Introduction

Purpose

The aim of mcetl is to ease the repeated processing of data files. Contrary to its name, mcetl can process any tabulated files (txt, csv, tsv, xlsx, etc.), and does not require that the files originate from materials characterization. However, the focus on materials characterization was selected because:

  • Most data files from materials characterization are relatively small in size (a few kB or MB).

  • Materials characterization files are typically cleanly tabulated and do not require handling messy or missing data.

  • It was the author's area of usage and naming things is hard...

mcetl requires only a very basic understanding of Python to use, and allows a single person to create a tool that their entire group can use to process data and produce Excel files with a consistent style. mcetl can create new Excel files when processing data or saving peak fitting results, or it can append to an existing Excel file to easily work with already created files.

Limitations

  • Data from files is fully loaded into memory for processing, so mcetl is not suited for processing files whose total memory size is large (e.g. cannot load a 10 GB file on a computer with only 8 GB of RAM).

  • mcetl does not provide any resources for processing data files directly from characterization equipment (such as .XRDML, .PAR, etc.). Other libraries such as xylib already exist and are capable of converting many such files to a format mcetl can use (txt, csv, etc.).

  • The peak fitting and plotting modules in mcetl are not as feature-complete as other alternatives such as Origin, fityk, SciDAVis, etc. The modules are included in mcetl in case those better alternatives are not available, and the author highly recommends using those alternatives over mcetl if available.