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https://github.com/daniel-muthukrishna/astrorapid
Real-time Automated Photometric IDentification (RAPID) of astronomical transients using deep learning
https://github.com/daniel-muthukrishna/astrorapid
Last synced: about 7 hours ago
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Real-time Automated Photometric IDentification (RAPID) of astronomical transients using deep learning
- Host: GitHub
- URL: https://github.com/daniel-muthukrishna/astrorapid
- Owner: daniel-muthukrishna
- License: mit
- Created: 2019-01-10T18:14:10.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-02-16T04:12:58.000Z (over 1 year ago)
- Last Synced: 2024-08-09T11:44:19.429Z (3 months ago)
- Language: Python
- Homepage: https://astrorapid.readthedocs.io
- Size: 13.1 MB
- Stars: 15
- Watchers: 5
- Forks: 14
- Open Issues: 15
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# astrorapid
Real-time Automated Photometric IDentification (RAPID) of astronomical transients using deep learning
For full documentation, please go to https://astrorapid.readthedocs.io
# Installation
```bash
pip install astrorapid
```
# Example Usage
```python
from astrorapid.classify import Classify
mjd = [57433.4816, 57436.4815, 57439.4817, 57451.4604, 57454.4397, 57459.3963, 57462.418 , 57465.4385, 57468.3768, 57473.3606, 57487.3364, 57490.3341, 57493.3154, 57496.3352, 57505.3144, 57513.2542, 57532.2717, 57536.2531, 57543.2545, 57546.2703, 57551.2115, 57555.2669, 57558.2769, 57561.1899, 57573.2133,57433.5019, 57436.4609, 57439.4587, 57444.4357, 57459.4189, 57468.3142, 57476.355 , 57479.3568, 57487.3586, 57490.3562, 57493.3352, 57496.2949, 57505.3557, 57509.2932, 57513.2934, 57518.2735, 57521.2739, 57536.2321, 57539.2115, 57543.2301, 57551.1701, 57555.2107, 57558.191 , 57573.1923, 57576.1749, 57586.1854]
flux = [2.0357230e+00, -2.0382695e+00, 1.0084588e+02, 5.5482742e+01, 1.4867026e+01, -6.5136810e+01, 1.6740545e+01, -5.7269131e+01, 1.0649184e+02, 1.5505235e+02, 3.2445984e+02, 2.8735449e+02, 2.0898877e+02, 2.8958893e+02, 1.9793906e+02, -1.3370536e+01, -3.9001358e+01, 7.4040916e+01, -1.7343750e+00, 2.7844931e+01, 6.0861992e+01, 4.2057487e+01, 7.1565346e+01, -2.6085690e-01, -6.8435440e+01, 17.573107 , 41.445435 , -110.72664 , 111.328964 , -63.48336 , 352.44907 , 199.59058 , 429.83075 , 338.5255 , 409.94604 , 389.71262 , 195.63905 , 267.13318 , 123.92461 , 200.3431 , 106.994514 , 142.96387 , 56.491238 , 55.17521 , 97.556946 , -29.263103 , 142.57687 , -20.85057 , -0.67210346, 63.353024 , -40.02601]
fluxerr = [42.784702, 43.83665 , 99.98704 , 45.26248 , 43.040398, 44.00679 , 41.856007, 49.354336, 105.86439 , 114.0044 , 45.697918, 44.15781 , 60.574158, 93.08788 , 66.04482 , 44.26264 , 91.525085, 42.768955, 43.228336, 44.178196, 62.15593 , 109.270035, 174.49638 , 72.6023 , 48.021034, 44.86118 , 48.659588, 100.97703 , 148.94061 , 44.98218 , 139.11194 , 71.4585 , 47.766987, 45.77923 , 45.610615, 60.50458 , 105.11658 , 71.41217 , 43.945534, 45.154167, 43.84058 , 52.93122 , 44.722775, 44.250145, 43.95989 , 68.101326, 127.122025, 124.1893 , 49.952255, 54.50728 , 114.91599]
passband = ['g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'g', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r', 'r']
photflag = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4096, 4096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 4096, 6144, 4096, 4096, 4096, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
objid = 'transient_1'
ra = 3.75464531293933
dec = 0.205076187109334
redshift = 0.233557
mwebv = 0.0228761
light_curve_list = [(mjd, flux, fluxerr, passband, photflag, ra, dec, objid, redshift, mwebv)]
classification = Classify()
predictions, time_steps = classification.get_predictions(light_curve_list)
print(predictions)
classification.plot_light_curves_and_classifications()
classification.plot_classification_animation()
```