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ZTF Figures Tutorial

Table of Contents

This tutorial demonstrates plotting ZTF cutouts and light curves. It is based heavily on https://github.com/ZwickyTransientFacility/ztf-avro-alert/blob/master/notebooks/Filtering_alerts.ipynb.

Prerequisites

  1. Load a ZTF alert to a dict or a pandas DataFrame. For examples, see:

Imports

import gzip
import io
from typing import Optional

import aplpy
import matplotlib as mpl
import numpy as np
import pandas as pd
from astropy.io import fits
from astropy.time import Time
from matplotlib import pyplot as plt

import pittgoogle

Plot a Light Curve

def plot_lightcurve(lightcurve_df: pd.DataFrame, days_ago: bool = True):
    """Plot the per-band light curve of a single ZTF object.
    Adapted from:
    https://github.com/ZwickyTransientFacility/ztf-avro-alert/blob/master/notebooks/Filtering_alerts.ipynb

    Parameters
    ----------
    lightcurve_df
        Lightcurve history of a ZTF object. Must contain columns
        ['jd','fid','magpsf','sigmapsf','diffmaglim']
    days_ago
        If True, x-axis will be number of days in the past.
        Else x-axis will be Julian date.
    """

    filter_code = pittgoogle.utils.ztf_fid_names()  # dict
    filter_color = {1: "green", 2: "red", 3: "pink"}

    # set the x-axis (time) details
    if days_ago:
        now = Time.now().jd
        t = lightcurve_df.jd - now
        xlabel = "Days Ago"
    else:
        t = lightcurve_df.jd
        xlabel = "Time (JD)"

    # plot lightcurves by band
    for fid, color in filter_color.items():
        # plot detections in this filter:
        w = (lightcurve_df.fid == fid) & ~lightcurve_df.magpsf.isnull()
        if np.sum(w):
            label = f"{fid}: {filter_code[fid]}"
            kwargs = {"fmt": ".", "color": color, "label": label}
            plt.errorbar(t[w], lightcurve_df.loc[w, "magpsf"], lightcurve_df.loc[w, "sigmapsf"], **kwargs)
        # plot nondetections in this filter
        wnodet = (lightcurve_df.fid == fid) & lightcurve_df.magpsf.isnull()
        if np.sum(wnodet):
            plt.scatter(
                t[wnodet],
                lightcurve_df.loc[wnodet, "diffmaglim"],
                marker="v",
                color=color,
                alpha=0.25,
            )

    plt.gca().invert_yaxis()
    plt.xlabel(xlabel)
    plt.ylabel("Magnitude")
    plt.legend()
plot_lightcurve(lightcurve_df)

Plot Cutouts

def plot_stamp(stamp, fig=None, subplot=None, **kwargs):
    """Adapted from:
    https://github.com/ZwickyTransientFacility/ztf-avro-alert/blob/master/notebooks/Filtering_alerts.ipynb
    """

    with gzip.open(io.BytesIO(stamp), "rb") as f:
        with fits.open(io.BytesIO(f.read())) as hdul:
            if fig is None:
                fig = plt.figure(figsize=(4, 4))
            if subplot is None:
                subplot = (1, 1, 1)
            ffig = aplpy.FITSFigure(hdul[0], figure=fig, subplot=subplot, **kwargs)
            ffig.show_grayscale(stretch="arcsinh")
    return ffig


def plot_cutouts(alert_dict):
    """Adapted from:
    https://github.com/ZwickyTransientFacility/ztf-avro-alert/blob/master/notebooks/Filtering_alerts.ipynb
    """

    # fig, axes = plt.subplots(1,3, figsize=(12,4))
    fig = plt.figure(figsize=(12, 4))
    for i, cutout in enumerate(["Science", "Template", "Difference"]):
        stamp = alert_dict["cutout{}".format(cutout)]["stampData"]
        ffig = plot_stamp(stamp, fig=fig, subplot=(1, 3, i + 1))
        ffig.set_title(cutout)
plot_cutouts(alert_dict)
plt.show(block=False)