https://github.com/esheldon/healpix_util

Some python tools to work with healpix
https://github.com/esheldon/healpix_util

Last synced: 11 days ago
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Some python tools to work with healpix

• Host: GitHub
• URL: https://github.com/esheldon/healpix_util
• Owner: esheldon
• Created: 2014-08-03T15:00:10.000Z (over 10 years ago)
• Default Branch: master
• Last Pushed: 2020-10-13T15:25:39.000Z (about 4 years ago)
• Last Synced: 2024-10-19T19:37:02.379Z (19 days ago)
• Language: C
• Homepage:
• Size: 85.9 KB
• Stars: 6
• Watchers: 2
• Forks: 3
• Open Issues: 1
• Metadata Files:
  • Readme: README.md

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README

        
healpix

=======

Some tools to work with healpix.

Note reader utilities use camelCase to avoid name collision

with the healpy readers.

a few examples

==============

```python

import healpix_util as hu

# create a HealPix object.  This will carry around the nside, scheme, npix,

# etc.

hpix=hu.HealPix("ring", 4096)

print(hpix)

scheme:       1

scheme_name:  RING

nside:        4096

npix:         201326592

ncap:         33546240

area:         6.24178e-08 square degrees

area:         0.000224704 square arcmin

area:         0.808935 square arcsec

# has many attributes, e.g. .scheme, .nside, .npix, .area, etc.

# find pixel number for input ra,dec arrays

pixnums = hpix.eq2pix(ra, dec)

ra, dec = hpix.pix2eq(pixnums)

# or for theta,phi

pixnums = hpix.ang2pix(theta, phi)

theta, phi = hpix.pix2ang(pixnums)

# query disc 

pixnums = hpix.query_disc(ra, dec, radius_degrees)

pixnums = hpix.query_disc(ra, dec, radius_degrees, inclusive=True)

pixnums = hpix.query_disc(theta, phi, radius_radians, system='ang')

# load a healpix.Map object

m = hu.readMap(filename)

m = hu.readMap(filename, column='I')

# load multiple Maps into an ordered dict, keyed by column name

maps=hu.readMaps(filename)

maps=hu.readMaps(filename,columns=["I","Q"])

# convert between schemes

mnest = m.convert("nest")

mring = m.convert("ring")

# find the mapval for the input coordinates

mapvals = m.get_mapval(ra, dec)

mapvals = m.get_mapval(theta, phi, system='ang')

# load a healpix.DensityMap, a special map that represents

# a spatial density

dmap=hu.readDensityMap(filename)

dmaps=hu.readDensityMaps(filename)

# generate random points from the map

ra,dec = dmap.genrand(100000)

# limit the range to a region known to have non-zero density, or

# weight.  This will speed it up greatly

ra,dec = dmap.genrand(100000, ra_range=[60.,95.], dec_range=[-62.,-42.])

# generate randoms in theta,phi

theta,phi = dmap.genrand(100000, system='ang', theta_range=tr, phi_range=pr)

# healpy routines are pulled into the healpix_util namespace

npix=hu.nside2npix(nside)

# obscure features

# check quadrants around the input points

# make sure weighted position ellipticity in adjacent quadrants

# less than 0.05

ellip_max=0.05

for i in xrange(ra.size):

    maskflags[i] = dmap.check_quad(ra[i], dec[i], radius_degrees[i], ellip_max)

# see docs on quad_check

# checking maskflags > 1 indicates the cluster is "good", but note during

# a lensing measurement you should be careful to always use sources from

# the same pair of quadrants (unless all are good)

```

dependencies

============

- numpy

- healpy

- fitsio # for file reading