""" BaSeL 2.2 library """
import numpy as np
from .simpletable import SimpleTable
try:
from astropy.io import fits as pyfits
except ImportError:
import pyfits
from .stellib import Stellib
from .config import libsdir
[docs]class BaSeL(Stellib):
""" BaSeL 2.2 library derived class
This library + Rauch is used in Pegase.2
The BaSeL stellar spectral energy distribution (SED) libraries are libraries
of theoretical stellar SEDs recalibrated using empirical photometric data.
Therefore, we call them semi-empirical libraries.
The BaSeL 2.2 library was calibrated using photometric data from solar
metallicity stars.
References
----------
* Lejeune, Cuisiner, and Buser, 1998 A&AS, 130, 65
* can be downloaded http://www.astro.unibas.ch/BaSeL_files/BaSeL2_2.tar.gz
"""
def __init__(self, *args, **kwargs):
self.name = 'BaSeL 2.2'
self.source = libsdir + '/stellib_BaSeL_v2.2.grid.fits'
self._load_()
Stellib.__init__(self, *args, **kwargs)
def _load_(self):
with pyfits.open(self.source) as f:
# load data
self._getWaveLength_(f)
self._getTGZ_(f)
self._getSpectra_(f)
self._getWaveLength_units(f)
def _getWaveLength_(self, f):
self._wavelength = f[0].data[-1]
def _getWaveLength_units(self, f):
self.wavelength_unit = 'angstrom'
def _getTGZ_(self, f):
self.grid = SimpleTable(f[1].data)
self.grid.header.update(f[1].header.items())
self.grid.header['NAME'] = 'TGZ'
[docs] def bbox(self, dlogT=0.05, dlogg=0.25):
""" Boundary of Basel 2.2 library
Parameters
----------
dlogT: float
log-temperature tolerance before extrapolation limit
dlogg: float
log-g tolerance before extrapolation limit
Returns
-------
bbox: ndarray
(logT, logg) edges of the bounding polygon
"""
bbox = [(3.301 - dlogT, 5.500 + dlogg),
(3.301 - dlogT, 3.500 - dlogg),
(3.544 - dlogT, 3.500 - dlogg),
(3.544 - dlogT, 1.000),
(3.477, 0.600 + dlogg),
(3.447 - dlogT, 0.600 + dlogg),
(3.398 - dlogT, 0.280 + dlogg),
(3.398 - dlogT, -1.020 - dlogg),
(3.398, -1.020 - dlogg),
(3.447, -1.020 - dlogg),
(3.505 + dlogT, -0.700 - dlogg),
(3.544 + dlogT, -0.510 - dlogg),
(3.574 + dlogT, -0.290 - dlogg),
(3.602 + dlogT, 0.000 - dlogg),
(3.778, 0.000 - dlogg),
(3.778 + dlogT, 0.000),
(3.875 + dlogT, 0.500),
(3.929 + dlogT, 1.000),
(3.954 + dlogT, 1.500),
(4.021 + dlogT, 2.000 - dlogg),
(4.146, 2.000 - dlogg),
(4.146 + dlogT, 2.000),
(4.279 + dlogT, 2.500),
(4.415 + dlogT, 3.000),
(4.491 + dlogT, 3.500),
(4.544 + dlogT, 4.000),
(4.602 + dlogT, 4.500),
(4.699 + dlogT, 5.000 - dlogg),
(4.699 + dlogT, 5.000 + dlogg),
(3.525 + dlogT, 5.000 + dlogg),
(3.525 + dlogT, 5.500 + dlogg),
(3.301 - dlogT, 5.500 + dlogg) ]
return np.array(bbox)
def _getSpectra_(self, f):
self.spectra = f[0].data[:-1]
@property
def logg(self):
return self.grid['logg']
@property
def logT(self):
return self.grid['logT']
@property
def Teff(self):
return self.grid['Teff']
@property
def Z(self):
return self.grid['Z']
@property
def logZ(self):
return self.grid['logZ']
@property
def NHI(self):
return self.grid['NHI']
@property
def NHeI(self):
return self.grid['NHeI']
@property
def NHeII(self):
return self.grid['NHeII']
