/usr/lib/python2.7/dist-packages/neo/io/neuroshareio.py is in python-neo 0.3.3-2.
This file is owned by root:root, with mode 0o644.
The actual contents of the file can be viewed below.
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"""
NeuroshareIO is a wrap with ctypes of neuroshare DLLs.
Neuroshare is a C API for reading neural data.
Neuroshare also provides a Matlab and a Python API on top of that.
Neuroshare is an open source API but each dll is provided directly by the vendor.
The neo user have to download separtatly the dll on neurosharewebsite:
http://neuroshare.sourceforge.net/
For some vendors (Spike2/CED , Clampfit/Abf, ...), neo.io also provides pure Python
Neo users you should prefer them of course :)
Supported : Read
Author: sgarcia
"""
import ctypes
import os
# file no longer exists in Python3
try:
file
except NameError:
import io
file = io.BufferedReader
import numpy as np
import quantities as pq
from neo.io.baseio import BaseIO
from neo.core import Segment, AnalogSignal, SpikeTrain, EventArray
from neo.io.tools import create_many_to_one_relationship
class NeuroshareIO(BaseIO):
"""
Class for reading file trougth neuroshare API.
The user need the DLLs in the path of the file format.
Usage:
>>> from neo import io
>>> r = io.NeuroshareIO(filename='a_file', dllname=the_name_of_dll)
>>> seg = r.read_segment(lazy=False, cascade=True, import_neuroshare_segment=True)
>>> print seg.analogsignals # doctest: +ELLIPSIS, +NORMALIZE_WHITESPACE
[<AnalogSignal(array([ -1.77246094e+02, -2.24707031e+02, -2.66015625e+02,
...
>>> print seg.spiketrains
[]
>>> print seg.eventarrays
[<EventArray: 1@1.12890625 s, 1@2.02734375 s, 1@3.82421875 s>]
Note:
neuroshare.ns_ENTITY_EVENT: are converted to neo.EventArray
neuroshare.ns_ENTITY_ANALOG: are converted to neo.AnalogSignal
neuroshare.ns_ENTITY_NEURALEVENT: are converted to neo.SpikeTrain
neuroshare.ns_ENTITY_SEGMENT: is something between serie of small AnalogSignal
and Spiketrain with associated waveforms.
It is arbitrarily converted as SpikeTrain.
"""
is_readable = True
is_writable = False
supported_objects = [Segment , AnalogSignal, EventArray, SpikeTrain ]
readable_objects = [Segment]
writeable_objects = [ ]
has_header = False
is_streameable = False
read_params = { Segment : [] }
write_params = None
name = 'neuroshare'
extensions = [ ]
mode = 'file'
def __init__(self , filename = '', dllname = '') :
"""
Arguments:
filename: the file to read
ddlname: the name of neuroshare dll to be used for this file
"""
self.dllname = dllname
self.filename = filename
BaseIO.__init__(self)
def read_segment(self, import_neuroshare_segment = True,
lazy=False, cascade=True):
"""
Arguments:
import_neuroshare_segment: import neuroshare segment as SpikeTrain with associated waveforms or not imported at all.
"""
seg = Segment( file_origin = os.path.basename(self.filename), )
neuroshare = ctypes.windll.LoadLibrary(self.dllname)
# API version
info = ns_LIBRARYINFO()
neuroshare.ns_GetLibraryInfo(ctypes.byref(info) , ctypes.sizeof(info))
seg.annotate(neuroshare_version = str(info.dwAPIVersionMaj)+'.'+str(info.dwAPIVersionMin))
if not cascade:
return seg
# open file
hFile = ctypes.c_uint32(0)
neuroshare.ns_OpenFile(ctypes.c_char_p(self.filename) ,ctypes.byref(hFile))
fileinfo = ns_FILEINFO()
neuroshare.ns_GetFileInfo(hFile, ctypes.byref(fileinfo) , ctypes.sizeof(fileinfo))
# read all entities
for dwEntityID in range(fileinfo.dwEntityCount):
entityInfo = ns_ENTITYINFO()
neuroshare.ns_GetEntityInfo( hFile, dwEntityID, ctypes.byref(entityInfo), ctypes.sizeof(entityInfo))
#~ print 'type', entityInfo.dwEntityType,entity_types[entityInfo.dwEntityType], 'count', entityInfo.dwItemCount
#~ print entityInfo.szEntityLabel
# EVENT
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_EVENT':
pEventInfo = ns_EVENTINFO()
neuroshare.ns_GetEventInfo ( hFile, dwEntityID, ctypes.byref(pEventInfo), ctypes.sizeof(pEventInfo))
#~ print pEventInfo.szCSVDesc, pEventInfo.dwEventType, pEventInfo.dwMinDataLength, pEventInfo.dwMaxDataLength
if pEventInfo.dwEventType == 0: #TEXT
pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength)
elif pEventInfo.dwEventType == 1:#CVS
pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength)
elif pEventInfo.dwEventType == 2:# 8bit
pData = ctypes.c_byte(0)
elif pEventInfo.dwEventType == 3:# 16bit
pData = ctypes.c_int16(0)
elif pEventInfo.dwEventType == 4:# 32bit
pData = ctypes.c_int32(0)
pdTimeStamp = ctypes.c_double(0.)
pdwDataRetSize = ctypes.c_uint32(0)
ea = EventArray(name = str(entityInfo.szEntityLabel),)
if not lazy:
times = [ ]
labels = [ ]
for dwIndex in range(entityInfo.dwItemCount ):
neuroshare.ns_GetEventData ( hFile, dwEntityID, dwIndex,
ctypes.byref(pdTimeStamp), ctypes.byref(pData),
ctypes.sizeof(pData), ctypes.byref(pdwDataRetSize) )
times.append(pdTimeStamp.value)
labels.append(str(pData))
ea.times = times*pq.s
ea.labels = np.array(labels, dtype ='S')
else :
ea.lazy_shape = entityInfo.dwItemCount
seg.eventarrays.append(ea)
# analog
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_ANALOG':
pAnalogInfo = ns_ANALOGINFO()
neuroshare.ns_GetAnalogInfo( hFile, dwEntityID,ctypes.byref(pAnalogInfo),ctypes.sizeof(pAnalogInfo) )
#~ print 'dSampleRate' , pAnalogInfo.dSampleRate , pAnalogInfo.szUnits
dwStartIndex = ctypes.c_uint32(0)
dwIndexCount = entityInfo.dwItemCount
if lazy:
signal = [ ]*pq.Quantity(1, pAnalogInfo.szUnits)
else:
pdwContCount = ctypes.c_uint32(0)
pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'f8')
neuroshare.ns_GetAnalogData ( hFile, dwEntityID, dwStartIndex,
dwIndexCount, ctypes.byref( pdwContCount) , pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
pszMsgBuffer = ctypes.create_string_buffer(" "*256)
neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256)
#~ print 'pszMsgBuffer' , pszMsgBuffer.value
signal = pData[:pdwContCount.value]*pq.Quantity(1, pAnalogInfo.szUnits)
#t_start
dwIndex = 0
pdTime = ctypes.c_double(0)
neuroshare.ns_GetTimeByIndex( hFile, dwEntityID, dwIndex, ctypes.byref(pdTime))
anaSig = AnalogSignal(signal,
sampling_rate = pAnalogInfo.dSampleRate*pq.Hz,
t_start = pdTime.value * pq.s,
name = str(entityInfo.szEntityLabel),
)
if lazy:
anaSig.lazy_shape = entityInfo.dwItemCount
seg.analogsignals.append( anaSig )
#segment
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_SEGMENT' and import_neuroshare_segment:
pdwSegmentInfo = ns_SEGMENTINFO()
neuroshare.ns_GetSegmentInfo( hFile, dwEntityID,
ctypes.byref(pdwSegmentInfo), ctypes.sizeof(pdwSegmentInfo) )
nsource = pdwSegmentInfo.dwSourceCount
pszMsgBuffer = ctypes.create_string_buffer(" "*256)
neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256)
#~ print 'pszMsgBuffer' , pszMsgBuffer.value
#~ print 'pdwSegmentInfo.dwSourceCount' , pdwSegmentInfo.dwSourceCount
for dwSourceID in range(pdwSegmentInfo.dwSourceCount) :
pSourceInfo = ns_SEGSOURCEINFO()
neuroshare.ns_GetSegmentSourceInfo( hFile, dwEntityID, dwSourceID,
ctypes.byref(pSourceInfo), ctypes.sizeof(pSourceInfo) )
if lazy:
sptr = SpikeTrain(times, name = str(entityInfo.szEntityLabel))
sptr.lazy_shape = entityInfo.dwItemCount
else:
pdTimeStamp = ctypes.c_double(0.)
dwDataBufferSize = pdwSegmentInfo.dwMaxSampleCount*pdwSegmentInfo.dwSourceCount
pData = np.zeros( (dwDataBufferSize), dtype = 'f8')
pdwSampleCount = ctypes.c_uint32(0)
pdwUnitID= ctypes.c_uint32(0)
nsample = pdwSampleCount.value
times = np.empty( (entityInfo.dwItemCount), drtype = 'f')
waveforms = np.empty( (entityInfo.dwItemCount, nsource, nsample), drtype = 'f')
for dwIndex in range(entityInfo.dwItemCount ):
neuroshare.ns_GetSegmentData ( hFile, dwEntityID, dwIndex,
ctypes.byref(pdTimeStamp), pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
dwDataBufferSize * 8, ctypes.byref(pdwSampleCount),
ctypes.byref(pdwUnitID ) )
#print 'dwDataBufferSize' , dwDataBufferSize,pdwSampleCount , pdwUnitID
times[dwIndex] = pdTimeStamp.value
waveforms[dwIndex, :,:] = pData[:nsample*nsource].reshape(nsample ,nsource).transpose()
sptr = SpikeTrain(times*pq.s,
waveforms = waveforms*pq.Quantity(1., str(pdwSegmentInfo.szUnits) ),
left_sweep = nsample/2./float(pdwSegmentInfo.dSampleRate)*pq.s,
sampling_rate = float(pdwSegmentInfo.dSampleRate)*pq.Hz,
name = str(entityInfo.szEntityLabel),
)
seg.spiketrains.append(sptr)
# neuralevent
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_NEURALEVENT':
pNeuralInfo = ns_NEURALINFO()
neuroshare.ns_GetNeuralInfo ( hFile, dwEntityID,
ctypes.byref(pNeuralInfo), ctypes.sizeof(pNeuralInfo))
#print pNeuralInfo.dwSourceUnitID , pNeuralInfo.szProbeInfo
if lazy:
times = [ ]*pq.s
else:
pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'f8')
dwStartIndex = 0
dwIndexCount = entityInfo.dwItemCount
neuroshare.ns_GetNeuralData( hFile, dwEntityID, dwStartIndex,
dwIndexCount, pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
times = pData*pq.s
sptr = SpikeTrain(times, name = str(entityInfo.szEntityLabel),)
if lazy:
sptr.lazy_shape = entityInfo.dwItemCount
seg.spiketrains.append(sptr)
# close
neuroshare.ns_CloseFile(hFile)
create_many_to_one_relationship(seg)
return seg
# neuroshare structures
class ns_FILEDESC(ctypes.Structure):
_fields_ = [('szDescription', ctypes.c_char*32),
('szExtension', ctypes.c_char*8),
('szMacCodes', ctypes.c_char*8),
('szMagicCode', ctypes.c_char*16),
]
class ns_LIBRARYINFO(ctypes.Structure):
_fields_ = [('dwLibVersionMaj', ctypes.c_uint32),
('dwLibVersionMin', ctypes.c_uint32),
('dwAPIVersionMaj', ctypes.c_uint32),
('dwAPIVersionMin', ctypes.c_uint32),
('szDescription', ctypes.c_char*64),
('szCreator',ctypes.c_char*64),
('dwTime_Year',ctypes.c_uint32),
('dwTime_Month',ctypes.c_uint32),
('dwTime_Day',ctypes.c_uint32),
('dwFlags',ctypes.c_uint32),
('dwMaxFiles',ctypes.c_uint32),
('dwFileDescCount',ctypes.c_uint32),
('FileDesc',ns_FILEDESC*16),
]
class ns_FILEINFO(ctypes.Structure):
_fields_ = [('szFileType', ctypes.c_char*32),
('dwEntityCount', ctypes.c_uint32),
('dTimeStampResolution', ctypes.c_double),
('dTimeSpan', ctypes.c_double),
('szAppName', ctypes.c_char*64),
('dwTime_Year',ctypes.c_uint32),
('dwTime_Month',ctypes.c_uint32),
('dwReserved',ctypes.c_uint32),
('dwTime_Day',ctypes.c_uint32),
('dwTime_Hour',ctypes.c_uint32),
('dwTime_Min',ctypes.c_uint32),
('dwTime_Sec',ctypes.c_uint32),
('dwTime_MilliSec',ctypes.c_uint32),
('szFileComment',ctypes.c_char*256),
]
class ns_ENTITYINFO(ctypes.Structure):
_fields_ = [('szEntityLabel', ctypes.c_char*32),
('dwEntityType',ctypes.c_uint32),
('dwItemCount',ctypes.c_uint32),
]
entity_types = { 0 : 'ns_ENTITY_UNKNOWN' ,
1 : 'ns_ENTITY_EVENT' ,
2 : 'ns_ENTITY_ANALOG' ,
3 : 'ns_ENTITY_SEGMENT' ,
4 : 'ns_ENTITY_NEURALEVENT' ,
}
class ns_EVENTINFO(ctypes.Structure):
_fields_ = [
('dwEventType',ctypes.c_uint32),
('dwMinDataLength',ctypes.c_uint32),
('dwMaxDataLength',ctypes.c_uint32),
('szCSVDesc', ctypes.c_char*128),
]
class ns_ANALOGINFO(ctypes.Structure):
_fields_ = [
('dSampleRate',ctypes.c_double),
('dMinVal',ctypes.c_double),
('dMaxVal',ctypes.c_double),
('szUnits', ctypes.c_char*16),
('dResolution',ctypes.c_double),
('dLocationX',ctypes.c_double),
('dLocationY',ctypes.c_double),
('dLocationZ',ctypes.c_double),
('dLocationUser',ctypes.c_double),
('dHighFreqCorner',ctypes.c_double),
('dwHighFreqOrder',ctypes.c_uint32),
('szHighFilterType', ctypes.c_char*16),
('dLowFreqCorner',ctypes.c_double),
('dwLowFreqOrder',ctypes.c_uint32),
('szLowFilterType', ctypes.c_char*16),
('szProbeInfo', ctypes.c_char*128),
]
class ns_SEGMENTINFO(ctypes.Structure):
_fields_ = [
('dwSourceCount',ctypes.c_uint32),
('dwMinSampleCount',ctypes.c_uint32),
('dwMaxSampleCount',ctypes.c_uint32),
('dSampleRate',ctypes.c_double),
('szUnits', ctypes.c_char*32),
]
class ns_SEGSOURCEINFO(ctypes.Structure):
_fields_ = [
('dMinVal',ctypes.c_double),
('dMaxVal',ctypes.c_double),
('dResolution',ctypes.c_double),
('dSubSampleShift',ctypes.c_double),
('dLocationX',ctypes.c_double),
('dLocationY',ctypes.c_double),
('dLocationZ',ctypes.c_double),
('dLocationUser',ctypes.c_double),
('dHighFreqCorner',ctypes.c_double),
('dwHighFreqOrder',ctypes.c_uint32),
('szHighFilterType', ctypes.c_char*16),
('dLowFreqCorner',ctypes.c_double),
('dwLowFreqOrder',ctypes.c_uint32),
('szLowFilterType', ctypes.c_char*16),
('szProbeInfo', ctypes.c_char*128),
]
class ns_NEURALINFO(ctypes.Structure):
_fields_ = [
('dwSourceEntityID',ctypes.c_uint32),
('dwSourceUnitID',ctypes.c_uint32),
('szProbeInfo',ctypes.c_char*128),
]
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