Preprocessing

classy offers basic preprocessing functionality for reflectance spectra: smoothing, truncating, normalising, resampling, and slope removal. They are demonstrated here using a spectrum of (402) Chloe from DeMeo+ 2009.

>>> import classy
>>> spectra = classy.Spectra("Chloe", shortbib="DeMeo+ 2009")
>>> spec = spectra[0]  # There is only one spectrum matching these criteria
>>> spec.plot()

Smoothing

Smoothing a spectrum aims to reduce high-frequency noise in the data. To smooth a spectrum, use the smooth() method. Two common smoothing algorithms are supported:

• Savitzky-Golay: Applies a smoothing filter using scipy.signal.savgol_filter

• Spline Fit: Applies smoothing using scipy.interpolate.UnivariateSpline

The main argument of smooth() is the method, which is either savgol or spline. All other arguments provided to smooth() are passed to the underlying smoothing function given above.

>>> spec.smooth(method='savgol', window_length=7, polyorder=3)  # arguments passed to scipy.signal.savgol_filter

The choice of smoothing parameters is often a tedious iterative process (smooth, plot, smooth, plot, …). classy facilitates this process by providing an interactive graphical user interface, allowing to adjust the parameters while inspecting the result in real time. Calling smooth() without any arguments launches the smoothing interface.

After storing the smoothing parameters, classy will automatically apply them the next time that the smooth() function is called, without opening the interface.

This way, smoothing spectra prior to analysis is quick and only done once. To smooth a batch of spectra using the smoothing interface, you can use these methods:

Command Line

Smooth all spectra in Marsset+ 2014.

$ classy smooth --shortbib 'Marsset+ 2014'

This will launch the smoothing interface for all spectra which have not been smoothed yet. You can use the --force flag to also smooth spectra that have already been smoothed. The syntax to select spectra is documented here.

python

Smooth all spectra in Marsset+ 2014.

>>> spectra = classy.Spectra(source='Marsset+ 2014')
>>> spectra.smooth()

This will launch the smoothing interface for all spectra which have not been smoothed yet. You can use the force=True argument to also smooth spectra that have already been smoothed. The syntax to select spectra is documented here.

Important

The original spectrum data file is never changed by classy. All preprocessing transformations are applied in memory.

Normalising

Two normalisation methods can be applied to a classy.Spectrum:

• wave: normalise a spectrum to unity at a given wavelength by division

• l2: set the L2-norm of the spectrum to unity

Normalisation is applied using the .normalize method. The method keyword accepts one of the two method names given above. The default is wave and requires to define the wavelength at which to normalise using the at argument. classy then normalises the spectrum to unity in the wavelength bin which is the closest to the provided at value.

>>> spec.normalize(method='l2')  # normalises L2-norm to unity
>>> spec.normalize(at=1.25)  # normalises to unity at closest existing wavelength point

Resampling

Resampling a classy.Spectrum can be used for extrapolation or for homogenisation of the sampling different spectra. The .resample() method uses scipy.interpolate.interp1d. The function takes a list of wavelengths at which to resample the spectrum. All other arguments are passed on to the scipy.interpolate.interp1d function.

>>> wave_new = classy.taxonomies.demeo.WAVE  # resample to DeMeo+ 2009 wavelength pattern
>>> spec.resample(wave_new)

The following example shows how to do linear and constant extrapolation. You can find more information in the documentation of scipy.interpolate.interp1d.

>>> spec.resample(wave_new, fill_value='extrapolate') # linear extrapolation
>>> spec.resample(wave_new, bounds_error=False, fill_value=(spec.refl[0], spec.refl[-1]) # constant extrapolation.

Warning

Resampling changes the shape of the wavelength and reflectance arrays (spec.wave and spec.refl). The reflectance error spec.refl_err cannot be resampled, meaning that it would keep its original shape. This can cause trouble in various places in classy. The error attribute is therefore set to None when resampling.

Truncating

Use the truncate() to quickly set the upper and lower wavelength bounds of the spectrum by specifying one or both of wave_min and wave_max.

>>> spec.truncate(wave_min=0.9)

Note

Unlike resampling, truncating does retain the reflectance error as the operation can be applied to the spec.refl_err array as well.

Remove Slope

Removing the slope of a spectrum means fitting a polynomial of degree 1 to the entire spectrum and to divide the reflectance values by the best-fit line. The intercept and slope of the fit solution are accessible via the added slope attribute of the spectrum:

>>> spec.remove_slope()
>>> spec.slope # tuple containing (slope, intercept) of fitted polynomial
(0.06839286, 1.02706994)

Remove Continuum

Removing the continuum of a spectrum means fitting a convex hull to the spectrum and dividing the reflectance values by the continuum. The reflectance will be overwritten. Use spectra._refl_original to get the original reflectance.

>>> spec  = classy.Spectra(16)[-2]
>>> spec.remove_continuum()