Source code for astro_prost.demo

import pandas as pd
from scipy.stats import gamma, halfnorm, uniform

from astro_prost.associate import associate_sample, prepare_catalog
from astro_prost.helpers import SnRateAbsmag

[docs] source = "ZTF BTS"
transient_catalog = pd.read_csv( "/Users/alexgagliano/Documents/Research/multimodal-supernovae/data/ZTFBTS/ZTFBTS_TransientTable.csv" ) # define priors for properties
[docs] priorfunc_z = halfnorm(loc=0.0001, scale=0.5)
# if you want the redshift prior to be based # on an observed distribution of transients within a given absmag range # transients are uniformly distributed in cosmological volume # between zmin and zmax and the subset # peaking above mag_cutoff sets the z distribution # cosmo = LambdaCDM(H0=70, Om0=0.3, Ode0=0.7) # priorfunc_z = PriorzObservedTransients(z_min=0, z_max=1, mag_cutoff=19, # absmag_mean=-19, absmag_min=-24, absmag_max=-17, cosmo=cosmo) # look at your distribution (only available for the above experiment) # priorfunc_z.plot()
[docs] priorfunc_offset = uniform(loc=0, scale=10)
[docs] priorfunc_absmag = uniform(loc=-30, scale=20)
[docs] likefunc_offset = gamma(a=0.75)
[docs] likefunc_absmag = SnRateAbsmag(a=-30, b=-10)
[docs] priors = {"offset": priorfunc_offset, "absmag": priorfunc_absmag, "z": priorfunc_z}
[docs] likes = {"offset": likefunc_offset, "absmag": likefunc_absmag}
# set up properties of the association run
[docs] verbose = 0
[docs] parallel = True
[docs] save = False
# list of catalogs to search -- options are (in order) glade, decals, panstarrs
[docs] catalogs = ["panstarrs"]
# the name of the coord columns in the dataframe
[docs] transient_coord_cols = ("ra", "dec")
# the column containing the transient names
[docs] transient_name_col = "name"
transient_catalog = prepare_catalog( transient_catalog, transient_name_col=transient_name_col, transient_coord_cols=transient_coord_cols ) # cosmology can be specified, else flat lambdaCDM is assumed with H0=70, Om0=0.3, Ode0=0.7
[docs] transient_catalog = associate_sample( transient_catalog, priors=priors, likes=likes, catalogs=catalogs, parallel=parallel, verbose=verbose, save=save, cat_cols=False, )