Machine learning for spectroscopy
Disclaimer: This is a work in progress
pip install git+https://github.com/MariaTsantaki/spectroscopy-ML
import specML
from specML import Data, Model, Minimizer
data = specML.get_data() # Data type
model = specML.get_model(data) # Model type
# Get a random spectrum
flux = data.y.sample(1)
minimizer = Minimizer(flux, model)
res = minimizer.minimize(method='Nelder-Mead')
# See the results
print(res)
# Plot the results
minimizer.plot()All scipy.optimize.minimize methods are available (although not all will work).
The minimization works by doing a chi squared minimization between the flux and
the calculated flux from, which is done with Machine Learning.