def __init__(self,
ebvRange,
wave,
enableDIB=False,
enableFlux=True,
extinctionLaw='gal3',
normRange=None):
self.points = np.array(ebvRange)
values = []
for ebv in ebvRange:
extinctFlux = np.ones(wave.shape)
if enableFlux:
extinct = pysynphot.Extinction(ebv, extinctionLaw)
extinctThroughPut = extinct.GetThroughput()[::-1]
f = interpolate.interp1d(extinct.wave[::-1], extinctThroughPut)
extinctFlux *= f(wave)
if enableDIB:
extinctFlux *= pydib.makeSpectrum(wave, ebv).flux
if normRange != None:
extinctFluxSpec = oned.onedspec(wave,
extinctFlux,
mode='waveflux')
extinctFlux /= np.mean(extinctFluxSpec[slice(*normRange)].flux)
values.append(extinctFlux)
self.values = np.array(values)
self.interpGrid = interpolate.interp1d(self.points,
self.values.transpose(),
fill_value=1,
bounds_error=False,
kind='cubic')
def __init__(self, ebvRange, wave, enableDIB=False, enableFlux=True, extinctionLaw="gal3", normRange=None):
self.points = np.array(ebvRange)
values = []
for ebv in ebvRange:
extinctFlux = np.ones(wave.shape)
if enableFlux:
extinct = pysynphot.Extinction(ebv, extinctionLaw)
extinctThroughPut = extinct.GetThroughput()[::-1]
f = interpolate.interp1d(extinct.wave[::-1], extinctThroughPut)
extinctFlux *= f(wave)
if enableDIB:
extinctFlux *= pydib.makeSpectrum(wave, ebv).flux
if normRange != None:
extinctFluxSpec = oned.onedspec(wave, extinctFlux, mode="waveflux")
extinctFlux /= np.mean(extinctFluxSpec[slice(*normRange)].flux)
values.append(extinctFlux)
self.values = np.array(values)
self.interpGrid = interpolate.interp1d(
self.points, self.values.transpose(), fill_value=1, bounds_error=False, kind="cubic"
)
def __call__(self, offset, teff, logg, feh, v_rad):
new_model = self.grid.interpolate_spectrum(teff=teff, logg=logg, feh=feh)
new_model = new_model.shift_velocity(v_rad, interp=False)
if abs(new_model.flux[0] + 1) < 1e-10 :
print 'outside of grid'
new_model.flux *= 1e10
return new_model.interpolate(self.spectrum.wave)
new_model_flux = np.interp(self.spectrum.wave, new_model.wave, new_model.flux) + offset
V = self.Vandermonde * (new_model_flux/ self.error2)[:,np.newaxis]
scl = np.sqrt((V * V).sum(0))
sol, resids, rank, s = np.linalg.lstsq(V/scl, self.fluxbye2, self.rcond)
sol = (sol.T/scl).T
if (rank != self.npol) and self.warn_conditioned:
msg = "The fit may be poorly conditioned"
warnings.warn(msg)
fit = np.dot(V, sol) * self.error2
fitspec = oned.onedspec(self.spectrum.wave, fit, mode='waveflux')
self.fitspec = fitspec
return fitspec
def getSpec(self, *args):
return oned.onedspec(self.wave, self.interpGrid(args), mode='waveflux')
def getSpecs(specDataDir, fnames, config, **kwargs):
#reading config
waveFName = config.get('wave', 'wave')
specDType = config.get('structure', 'datatype')
specSize = config.getint('structure', 'specsize')
specsize = config.getint('wave', 'islog')
R = config.getfloat('params', 'r')
if kwargs.has_key('smoothres'):
if kwargs['smoothres']>R:
raise ValueError('requested resolution (R=%f) higher than'
'models intrinsic resolution (R=%f)' % (kwargs['smoothres'], R))
#Reading wave solution
wave = np.fromfile(os.path.join(specDataDir, waveFName))
if kwargs.has_key('wave'):
gridSize = len(fnames) * len(kwargs['wave'].tostring()) / 1024**2
else:
gridSize = len(fnames) * specSize / 1024**2
print "Processing %d spectra" % len(fnames)
print "Processing %.3f MB in to Memory." % gridSize
specs = []
if kwargs.has_key('normrange'):
if kwargs.has_key('normmode'):
normMode = kwargs['normmode']
else:
normMode = 'simple'
if normMode == 'simple':
normSlice = slice(*[wave.searchsorted(item) for item in kwargs['normrange']])
else:
raise NotImplementedError('Normalisation mode %s has not been implemented yet' % normMode)
startTime = time.time()
for i, specFName in enumerate(fnames):
flux = np.fromfile(os.path.join(specDataDir, 'data', specFName))
spec = oned.onedspec(wave, flux, mode='waveflux')
if i%100 == 0:
print "@%d took %.2f s" % (i, time.time() - startTime)
startTime = time.time()
if kwargs.has_key('wave'):
spec = spec.interpolate(kwargs['wave'])
if kwargs.has_key('normrange'):
normFac = np.mean(spec[normSlice].flux)
spec /= normFac
if kwargs.has_key('smoothres') or kwargs.has_key('smoothrot'):
if kwargs.has_key('wave'):
tmpSpec = spec[float(kwargs['wave'].min()):float(kwargs['wave'].max())]
logDelta, logSpec = tmpSpec.interpolate_log()
else:
logDelta, logSpec = spec.interpolate_log()
if kwargs.has_key('smoothres'):
logSpec = logSpec.convolve_profile(kwargs['smoothres'], smallDelta=logDelta)
if kwargs.has_key('smoothrot'):
logSpec = logSpec.convolve_rotation(kwargs['smoothrot'], smallDelta=logDelta)
spec = logSpec
specs.append(spec.flux)
if kwargs.has_key('wave'):
return kwargs['wave'], np.array(specs)
else:
return wave, np.array(specs)
def getSpec(self, *args):
return oned.onedspec(self.wave, self(*args), mode="waveflux")
def getSpecs(specDataDir, fnames, config, **kwargs):
# reading config
waveFName = config.get("wave", "wave")
specDType = config.get("structure", "datatype")
specSize = config.getint("structure", "specsize")
specsize = config.getint("wave", "islog")
R = config.getfloat("params", "r")
if kwargs.has_key("smoothres"):
if kwargs["smoothres"] > R:
raise ValueError(
"requested resolution (R=%f) higher than"
"models intrinsic resolution (R=%f)" % (kwargs["smoothres"], R)
)
# Reading wave solution
wave = np.fromfile(os.path.join(specDataDir, waveFName))
if kwargs.has_key("wave"):
gridSize = len(fnames) * len(kwargs["wave"].tostring()) / 1024 ** 2
else:
gridSize = len(fnames) * specSize / 1024 ** 2
print "Processing %d spectra" % len(fnames)
print "Processing %.3f MB in to Memory." % gridSize
specs = []
startTime = time.time()
for i, specFName in enumerate(fnames):
flux = np.fromfile(os.path.join(specDataDir, "data", specFName))
spec = oned.onedspec(wave, flux, mode="waveflux")
if i % 100 == 0:
print "@%d took %.2f s" % (i, time.time() - startTime)
startTime = time.time()
if kwargs.has_key("normrange"):
normrange = kwargs["normrange"]
normFac = np.mean(spec[slice(*normrange)].flux)
spec.flux /= normFac
if kwargs.has_key("smoothres") or kwargs.has_key("smoothrot"):
if kwargs.has_key("wave"):
tmpSpec = spec[float(kwargs["wave"].min()) : float(kwargs["wave"].max())]
logDelta, logSpec = tmpSpec.interpolate_log()
else:
logDelta, logSpec = spec.interpolate_log()
if kwargs.has_key("smoothres"):
logSpec = logSpec.convolve_profile(kwargs["smoothres"], smallDelta=logDelta)
if kwargs.has_key("smoothrot"):
logSpec = logSpec.convolve_rotation(kwargs["smoothrot"], smallDelta=logDelta)
spec = logSpec
if kwargs.has_key("wave"):
spec = spec.interpolate(kwargs["wave"])
else:
spec = spec.interpolate(wave)
specs.append(spec.flux)
if kwargs.has_key("wave"):
return kwargs["wave"], np.array(specs)
else:
return wave, np.array(specs)
def getSpec(self, *args):
return oned.onedspec(self.wave, self(*args), mode='waveflux')
def getSpecs(specDataDir, fnames, config, **kwargs):
#reading config
waveFName = config.get('wave', 'wave')
specDType = config.get('structure', 'datatype')
specSize = config.getint('structure', 'specsize')
specsize = config.getint('wave', 'islog')
R = config.getfloat('params', 'r')
if kwargs.has_key('smoothres'):
if kwargs['smoothres'] > R:
raise ValueError('requested resolution (R=%f) higher than'
'models intrinsic resolution (R=%f)' %
(kwargs['smoothres'], R))
#Reading wave solution
wave = np.fromfile(os.path.join(specDataDir, waveFName))
if kwargs.has_key('wave'):
gridSize = len(fnames) * len(kwargs['wave'].tostring()) / 1024**2
else:
gridSize = len(fnames) * specSize / 1024**2
print "Processing %d spectra" % len(fnames)
print "Processing %.3f MB in to Memory." % gridSize
specs = []
startTime = time.time()
for i, specFName in enumerate(fnames):
flux = np.fromfile(os.path.join(specDataDir, 'data', specFName))
spec = oned.onedspec(wave, flux, mode='waveflux')
if i % 100 == 0:
print "@%d took %.2f s" % (i, time.time() - startTime)
startTime = time.time()
if kwargs.has_key('normrange'):
normrange = kwargs['normrange']
normFac = np.mean(spec[slice(*normrange)].flux)
spec.flux /= normFac
if kwargs.has_key('smoothres') or kwargs.has_key('smoothrot'):
if kwargs.has_key('wave'):
tmpSpec = spec[float(kwargs['wave'].min()
):float(kwargs['wave'].max())]
logDelta, logSpec = tmpSpec.interpolate_log()
else:
logDelta, logSpec = spec.interpolate_log()
if kwargs.has_key('smoothres'):
logSpec = logSpec.convolve_profile(kwargs['smoothres'],
smallDelta=logDelta)
if kwargs.has_key('smoothrot'):
logSpec = logSpec.convolve_rotation(kwargs['smoothrot'],
smallDelta=logDelta)
spec = logSpec
if kwargs.has_key('wave'):
spec = spec.interpolate(kwargs['wave'])
else:
spec = spec.interpolate(wave)
specs.append(spec.flux)
if kwargs.has_key('wave'):
return kwargs['wave'], np.array(specs)
else:
return wave, np.array(specs)
def getSpec(self, *args):
return oned.onedspec(self.wave, self.interpGrid(args), mode='waveflux')
