def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
chx = ChannelIndex(name='chx1', index=np.arange(signal.shape[1]))
chx.analogsignals = [signal]
chx.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects,
('Segment', 'ChannelIndex'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers,
('segment', 'channel_index'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._parent_containers,
('segment', 'channel_index'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'chx1')
assert_neo_object_is_compliant(signal)
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 test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
chx = ChannelIndex(name='chx1', index=np.arange(signal.shape[1]))
chx.analogsignals = [signal]
chx.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers, ('segment', 'channel_index'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._parent_containers, ('segment', 'channel_index'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'chx1')
assert_neo_object_is_compliant(signal)
def test__children(self):
chx = ChannelIndex(index=np.arange(self.nchildren), name='chx1')
chx.units = [self.unit1]
chx.create_many_to_one_relationship()
assert_neo_object_is_compliant(self.unit1)
assert_neo_object_is_compliant(chx)
self.assertEqual(self.unit1._container_child_objects, ())
self.assertEqual(self.unit1._data_child_objects, ('SpikeTrain', ))
self.assertEqual(self.unit1._single_parent_objects, ('ChannelIndex', ))
self.assertEqual(self.unit1._multi_child_objects, ())
self.assertEqual(self.unit1._multi_parent_objects, ())
self.assertEqual(self.unit1._child_properties, ())
self.assertEqual(self.unit1._single_child_objects, ('SpikeTrain', ))
self.assertEqual(self.unit1._container_child_containers, ())
self.assertEqual(self.unit1._data_child_containers, ('spiketrains', ))
self.assertEqual(self.unit1._single_child_containers,
('spiketrains', ))
self.assertEqual(self.unit1._single_parent_containers,
('channel_index', ))
self.assertEqual(self.unit1._multi_child_containers, ())
self.assertEqual(self.unit1._multi_parent_containers, ())
self.assertEqual(self.unit1._child_objects, ('SpikeTrain', ))
self.assertEqual(self.unit1._child_containers, ('spiketrains', ))
self.assertEqual(self.unit1._parent_objects, ('ChannelIndex', ))
self.assertEqual(self.unit1._parent_containers, ('channel_index', ))
self.assertEqual(len(self.unit1._single_children), self.nchildren)
self.assertEqual(len(self.unit1._multi_children), 0)
self.assertEqual(len(self.unit1.data_children), self.nchildren)
self.assertEqual(len(self.unit1.data_children_recur), self.nchildren)
self.assertEqual(len(self.unit1.container_children), 0)
self.assertEqual(len(self.unit1.container_children_recur), 0)
self.assertEqual(len(self.unit1.children), self.nchildren)
self.assertEqual(len(self.unit1.children_recur), self.nchildren)
self.assertEqual(self.unit1._multi_children, ())
self.assertEqual(self.unit1.container_children, ())
self.assertEqual(self.unit1.container_children_recur, ())
assert_same_sub_schema(list(self.unit1._single_children),
self.trains1a)
assert_same_sub_schema(list(self.unit1.data_children), self.trains1a)
assert_same_sub_schema(list(self.unit1.data_children_recur),
self.trains1a)
assert_same_sub_schema(list(self.unit1.children), self.trains1a)
assert_same_sub_schema(list(self.unit1.children_recur), self.trains1a)
self.assertEqual(len(self.unit1.parents), 1)
self.assertEqual(self.unit1.parents[0].name, 'chx1')
def test_channel_index_write(self):
block = Block(name=self.rword())
chx = ChannelIndex(name=self.rword(),
description=self.rsentence(),
index=[1, 2, 3, 5, 8, 13])
block.channel_indexes.append(chx)
self.write_and_compare([block])
chx.annotate(**self.rdict(3))
self.write_and_compare([block])
def test_channel_index_write(self):
block = Block(name=self.rword())
chx = ChannelIndex(name=self.rword(),
description=self.rsentence(),
index=[1, 2, 3, 5, 8, 13])
block.channel_indexes.append(chx)
self.write_and_compare([block])
chx.annotate(**self.rdict(3))
self.write_and_compare([block])
def read_block(self,
lazy = False,
cascade = True,
):
"""
"""
tree = ElementTree.parse(self.filename)
root = tree.getroot()
acq = root.find('acquisitionSystem')
nbits = int(acq.find('nBits').text)
nbchannel = int(acq.find('nChannels').text)
sampling_rate = float(acq.find('samplingRate').text)*pq.Hz
voltage_range = float(acq.find('voltageRange').text)
#offset = int(acq.find('offset').text)
amplification = float(acq.find('amplification').text)
bl = Block(file_origin = os.path.basename(self.filename).replace('.xml', ''))
if cascade:
seg = Segment()
bl.segments.append(seg)
# RCG
for i, xml_chx in enumerate(root.find('anatomicalDescription').find('channelGroups').findall('group')):
n_channels = len(xml_chx)
chx = ChannelIndex(name='Group {0}'.format(i),
index=np.arange(n_channels, dtype = int))
chx.channel_ids = np.array([int(xml_rc.text) for xml_rc in xml_chx])
chx.channel_names = np.array(['Channel{0}'.format(id) for id in chx.channel_ids], dtype = 'S')
bl.channel_indexes.append(chx)
# AnalogSignals
reader = RawBinarySignalIO(filename = self.filename.replace('.xml', '.dat'))
seg2 = reader.read_segment(cascade = True, lazy = lazy,
sampling_rate = sampling_rate,
t_start = 0.*pq.s,
unit = pq.V, nbchannel = nbchannel,
bytesoffset = 0,
dtype = np.int16 if nbits<=16 else np.int32,
rangemin = -voltage_range/2.,
rangemax = voltage_range/2.,)
for s, sig in enumerate(seg2.analogsignals):
if not lazy:
sig /= amplification
sig.segment = seg
seg.analogsignals.append(sig)
chx.analogsignals.append(sig)
bl.create_many_to_one_relationship()
return bl
def test_channel_index_write(self):
block = Block(name=self.rword())
chx = ChannelIndex(name=self.rword(),
description=self.rsentence(),
channel_ids=[10, 20, 30, 50, 80, 130],
index=[1, 2, 3, 5, 8, 13])
block.channel_indexes.append(chx)
self.write_and_compare([block])
chx.annotate(**self.rdict(3))
self.write_and_compare([block])
chx.channel_names = ["one", "two", "three", "five",
"eight", "xiii"]
self.write_and_compare([block])
def test_channel_index_write(self):
block = Block(name=self.rword())
chx = ChannelIndex(name=self.rword(),
description=self.rsentence(),
channel_ids=[10, 20, 30, 50, 80, 130],
index=[1, 2, 3, 5, 8, 13])
block.channel_indexes.append(chx)
self.write_and_compare([block])
chx.annotate(**self.rdict(3))
self.write_and_compare([block])
chx.channel_names = ["one", "two", "three", "five",
"eight", "xiii"]
self.write_and_compare([block])
def proc_src_units(srcfile, filename):
'''Get the units in an src file that has been processed by the official
matlab function. See proc_src for details'''
chx = ChannelIndex(file_origin=filename, index=np.array([], dtype=int))
un_unit = Unit(name='UnassignedSpikes',
file_origin=filename,
elliptic=[],
boundaries=[],
timestamp=[],
max_valid=[])
chx.units.append(un_unit)
sortInfo = srcfile['sortInfo'][0, 0]
timeslice = sortInfo['timeslice'][0, 0]
maxValid = timeslice['maxValid'][0, 0]
cluster = timeslice['cluster'][0, 0]
if len(cluster):
maxValid = maxValid[0, 0]
elliptic = [res.flatten() for res in cluster['elliptic'].flatten()]
boundaries = [res.flatten() for res in cluster['boundaries'].flatten()]
fullclust = zip(elliptic, boundaries)
for ielliptic, iboundaries in fullclust:
unit = Unit(file_origin=filename,
boundaries=[iboundaries],
elliptic=[ielliptic],
timeStamp=[],
max_valid=[maxValid])
chx.units.append(unit)
return chx
def read_block(self, lazy=False, cascade=True, channel_index=None):
"""
Arguments:
Channel_index: can be int, iterable or None to select one, many or all channel(s)
"""
blk = Block()
if cascade:
seg = Segment(file_origin=self._filename)
blk.segments += [seg]
if channel_index:
if type(channel_index) is int: channel_index = [channel_index]
if type(channel_index) is list:
channel_index = np.array(channel_index)
else:
channel_index = np.arange(0, self._attrs['shape'][1])
chx = ChannelIndex(name='all channels', index=channel_index)
blk.channel_indexes.append(chx)
ana = self.read_analogsignal(channel_index=channel_index,
lazy=lazy,
cascade=cascade)
ana.channel_index = chx
seg.duration = (self._attrs['shape'][0] /
self._attrs['kwik']['sample_rate']) * pq.s
# neo.tools.populate_RecordingChannel(blk)
blk.create_many_to_one_relationship()
return blk
def test__construct_subsegment_by_unit(self):
nb_seg = 3
nb_unit = 7
unit_with_sig = np.array([0, 2, 5])
signal_types = ['Vm', 'Conductances']
sig_len = 100
# channelindexes
chxs = [ChannelIndex(name='Vm',
index=unit_with_sig),
ChannelIndex(name='Conductance',
index=unit_with_sig)]
# Unit
all_unit = []
for u in range(nb_unit):
un = Unit(name='Unit #%d' % u, channel_indexes=np.array([u]))
assert_neo_object_is_compliant(un)
all_unit.append(un)
blk = Block()
blk.channel_indexes = chxs
for s in range(nb_seg):
seg = Segment(name='Simulation %s' % s)
for j in range(nb_unit):
st = SpikeTrain([1, 2], units='ms',
t_start=0., t_stop=10)
st.unit = all_unit[j]
for t in signal_types:
anasigarr = AnalogSignal(np.zeros((sig_len,
len(unit_with_sig))),
units='nA',
sampling_rate=1000. * pq.Hz,
channel_indexes=unit_with_sig)
seg.analogsignals.append(anasigarr)
blk.create_many_to_one_relationship()
for unit in all_unit:
assert_neo_object_is_compliant(unit)
for chx in chxs:
assert_neo_object_is_compliant(chx)
assert_neo_object_is_compliant(blk)
# what you want
newseg = seg.construct_subsegment_by_unit(all_unit[:4])
assert_neo_object_is_compliant(newseg)
def create_all_annotated(cls):
times = cls.rquant(1, pq.s)
signal = cls.rquant(1, pq.V)
blk = Block()
blk.annotate(**cls.rdict(3))
cls.populate_dates(blk)
seg = Segment()
seg.annotate(**cls.rdict(4))
cls.populate_dates(seg)
blk.segments.append(seg)
asig = AnalogSignal(signal=signal, sampling_rate=pq.Hz)
asig.annotate(**cls.rdict(2))
seg.analogsignals.append(asig)
isig = IrregularlySampledSignal(times=times,
signal=signal,
time_units=pq.s)
isig.annotate(**cls.rdict(2))
seg.irregularlysampledsignals.append(isig)
epoch = Epoch(times=times, durations=times)
epoch.annotate(**cls.rdict(4))
seg.epochs.append(epoch)
event = Event(times=times)
event.annotate(**cls.rdict(4))
seg.events.append(event)
spiketrain = SpikeTrain(times=times, t_stop=pq.s, units=pq.s)
d = cls.rdict(6)
d["quantity"] = pq.Quantity(10, "mV")
d["qarray"] = pq.Quantity(range(10), "mA")
spiketrain.annotate(**d)
seg.spiketrains.append(spiketrain)
chx = ChannelIndex(name="achx", index=[1, 2], channel_ids=[0, 10])
chx.annotate(**cls.rdict(5))
blk.channel_indexes.append(chx)
unit = Unit()
unit.annotate(**cls.rdict(2))
chx.units.append(unit)
return blk
def proc_dam(filename):
'''Load an dam file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareDamIO to
make sure BrainwareDamIO is working properly
block = proc_dam(filename)
filename: The file name of the numpy file to load. It should end with
'*_dam_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.dam', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_dam_py2.npz'
dam file name = 'file1.dam'
'''
with np.load(filename) as damobj:
damfile = damobj.items()[0][1].flatten()
filename = os.path.basename(filename[:-12] + '.dam')
signals = [res.flatten() for res in damfile['signal']]
stimIndexes = [int(res[0, 0].tolist()) for res in damfile['stimIndex']]
timestamps = [res[0, 0] for res in damfile['timestamp']]
block = Block(file_origin=filename)
chx = ChannelIndex(file_origin=filename,
index=np.array([0]),
channel_ids=np.array([1]),
channel_names=np.array(['Chan1'], dtype='S'))
block.channel_indexes.append(chx)
params = [res['params'][0, 0].flatten() for res in damfile['stim']]
values = [res['values'][0, 0].flatten() for res in damfile['stim']]
params = [[res1[0] for res1 in res] for res in params]
values = [[res1 for res1 in res] for res in values]
stims = [dict(zip(param, value)) for param, value in zip(params, values)]
fulldam = zip(stimIndexes, timestamps, signals, stims)
for stimIndex, timestamp, signal, stim in fulldam:
sig = AnalogSignal(signal=signal * pq.mV,
t_start=timestamp * pq.d,
file_origin=filename,
sampling_period=1. * pq.s)
segment = Segment(file_origin=filename,
index=stimIndex,
**stim)
segment.analogsignals = [sig]
block.segments.append(segment)
block.create_many_to_one_relationship()
return block
def read_block(self, lazy=False, cascade=True, **kargs):
'''
Reads a block from the simple spike data file "fname" generated
with BrainWare
'''
# there are no keyargs implemented to so far. If someone tries to pass
# them they are expecting them to do something or making a mistake,
# neither of which should pass silently
if kargs:
raise NotImplementedError('This method does not have any '
'argument implemented yet')
self._fsrc = None
self.__lazy = lazy
self._blk = Block(file_origin=self._filename)
block = self._blk
# if we aren't doing cascade, don't load anything
if not cascade:
return block
# create the objects to store other objects
chx = ChannelIndex(file_origin=self._filename,
index=np.array([], dtype=np.int))
self.__unit = Unit(file_origin=self._filename)
# load objects into their containers
block.channel_indexes.append(chx)
chx.units.append(self.__unit)
# initialize values
self.__t_stop = None
self.__params = None
self.__seg = None
self.__spiketimes = None
# open the file
with open(self._path, 'rb') as self._fsrc:
res = True
# while the file is not done keep reading segments
while res:
res = self.__read_id()
block.create_many_to_one_relationship()
# cleanup attributes
self._fsrc = None
self.__lazy = False
self._blk = None
self.__t_stop = None
self.__params = None
self.__seg = None
self.__spiketimes = None
return block
def create_all_annotated(cls):
times = cls.rquant(1, pq.s)
signal = cls.rquant(1, pq.V)
blk = Block()
blk.annotate(**cls.rdict(3))
seg = Segment()
seg.annotate(**cls.rdict(4))
blk.segments.append(seg)
asig = AnalogSignal(signal=signal, sampling_rate=pq.Hz)
asig.annotate(**cls.rdict(2))
seg.analogsignals.append(asig)
isig = IrregularlySampledSignal(times=times, signal=signal,
time_units=pq.s)
isig.annotate(**cls.rdict(2))
seg.irregularlysampledsignals.append(isig)
epoch = Epoch(times=times, durations=times)
epoch.annotate(**cls.rdict(4))
seg.epochs.append(epoch)
event = Event(times=times)
event.annotate(**cls.rdict(4))
seg.events.append(event)
spiketrain = SpikeTrain(times=times, t_stop=pq.s, units=pq.s)
d = cls.rdict(6)
d["quantity"] = pq.Quantity(10, "mV")
d["qarray"] = pq.Quantity(range(10), "mA")
spiketrain.annotate(**d)
seg.spiketrains.append(spiketrain)
chx = ChannelIndex(name="achx", index=[1, 2], channel_ids=[0, 10])
chx.annotate(**cls.rdict(5))
blk.channel_indexes.append(chx)
unit = Unit()
unit.annotate(**cls.rdict(2))
chx.units.append(unit)
return blk
def read_channelindex(self,
path,
cascade=True,
lazy=False,
read_waveforms=True):
channel_group = self._exdir_directory[path]
group_id = channel_group.attrs['electrode_group_id']
chx = ChannelIndex(
name='Channel group {}'.format(group_id),
index=channel_group.attrs['electrode_idx'],
channel_ids=channel_group.attrs['electrode_identities'],
**{
'group_id': group_id,
'exdir_path': path
})
if 'LFP' in channel_group:
for lfp_group in channel_group['LFP'].values():
ana = self.read_analogsignal(lfp_group.name,
cascade=cascade,
lazy=lazy)
chx.analogsignals.append(ana)
ana.channel_index = chx
if 'MUA' in channel_group:
for mua_group in channel_group['MUA'].values():
ana = self.read_analogsignal(mua_group.name,
cascade=cascade,
lazy=lazy)
chx.analogsignals.append(ana)
ana.channel_index = chx
sptrs = []
if 'UnitTimes' in channel_group:
for unit_group in channel_group['UnitTimes'].values():
unit = self.read_unit(unit_group.name,
cascade=cascade,
lazy=lazy,
read_waveforms=read_waveforms)
unit.channel_index = chx
chx.units.append(unit)
sptr = unit.spiketrains[0]
sptr.channel_index = chx
elif 'EventWaveform' in channel_group:
sptr = self.read_spiketrain(channel_group['EventWaveform'].name,
cascade=cascade,
lazy=lazy,
read_waveforms=read_waveforms)
unit = Unit(name=sptr.name, **sptr.annotations)
unit.spiketrains.append(sptr)
unit.channel_index = chx
sptr.channel_index = chx
chx.units.append(unit)
return chx
def setUp(self):
self.data1 = np.arange(10.0)
self.data1quant = self.data1 * pq.mV
self.time1 = np.logspace(1, 5, 10)
self.time1quant = self.time1*pq.ms
self.signal1 = IrregularlySampledSignal(self.time1quant,
signal=self.data1quant,
name='spam',
description='eggs',
file_origin='testfile.txt',
arg1='test')
self.signal1.segment = Segment()
self.signal1.channel_index = ChannelIndex([0])
def create_channelindex(self, parent=None, name='ChannelIndex', analogsignals=None):
channels_num = min([signal.shape[1] for signal in analogsignals])
channelindex = ChannelIndex(index=np.arange(channels_num),
channel_names=['Channel{}'.format(i) for i in range(channels_num)],
channel_ids=np.arange(channels_num),
coordinates=([[1.87, -5.2, 4.0]] * channels_num) * pq.cm)
for signal in analogsignals:
channelindex.analogsignals.append(signal)
self._assign_basic_attributes(channelindex, name)
self._assign_annotations(channelindex)
return channelindex
def _source_chx_to_neo(self, nix_source):
neo_attrs = self._nix_attr_to_neo(nix_source)
chx = list(
self._nix_attr_to_neo(c) for c in nix_source.sources
if c.type == "neo.channelindex")
chan_names = list(c["neo_name"] for c in chx if "neo_name" in c)
if chan_names:
neo_attrs["channel_names"] = chan_names
neo_attrs["index"] = np.array([c["index"] for c in chx])
if "coordinates" in chx[0]:
coord_units = chx[0]["coordinates.units"]
coord_values = list(c["coordinates"] for c in chx)
neo_attrs["coordinates"] = pq.Quantity(coord_values, coord_units)
rcg = ChannelIndex(**neo_attrs)
self._object_map[nix_source.id] = rcg
return rcg
def read_block(self, lazy=False, cascade=True, **kargs):
'''
Reads a block from the raw data file "fname" generated
with BrainWare
'''
# there are no keyargs implemented to so far. If someone tries to pass
# them they are expecting them to do something or making a mistake,
# neither of which should pass silently
if kargs:
raise NotImplementedError('This method does not have any '
'arguments implemented yet')
self._fsrc = None
block = Block(file_origin=self._filename)
# if we aren't doing cascade, don't load anything
if not cascade:
return block
# create the objects to store other objects
chx = ChannelIndex(file_origin=self._filename,
channel_ids=np.array([1]),
index=np.array([0]),
channel_names=np.array(['Chan1'], dtype='S'))
# load objects into their containers
block.channel_indexes.append(chx)
# open the file
with open(self._path, 'rb') as fobject:
# while the file is not done keep reading segments
while True:
seg = self._read_segment(fobject, lazy)
# if there are no more Segments, stop
if not seg:
break
# store the segment and signals
seg.analogsignals[0].channel_index = chx
block.segments.append(seg)
# remove the file object
self._fsrc = None
block.create_many_to_one_relationship()
return block
def test_anonymous_objects_write(self):
nblocks = 2
nsegs = 2
nanasig = 4
nirrseg = 2
nepochs = 3
nevents = 4
nspiketrains = 3
nchx = 5
nunits = 10
times = self.rquant(1, pq.s)
signal = self.rquant(1, pq.V)
blocks = []
for blkidx in range(nblocks):
blk = Block()
blocks.append(blk)
for segidx in range(nsegs):
seg = Segment()
blk.segments.append(seg)
for anaidx in range(nanasig):
seg.analogsignals.append(AnalogSignal(signal=signal,
sampling_rate=pq.Hz))
for irridx in range(nirrseg):
seg.irregularlysampledsignals.append(
IrregularlySampledSignal(times=times,
signal=signal,
time_units=pq.s)
)
for epidx in range(nepochs):
seg.epochs.append(Epoch(times=times, durations=times))
for evidx in range(nevents):
seg.events.append(Event(times=times))
for stidx in range(nspiketrains):
seg.spiketrains.append(SpikeTrain(times=times,
t_stop=times[-1]+pq.s,
units=pq.s))
for chidx in range(nchx):
chx = ChannelIndex(name="chx{}".format(chidx),
index=[1, 2],
channel_ids=[11, 22])
blk.channel_indexes.append(chx)
for unidx in range(nunits):
unit = Unit()
chx.units.append(unit)
self.writer.write_all_blocks(blocks)
self.compare_blocks(blocks, self.reader.blocks)
def test_multiref_write(self):
blk = Block("blk1")
signal = AnalogSignal(name="sig1",
signal=[0, 1, 2],
units="mV",
sampling_period=pq.Quantity(1, "ms"))
othersignal = IrregularlySampledSignal(name="i1",
signal=[0, 0, 0],
units="mV",
times=[1, 2, 3],
time_units="ms")
event = Event(name="Evee", times=[0.3, 0.42], units="year")
epoch = Epoch(name="epoche",
times=[0.1, 0.2] * pq.min,
durations=[0.5, 0.5] * pq.min)
st = SpikeTrain(name="the train of spikes",
times=[0.1, 0.2, 10.3],
t_stop=11,
units="us")
for idx in range(3):
segname = "seg" + str(idx)
seg = Segment(segname)
blk.segments.append(seg)
seg.analogsignals.append(signal)
seg.irregularlysampledsignals.append(othersignal)
seg.events.append(event)
seg.epochs.append(epoch)
seg.spiketrains.append(st)
chidx = ChannelIndex([10, 20, 29])
seg = blk.segments[0]
st = SpikeTrain(name="choochoo",
times=[10, 11, 80],
t_stop=1000,
units="s")
seg.spiketrains.append(st)
blk.channel_indexes.append(chidx)
for idx in range(6):
unit = Unit("unit" + str(idx))
chidx.units.append(unit)
unit.spiketrains.append(st)
self.writer.write_block(blk)
self.compare_blocks([blk], self.reader.blocks)
def test_no_segment_write(self):
# Tests storing AnalogSignal, IrregularlySampledSignal, and SpikeTrain
# objects in the secondary (ChannelIndex) substructure without them
# being attached to a Segment.
blk = Block("segmentless block")
signal = AnalogSignal(name="sig1",
signal=[0, 1, 2],
units="mV",
sampling_period=pq.Quantity(1, "ms"))
othersignal = IrregularlySampledSignal(name="i1",
signal=[0, 0, 0],
units="mV",
times=[1, 2, 3],
time_units="ms")
sta = SpikeTrain(name="the train of spikes",
times=[0.1, 0.2, 10.3],
t_stop=11,
units="us")
stb = SpikeTrain(name="the train of spikes b",
times=[1.1, 2.2, 10.1],
t_stop=100,
units="ms")
chidx = ChannelIndex([8, 13, 21])
blk.channel_indexes.append(chidx)
chidx.analogsignals.append(signal)
chidx.irregularlysampledsignals.append(othersignal)
unit = Unit()
chidx.units.append(unit)
unit.spiketrains.extend([sta, stb])
self.writer.write_block(blk)
self.compare_blocks([blk], self.reader.blocks)
self.writer.close()
reader = NixIO(self.filename, "ro")
blk = reader.read_block(neoname="segmentless block")
chx = blk.channel_indexes[0]
self.assertEqual(len(chx.analogsignals), 1)
self.assertEqual(len(chx.irregularlysampledsignals), 1)
self.assertEqual(len(chx.units[0].spiketrains), 2)
def test_channel_index_write(self):
block = Block(name=self.rword())
chx = ChannelIndex(name=self.rword(),
description=self.rsentence(),
channel_ids=[10, 20, 30, 50, 80, 130],
index=[1, 2, 3, 5, 8, 13])
block.channel_indexes.append(chx)
self.write_and_compare([block])
chx.annotate(**self.rdict(3))
self.write_and_compare([block])
chx.channel_names = ["one", "two", "three", "five", "eight", "xiii"]
chx.coordinates = self.rquant((6, 3), pq.um)
self.write_and_compare([block])
# add an empty channel index and check again
newchx = ChannelIndex(np.array([]))
block.channel_indexes.append(newchx)
self.write_and_compare([block])
def test_multiref_write(self):
blk = Block("blk1")
signal = AnalogSignal(name="sig1", signal=[0, 1, 2], units="mV",
sampling_period=pq.Quantity(1, "ms"))
for idx in range(3):
segname = "seg" + str(idx)
seg = Segment(segname)
blk.segments.append(seg)
seg.analogsignals.append(signal)
chidx = ChannelIndex([10, 20, 29])
seg = blk.segments[0]
st = SpikeTrain(name="choochoo", times=[10, 11, 80], t_stop=1000,
units="s")
seg.spiketrains.append(st)
blk.channel_indexes.append(chidx)
for idx in range(6):
unit = Unit("unit" + str(idx))
chidx.units.append(unit)
unit.spiketrains.append(st)
self.writer.write_block(blk)
self.compare_blocks([blk], self.reader.blocks)
def _create_channelindex(self, group):
return ChannelIndex(index=self._read_array(group, 'index'),
channel_names=self._read_array(group, 'channel_names'),
channel_ids=self._read_array(group, 'channel_ids'),
coordinates=self._read_array(group, 'coordinates'))
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
def read_block(self,
block_index=0,
lazy=False,
signal_group_mode=None,
units_group_mode=None,
load_waveforms=False):
"""
:param block_index: int default 0. In case of several block block_index can be specified.
:param lazy: False by default.
:param signal_group_mode: 'split-all' or 'group-by-same-units' (default depend IO):
This control behavior for grouping channels in AnalogSignal.
* 'split-all': each channel will give an AnalogSignal
* 'group-by-same-units' all channel sharing the same quantity units ar grouped in
a 2D AnalogSignal
:param units_group_mode: 'split-all' or 'all-in-one'(default depend IO)
This control behavior for grouping Unit in ChannelIndex:
* 'split-all': each neo.Unit is assigned to a new neo.ChannelIndex
* 'all-in-one': all neo.Unit are grouped in the same neo.ChannelIndex
(global spike sorting for instance)
:param load_waveforms: False by default. Control SpikeTrains.waveforms is None or not.
"""
if signal_group_mode is None:
signal_group_mode = self._prefered_signal_group_mode
if self._prefered_signal_group_mode == 'split-all':
self.logger.warning("the default signal_group_mode will change from "\
"'split-all' to 'group-by-same-units' in next release")
if units_group_mode is None:
units_group_mode = self._prefered_units_group_mode
# annotations
bl_annotations = dict(self.raw_annotations['blocks'][block_index])
bl_annotations.pop('segments')
bl_annotations = check_annotations(bl_annotations)
bl = Block(**bl_annotations)
# ChannelIndex are plit in 2 parts:
# * some for AnalogSignals
# * some for Units
# ChannelIndex for AnalogSignals
all_channels = self.header['signal_channels']
channel_indexes_list = self.get_group_channel_indexes()
for channel_index in channel_indexes_list:
for i, (ind_within,
ind_abs) in self._make_signal_channel_subgroups(
channel_index,
signal_group_mode=signal_group_mode).items():
if signal_group_mode == "split-all":
chidx_annotations = self.raw_annotations[
'signal_channels'][i]
elif signal_group_mode == "group-by-same-units":
# this should be done with array_annotation soon:
keys = list(self.raw_annotations['signal_channels'][
ind_abs[0]].keys())
# take key from first channel of the group
chidx_annotations = {key: [] for key in keys}
for j in ind_abs:
for key in keys:
v = self.raw_annotations['signal_channels'][j].get(
key, None)
chidx_annotations[key].append(v)
if 'name' in list(chidx_annotations.keys()):
chidx_annotations.pop('name')
chidx_annotations = check_annotations(chidx_annotations)
# this should be done with array_annotation soon:
ch_names = all_channels[ind_abs]['name'].astype('S')
neo_channel_index = ChannelIndex(
index=ind_within,
channel_names=ch_names,
channel_ids=all_channels[ind_abs]['id'],
name='Channel group {}'.format(i),
**chidx_annotations)
bl.channel_indexes.append(neo_channel_index)
# ChannelIndex and Unit
# 2 case are possible in neo defifferent IO have choosen one or other:
# * All units are grouped in the same ChannelIndex and indexes are all channels:
# 'all-in-one'
# * Each units is assigned to one ChannelIndex: 'split-all'
# This is kept for compatibility
unit_channels = self.header['unit_channels']
if units_group_mode == 'all-in-one':
if unit_channels.size > 0:
channel_index = ChannelIndex(index=np.array([], dtype='i'),
name='ChannelIndex for all Unit')
bl.channel_indexes.append(channel_index)
for c in range(unit_channels.size):
unit_annotations = self.raw_annotations['unit_channels'][c]
unit_annotations = check_annotations(unit_annotations)
unit = Unit(**unit_annotations)
channel_index.units.append(unit)
elif units_group_mode == 'split-all':
for c in range(len(unit_channels)):
unit_annotations = self.raw_annotations['unit_channels'][c]
unit_annotations = check_annotations(unit_annotations)
unit = Unit(**unit_annotations)
channel_index = ChannelIndex(index=np.array([], dtype='i'),
name='ChannelIndex for Unit')
channel_index.units.append(unit)
bl.channel_indexes.append(channel_index)
# Read all segments
for seg_index in range(self.segment_count(block_index)):
seg = self.read_segment(block_index=block_index,
seg_index=seg_index,
lazy=lazy,
signal_group_mode=signal_group_mode,
load_waveforms=load_waveforms)
bl.segments.append(seg)
# create link to other containers ChannelIndex and Units
for seg in bl.segments:
for c, anasig in enumerate(seg.analogsignals):
bl.channel_indexes[c].analogsignals.append(anasig)
nsig = len(seg.analogsignals)
for c, sptr in enumerate(seg.spiketrains):
if units_group_mode == 'all-in-one':
bl.channel_indexes[nsig].units[c].spiketrains.append(sptr)
elif units_group_mode == 'split-all':
bl.channel_indexes[nsig +
c].units[0].spiketrains.append(sptr)
bl.create_many_to_one_relationship()
return bl
def test__children(self):
chx = ChannelIndex(index=np.arange(self.nchildren), name='chx1')
chx.units = [self.unit1]
chx.create_many_to_one_relationship()
assert_neo_object_is_compliant(self.unit1)
assert_neo_object_is_compliant(chx)
self.assertEqual(self.unit1._container_child_objects, ())
self.assertEqual(self.unit1._data_child_objects, ('SpikeTrain',))
self.assertEqual(self.unit1._single_parent_objects,
('ChannelIndex',))
self.assertEqual(self.unit1._multi_child_objects, ())
self.assertEqual(self.unit1._multi_parent_objects, ())
self.assertEqual(self.unit1._child_properties, ())
self.assertEqual(self.unit1._single_child_objects, ('SpikeTrain',))
self.assertEqual(self.unit1._container_child_containers, ())
self.assertEqual(self.unit1._data_child_containers, ('spiketrains',))
self.assertEqual(self.unit1._single_child_containers, ('spiketrains',))
self.assertEqual(self.unit1._single_parent_containers,
('channel_index',))
self.assertEqual(self.unit1._multi_child_containers, ())
self.assertEqual(self.unit1._multi_parent_containers, ())
self.assertEqual(self.unit1._child_objects, ('SpikeTrain',))
self.assertEqual(self.unit1._child_containers, ('spiketrains',))
self.assertEqual(self.unit1._parent_objects,
('ChannelIndex',))
self.assertEqual(self.unit1._parent_containers,
('channel_index',))
self.assertEqual(len(self.unit1._single_children), self.nchildren)
self.assertEqual(len(self.unit1._multi_children), 0)
self.assertEqual(len(self.unit1.data_children), self.nchildren)
self.assertEqual(len(self.unit1.data_children_recur), self.nchildren)
self.assertEqual(len(self.unit1.container_children), 0)
self.assertEqual(len(self.unit1.container_children_recur), 0)
self.assertEqual(len(self.unit1.children), self.nchildren)
self.assertEqual(len(self.unit1.children_recur), self.nchildren)
self.assertEqual(self.unit1._multi_children, ())
self.assertEqual(self.unit1.container_children, ())
self.assertEqual(self.unit1.container_children_recur, ())
assert_same_sub_schema(list(self.unit1._single_children),
self.trains1a)
assert_same_sub_schema(list(self.unit1.data_children),
self.trains1a)
assert_same_sub_schema(list(self.unit1.data_children_recur),
self.trains1a)
assert_same_sub_schema(list(self.unit1.children),
self.trains1a)
assert_same_sub_schema(list(self.unit1.children_recur),
self.trains1a)
self.assertEqual(len(self.unit1.parents), 1)
self.assertEqual(self.unit1.parents[0].name, 'chx1')
def proc_f32(filename):
'''Load an f32 file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareF32IO to
make sure BrainwareF32IO is working properly
block = proc_f32(filename)
filename: The file name of the numpy file to load. It should end with
'*_f32_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.f32', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_f32_py2.npz'
f32 file name = 'file1.f32'
'''
filenameorig = os.path.basename(filename[:-12] + '.f32')
# create the objects to store other objects
block = Block(file_origin=filenameorig)
chx = ChannelIndex(file_origin=filenameorig,
index=np.array([], dtype=np.int),
channel_names=np.array([], dtype='S'))
unit = Unit(file_origin=filenameorig)
# load objects into their containers
block.channel_indexes.append(chx)
chx.units.append(unit)
try:
with np.load(filename) as f32obj:
f32file = f32obj.items()[0][1].flatten()
except IOError as exc:
if 'as a pickle' in exc.message:
block.create_many_to_one_relationship()
return block
else:
raise
sweeplengths = [res[0, 0].tolist() for res in f32file['sweeplength']]
stims = [res.flatten().tolist() for res in f32file['stim']]
sweeps = [res['spikes'].flatten() for res in f32file['sweep'] if res.size]
fullf32 = zip(sweeplengths, stims, sweeps)
for sweeplength, stim, sweep in fullf32:
for trainpts in sweep:
if trainpts.size:
trainpts = trainpts.flatten().astype('float32')
else:
trainpts = []
paramnames = ['Param%s' % i for i in range(len(stim))]
params = dict(zip(paramnames, stim))
train = SpikeTrain(trainpts,
units=pq.ms,
t_start=0,
t_stop=sweeplength,
file_origin=filenameorig)
segment = Segment(file_origin=filenameorig, **params)
segment.spiketrains = [train]
unit.spiketrains.append(train)
block.segments.append(segment)
block.create_many_to_one_relationship()
return block
def test__issue_285(self):
##Spiketrain
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch([0, 10, 20], [2, 2, 2], ["a", "b", "c"], units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
os.remove('blk.pkl')
##Epoch
train = Epoch(times=np.arange(0, 30, 10)*pq.s,durations=[10, 5, 7]*pq.ms,labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##Event
train = Event(np.arange(0, 30, 10)*pq.s,labels=np.array(['trig0', 'trig1', 'trig2'],dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##IrregularlySampledSignal
train = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],units='mV', time_units='ms')
train.segment = Segment()
unit = Unit()
train.channel_index = ChannelIndex(1)
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
blk.segments[0].block = blk
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
self.assertIsInstance(r_seg.spiketrains[0].channel_index, ChannelIndex)
os.remove('blk.pkl')
from neo.core import (Block, Segment, ChannelIndex, AnalogSignal)
from quantities import nA, kHz
import numpy as np
import neo
import pickle
blk = Block()
seg = Segment(name='segment foo')
blk.segments.append(seg)
source_ids = np.arange(64)
channel_ids = source_ids + 42
chx = ChannelIndex(name='Array probe', index=np.arange(64),
channel_ids=channel_ids,
channel_names=['Channel %i' % chid for chid in channel_ids])
blk.channel_indexes.append(chx)
a = AnalogSignal(np.random.randn(10000, 64)*nA, sampling_rate=10*kHz)
# link AnalogSignal and ID providing channel_index
a.channel_index = chx
chx.analogsignals.append(a)
seg.analogsignals.append(a)
seg1 = blk.segments[0]
a1 = seg1.analogsignals[0]
chx1 = a1.channel_index
print(chx1)
print(chx1.index)
io = neo.io.PickleIO(filename="test.pickle")
def __init__(self, filename):
"""
Arguments:
filename : the filename
"""
BaseIO.__init__(self)
self._absolute_filename = filename
self._path, relative_filename = os.path.split(filename)
self._base_filename, extension = os.path.splitext(relative_filename)
if extension != ".set":
raise ValueError("file extension must be '.set'")
with open(self._absolute_filename, "r") as f:
text = f.read()
params = parse_params(text)
self._adc_fullscale = (float(params["ADC_fullscale_mv"]) * 1000.0 *
pq.uV)
self._duration = float(
params["duration"]) * pq.s # TODO convert from samples to seconds
self._tracked_spots_count = int(params["tracked_spots"])
self._params = params
# TODO this file reading can be removed, perhaps?
channel_group_files = glob.glob(
os.path.join(self._path, self._base_filename) + ".[0-9]*")
self._channel_to_channel_index = {}
self._channel_group_to_channel_index = {}
self._channel_count = 0
self._channel_group_count = 0
self._channel_indexes = []
for channel_group_file in channel_group_files:
# increment before, because channel_groups start at 1
self._channel_group_count += 1
group_id = self._channel_group_count # TODO count from 0?
with open(channel_group_file, "rb") as f:
channel_group_params = parse_header_and_leave_cursor(f)
num_chans = channel_group_params["num_chans"]
channel_ids = []
channel_names = []
for i in range(num_chans):
channel_id = self._channel_count + i
channel_ids.append(channel_id)
channel_names.append("channel_{}_group_{}_internal_{}\
".format(channel_id, group_id, i))
chan_name = 'group_id #{}'.format(group_id)
channel_names = np.array(channel_names, dtype="S")
channel_index = ChannelIndex(name=chan_name,
channel_names=channel_names,
index=np.arange(num_chans),
channel_ids=np.array(channel_ids),
**{'group_id': group_id})
self._channel_indexes.append(channel_index)
self._channel_group_to_channel_index[group_id] = channel_index
for i in range(num_chans):
channel_id = self._channel_count + i
self._channel_to_channel_index[channel_id] = channel_index
# increment after, because channels start at 0
self._channel_count += num_chans
# TODO add channels only for files that exist
self._channel_ids = np.arange(self._channel_count)
def read_block(
self,
lazy=False,
get_waveforms=True,
cluster_group=None,
raw_data_units='uV',
get_raw_data=False,
):
"""
Reads a block with segments and channel_indexes
Parameters:
get_waveforms: bool, default = False
Wether or not to get the waveforms
get_raw_data: bool, default = False
Wether or not to get the raw traces
raw_data_units: str, default = "uV"
SI units of the raw trace according to voltage_gain given to klusta
cluster_group: str, default = None
Which clusters to load, possibilities are "noise", "unsorted",
"good", if None all is loaded.
"""
assert not lazy, 'Do not support lazy'
blk = Block()
seg = Segment(file_origin=self.filename)
blk.segments += [seg]
for model in self.models:
group_id = model.channel_group
group_meta = {'group_id': group_id}
group_meta.update(model.metadata)
chx = ChannelIndex(name='channel group #{}'.format(group_id),
index=model.channels,
**group_meta)
blk.channel_indexes.append(chx)
clusters = model.spike_clusters
for cluster_id in model.cluster_ids:
meta = model.cluster_metadata[cluster_id]
if cluster_group is None:
pass
elif cluster_group != meta:
continue
sptr = self.read_spiketrain(cluster_id=cluster_id,
model=model,
get_waveforms=get_waveforms,
raw_data_units=raw_data_units)
sptr.annotations.update({
'cluster_group': meta,
'group_id': model.channel_group
})
sptr.channel_index = chx
unit = Unit(cluster_group=meta,
group_id=model.channel_group,
name='unit #{}'.format(cluster_id))
unit.spiketrains.append(sptr)
chx.units.append(unit)
unit.channel_index = chx
seg.spiketrains.append(sptr)
if get_raw_data:
ana = self.read_analogsignal(model, units=raw_data_units)
ana.channel_index = chx
seg.analogsignals.append(ana)
seg.duration = model.duration * pq.s
blk.create_many_to_one_relationship()
return blk