Preprocessing module

Preprocessing.MeanSignal(signals, key='Y')

Calculates and return mean from specific key in signal dictionary, item of signals list

Parameters:

• signals -- list list of dictionaries of signal data.

• key -- str/int, default to 'Y' key of dictionary

Returns:

np.array computed mean

Preprocessing.StdDevSignal(signals, key=None)

Calculates and return standard deviation from specific key in signal dictionary, item of signals list

Parameters:

• signals -- list, list of dictionaries of signal data.

• key -- str/int

Returns:

np.array

Preprocessing.call_function(func_name, param_dict, module='ss')

Call a function from module by name, print its documentation, and handle exceptions. If given arguments dont work, function retries with default arguments given by module.

Modules:

• 'ss': scipy.signal

• 'mtf': mne.time_frequency

• 'si': scipy.integrate

• 'ssw': scipy.signal.windows

• 'np': numpy

Parameters:

• func_name -- str, function name

• param_dict -- dict, required and optional arguments

• module -- str, module name

Returns:

output of func(*args,**kwargs)

Preprocessing.call_function2(func_name, *args, **kwargs)

Call a function from scipy.signal by name, print its documentation, and handle exceptions. If given arguments dont work, function retries with default arguments given by module.

Parameters:

• func_name -- str, function name

• args -- dict, required arguments

• kwargs -- dict, optional keyword arguments

Returns:

output of func(*args,**kwargs)

Preprocessing.convert_from_db(value)

Convert a dB value back to linear scale using 10^(dB/10).

Parameters:

value -- int/float

Returns:

int/float

Preprocessing.convert_to_db(value)

Convert a value to dB scale using 10 * log10.

Parameters:

value -- int/float

Returns:

int/float

Preprocessing.extract_params_and_returns(docstring)

Extracts the 'Parameters' and 'Returns' sections from a Numpydoc-formatted documentation string.

Parameters:

docstring (str) -- str, documentation string of a function.

Returns:

A string containing only the 'Parameters' and 'Returns' sections.

Return type:

str

Preprocessing.get_date_from_ts(ts)

Returns datetime object correspondant to the given timestamp ts

Parameters:

ts -- int/float

Returns:

datetime.datetime

Preprocessing.get_default_kwargs(func_name, module='ss')

Retrieves optional keywords parameters, and default values, from func_name.

Modules:

• 'ss': scipy.signal

• 'mtf': mne.time_frequency

• 'si': scipy.integrate

• 'ssw': scipy.signal.windows

• 'np': numpy

Parameters:

• func_name -- str, function name

• module -- str, module name. Defaults to ss

Returns:

dict

Preprocessing.get_functions(module='ss')

Retrieves and saves all functions names and callbacks into a dictionary

Modules:

• 'ss': scipy.signal

• 'mtf': mne.time_frequency

• 'si': scipy.integrate

• 'ssw': scipy.signal.windows

• 'np': numpy

Parameters:

module -- str

Returns:

dict, {func_naame: obj}

Preprocessing.get_required_params(func_name, module='ss')

Retrieves required parameters, and default values, from func_name.

Modules:

• 'ss': scipy.signal

• 'mtf': mne.time_frequency

• 'si': scipy.integrate

• 'ssw': scipy.signal.windows

• 'np': numpy

Parameters:

• func_name -- str, function name

• module -- str, module name. Defaults to ss

Returns:

dict

Preprocessing.removeCardiacComponent(raw, band, filterMethod='Fixed Peak Find')

Applies 5th order Butterworth bandpass filter with cardiac artifact removal. With Wiener Filter and scipy.find_peaks, an ECG template is created and subtracted to the filtered signal, removing the cardiac component. Adapted from https://github.com/Fixel-Institute/BRAVO_SSR/blob/main/modules/LocalPerceptDatabase.py#L327

Parameters:

• raw -- dict, {'Y': signal values}

• band -- Description

• filterMethod -- Description

Returns:

filtered signal, signal with only bandpass