Metadata-Version: 2.1
Name: sciopy
Version: 0.6.4.8
Summary: Python based interface module for communication with the ScioSpec Electrical Impedance Tomography (EIT) device.
Home-page: https://github.com/spatialaudio/sciopy.git
Author: Jacob Peter Thönes
Author-email: jacob.thoenes@uni-rostock.de
Keywords: ScioSpec,EIT
Platform: any
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
License-File: LICENSE

# ![sciopy](https://raw.githubusercontent.com/spatialaudio/sciopy/dev/doc/images/logo_sciopy.jpg)

This package offers the serial interface for communication with an EIT device from ScioSpec. Commands can be written serially and the system response can be read out. With the current version, it is possible to start and stop measurements with defined burst counts and to read out the measurement data. In addition, the measurement data is packed into a data class for better further processing.

## Install Requirements

All requirements are provided inside the `requirements.txt`. To install them navigate inside the sciopy directory and type:

    pip3 install -r requirements.txt # Linux, macOS, Windows
    pip install -r requirements.txt  # Windows

## Run Example Script

For a single measurement, you can simply run the `example` scripts using the command:

    python measurement_n_el_16.py

This script establishes a serial connection to the ScioSpec device, sends the measurement configuration, and starts a 
measurement with a burst count of 10. For a successful measurement, you have to plug in the 16 electrodes to the port "channel 1-16" at the ScioSpec device. For saving the data, set `save = True` and insert a valid `s_path` to the `scio_spec_measurement_config` at the beginning of the script.

The second provided example script `prep_data_for_ml.py` can be used for the conversion of a finished measurement.
This script creates a new folder with the ending `_prepared` and puts together the potential values and object positions for all measurements. This could be useful for later application of machine learning. 

## Explanation of stored files (.npz)


- `potential matrix` (e.g. variable P) is a 16x16 matrix (n_el=16). If you visualize it using `from sciopy import plot_potential_matrix` you can recognize the used excitation pattern or if an electrode is a defect.
- `p_with_exc` is the matrix P with the excitation electrodes
- `p_without_exc` is the matrix P without the excitation electrodes
- `abs_p_norm_without_ext` is the matrix, normalized between (I think) 0-1 without the excitation electrodes.
- `v_with_ext` is the computed voltage from the potential values containing the excitation electrodes
- `v_without_ext` is the computed voltage from the potential values without the excitation electrodes
- `abs_v_norm_without_ext` is the voltage data, normalized between 0-1 without the excitation electrodes.
- `config` contains some information regarding the measurement procedure.

## TBD

- [ ] Communication using LAN connection.
- [ ] Measurements using 48, 64 electrodes.
- [ ] Adjacent and opposite injection pattern combination.
- [x] Measurements using 32 electrodes.
- [x] Parsing measurements of 32 electrodes.
- [x] Adjacent and opposite drive patterns for 32 electrodes.
- [x] Measurement using 16 electrodes in opposite and adjacent injection pattern.

## Contact

Email: jacob.thoenes@uni-rostock.de
