fitspy.spectrum module

Class dedicated to spectrum processing

fitspy.spectrum.create_model(model, model_name, prefix=None)

Return a ‘model’ (peak_model or ‘bkg_model’) object

class fitspy.spectrum.Spectrum

Bases: object

Class dedicated to spectrum processing

fname

Filename associated to spectrum to handle

Type:

str

range_min, range_max

Range associated to the spectrum support to work with

Type:

floats

x0, y0

Arrays related to the spectrum raw support and intensity (resp.)

Type:

numpy.array((n0))

x, y

Arrays related to spectrum modified support and intensity (resp.)

Type:

numpy.array((n))

normalize

Activation keyword for the spectrum profile normalization

Type:

bool

normalize_range_min, normalize_range_max

Ranges for searching the maximum value used in the normalization

Type:

floats

outliers_limit

Array related to the outliers limit associated to the raw data (x0, y0)

Type:

numpy.array((n0))

baseline

Baseline associated to the spectrum (to subract)

Type:

Baseline object

baseline_history

Concatenation list of all baselines applied to the spectrum. Each item of the list consist in the max order of the baseline polynom and the (x, y) baseline points coordinates.

Type:

list of list - DEPRECATED from v2024.2

bkg_model

Background model to fit with the composite peaks models, among : [None, ‘ConstantModel’, ‘LinearModel’, ‘ParabolicModel’, ‘ExponentialModel’]

Type:

lmfit.Model

peak_models

List of peak models

Type:

list of lmfit.Model

peak_labels

List of labels associated to the peak models. Default is [‘1’, ‘2’, …]

Type:

list of str

peak_index

Counter used for peak models indexing when creating a lmfit.Model

Type:

itertools.count

fit_params

Dictionary used to manage the fit parameters: * method: str

Method used for fitting. See lmfit.Model.fit(). Default method is ‘Leastsq’.

  • fit_negative: bool

    Activation keyword to take into account negative values when fitting. Default is False.

  • fit_outliers: bool

    Activation keyword to take into account outliers points when fitting. Default is False.

  • max_ite: int

    Maximum number of iteration associated to the fitting process. An iteration consists in evaluating all the ‘free’ parameters once. Default is 200.

  • coef_noise: float

    Coefficient applied to the estimated noise amplitude to define a threshold below which the fit weights are set to 0, and local peak models are disabled . Default is 2.

  • xtol: float

    Relative error desired in the solution approximated by the ‘Leastsq’ or the ‘Least_square’ algorithm. Default is 1e-4.

Type:

dict

result_fit

Object resulting from lmfit fitting. Default value is a ‘None’ object (function) that enables to address a ‘result_fit.success’ status.

Type:

lmfit.ModelResult

reinit()

Reinitialize the main attributes

set_attributes(model_dict)

Set attributes from a dictionary (obtained from a .json reloading)

preprocess()

Preprocess the spectrum: call successively load_profile(), apply_range(), eval_baseline(), subtract_baseline() and normalization()

load_profile(fname)

Load profile from ‘fname’ with 1 header line and 2 (x,y) columns

apply_range(range_min=None, range_max=None)

Apply range to the raw spectrum

calculate_outliers()

Return outliers points (x,y) coordinates

property y_no_outliers

Return spectrum profile where outliers have been removed and replaced by interpolated values

normalization()

Normalize spectrum according to the ‘Maximum’ value or the nearest ‘Attractor’ value from reference position, assuming that the baseline has been correctly defined (y_min value ~ 0)

static create_peak_model(index, model_name, x0, ampli, fwhm=1.0, fwhm_l=1.0, fwhm_r=1.0, alpha=0.5)

Create a ‘lmfit’ model associated to one peak

Parameters:

  • index (int) – Index used to create the ‘prefix’ associated to the peak model

  • model_name (str) – Model name among ‘Gaussian’, ‘Lorentzian’, ‘PseudoVoigt’, ‘GaussianAsym’, ‘LorentzianAsym’

  • x0 (float) – Position of the peak model

  • ampli (float) – Amplitude of the peak model.

  • fwhm (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are 1.

  • fwhm_l (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are 1.

  • fwhm_r (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are 1.

  • alpha (float, optional) – Optional parameter passed to the ‘PseudoVoigt’ model. Default value is 0.5.

Returns:

peak_model

Return type:

lmfit.Model

reassign_params()

Reassign fitted ‘params’ values to the ‘models’

add_peak_model(model_name, x0, ampli=None, fwhm=None, fwhm_l=None, fwhm_r=None, alpha=0.5)

Add a peak model passing model_name and indice position or parameters

Parameters:

  • model_name (str) – Model name among ‘Gaussian’, ‘Lorentzian’, ‘GaussianAsym’, ‘LorentzianAsym’

  • x0 (float) – Position of the peak model

  • ampli (float, Optional) – Amplitude of the peak model. If None, consider the amplitude of the spectrum profile at position x0

  • fwhm (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are x-step size (x[1]-x[0]).

  • fwhm_l (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are x-step size (x[1]-x[0]).

  • fwhm_r (floats, optional) – Optional parameters passed to the model related to the Full Width at Half Maximum. Default values are x-step size (x[1]-x[0]).

  • alpha (float, optional) – Optional parameter passed to the ‘PseudoVoigt’ model. Default values is 0.5.

del_peak_model(i)

Delete the ith-peak model

reorder(key='x0', reverse=False)

Return increasing or decreasing ordered models list from ‘key’

static get_model_name(model)

from model class attribute return the function name associated Ex: Model(‘LorentzianAsym’…) -> ‘lorentzian_asym’

remove_models()

Remove all the models

set_bkg_model(bkg_name)

Set the ‘bkg_model’ attribute from ‘bkg_name’

fit(fit_method=None, fit_negative=None, fit_outliers=None, max_ite=None, coef_noise=None, xtol=None, reinit_guess=True, **kwargs)

Fit the peaks and background models

Parameters:

  • fit_method (str, optional) – Method passed to lmfit.fit() like ‘leastsq’, ‘least_squares’, ‘nelder’, ‘slsqp’, … (see the lmfit documentation). Default value is ‘leastsq’.

  • fit_negative (bool, optional) – Activation key to take into account negative values during the fit. Default value is False.

  • fit_outliers (bool, optional) – Activation key to take into account outliers during the fit. Default value is False.

  • max_ite (int, optional) – Number of maximum iterations (1 iteration corresponds to 1 gradient descent of all the variables). Default value is 200.

  • coef_noise (float, optional) – Multiplication factor associated with the estimated noise level. Default value is 1.

  • xtol (float, optional) – Relative tolerance associated with the ‘leastsq’ and the ‘least_squares’ fit algorithm. Default value is 0.0001.

  • reinit_guess (bool, optional) – Key to adapt initial values for ‘ampli’ and ‘fwhm’, ‘fwhm_l’ or ‘fwhm_r’ to the spectrum intensity at the corresponding point ‘x0’. Default value is True.

  • kwargs (dict, optional) – Dictionary of optional arguments passed to lmfit.fit()

auto_baseline()

set baseline.mode to ‘Semi-Auto

eval_baseline()

Evaluate baseline profile

subtract_baseline()

Subtract the baseline to the spectrum profile

auto_peaks(model_name)

Create automatically ‘model_name’ peak-models in the limit of 5% of the maximum intensity peaks and nmax_nfev=400

plot(ax, show_outliers=True, show_outliers_limit=True, show_negative_values=True, show_noise_level=True, show_baseline=True, show_background=True, show_peak_models=True, show_result=True, subtract_baseline=True)

Plot the spectrum with the peak models

plot_residual(ax, factor=1)

Plot the residual x factor obtained after fitting

save_params(dirname_params)

Save fit parameters in a ‘.csv’ file located in ‘dirname_params’

save_stats(dirname_stats)

Save statistics in a ‘.txt’ file located in ‘dirname_stats’

save(fname_json=None)

Return a ‘model_dict’ dictionary from the spectrum attributes and Save it if a ‘fname_json’ is given

static load(fname_json, preprocess=False)

Return a Spectrum object created from a .json file reloading