def get_spikes(cerebus_times_ms=None, t1_ms=-50, t2_ms=150, fun_args=None):
"""A reading function that reads in data from the cerebus spike files.
Inputs:
cerebus_times_ms : an array of cerebus times indicating the start of the
stimuli
t1_ms : start time relative to cerebus_times_ms
t2_ms : end time relative to cerebus_times_ms
fun_args : dictionary:
'nev_basic_header',
'nev_extended_header',
'frag_dir', - directory where fragmented nev file is (absolute)
'channel', - the channel we worry about
'unit' the unit
Outputs:
neural_response : a 7D list with ultimate cells containing a list, array
or any other data structure
comment : a string that should explain what the data is
"""
nev_basic_header = fun_args['nev basic header']
nev_extended_header = fun_args['nev extended header']
frag_dir = fun_args['frag dir']
channel = fun_args['channel']
unit = fun_args['unit']
# Read in the whole spike train - warning, if you read in the spike
# waveforms as well, you may run outta memory
data, dummy = nev.read_frag_unit(frag_dir,
nev_basic_header,
nev_extended_header,
channel=channel,
unit=unit,
tstart_ms=0.0,
tdur_ms=-1,
load_waveform=False,
buffer_increment_size=1000)
all_spike_time_ms = data['spike time ms']
spike_counts = pylab.zeros((cerebus_times_ms.size), dtype=float)
#mean_latency_ms = pylab.ones((cerebus_times_ms.size),dtype=float) * 1000
spike_time_ms = [None] * cerebus_times_ms.size
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
tstop_ms = cerebus_times_ms[n] + t2_ms
idx = pylab.find((all_spike_time_ms >= tstart_ms)
& (all_spike_time_ms <= tstop_ms))
spike_counts[n] = idx.size
if idx.size > 0:
rel_spike_time_ms = all_spike_time_ms[idx] - cerebus_times_ms[n]
#mean_latency_ms[n] = rel_spike_time_ms.mean()
spike_time_ms[n] = rel_spike_time_ms
data = {
'trials': cerebus_times_ms.size,
'spike counts': spike_counts,
#'mean latency ms': mean_latency_ms,
'spike times ms': spike_time_ms
}
return data
def get_spikes(cerebus_times_ms = None,
t1_ms = -50,
t2_ms = 150,
fun_args = None):
"""A reading function that reads in data from the cerebus spike files.
Inputs:
cerebus_times_ms : an array of cerebus times indicating the start of the
stimuli
t1_ms : start time relative to cerebus_times_ms
t2_ms : end time relative to cerebus_times_ms
fun_args : dictionary:
'nev_basic_header',
'nev_extended_header',
'frag_dir', - directory where fragmented nev file is (absolute)
'channel', - the channel we worry about
'unit' the unit
Outputs:
neural_response : a 7D list with ultimate cells containing a list, array
or any other data structure
comment : a string that should explain what the data is
"""
nev_basic_header = fun_args['nev basic header']
nev_extended_header = fun_args['nev extended header']
frag_dir = fun_args['frag dir']
channel = fun_args['channel']
unit = fun_args['unit']
# Read in the whole spike train - warning, if you read in the spike
# waveforms as well, you may run outta memory
data, dummy = nev.read_frag_unit(
frag_dir, nev_basic_header, nev_extended_header,
channel = channel,
unit = unit,
tstart_ms = 0.0,
tdur_ms = -1,
load_waveform = False,
buffer_increment_size = 1000)
all_spike_time_ms = data['spike time ms']
spike_counts = pylab.zeros((cerebus_times_ms.size),dtype=float)
#mean_latency_ms = pylab.ones((cerebus_times_ms.size),dtype=float) * 1000
spike_time_ms = [None] * cerebus_times_ms.size
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
tstop_ms = cerebus_times_ms[n] + t2_ms
idx = pylab.find((all_spike_time_ms >= tstart_ms) & (all_spike_time_ms <= tstop_ms))
spike_counts[n] = idx.size
if idx.size > 0:
rel_spike_time_ms = all_spike_time_ms[idx] - cerebus_times_ms[n]
#mean_latency_ms[n] = rel_spike_time_ms.mean()
spike_time_ms[n] = rel_spike_time_ms
data = {'trials': cerebus_times_ms.size,
'spike counts': spike_counts,
#'mean latency ms': mean_latency_ms,
'spike times ms': spike_time_ms}
return data
def get_spikes_in_window(cerebus_times_ms=None,
t1_ms=-50,
t2_ms=150,
nev_basic_header=None,
nev_extended_header=None,
frag_dir=None,
channel=0,
unit=0):
"""A reading function that reads in data from the cerebus spike files.
(Replaces get_spikes)
Inputs:
cerebus_times_ms : an array of cerebus times indicating the start of the
stimuli
t1_ms : start time relative to cerebus_times_ms
t2_ms : end time relative to cerebus_times_ms
nev_basic_header
nev_extended_header
frag_dir - directory where fragmented nev file is (absolute)
channel - the channel we worry about
unit - the unit
Outputs:
spike_time_ms : list (length same as cerebus_times) of spike times, given
relative to the cerebus_time
"""
# Read in the whole spike train - warning, if you read in the spike
# waveforms as well, you may run outta memory
data, dummy = nev.read_frag_unit(frag_dir,
nev_basic_header,
nev_extended_header,
channel=channel,
unit=unit,
tstart_ms=0.0,
tdur_ms=-1,
load_waveform=False,
buffer_increment_size=1000)
all_spike_time_ms = data['spike time ms']
spike_time_ms = [None] * cerebus_times_ms.size
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
tstop_ms = cerebus_times_ms[n] + t2_ms
idx = pylab.find((all_spike_time_ms >= tstart_ms)
& (all_spike_time_ms <= tstop_ms))
if idx.size > 0:
rel_spike_time_ms = all_spike_time_ms[idx] - cerebus_times_ms[n]
spike_time_ms[n] = rel_spike_time_ms
return spike_time_ms
def get_spikes_in_window(cerebus_times_ms = None,
t1_ms = -50,
t2_ms = 150,
nev_basic_header = None,
nev_extended_header = None,
frag_dir = None,
channel = 0,
unit = 0):
"""A reading function that reads in data from the cerebus spike files.
(Replaces get_spikes)
Inputs:
cerebus_times_ms : an array of cerebus times indicating the start of the
stimuli
t1_ms : start time relative to cerebus_times_ms
t2_ms : end time relative to cerebus_times_ms
nev_basic_header
nev_extended_header
frag_dir - directory where fragmented nev file is (absolute)
channel - the channel we worry about
unit - the unit
Outputs:
spike_time_ms : list (length same as cerebus_times) of spike times, given
relative to the cerebus_time
"""
# Read in the whole spike train - warning, if you read in the spike
# waveforms as well, you may run outta memory
data, dummy = nev.read_frag_unit(
frag_dir, nev_basic_header, nev_extended_header,
channel = channel,
unit = unit,
tstart_ms = 0.0,
tdur_ms = -1,
load_waveform = False,
buffer_increment_size = 1000)
all_spike_time_ms = data['spike time ms']
spike_time_ms = [None] * cerebus_times_ms.size
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
tstop_ms = cerebus_times_ms[n] + t2_ms
idx = pylab.find((all_spike_time_ms >= tstart_ms) & (all_spike_time_ms <= tstop_ms))
if idx.size > 0:
rel_spike_time_ms = all_spike_time_ms[idx] - cerebus_times_ms[n]
spike_time_ms[n] = rel_spike_time_ms
return spike_time_ms
read_non_neural = False
load_waveform = False
logger.info('Reading headers')
f = open(options.nevfile)
basic_header = nev.read_basic_header(f)
extended_header = nev.read_extended_header(f, basic_header)
if fragment:
logger.info('Fragmenting')
nev.fragment(f, basic_header, extended_header,
frag_dir = options.fragdir,
channel_list = pylab.arange(1,97))
f.close()
if read_non_neural:
logger.info('Reading non neural events')
time_stamp_ms, codes = \
nev.read_frag_nonneural_digital(options.fragdir, basic_header)
if load_waveform:
logger.info('Loading all waveforms')
data, read_errors = \
nev.read_frag_unit(options.fragdir, basic_header, extended_header,
channel = 1,
unit = 0,
tstart_ms = 0.0,
tdur_ms = -1.0,
load_waveform = True)
(options, args) = parser.parse_args()
fragment = False
read_non_neural = False
load_waveform = True
logger.info('Reading headers')
f = open(options.nevfile)
basic_header = nev.read_basic_header(f)
extended_header = nev.read_extended_header(f, basic_header)
unit = 0#Unsorted
for channel in range(1,97):
fout_name = options.outdir + '/channel%02d.asc' %(channel)
fout = open(fout_name,'w')
write_header(fout, basic_header, extended_header, 1, 1)#each file gets a separate channel
data, data_ok = \
nev.read_frag_unit(options.fragdir, basic_header, extended_header,
channel = channel,
unit = unit,
tstart_ms = 0.0,
tdur_ms = -1.0,#Read all of it
load_waveform = True)
if data_ok:
write_data(fout, 1, unit, data, options.threshold)
fout.close()
f.close()
logger.info('Reading headers')
f = open(options.nevfile)
basic_header = nev.read_basic_header(f)
extended_header = nev.read_extended_header(f, basic_header)
if fragment:
logger.info('Fragmenting')
nev.fragment(f,
basic_header,
extended_header,
frag_dir=options.fragdir,
channel_list=pylab.arange(1, 97))
f.close()
if read_non_neural:
logger.info('Reading non neural events')
time_stamp_ms, codes = \
nev.read_frag_nonneural_digital(options.fragdir, basic_header)
if load_waveform:
logger.info('Loading all waveforms')
data, read_errors = \
nev.read_frag_unit(options.fragdir, basic_header, extended_header,
channel = 1,
unit = 0,
tstart_ms = 0.0,
tdur_ms = -1.0,
load_waveform = True)
(options, args) = parser.parse_args()
fragment = False
read_non_neural = False
load_waveform = True
logger.info('Reading headers')
f = open(options.nevfile)
basic_header = nev.read_basic_header(f)
extended_header = nev.read_extended_header(f, basic_header)
unit = 0 #Unsorted
for channel in range(1, 97):
fout_name = options.outdir + '/channel%02d.asc' % (channel)
fout = open(fout_name, 'w')
write_header(fout, basic_header, extended_header, 1,
1) #each file gets a separate channel
data, data_ok = \
nev.read_frag_unit(options.fragdir, basic_header, extended_header,
channel = channel,
unit = unit,
tstart_ms = 0.0,
tdur_ms = -1.0,#Read all of it
load_waveform = True)
if data_ok:
write_data(fout, 1, unit, data, options.threshold)
fout.close()
f.close()
