IWT signal handling tools

Various tools to handle signal processing. Mainly developed to handle measurements made using IWT MesuSoft, but should be applicable to arbitrary signals as well.

Functions

File handling

• ui_get_file_path helper function to select a file via a GUI for further handling
• get_mesusoft_measurement reads an IWT MesuSoft Measurement and saves it as a dataframe
• save_dataframe_to_tdms save a dataframe to a TDMS file

Idle detection

These functions are inspired by the pydub package v0.25.1
(c) James Robert, MIT License

• detect_idle detect idle segments in a measurements, i.e. rms amplitude
  or peak-to-valley below a given threshold, returns index ranges
• detect_nonidle inverse of detect idle
• split_on_ranges split a dataframe at given ranges
• split_on_idle split a dataframe on idle segments
• detect_leading_idle detect leading idle in a dataframe

Signal normalization

• normalize normalize a signal to [min|max]
• normalize_to_interval normalize a signal to a given interval

Classes

• none

Usage

Example: handle machining data with idle segments

Force measurements from raster milling operations are characterized by featuring relevant measurement data, i.e. when the cutter is engaged in cutting as well as idle portions when the cutter is not engaged, e.g. as the machine is repositioning. The idle detection functions are applied in this case to separate the measurements in chunks of relevant data and saved bach to new TDMS files.

• use ui_get_file_path to get filename or use a string/Path object
• read the measurement to a dataframe with get_mesusoft_measurement
• manipulate dataframe, e.g. by split_on_idle
• save manipulated dataframe to new TDMS using save_dataframe_to_tdms

Contact

Leibniz-Institute for Materials Engineering IWT
Laboratory for Precision Machining LFM
Dr.-Ing. Lars Schönemann
Badgasteiner Straße 3
28359 Bremen
Germany
schoenemann@iwt.uni-bremen.de