Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
drop aplpy, matplotlib, numpy dependencies. add tutorial ztf-figures. update changelog.
- Loading branch information
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,137 @@ | ||
| .. _ztf figures: | ||
|
|
||
| ZTF Figures Tutorial | ||
| ============================== | ||
|
|
||
| .. contents:: Table of Contents | ||
| :depth: 1 | ||
| :local: | ||
|
|
||
| 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: | ||
|
|
||
| - :ref:`cloud storage` | ||
| - :ref:`bigquery` | ||
|
|
||
| Imports | ||
| --------- | ||
|
|
||
| .. code:: python | ||
| 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 | ||
| ------------------ | ||
|
|
||
| .. code:: python | ||
| 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() | ||
| .. code:: python | ||
| plot_lightcurve(lightcurve_df) | ||
| Plot Cutouts | ||
| ------------ | ||
|
|
||
| .. code:: python | ||
| 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) | ||
| .. code:: python | ||
| plot_cutouts(alert_dict) | ||
| plt.show(block=False) |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Oops, something went wrong.