def _setup_mockparams_densfunc(type,sample):
"""Return the parameters of the mock density for this type"""
if type.lower() == 'exp':
if sample.lower() == 'lowlow':
return [0.,1./0.3]
elif sample.lower() == 'solar':
return [1./3.,1./0.3]
else:
return [1./3.,1./0.3]
elif type.lower() == 'expplusconst':
if sample.lower() == 'lowlow':
return [0.,1./0.3,numpy.log(0.1)]
else:
return [1./3.,1./0.3,numpy.log(0.1)]
elif type.lower() == 'twoexp':
return [1./3.,1./0.3,1./4.,1./0.5,densprofiles.logit(0.5)]
elif type.lower() == 'brokenexp':
if sample.lower() == 'lowlow':
return [-0.2,1./.3,0.2,numpy.log(11.)]
elif sample.lower() == 'solar':
return [-1./6.,1./0.3,1./2.,numpy.log(8.)]
else:
return [-1./6.,1./0.3,1./2.,numpy.log(6.)]
elif type.lower() == 'brokenexpflare':
if sample.lower() == 'lowlow':
return [-0.2,1./.3,0.2,numpy.log(11.),-0.1]
elif sample.lower() == 'solar':
return [-1./6.,1./0.3,1./2.,numpy.log(8.),-0.1]
else:
return [-1./6.,1./0.3,1./2.,numpy.log(6.),-0.1]
elif type.lower() == 'gaussexp':
if sample.lower() == 'lowlow':
return [.4,1./0.3,numpy.log(11.)]
else:
return [1./3.,1./0.3,numpy.log(10.)]
def _setup_mockparams_densfunc(type, sample):
"""Return the parameters of the mock density for this type"""
if type.lower() == 'exp':
if sample.lower() == 'lowlow':
return [0., 1. / 0.3]
elif sample.lower() == 'solar':
return [1. / 3., 1. / 0.3]
else:
return [1. / 3., 1. / 0.3]
elif type.lower() == 'expplusconst':
if sample.lower() == 'lowlow':
return [0., 1. / 0.3, numpy.log(0.1)]
else:
return [1. / 3., 1. / 0.3, numpy.log(0.1)]
elif type.lower() == 'twoexp':
return [1. / 3., 1. / 0.3, 1. / 4., 1. / 0.5, densprofiles.logit(0.5)]
elif type.lower() == 'brokenexp':
if sample.lower() == 'lowlow':
return [-0.2, 1. / .3, 0.2, numpy.log(11.)]
elif sample.lower() == 'solar':
return [-1. / 6., 1. / 0.3, 1. / 2., numpy.log(8.)]
else:
return [-1. / 6., 1. / 0.3, 1. / 2., numpy.log(6.)]
elif type.lower() == 'brokenexpflare':
if sample.lower() == 'lowlow':
return [-0.2, 1. / .3, 0.2, numpy.log(11.), -0.1]
elif sample.lower() == 'solar':
return [-1. / 6., 1. / 0.3, 1. / 2., numpy.log(8.), -0.1]
else:
return [-1. / 6., 1. / 0.3, 1. / 2., numpy.log(6.), -0.1]
elif type.lower() == 'gaussexp':
if sample.lower() == 'lowlow':
return [.4, 1. / 0.3, numpy.log(11.)]
else:
return [1. / 3., 1. / 0.3, numpy.log(10.)]
def _setup_initparams_densfunc(type,data):
"""Return the initial parameters of the density for this type, might depend on the data"""
if type.lower() == 'exp':
return [1./3.,1./0.3]
elif type.lower() == 'expplusconst':
return [1./3.,1./0.3,numpy.log(0.1)]
elif type.lower() == 'twoexp':
return [1./3.,1./0.3,1./4.,1./0.5,densprofiles.logit(0.5)]
elif type.lower() == 'brokenexp':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H']))]
elif type.lower() == 'tribrokenexp':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H']))]
elif type.lower() == 'symbrokenexp':
return [0.4,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenexpflare':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
-1./5.]
elif type.lower() == 'tribrokenexpflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
-1./5.]
elif type.lower() == 'tribrokenexpfixedflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H']))]
elif type.lower() == 'brokentwoexp':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
densprofiles.logit(0.5),1./0.8]
elif type.lower() == 'brokentwoexpflare':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
densprofiles.logit(0.5),1./0.8,-0.2]
elif type.lower() == 'tribrokentwoexp':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
densprofiles.logit(0.5),1./0.8]
elif type.lower() == 'gaussexp':
return [1./3.,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenquadexp':
return [1./3.,1./0.3,1./3.,numpy.log(10.)]
elif type.lower() == 'symbrokenquadexp':
return [1./3.,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenexpfixedspiral':
return [1./6.,1./0.3,1./2.,numpy.log(14.),numpy.log(1.)]
elif type.lower() == 'tribrokenexplinflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
0.]
elif type.lower() == 'tribrokenexpinvlinflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data['RC_GALR_H'])),
-1./5.]
def _setup_initparams_densfunc(type,data, pos_keys=['RC_GALR_H', 'RC_GALPHI_H', 'RC_GALZ_H']):
"""Return the initial parameters of the density for this type, might depend on the data"""
if type.lower() == 'exp':
return [1./3.,1./0.3]
elif type.lower() == 'expplusconst':
return [1./3.,1./0.3,numpy.log(0.1)]
elif type.lower() == 'twoexp':
return [1./3.,1./0.3,1./4.,1./0.5,densprofiles.logit(0.5)]
elif type.lower() == 'brokenexp':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]]))]
elif type.lower() == 'tribrokenexp':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]]))]
elif type.lower() == 'symbrokenexp':
return [0.4,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenexpflare':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
-1./5.]
elif type.lower() == 'tribrokenexpflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
-1./5.]
elif type.lower() == 'tribrokenexpfixedflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]]))]
elif type.lower() == 'brokentwoexp':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
densprofiles.logit(0.5),1./0.8]
elif type.lower() == 'brokentwoexpflare':
return [-1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
densprofiles.logit(0.5),1./0.8,-0.2]
elif type.lower() == 'tribrokentwoexp':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
densprofiles.logit(0.5),1./0.8]
elif type.lower() == 'gaussexp':
return [1./3.,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenquadexp':
return [1./3.,1./0.3,1./3.,numpy.log(10.)]
elif type.lower() == 'symbrokenquadexp':
return [1./3.,1./0.3,numpy.log(10.)]
elif type.lower() == 'brokenexpfixedspiral':
return [1./6.,1./0.3,1./2.,numpy.log(14.),numpy.log(1.)]
elif type.lower() == 'tribrokenexplinflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
0.]
elif type.lower() == 'tribrokenexpinvlinflare':
return [1./3.,1./0.3,1./3.,numpy.log(numpy.median(data[pos_keys[0]])),
-1./5.]
