/usr/lib/python2.7/dist-packages/neo/io/neuroexplorerio.py is in python-neo 0.3.3-2.
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"""
Class for reading data from NeuroExplorer (.nex)
Documentation for dev :
http://www.neuroexplorer.com/code.html
Depend on: scipy
Supported : Read
Author: sgarcia,luc estebanez
"""
import os
import struct
import numpy as np
import quantities as pq
from neo.io.baseio import BaseIO
from neo.core import Segment, AnalogSignal, SpikeTrain, EpochArray, EventArray
from neo.io.tools import create_many_to_one_relationship
class NeuroExplorerIO(BaseIO):
"""
Class for reading nex file.
Usage:
>>> from neo import io
>>> r = io.NeuroExplorerIO(filename='File_neuroexplorer_1.nex')
>>> seg = r.read_segment(lazy=False, cascade=True)
>>> print seg.analogsignals # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<AnalogSignal(array([ 39.0625 , 0. , 0. , ..., -26.85546875, ...
>>> print seg.spiketrains # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<SpikeTrain(array([ 2.29499992e-02, 6.79249987e-02, 1.13399997e-01, ...
>>> print seg.eventarrays # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<EventArray: @21.1967754364 s, @21.2993755341 s, @21.350725174 s, @21.5048999786 s, ...
>>> print seg.epocharrays # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<neo.core.epocharray.EpochArray object at 0x10561ba90>, <neo.core.epocharray.EpochArray object at 0x10561bad0>]
"""
is_readable = True
is_writable = False
supported_objects = [Segment , AnalogSignal, SpikeTrain, EventArray, EpochArray]
readable_objects = [ Segment]
writeable_objects = []
has_header = False
is_streameable = False
# This is for GUI stuf : a definition for parameters when reading.
read_params = {
Segment : [ ]
}
write_params = None
name = 'NeuroExplorer'
extensions = [ 'nex' ]
mode = 'file'
def __init__(self , filename = None) :
"""
This class read a nex file.
Arguments:
filename : the filename to read you can pu what ever it do not read anythings
"""
BaseIO.__init__(self)
self.filename = filename
def read_segment(self,
lazy = False,
cascade = True,
):
fid = open(self.filename, 'rb')
globalHeader = HeaderReader(fid , GlobalHeader ).read_f(offset = 0)
#~ print globalHeader
#~ print 'version' , globalHeader['version']
seg = Segment()
seg.file_origin = os.path.basename(self.filename)
seg.annotate(neuroexplorer_version = globalHeader['version'])
seg.annotate(comment = globalHeader['comment'])
if not cascade :
return seg
offset = 544
for i in range(globalHeader['nvar']):
entityHeader = HeaderReader(fid , EntityHeader ).read_f(offset = offset+i*208)
entityHeader['name'] = entityHeader['name'].replace('\x00','')
#print 'i',i, entityHeader['type']
if entityHeader['type'] == 0:
# neuron
if lazy:
spike_times = [ ]*pq.s
else:
spike_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
spike_times = spike_times.astype('f8')/globalHeader['freq']*pq.s
sptr = SpikeTrain( times= spike_times,
t_start = globalHeader['tbeg']/globalHeader['freq']*pq.s,
t_stop = globalHeader['tend']/globalHeader['freq']*pq.s,
name = entityHeader['name'],
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index = entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 1:
# event
if lazy:
event_times = [ ]*pq.s
else:
event_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
event_times = event_times.astype('f8')/globalHeader['freq'] * pq.s
labels = np.array(['']*event_times.size, dtype = 'S')
evar = EventArray(times = event_times, labels=labels, channel_name = entityHeader['name'] )
if lazy:
evar.lazy_shape = entityHeader['n']
seg.eventarrays.append(evar)
if entityHeader['type'] == 2:
# interval
if lazy:
start_times = [ ]*pq.s
stop_times = [ ]*pq.s
else:
start_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
start_times = start_times.astype('f8')/globalHeader['freq']*pq.s
stop_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset']+entityHeader['n']*4,
)
stop_times = stop_times.astype('f')/globalHeader['freq']*pq.s
epar = EpochArray(times = start_times,
durations = stop_times - start_times,
labels = np.array(['']*start_times.size, dtype = 'S'),
channel_name = entityHeader['name'])
if lazy:
epar.lazy_shape = entityHeader['n']
seg.epocharrays.append(epar)
if entityHeader['type'] == 3:
# spiketrain and wavefoms
if lazy:
spike_times = [ ]*pq.s
waveforms = None
else:
spike_times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
spike_times = spike_times.astype('f8')/globalHeader['freq'] * pq.s
waveforms = np.memmap(self.filename , np.dtype('i2') ,'r' ,
shape = (entityHeader['n'] , 1,entityHeader['NPointsWave']),
offset = entityHeader['offset']+entityHeader['n'] *4,
)
waveforms = (waveforms.astype('f')* entityHeader['ADtoMV'] + entityHeader['MVOffset'])*pq.mV
t_stop = globalHeader['tend']/globalHeader['freq']*pq.s
if spike_times.size>0:
t_stop = max(t_stop, max(spike_times))
sptr = SpikeTrain( times = spike_times,
t_start = globalHeader['tbeg']/globalHeader['freq']*pq.s,
#~ t_stop = max(globalHeader['tend']/globalHeader['freq']*pq.s,max(spike_times)),
t_stop = t_stop,
name = entityHeader['name'],
waveforms = waveforms,
sampling_rate = entityHeader['WFrequency']*pq.Hz,
left_sweep = 0*pq.ms,
)
if lazy:
sptr.lazy_shape = entityHeader['n']
sptr.annotate(channel_index = entityHeader['WireNumber'])
seg.spiketrains.append(sptr)
if entityHeader['type'] == 4:
# popvectors
pass
if entityHeader['type'] == 5:
# analog
timestamps= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
timestamps = timestamps.astype('f8')/globalHeader['freq']
fragmentStarts = np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
fragmentStarts = fragmentStarts.astype('f8')/globalHeader['freq']
t_start = timestamps[0] - fragmentStarts[0]/float(entityHeader['WFrequency'])
del timestamps, fragmentStarts
if lazy :
signal = [ ]*pq.mV
else:
signal = np.memmap(self.filename , np.dtype('i2') ,'r' ,
shape = (entityHeader['NPointsWave'] ),
offset = entityHeader['offset'],
)
signal = signal.astype('f')
signal *= entityHeader['ADtoMV']
signal += entityHeader['MVOffset']
signal = signal*pq.mV
anaSig = AnalogSignal(signal=signal, t_start=t_start * pq.s,
sampling_rate=
entityHeader['WFrequency'] * pq.Hz,
name=entityHeader['name'],
channel_index=entityHeader['WireNumber'])
if lazy:
anaSig.lazy_shape = entityHeader['NPointsWave']
seg.analogsignals.append( anaSig )
if entityHeader['type'] == 6:
# markers : TO TEST
if lazy:
times = [ ]*pq.s
labels = np.array([ ], dtype = 'S')
markertype = None
else:
times= np.memmap(self.filename , np.dtype('i4') ,'r' ,
shape = (entityHeader['n'] ),
offset = entityHeader['offset'],
)
times = times.astype('f8')/globalHeader['freq'] * pq.s
fid.seek(entityHeader['offset'] + entityHeader['n']*4)
markertype = fid.read(64).replace('\x00','')
labels = np.memmap(self.filename, np.dtype('S' + str(entityHeader['MarkerLength'])) ,'r',
shape = (entityHeader['n'] ),
offset = entityHeader['offset'] + entityHeader['n']*4 + 64
)
ea = EventArray( times = times,
labels = labels.view(np.ndarray),
name = entityHeader['name'],
channel_index = entityHeader['WireNumber'],
marker_type = markertype
)
if lazy:
ea.lazy_shape = entityHeader['n']
seg.eventarrays.append(ea)
create_many_to_one_relationship(seg)
return seg
GlobalHeader = [
('signature' , '4s'),
('version','i'),
('comment','256s'),
('freq','d'),
('tbeg','i'),
('tend','i'),
('nvar','i'),
]
EntityHeader = [
('type' , 'i'),
('varVersion','i'),
('name','64s'),
('offset','i'),
('n','i'),
('WireNumber','i'),
('UnitNumber','i'),
('Gain','i'),
('Filter','i'),
('XPos','d'),
('YPos','d'),
('WFrequency','d'),
('ADtoMV','d'),
('NPointsWave','i'),
('NMarkers','i'),
('MarkerLength','i'),
('MVOffset','d'),
('dummy','60s'),
]
MarkerHeader = [
('type' , 'i'),
('varVersion','i'),
('name','64s'),
('offset','i'),
('n','i'),
('WireNumber','i'),
('UnitNumber','i'),
('Gain','i'),
('Filter','i'),
]
class HeaderReader():
def __init__(self,fid ,description ):
self.fid = fid
self.description = description
def read_f(self, offset =0):
self.fid.seek(offset)
d = { }
for key, fmt in self.description :
val = struct.unpack(fmt , self.fid.read(struct.calcsize(fmt)))
if len(val) == 1:
val = val[0]
else :
val = list(val)
d[key] = val
return d
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