SPI: Spectrum Polynomial Interpolations^*

Doing the basic regression thing.

	from spi.library_models import MILESInterpolator as MILES
	psi = MILES(training_data='miles_prugniel.h5', normalize_labels=False)
	psi.renormalize_library_spectra(bylabel='luminosity')
	# Only train on warm stars
	psi.restrict_sample({'teff':(4000.0, 9000.0)})
	# Choose polynomial features to train on, here linear terms + logt^2
	psi.features = (['logt'], ['feh'], ['logg'], ['logt', 'logt'])
	psi.train()

    # Plot a predicted spectrum
	spectrum = spi.get_star_spectrum(logt=3.617, logg=4.5, feh=0.0)
	plot(spi.wavelengths, spectrum)

References:

• Worthey, G., Faber, S. M., Gonzalez, J. J., & Burstein, D. 1994, ApJS, 94, 687 (indices)
• Wu, Y., Singh, H. P., Prugniel, P., Gupta, R., & Koleva, M. 2011, A&A, 525, A71
• Prugniel, P., Vauglin, I., & Koleva, M., 2011, A&A, 531, A165
• Ness, M., Hogg, D. W., Rix, H.-W., Ho, A. Y. Q., & Zasowski, G. 2015, ApJ, 808, 16
• Sharma, K., Prugniel, P., & Singh, H. P. 2016, A&A, 585, A64
• Rix, H.-W., Ting, Y.-S., Conroy, C. & Hogg, D. W. 2016

* More precisely, approximations rather than interpolations. But SPI sounds cooler than SPA.