| Name | skykatana JSON |
| Version |
0.1.3
JSON |
| download |
| home_page | None |
| Summary | Tookit to create sky masks in a pipeline way |
| upload_time | 2024-09-22 15:09:08 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2024 Emilio Donoso Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
astronomy
surveys
sky masks
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# <img src="./docs/croppedSCR.png">
---
## **Cutting pixelized sky masks in a pipeline way**
---
**Skykatana** is a pacakge to create and maniputate boolean spatial masks on the
celestial sphere, by combining [healsparse](https://github.com/LSSTDESC/healsparse) pixel maps
accounting for various effects such as cutting out regions around bright stars, low depth, bad
seeing, extended sources, among others. We call these partial maps **stages**, which are then
combined into a final mask.
For each stage you can generate random points, quickly visualize masks, do plots overlaying
bright stars, and apply the mask to an arbitrary catalog to select sources located inside.
Although mainly designed to work with the [HSC-SSP survey](https://hsc-release.mtk.nao.ac.jp/doc/),
it is flexible to accomodate other surveys such as the upcoming half-sky dataset of the
[Vera Rubin Observatory](https://rubinobservatory.org/).
Main Class
-------------
* ``SkyMaskPipe()``
Main class for assembling and handling pixelized masks
Main Methods
-------------
* ``build_footprint_mask(), build_patch_mask(), build_holes_mask(), etc``
--> Generate maps for each stage
* ``combine_mask()``
--> Merge the maps created above to generate a final mask
* ``plot()``
--> Visualize a mask stage by plotting randoms. Options to zoom, oveplot stars, etc.
* ``plot2compare()``
--> Compare input sources on the left and a mask stage on the right
* ``makerans()``
--> Generate randoms over a mask stage
* ``apply()``
--> Cut out sources outside of a given mask stage
Dependencies
------------
* [lsdb](https://github.com/astronomy-commons/lsdb), [healsparse](https://github.com/LSSTDESC/healsparse),
[tqdm](https://github.com/tqdm/tqdm)
Install
-------
1. `pip install skykatana`
2. Clone the repo, switch to the pacakge directory and do `pip install .` . This has the advantage that you will
get the latest version and all the files in /example_data (~210 MB)
Documentation
-------------
* A quick introductory notebook is availables [here](https://github.com/samotracio/skykatana/blob/main/notebooks/quick_example_hsc.ipynb)
* An indepth tutorial notebook can be found [here](https://github.com/samotracio/skykatana/blob/main/notebooks/indepth_usage.ipynb)
* The full documentation is available [here](https://skykatana.readthedocs.io/en/latest/)
Credits
-------
* Main author: [Emilio Donoso](mailto:emiliodon@gmail.com)
* Contributors: [Mariano Dominguez](mailto:mariano.dominguez@unc.edu.ar),
[Claudio Lopez](mailto:yoclaudioantonio1@gmail.com), [Konstantin Malanchev](mailto:hombit@gmail.com)
Acknowledgements
----------------
This software was partially developed with the generous support of the [LINCC Frameworks Incubator Program](https://lsstdiscoveryalliance.org/programs/lincc-frameworks/incubators/) using LINCC resources. The [healsparse](https://github.com/LSSTDESC/healsparse) code was written by Eli Rykoff and Javier Sanchez
Raw data
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"description": "# <img src=\"./docs/croppedSCR.png\">\n---\n## **Cutting pixelized sky masks in a pipeline way**\n---\n**Skykatana** is a pacakge to create and maniputate boolean spatial masks on the\ncelestial sphere, by combining [healsparse](https://github.com/LSSTDESC/healsparse) pixel maps\naccounting for various effects such as cutting out regions around bright stars, low depth, bad\nseeing, extended sources, among others. We call these partial maps **stages**, which are then\ncombined into a final mask.\n\nFor each stage you can generate random points, quickly visualize masks, do plots overlaying\nbright stars, and apply the mask to an arbitrary catalog to select sources located inside.\n\nAlthough mainly designed to work with the [HSC-SSP survey](https://hsc-release.mtk.nao.ac.jp/doc/),\nit is flexible to accomodate other surveys such as the upcoming half-sky dataset of the\n[Vera Rubin Observatory](https://rubinobservatory.org/).\n\nMain Class\n-------------\n* ``SkyMaskPipe()``\n Main class for assembling and handling pixelized masks\n\nMain Methods\n-------------\n* ``build_footprint_mask(), build_patch_mask(), build_holes_mask(), etc``\n --> Generate maps for each stage\n* ``combine_mask()``\n --> Merge the maps created above to generate a final mask\n* ``plot()``\n --> Visualize a mask stage by plotting randoms. Options to zoom, oveplot stars, etc.\n* ``plot2compare()``\n --> Compare input sources on the left and a mask stage on the right\n* ``makerans()``\n --> Generate randoms over a mask stage\n* ``apply()``\n --> Cut out sources outside of a given mask stage\n\nDependencies\n------------\n* [lsdb](https://github.com/astronomy-commons/lsdb), [healsparse](https://github.com/LSSTDESC/healsparse),\n[tqdm](https://github.com/tqdm/tqdm)\n\nInstall\n-------\n1. `pip install skykatana`\n2. Clone the repo, switch to the pacakge directory and do `pip install .` . This has the advantage that you will\nget the latest version and all the files in /example_data (~210 MB)\n\nDocumentation\n-------------\n* A quick introductory notebook is availables [here](https://github.com/samotracio/skykatana/blob/main/notebooks/quick_example_hsc.ipynb)\n* An indepth tutorial notebook can be found [here](https://github.com/samotracio/skykatana/blob/main/notebooks/indepth_usage.ipynb)\n* The full documentation is available [here](https://skykatana.readthedocs.io/en/latest/)\n\nCredits\n-------\n* Main author: [Emilio Donoso](mailto:emiliodon@gmail.com)\n* Contributors: [Mariano Dominguez](mailto:mariano.dominguez@unc.edu.ar),\n[Claudio Lopez](mailto:yoclaudioantonio1@gmail.com), [Konstantin Malanchev](mailto:hombit@gmail.com)\n\nAcknowledgements\n----------------\nThis software was partially developed with the generous support of the [LINCC Frameworks Incubator Program](https://lsstdiscoveryalliance.org/programs/lincc-frameworks/incubators/) using LINCC resources. The [healsparse](https://github.com/LSSTDESC/healsparse) code was written by Eli Rykoff and Javier Sanchez\n\n",
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"license": "MIT License Copyright (c) 2024 Emilio Donoso Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. ",
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