np.sin(phi)],[np.sin(phi),np.cos(phi)]])
    T =,S)
    data =,data)
    data[0] += x_cent
    data[1] += y_cent

    # Output data?
    if data_out == True:
        return data

    # Plot!
    return_fig = False
    if ax is None:
        return_fig = True
        fig,ax = plt.subplots()

    if plot_kwargs is None:

    if fill == True:

    if return_fig == True:
        return fig

To clarify, semimaj is the length of the semimajor axis; semimin is the length of the semiminor axis; phi is the angle in radians the semimajor axis is from the positive x axis; x_cent and y_cent are the xy centers; theta_num are the number of points from 0 to 2pi to use in drawing the ellipse; ax is a pyplot axis; and *_kwargs are dictionaries with plotting keyword arguments.

For the first scenario, we will only feed it the most basic information. Assuming we have already loaded in the above script, we can use the code below to make a simple plot.



For the next plot, we will make some more specifications, and use our own previously generated figure and axes.

fig = mp.figure()
ax = fig.add_subplot(111,aspect='equal')
ax.set_xlabel('x axis',fontsize=20)
ax.set_ylabel('y axis',fontsize=20)

plot_kwargs = {'color':'r','linestyle':'-','linewidth':3,'alpha':0.5}
fill_kwargs = {'color':'r','alpha':0.3}

plot_kwargs = {'color':'b','linestyle':'-','linewidth':3,'alpha':0.8}
fill_kwargs = {'color':'b','alpha':0.7}