You can build and use cutouts using the included Dockerfile and docker-compose.yml:
docker compose build cutouts
docker compose run -it cutouts
To get the latest released version and install it into a conda environment:
conda install -c asteroid-institute cutouts
To install the latest from source:
pip install git+https://github.com/B612-Asteroid-Institute/cutouts.git@main#egg=cutouts
To reproduce the example that ships with the repository:
import pandas as pd
import matplotlib.pyplot as plt
from astropy.time import Time
from cutouts import get_cutouts
from cutouts import plot_cutouts
# Read in observations and predicted ephemerides
observations = pd.read_csv("examples/2013_RR163.csv", index_col=False)
# Extract relevant quantities
ra = observations["pred_ra_deg"].values
dec = observations["pred_dec_deg"].values
vra = observations["pred_vra_degpday"].values
vdec = observations["pred_vdec_degpday"].values
obscode = observations["obscode"].values
mag = observations["mag"].values
filter = observations["filter"].values
mag_sigma = observations["mag_sigma"].values
times = Time(observations["mjd_utc"].values, scale="utc", format="mjd")
exposure_id = observations["exposure_id"].values
# Find cutouts and save them
NSC_SIA_URL = "https://datalab.noirlab.edu/sia/nsc_dr2"
cutout_paths, cutout_results = get_cutouts(
times, ra, dec,
sia_url=NSC_SIA_URL,
exposure_id=exposure_id,
out_dir="examples/cutouts"
)
exposure_time = cutout_results["exptime"].values.astype(int)
# Plot cutouts
fig, ax = plot_cutouts(
cutout_paths,
times,
ra, dec,
vra, vdec,
filters=filter,
mag=mag,
mag_sigma=mag_sigma,
exposure_time=exposure_time,
cutout_height=75,
cutout_width=75,
)
fig.suptitle(f"(2013 RR163)", y=1.0)
fig.savefig(f"examples/2013_RR163.jpg", bbox_inches="tight")
cutouts also comes with a simple command line interface (CLI) to quickly produce a grid of cutouts from a file
of predicted ephemerides and observations.
cutouts examples/2013_RR163.csv --out_dir examples/cli_test