Fit Something Fast!

Fit Something Fast!#

Here are some scripts which can be used like configuration files. Simply fill in the blanks at the top of the file and a basic fitting routine will run on your data. This is useful to get results fast on a fairly standard dataset, or to use as a starting point when building a more sophisticated analysis routine.

Fit a single isolated object#

Get the script here: single_model_fit.py

This script will fit a single object with a single model (plus a sky model if requested).

basic usage is to fill in these blanks at the top of the file. Even just filling the target_file is enough to get started:

name = "object_name"  # used for saving files
target_file = "<required>.fits"  # can be a numpy array instead
mask_file = None  # "<path to mask>.fits" # can be a numpy array instead
psf_file = None  # "<path to psf>.fits" # can be a numpy array instead
variance_file = None  # "<path to variance>.fits" # or numpy array or "auto"
pixelscale = 0.1  # arcsec/pixel
zeropoint = 22.5  # mag
initial_params = None  # e.g. {"center": [3, 3], "q": {"value": 0.8, "locked": True}}
window = None  # None to fit whole image, otherwise ((xmin,xmax),(ymin,ymax)) pixels
initial_sky = None  # If None, sky will be estimated
sky_locked = False
model_type = "sersic galaxy model"

then run the script from the command line as a python file:

>>> python single_model_fit.py

This will output the fitted parameters and save the model and residual images as fits files. See the Getting Started with AstroPhot tutorial for more information.

Fit all objects in an image#

Get the script here: segmap_models_fit.py

This script will fit all objects in an image with a single model type. It will also fit a sky model (if requested) and a single special model as the “primary obejct” (if requested).

basic usage is to fill in these blanks at the top of the file. Even just filling the target_file and segmap_file is enough to get started:

name = "field_name"  # used for saving files
target_file = "<required>.fits"  # can be a numpy array instead
segmap_file = "<required>.fits"  # can be a numpy array instead
mask_file = None  # "<path to mask>.fits" # can be a numpy array instead
psf_file = None  # "<path to psf>.fits" # can be a numpy array instead
variance_file = None  # "<path to variance>.fits" # or numpy array or "auto"
pixelscale = 0.1  # arcsec/pixel
zeropoint = 22.5  # mag
initial_sky = None  # If None, sky will be estimated. Recommended to set manually
sky_locked = False
model_type = "sersic galaxy model"  # model type for segmap entries
segmap_filter = {}  # in pixels or ADU: min_size, min_area, min_flux
segmap_filter_ids = []  # list of segmap ids to remove from fit
segmap_override_init_params = {}  # Override some initial parameters for segmap models
primary_key = None  # segmentation map id, use None to have no primary object
primary_name = "primary object"  # name for primary object
primary_model_type = "sersic galaxy model"
primary_initial_params = None  # {"center": [3, 3], "q": {"value": 0.8, "locked": True}}

then run the script from the command line as a python file:

>>> python segmap_models_fit.py

This will output the fitted parameters and save the model and residual images as fits files. See the Group Models tutorial for more information.

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