CGCD

This Python library enables the analysis of thermoluminescent (TL) glow curves. It currently includes two main functions:

PeakDetection This function detects potential peaks present in a TL glow curve. It requires the following input parameters:

DataFrame: A pandas.DataFrame with two columns. The first column should contain temperature values (in degrees Celsius), and the second column should contain the corresponding light intensity for each temperature point.

Smoothing level (smoothing_level): An integer between 0 and 4. A value of 0 applies very strong smoothing, while 4 applies minimal smoothing.

Resolution (channels): An integer indicating the number of channels used to analyze the curve, which affects the resolution of peak detection.

Derivative factor (derivative_factor): A numerical value that multiplies the intensity of the second derivative, used to enhance peak detection.

As a result, the function returns a pandas.DataFrame containing the intensity of each detected peak and the corresponding temperature at which the maximum is estimated to occur.

Deconvolution This function performs a deconvolution fit of experimental TL glow curves using a mathematical model based on first-order kinetics. It supports both discrete and continuous trapped charge density distributions, allowing either Gaussian, exponential, or localized (discrete) peak modeling.

Input parameters:

df: A pandas.DataFrame containing two columns — temperature values (in °C or K) and corresponding TL intensities.

p: A 4×n vector (as a 1D list or NumPy array of length 4×n), representing the initial guess for each kinetic parameter of the n peaks, in the following order: [I₁_max, E₁_a, T₁_max, σ₁, ..., Iₙ_max, Eₙ_a, Tₙ_max, σₙ]

l_w: A 4×n vector specifying the lower bounds for each parameter during the fitting process, in the same format as p.

p_w: A 4×n vector specifying the upper bounds for each parameter during fitting, also in the same format as p.

max_iter: An integer specifying the maximum number of iterations allowed for the numerical optimization method. This controls the convergence behavior during the fitting process.

ROI: A tuple or list defining the region of interest, as the minimum and maximum temperatures that delimit the fitting area (e.g., [start_temp, end_temp]).

models: A list or vector of length n defining the model type for each component peak. Each element must be one of:

'G' – for a continuous Gaussian trap distribution

'E' – for a continuous Exponential trap distribution

'L' – for a Localized (discrete) trap distribution

The function returns two pandas.DataFrame objects:

Kinetic Parameters DataFrame: Contains the estimated parameters for each contribution:

I_max (a.u.)

E_a (eV)

T_max (K)

σ (eV)

Statistical Residuals DataFrame: Reports a statistical analysis of the residuals from the curve fitting, helping evaluate the quality and reliability of the deconvolution.

Characteristics

• ✅ Easy to use
• ⚡ Fast and efficient
• 🔧 Compatible with Python 3.7+

Installation

You can install the library using pip:

pip install CGCD==0.0.0