def _read_recordingchannelgroup(self, node, parent):
# todo: handle Units
attributes = self._get_standard_attributes(node)
channel_indexes = node["channel_indexes"].value
channel_names = node["channel_names"].value
if channel_indexes.size:
if len(node['recordingchannels']):
raise MergeError(
"Cannot handle a RecordingChannelGroup which both has a "
"'channel_indexes' attribute and contains "
"RecordingChannel objects")
raise NotImplementedError(
"todo") # need to handle node['analogsignalarrays']
else:
channels = []
for name, child_node in node['recordingchannels'].items():
if "RecordingChannel" in name:
channels.append(self._read_recordingchannel(child_node))
channel_index = ChannelIndex(None, **attributes)
channel_index._channels = channels
# construction of the index is deferred until we have processed
# all RecordingChannelGroup nodes
units = []
for name, child_node in node['units'].items():
if "Unit" in name:
units.append(
self._read_unit(child_node, parent=channel_index))
channel_index.units = units
channel_index.block = parent
return channel_index
def _read_recordingchannelgroup(self, node, parent):
# todo: handle Units
attributes = self._get_standard_attributes(node)
channel_indexes = node["channel_indexes"].value
channel_names = node["channel_names"].value
if channel_indexes.size:
if len(node['recordingchannels']) :
raise MergeError("Cannot handle a RecordingChannelGroup which both has a "
"'channel_indexes' attribute and contains "
"RecordingChannel objects")
raise NotImplementedError("todo") # need to handle node['analogsignalarrays']
else:
channels = []
for name, child_node in node['recordingchannels'].items():
if "RecordingChannel" in name:
channels.append(self._read_recordingchannel(child_node))
channel_index = ChannelIndex(None, **attributes)
channel_index._channels = channels
# construction of the index is deferred until we have processed
# all RecordingChannelGroup nodes
units = []
for name, child_node in node['units'].items():
if "Unit" in name:
units.append(self._read_unit(child_node, parent=channel_index))
channel_index.units = units
channel_index.block = parent
return channel_index
def read_block(self,
lazy=False,
cascade=True,
t_starts=None,
t_stops=None,
electrode_list=None,
unit_list=None,
analogsignals=True,
events=False,
waveforms=False):
"""
Reads data in a requested time window and returns block with single segment
containing these data.
Arguments:
lazy : Postpone actual reading of the data files. Default 'False'.
cascade : Do not postpone reading subsequent neo types (segments).
Default 'True'.
t_starts : list of quantities or quantity describing the start of the
requested time window to load. If None or [None]
the complete session is loaded. Default 'None'.
t_stops : list of quantities or quantity describing the end of the
requested time window to load. Has to contain the
same number of values as t_starts. If None or [None]
the complete session is loaded. Default 'None'.
electrode_list : list of integers containing the IDs of the requested
units to load. If [] or None all available units
will be loaded. If False, no unit will be loaded.
Default: None.
unit_list : list of integers containing the IDs of the requested
units to load. If [] all available units will be
loaded.
Default: None.
events : Loading events. If True all available events in the given
time window will be read. Default: False.
load_waveforms : Load waveform for spikes in the requested time
window. Default: False.
Returns: Block object containing the requested data in neo structures.
Usage:
from neo import io
import quantities as pq
import matplotlib.pyplot as plt
session_folder = '../Data/2014-07-24_10-31-02'
NIO = io.NeuralynxIO(session_folder,print_diagnostic = True)
block = NIO.read_block(lazy = False, cascade = True,
t_starts = 0.1*pq.s, t_stops = 0.2*pq.s,
channel_list = [1,5,10], unit_list = [1,2,3],
events = True, load_waveforms = True)
"""
# Load neo block
block = neo.io.NeuralynxIO.read_block(self,
lazy=lazy,
cascade=cascade,
t_starts=t_starts,
t_stops=t_stops,
electrode_list=electrode_list,
unit_list=unit_list,
analogsignals=analogsignals,
events=events,
waveforms=waveforms)
# TODO: odML <-> data files consistency checks? Low priority
# Add annotations of odML meta data info
if self.odML_avail:
"""
TODO:
* Add electroporation area to recording channel group
"""
area_dict = self.get_electrodes_by_area()
electroporated_areas = self.get_electroporation()
channel_indexes = [
r for r in block.channel_indexes if r.name == 'all channels'
]
for area, channels in area_dict.items():
electroporated, expression = False, None
if area in electroporated_areas.keys():
electroporated = True
expression = electroporated_areas[area]
chidx = ChannelIndex(
name='%s channels' % area,
channel_indexes=channels,
channel_names=['channel %i' % i for i in channels],
electroporated=electroporated,
expression=expression)
chidx.block = block
block.channel_indexes.append(chidx)
# raise NotImplementedError('neo block annotation using odmls is not implemented yet.')
# ########### Annotate information of 'Recording' Section ############
# # Annotate Amplifier Information
# amp_properties = ['LowpassCutoff','HighpassCutoff','SamplingRate']
# ff = lambda x: x.name in amp_properties and 'Amplifier' in x.parent.get_path()
# pobj = {p.name:p.value.data for p in self.odML_doc.iterproperties(filter_func=ff)}
# block.annotate(amplifier= pobj)
#
# # Consistency Check with Analogsignal Sampling Rate
# if any([pobj['SamplingRate'] != asa.annotations['SamplingFrequency']
# for asa in block.segments[0].analogsignalarrays]):
# raise ValueError('Inconsistent sampling rates detected in odml'
# ' and original data files (%s / %s)'%(
# pobj['SamplingRate'],
# [asa.annotations['SamplingFrequency'] for asa in
# block.segments[0].analogsignalarray]))
#
# # Annotate different Recording Areas
# # Extracting Recording Area sections
# ff = lambda x: 'RecordingArea' in x.name and 'Probe' in x.sections
# recording_secs = [p for p in self.odML_doc.itersections(filter_func=ff)]
# rec_properties = ['Hemisphere','Probe ID','Channels',
# 'SpikingChannels','BrokenChannels','Quality']
# ff2 = lambda x: x.name in rec_properties
# area_dict = {}
# for recording_sec in recording_secs:
# # extracting specific properties of each recording area section
# area_dict[recording_sec.name] = {a.name:a.value.data for a in
# recording_sec.iterproperties(filter_func=ff2)}
# # adding two 'area' properties manually as they have the same name
# area_dict[recording_sec.name]['RecordingArea'] = \
# recording_sec.properties['Area'].value.data
# area_dict[recording_sec.name]['ReferenceArea'] = \
# recording_sec.get_property_by_path('Reference:Area').value.data
# block.annotate(recordingareas=area_dict)
return block
