Photometry (local and global) for host galaxy of supernovae
Project description
HostPhot
Conda environment
It is recommended to create an environment for every new project:
conda create -n hostphot pip
conda activate hostphot
pip install hostphot
Modules
Cutouts
This module allows you to download image cutouts from PS1
, DES
and SDSS
. For this, you can use get_PS1_images()
, get_DES_images()
and get_SDSS_images()
, respectively. For example:
from hostphot.cutouts import get_PS1_images
ra, dec = 30, 100
size = 400 # in pixels
filters = 'grizy'
fits_images = get_PS1_images(ra, dec, size, filters)
where fits_images
is a list with the fits images in the given filters.
You can also use download_multiband_images()
for multiple images:
from hostphot.cutouts import download_multiband_images
download_multiband_images(sn_name, ra, dec, size,
work_dir, filters,
overwrite, survey)
where work_dir
is where all the images will be downloaded. A Subdirectory inside work_dir
will be created with the SN name as the directory name.
Local Photometry
Local photometry can be obtained for the downloaded images. For this, use extract_local_photometry()
for a single image:
from hostphot.local_photometry import extract_local_photometry
fits_file = 'path/to/local/fits_file'
ra, dec = 30, 100
z = 0.01 # redshift
ap_radius = 4 # aperture for the photometry in kpc
survey = 'PS1'
extract_local_photometry(fits_file, ra, dec, z, ap_radius, survey)
which returns mag
and mag_err
. You can also use multi_local_photometry()
for multiple images:
from hostphot.local_photometry import multi_local_photometry
multi_local_photometry(name_list, ra_list, dec_list, z_list,
ap_radius, work_dir, filters,
survey, correct_extinction)
where work_dir
should be the same as used in download_multiband_images()
and name_list
should contain the names of the SNe used in download_multiband_images()
as well. This produces a pandas DataFrame as an output where, e.g., column g
is the g-band magnitude and g_err
its uncertainty.
Global Photometry
Global photometry can be obtained in a similar way to local photometry. Use extract_global_photometry()
for a single image:
from hostphot.global_photometry import extract_global_photometry
survey = 'PS1'
extract_global_photometry(fits_file, host_ra, host_ra, survey=survey)
which returns mag
and mag_err
. You can also use multi_global_photometry()
for multiple images:
from hostphot.global_photometry import multi_global_photometry
survey = 'PS1'
correct_extinction = True
multi_global_photometry(name_list, host_ra_list, host_dec_list, work_dir, filters,
survey=survey, correct_extinction=correct_extinction)
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