Source code for astroML.datasets.rrlyrae_mags

import os

import numpy as np

from . import get_data_home
from . import fetch_sdss_S82standards
from .tools import download_with_progress_bar

DATA_URL = ("https://github.com/astroML/astroML-data/raw/master/datasets/"
            "RRLyrae.fit")


[docs]def fetch_rrlyrae_mags(data_home=None, download_if_missing=True): """Loader for RR-Lyrae 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 : 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 : recarray, shape = (483,) record array containing imaging data Examples -------- >>> from astroML.datasets import fetch_rrlyrae_mags >>> data = fetch_rrlyrae_mags() # doctest: +IGNORE_OUTPUT +REMOTE_DATA >>> # number of objects in dataset >>> data.shape # doctest: +REMOTE_DATA (483,) Notes ----- This data is from table 1 of Sesar et al 2010 ApJ 708:717 """ # fits is an optional dependency: don't import globally from astropy.io import fits 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') fitsdata = download_with_progress_bar(DATA_URL) open(archive_file, 'wb').write(fitsdata) hdulist = fits.open(archive_file) return np.asarray(hdulist[1].data)
[docs]def fetch_rrlyrae_combined(data_home=None, download_if_missing=True): """Loader for RR-Lyrae combined data This returns the combined RR-Lyrae colors and SDSS standards colors. The RR-Lyrae sample is confirmed through time-domain observations; this result in a nice dataset for testing classification routines. 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 : 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 ------- X : ndarray a shape (n_samples, 4) array. Columns are u-g, g-r, r-i, i-z y : ndarray a shape (n_samples,) array of labels. 1 indicates an RR Lyrae, 0 indicates a background star. """ # ---------------------------------------------------------------------- # Load data kwds = dict(data_home=data_home, download_if_missing=download_if_missing) rrlyrae = fetch_rrlyrae_mags(**kwds) standards = fetch_sdss_S82standards(**kwds) # ------------------------------------------------------------ # perform color cuts on standard stars # these come from eqns 1-4 of Sesar et al 2010, ApJ 708:717 u_g = standards['mmu_u'] - standards['mmu_g'] g_r = standards['mmu_g'] - standards['mmu_r'] r_i = standards['mmu_r'] - standards['mmu_i'] i_z = standards['mmu_i'] - standards['mmu_z'] standards = standards[(u_g > 0.7) & (u_g < 1.35) & (g_r > -0.15) & (g_r < 0.4) & (r_i > -0.15) & (r_i < 0.22) & (i_z > -0.21) & (i_z < 0.25)] # ---------------------------------------------------------------------- # get magnitudes and colors; split into train and test sets mags_rr = np.vstack([rrlyrae[f + 'mag'] for f in 'ugriz']) colors_rr = mags_rr[:-1] - mags_rr[1:] mags_st = np.vstack([standards['mmu_' + f] for f in 'ugriz']) colors_st = mags_st[:-1] - mags_st[1:] # stack the two sets of colors together X = np.vstack((colors_st.T, colors_rr.T)) y = np.zeros(X.shape[0]) y[-colors_rr.shape[1]:] = 1 return X, y