def test_pix2ring(self):
with NumpyRNGContext(12345):
nside = 1024
hpx = Healpix(nside)
npix = hpx.npix
hpx_idx = np.random.random_integers(0, npix - 1, 10000)
assert_equal(hp.pix2ring(nside, hpx_idx),
hpx.pix2ring_many(hpx_idx))
def test_pix2ring(self):
if not DO_HEALPY:
# not possible on Windows
return
with NumpyRNGContext(12345):
nside = 1024
hpx = Healpix(nside)
npix = hpx.npix
hpx_idx = np.random.random_integers(0, npix - 1, 10000)
assert_equal(hp.pix2ring(nside, hpx_idx),
hpx.pix2ring_many(hpx_idx))
def create_ring_profile(input_map):
input_map = np.abs(input_map)
npix = input_map.shape[0]
nside = hp.npix2nside(npix)
rings = hp.pix2ring(nside, np.arange(npix)) - 1
mask = np.ones([npix])
mask[np.isnan(input_map)] = 0
rho = np.bincount(rings, weights=mask)
averages = np.bincount(rings, weights=np.nan_to_num(input_map)) / rho
# set profile for empty rings to 1
averages[np.isnan(averages)] = 1
result = averages[rings]
return result
def test_pix2ring(self):
if not DO_HEALPY:
# not possible on Windows
return
with NumpyRNGContext(12345):
nside = 1024
hpx = Healpix(nside)
npix = hpx.npix
hpx_idx = np.random.random_integers(0, npix - 1, 10000)
assert_equal(
hp.pix2ring(nside, hpx_idx),
hpx.pix2ring_many(hpx_idx)
)
def calcAvgBkg(ax, datamap, basename, **opts):
nside = hp.get_nside(datamap)
npix = hp.nside2npix(nside)
counts = zeros(4*nside, dtype=float)
norm = zeros(4*nside, dtype=float)
ringId = hp.pix2ring(nside, np.array(range(npix)))
for i, ring in enumerate(ringId):
counts[ring] += datamap[i]
norm[ring] += 1.
bgmap = zeros(npix, dtype=float)
mapmax = max(datamap)
for i, ring in enumerate(ringId):
bgmap[i] = counts[ring] / norm[ring]
ra, ri, sigmax, sigmay = returnRI(bgmap, datamap, **opts)
ax.errorbar(ra, ri, xerr=0*sigmax, yerr=sigmay, marker='o', fmt='.',\
capsize=4, label=basename+" (avg)", linewidth=2, markersize=8, mew=0)
def polar_profile(m, nest=False):
"""Obtain the marginalized polar profile of sky map.
Parameters
----------
m : np.ndarray
The input HEALPix array.
nest : bool, default=False
Indicates whether the input sky map is in nested rather than
ring-indexed HEALPix coordinates (default: ring).
Returns
-------
theta : np.ndarray
The polar angles (i.e., the colatitudes) of the isolatitude rings.
m_int : np.ndarray
The normalized probability density, such that `np.trapz(m_int, theta)`
is approximately `np.sum(m)`.
"""
npix = len(m)
nside = hp.npix2nside(npix)
nrings, = hp.pix2ring(nside, np.asarray([npix]))
startpix, ringpix, costheta, sintheta, _ = hp.ringinfo(
nside, np.arange(1, nrings))
if nest:
m = hp.reorder(m, n2r=True)
theta = np.arccos(costheta)
m_int = np.asarray([
m[i:i + j].sum() * stheta * 0.5 * npix / j
for i, j, stheta in zip(startpix, ringpix, sintheta)
])
return theta, m_int
def polar_profile(m, nest=False):
"""Obtain the marginalized polar profile of sky map.
Parameters
----------
m : np.ndarray
The input HEALPix array.
nest : bool, default=False
Indicates whether the input sky map is in nested rather than
ring-indexed HEALPix coordinates (default: ring).
Returns
-------
theta : np.ndarray
The polar angles (i.e., the colatitudes) of the isolatitude rings.
m_int : np.ndarray
The normalized probability density, such that `np.trapz(m_int, theta)`
is approximately `np.sum(m)`.
"""
npix = len(m)
nside = hp.npix2nside(npix)
nrings, = hp.pix2ring(nside, np.asarray([npix]))
startpix, ringpix, costheta, sintheta, _ = hp.ringinfo(
nside, np.arange(1, nrings))
if nest:
m = hp.reorder(m, n2r=True)
theta = np.arccos(costheta)
m_int = np.asarray(
[m[i:i+j].sum() * stheta * 0.5 * npix / j
for i, j, stheta in zip(startpix, ringpix, sintheta)])
return theta, m_int
