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- # -*- coding: utf-8 -*-
- """
- Reading from neuroscope format files.
- Ref: http://neuroscope.sourceforge.net/
- It is an old format from Buzsaki's lab
- Some old open datasets from spike sorting
- are still using this format.
- This only the signals.
- This should be done (but maybe never will):
- * SpikeTrain file '.clu' '.res'
- * Event '.ext.evt' or '.evt.ext'
- Author: Samuel Garcia
- """
- from __future__ import unicode_literals, print_function, division, absolute_import
- from .baserawio import (BaseRawIO, _signal_channel_dtype, _unit_channel_dtype,
- _event_channel_dtype)
- import numpy as np
- from xml.etree import ElementTree
- class NeuroScopeRawIO(BaseRawIO):
- extensions = ['xml', 'dat']
- rawmode = 'one-file'
- def __init__(self, filename=''):
- BaseRawIO.__init__(self)
- self.filename = filename
- def _source_name(self):
- return self.filename.replace('.xml', '').replace('.dat', '')
- def _parse_header(self):
- filename = self.filename.replace('.xml', '').replace('.dat', '')
- tree = ElementTree.parse(filename + '.xml')
- root = tree.getroot()
- acq = root.find('acquisitionSystem')
- nbits = int(acq.find('nBits').text)
- nb_channel = int(acq.find('nChannels').text)
- self._sampling_rate = float(acq.find('samplingRate').text)
- voltage_range = float(acq.find('voltageRange').text)
- # offset = int(acq.find('offset').text)
- amplification = float(acq.find('amplification').text)
- # find groups for channels
- channel_group = {}
- for grp_index, xml_chx in enumerate(
- root.find('anatomicalDescription').find('channelGroups').findall('group')):
- for xml_rc in xml_chx:
- channel_group[int(xml_rc.text)] = grp_index
- if nbits == 16:
- sig_dtype = 'int16'
- gain = voltage_range / (2 ** 16) / amplification / 1000.
- # ~ elif nbits==32:
- # Not sure if it is int or float
- # ~ dt = 'int32'
- # ~ gain = voltage_range/2**32/amplification
- else:
- raise (NotImplementedError)
- self._raw_signals = np.memmap(filename + '.dat', dtype=sig_dtype,
- mode='r', offset=0).reshape(-1, nb_channel)
- # signals
- sig_channels = []
- for c in range(nb_channel):
- name = 'ch{}grp{}'.format(c, channel_group[c])
- chan_id = c
- units = 'mV'
- offset = 0.
- group_id = 0
- sig_channels.append((name, chan_id, self._sampling_rate,
- sig_dtype, units, gain, offset, group_id))
- sig_channels = np.array(sig_channels, dtype=_signal_channel_dtype)
- # No events
- event_channels = []
- event_channels = np.array(event_channels, dtype=_event_channel_dtype)
- # No spikes
- unit_channels = []
- unit_channels = np.array(unit_channels, dtype=_unit_channel_dtype)
- # fille into header dict
- self.header = {}
- self.header['nb_block'] = 1
- self.header['nb_segment'] = [1]
- self.header['signal_channels'] = sig_channels
- self.header['unit_channels'] = unit_channels
- self.header['event_channels'] = event_channels
- self._generate_minimal_annotations()
- def _segment_t_start(self, block_index, seg_index):
- return 0.
- def _segment_t_stop(self, block_index, seg_index):
- t_stop = self._raw_signals.shape[0] / self._sampling_rate
- return t_stop
- def _get_signal_size(self, block_index, seg_index, channel_indexes):
- return self._raw_signals.shape[0]
- def _get_signal_t_start(self, block_index, seg_index, channel_indexes):
- return 0.
- def _get_analogsignal_chunk(self, block_index, seg_index, i_start, i_stop, channel_indexes):
- if channel_indexes is None:
- channel_indexes = slice(None)
- raw_signals = self._raw_signals[slice(i_start, i_stop), channel_indexes]
- return raw_signals
|
