def MAKE_model_dictionary(cat, filters, clobbermodel=False):
"""
Create model dictionary for all redshifts in z-array and filters
input
cat - catalog settings
filters - filter settings
clobbermodel - remove any existing dictionary (default - False)
ouput
modelsdict
"""
t0= time.time()
if clobbermodel and os.path.lexists(cat['dict_path']):
print "removing model dictionary "+cat['dict_path']
os.system('rm -rf '+ cat['dict_path'])
if not os.path.lexists(cat['dict_path']):
MODELFILES.construct(cat['path'])
mydict = MODELSDICT(cat['dict_path'], cat['path'], filters)
mydict.build()
print '_____________________________________________________'
print 'For this dictionary creation %.2g min elapsed'% ((time.time() - t0)/60.)
Modelsdict = cPickle.load(file(cat['dict_path'], 'rb'))
return Modelsdict
def MAKE_model_dictionary(cat, filters, clobbermodel=False):
"""
Create model dictionary for all redshifts in z-array and filters
input
cat - catalog settings
filters - filter settings
clobbermodel - remove any existing dictionary (default - False)
ouput
modelsdict
"""
t0 = time.time()
if clobbermodel and os.path.lexists(cat['dict_path']):
print "removing model dictionary " + cat['dict_path']
os.system('rm -rf ' + cat['dict_path'])
if not os.path.lexists(cat['dict_path']):
MODELFILES.construct(cat['path'])
mydict = MODELSDICT(cat['dict_path'], cat['path'], filters)
mydict.build()
print '_____________________________________________________'
print 'For this dictionary creation %.2g min elapsed' % (
(time.time() - t0) / 60.)
Modelsdict = cPickle.load(file(cat['dict_path'], 'rb'))
return Modelsdict
def RUN_AGNfitter_onesource_independent(cat, line, data_obj, filtersz, clobbermodel=False):
"""
Main function for fitting a single source in line 'line' and create it's modelsdict independently.
"""
mc = MCMC_settings()
out = OUTPUT_settings()
data = DATA(data_obj,line)
print ''
print 'Fitting sources from catalog: ', data.catalog
print '- Sourceline: ', line
print '- Sourcename: ', data.name
## 0. CONSTRUCT DICTIONARY for this redshift
t0= time.time()
# needs a list/array of z
filtersz['dict_zarray'] = [data.z]
# save the dictionary for this source in the OUTPUT folder for this source
# create this source output folder if it doesn't exist
if not os.path.lexists(cat['output_folder'] +'/'+str(data.name)):
os.system('mkdir -p ' + cat['output_folder'] +'/'+str(data.name))
dictz = cat['output_folder'] +'/'+str(data.name) +'/MODELSDICT_' + str(data.name)
# remove this source modelsdict if it already exists and we want to remove it
if clobbermodel and os.path.lexists(dictz):
os.system('rm -rf '+dictz)
print "removing source model dictionary "+dictz
if not os.path.lexists(dictz):
zdict = MODELSDICT(dictz, cat['path'], filtersz)
Modelsdictz = zdict.build(save=False)
print '_____________________________________________________'
print 'For this dictionary creation %.2g min elapsed'% ((time.time() - t0)/60.)
else:
Modelsdictz = cPickle.load(file(dictz, 'rb'))
data.DICTS(filtersz, Modelsdictz)
P = parspace.Pdict (data) # Dictionary with all parameter space especifications.
# From PARAMETERSPACE_AGNfitter.py
t1= time.time()
MCMC_AGNfitter.main(data, P, mc)
print 'fitting took %.2g min'% ((time.time() - t1)/60.)
t2= time.time()
PLOTandWRITE_AGNfitter.main(data, P, out)
print 'plotting took %.2g min '% ((time.time() - t2)/60.)
print '_____________________________________________________'
print 'Processing this source took %.2g min '% ((time.time() - t0)/60.)
return
def RUN_AGNfitter_onesource_independent( line, data_obj, filtersz, models, clobbermodel=False):
"""
Main function for fitting a single source in line and create it's modelsdict independently.
"""
mc = MCMC_settings()
out = OUTPUT_settings()
data = DATA(data_obj,line)
print ''
print '________________________'
print 'Fitting sources from catalog: ', data.catalog
print '- Sourceline: ', line
print '- Sourcename: ', data.name
## 0. CONSTRUCT DICTIONARY for this redshift
t0= time.time()
## needs a list/array of z
filtersz['dict_zarray'] = [data.z]
## save the dictionary for this source in the OUTPUT folder for this source
## create this source output folder if it doesn't exist
if not os.path.lexists(cat['output_folder']+str(data.name)):
os.system('mkdir -p ' + cat['output_folder'] +str(data.name))
dictz = cat['output_folder'] +str(data.name) +'/MODELSDICT_' + str(data.name)
## remove this source modelsdict if it already exists and we want to remove it
if clobbermodel and os.path.lexists(dictz):
os.system('rm -rf '+dictz)
print "removing source model dictionary "+dictz
if not os.path.lexists(dictz):
zdict = MODELSDICT(dictz, cat['path'], filtersz, models)
zdict.build()
f = open(zdict.filename, 'wb')
cPickle.dump(zdict, f, protocol=2)
f.close()
print '_____________________________________________________'
print 'For this dictionary creation %.2g min elapsed'% ((time.time() - t0)/60.)
else:
zdict = cPickle.load(file(dictz, 'rb'))
Modelsdictz = zdict.MD
data.DICTS(filtersz, Modelsdictz)
P = parspace.Pdict (data) # Dictionary with all parameter space especifications.
# From PARAMETERSPACE_AGNfitter.py
t1= time.time()
MCMC_AGNfitter.main(data, P, mc)
PLOTandWRITE_AGNfitter.main(data, P, out, models)
print '_____________________________________________________'
print 'For this fit %.2g min elapsed'% ((time.time() - t1)/60.)
return
def MAKE_model_dictionary(cat, filters, models, clobbermodel=False):
"""
Create model dictionary for all redshifts in z-array and filters
input
cat - catalog settings
filters - filter settings
clobbermodel - remove any existing dictionary (default - False)
ouput
modelsdict
"""
t0= time.time()
modelsdict_name = cat['path']+models['path']+ filters['filterset']+models['modelset']
if clobbermodel and os.path.lexists(modelsdict_name) : ## If overwriting of model dictionary mode is on
print "> Overwriting model dictionary (-o) "+modelsdict_name
print '_______________________'
os.system('rm -rf '+ modelsdict_name)
if not os.path.lexists(modelsdict_name): ## If model dictionary does not exist yet
print '> Constructing new MODELS DICTIONARY:'
print 'SAVED AS : ', modelsdict_name
print 'FILTERSET USED : ', filters['filterset']
print '________________________'
print 'This process might take some time but you have to do it only once.'
print 'If you interrupt it, please trash the empty file created.'
print ''
mydict = MODELSDICT( modelsdict_name, cat['path'], filters, models)
mydict.build()
f = open(mydict.filename, 'wb')
cPickle.dump(mydict, f, protocol=2)
f.close()
print '________________________'
print 'The models dictionary ' + modelsdict_name +' has been created.'\
'(%.2g min elapsed)'% ((time.time() - t0)/60.)
else: ## If model dictionary exists and you want to reuseit
mydict = cPickle.load(file(modelsdict_name, 'rb'))
print '________________________'
print 'MODELS DICTIONARY currently in use:'
print 'SAVED AS : ', mydict.filename
print 'FILTERSET USED : ', mydict.filterset_name
test_settingschanges= [mydict.filters_list[i]==filters[i] for i in filters.keys() if type(filters[i]) is BooleanType]
if False in test_settingschanges : ## compare nr of data bands with n of model filters
print '________________________'
print '*** WARNING ***'
print 'You have changed entries in your FILTER_settings without updating the filterset name.'
print 'The MODEL DICTIONARY will NOT be updated correctly.'
print ''
py3 = sys.version_info[0] > 2 #creates boolean value for test that Python major version > 2
if py3:
response = input(">> You want to continue with the old model dictionary nevertheless? (yes/no)")
else:
response = raw_input(">> You want to continue with the old model dictionary nevertheless? (yes/no)")
while True:
if response== 'no':
sys.exit('\nTo update the MODEL DICTIONARY , you have these options: \n\
* change the filterset name (filters["filterset"]), or \n\
* run the code in model-overwriting mode (-o)')
elif response== 'yes':
print '________________________'
print 'MODELS DICTIONARY currently in use:'
print 'SAVED AS : ', mydict.filename
print 'FILTERSET USED : ', mydict.filterset_name
break
else:
print "Please answer (yes/no)"
break
Modelsdict = mydict.MD ## MD is the model dictionary saved as a class of the method MODELSDICT
bands_in_cat =len(cat['freq/wl_list'])
bands_in_dict = len(Modelsdict[Modelsdict.keys()[0]][1][Modelsdict[Modelsdict.keys()[0]][1].keys()[0]][0])
if bands_in_cat!= bands_in_dict :
sys.exit('________________________\n*** ERROR ***\n'+ \
'Number of bands in catalog ('+str(bands_in_cat)+'), does not match the numbers of filters in models (' + str(bands_in_dict)+')\n'+\
'This will produce a mismatched fitting. Make sure the filterset contains only/all the photometric bands corresponding your catalog.\n'+ \
'They do NOT need to be sorted in the same order.')
return Modelsdict
