def get_lfp_in_window(cerebus_times_ms = None,
t1_ms = -50,
t2_ms = 150,
p_thresh = +400,
n_thresh = -400,
hp_hz = None,
lp_hz = None,
nsx_basic_header = None,
f_nsx = None,
channel = 0):
"""A reading function that reads in data from the cerebus ns3 files to grab the
lfp.
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
p_thresh : If waveform exceeds this positive threshold, discard
n_thresh : If waveform exceeds this negative threshold, discard
hp_hz : high pass filter characteristic (no filtering if none)
lp_hz : low pass filter characteristic (no filtering if none)
nsx_basic_header :
f_nsx : file handle
channel = 0
Outputs:
lfps : a list of arrays the same length as cerebus_times. Traces that have
been thrown are marked by Nones
mean_lfp : the mean lfp
"""
trace_count = 0
tdur_ms = t2_ms - t1_ms
N_lfp = nsx.length_of_lfp(nsx_basic_header, tdur_ms)
lfps = []
mean_lfp = pylab.zeros(N_lfp, dtype=float)
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
this_lfp = nsx.read_channel(f_nsx, nsx_basic_header,
channel,
tstart_ms = tstart_ms,
tdur_ms = tdur_ms)
if (this_lfp.max() < p_thresh) and (this_lfp.min() > n_thresh):
#if threshold == None or pylab.absolute(this_lfp.max()) < threshold:
lfps.append(this_lfp)
mean_lfp += this_lfp
trace_count += 1
else:
lfps.append(None)
mean_lfp /= float(trace_count)#cerebus_times_ms.size
Fs = float(nsx_basic_header['Fs Hz'])
t_ms = 1000*pylab.arange(N_lfp)/Fs + t1_ms
return lfps, mean_lfp, t_ms
def old_get_lfp(cerebus_times_ms=None, t1_ms=-50, t2_ms=150, fun_args=None):
"""A reading function that reads in data from the cerebus ns3 files to grab the
lfp.
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 : dict:
'basic nsx header',
'fnsx', - nsx file handle
'channel', - channel we are interested in
'pos threshold', -
'neg threshold' - discard traces with values outside this range
(if not give, defaults to +/1 400 uV)
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
"""
basic_header = fun_args['basic nsx header']
f_nsx = fun_args['fnsx']
channel = fun_args['channel']
p_thresh = fun_args.get('pos threshold', +400)
n_thresh = fun_args.get('neg threshold', -400)
trace_count = 0
tdur_ms = t2_ms - t1_ms
N_lfp = nsx.length_of_lfp(basic_header, tdur_ms)
mean_lfp = pylab.zeros(N_lfp, dtype=float)
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
this_lfp = nsx.read_channel(f_nsx,
basic_header,
channel,
tstart_ms=tstart_ms,
tdur_ms=tdur_ms)
if (this_lfp.max() < p_thresh) and (this_lfp.min() > n_thresh):
#if threshold == None or pylab.absolute(this_lfp.max()) < threshold:
mean_lfp += this_lfp
trace_count += 1
mean_lfp /= float(trace_count) #cerebus_times_ms.size
data = {
'total trials': cerebus_times_ms.size,
'trials': trace_count, #The number of valid trials
'mean lfp': mean_lfp,
'Fs Hz': float(basic_header['Fs Hz'])
}
return data
def old_get_lfp(cerebus_times_ms = None,
t1_ms = -50,
t2_ms = 150,
fun_args = None):
"""A reading function that reads in data from the cerebus ns3 files to grab the
lfp.
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 : dict:
'basic nsx header',
'fnsx', - nsx file handle
'channel', - channel we are interested in
'pos threshold', -
'neg threshold' - discard traces with values outside this range
(if not give, defaults to +/1 400 uV)
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
"""
basic_header = fun_args['basic nsx header']
f_nsx = fun_args['fnsx']
channel = fun_args['channel']
p_thresh = fun_args.get('pos threshold', +400)
n_thresh = fun_args.get('neg threshold', -400)
trace_count = 0
tdur_ms = t2_ms - t1_ms
N_lfp = nsx.length_of_lfp(basic_header, tdur_ms)
mean_lfp = pylab.zeros(N_lfp, dtype=float)
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
this_lfp = nsx.read_channel(f_nsx, basic_header,
channel,
tstart_ms = tstart_ms,
tdur_ms = tdur_ms)
if (this_lfp.max() < p_thresh) and (this_lfp.min() > n_thresh):
#if threshold == None or pylab.absolute(this_lfp.max()) < threshold:
mean_lfp += this_lfp
trace_count += 1
mean_lfp /= float(trace_count)#cerebus_times_ms.size
data = {'total trials':cerebus_times_ms.size,
'trials': trace_count, #The number of valid trials
'mean lfp': mean_lfp,
'Fs Hz': float(basic_header['Fs Hz'])}
return data
def inspect_lfp(nsx_fname, channel = 1):
"""Plot the whole lfp for the given file."""
f_nsx = open(nsx_fname)
nsx_basic_header = nsx.read_basic_header(f_nsx)
this_lfp = nsx.read_channel(f_nsx, nsx_basic_header,
channel,
tstart_ms = 0,
tdur_ms = -1)
Fs = float(nsx_basic_header['Fs Hz'])
t_ms = 1000*pylab.arange(this_lfp.size)/Fs
pylab.plot(t_ms, this_lfp)
def inspect_lfp(nsx_fname, channel=1):
"""Plot the whole lfp for the given file."""
f_nsx = open(nsx_fname)
nsx_basic_header = nsx.read_basic_header(f_nsx)
this_lfp = nsx.read_channel(f_nsx,
nsx_basic_header,
channel,
tstart_ms=0,
tdur_ms=-1)
Fs = float(nsx_basic_header['Fs Hz'])
t_ms = 1000 * pylab.arange(this_lfp.size) / Fs
pylab.plot(t_ms, this_lfp)
def get_lfp_in_window(cerebus_times_ms=None,
t1_ms=-50,
t2_ms=150,
p_thresh=+400,
n_thresh=-400,
hp_hz=None,
lp_hz=None,
nsx_basic_header=None,
f_nsx=None,
channel=0):
"""A reading function that reads in data from the cerebus ns3 files to grab the
lfp.
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
p_thresh : If waveform exceeds this positive threshold, discard
n_thresh : If waveform exceeds this negative threshold, discard
hp_hz : high pass filter characteristic (no filtering if none)
lp_hz : low pass filter characteristic (no filtering if none)
nsx_basic_header :
f_nsx : file handle
channel = 0
Outputs:
lfps : a list of arrays the same length as cerebus_times. Traces that have
been thrown are marked by Nones
mean_lfp : the mean lfp
"""
trace_count = 0
tdur_ms = t2_ms - t1_ms
N_lfp = nsx.length_of_lfp(nsx_basic_header, tdur_ms)
lfps = []
mean_lfp = pylab.zeros(N_lfp, dtype=float)
for n in range(cerebus_times_ms.size):
tstart_ms = cerebus_times_ms[n] + t1_ms #all times in ms
this_lfp = nsx.read_channel(f_nsx,
nsx_basic_header,
channel,
tstart_ms=tstart_ms,
tdur_ms=tdur_ms)
if (this_lfp.max() < p_thresh) and (this_lfp.min() > n_thresh):
#if threshold == None or pylab.absolute(this_lfp.max()) < threshold:
lfps.append(this_lfp)
mean_lfp += this_lfp
trace_count += 1
else:
lfps.append(None)
mean_lfp /= float(trace_count) #cerebus_times_ms.size
Fs = float(nsx_basic_header['Fs Hz'])
t_ms = 1000 * pylab.arange(N_lfp) / Fs + t1_ms
return lfps, mean_lfp, t_ms