KiMoPack 6.11.2

A comprehensive package for the analysis of kinetic data.

KiMoPack

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.

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 distribution is using the Anaconda package manager, The conda type installation. For both please open a command line (e.g. using “cmd” in windows) and execute the following commands. The Jupyter notebooks are copied during the install process. The notebooks can also be downloaded from the github server https://github.com/erdzeichen/KiMoPack.

Install using “pip”:

$ pip install KiMoPack

Upgrade if already installed:

$ pip install KiMoPack -U

Install and update using “conda” from the channel erdzeichen:

$ conda install -c erdzeichen kimopack

Best usage

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.n These notebooks can be downloaded from https://github.com/erdzeichen/KiMoPack/tree/main/Workflow_tools or by command line.

To do that start any console (under windows e.g. type “cmd” and hit enter). In the console you then start python by typing “python” and hit enter, lastly you import Kimopack and run a function that downloads the files for you by typing “import KiMoPack.plot_func as pf; pf.download_all()” This downloads the notebooks and tutorials from github for you. If you instead use “import KiMoPack.plot_func as pf; pf.download_notebooks()” then only the workflow tools are downloaded. 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 https://doi.org/10.1021/acs.jpca.2c00907, the tutorial videos, or the tutorial notebooks under https://github.com/erdzeichen/KiMoPack/tree/main/Tutorial_Notebooks_for_local_use.

Citation

We have written and submitted a paper introducing the toolbox under https://doi.org/10.1021/acs.jpca.2c00907

Links

• Publication: https://pubs.acs.org/doi/10.1021/acs.jpca.2c00907

• Documentation: https://kimopack.readthedocs.io/

• PyPI Releases: https://pypi.org/project/KiMoPack/

• Source Code: https://github.com/erdzeichen/KiMoPack

• Issue Tracker: https://github.com/erdzeichen/KiMoPack/issues

• Website: https://www.chemphys.lu.se/research/projects/kimopack/

• Zenodo: https://zenodo.org/badge/latestdoi/400527965

• Tutorial videos: https://www.youtube.com/channel/UCmhiK0P9wXXjs_PJaitx8BQ