https://github.com/hover2pi/specialsoss
SPECtral Image AnaLysis for SOSS
https://github.com/hover2pi/specialsoss
Last synced: 8 months ago
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SPECtral Image AnaLysis for SOSS
- Host: GitHub
- URL: https://github.com/hover2pi/specialsoss
- Owner: hover2pi
- License: mit
- Created: 2018-10-08T16:36:32.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2022-12-26T20:46:35.000Z (over 3 years ago)
- Last Synced: 2023-04-01T11:12:39.750Z (about 3 years ago)
- Language: Jupyter Notebook
- Homepage:
- Size: 217 MB
- Stars: 1
- Watchers: 3
- Forks: 1
- Open Issues: 5
-
Metadata Files:
- Readme: README.rst
- Changelog: HISTORY.rst
- Contributing: CONTRIBUTING.rst
- License: LICENSE
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README
===========
specialsoss
===========
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SPECtral Image AnaLysis for SOSS
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Authors: Joe Filippazzo
This pure Python 3.6+ package performs optimal spectral extraction routines
for the Single Object Slitless Spectroscopy (SOSS) mode of the
Near-Infrared Imager and Slitless Spectrograph (NIRISS) instrument
onboard the James Webb Space Telescope (JWST).
Additional resources:
- `Full documentation `_
- `Jupyter notebook `_
- `Build history `_
Extracting Spectra from SOSS Observations
-----------------------------------------
The headers in JWST data products provide almost all the information
needed to perform the spectral extraction, making the path to your data
the only required input. To load your SOSS exposure, simply do
.. code:: python
# Imports
import numpy as np
from specialsoss import SossExposure
from pkg_resources import resource_filename
# Load the exposure
file = resource_filename('specialsoss', 'files/SUBSTRIP256_CLEAR_uncal.fits')
obs = SossExposure(file)
Now we can scroll through the ingested data by plotting the frames.
.. code:: python
obs.plot_frames()
.. figure:: specialsoss/files/images/plot_frames.png
:alt: SOSS Frames
To run a spectral extraction routine, use the `extract` method with
the name of the routine you would like to use. In this example, we'll
use the vanilla column sum extraction.
.. code:: python
obs.extract('sum', 'uncal')
Finally, we can see the extracted time-series spectra and the monochromatic lightcurves with:
.. code:: python
obs.plot_results()