spelunker: a library to extract guidestar data and observe technical and stellar events
Project description
Spelunker — NIRISS FGS quicklook pipeline
spelunker
is a package that assists on studying JWST FGS/NIRISS guidestar data.
Authors: Derod Deal (dealderod@ufl.edu), Néstor Espinoza (nespinoza@stsci.edu)
Statement of need
Every time JWST observes an object, it simultaneously observes a nearby star --- a so-called "guide star" --- with the NIRISS Fine Guidance Sensor (FGS) that is used to keep the telescope locked on the target of interest. While researchers typically focus on their science targets, the guide star data can be extremely interesting on its own right both to detect anomalies on science data, as well as to explore time-series data of guidestars themselves. spelunker
provides an easy-to-access ("plug-and-play") library to access this guide star data. The library is able to generate time-series for several metrics of the FGS data in an automated fashion, including fluxes and PSF variations, along with derived products from those such as periodograms that can aid on their analysis given only a JWST program ID number.
Installation
To install spelunker
, use pip install
.
pip install spelunker
Using the library
Get started with spelunker
with only two lines of code.
import spelunker
spk = spelunker.load(pid=1534)
This will download guidestar data for Program ID 1534; the spk
object itself can then be used to explore this guidestar data! For example, let's make a plot of the guidestar time-series for the first minutes of this PID:
import matplotlib.pyplot as plt
# Convert times from MJD to minutes:
plt.plot( ( spk.fg_time - spk.fg_time[0] ) * 24 * 60, spk.fg_flux )
plt.xlim(0,10)
plt.xlabel('Time from start (minutes)')
plt.ylabel('Counts')
(See below on more information that can be extracted, including fitting 2D gaussians to each FGS integration!). We can even make a plot of the tracked guidestars within this Program ID:
spk.guidestar_plot()
Mnemonics from JWST technical events can be overplotted on any timeseries, such as high-gain antenna (HGA) movement or to identify if the FGS tracks a new guidestar if the jwstuser
package is also installed:
import matplotlib.pyplot as plt
spk.mast_api_token = 'insert a token from auth.MAST here'
fig, ax = plt.subplots(figsize=(12,4),dpi=200)
ax = spk.mnemonics_local('GUIDESTAR')
ax = spk.mnemonics('SA_ZHGAUPST', 60067.84, 60067.9)
ax.plot(spk.fg_time, spk.fg_flux)
plt.legend(loc=3)
plt.xlim(60067.84, 60067.9)
plt.show()
For more information on the tools under spelunker
and how to get started, visit the quickstart guide or checkout our readthedocs. Get acquainted with spelunker
with the following example notebooks:
Licence and attribution
This project is under the MIT License, which can be viewed here.
Acknowledgments
DD and NE would like to thank the STScI's Space Astronomy Summer Program (SASP) as well as the National Astronomy Consortium (NAC) program which made it possible for them to work together on this fantastic project!
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
File details
Details for the file spelunker-1.0.4.tar.gz
.
File metadata
- Download URL: spelunker-1.0.4.tar.gz
- Upload date:
- Size: 22.1 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 2dbe3247f7189f339de9a9950d7c6996fba088235a5ad9cfc6846bfe6189c56d |
|
MD5 | 4efaeb6e0e424387b033dc41b24b15f0 |
|
BLAKE2b-256 | 8d92dc8050d860635cb8ddd516e0f6110631d2f1f42cb7403410d6e52c7a23f1 |
File details
Details for the file spelunker-1.0.4-py3-none-any.whl
.
File metadata
- Download URL: spelunker-1.0.4-py3-none-any.whl
- Upload date:
- Size: 22.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.9.18
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 776c0b1ad3aed15faf37ada148d635eb73004f3b26979ad0c64327c7fbe8f10f |
|
MD5 | d8f1e18964649a395577bebe90fec6be |
|
BLAKE2b-256 | 4b894c8775d358d4019669da960d90a6948f7755cc15b69fe3fdfac3521afb0c |