How to use the spikeextractors.MdaRecordingExtractor function in spikeextractors

To help you get started, we’ve selected a few spikeextractors examples, based on popular ways it is used in public projects.

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github SpikeInterface / spikeextractors / tests / test_extractors.py View on Github external
def test_mda_extractor(self):
        path1 = self.test_dir + '/mda'
        path2 = path1 + '/firings_true.mda'
        se.MdaRecordingExtractor.write_recording(self.RX, path1)
        se.MdaSortingExtractor.write_sorting(self.SX, path2)
        RX_mda = se.MdaRecordingExtractor(path1)
        SX_mda = se.MdaSortingExtractor(path2)
        self._check_recording_return_types(RX_mda)
        self._check_recordings_equal(self.RX, RX_mda)
        self._check_sorting_return_types(SX_mda)
        self._check_sortings_equal(self.SX, SX_mda)
github SpikeInterface / spikeextractors / spikeextractors / example_datasets / real.py View on Github external
def real(name='franklab_tetrode', download=True):
    if name == 'franklab_tetrode':
        dsdir = 'kbucket://b5ecdf1474c5/datasets/neuron_paper/franklab_tetrode'
        IX = se.MdaRecordingExtractor(dir_path=dsdir)
        return (IX, None)
    else:
        raise Exception('Unrecognized name for real dataset: ' + name)
github SpikeInterface / spiketoolkit / spiketoolkit / sorters / ironclust / ironclust.py View on Github external
def _setup_recording(self, recording, output_folder):
        from mountainlab_pytools import mdaio
        p = self.params

        if not check_if_installed(IronclustSorter.ironclust_path):
            raise ImportError(IronclustSorter.installation_mesg)

        dataset_dir = (output_folder / 'ironclust_dataset').absolute()
        if not dataset_dir.is_dir():
            dataset_dir.mkdir()

        # Generate three files in the dataset directory: raw.mda, geom.csv, params.json
        se.MdaRecordingExtractor.write_recording(recording=recording, save_path=dataset_dir)
        samplerate = recording.get_sampling_frequency()

        if self.debug:
            print('Reading timeseries header...')
        HH = mdaio.readmda_header(str(dataset_dir / 'raw.mda'))
        num_channels = HH.dims[0]
        num_timepoints = HH.dims[1]
        duration_minutes = num_timepoints / samplerate / 60
        if self.debug:
            print('Num. channels = {}, Num. timepoints = {}, duration = {} minutes'.format(num_channels, num_timepoints,
                                                                                       duration_minutes))

        if self.debug:
            print('Creating .params file...')
        txt = ''
        txt += 'samplerate={}\n'.format(samplerate)