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KiMoPack

Introduction

KiMoPack is a project for the handling of spectral data measure at multiple time-points. The current design is optimised for the use with optical transient absorption data, but it has been successfully adapted for the use with transient x-ray emission and spectro-electro chemistry data.

It focuses on the main tasks an experimentator has: Loading and shaping of experiments, plotting of experiments, comparing of experiments, analysing experiments with fast and/or advanced fitting routines and saving/exporting/presenting the results. For typical use a series of juypter notebooks are provided that guide through the a number of different use scenarios, and are suggesting the parameter that are typically set.

This is a guide for the common tasks and tools provided for it. For more information please see the documentation and the paper.

If you use KiMoPack in your research please cite this paper DOI: 10.1021/acs.jpca.2c00907  

A guide to to the main analysis tasks

Installation

The basis of the program is a module called "plot_func.py" that contains all the necessary functions and classes.  We provide a series of jupyter based work flow packages that guide the user through a series of typical tasks during the analysis of optical transient absorption data and that we strongly recommend.

The files can be downloaded from the github directory https://github.com/erdzeichen/KiMoPack and manually installed (added to the path). Alternatively we recommend the usage of the usual python install commands "pip" or if the Anaconda

package manager "conda". For details please see this webpage: https://kimopack.readthedocs.io/en/latest/Installation.html

 

While KiMoPack is a python library, we facilitate its use with Jupyter notebooks. For the typical analysis tasks we have developed a series of Notebooks that guide through the tasks.

These notebooks can be found in the installation folder under "Workflowtools" or can be downloaded from https://github.com/erdzeichen/KiMoPack/tree/main/Workflow_tools. Please copy one of these notebooks into your data analysis folder and rename them to create a analysis log of your session. For more information please see the publication, the documentation on ReadTheDocs, the tutorial videos, or the tutorial notebooks (links below)

The publication was accepted and is available under  https://doi.org/10.1021/acs.jpca.2c00907