Appendix

Validity Verifications

When instantiating a Spectrum, classy verifies the validity of the passed data in the following way.

Argument	Value	Action
wave	Positive	Keep value.
wave	Negative	Discard value and matching reflectance.
wave	NaN	Discard value and matching reflectance.
refl	Positive	Keep value.
refl	Negative	Keep value.
refl	NaN	Discard value and matching wavelength.

The examples below show how the wave and refl arrays are adapted.

>>> # Negative wavelength -> remove value
>>> wave = [-1, 2, 3, 4]
>>> refl = [1, 2, 3, 4]
>>> spec = classy.Spectrum(wave, refl)
>>> print(spec.wave)
>>> print(spec.refl)

>>> # NaN wavelength -> remove value
>>> wave = [np.nan, 2, 3, 4]
>>> refl = [1, 2, 3, 4]
>>> spec = classy.Spectrum(wave, refl)
>>> print(spec.wave)
>>> print(spec.refl)

>>> # Negative reflectance -> keep value
>>> wave = [1, 2, 3, 4]
>>> refl = [-1, 2, 3, 4]
>>> spec = classy.Spectrum(wave, refl)
>>> print(spec.wave)
>>> print(spec.refl)

>>> # NaN reflectance -> remove value
>>> wave = [1, 2, 3, 4]
>>> refl = [np.nan, 2, 3, 4]
>>> spec = classy.Spectrum(wave, refl)
>>> print(spec.wave)
>>> print(spec.refl)

These validity verifications can thus affect the shape of the wave and refl values. Attributes which are applied on a per-point bases (like quality flags) are not handled by classy, their adaptation is up to you. To facilitate this, classy adds the mask_valid attribute, which has the same shape as the original data and is True if a value is kept and False otherwise.`

>>> # NaN reflectance -> remove value
>>> wave = [1, 2, 3, 4]
>>> refl = [np.nan, 2, 3, 4]
>>> flag = [0, 1, 1, 2]
>>> spec = classy.Spectrum(wave, refl, flag=flag)
>>> print(spec.wave)
>>> print(spec.refl)
>>> print(spec.flag)
>>> spec.flag = flag[spec.mask_valid]
>>> print(spec.flag)

Finally, classy ensures that the passed wavelength values are sorted in increasing order. If they are not sorted yet, wave will be sorted and refl and refl_err will be sorted accordingly. To ensure that your metadata has the correct order, you can run

>>> flag = sorted(flag, key=wave)

prior to instantiation.

Sharing feature index entires

Locate the features.csv file in your CLASSY_DATA_DIR (default locations are given here). Each line represents one feature (column feature) in a given spectrum (identified by the filename column). All the necessary data to parametrize the feature using classy is contained in these single lines. To share the parametrization of certain features, select the corresponding lines from the features.csv index file and share them with your collaborators.