Source code for astroML.datasets.sdss_sspp
import os
import numpy as np
from astropy.table import Table
from . import get_data_home
DATA_URL = ("https://github.com/astroML/astroML-data/raw/master/datasets/"
"SDSSssppDR9_rerun122.fit.gz")
def compute_distances(data):
"""Compute the distances to select stars in the sdss_sspp sample.
Distance are determined using empirical color/magnitude fits from
Ivezic et al 2008, ApJ 684:287
Extinction correcctions come from Berry et al 2011, arXiv 1111.4985
This distance only works for stars with log(g) > 3.3
Other stars will have distance=-1
"""
# extinction terms from Berry et al
Ar = data['Ar']
Au = 1.810 * Ar
Ag = 1.400 * Ar
Ai = 0.759 * Ar
Az = 0.561 * Ar
# compute corrected mags and colors
gmag = data['gpsf'] - Ag
rmag = data['rpsf'] - Ar
imag = data['ipsf'] - Ai
gi = gmag - imag
# compute distance fit from Ivezic et al
FeH = data['FeH']
Mr0 = (-5.06 + 14.32 * gi - 12.97 * gi ** 2 +
6.127 * gi ** 3 - 1.267 * gi ** 4 + 0.0967 * gi ** 5)
FeHoffset = 4.50 - 1.11 * FeH - 0.18 * FeH ** 2
Mr = Mr0 + FeHoffset
dist = 0.01 * 10 ** (0.2 * (rmag - Mr))
# stars with log(g) < 3.3 don't work for this fit: set distance to -1
dist[data['logg'] < 3.3] = -1
return dist
[docs]def 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 astroML data is stored in '~/astroML_data'.
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() # doctest: +IGNORE_OUTPUT +REMOTE_DATA
>>> # number of objects in dataset
>>> data.shape # doctest: +REMOTE_DATA
(327260,)
>>> # names of the first five columns
>>> print(data.dtype.names[:5]) # doctest: +REMOTE_DATA
('ra', 'dec', 'Ar', 'upsf', 'uErr')
>>> # first RA value
>>> print(data['ra'][:1]) # doctest: +REMOTE_DATA
[49.6275024]
>>> # first DEC value
>>> print(data['dec'][:1]) # doctest: +REMOTE_DATA
[-1.04175591]
"""
data_home = get_data_home(data_home)
archive_file = os.path.join(data_home, os.path.basename(DATA_URL))
if not os.path.exists(archive_file):
if not download_if_missing:
raise IOError('data not present on disk. '
'set download_if_missing=True to download')
data = Table.read(DATA_URL)
data.write(archive_file)
else:
data = Table.read(archive_file)
if cleaned:
# -1.1 < FeH < 0.1
data = data[(data['FeH'] > -1.1) & (data['FeH'] < 0.1)]
# -0.03 < alpha/Fe < 0.57
data = data[(data['alphFe'] > -0.03) & (data['alphFe'] < 0.57)]
# 5000 < Teff < 6500
data = data[(data['Teff'] > 5000) & (data['Teff'] < 6500)]
# 3.5 < log(g) < 5
data = data[(data['logg'] > 3.5) & (data['logg'] < 5)]
# 0 < error for FeH < 0.1
data = data[(data['FeHErr'] > 0) & (data['FeHErr'] < 0.1)]
# 0 < error for alpha/Fe < 0.05
data = data[(data['alphFeErr'] > 0) & (data['alphFeErr'] < 0.05)]
# 15 < g mag < 18
data = data[(data['gpsf'] > 15) & (data['gpsf'] < 18)]
# abs(radVel) < 100 km/s
data = data[(abs(data['radVel']) < 100)]
return np.asarray(data)