for i, x in enumerate(xs)
]
return res
fitres = leastsq(minimize, arg)
return fitres
# IF-curve
filename = '/Users/jenskremkow/src/NeuroTools/examples/spike2/IF-Curve-example-data-provided-by-Florian-Rau-University-of-Freiburg-2008.smr'
# load all channels in the file
#all_channels = spike2.load(filename)
# or only selected channels
all_channels = spike2.load(filename, channels=[1, 2, 32])
# seperate the channels, just for better reading
vm = all_channels[2]
dc = all_channels[1]
dc_onset_marker = all_channels[32]
# cutout the dc and vm around a dc step, markers are in seconds, we need them in milliseonds
dc_sequence = dc.slice_by_events(dc_onset_marker.times * 1000,
t_min=500,
t_max=1000)
vm_sequence = vm.slice_by_events(dc_onset_marker.times * 1000,
t_min=500,
t_max=1000)
xlim = [1000, 27000]
- loads content from a CED Son file which contains data from a IF-curve experiment
- then the data is processed and a IF-Curve is plotted
Performed at the NeuroTools demo session, INCF booth,
SfN annual meeting 2008, Washington. DC.
"""
# IF-curve
filename = 'IF-Curve-example-data-provided-by-Florian-Rau-University-of-Freiburg-2008.smr'
# load all channels in the file
#all_channels = spike2.load(filename)
# or only selected channels
all_channels = spike2.load(filename,channels=[1,2,32])
# seperate the channels, just for better reading
vm = all_channels[2]
dc = all_channels[1]
dc_onset_marker = all_channels[32]
pylab.close('all')
# show original data
# vm
pylab.figure()
pylab.plot(vm.time_axis(),vm.signal())
# currents
pylab.figure()
pylab.plot(dc.time_axis(),dc.signal())
# dc_onset_markers
import NeuroTools.spike2.spike2channels as spike2
import pylab, numpy
from scipy.optimize import leastsq
pylab.rcParams['text.usetex'] = True
# helper function
def fit_exp(xs,ys,arg):
def minimize(arg):
tau, offset, a = arg
res = [a*numpy.exp(-x/tau)+offset - ys[i] for i,x in enumerate(xs)]
return res
fitres = leastsq(minimize,arg)
return fitres
filename = '/Users/jenskremkow/src/NeuroTools/examples/spike2/080814_cortex1_1cell001.smr'
a = spike2.load(filename)
current = a[1]
vm = a[2]
#marker = a[32]
t_min = 200.
t_max = 800.
# since the markers dont really work
marker = current.threshold_detection(threshold=-30.,sign='below')
# current average
current_average, time_axis = current.event_triggered_average(marker,average=True,t_min=t_min,t_max=t_max, with_time = True)
I_base = current_average[0:t_min/current.dt].mean()
I = current_average.min()-I_base
# vm average
vm_average= vm.event_triggered_average(marker,average=True,t_min=t_min,t_max=t_max)
vm_base = vm_average[0:t_min/vm.dt].mean()
# vm average for fit, I am just to lazy to slice the vm_avrage, so I recalculate it, starting with t_min 0.0
vm_average_for_fit, time_axis_for_fit = vm.event_triggered_average(marker,average=True,t_min=0.0,t_max=60., with_time=True)
# fit
