Usage

This page provides an overview of how to use the Pkynetics library for thermal analysis kinetic methods, focusing on the latest implemented features.

Data Import

Pkynetics provides importers for TGA and DSC data from various manufacturers:

from pkynetics.data_import import tga_importer, dsc_importer

# Import TGA data
tga_data = tga_importer('path/to/tga_data.csv', manufacturer='auto')

# Import DSC data
dsc_data = dsc_importer('path/to/dsc_data.csv', manufacturer='auto')

The manufacturer parameter can be set to ‘auto’, ‘TA’, ‘Mettler’, ‘Netzsch’, or ‘Setaram’.

Model Fitting Methods

Pkynetics implements several model fitting methods with improved data handling:

Coats-Redfern Method

For kinetic analysis:

from pkynetics.model_fitting_methods import coats_redfern_method
from pkynetics.result_visualization import plot_coats_redfern

e_a, a, r_squared, x, y, x_fit, y_fit = coats_redfern_method(temperature, alpha, heating_rate, n=1)
plot_coats_redfern(x, y, x_fit, y_fit, e_a, a, r_squared)

print(f"Activation energy (E_a): {e_a/1000:.2f} kJ/mol")
print(f"Pre-exponential factor (A): {a:.2e} min^-1")
print(f"R-squared: {r_squared:.4f}")

Result Visualization

Pkynetics now offers enhanced visualization capabilities:

from pkynetics.result_visualization import (
    plot_arrhenius,
    plot_conversion_vs_temperature,
    plot_derivative_thermogravimetry,
    plot_activation_energy_vs_conversion
)

# Example: Plot conversion vs temperature
plot_conversion_vs_temperature([temperature1, temperature2], [alpha1, alpha2], [heating_rate1, heating_rate2])

For more detailed usage instructions and examples of other methods, please refer to the API documentation and the Examples section.