def filters1( model_nus, model_fluxes, filterdict, z ):
"""
Projects the model SEDs into the filter curves of each photometric band.
##input:
- model_nus: template frequencies [log10(nu)]
- model_fluxes: template fluxes [F_nu]
- filterdict: dictionary with all band filter curves' information.
To change this, add one band and filter curve, etc,
look at DICTIONARIES_AGNfitter.py
- z: redshift
##output:
- bands [log10(nu)]
- Filtered fluxes at these bands [F_nu]
"""
bands, files_dict, lambdas_dict, factors_dict = filterdict
filtered_model_Fnus = []
# Costumize model frequencies and fluxes [F_nu]
# to same units as filter curves (to wavelengths [angstrom] and F_lambda)
model_lambdas = nu2lambda_angstrom(model_nus) * (1+z)
model_lambdas = model_lambdas[::-1]
model_fluxes_nu = model_fluxes[::-1]
model_fluxes_lambda = fluxnu_2_fluxlambda(model_fluxes_nu, model_lambdas)
mod2filter_interpol = interp1d(model_lambdas, model_fluxes_lambda, kind = 'nearest', bounds_error=False, fill_value=0.)
# For filter curve at each band.
# (Vectorised integration was not possible -> different filter-curve-arrays' sizes)
for iband in bands:
# Read filter curves info for each data point
# (wavelengths [angstrom] and factors [non])
lambdas_filter = np.array(lambdas_dict[iband])
factors_filter = np.array(factors_dict[iband])
iband_angst = nu2lambda_angstrom(iband)
# Interpolate the model fluxes to
#the exact wavelengths of filter curves
modelfluxes_at_filterlambdas = mod2filter_interpol(lambdas_filter)
# Compute the flux ratios, equivalent to the filtered fluxes:
# F = int(model)/int(filter)
integral_model = trapz(modelfluxes_at_filterlambdas*factors_filter, x= lambdas_filter)
integral_filter = trapz(factors_filter, x= lambdas_filter)
filtered_modelF_lambda = (integral_model/integral_filter)
# Convert all from lambda, F_lambda to Fnu and nu
filtered_modelFnu_atfilter_i = fluxlambda_2_fluxnu(filtered_modelF_lambda, iband_angst)
filtered_model_Fnus.append(filtered_modelFnu_atfilter_i)
return bands, np.array(filtered_model_Fnus)
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