Core Features

arora.core.get_signal

arora.core.get_signal(file, signal_name)

Parameters:

filestr

Path to the edf file that is to be imported

signal_namestr or list of str or list of dict

The name of the signals that is wanted, can also be a list of signals that are wanted

Returns:

List of all the signals that were requested.

For example:

>>> foldername = "path/to/file/file.edf"
>>> arora.core.get_signals(foldername, 'cRIP Flow')
[array([2.42926359e-06, 2.45956329e-06, 2.42926589e-06, ...,
        2.45226589e-06, 2.45226389e-06, 2.45926359e-06])]

arora.core.epochize

arora.core.epochize(data, channel_name, epoch_len, sampling_freq)

Note

This method is in development.

Parameters:

datalist of int

The signal data

channel_namelist of str

The names of the channels to be used

epoch_lenint

The length of the epoch

sampling_freqint or float

The sampling frequency of the data

start_timestamp : ?

Returns

A dataframe with the epochs - will be changed later on to a tuple with the epoch, start and duration

For example:

>>> arora.core.epochize()

arora.core.segmentation

arora.core.segmentation(signals, freq, onset, duration, only_signal, time_unit)

Parameters:

signalslist of int or float

List of the signals given in a 1D array

freqfloat, default is 250

The frequency of the signals

onsetint, default is 0

The beginning onset in seconds

durationint, default 30

The duration of the segment in the time_unit specified (sec, min, hour)

only_signalbool, default is False

If the boolean is true then it will only return the signal list. This can be used to lessen the overhead of the signal

time_unitstr, optional, default is sec

the time unit for the duration option of sec, min and hour

Returns:

If only_signal is False then:

A list of EEG signals and pandas DataFrame including the beginning and end index of the sample

If only_signal is True then:

A list of EEG signals

For example:

>>> signals = [823, 99, 5, 5, 16, 84, 26, 11, 10, 12, 55, 29, 30, 82, 99, 5, 5, 16, 84, 26, 11, 10, 12, 55, 29, 30, 82, 99, 5, 5, 16, 84, 26, 11, 10, 12, 55, 29, 30] # dummy data
>>> arora.core.segmentation(signals, 1, 2, 10)
([[5, 5, 16, 84, 26, 11, 10, 12, 55, 29, 30],
  [82, 99, 5, 5, 16, 84, 26, 11, 10, 12],
  [55, 29, 30, 82, 99, 5, 5, 16, 84, 26]],
    beg_index  end_index
 0         23         32
 1         13         22
 2          2         12)