- Version: 0.0.1
- Author: Adina Feinstein
- Created: 9 August, 2022
- Update: 9 August, 2022
If you have any questions that are beyond the scope of this help file, Please feel free to open a GitHub Issue.
The purpose of
nirHiss is to extract 1D spectra from JWST
Near-Infrared Imager and Slitless Spectrograph (NIRISS) instrument.
We have two primary ways to install
- Install through the Python Package Index (PyPI)
pip install nirhiss
- Install the developer's version on GitHub:
git clone https://github.com/afeinstein20/nirHiss
python setup.py install
nirHiss saves all outputs as a FITS file.
Citing nirHiss & dependencies
If you find
nirHiss useful for your work, please cite the
Here are some frequently asked questions (FAQs) and their answers!
nirHissperforms the following steps:
- Determines in which sectors this target was observed.
- Locates the object on TESS's many cameras/chips.
- Downloads a time series of "postcards" containing TESS data for the object and its immediate surroundings.
- Creates and stores a target pixel file (TPF) of the object.
- Traces centroid shifts for the object across the time series.
- Chooses an optimal pixel aperture for photometry.
- Creates a light curve using the chosen aperture and centroid trace.
- Performs basic systematics corrections on the light curve and stores it.
ffi(sector=None, camera=None, chip=None)¶
- This class allows the user to download all full-frame images for a given sector,
camera, and chip. It also allows the user to create their own pointing model based on each cadence for a given combination of sector, camera, and chip.
- No individual user should have to download all of the full-frame images because
stacked postcards will be available for the user to download from MAST.
sector (int, optional) –
camera (int, optional) –
chip (int, optional) –
build_pointing_model(pos_predicted, pos_inferred, outlier_removal=False)¶
- Builds an affine transformation to correct the positions of stars
from a possibly incorrect WCS.
pos_predicted (tuple) – Positions taken straight from the WCS; [[x,y],[x,y],…] format.
pos_inferred (tuple) – Positions taken using any centroiding method; [[x,y],[x,y],…] format.
outlier_removal (bool, optional) – Whether to clip 1-sigma outlier frames. Default False.
xhat – (3, 3) affine transformation matrix between WCS positions and inferred positions.
- Return type
Sorts FITS files by start date of observation.
Applies pointing model to correct the position of star(s) on postcard.
coords (tuple) – (x, y) position of star(s).
pointing_model (astropy.table.Table) – pointing_model for ONE cadence.
coords – Corrected position of star(s).
- Return type
See what's new added, changed, fixed, improved or updated in the latest versions.
Version 0.0.1 (9 August, 2022)
- Added Ability to use local postcards
nirHiss.Update()for automatic sector updates
- Updated Significant speedups when TIC, Coords, and a Gaia ID are all provided