def sample_lonlat(self, n):
"""
Sample 2D distribution of points in lon, lat
"""
# From http://en.wikipedia.org/wiki/Ellipse#General_parametric_form
# However, Martin et al. (2009) use PA theta "from North to East"
# Definition of phi (position angle) is offset by pi/4
# Definition of t (eccentric anamoly) remains the same (x,y-frame usual)
# In the end, everything is trouble because we use glon, glat...
radius = self.sample_radius(n)
a = radius
b = self.jacobian * radius
t = 2. * np.pi * numpy.random.rand(n)
cost, sint = np.cos(t), np.sin(t)
phi = np.pi / 2. - np.deg2rad(self.theta)
cosphi, sinphi = np.cos(phi), np.sin(phi)
x = a * cost * cosphi - b * sint * sinphi
y = a * cost * sinphi + b * sint * cosphi
if self.projector is None:
logger.debug("Creating AITOFF projector for sampling")
projector = Projector(self.lon, self.lat, 'ait')
else:
projector = self.projector
lon, lat = projector.imageToSphere(x, y)
return lon, lat
def findObjects(pixels, values, nside, zvalues, rev, good):
"""
Characterize labelled candidates in a multi-dimensional HEALPix map.
Parameters:
values : (Sparse) HEALPix array of data values
nside : HEALPix dimensionality
pixels : Pixel values associated to (sparse) HEALPix array
zvalues : Values of the z-dimension (usually distance modulus)
rev : Reverse indices for pixels in each "island"
good : Array containg labels for each "island"
Returns:
objs : numpy.recarray of object characteristics
"""
ngood = len(good)
objs = numpy.recarray((ngood, ),
dtype=[
('LABEL', 'i4'),
('NPIX', 'i4'),
('VAL_MAX', 'f4'),
('IDX_MAX', 'i4'),
('ZIDX_MAX', 'i4'),
('PIX_MAX', 'i4'),
('X_MAX', 'f4'),
('Y_MAX', 'f4'),
('Z_MAX', 'f4'),
('X_CENT', 'f4'),
('Y_CENT', 'f4'),
('Z_CENT', 'f4'),
('X_BARY', 'f4'),
('Y_BARY', 'f4'),
('Z_BARY', 'f4'),
('CUT', 'i2'),
])
objs['CUT'][:] = 0
shape = values.shape
ncol = shape[1]
for i in range(0, ngood):
logger.debug("i=%i", i)
# This code could use some cleanup...
indices = rev[rev[good[i]]:rev[good[i] + 1]]
npix = len(indices)
idx = indices // ncol # This is the spatial index
zidx = indices % ncol # This is the distance index
pix = pixels[idx] # This is the healpix pixel
xval, yval = pix2ang(nside, pix)
zval = zvalues[zidx]
objs[i]['LABEL'] = good[i]
objs[i]['NPIX'] = npix
logger.debug("LABEL=%i" % objs[i]['LABEL'])
logger.debug("NPIX=%i" % objs[i]['NPIX'])
island = values[idx, zidx]
idxmax = island.argmax()
xval_max, yval_max, zval_max = xval[idxmax], yval[idxmax], zval[
idxmax]
objs[i]['VAL_MAX'] = island[idxmax]
objs[i]['IDX_MAX'] = idx[idxmax]
objs[i]['ZIDX_MAX'] = zidx[idxmax]
objs[i]['PIX_MAX'] = pix[idxmax]
objs[i]['X_MAX'] = xval_max
objs[i]['Y_MAX'] = yval_max
objs[i]['Z_MAX'] = zval_max
proj = Projector(xval_max, yval_max)
xpix, ypix = proj.sphereToImage(xval, yval)
# Projected centroid
x_cent, y_cent, zval_cent = numpy.average([xpix, ypix, zval],
axis=1)
xval_cent, yval_cent = proj.imageToSphere(x_cent, y_cent)
objs[i]['X_CENT'] = xval_cent
objs[i]['Y_CENT'] = yval_cent
objs[i]['Z_CENT'] = zval_cent
# Projected barycenter
weights = [island, island, island]
x_bary, y_bary, zval_bary = numpy.average([xpix, ypix, zval],
weights=weights,
axis=1)
xval_bary, yval_bary = proj.imageToSphere(x_bary, y_bary)
objs[i]['X_BARY'] = xval_bary
objs[i]['Y_BARY'] = yval_bary
objs[i]['Z_BARY'] = zval_bary
return objs
def projector(self):
if self.proj is None or self.proj.lower() == 'none':
return None
else:
return Projector(self.lon, self.lat, self.proj)
def findObjects(pixels, values, nside, zvalues, rev, good):
"""
Characterize labelled candidates in a multi-dimensional HEALPix map.
Parameters:
values : (Sparse) HEALPix array of data values
nside : HEALPix dimensionality
pixels : Pixel values associated to (sparse) HEALPix array
zvalues : Values of the z-dimension (usually distance modulus)
rev : Reverse indices for pixels in each "island"
good : Array containg labels for each "island"
Returns:
objs : numpy.recarray of object characteristics
"""
ngood = len(good)
objs = numpy.recarray((ngood,),
dtype=[('LABEL','i4'),
('NPIX','i4'),
('VAL_MAX','f4'),
('IDX_MAX','i4'),
('ZIDX_MAX','i4'),
('PIX_MAX','i4'),
('X_MAX','f4'),
('Y_MAX','f4'),
('Z_MAX','f4'),
('X_CENT','f4'),
('Y_CENT','f4'),
('Z_CENT','f4'),
('X_BARY','f4'),
('Y_BARY','f4'),
('Z_BARY','f4'),
('CUT','i2'),])
objs['CUT'][:] = 0
shape = values.shape
ncol = shape[1]
for i in range(0,ngood):
logger.debug("i=%i",i)
# This code could use some cleanup...
indices=rev[rev[good[i]]:rev[good[i]+1]]
npix = len(indices)
idx = indices // ncol # This is the spatial index
zidx = indices % ncol # This is the distance index
pix = pixels[idx] # This is the healpix pixel
xval,yval = pix2ang(nside, pix)
zval = zvalues[zidx]
objs[i]['LABEL'] = good[i]
objs[i]['NPIX'] = npix
logger.debug("LABEL=%i"%objs[i]['LABEL'])
logger.debug("NPIX=%i"%objs[i]['NPIX'])
island = values[idx,zidx]
idxmax = island.argmax()
xval_max,yval_max,zval_max = xval[idxmax],yval[idxmax],zval[idxmax]
objs[i]['VAL_MAX'] = island[idxmax]
objs[i]['IDX_MAX'] = idx[idxmax]
objs[i]['ZIDX_MAX'] = zidx[idxmax]
objs[i]['PIX_MAX'] = pix[idxmax]
objs[i]['X_MAX'] = xval_max
objs[i]['Y_MAX'] = yval_max
objs[i]['Z_MAX'] = zval_max
proj = Projector(xval_max,yval_max)
xpix,ypix = proj.sphereToImage(xval,yval)
# Projected centroid
x_cent,y_cent,zval_cent = numpy.average([xpix,ypix,zval],axis=1)
xval_cent, yval_cent = proj.imageToSphere(x_cent,y_cent)
objs[i]['X_CENT'] = xval_cent
objs[i]['Y_CENT'] = yval_cent
objs[i]['Z_CENT'] = zval_cent
# Projected barycenter
weights=[island,island,island]
x_bary,y_bary,zval_bary = numpy.average([xpix,ypix,zval],weights=weights,axis=1)
xval_bary,yval_bary = proj.imageToSphere(x_bary, y_bary)
objs[i]['X_BARY'] = xval_bary
objs[i]['Y_BARY'] = yval_bary
objs[i]['Z_BARY'] = zval_bary
return objs
def projector(self):
"""Projector used to transform to local sky coordinates."""
if self.proj is None or self.proj.lower() == 'none':
return None
else:
return Projector(self.lon, self.lat, self.proj)