Basic Usage#

There are two methods to run PyGriFE. The recommended method is to use a text-based python session, although there is an optional GUI.

Python#

First, begin by importing the main class into the python environment.

from pygrife import GrismExtractor

To instantiate the GrismExtractor object, we need to supply the root_name of the processed files, their current location (see necessary files), and the output directory. GrismExtractor copies the relevant files to the output directory, to preserve the originals and prevent any unexpected modifications.

ge = GrismExtractor(
    field_root="nis-wfss",
    in_dir="probably/called/Prep",
    out_dir="use/a/new/name/for/this",
)

Next, we need to load the original segmentation map.

ge.load_orig_seg_map(
    "/path/to/nis-wfss-ir_seg.fits",
)

We can now begin modifying the segmentation map to our liking. ge.seg_map is a 2D numpy array, and can be modified using any relevant operation. Several convenience methods are also built into GrismExtractor, such as the ability to read in a “regions” file written by DS9.

new_id = ge.add_reg_obj(
    reg_path="/some/path/to/targets_ds9_A2744_03.reg",
)

Alternatively, we could extract an object within a particular radius of a point (with many different ways to express this; see the documentation of add_circ_obj for further details).

import numpy as np
import astropy.units as u

new_ids = np.append(
    new_id,
    ge.add_circ_obj(
        ra=3.61066,
        dec=-30.39560,
        unit="deg",
        radius=3*u.arcsec,
    ),
)

Now we’ve modified the segmentation map, we need to regenerate the object catalogue, and load the processed grism data.

ge.regen_multiband_catalogue()
ge.load_grism_files(cpu_count=6)

Any objects can then be extracted, by supplying an array of IDs, and specifying the redshift range to search through.

ge.extract_spectra(
    new_ids,
    z_range=[0.25,0.35],
)

If you’ve made it this far without any problems, congratulations! You should now have all the beam cutouts, extracted spectra, and other paraphernalia associated with your objects. Of course, if the sources are in a heavily contaminated part of the field, the contamination model can be refined using the best-fit spectra, and the process repeated until a satisfactory extraction is obtained.

ge.refine_contam_model_with_fits(
    fit_files=[ge.out_dir / f"{ge.field_root}_{o_id:0>5}.full.fits" for o_id in new_ids],
)
ge.extract_spectra(
    new_ids,
    z_range=[0.25,0.35],
)

GUI#

PyGriFE also includes a graphical interface, to view and modify the segmentation map. This feature is under active development, but can be run after installing the optional dependencies (see Installation: GUI for more details). The GUI itself can be used by running the following.

from pygrife.GUI import run_GUI

run_GUI()

Note that even when feature complete, at least one thread will be dedicated to running the GUI, and so the method detailed above in Basic Usage: Python will be slightly more performant.

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