Getting Started¶

Generating a galaxy catalog with intrinsic properties can be done with just a few lines of code. Initialize the GalSBI class with the desired model and call it. If you run a model for the first time, it might take a bit longer since some files have to be loaded to cache. A default configuration will be used to generate an intrinsic catalog. It can be loaded directly and plotted. By default, the catalog will be saved in the current directory.

from galsbi import GalSBI
from trianglechain import TriangleChain
import matplotlib.pyplot as plt

model = GalSBI("Fischbacher+24")
model()
cats = model.load_catalogs()

ranges = {
    "mag": [18, 27],
    "r50": [0, 4],
    "sersic_n": [0, 5],
    "z": [0, 6]
}
tri = TriangleChain(ranges=ranges, params=list(ranges.keys()), fill=True)
tri.contour_cl(cat);

This is a simple example of how to generate a catalog from one sample of the posterior distribution. To obtain catalogs from different posterior samples, one can change the model_index (which is by default 0). The positions of the galaxies are by default in the first pixel of a healpix map with nside 64. This can easily be changed by passing a boolean healpix mask to the GalSBI class.

import healpy as hp
import numpy as np
import matplotlib.pyplot as plt

nside = 2048
npix = hp.nside2npix(nside)

mask = np.zeros(npix)

# Define a circular patch around the point (theta, phi) = (45 degrees, 45 degrees)
theta_center = np.radians(45.0)
phi_center = np.radians(45.0)

# Set a 0.5 degree radius
radius = np.radians(0.5)

# Find all pixel indices within this patch and set mask to 1
vec_center = hp.ang2vec(theta_center, phi_center)
patch_pixels = hp.query_disc(nside, vec_center, radius)
mask[patch_pixels] = 1

model = GalSBI("Fischbacher+24", model_index=42)
model(healpix_map=mask)

The catalogs are saved as {catalog_name}_{model_index}_{band}_ucat.gal.cat and {catalog_name}_{model_index}_{band}_ucat.star.cat. To change the path where the catalogs are saved, pass a different catalog_name argument to the call. The catalogs are saved as structured numpy arrays but they can be loaded as pandas dataframes (output_format="df") or fits tables (output_format="fits").

cats_as_pd_df = model.load_catalogs(output_format="df")
cats_as_fits = model.load_catalogs(output_format="fits")

By default, the catalogs are returned separately for each band. To get a single catalog with all bands, pass combine=True.

cats_combined = model.load_catalogs(combine=True)

Finally, to see which papers should be cited given the specific setup used, call the cite method.

model.cite()