def __init__(self,
surveyName,
fieldName,
prange=False,
cosmo=FLARE.default_cosmo(),
SPS=False,
verbose=False,
make_plots=False):
self.SimType = 'idealised'
self.surveyName = surveyName
self.fieldName = fieldName
self.prange = prange
self.cosmo = cosmo
self.verbose = verbose
self.make_plots = make_plots
self.Field = FLARE.surveys.surveys[self.surveyName].fields[
self.fieldName]
self.Filters = self.Field.filters
self.Backgrounds = FLARE.obs.FieldBackgrounds(
self.Field, verbose=self.verbose) # --- create Background object
self.SPS = SPS # necessary for SED generating if not \beta model
def __init__(self,
Filters,
prange,
cosmo=FLARE.default_cosmo(),
SPS=False,
verbose=False,
SimType='const'):
self.SimType = SimType
self.Filters = Filters
self.prange = prange
self.cosmo = cosmo
self.verbose = verbose
self.SPS = SPS # necessary for SED generating if not \beta model
def __init__(self,
DepthModel,
ReferenceFilter,
Filters,
prange,
cosmo=FLARE.default_cosmo(),
SPS=False,
verbose=False):
self.DepthModel = DepthModel
self.depths = FLARE.surveys.depthmodel[self.DepthModel]
self.ReferenceFilter = ReferenceFilter
self.Filters = Filters
self.prange = prange
self.cosmo = cosmo
self.verbose = verbose
self.SPS = SPS # necessary for SED generating if not \beta model
def __init__(self,
surveyName,
fieldName,
DetectionFilters,
prange=False,
cosmo=FLARE.default_cosmo(),
CutoutWidth=101,
threshold=2.5,
npixels=5,
SPS=False,
verbose=False,
make_plots=False):
self.SimType = 'idealised'
self.surveyName = surveyName
self.fieldName = fieldName
self.DetectionFilters = DetectionFilters
self.prange = prange
self.cosmo = cosmo
self.CutoutWidth = CutoutWidth
self.threshold = threshold
self.npixels = npixels
self.verbose = verbose
self.make_plots = make_plots
self.Field = FLARE.surveys.surveys[self.surveyName].fields[
self.fieldName]
self.Filters = self.Field.filters
self.Backgrounds = FLARE.obs.FieldBackgrounds(
self.Field, verbose=self.verbose) # --- create Background object
# --- define PSF
TargetPSF = PSF.PSF(self.Filters[-1])
self.PSFs = {f: TargetPSF
for f in self.Filters
} # force all to use the PSF of the last filter
self.SPS = SPS # necessary for SED generating if not \beta model
def __init__(self,
surveyName,
fieldName,
detection_criteria,
prange=False,
cosmo=FLARE.default_cosmo(),
SPS=False,
verbose=False,
make_plots=False):
self.SimType = 'simple'
self.surveyName = surveyName
self.fieldName = fieldName
self.prange = prange
self.cosmo = cosmo
self.verbose = verbose
self.make_plots = make_plots
self.Field = FLARE.surveys.surveys[self.surveyName].fields[
self.fieldName]
self.Filters = self.Field.filters
self.SPS = SPS # necessary for SED generating if not \beta model
self.depth = {
f: FLARE.photom.m_to_flux(self.Field.depths[f])
for f in self.Filters
}
self.detection_filter = detection_criteria[0]
self.detection_snr = detection_criteria[1]
self.detection_stretch = detection_criteria[2]
if verbose:
for f in self.Filters:
print(f'{f} {self.depth[f]:.2f}')
model.create_Lnu_grid(F) # --- create new L grid for each filter
Lnu = models.generate_Lnu(model, test.Masses, test.Ages, test.Metallicities, test.MetSurfaceDensities, F) # --- calculate rest-frame L uminosity
for f in F['filters']:
print(f, Lnu[f])
# --- create observed frame fluxes
z = 8.
F = FLARE.filters.add_filters(FLARE.filters.NIRCam_W, new_lam = model.lam * (1. + z))
cosmo = FLARE.default_cosmo()
model.create_Fnu_grid(F, z, cosmo) # --- create new Fnu grid for each filter
Fnu = models.generate_Fnu(model, test.Masses, test.Ages, test.Metallicities, test.MetSurfaceDensities, F) # --- calculate rest-frame L uminosity
for f in F['filters']:
print(f, Fnu[f])
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
import FLARE
import FLARE.LF
from FLARE.LF import evo, lf_parameters, LF_plots, completeness
from FLARE.photom import m_to_flux
cosmo = FLARE.default_cosmo() # WMAP9
f_limit_deep = FLARE.photom.m_to_flux(26.)
area = 60.*60.*40.
print('area of Euclid deep: {0} arcmin2'.format(area))
# load in lf parameters
m = getattr(lf_parameters, 'FLARES')()
evo_model = evo.linear(m) # initialises the linear evolution model with the FLARES parameters
bin_edges, bin_centres, N = evo_model.N(cosmo = cosmo, redshift_limits = [8., 15.], log10L_limits = [27., 30.], dz = 0.1, dlog10L = 0.01)
c = completeness.completeness_erf(bin_centres, f_limit_deep) # calculates the completeness with a flux limit
# model_definitions = {'SPS': 'BPASSv2.2.1.binary', 'IMF': 'ModSalpeter_300', 'SFZH': 'constant_constant', 'dust': False, 'cosmo': FLARE.default_cosmo()}
# redshift = z
# ID = 'XDF_fixedz'
# filters = FLARE.filters.XDF
# model_definitions = {'SPS': 'BPASSv2.2.1.binary', 'IMF': 'ModSalpeter_300', 'SFZH': 'constant_constant', 'dust': 'very_simple', 'cosmo': FLARE.default_cosmo()}
# redshift = z
ID = 'XDF'
filters = FLARE.filters.XDF
model_definitions = {
'SPS': 'BPASSv2.2.1.binary',
'IMF': 'ModSalpeter_300',
'SFZH': 'constant_constant',
'dust': 'very_simple',
'cosmo': FLARE.default_cosmo()
}
redshift = False
# --- generate fake data
parameters = {
'log10_duration': 8.,
'log10Z': -2.,
'log10M*': 8.,
'log10fesc': -1.,
'log10tau_V': -1.0,
'z': z
}
# --- full SED