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astro-delight-0.0.5
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشترتوضیحات
Deep Learning Identification of Galaxy Hosts in Transients, a package to automatically identify host galaxies of transient candidates
| ویژگی | مقدار |
|---|---|
| سیستم عامل | POSIX :: Linux |
| نام فایل | astro-delight-0.0.5 |
| نام | astro-delight |
| نسخه کتابخانه | 0.0.5 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Francisco Förster |
| ایمیل نویسنده | francisco.forster@gmail.com |
| آدرس صفحه اصلی | https://github.com/fforster/delight |
| آدرس اینترنتی | https://pypi.org/project/astro-delight/ |
| مجوز | GNU GPLv3 |
<p float="left">
<img src="http://alerce.science/static/img/alerce_logo.cc79ccea2406.png" alt="drawing" width="300"/>
<img src="https://raw.githubusercontent.com/fforster/DELIGHT/main/figures/Delight.png" alt="drawing" width="200"/>
</p>
The **Deep Learning Identification of Galaxy Hosts in Transients (DELIGHT, Förster et al. 2022, *in preparation*)** is a library created by the [ALeRCE broker](http://alerce.science) to automatically identify host galaxies of transient candidates using multi-resolution images and a convolutional neural network (you can test it with our [example notebook](https://nbviewer.org/github/fforster/DELIGHT/blob/main/notebook/Delight_example_notebook.ipynb)).
You can install it using `pip install astro-delight`, but we recommend cloning this repository and `pip install .` from there.
The library has a class with several methods that allow you to get the most likely host coordinates starting from given transient coordinates. In order to do this, the delight object needs a list of object identifiers and coordinates (`oid, ra, dec`). With this information, it downloads [PanSTARRS](https://outerspace.stsci.edu/display/PANSTARRS/) images centered around the position of the transients (2 arcmin x 2 arcmin), gets their WCS solutions, creates the multi-resolution images, does some extra preprocessing of the data, and finally predicts the position of the hosts using a multi-resolution image and a convolutional neural network. It can also estimate the host's semi-major axis if requested taking advantage of the multi-resolution images.
Note that DELIGHT's prediction time is currently dominated by the time to download [PanSTARRS](https://outerspace.stsci.edu/display/PANSTARRS/) images using the [panstamps service](https://panstamps.readthedocs.io/en/master/). In the future, we expect that there will be services that directly provide multi-resolution images, which should be more lightweight with no significant loss of information. Once these images are obtained, the processing times are only milliseconds per host.
---
*Classes*:
* **Delight**: the main class containing the methods to predict host galaxy positions starting from transient coordinates
*Methods* (most important):
* **init**: class constructor, it requires a list of object identifiers, a list of right ascensions, and a list of declinations
* **download**: downloads [PanSTARRS](https://outerspace.stsci.edu/display/PANSTARRS/) fits files using the [panstamps](https://panstamps.readthedocs.io/en/master/) servive.
* **get_pix_coords**: gets the WCS solution in the fits files to move from pixel to celestial coordinates.
* **compute_multiresolution**: transform the [PanSTARRS](https://outerspace.stsci.edu/display/PANSTARRS/) images to multi-resolution images
* **load_model**: load DELIGHT's [Tensorflow](https://www.tensorflow.org/) model
* **predict**: predict the host positions
* **plot_host**: plot the original host image, the multi-resolution images, and the transient and predicted host position
* **get_hostsize**: estimate the host semi-major axis
* **save**: save the resulting dataframe
* **load**: load the resulting dataframe
*Requirements*:
* xarray (`python -m pip install xarray`)
* astropy (`pip install astropy`)
* sep (`pip install sep`)
* tensorflow (https://www.tensorflow.org/install/pip, `pip install tensorflow`)
* pantamps (`pip install panstamps`)
---
**DELIGHT's multi-resolution images and prediction vector:**
<img src="https://raw.githubusercontent.com/fforster/DELIGHT/main/figures/multi-resolution.png" alt="drawing" width="800"/>
**DELIGHT's neural network architecture:**
<img src="https://raw.githubusercontent.com/fforster/DELIGHT/main/figures/delight_architecture.png" alt="drawing" width="800"/>
نیازمندی
| مقدار | نام |
|---|---|
| - | astropy |
| - | sep |
| - | xarray |
| - | panstamps |
| - | matplotlib |
| - | numpy |
| - | tensorflow |
نحوه نصب
نصب پکیج whl astro-delight-0.0.5:
pip install astro-delight-0.0.5.whl
نصب پکیج tar.gz astro-delight-0.0.5:
pip install astro-delight-0.0.5.tar.gz