Skip to main content

Parsing of IVOA S_REGION strings

Project description

python package

sregion

Parsing of IVOA S_REGION strings

The STS-C formalism is described at http://www.ivoa.net/Documents/latest/STC-S.html, though it seems that it was never adopted as an official standard. Nevertheless, the s_region strings do seem to have been adopted as a sort of pseudostandard in IVOA-compliant datasets / databases.

astropy-regions would probably be a better place to put this, but I'm not interested in all of the full astropy coordinate compatibility for now.

Examples

>>> import numpy as np
>>> from sregion import SRegion

#
# Polygon string
#
>>> sr = SRegion('POLYGON 0.0 0.0 0.0 1.0 1.0 1.0 1.0 0.0')
>>> print(sr.area)
[1.0]
>>> print(sr.centroid)
[array([0.5, 0.5])]

#
# Circle string
#
>>> for i in range(4,10):
>>>     sr = SRegion('CIRCLE 10 10 1', ncircle=2**i)
>>>     print(f'ncircle={2**i:>3} {sr.area[0]/np.pi:.5f} {sr.centroid[0]}')
ncircle= 16 0.97450 [10. 10.]
ncircle= 32 0.99359 [10. 10.]
ncircle= 64 0.99839 [10. 10.]
ncircle=128 0.99960 [10. 10.]
ncircle=256 0.99990 [10. 10.]
ncircle=512 0.99997 [10. 10.]

# Circle with radius in angular units
>>> import astropy.units as u
>>> sr = SRegion('CIRCLE 10 10 1"', ncircle=256)
>>> print(f'{sr.sky_area(unit=u.arcsec**2)[0]:.5f}')
3.14128 arcsec2

#
# From WCS objects
#
>>> from astropy.wcs import WCS
>>> wcs = WCS()
>>> wcs.pixel_shape = [601,601]
>>> wcs.wcs.cdelt *= 0.1/3600
>>> wcs.wcs.crpix[1] = 300
>>> wcs.wcs.crval = [0,0]
>>> print(SRegion(wcs).sky_area())
[<Quantity 1. arcmin2>]

#
# From arrays
#
>>> x = np.array([0, 0, 1, 1])
>>> y = np.array([0, 1, 1, 0])
>>> sr = SRegion(np.array([x, y]).T)
>>> print(sr.area)
[1.0]
>>> print(sr.centroid)
[array([0.5, 0.5])]

# 
# To s_region string
#
>>> print(sr.s_region)
POLYGON 0.000000 0.000000 0.000000 1.000000 1.000000 1.000000 1.000000 0.000000

#
# To matplotlib path object(s)
#
>>> print(sr.path[0].contains_point([0.5, 0.5]))
True
>>> print(sr.path[0].contains_points([[0.5, 0.5], [2.0, 2.0]]))
[ True False]

#
# To matplotlib patch(es)
#
>>> import matplotlib.pyplot as plt
>>> fig, ax = plt.subplots(1,1,figsize=(2,2))
>>> for p in sr.patch(alpha=0.5, fc='r'):
>>>     ax.add_patch(p)
>>> ax.set_xlim(-1, 2)
>>> ax.set_ylim(*ax.get_xlim())
>>> ax.grid()

#
# To shapely polygons
# 
>>> sr.shapely
[<shapely.geometry.polygon.Polygon at 0x18055b910>]

#
# To DS9 region(s)
#
>>> for r in sr.region:
>>>    print(r)
polygon(0.000000,0.000000,0.000000,1.000000,1.000000,1.000000,1.000000,0.000000)

>>> sr.ds9_properties = 'color=red width=2'
>>> sr.label = 'my_group'
>>> for r in sr.region:
>>>    print(r)
polygon(0.000000,0.000000,0.000000,1.000000,1.000000,1.000000,1.000000,0.000000) # color=red width=2 text={my_group}

    

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

sregion-1.3.3.tar.gz (12.8 kB view details)

Uploaded Source

File details

Details for the file sregion-1.3.3.tar.gz.

File metadata

  • Download URL: sregion-1.3.3.tar.gz
  • Upload date:
  • Size: 12.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.9.19

File hashes

Hashes for sregion-1.3.3.tar.gz
Algorithm Hash digest
SHA256 05356d73c0b171535a02f407f13703323eadcb1e7d4841611efedc456fda053b
MD5 7b429a1c3a093f6bf6a9fa251ceda6ec
BLAKE2b-256 ce97e9a83444d95d06e8554ab32afea59597c850321ad59d25658539fe76db50

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page