Welcome to Tihi documentation!

Contents:

Indices and tables

tihi package

tihi module

app module

class tihi.app.Window

Bases: QMainWindow

Main window of the application

finish_wizard()
normalize()
plot_approx()
plot_decompositions()
plot_input_data()

Plot the input data called by the self.read_file function

read_file()
Reads a signal file data in the following format:

  • first column : independent variable data

  • second column : dependent variable data

run_wizard()
save_parameters()
set_max_value()

sets the max x-value of the input signal

set_min_value()

sets the min x-value of the input signal

set_title()

Set the title of the plot

set_xlabel()

Set the x-axis label of the plot

set_ylabel()

Set the y-axis label of the plot

class tihi.app.WizardWindow(x_vals, y_vals, x_label, y_label, title)

Bases: QMainWindow

closure()
tihi.app.main()

wizard module

class tihi.wizard.MagicWizard(x_vals, y_vals, x_label='x-axis', y_label='y-axis', title='title')

Bases: QWizard

Wizard for peak profiling using PyQt5 and custom pages.

out_profiles

List to store output profiles.

Type:

list

out_params

List to store output parameters.

Type:

list

out_model

Placeholder for output model.

page_num

Current page number.

Type:

int

interpolate

Interpolation page instance.

Type:

ipp

baseline

Baseline fitting page instance.

Type:

blp

peak_detect

Peak detection page instance.

Type:

pdp

distribution

Distribution fitting page instance.

Type:

dfp

back_button()

Placeholder method for handling the back button.

Returns:

None

finish_line()

Placeholder method for finishing the wizard.

Returns:

None

handlePageChange()

Handle changes in wizard pages.

Disables the “go back” button on the first page.

initializePage(pageId)

Initialize wizard pages and manage the “go back” button’s visibility.

Parameters:

pageId – ID of the wizard page being initialized.

Returns:

None

next_button()

Move to the next wizard page based on the current page number.

Returns:

None

tihi_utils package

tihi_utils module

tihi_utils.baseline_corrector module

tihi.tihi_utils.baseline_corrector.airPLS(y, lamb=100, niter=15)

Perform AirPLS baseline correction algorithm on spectral data.

:param y (ndarray) : Input signal (spectral data). :param lamb (float, optional (default=100)) : Parameter controlling the smoothness of the baseline. :param niter (int, optional (default=15)) : Maximum number of iterations. :return (ndarray) : airPLS baseline.

tihi.tihi_utils.baseline_corrector.arPLS(y, ratio=1e-06, lam=100, niter=100, full_output=False)

Perform arPLS baseline correction algorithm on spectral data.

:param y (ndarray) : Input signal (spectral data). :param ratio (float, optional (default=1e-6)) : Convergence threshold. :param lam (float, optional (default=100)) : Parameter controlling the smoothness of the baseline. :param niter (int, optional (default=100)) : Maximum number of iterations. :param full_output (bool, optional (default=False)) : If True, return additional information about the iteration. :return (ndarray) : arPLS baseline. :return (dict, if full_output=True) : Dictionary containing information about the iteration (num_iter, stop_criterion).

tihi.tihi_utils.baseline_corrector.linear_baseline_correction(x_val, y_val, window_length=31, percentile=5)

Perform linear baseline correction using a percentile threshold on smoothed data.

:param x_val (ndarray) : x values of the data points. :param y_val (ndarray) : y values of the data points. :param window_length (int, optional (default=31)) : Length of the window for median filtering. :param percentile (int, optional (default=5)) : Percentile threshold for selecting baseline points. :return (ndarray) : Linear baseline y values.

tihi_utils.distributions module

class tihi.tihi_utils.distributions.GaussianFitter(InterpolatedData, peaks, max_iter=100)

Bases: object

approximator(max_iter)

Perform Gaussian fitting using least squares optimization.

:param max_iter (int) : Maximum number of iterations for fitting. :return error (float) : Mean absolute error of the fitting. :Notes : Uses soft L1 loss and bounds parameters to constrain optimization.

gaussian(x, center, amplitude, sigma)

Calculate a Gaussian function.

:param x (ndarray) : X values. :param center (float) : Center of the Gaussian function. :param amplitude (float) : Amplitude of the Gaussian function. :param sigma (float) : Standard deviation of the Gaussian function. :return (ndarray) : Calculated Gaussian function values.

gaussian_sum(x, params)

Calculate the sum of Gaussian functions.

:param x (ndarray) : X values. :param params (ndarray) : Array of parameters for Gaussian functions. :return (ndarray) : Sum of Gaussian functions.

residual(params, x_vals, y_vals)

Calculate residual between data and Gaussian fit.

:param params (ndarray) : Array of parameters for Gaussian functions. :param x_vals (ndarray) : X values of the data. :param y_vals (ndarray) : Y values of the data. :return (ndarray) : Residual values.

class tihi.tihi_utils.distributions.LorentzianFitter(InterpolatedData, peaks, max_iter=100)

Bases: object

Lorentzian peak fitting class.

:param InterpolatedData (object) : Interpolated data object containing x_val and y_val. :param peaks (array_like) : Indices of peaks in the data. :param max_iter (int, optional (default=100)) : Maximum number of iterations for fitting. :attribute x_vals (ndarray) : X values from InterpolatedData. :attribute y_vals (ndarray) : Y values from InterpolatedData. :attribute centers (ndarray) : Initial centers of Lorentzian peaks. :attribute amplitudes (ndarray) : Initial amplitudes of Lorentzian peaks. :attribute gammas (list) : Initial full width at half maximum (FWHM) of Lorentzian peaks. :attribute params (ndarray) : Array of initial parameters for least squares fitting. :attribute start_params (list) : Flattened list of initial parameters. :attribute decompositions (list) : List to store individual Lorentzian functions.

approximator(max_iter)

Perform Lorentzian fitting using least squares optimization.

:param max_iter (int) : Maximum number of iterations for fitting. :return error (float) : Mean absolute error of the fitting. :Notes : Uses soft L1 loss and bounds parameters to constrain optimization.

lorentzian(x, center, amplitude, gamma)

Calculate a Lorentzian function.

:param x (ndarray) : X values. :param center (float) : Center of the Lorentzian function. :param amplitude (float) : Amplitude of the Lorentzian function. :param gamma (float) : Full width at half maximum (FWHM) of the Lorentzian function. :return (ndarray) : Calculated Lorentzian function values.

lorentzian_sum(x, params)

Calculate the sum of Lorentzian functions.

:param x (ndarray) : X values. :param params (ndarray) : Array of parameters for Lorentzian functions. :return (ndarray) : Sum of Lorentzian functions.

residual(params, x_vals, y_vals)

Calculate residual between data and Lorentzian fit.

:param params (ndarray) : Array of parameters for Lorentzian functions. :param x_vals (ndarray) : X values of the data. :param y_vals (ndarray) : Y values of the data. :return (ndarray) : Residual values.

class tihi.tihi_utils.distributions.VoigtFitter(InterpolatedData, peaks, max_iter=50)

Bases: object

approximator(max_iter)

Perform Voigt fitting using least squares optimization.

:param max_iter (int) : Maximum number of iterations for fitting. :return error (float) : Mean absolute error of the fitting. :Notes : Uses soft L1 loss and bounds parameters to constrain optimization.

residual(params, x_vals, y_vals)

Calculate residual between data and Voigt fit.

:param params (ndarray) : Array of parameters for Voigt profiles. :param x_vals (ndarray) : X values of the data. :param y_vals (ndarray) : Y values of the data. :return (ndarray) : Residual values.

voigt(x, center, amplitude, gauss_width, lorentz_width)

Calculate a Voigt profile using Faddeeva function approximation.

:param x (ndarray) : X values. :param center (float) : Center of the Voigt profile. :param amplitude (float) : Amplitude of the Voigt profile. :param gauss_width (float) : Gaussian component width of the Voigt profile. :param lorentz_width (float) : Lorentzian component width of the Voigt profile. :return (ndarray) : Calculated Voigt profile values.

voigt_sum(x, params)

Calculate the sum of Voigt profiles.

:param x (ndarray) : X values. :param params (ndarray) : Array of parameters for Voigt profiles. :return (ndarray) : Sum of Voigt profiles.

tihi_utils.interpolate module

class tihi.tihi_utils.interpolate.Interpolate(input_x, input_y, degree_spline=3, gratings=1000, denoising_window_size=10, denoising_order=3)

Bases: object

denoise_signal()

Apply Savitzky-Golay filter for signal denoising.

interpolate(x_in, y_in)

Perform spline interpolation.

Parameters:

  • x_in (1D np array) – input x values

  • y_in (1D np array) – input y values

tihi_utils.peak_detection module

tihi.tihi_utils.peak_detection.find_peaks(class_interpolate, window_size=10, threshold=0.01, min_amp=0.0)

Find peaks in the interpolated signal based on the second derivative.

Parameters:

  • class_interpolate (Interpolate) – Instance of Interpolate containing interpolated data.

  • window_size (int, optional) – Size of the window for peak detection. Defaults to 10.

  • threshold (float, optional) – Threshold for peak detection. Defaults to 0.01.

  • min_amp (float, optional) – Minimum amplitude of peaks. Defaults to 0.0.

Returns:

List of indices where peaks are detected.

Return type:

peaks (list)

tihi.tihi_utils.peak_detection.second_derivative(class_interpolate)

Compute the second derivative of the interpolated signal.

Parameters:

class_interpolate (Interpolate) – Instance of Interpolate containing interpolated data.

Returns:

Array of second derivative values.

Return type:

second_deriv (np.array)

tihi_wizardPages package

tihi_wizardPages module

tihi_wizardPages.baselinePage module

class tihi.tihi_wizardPages.baselinePage.BaselinePage(x_vals, y_vals, x_label='x-axis', y_label='y-axis', title='title')

Bases: QWizardPage

Wizard page for baseline correction using various methods.

x_vals : x-axis values of the data y_vals : y-axis values of the data x_label : label for the x-axis y_label : label for the y-axis title : title for the plot

clear()

Clears the plot and resets the x and y values to the original data.

lambda_changes()

Updates the lambda parameter for PLS methods based on user input.

method_changes()

Updates the baseline correction method based on user selection.

plot_input_data()

Plots the input data along with the baseline if it exists.

ratio_changes()

Updates the ratio parameter for arPLS based on user input.

run()

Executes the baseline correction based on the selected method.

class tihi.tihi_wizardPages.baselinePage.QIComboBox(parent=None)

Bases: QComboBox

Custom ComboBox class inheriting from QComboBox.

tihi_wizardPages.distributionPage module

class tihi.tihi_wizardPages.distributionPage.DistributionFittingPage(interpolation_class, x_label='x-axis', y_label='y-axis', title='title')

Bases: QWizardPage

Wizard page for fitting distributions to data.

interpolation_class : Instance of a class containing x_val and y_val attributes for data. x_label : Label for the x-axis. y_label : Label for the y-axis. title : Title for the plot.

clear()

Clears the plot and re-plots the original data.

distribution_type_changes()

Updates the selected distribution type based on user input.

max_iter_changes()

Updates the maximum iteration value based on user input.

method_changes()

Updates the optimizer loss type based on user input.

plot_all()

Plots all decompositions.

plot_input_data(plot_approximation=False, plot_all_distributions=False)

Plots the input data along with the approximation or all decompositions if specified.

plot_approximation : If True, plot the approximation. (the sum of distributions) plot_all_distributions: If True, plot all decompositions.

run()

Executes the decomposition based on the selected distribution type.

class tihi.tihi_wizardPages.distributionPage.QIComboBox(parent=None)

Bases: QComboBox

Custom ComboBox class inheriting from QComboBox.

tihi_wizardPages.interpolatePage module

class tihi.tihi_wizardPages.interpolatePage.InterpolationPage(x_vals, y_vals, x_label='x-axis', y_label='y-axis', title='title')

Bases: QWizardPage

Wizard page for interpolating and denoising data.

x_vals : Input x-axis values. y_vals : Input y-axis values. x_label : Label for the x-axis. y_label : Label for the y-axis. title : Title for the plot.

change_denoise_window_size()

Updates the denoise window size based on user input.

change_numpoints()

Updates the number of points to use for interpolation based on user input.

clear()

Clears the plot and re-plots the original data.

denoise()

Denoises the interpolated data and updates the plot.

interpolate_data()

Interpolates the original data and updates the plot.

plot_input_data(denoise=False)

Plots the input data.

denoise: If True, plot with denoising (green line).

tihi_wizardPages.peak_detectionPage module

class tihi.tihi_wizardPages.peak_detectionPage.PeakDetectionPage(interpolation_class, x_label='x-axis', y_label='y-axis', title='title')

Bases: QWizardPage

Wizard page for peak detection on interpolated data.

interpolation_class : Interpolation class containing x_val and y_val attributes from the Interpolation Page. x_label : Label for the x-axis. y_label : Label for the y-axis. title : Title for the plot.

clear()

Clears the plot area.

min_amp_changes()

Updates the minimum amplitude for peak detection based on user input.

plot_input_data(peak=False)

Plot the input data called by the self.read_file function

run()

Executes peak detection using specified parameters and updates the plot.

threshold_changes()

Updates the second-derivative difference threshold for peak detection based on user input.

window_size_changes()

Updates the window size for peak detection based on user input.