fitspy.app.callbacks module

Callback functions encapsulated in a class to interact with the GUI

class fitspy.app.callbacks.Callbacks

Bases: object

Callback functions to interact with the GUI of the spectra fitting appli

spectra

List that contains all Spectrum objects

Type:

Spectra object

current_spectrum

The current selected spectrum to work with

Type:

Spectrum object

cids

connection ids that can be used with FigureCanvasBase.mpl_disconnect

Type:

list of ints

show_plot

Activation key for (re)plotting

Type:

bool

lines

Lines related to all the spectra displaying

Type:

list of Matplotlib.Lines2D

nearest_lines

The nearest profiles of the mouse position when clicking (limited to 10)

Type:

list Matplotlib.Lines2D

tmp

Annotation to display fit parameters in the plot

Type:

matplotlib.text.Annotation

selected_frame

Frame to enable between ‘Baseline’ and ‘Peaks’

Type:

str

model

Dictionary issued from a .json model reloading

Type:

dict

ncpus

Number of CPUs to work with in fitting. If ncpus = “auto”, determine automatically this number according to the number of spectra to handle and the number of available CPUs. if ncpus is None (default value), consider the value passed through fit_settings.params[‘ncpus’]

Type:

int or str

update_figure_settings()

Update figure settings

rescale()

Rescale the figure

outliers_calculation()

Calculate the outliers (limit)

set_outliers_coef()

Set the outliers coefficient

show_all()

Show all spectra and highlight spectrum on mouse over

show_hide_results(view)

Show/Hide the ‘paramsview’ or the ‘statsview’

save_results(dirname_res=None)

Save all results (peaks parameters) in .csv files

load_model(fname_json=None)

Load a model from a .json file

get_ncpus(nfiles)

Return the number of CPUs to work with

apply_model(model_dict=None, fnames=None, fit_params=None)

Apply model to the selected spectra

apply_model_to_all()

Apply model to the all the spectra

messagebox_continue(fnames)

Open a messagebox if no models are found and return True/False concerning process continuation

save(fnames, fname_json=None)

Save spectra in a .json file

save_selection(fname_json=None)

Save selected spectra models in a .json file

save_all(fname_json=None)

Save all spectra models in a .json file

reload(fname_json=None)

Reload spectra models from a .json file

plot()

Plot baseline and peak models after ‘ax’ clearing

on_press_baseline_peaks(event)

Callback function associated to the mouse press event in the ‘Baseline’ and ‘Peaks’ LabelFrames for enabling/disabling

baseline_is_subtracted_message()

Show an error message associated with the baseline

add_baseline_point(x, y)

Add baseline point from the (x,y)-coordinate

del_baseline_point(x, _)

Delete the closest baseline ‘x’-point

apply_baseline_settings(fnames=None)

Apply baseline settings

apply_baseline_settings_to_all()

Apply baseline settings to all the spectra

set_baseline_settings()

Set baseline settings from the baseline to the appli

load_baseline(fname=None)

Load a baseline from a row-column .txt file

load_user_model(model)

Load users model from file to be added to PEAK_MODELS or BKG_MODEL

add_peaks_point(x, y)

Add peak from the (x,y)-coordinates

del_peaks_point(x, _)

Delete the closest peak ‘x’-point

auto_peaks(model_name=None)

Define peaks from automatic detection

set_bkg_model()

Set bkg_model

update_fit_settings()

Update fit settings

colorize_from_fit_status(fnames)

Colorize the fileselector items from the fit success status

fit(fnames=None)

Fit the peaks

fit_all()

Fit the peaks for all the spectra

set_range()

Set range from the spectrum to the appli

apply_range(fnames=None)

Set an apply range to the spectrum/spectra

apply_range_to_all()

Set and apply range to all the spectra

update_normalize()

Update ‘normalize’ to all the spectra

get_value(name)

Return the float value related to the ‘name’ attribute

update_normalize_range()

Update the normalization ranges to all the spectra

set_normalize_settings()

Set normalize settings from the spectrum to the appli

reinit(fnames=None)

Reinitialize the spectrum

reinit_all()

Reinitialize all the spectra

reassign_current_spectrum(fname)

Reassign the current spectrum from ‘fname’

remove(delete_tabview=True)

Remove all the features (spectrum attributes, baseline, tabview)

auto_eval(model_name=None, fnames=None)

Fit spectrum after evaluating baseline and peaks automatically

auto_eval_all(model_name=None)

Apply automatic fitting on all spectra

delete_all(_)

Delete all spectra

delete(fnames=None)

Delete items from spectra selected in the ‘fileselector’ or passed as argument

add_items_from_dir(dirname)

Add new items related to a ‘dirname’

add_items(fnames=None)

Add new items from a ‘fnames’ list

update_markers(fname)

Markers management in 2D-maps

update(fname=None)

Update the appli with the spectrum selected in the ‘fileselector’ or passed as argument

create_map(fname)

Create the 2D-map that consists in replacing the current spectra by the ones issued from the 2D-map extrusion