This documentation is for astroML version 0.2

This page

Links

astroML Mailing List

GitHub Issue Tracker

Videos

Scipy 2012 (15 minute talk)

Scipy 2013 (20 minute talk)

Citing

If you use the software, please consider citing astroML.

Linear Sum of GaussiansΒΆ

Fitting a spectrum with a linear sum of gaussians.

../../_images/plot_spectrum_sum_of_norms_1.png ../../_images/plot_spectrum_sum_of_norms_2.png ../../_images/plot_spectrum_sum_of_norms_3.png
# Author: Jake VanderPlas <vanderplas@astro.washington.edu>
# License: BSD
#   The figure is an example from astroML: see http://astroML.github.com
from matplotlib import pyplot as plt
from astroML.datasets import fetch_vega_spectrum
from astroML.sum_of_norms import sum_of_norms, norm

# Fetch the data
x, y = fetch_vega_spectrum()

# truncate the spectrum
mask = (x >= 2000) & (x < 10000)
x = x[mask]
y = y[mask]

for n_gaussians in (10, 50, 100):
    # compute the best-fit linear combination
    w_best, rms, locs, widths = sum_of_norms(x, y, n_gaussians,
                                             spacing='linear',
                                             full_output=True)

    norms = w_best * norm(x[:, None], locs, widths)

    # plot the results
    plt.figure()
    plt.plot(x, y, '-k', label='input spectrum')
    ylim = plt.ylim()

    plt.plot(x, norms, ls='-', c='#FFAAAA')
    plt.plot(x, norms.sum(1), '-r', label='sum of gaussians')
    plt.ylim(-0.1 * ylim[1], ylim[1])

    plt.legend(loc=0)

    plt.text(0.97, 0.8,
             "rms error = %.2g" % rms,
             ha='right', va='top', transform=plt.gca().transAxes)
    plt.title("Fit to a Spectrum with a Sum of %i Gaussians" % n_gaussians)

plt.show()