# Run the fitting
fit('data_glimpse',
filters,
apertures,
model_dir,
'output.fitinfo',
extinction_law=extinction,
distance_range=[1., 2.] * u.kpc,
av_range=[0., 40.])
# For the remaining commands, we always select the models with chi^2-chi_best^2
# per datapoint less than 3.
select_format = ('F', 3)
# Make SED plots
plot('output.fitinfo', 'plots_seds', plot_max=100, select_format=select_format)
# Make histograms of the disk mass
plot_params_1d('output.fitinfo',
'MDISK',
'plots_mdisk',
log_x=True,
select_format=select_format)
# Make 2-d plots of the envelope infall rate vs disk mass
plot_params_2d('output.fitinfo',
'MDISK',
'MDOT',
'plots_mdot_mdisk',
log_x=True,
log_y=True,
def fit_all_sources(sourcetable):
'''
Wrap around Robitaille's fitting routine
'''
from sedfitter.extinction import Extinction
# load up extinction law as suggested by website; might switch over to other dust files
extinction = Extinction.from_file(SEDfolder+'extinction_law.ascii',columns=[0,1],wav_unit=u.micron,chi_unit=u.cm**2/u.g)
# only fit the isolated and clustered sources
types = sourcetable.group_by('Property')
totsourcetable = vstack([types.groups[0],types.groups[2]])
# list of columns with fluxes to be used to fit
columnlist = ['j','h','ks','i1','i2','i3','i4','F11','F19','m1','F31','F37','m2','S450','S850','H70','H160','H250','H350','H500']
errorlist = ["e_"+col for col in columnlist]
flaglist = ["flag_"+col for col in columnlist if "flag_"+col in totsourcetable.columns]
# set up input table to be fed to sedfitter
# fluxlist = []
# for col in columnlist:
# fluxlist.append(col);fluxlist.append('e_'+col)
# cluster names and distances
fieldlist = Table(names=["CepA","CepC","IRAS20050","NGC2264","NGC1333","NGC7129","Oph","S140","S171","NGC2071"])
fieldlist.add_row([730,730,700,760,240,1000,160,900,850,490])
# for each cluster
clusters = totsourcetable.group_by('Cluster')
for key,group in izip(clusters.groups.keys,clusters.groups):
# isolate source and use only relevant columns
newsourcetable = group[['SOFIA_name','RA','DEC']+columnlist+errorlist+flaglist]
print "Working on cluster ",key['Cluster']
print newsourcetable
# number of sources in that cluster
L = len(newsourcetable)
# for each column
for col in columnlist:
# re-check that there are no negative fluxes that are not masked
for i in range(len(newsourcetable)):
if newsourcetable[col][i]<0:
newsourcetable.mask[col][i] = True
# create the flag table based on mask
newsourcetable["flag"+col] = np.ma.array(~newsourcetable[col].mask).astype(int)
# make sure the errors are flagged as well
newsourcetable['e_'+col].mask = newsourcetable[col].mask
# multiplies the flag by 3 for the Herschel fluxes that we know are taken with a very large aperture
for col in ['H250','H350','H500']:
newsourcetable["flag"+col] *= 3
for col in columnlist:
for i in range(len(newsourcetable)):
# if flag string contains a 'U', then the flux is an upper limit
if 'flag_'+col in newsourcetable.columns:
if 'U' in newsourcetable['flag_'+col][i] and newsourcetable["flag"+col][i] != 3:
newsourcetable["flag"+col][i] *= 3
# convert to mJy
for col in columnlist:
newsourcetable[col] *= 1000.0
newsourcetable['e_'+col] *= 1000.0
# creates the list of columns containing the flags
newflaglist = ["flag"+col for col in columnlist]
# create proper flux;error lists
fluxlist = []
for col in columnlist:
fluxlist.append(col);fluxlist.append('e_'+col)
# creates the table in proper format for feeding to sedfitter
final = newsourcetable[['SOFIA_name','RA','DEC']+newflaglist+fluxlist].filled(-1.0)
final.write(folder_export+key['Cluster']+'_sedfitter.tab',format='ascii')
# remove first line (column headers)
os.system('sed -i -e "1d" %s' % (folder_export+key['Cluster']+'_sedfitter.tab'))
# name of the filter names for use in sedfitter
sednamelist = ['2J','2H','2K','I1','I2','I3','I4','F11','F19','M1','F31','F37','M2','W1','W2','H70','H160','H250','H350','H500']
# list of aperture sizes
apertures = [8,8,8,12,12,12,12,9,9,35,9,9,45,20,40,12,22,22,30,42] *u.arcsec ### CHECK APERTURE SIZES ,12,22,22,30,42
# distance to the current cluster
distance = fieldlist[key['Cluster']][0]
# cleans up the directories before starting
os.system('rm -rf %s' % (folder_export+'plot_'+key['Cluster']))
os.system('rm %s' % (SEDfolder+'output_'+key['Cluster']+'.fitinfo'))
# fitting routine
fit(folder_export+key['Cluster']+'_sedfitter.tab',sednamelist,apertures,
model_dir,SEDfolder+'output_'+key['Cluster']+'.fitinfo',
extinction_law = extinction,
distance_range = [distance*0.8,distance*1.2] * u.pc,
av_range = [0.,10])
print "Generating some plots"
plot(SEDfolder+'output_'+key['Cluster']+'.fitinfo',folder_export+'plot_'+key['Cluster'],select_format=('N',10.))
#plot_params_1d(SEDfolder+'output_'+key['Cluster']+'.fitinfo','MDISK',output_dir=folder_export+'plot_'+key['Cluster']+"/1d",log_x=True,select_format=('F',3.))
print "Extracting the parameters from the fits"
write_parameters(SEDfolder+'output_'+key['Cluster']+'.fitinfo',folder_export+'plot_'+key['Cluster']+'/params.txt',select_format=('N',10.))
write_parameter_ranges(SEDfolder+'output_'+key['Cluster']+'.fitinfo',folder_export+'plot_'+key['Cluster']+'/params_ranges.txt',select_format=('N',10.))
print "Parsing results..."
parse_params_table(folder_export+'plot_'+key['Cluster'])
print "Done parsing results"
from sedfitter.extinction import Extinction
# Define path to models
model_dir = '/Users/cadeadams/Desktop/PythonPrac/models_kurucz'
# Read in extinction law)
extinction = Extinction.from_file('kmh94.par',
columns=[0, 3],
wav_unit=u.micron,
chi_unit=u.cm**2 / u.g)
# Define filters and apertures
filt = [
'2H', '2J', '2K', 'S4', 'S1', 'S2', 'S3', 'WISE1', 'WISE2', 'WISE3',
'WISE4'
]
apertures = [3., 3., 3., 3., 3., 3., 3., 3., 3., 3., 3.] * u.arcsec
# Run the fitting
fit('V380Ori.dat',
filt,
apertures,
model_dir,
'output.fitinfo',
extinction_law=extinction,
distance_range=[1., 2.] * u.kpc,
av_range=[0., 40.])
from sedfitter import plot
plot('output.fitinfo', 'plots_seds')
output_format=select_format)
except Exception as e:
bad.write("%s %s\n" % (nospacename, e))
thisbad = True
print(e)
#info = fitter.fit(source)
#FitInfo.write(info,nospacename+'.sedfit')
if thisbad:
continue
else:
#z = FitInfoFile(outdir+nospacename+'.sedfit',mode="r")
##print(type(z))
#good.append(z)
write_parameters(outfit,
outdir + "/" + nospacename + "_parameters.txt",
select_format=select_format)
write_parameter_ranges(outfit,
outdir + "/" + nospacename +
"_parameters_ranges.txt",
select_format=select_format)
plot(input_fits=outfit,
output_dir=plotdir + nospacename,
format='png',
plot_mode='A',
plot_name=True,
select_format=select_format,
show_convolved=False,
show_sed=True)
bad.close()
labels = []
labels.append(
"Best Fit: A$_{\\rm V}=9.7$, M$_{\\rm disk}=1.4\\times 10^{-4} {\\rm M}_{\\odot}$, $\\dot{{\\rm M}}=0$, T$_{\\rm eff}=4000$, L$= 0.5{\\rm L_{\odot}}$"
)
labels.append(
"w/Optical+M24 data: A$_{\\rm V}\\sim 2$, M$_{\\rm disk}\\sim 10^{-6} {\\rm M}_{\\odot}$, $\dot{{\\rm M}}\\sim 5\\times 10^{-8}$, T$_{\\rm eff}=4000$, L$= 0.25{\\rm L_{\\odot}}$"
)
outfile = data_dir + 'one_source_sedfit_output.info' + ext
plot(outfile,
'plots_seds' + ext,
plot_max=100,
select_format=select_format,
mysources=mysources,
myfilters=myfilters,
y_mode='M',
y_range=(6E-14, 5E-10),
x_mode='M',
x_range=(2E-1, 80),
plot_info=False,
plot_name=False,
mylabels=labels)
ext = "onesource+mips"
labels = []
labels.append(
"Best Fit: A$_{\\rm V}=0$, M$_{\\rm disk}=5\\times 10^{-3} {\\rm M}_{\\odot}$, $\\dot{{\\rm M}}=2\\times 10^{-7}$, T$_{\\rm eff}=4000$, L$= 3.0{\\rm L_{\\odot}}$"
)
labels.append(
"w/Optical+M24 data: A$_{\\rm V}\\sim 2$, M$_{\\rm disk}\\sim 10^{-6} {\\rm M}_{\\odot}$, $\\dot{{\\rm M}}\\sim 5\\times 10^{-8}$, T$_{\\rm eff}=4000$, L$= 0.25{\\rm L_{\\odot}}$"
def fit_all_sources(sourcetable):
'''
Wrap around Robitaille's fitting routine
'''
from sedfitter.extinction import Extinction
# load up extinction law as suggested by website; might switch over to other dust files
extinction = Extinction.from_file(SEDfolder + 'extinction_law.ascii',
columns=[0, 1],
wav_unit=u.micron,
chi_unit=u.cm**2 / u.g)
# only fit the isolated and clustered sources
types = sourcetable.group_by('Property')
totsourcetable = vstack([types.groups[0], types.groups[2]])
# list of columns with fluxes to be used to fit
columnlist = [
'j', 'h', 'ks', 'i1', 'i2', 'i3', 'i4', 'F11', 'F19', 'm1', 'F31',
'F37', 'm2', 'S450', 'S850', 'H70', 'H160', 'H250', 'H350', 'H500'
]
errorlist = ["e_" + col for col in columnlist]
flaglist = [
"flag_" + col for col in columnlist
if "flag_" + col in totsourcetable.columns
]
# set up input table to be fed to sedfitter
# fluxlist = []
# for col in columnlist:
# fluxlist.append(col);fluxlist.append('e_'+col)
# cluster names and distances
fieldlist = Table(names=[
"CepA", "CepC", "IRAS20050", "NGC2264", "NGC1333", "NGC7129", "Oph",
"S140", "S171", "NGC2071"
])
fieldlist.add_row([730, 730, 700, 760, 240, 1000, 160, 900, 850, 490])
# for each cluster
clusters = totsourcetable.group_by('Cluster')
for key, group in izip(clusters.groups.keys, clusters.groups):
# isolate source and use only relevant columns
newsourcetable = group[['SOFIA_name', 'RA', 'DEC'] + columnlist +
errorlist + flaglist]
print "Working on cluster ", key['Cluster']
print newsourcetable
# number of sources in that cluster
L = len(newsourcetable)
# for each column
for col in columnlist:
# re-check that there are no negative fluxes that are not masked
for i in range(len(newsourcetable)):
if newsourcetable[col][i] < 0:
newsourcetable.mask[col][i] = True
# create the flag table based on mask
newsourcetable["flag" + col] = np.ma.array(
~newsourcetable[col].mask).astype(int)
# make sure the errors are flagged as well
newsourcetable['e_' + col].mask = newsourcetable[col].mask
# multiplies the flag by 3 for the Herschel fluxes that we know are taken with a very large aperture
for col in ['H250', 'H350', 'H500']:
newsourcetable["flag" + col] *= 3
for col in columnlist:
for i in range(len(newsourcetable)):
# if flag string contains a 'U', then the flux is an upper limit
if 'flag_' + col in newsourcetable.columns:
if 'U' in newsourcetable[
'flag_' +
col][i] and newsourcetable["flag" + col][i] != 3:
newsourcetable["flag" + col][i] *= 3
# convert to mJy
for col in columnlist:
newsourcetable[col] *= 1000.0
newsourcetable['e_' + col] *= 1000.0
# creates the list of columns containing the flags
newflaglist = ["flag" + col for col in columnlist]
# create proper flux;error lists
fluxlist = []
for col in columnlist:
fluxlist.append(col)
fluxlist.append('e_' + col)
# creates the table in proper format for feeding to sedfitter
final = newsourcetable[['SOFIA_name', 'RA', 'DEC'] + newflaglist +
fluxlist].filled(-1.0)
final.write(folder_export + key['Cluster'] + '_sedfitter.tab',
format='ascii')
# remove first line (column headers)
os.system('sed -i -e "1d" %s' %
(folder_export + key['Cluster'] + '_sedfitter.tab'))
# name of the filter names for use in sedfitter
sednamelist = [
'2J', '2H', '2K', 'I1', 'I2', 'I3', 'I4', 'F11', 'F19', 'M1',
'F31', 'F37', 'M2', 'W1', 'W2', 'H70', 'H160', 'H250', 'H350',
'H500'
]
# list of aperture sizes
apertures = [
8, 8, 8, 12, 12, 12, 12, 9, 9, 35, 9, 9, 45, 20, 40, 12, 22, 22,
30, 42
] * u.arcsec ### CHECK APERTURE SIZES ,12,22,22,30,42
# distance to the current cluster
distance = fieldlist[key['Cluster']][0]
# cleans up the directories before starting
os.system('rm -rf %s' % (folder_export + 'plot_' + key['Cluster']))
os.system('rm %s' %
(SEDfolder + 'output_' + key['Cluster'] + '.fitinfo'))
# fitting routine
fit(folder_export + key['Cluster'] + '_sedfitter.tab',
sednamelist,
apertures,
model_dir,
SEDfolder + 'output_' + key['Cluster'] + '.fitinfo',
extinction_law=extinction,
distance_range=[distance * 0.8, distance * 1.2] * u.pc,
av_range=[0., 10])
print "Generating some plots"
plot(SEDfolder + 'output_' + key['Cluster'] + '.fitinfo',
folder_export + 'plot_' + key['Cluster'],
select_format=('N', 10.))
#plot_params_1d(SEDfolder+'output_'+key['Cluster']+'.fitinfo','MDISK',output_dir=folder_export+'plot_'+key['Cluster']+"/1d",log_x=True,select_format=('F',3.))
print "Extracting the parameters from the fits"
write_parameters(SEDfolder + 'output_' + key['Cluster'] + '.fitinfo',
folder_export + 'plot_' + key['Cluster'] +
'/params.txt',
select_format=('N', 10.))
write_parameter_ranges(
SEDfolder + 'output_' + key['Cluster'] + '.fitinfo',
folder_export + 'plot_' + key['Cluster'] + '/params_ranges.txt',
select_format=('N', 10.))
print "Parsing results..."
parse_params_table(folder_export + 'plot_' + key['Cluster'])
print "Done parsing results"
filters = ['2J', '2H', '2K', 'I1', 'I2', 'I3', 'I4']
apertures = [3., 3., 3., 3., 3., 3., 3.] * u.arcsec
# Run the fitting
fit('data_glimpse', filters, apertures, model_dir,
'output.fitinfo',
extinction_law=extinction,
distance_range=[1., 2.] * u.kpc,
av_range=[0., 40.])
# For the remaining commands, we always select the models with chi^2-chi_best^2
# per datapoint less than 3.
select_format = ('F', 3)
# Make SED plots
plot('output.fitinfo', 'plots_seds', plot_max=100, select_format=select_format)
# Make histograms of the disk mass
plot_params_1d('output.fitinfo', 'MDISK', 'plots_mdisk',
log_x=True, select_format=select_format)
# Make 2-d plots of the envelope infall rate vs disk mass
plot_params_2d('output.fitinfo', 'MDISK', 'MDOT', 'plots_mdot_mdisk',
log_x=True, log_y=True, select_format=select_format)
# Write out all models with a delta chi^2-chi_best^2 per datapoint < 3
write_parameters('output.fitinfo', 'parameters.txt',
select_format=select_format)
# Write out the min/max ranges corresponding to the above file
write_parameter_ranges('output.fitinfo', 'parameter_ranges.txt',