** Feature request**
Some rail estimators also have an "engine". This is a means to create samples of photometry. In the first instance this would be a means to sample uniformly from the lephare model magnitudes. In a more advanced version it would be possible to fit a set of model weights to a set of photometry measurements and then to sample from that "prior" distribution of models.
Can we do this using the MagSVC class? In pseudocode:
def lephare_sampler(config, type, n, weights=None):
"""Draw n samples from the models with weights.
If weights=None draw uniformly
"""
service=lp.MagSvc.from_config(type, config_file)
mags=[]
for i in np.arange(n):
# Set redshift grid from config
# Sample from z grid
# Sample from models
# Sample from other parameters of model...
# Calculate mags
mags.append(service(z,model,model_params))
return np.array(mags)
magnitudes=lephare_sampler(config, "GAL", 10000)
Before submitting
Please check the following:
** Feature request**
Some rail estimators also have an "engine". This is a means to create samples of photometry. In the first instance this would be a means to sample uniformly from the lephare model magnitudes. In a more advanced version it would be possible to fit a set of model weights to a set of photometry measurements and then to sample from that "prior" distribution of models.
Can we do this using the MagSVC class? In pseudocode:
Before submitting
Please check the following: