fitspy.spectra module

Class dedicated to handle ‘Spectrum’ objects contained in a list managed by “Spectra”

class fitspy.spectra.Spectra(spectra_list=None)

Bases: list

Class dedicated to handle ‘Spectrum’ objects contained in a list

spectra_maps
Type:

list of SpectraMap objects

Parameters:

spectra_list (list of Spectrum objects, optional) –

property fnames

Return all the fnames related to spectra AND spectra maps

property all

Return all the spectra related to spectra AND spectra maps

get_objects(fname)

Return spectrum and parent (spectra or spectra map) related to ‘fname’

save_results(dirname_res, fnames=None)

Save spectra results (peaks parameters and statistics) in .csv files

Parameters:
  • dirname_res (str) – Dirname where to save the .csv files

  • fnames (list of str, optional) – List of the spectrum ‘fnames’ to save. If None, consider all the spectrum contained in the ‘spectra’ list

save_figures(dirname_fig, fnames=None, bounds=None)

Save spectra figures

Parameters:
  • dirname_fig (str) – Dirname where to save the figures

  • fnames (list of str, optional) – List of the spectrum ‘fnames’ to save. If None, consider all the spectrum contained in the ‘spectra’ list

  • bounds (tuple of 2 tuples, optional) – Axis limits corresponding to ((xmin, xmax), (ymin, ymax))

static load_model(fname_json, ind=0)

Return a fitspy model (‘model_dict’) from a ‘.json’ file

Parameters:
  • fname_json (str) – Filename associated to the spectra .json file where to extract the fitspy model

  • ind (int, optional) – Spectrum index to consider as model in the spectra issued from the .json file reloading

Returns:

model_dict – The corresponding fitspy model

Return type:

dict

apply_model(model_dict, fnames=None, ncpus=1, fit_only=False, tk_progressbar=None, **fit_kwargs)

Apply ‘model’ to all or part of the spectra

Parameters:
  • model_dict (dict) – Dictionary related to the Spectrum object attributes (obtained from Spectrum.save() for instance)

  • fnames (list of str, optional) – List of the spectrum.fname to handle. If None, apply the model to all the spectra

  • ncpus (int, optional) – Number of CPU to work with in fitting

  • fit_only (bool, optional) – Activation key to process only fitting

  • tk_progressbar (ProgressBar obj, optional) – Progression bar using tkinter.ttk.Progressbar to follow the ‘apply_model’ progression

  • fit_kwargs (dict) – Keywords arguments passed to spectrum.fit()

save(fname_json, fnames=None)

Save spectra in a .json file

Parameters:
  • fname_json (str) – Filename associated to the .json file for the spectra saving

  • fnames (list of str, optional) – List of the spectrum ‘fnames’ to save. If None, consider all the spectrum contained in the ‘spectra’ list

static load(fname_json)

Return a Spectra object from a .json file

fitspy.spectra.progressbar(queue_incr, ntot, tk_progressbar=None)

Progress bar