picnik 0.1.0

A package to make isoconversional computations for non-isothermal kinetics

pICNIK

pICNIK is a package for isoconversional computations for non-isothermal kinetcis.
The package has an object oriented interface with two classes: DataExtraction and ActivationEnergy, with the purpose of managing the experimental data and computing activation energies ($E_{\alpha}$) with the next isoconversional methods:

• Ozawa-Flynn-Wall (OFW)
  $\ln{\left(\beta_{i}\right)} = \left[\ln{\left(\frac{A_{\alpha}E_{\alpha}}{g(\alpha)R}\right)}-5.331\right]-1.052\frac{E_{\alpha}}{RT_{\alpha,i}}}$

• Kissinger-Akahira-Sunose (KAS)\ $\ln{\left(\frac{\beta_{i}}{T_{\alpha ,i}^{2}}\right)}\approx\ln{\left[\frac{A_\alpha R}{E_\alpha g(\alpha)}\right]}-\frac{E_\alpha}{RT_{\alpha ,i}}$

• Friedman (Fr)
  $\ln{\left(\frac{d\alpha}{dt}\right){\alpha ,i}} = \ln{\left[A{\alpha}f\left(\alpha\right)\right]} - \frac{E_{\alpha}}{RT_{\alpha ,i}}$

• Vyazovkin (Vy)
  $\phi=n(n-1) - \sum_{i}^{n} \sum_{j \neq i}^{n-1} \frac{\beta_j I(E_{\alpha},T_{\alpha ,i})}{\beta_i I(E_{\alpha},T_{\alpha ,j})}$

• Advanced method of Vyazovkin (aVy)
  $\phi=n(n-1) - \sum_{i}^{n} \sum_{j \neq i}^{n-1} \frac{\beta_j J(E_{\Delta\alpha},T_{\Delta\alpha ,i})}{\beta_i J(E_{\Delta\alpha},T_{\Delta\alpha ,j})}$

Installation

picnik can be installed from PyPi with pip: $ pip install picnik

DataExtractioin class

It has methods to open the .csv files containing the thermogravimetric data as pandas DataFrames for the experimental data, computing and adding the conversion for the process ($\alpha$) and the conversion rate ($d\alpha/dt$) as columns in the DataFrame.
The class also has methods for creating isoconversional DataFrames of time, temperature, conversion rates (for the OFW, KAS, Fr and Vy methods) and also "advanced" DataFrames of time and temperature (for the aVy method).
Example:

import picnik as pnk

files = ["HR_1.csv","HR_2.csv",...,"HR_n.csv"]
xtr = pnk.DataExtraction()
xtr.set_data(files)
xtr.data_extraction()
xtr.isoconversional()
xtr.adv_isoconversional()

The DataFrames are stored as attributes of the xtr object

ActivationEnergy class

This class has methods to compute the activation energies with the DataFrames created with the xtr object along with its associated error. The Fr(),OFW(),KAS() methods return a tuple of three, two and two elements respectively. The first element of the tuples is a numpy array containing the isoconversional activation energies. The second element contains the associated error within a 95% confidence interval. The third element in the case of the Fr() method is a numpy array containing the intercept of the Friedman method. The Vy() and aVy() only return a numpy array of isoconversional activation energies, the error associated to this methods are obtained with the Vy_error() and aVy_error() methods Example:

ace = pnk.ActivationEnergy(xtr.Beta,
                           xtr.TempIsoDF,
                           xtr.diffIsoDF,
                           xtr.TempAdvIsoDF,
                           xtr.timeAdvIsoDF)
E_Fr, E_OFW, E_KAS, E_Vy, E_aVy = ace.Fr(), ace.OFW(), ace.KAS(), ace.Vy(), ace.aVy()
Vy_e, aVy_e = ace.Vy_error(), ace.aVy_error()

The constructor of this class needs five arguments, a list/array/tuple of Temperature rates, and four DataFrames: one of temperature, one of convertsion rates and two "advanced" one of temperature and the other of time.

Saving results

The DataExtractionclass also has a method to export the results as .csv or .xlsx files:

xtr.save_df(E_Fr = E_Fr[0], 
            E_OFW = E_OFW[0], 
            E_KAS = E_KAS[0], 
            E_Vy = E_Vy, 
            E_aVy = E_aVy,
            file_t="xlsx" )