WMAP power spectrum analysis with HealPyΒΆ

This demonstrates how to plot and take a power spectrum of the WMAP data using healpy, the python wrapper for healpix. Healpy is available for download at the github site

../../_images/plot_wmap_power_spectra_1.png ../../_images/plot_wmap_power_spectra_3.png ../../_images/plot_wmap_power_spectra_2.png
NSIDE = 512
ORDERING = NESTED in fits file
INDXSCHM = IMPLICIT
Ordering converted to RING
NSIDE = 512
ORDERING = NESTED in fits file
INDXSCHM = IMPLICIT
Ordering converted to RING
NSIDE = 512
ORDERING = NESTED in fits file
INDXSCHM = IMPLICIT
Ordering converted to RING
# Author: Jake VanderPlas <vanderplas@astro.washington.edu>
# License: BSD
#   The figure is an example from astroML: see http://astroML.github.com
import numpy as np
from matplotlib import pyplot as plt

# warning: due to a bug in healpy, importing it before pylab can cause
#  a segmentation fault in some circumstances.
import healpy as hp

from astroML.datasets import fetch_wmap_temperatures


#------------------------------------------------------------
# Fetch the data
wmap_unmasked = fetch_wmap_temperatures(masked=False)
wmap_masked = fetch_wmap_temperatures(masked=True)
white_noise = np.ma.asarray(np.random.normal(0, 0.062, wmap_masked.shape))

#------------------------------------------------------------
# plot the unmasked map
fig = plt.figure(1)
hp.mollview(wmap_unmasked, min=-1, max=1, title='Unmasked map',
            fig=1, unit=r'$\Delta$T (mK)')

#------------------------------------------------------------
# plot the masked map
#  filled() fills the masked regions with a null value.
fig = plt.figure(2)
hp.mollview(wmap_masked.filled(), title='Masked map',
            fig=2, unit=r'$\Delta$T (mK)')

#------------------------------------------------------------
# compute and plot the power spectrum
cl = hp.anafast(wmap_masked.filled(), lmax=1024)
ell = np.arange(len(cl))

cl_white = hp.anafast(white_noise, lmax=1024)

fig = plt.figure(3)
ax = fig.add_subplot(111)
ax.scatter(ell, ell * (ell + 1) * cl,
           s=4, c='black', lw=0,
           label='data')
ax.scatter(ell, ell * (ell + 1) * cl_white,
           s=4, c='gray', lw=0,
           label='white noise')

ax.set_xlabel(r'$\ell$')
ax.set_ylabel(r'$\ell(\ell+1)C_\ell$')
ax.set_title('Angular Power (not mask corrected)')
ax.legend(loc='upper right')
ax.grid()
ax.set_xlim(0, 1100)

plt.show()