pyGCG 0.7.15

A GUI to aid in viewing and classifying NIRISS data products.

pyGCG: Grism Classification GUI

A Python GUI to aid in viewing and classifying NIRISS data products. This was originally designed for use by the GLASS-JWST collaboration, but has been tested against the data products from the PASSAGE collaboration as well.

Installation

In all cases, it is strongly recommended to install pyGCG into a new virtual environment, to minimise dependency conflicts (see Requirements).

Using pip (recommended)

pyGCG can be installed directly from the Python Package Index (PyPI), by running:

pip install --upgrade pygcg

Building from source

Alternatively, to clone the latest GitHub repository, use this command:

git clone https://github.com/PJ-Watson/pyGCG.git

To build and install pyGCG, run (from the root of the source tree):

pip install .

Usage

Launching the GUI

In the most basic configuration, pyGCG can be run in a Python session as follows:

from pygcg.GUI_main import run_app
run_app()

Alternatively, pyGCG can be launched from the terminal using a single line:

python -c "from pygcg.GUI_main import run_app; run_app()"

Configuration file

When launching pyGCG, one can pass the path of a configuration file using the config_file keyword:

from pygcg.GUI_main import run_app
run_app(config_file="/path/to/your/config.toml")

By default, pyGCG will look for config.toml in the current working directory, and will create this file if it doesn't exist, using the included example_config.toml. This file will also be created if the supplied configuration file is invalid.

The configuration file is TOML-formatted and organised into various sections, or tables.

Files

This table describes the location of the necessary files and directories.

Key	Description
extractions_dir	The directory in which NIRISS extractions are stored. By default, this is assumed to contain all ancillary data (catalogue, segmentation maps, direct images).
out_dir	The directory in which the pyGCG output will be stored. If no directory is provided, or it is not possible to create the supplied directory, pyGCG will run in read-only mode.
cat_path	The file path of the input catalogue. By default, pyGCG will search for a file matching *ir.cat.fits inside extractions_dir. The catalogue must contain columns that can be interpreted as id, ra, and dec (see Catalogue).
prep_dir	If different to extractions_dir, this can be used to specify the directory containing the segmentation map and direct images.
cube_path	The file path of the corresponding MUSE datacube.
temp_dir	The directory in which temporary files are stored. Defaults to {out_dir}/.temp/.
skip_existing	If True, pyGCG will skip loading objects which already exist in the output catalogue.
out_cat_name	The name of the output catalogue. Defaults to pyGCG_output.fits.

Grisms

This table specifies the grism filters and position angles used in observations.

Key	Default	Description
R	"F200W"	The name of the grism filter that will be mapped to the red channel in the RGB image. Conventionally, this would be the filter covering the longest wavelengths.
G	"F150W"	Same as above, but for the green channel.
B	"F115W"	Same as above, but for the blue channel.
PA1	72.0	The position angle (in degrees) of the first grism orientation.
PA2	341.0	Same as above, but for the second grism orientation.

Catalogue

This table can be used to specify non-standard column names (compared to the default grizli catalogue).

Key	Default	Description
id	"NUMBER"	The unique label used to identify objects. Any type which can be represented as a string is acceptable.
ra	"X_WORLD"	The right ascension of the object, assumed to be in degrees if no unit is present.
dec	"Y_WORLD"	The declination of the object, assumed to be in degrees if no unit is present.
seg_id	"NUMBER"	The unique number corresponding to the object identification in the grizli segmentation map and extractions (e.g. nis-wfss_{seg_id}.1D.fits). By default, this is also used as the object id. If seg_id is supplied, but not a valid catalogue column name, a warning will be raised.
seg_id_length	5	The number of characters used for seg_id, which is assumed to be zero-padded (e.g. 76 -> 00076).
mag	"MAG_AUTO"	The magnitude of the object. If not supplied, or the column name does not exist, this will not be displayed rather than raising an error.
radius	5	The radius of the object. This will fail silently if not found, in the same way as mag.
plate_scale	None	In arcsec/pixel. By default, radius will be displayed alongside any units included in the catalogue. If plate scale is specified, radius is taken to be in pixels, and converted to angular units using plate_scale.

Lines

In the Spectrum tab, it is possible to overlay the positions of reference lines at a given redshift. These take the following format:

[lines.emission.Lyman_alpha]
tex_name = 'Ly$\alpha$'
centre = 1215.24

pyGCG currently supports grouping lines into two categories, emission and absorption. The visibility of these groups can be toggled separately.

The key for each line, [lines.emission.XXX], must be unique. There is no such requirement for tex_name, which uses the Matplotlib Mathtext parser to render the name on the plot. Note that single quotation marks are used to represent a string literal in TOML. centre is self-evidently the centre of the line, and is given in angstroms.

Appearance

These options can be used to change the appearance of the GUI.

Key	Default	Description
appearance	"system"	The overall appearance. Can be one of system (default), light, or dark.
theme	"blue"	The CustomTkinter colour theme. This can be one of blue (default), dark-blue, or green. This can also point to the location of a custom .json file describing the desired theme.

Requirements

pyGCG has the following strict requirements:

• Python 3.10 or later
• NumPy 1.24 or later
• Matplotlib 3.6 or later
• Astropy 5.3 or later
• CustomTkinter 5.2 or later
• CTkMessageBox 2.5 or later
• TOML Kit 0.12 or later
• tqdm 4.66 or later

pyGCG has been tested with Python 3.10-3.12, across multiple operating systems, and is developed primarily on Python 3.12 and Ubuntu 22.04.5 LTS.