spike2py-preprocess 0.1.14

"Preprocess data with spike2py."

spike2py_preprocess provides a simple way to batch (pre)process data with spike2py.

spike2py_preprocess can be used to batch read a series of .mat files and save them to .pkl files. However, the power of spike2py_preprocess is its ability to also preprocess the data, and this for a single trial, all trials from a subject, or all trials from a study. Moreover, spike2py_preprocess can be used to extract only relevant sections of data; simply add two Spike2 TextMarks to mark the section of data to be extracted. More than one section can be extracted per trial.

Trial

In python:

>>> from spike2py.trial import TrialInfo
>>> from spike2py_preprocess.trial import trial
>>> trial_info = TrialInfo(file="0004.mat",
                           name='h_reflex_curve',
                           subject_id='sub01',
                           path_save_trial='./proc')
>>> trial(trial_info)

On the command line:

$ python -m spike2py_preprocess trial --help
$ python -m spike2py_preprocess trial <path_to_trial_info_json>

or simply:

$ spike2py_preprocess trial --help
$ spike2py_preprocess trial <path_to_trial_info_json>

Here, we need to point spike2py_preprocess.py to a valid json file. The json file requires the following fields:

{
  "file": "/home/maple/study/sub01/data/raw/sub01_DATA000_H_B.mat",
  "channels": ["FDI", "W_EXT", "stim"],
  "name": "biphasic_high_fq",
  "subject_id": "sub01",
  "path_save_trial": "/home/maple/study/sub01/data/proc"
}

Subject:

In Python:

>>> from spike2py_preprocess.subject import subject
>>> from pathlib import Path
>>> subject_folder = Path('sub01')
>>> subject(subject_folder)

On the command line:

$ python -m spike2py_preprocess subject --help
$ python -m spike2py_preprocess subject /home/maple/study/sub01

or simply:

$ spike2py_preprocess subject --help
$ spike2py_preprocess subject /home/maple/study/sub01

Study:

In Python:

>>> from spike2py_preprocess.study import study
>>> from pathlib import Path
>>> study_folder = Path('great_study')
>>> study(study_folder)

On the command line:

$ python -m spike2py_preprocess study --help
$ python -m spike2py_preprocess study /home/maple/study/

or simply:

$ spike2py_preprocess study --help
$ spike2py_preprocess study /home/maple/study/

Preprocess

You can specify the preprocessing settings to apply to one or more channels by including one or more <level>_preprocess.json files.

For a single trial, spike2py_preprocess looks for <trialname.mat>_preprocess.json in the same folder as the .mat file.

For all trials for a subject, spike2py_preprocess looks for subject_preprocess.json in the provided subject folder.

Finally, for all trials in a study, spike2py_preprocess looks for study_preprocess.json in the provided study folder.

Controlling the preprocessing

By including study_preprocess.json, subject_preprocess.json and <trialname.mat>_preprocess.json files in a given file structure, it is possible to provide a general preprocess scheme, but that can be overridden for a given subject or a given trial.

Below is an example of what could be included in a preprocess file. As you can see, each item is the name of a channel that exists in the .mat file.

For each channel, a specific preprocessing step is specified. At present, the preprocessing steps that are possible are those included in the spike2py SignalProcessing mixin class.

The keys are the call to the preprocessing method, and the values are the inputs to those methods (if required).

{
  "Fdi": {
    "remove_mean": "",
    "lowpass": "cutoff=200"
  },
  "W_Ext": {
    "remove_mean": "",
    "bandstop": "cutoff = [49, 51]"
  },
  "Stim": {
    "lowpass": "cutoff=20, order=8"
    }
}

IMPORTANT

Note that, in our example above, the channels were specified as "channels": ["FDI", "W_EXT", "stim"], but here they are specified as Fdi, W_Ext, and Stim. The reason for this is the researchers often used a wide variety of styles to label channels, sometimes with ALLCAPS, other times with camel_case, or a combination of many. To standardise things, spike2py applied TitleCase to each of the channel names. If you are not 100% certain what the resulting channel name will be, simply apply spike2py_preprocess with you preprocess specified. This will result in a pickle (.pkl) file that can be opened with spike2py.

import spike2py as s2p
from pathlib import Path

tutorial = s2p.trial.load(file=Path('data.pkl'))

Now if you simply type tutorial and hit return, you will see information about the data, include the channel names.

File structure

Below is an example of the required file/folder structure for spike2py_preprocess.

In the example, sub02_DATA000_H_B.mat has its own preprocess details located in preprocess_sub02_DATA000_H_B.json.

Similarly, at the subject level, sub02 has a subject_preprocess.json file. This means all their files (excluding sub02_DATA000_H_B.mat) will be preprocessed in the same way.

Finally, because sub01 does not include a dedicated .json file, their data would simply be read and saved as .pkl files if their data was analysed on their own. However, if spike2py_preprocess was used to preprocess all trials in the study, trials from sub01 would be preprocessed with the details provided in study_preprocess.json.

Study folder structure and required files

Below is an example study file structure. The study folder can have whatever name you like, study1 in this case. Similarly, the subject folders can have whatever names you like, but they should match the list of subjects you include in the study_info.json file (see below for more details).

The raw data, in .mat format for each subject must be located in a folder with the name raw.

study1/
├── study_info.json
├── study_preprocess.json
├── sub01
│   ├── raw
│   │   ├── sub01_DATA000_H_B_trial_info.json
│   │   ├── sub01_DATA000_H_B.mat
│   │   ├── sub01_DATA001_C_B.mat
│   │   ├── sub01_DATA002_C_M.mat
│   │   └── sub01_DATA003_H_M.mat
│   └── subject_info.json
└── sub02
    ├── raw
    │   ├── preprocess_sub02_DATA000_H_B.json
    │   ├── sub02_DATA000_H_B.mat
    │   ├── sub02_DATA001_C_B.mat
    │   ├── sub02_DATA002_C_M.mat
    │   └── sub02_DATA003_H_M.mat
    ├── subject_info.json
    └── subject_preprocess.json

subject_info.json

This file contains details about the subject. Additional information can appear in this file, but at a minimum it requires that "subject_id" be provided, as well as "trials", which contains the various trials to be processed for this subject. For each trial, the minimum data required is "name" and "file". If "channels" is provided, only these channels will be included and preprocessed; if not provided, all channels will be included.

{
  "subject_id": "sub01",
  "age": 50,
  "gender": "F",
  "trials": {
    "trial1": {
      "name": "conv_biphasic",
      "file": "sub01_001.mat"
    },
    "trial2": {
      "name": "khz_biphasic",
      "file": "sub01_002.mat",
      "channels": ["FDI", "W_EXT", "stim"]
    }
  }
}

study_info.json

This file contains details about the study. Additional information can appear in this file, but at a minimum it requires that "name" and "subjects" be provided. If "channels" is provided, only these channels will be included and preprocessed, noting that this can be trumped

{
    "name": "TSS_H-reflex",
    "subjects": [
      "sub01",
      "sub02"
    ],
  "channels": ["FDI", "W_EXT", "stim"]
}

Spike2 TextMarks

Please refer to the document entitled "How_to_add_TextMarks_in_Spike2.pdf" for a guide on how to add TextMarks in Spike2.

If you add two TextMarks with the same label (e.g. 'MVC'), the section of data between the two TextMarks will be extracted and saved to a .pkl file. Many such pairs of TextMarks can be included in a trial.

If you have two related sections of data, but want to exclude a middle section that is not useful or relevant, you can add four labels, two around each section of data of interest, that have the same label, the data from both sections will be concatenated and extracted.

Note that Spike2 TextMarks need to be added prior to batch exporting the trial to .mat.

Installing

spike2py_preprocess is available on PyPI:

$ python -m pip install spike2py_preprocess

spike2py officially supports Python 3.8+.

Contributing

Like this project? Want to help? We would love to have your contribution! Please see CONTRIBUTING to get started.

Code of conduct

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to [e-mail].

License

GPLv3