Source code for astroML.datasets.generated

import numpy as np
from astropy.cosmology import FlatLambdaCDM

from ..density_estimation import FunctionDistribution
from sklearn.utils import check_random_state

def redshift_distribution(z, z0):
    return (z / z0) ** 2 * np.exp(-1.5 * (z / z0))

[docs]def generate_mu_z(size=1000, z0=0.3, dmu_0=0.1, dmu_1=0.02, random_state=None, cosmo=None): """Generate a dataset of distance modulus vs redshift. Parameters ---------- size : int or tuple size of generated data z0 : float parameter in redshift distribution: p(z) ~ (z / z0)^2 exp[-1.5 (z / z0)] dmu_0, dmu_1 : float specify the error in mu, dmu = dmu_0 + dmu_1 * mu random_state : None, int, or np.random.RandomState instance random seed or random number generator cosmo : astropy.cosmology instance specifying cosmology to use when generating the sample. If not provided, a Flat Lambda CDM model with H0=71, Om0=0.27, Tcmb=0 is used. Returns ------- z, mu, dmu : ndarrays arrays of shape ``size`` """ if cosmo is None: cosmo = FlatLambdaCDM(H0=71, Om0=0.27, Tcmb0=0) random_state = check_random_state(random_state) zdist = FunctionDistribution(redshift_distribution, func_args=dict(z0=z0), xmin=0.1 * z0, xmax=10 * z0, random_state=random_state) z_sample = zdist.rvs(size) mu_sample = cosmo.distmod(z_sample).value dmu = dmu_0 + dmu_1 * mu_sample mu_sample = random_state.normal(mu_sample, dmu) return z_sample, mu_sample, dmu