Source code for astroML.datasets.sdss_filters

from __future__ import print_function, division

import os

import numpy as np

from astroML.datasets import get_data_home
from ..py3k_compat import urlopen

# Info on vega spectrum: http://www.stsci.edu/hst/observatory/cdbs/calspec.html
VEGA_URL = 'http://www.astro.washington.edu/users/ivezic/DMbook/data/1732526_nic_002.ascii'
FILTER_URL = 'http://classic.sdss.org/dr7/instruments/imager/filters/%s.dat'


[docs]def fetch_sdss_filter(fname, data_home=None, download_if_missing=True): """Loader for SDSS Filter profiles Parameters ---------- fname : str filter name: must be one of 'ugriz' 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 : 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. Returns ------- data : ndarray data is an array of shape (5, Nlam) first row: wavelength in angstroms second row: sensitivity to point source, airmass 1.3 third row: sensitivity to extended source, airmass 1.3 fourth row: sensitivity to extended source, airmass 0.0 fifth row: assumed atmospheric extinction, airmass 1.0 """ if fname not in 'ugriz': raise ValueError("Unrecognized filter name '%s'" % fname) url = FILTER_URL % fname data_home = get_data_home(data_home) archive_file = os.path.join(data_home, '%s.dat' % fname) 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') print("downloading from %s" % url) F = urlopen(url) open(archive_file, 'wb').write(F.read()) F = open(archive_file) return np.loadtxt(F, unpack=True)
[docs]def fetch_vega_spectrum(data_home=None, download_if_missing=True): """Loader for Vega reference spectrum Parameters ---------- fname : str filter name: must be one of 'ugriz' 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 : 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. Returns ------- data : ndarray data[0] is the array of wavelength in angstroms data[1] is the array of fluxes in Jy (F_nu, not F_lambda) """ data_home = get_data_home(data_home) archive_name = os.path.join(data_home, VEGA_URL.split('/')[-1]) if not os.path.exists(archive_name): if not download_if_missing: raise IOError('data not present on disk. ' 'set download_if_missing=True to download') print("downnloading from %s" % VEGA_URL) F = urlopen(VEGA_URL) open(archive_name, 'wb').write(F.read()) F = open(archive_name, 'r') return np.loadtxt(F, unpack=True)