Source code for neurokin.neural_data

from neurokin.utils.neural import importing
from numpy.typing import ArrayLike




class NeuralData:
    """
    This class represents the neural data recording of a single recording experiment.
    It allows to load neural data from multiple recording systems and to perform basic analysis on the raw data.

    """

    def __init__(self, path):
        self.path = path
        self.raw: ArrayLike
        self.fs: float
        self.sync_data: ArrayLike
        self.fs_sync: float
        self.pulses_count: int
        self.recording: int
        self.freq: ArrayLike[ArrayLike]
        self.pxx: ArrayLike[ArrayLike]
        self.recording_duration: float



    def load_tdt_data(self, sync_present: bool = False, stream_name="Wav1", stim_stream_name="Wav1", t1=0, t2=-1):
        """
        Loads TDT data in the classes attributes.

        :param sync_present: whether there is a sync channel or not (e.g. stimulation trace)
        :param stream_name:  name of the stream where the neural data is stored
        :param stim_stream_name: name of the stream where the sync or stimulation data is stored
        :param t1: first time in second from which to import
        :param t2: last time in second up until which to import
        :return:
        """
        self.fs, self.raw, self.sync_data, self.fs_sync = importing.import_tdt_channel_data(folderpath=self.path,
                                                                                            stream_name=stream_name,
                                                                                            stim_name=stim_stream_name,
                                                                                            sync_present=sync_present,
                                                                                            t1=t1,
                                                                                            t2=t2)




    def load_open_ephys(self, experiment, recording, channels=None, sync_present: bool = False, sync_ch: int = None,
                        source_processor: str = None):
        """
        Loads OpenEphys data in the classes attributes

        :param experiment: name of the experiment folder, as named by OE
        :param recording: name of the recording folder, as named by OE
        :param channels: which channels to load
        :param sync_present: whether there is a sync channel or not (e.g. stimulation trace)
        :param sync_ch: number where the sync or stimulation data is stored
        :param source_processor: name of the source processor as named by OE
        :return:
        """
        self.fs, self.raw, self.sync_data = importing.import_open_ephys_channel_data(folderpath=self.path,
                                                                                     experiment=experiment,
                                                                                     recording=recording,
                                                                                     channels=channels,
                                                                                     sync_present=sync_present,
                                                                                     sync_ch=sync_ch,
                                                                                     source_processor=source_processor)
        self.recording = recording





    def compute_recording_duration(self):
        """
        Computes recording duration based on len of the dataset over the sampling frequency and sets it as a class
        attribute

        :return:
        """
        self.recording_duration = self.raw.shape[1] / self.fs





    def pick_sync_data(self, idx):
        """
        In TDT loading, if the sync_data is accidentally a multichannel, use this to pick the correct one, based on
        a 0-based index.

        :param idx: index of the correct sync data
        :return:
        """
        self.sync_data = self.sync_data[idx]