def make_grid():
from copy import deepcopy
simpar = deepcopy(simParams)
simpar['FileName'] = 'boss_grid_sim'
simpar['GridParams']['GridType'] = 'FluxRedshiftGrid'
del simpar['GridParams']['QLFmodel']
del simpar['GridParams']['QLFargs']
simpar['GridParams']['mRange'] += (30, )
simpar['GridParams']['zRange'] += (20, )
simpar['GridParams']['nPerBin'] = 100
simpar['ForestParams']['NumLinesOfSight'] = 1000
simpar['ForestParams']['FileName'] = 'boss_grid_forest'
qsoSimulation(simpar, verbose=5)
def make_grid():
from copy import deepcopy
simpar = deepcopy(simParams)
simpar['FileName'] = 'boss_grid_sim'
simpar['GridParams']['GridType'] = 'FluxRedshiftGrid'
del simpar['GridParams']['QLFmodel']
del simpar['GridParams']['QLFargs']
simpar['GridParams']['mRange'] += (30,)
simpar['GridParams']['zRange'] += (20,)
simpar['GridParams']['nPerBin'] = 100
simpar['ForestParams']['NumLinesOfSight'] = 1000
simpar['ForestParams']['FileName'] = 'boss_grid_forest'
qsoSimulation(simpar,verbose=5)
from simqso import qsoSimulation from QLFz4.simulations.sdsssims import simpars # (1) Train Classifier # Input) Training Data Set # Output) .pkl of sklearn classifier # list of feature columns used by classifier # This step is currently manual # (2) Simulate quasars and their classifiers using simqso # Input) Simulation paramter file: (SED model, flux/redshift distribution) # Output) set of quasars (M, z, ugriz) # !python sdsssims.py qsoSimulation(simpars, noPhotoMap=True, saveSpectra=True, writeFeatures=True) # (3) Add variability params to simulated quasars # Input) Output from simqso + Training set + list of feature columns used by classifier # Ouput) Set of simulated quasars (M, z, ugriz, variability) # 4) Run simulated quasars though classifier # Input) Set of simulated quasars (M, z, ugriz, variability) # Ouput) completeness grid: fraction(M, z) # 5) Run candidates through classifier # Input) candidates # Output) N(M, z) # 6) "QuasarSurvey" # Input) N(M, z), fraction(M, z), m2M/M2m, area
# (1) Train Classifier # Input) Training Data Set # Output) .pkl of sklearn classifier # list of feature columns used by classifier # This step is currently manual # (2) Simulate quasars and their classifiers using simqso # Input) Simulation paramter file: (SED model, flux/redshift distribution) # Output) set of quasars (M, z, ugriz) # !python sdsssims.py qsoSimulation(simpars, noPhotoMap=True, saveSpectra=True, writeFeatures=True) # (3) Add variability params to simulated quasars # Input) Output from simqso + Training set + list of feature columns used by classifier # Ouput) Set of simulated quasars (M, z, ugriz, variability) # 4) Run simulated quasars though classifier # Input) Set of simulated quasars (M, z, ugriz, variability) # Ouput) completeness grid: fraction(M, z) # 5) Run candidates through classifier # Input) candidates # Output) N(M, z)
p['QuasarModelParams'] = copy(quasarmodel) p['PhotoMapParams'] = cfhtls_photo # this is a bit of a hack, but because each survey shares a random seed, # as long as the shape of the luminosity and flux grids are the same, # the random sampling of the redshifts should be identical, and thus # the forest model can be shared and only generated once for each survey p['RandomSeed'] = uint32(hash(survey)) pprint.pprint(p) return p if __name__=='__main__': #survey,gridtype = sys.argv[1:3] #try: # extraargs = sys.argv[3:] #except: # extraargs = () survey = 'CFHTLS_Wide' gridtype = 'flux' quasarmodel = fiducial_linetweak_model forestscale = None p = get_params(survey,gridtype,quasarmodel)#,forestscale,extraargs) if survey=='test': simdir = './' qsoSimulation(p, # noPhotoMap=True, # saveSpectra=True, writeFeatures=True, # forestOnly=True, outputDir=simdir)
'IronEmissionParams': {
'FeScalings': [(0, 1540, 0.5), (1540, 1680, 2.0), (1680, 1868, 1.6),
(1868, 2140, 1.0), (2140, 3500, 1.0)],
},
'DustExtinctionParams': {
'DustExtinctionModel': 'Exponential E(B-V) Distribution',
'DustModelName': 'SMC',
'E(B-V)': 0.033,
#'DustLOSfraction':1.0,
},
}
def make_grid():
from copy import deepcopy
simpar = deepcopy(simParams)
simpar['FileName'] = 'boss_grid_sim'
simpar['GridParams']['GridType'] = 'FluxRedshiftGrid'
del simpar['GridParams']['QLFmodel']
del simpar['GridParams']['QLFargs']
simpar['GridParams']['mRange'] += (30, )
simpar['GridParams']['zRange'] += (20, )
simpar['GridParams']['nPerBin'] = 100
simpar['ForestParams']['NumLinesOfSight'] = 1000
simpar['ForestParams']['FileName'] = 'boss_grid_forest'
qsoSimulation(simpar, verbose=5)
if __name__ == '__main__':
qsoSimulation(simParams)
p['PhotoMapParams'] = cfhtls_photo
# this is a bit of a hack, but because each survey shares a random seed,
# as long as the shape of the luminosity and flux grids are the same,
# the random sampling of the redshifts should be identical, and thus
# the forest model can be shared and only generated once for each survey
p['RandomSeed'] = uint32(hash(survey))
pprint.pprint(p)
return p
if __name__ == '__main__':
#survey,gridtype = sys.argv[1:3]
#try:
# extraargs = sys.argv[3:]
#except:
# extraargs = ()
survey = 'CFHTLS_Wide'
gridtype = 'flux'
quasarmodel = fiducial_linetweak_model
forestscale = None
p = get_params(survey, gridtype, quasarmodel) #,forestscale,extraargs)
if survey == 'test':
simdir = './'
qsoSimulation(
p,
# noPhotoMap=True,
# saveSpectra=True,
writeFeatures=True,
# forestOnly=True,
outputDir=simdir)
# 'FileName':name+'_forest',
'ForestModel':'Worseck&Prochaska2011',
'ForestType':'Sightlines', # 'Grid' 'OneToOne'
'zRange':(0.0,4.5),
'NumLinesOfSight':2000,
'Rmin':30000.,
},
'PhotoMapParams':{
'PhotoSystems':[
('SDSS','Legacy'),
('UKIRT','UKIDSS_LAS'),
]
},
}
if __name__=='__main__':
import sys
survey,gridType = sys.argv[1:]
simpars['FileName'] = survey+'_'+gridType
simpars['GridParams'] = grids[survey+'_'+gridType]
if gridType == 'LF':
simpars['ForestParams']['FileName'] = survey+'_LF_forest'
else:
simpars['ForestParams']['FileName'] = survey+'_grid_forest'
# makes the lum and flux grids use the same forest
simpars['RandomSeed'] = uint32(hash(survey))
simpars['QuasarModelParams'] = fiducial_linetweak_model
qsoSimulation(simpars,writeFeatures=True)#,forestOnly=True)
'IronEmissionParams':{
'FeScalings':[(0,1540,0.5),(1540,1680,2.0),(1680,1868,1.6),
(1868,2140,1.0),(2140,3500,1.0)],
},
'DustExtinctionParams':{
'DustExtinctionModel':'Exponential E(B-V) Distribution',
'DustModelName':'SMC',
'E(B-V)':0.033,
#'DustLOSfraction':1.0,
},
}
def make_grid():
from copy import deepcopy
simpar = deepcopy(simParams)
simpar['FileName'] = 'boss_grid_sim'
simpar['GridParams']['GridType'] = 'FluxRedshiftGrid'
del simpar['GridParams']['QLFmodel']
del simpar['GridParams']['QLFargs']
simpar['GridParams']['mRange'] += (30,)
simpar['GridParams']['zRange'] += (20,)
simpar['GridParams']['nPerBin'] = 100
simpar['ForestParams']['NumLinesOfSight'] = 1000
simpar['ForestParams']['FileName'] = 'boss_grid_forest'
qsoSimulation(simpar,verbose=5)
if __name__=='__main__':
qsoSimulation(simParams)