def bolometric_correction(self, component = 'tot', filternamelist = None):
lightspeed = 2.998e18 #AA/s
filterlist = observate.load_filters(filternamelist)
mags = observate.getSED(self.spectrum[component]['wavelength'],
self.spectrum[component]['f_lambda_int'],
filterlist)
nueff = np.array([lightspeed/f.wave_effective for f in filterlist])
#log_nuLnu =
log_bc = self.spectrum[component]['log_Lbol'] - log_nuLnu
return bc
imnamelist = ['mips_24.6arcsec.Jypix','pacs_70.6arcsec.Jypix',
'pacs_100.6arcsec.Jypix','pacs_160.6arcsec.Jypix',
'spire_250.6arcsec.Jypix']
errnamelist = ['x','pacs_70.6arcsec.sig','pacs_100.6arcsec.sig',
'pacs_160.6arcsec.sig','x']
rp['fudge_err'] = [0.1,0.1,0.1,0.1,0.15] #errors to use when true error images don't exist
rp['impath'] = os.getenv('pyxydust')+'/imdata/NGC6822'
rp['imnamelist'] = ['{0}_conv250_{1}.fits'.format(rp['impath'],name) for name in imnamelist]
rp['errnamelist'] = ['{0}_{1}.fits'.format(rp['impath'],name) for name in errnamelist]
##### load the filters and the DL07 model grid
dl07 = dustmodel.DraineLi()
filterlist = observate.load_filters(rp['fnamelist'])
############### END USER INPUT ##########
############### MAIN ####################
#do it this way so the main function can come first
def main(rp):
print('starting main')
##### read the images and errors
data_mag, data_magerr, header = datacube.loadImageCube(rp['imnamelist'],rp['errnamelist'],rp['fudge_err'])
dm = 5.0*np.log10(rp['dist'])+25
data_mag = np.where(data_mag != 0.0, data_mag-dm, 0.0)
nx, ny = data_mag.shape[0], data_mag.shape[1]
dust = attenuation.Attenuator(dust_type = rp['dust_type']) fitter = sf.StarfitterGrid(rp) #set up priors in the grid fitter.initialize_grid(params = None) fitter.set_params_from_isochrone(Z = [0.0077]) fitter.stargrid.pars['R_V'] = 3.0 #fix extra dust parameters (should marginalize over, but requires thought about prior) fitter.stargrid.pars['F_BUMP'] = 0.5 #fix extra dust parameters (should marginalize over, but requires thought about prior) fitter.build_grid(attenuator = dust) ## Set up for predicting the emission in any band ## Do this after running fitter.initialize_grid() but before ## running fitter.fit_image() pred_fnamelist = ['galex_NUV', 'galex_FUV'] pred_filt = observate.load_filters(pred_fnamelist) prediction_sed, tmp1, tmp2 = fitter.basel.generateSEDs(fitter.stargrid.pars, pred_filt,attenuator = dust, wave_min = 92, wave_max = 1e7) #for i in xrange(len(pred_filt)-1) : fitter.stargrid.add_par(prediction_sed[:,0] + 5.0*np.log10(rp['dist'])+25,pred_filt[0].name) fitter.rp['outparnames']+= [pred_filt[0].name] fitter.load_data() #Chhange the number of lines to extract #fitter.rp['nlines'] = 1e4 #fitter.rp['lines'] =None #fitter.load_data() fitter.fit_image() fitter.write_catalog(outparlist = ['galex_NUV', 'LOGT','LOGL', 'A_V'])
#Example hrdspy usage. determine dispersion in integrated spectrum
#as a function of wavelength for a given mass and age.
import numpy as np
import starmodel
import isochrone
import observate
import cluster
import matplotlib.pyplot as pl
import time
# choose a few filters and load them
filternamelist = ['ctio_mosaic_ii_Uj','ctio_mosaic_ii_B','ctio_mosaic_ii_V',
'ctio_mosaic_ii_Rc','ctio_mosaic_ii_Ic']
filterlist = observate.load_filters(filternamelist)
j, k = 1, 3 #color to plot
# instantiate and load the isochrones and spectra first so you
# don't have to do it for each cluster realization
isoc=isochrone.Padova2007()
isoc.load_all_isoc()
speclib = starmodel.BaSeL3()
speclib.read_all_Z()
# set cluster parameters
Z = 0.0076 #LMC metallicity
mtot = 1e3 #1000 solar masses
logage = np.arange(7.2,9.2,0.2)
nreal = 10 #10 realizations of the cluster
