astrorapid 0.1.21
Real-time Automated Photometric IDentification of astronomical transients
License: MIT
Keywords: supernova photometric light curve classification deep machine learning

# 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']
zeropoint = [27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5, 27.5]
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, zeropoint, photflag, ra, dec, objid, redshift, mwebv)]

classification = Classify(light_curve_list)
predictions, time_steps = classification.get_predictions()
print(predictions)

classification.plot_light_curves_and_classifications()
classification.plot_classification_animation()

```