eisplottingtool 0.1.5

A tool used to plot EIS data and other battery related data

eisplottingtools

This package was created during my civil service at EMPA. to process, plot and fit electrochemical impedance spectroscopy (EIS) data.

This package itself centered around the class EISFrame, which itself is a wrapper around a pandas.DataFrame. The EISFrame contains additional methods to plot, fit and manipulate EIS data and also other electrochemical data.

Installation

This package is currently not on PyPI.
To install and use this package fork or clone the repository on GitHub. Move to the folder where the repository is located on your local machine and open a Terminal.

To install run:

> py -m pip install .

to install and editable version use:

> py -m pip install -e .

Example Usage

Step 1: Import data & create EISFrame

To load data and to create an EISFramethe load_datafunction can be used.
The following file types are supported to load data from: csv, txt, mpr, mpt.

import eisplottingtool as ept

data = ept.load_data("Example_Data.mpr")

Step 2: Manipulate Data

An EISFrame is essentially a wrapper around a pandas.DataFrame with additional functionality.
Like a DataFrame the data in an EisFrame can be accessed directly with the index operator [...].

• int for accessing cycles

Step 3: Define an equivalent circuit

To fit our data an equivalent circuit is needed, defined as a string.
To combine Elements are combined in series by a dash (-) is used. Elements in parallel are wrapped by p( , ).
An element is defined by a symbol made of letters followed by a digit. Implemented components are:

For example the following circuit would be defined as:

Implemented components

Name	Symbol	Parameters	Default Bounds	Units
Resistor	R	R	(1e-6, 1e6)	Ohm
Capacitance	C	C	(1e-20, 1)	Farad
Constant Phase Element	CPE	CPE_Q	(1e-20, 1)	Ohm^-1 s^n
		CPE_n	(0, 1)
Warburg element	W	W	(0, 1e10)	Ohm^-1 s^0.5
Warburg short element	Ws	Ws_R	(0, 1e10)	Ohm
		Ws_T	(1e-10, 1e10)	s
Warburg open element	Wo	Wo_R	(0, 1e10)	Ohm
		Wo_T	(1e-10, 1e10)	s
Warburg short element special	Wss	Wss_R	(0, 1e10)	Ohm
		Wss_T	(1e-10, 1e10)	s
		Wss_n	(0, 1)
Warburg open element special	Wos	Wos_R	(0, 1e10)	Ohm
		Wos_T	(1e-10, 1e10)	s
		Wos_n	(0, 1)

Step 4: Fit the data

Least_squares is a good fitting method but will get stuck in local minima. For this reason, the Nelder-Mead-Simplex algorithm is used to get out of these local minima. The fitting routine is inspired by Relaxis 3 fitting procedure. More information about it can be found on page 188 of revision 1.25 of Relaxis User Manual.

Step 5: visualize the data

Functions

Extended Backus–Naur form (EBNF) parser for a circuit string.

Implements an extended Backus–Naur form (EBNF) to parse a string that describes a circuit Already implemented circuit elements are located in CircuitComponents.py To use a component in the circuit string use its symbol. The symbol can be followed by a digit to differentiate similar components.

To put elements in series connect them through '-' Parallel elements are created by p(Elm1, Elm2,...)

The syntax of the EBNF is given by:

• circuit = element | element-circuit - element = component | parallel - parallel = p(circuit {,circuit})
• component = a circuit component defined in circuit_components

From this an equation for the impedance is generated and evaluated.