def gensubgrid(theta_av):
"""
**Partial function**
Generate the chunk of grid corresponding to a given theta_av
based on the initial grid g0
"""
_args = {}
for e, k in enumerate(kwargs):
if hasattr(theta_av, '__iter__'):
_args[k] = theta_av[e]
else:
_args[k] = theta_av
tau = oAv.function(g0.lamb * 1e-4, Alambda=True, **_args)
outputSEDs = g0.seds * numpy.exp(-tau)[None, :]
#copy original grid
t = Table(g0.grid.data,
header=g0.grid.header,
name=g0.grid.header['NAME'])
for k, v in _args.iteritems():
t.addCol([v] * t.nrows, name=k)
g = grid.SpectralGrid()
g.lamb = g0.lamb[:]
g.seds = outputSEDs
g.grid = t
return g, theta_av
def test_spectralgrid_fit_profiles():
import os
reload(grid)
file_dir = '/d/bip3/ezbc/taurus/data/galfa/'
file_name = 'taurus.galfa.cube.bin.4arcmin.fits'
box = [300,300,325,325]
noise_range = [(-110,-90),(90,110)]
spGrid = grid.SpectralGrid(file_dir + file_name, box = box,
noiseRange = noise_range,
basesubtract = True)
guesses = [40,5,6]
spGrid.fit_profiles(
grow_pos = (312,312),
guesses=guesses,
ncomp=len(guesses)/3,
alpha=0.001,
coords='image',
number_of_fits=10,
verbose=True)
os.system('rm -rf Test')
def test_spectralgrid_init():
file_dir = '/d/bip3/ezbc/taurus/data/galfa/'
file_name = 'taurus.galfa.cube.bin.4arcmin.fits'
box = [300,300,325,325]
noise_range = [(-110,-90),(90,110)]
spGrid = grid.SpectralGrid(file_dir + file_name, box = box,
noiseRange = noise_range,
basesubtract = True)
assert spGrid.cube is not None
def getFakeCluster(g,
age,
Z,
filts,
err=0.1,
suffix='0',
Nstars=None,
oAv=None,
**kwargs):
""" Generate a fake sed from a model grid
INPUTS:
idx int index number on the grid
age float age of the desired population
Z float metallicity of the desired population
filters list[filter] list of filter names
OUTPUTS:
fakein int the index of the model on the grid
lamb ndarray[float, ndim=1] wavelength taken from the grid
fakesed ndarray[float, ndim=1] resulting SED
KEYWORDS:
suffix str suffix used to store the cluster table
err float proportional error to consider on the fluxes
Nstars int number of stars in the population (None = all possible stars)
oAv ExtinctionLaw if provided, apply extinction function using **kwargs
**kwargs if provided, extra keywords are used to apply an extinction
"""
# find all the stars matching age and Z criteria
glogA = numpy.unique(g.logA)
gZ = numpy.unique(g.Z)
_logA = glogA[numpy.argmin(abs(glogA - numpy.log10(age)))]
_Z = gZ[numpy.argmin(abs(gZ - Z))]
idx = numpy.where((g.logA == _logA) & (g.Z == _Z))[0]
# Restrict to Nstars is provided
if Nstars is not None:
idx = numpy.random.randint(0, len(idx), min([len(idx), Nstars]))
fakeseds = g.seds[idx]
# Compute extinction is requested
if (oAv is not None) & (len(kwargs) > 0):
tau = oAv(g.lamb * 1e-4, Alambda=True, **kwargs)
fakeseds *= exp(-tau)[None, :]
## extract photometry
gg = grid.SpectralGrid()
gg.lamb = g.lamb
gg.seds = fakeseds
gg.grid = g.grid[idx]
seds = gg.getSEDs(filts, absFlux=True)
seds.grid.addCol(idx, name='idx')
for k, v in kwargs.iteritems():
seds.grid.header[k] = v
filter_names = []
for ek, nk in enumerate(filts):
fname = nk.name
seds.grid.addCol(seds.seds[:, ek], name=fname)
seds.grid.addCol(seds.seds[:, ek] * err, name=fname + 'err')
filter_names.append(nk.name)
seds.grid.header['logA'] = _logA
seds.grid.header['Z'] = _Z
seds.grid.write('Tests/cl_%s.fits' % suffix)
del seds
obs = observations.Observations('Tests/cl_%s.fits' % suffix)
obs.setFilters(filter_names)
return obs
################################################################################
# Loading cfa grid of persues
################################################################################
import grid
cfa = grid.load_grid('perseus.cfa.138_62.5sigma')
grid.plot_ncompImage(cfa)
box=[0,36,180,180]
reload(grid)
perseus_galfa = grid.SpectralGrid('../galfa/'+\
'perseus.galfa.cube.bin.4arcmin.fits',
box=box,
noiseScale=10.,
noiseRange=((-110,-90),(90,110)),
basesubtract=True)
guesses = [48,5,10]
perseus_galfa.fit_profiles(
growPos = (138,62),
tileSaveFreq=100,
threshold = 1,
#filename='perseus.galfa.138_62.10',
guesses=guesses,
ncomp=len(guesses)/3,
alpha=0.001,
coords='image',
numberOfFits=1e6,
COcube=cfa,
COwidthScale=1.)