Skip to main content

Global and local photometry of galaxies hosting supernovae or other transients

Project description

drawing

Global and local photometry of galaxies hosting supernovae or other transients

repo documentation status license Tests and Publish Python Version PyPI DOI DOI Coverage

Read the full documentation at hostphot.readthedocs.io. It is recommended to read the Further Information section to understand how HostPhot works.


Conda environment

It is recommended to create an environment before installing HostPhot:

conda create -n hostphot pip
conda activate hostphot
pip install hostphot

Requirements

HostPhot has the following requirements:

numpy
pandas
matplotlib
python-dotenv
astropy
reproject
photutils
astroquery
extinction
sfdmap
pyvo
sep
ipywidgets (optional: for interactive aperture)
ipympl (optional: for interactive aperture)
ipython (optional: for interactive aperture)
pytest (optional: for testing the code)

Tests

To run the tests, go to the parent directory and run the following command:

pytest -v

Modules

Cutouts

This module allows you to download image cutouts from different surveys (e.g. PS1):

from hostphot.cutouts import download_images

name = 'SN2004eo'
host_ra, host_dec = 308.2092, 9.92755  # coords of host galaxy of SN2004eo
survey = 'PS1'
download_images(name, host_ra, host_dec, survey=survey)

Image Pre-processing

Coadds can be created and stars can be masked out of the images:

from hostphot.coadd import coadd_images

coadd_filters = 'riz'
coadd_images(name, filters=coadd_filters, survey=survey)  # creates a new fits file
from hostphot.image_masking import create_mask

# one can extract the mask parameters from the coadd
# this also creates new fits files
coadd_mask_params = create_mask(name, host_ra, host_dec,
                                filt=coadd_filters, survey=survey,
                                extract_params=True)  

for filt in 'grizy':
    create_mask(name, host_ra, host_dec, filt, survey=survey,
                common_params=coadd_mask_params)

If the user is not happy with the result of the masking, there are a few parameters that can be adjusted. For instance, threshold sets the threshold used by sep for detecting objects. Lowering it will allow the detection of fainter objects. sigma is the width of the gaussian used for convolving the image and masking the detected objects. If crossmatch is set to True, the detected objects are cross-matched with the Gaia catalog and only those in common are kept. This is useful for very nearby host galaxies (e.g. that of SN 2011fe) so the structures of the galaxy are not maked out, artificially lowering its flux.

Local Photometry

Local photometry can be obtained for multiple circular apertures:

import hostphot.local_photometry as lp

ap_radii = [3, 4]  # aperture radii in units of kpc
ra, dec =  308.22579, 9.92853 # coords of SN2004eo
z = 0.0157  # redshift

results = lp.multi_band_phot(name, ra, dec, z,
                             survey=survey, ap_radii=ap_radii, 
                             use_mask=True, save_plots=True)

If the results return NaN values, this means that the flux is below the detection limit for the given survey.

Global Photometry

Global photometry can be obtained in a similar way to local photometry, using common aperture:

import hostphot.global_photometry as gp

results = gp.multi_band_phot(name, host_ra, host_dec, 
                             survey=survey, ra=ra, dec=dec,
                             use_mask=True, common_aperture=True, 
                             coadd_filters='riz', save_plots=True)

Contributing

To contribute, either open an issue or send a pull request (prefered option). You can also contact me directly (check my profile: https://github.com/temuller).

Adding other surveys

If you wish a survey to be added to HostPhot, there are a couple of ways of doing it. 1) You can do a pull request, following the same structure as used for the surveys that are already implemented, or 2) open an issue asking for a survey to be added. Either way, there are a fews things needed to add a survey: where to download the images from (e.g., using astroquery), zero-points to convert the images's flux/counts values into magnitudes, the magnitude system (e.g. AB, Vega), the pixel scaling of the images (in units of arcsec/pixel), the filters transmission functions and any other piece of information necessary to properly estimate magnitudes, errors, etc. If you open an issue asking for a survey to be added, please include all this information. For more information, please check the Adding New Surveys section of the documentation.

Citing HostPhot

If you make use of HostPhot, please cite the following paper:

@article{Müller-Bravo2022, 
  author = {Tomás E. Müller-Bravo and Lluís Galbany},
  title = {HostPhot: global and local photometry of galaxies hosting supernovae or other transients},
  doi = {10.21105/joss.04508}, 
  url = {https://doi.org/10.21105/joss.04508}, 
  year = {2022}, 
  publisher = {The Open Journal}, 
  volume = {7}, 
  number = {76}, 
  pages = {4508},  
  journal = {Journal of Open Source Software} 
} 

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

HostPhot-2.1.1.tar.gz (1.7 MB view details)

Uploaded Source

Built Distribution

HostPhot-2.1.1-py3-none-any.whl (1.8 MB view details)

Uploaded Python 3

File details

Details for the file HostPhot-2.1.1.tar.gz.

File metadata

  • Download URL: HostPhot-2.1.1.tar.gz
  • Upload date:
  • Size: 1.7 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.4

File hashes

Hashes for HostPhot-2.1.1.tar.gz
Algorithm Hash digest
SHA256 5042499064481c05f7e6287ea6b6dddbd1cae7bb0408de2e5433b0f4ab1589ed
MD5 07113ad781b2549101dfa986c358f06a
BLAKE2b-256 6a15d9eb077b531c571f26a16c3f2a5be4de4325da2819ecf11577b85cb41713

See more details on using hashes here.

File details

Details for the file HostPhot-2.1.1-py3-none-any.whl.

File metadata

  • Download URL: HostPhot-2.1.1-py3-none-any.whl
  • Upload date:
  • Size: 1.8 MB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.1 CPython/3.10.4

File hashes

Hashes for HostPhot-2.1.1-py3-none-any.whl
Algorithm Hash digest
SHA256 98452726bff55458cfd7b4e63195183dacc58d8220d900e95e5d32f9b53560cc
MD5 616934919458b2818fea566753465a8d
BLAKE2b-256 413f03da83461dcc472cd5593bb1c0847735d779827f8102936629c8a98678e7

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page