Getting started

pyLick is currently based on five modules:

1. io implements the built-in methods for spectra I/O and preliminary analysis;

2. indices loads the index library with pass-bands definitions;

3. measurements derive multiple indices on one spectrum;

4. analysis contains two main classes:

   1. Galaxy, optimized to analyze a single spectrum (wavelength, flux, flux error, [mask])

   2. Catalog, optimized to perform the analysis of a catalog of spectra

5. plot plotting routines.

The examples below show typical usage of pyLick.

Workflow 1

1. Import a .fits spectrum, (2) load the table of spectral indices to measure, (3) call the Galaxy class,

import pylick.io as io
from pylick.indices import IndexLibrary
from pylick.analysis import Galaxy

spectrum     = io.load_spec_fits(dir_spec, filename, colnames=['lambda', 'flux', 'flux_err'])
ind_library  = IndexLibrary(index_keys)

ind_measured = Galaxy(ID, index_list, spec_wave=spectrum[0], spec_flux=spectrum[1], spec_err=spectrum[2], z=z)
vals, errs   = ind_measured.vals, ind_measured.errs
print(ind_measured)

Workflow 2

Define a function to load the spectra from a catalog folder, (2) load the table of spectral indices to measure, (3) call the Catalog class,

import pylick.io as io
from pylick.indices import IndexLibrary
from pylick.analysis import Catalog

def load_spec(ID):
   ...
   return [wave, flux, ferr, mask]

IDs = [...]
ind_library  = IndexLibrary(index_keys)

ind_measured = Catalog(IDs, load_spec, index_keys, z=zs, do_plot=True, verbose=True)

Workflow 3

Call the MeasSpectrum class defined in pylick.measurement to verify intermediate measurements for debug purposes.