Cone search demo

Cone search demo#

This notebook walks through performing a cone search of the pixels in a HATS catalog. This shows strategies for visualizing a catalog’s partitions, and using hats’s spatial metadata to improve performance in targeted queries.

[1]:

import hats
import numpy as np

## Fill in these variables with what's relevant in your use case:

### Change this path!!!
catalog_path = "../../tests/data/small_sky_order1"

ra = 0  # degrees
dec = -80  # degrees
radius_degrees = 10  # degrees

[2]:

## Load catalog

catalog = hats.read_hats(catalog_path)

[3]:

## Plot catalog pixels

hats.inspection.plot_pixels(catalog)

[3]:

(<Figure size 1000x500 with 2 Axes>,
 <WCSAxes: title={'center': 'Catalog pixel density map - small_sky_order1'}>)
../_images/notebooks_cone_search_3_1.png 
[4]:

import astropy.units as u
from astropy.coordinates import SkyCoord
from astropy.visualization.wcsaxes import SphericalCircle

fig, ax = hats.inspection.plot_pixels(catalog)
cone_center = SkyCoord(ra=ra * u.deg, dec=dec * u.deg)
s = SphericalCircle(
    cone_center,
    radius_degrees * u.degree,
    edgecolor="purple",
    facecolor="none",
    transform=ax.get_transform("icrs"),
)
ax.add_patch(s)

[4]:

<astropy.visualization.wcsaxes.patches.SphericalCircle at 0x701f845ccd50>
../_images/notebooks_cone_search_4_1.png 
[5]:

## Filter catalog and plot filtered pixels

radius_arcseconds = radius_degrees * 3600
filtered_catalog = catalog.filter_by_cone(ra, dec, radius_arcseconds)

hats.inspection.plot_pixels(filtered_catalog)

[5]:

(<Figure size 1000x500 with 2 Axes>,
 <WCSAxes: title={'center': 'Catalog pixel density map - small_sky_order1'}>)
../_images/notebooks_cone_search_5_1.png