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11.4.9. astroML.datasets.fetch_sdss_sspp

astroML.datasets.fetch_sdss_sspp(data_home=None, download_if_missing=True, cleaned=False)

Loader for SDSS SEGUE Stellar Parameter Pipeline data

Parameters :

data_home : optional, default=None

Specify another download and cache folder for the datasets. By default all scikit learn data is stored in ‘~/astroML_data’ subfolders.

download_if_missing : bool (optional) default=True

If False, raise a IOError if the data is not locally available instead of trying to download the data from the source site.

cleaned : bool (optional) default=False

if True, then return a cleaned catalog where objects with extreme values are removed.

Returns :

data : recarray, shape = (327260,)

record array containing pipeline parameters

Notes

Here are the comments from the fits file header:

Imaging data and spectrum identifiers for a sample of 327,260 stars with SDSS spectra, selected as:

  1. available SSPP parameters in SDSS Data Release 9 (SSPP rerun 122, file from Y.S. Lee)
  2. 14 < r < 21 (psf magnitudes, uncorrected for ISM extinction)
  3. 10 < u < 25 & 10 < z < 25 (same as above)
  4. errors in ugriz well measured (>0) and <10
  5. 0 < u-g < 3 (all color cuts based on psf mags, dereddened)
  6. -0.5 < g-r < 1.5 & -0.5 < r-i < 1.0 & -0.5 < i-z < 1.0
  7. -200 < pmL < 200 & -200 < pmB < 200 (proper motion in mas/yr)
  8. pmErr < 10 mas/yr (proper motion error)
  9. 1 < log(g) < 5
  10. TeffErr < 300 K

Teff and TeffErr are given in Kelvin, radVel and radVelErr in km/s. (ZI, Feb 2012, ivezic@astro.washington.edu)

Examples

>>> from astroML.datasets import fetch_sdss_sspp
>>> data = fetch_sdss_sspp()
>>> data.shape  # number of objects in dataset
(327260,)
>>> print data.names[:5]  # names of the first five columns
['ra', 'dec', 'Ar', 'upsf', 'uErr']
>>> print data['ra'][:2]  # first two RA values
[ 49.62750244  40.27209091]
>>> print data['dec'][:2]  # first two DEC values
[-1.04175591 -0.64250112]