def analyzeSweep(abf, plotToo=True, color=None, label=None):
Y = abf.sweepYsmartbase()[abf.pointsPerSec * .5:]
#Y=abf.sweepY[abf.pointsPerSec*.5:]
AV, SD = np.average(Y), np.std(Y)
dev = 5 # number of stdevs from the avg to set the range
R1, R2 = [(AV - SD) * dev, (AV + SD) * dev]
nBins = 1000
hist, bins = np.histogram(Y, bins=nBins, range=[R1, R2], density=True)
histSmooth = abf.convolve(hist, cm.kernel_gaussian(nBins / 5))
peakI = np.where(histSmooth == max(histSmooth))[0][0]
peakX = bins[peakI]
# center the peak at 0 pA
hist = np.roll(hist, int(nBins / 2 - peakI))
histSmooth = np.roll(histSmooth, int(nBins / 2 - peakI))
hist, histSmooth = hist / max(histSmooth), histSmooth / max(
histSmooth) # normalize height to 1
if plotToo:
plt.plot(bins[1:], hist, '.', color=color, alpha=.2, ms=10)
plt.plot(bins[1:],
histSmooth,
'-',
color=color,
lw=5,
alpha=.5,
label=label)
return
def analyzeSweep(abf, plotToo=True, color=None, label=None):
Y = abf.sweepYsmartbase()[abf.pointsPerSec * .5:]
AV, SD = np.average(Y), np.std(Y)
dev = 5 # number of stdevs from the avg to set the range
R1, R2 = [(AV - SD) * dev, (AV + SD) * dev]
nBins = 1000
hist, bins = np.histogram(Y, bins=nBins, range=[R1, R2], density=True)
histSmooth = abf.convolve(hist, cm.kernel_gaussian(nBins / 5))
if plotToo:
plt.plot(bins[1:], hist, '.', color=color, alpha=.2, ms=10)
plt.plot(bins[1:],
histSmooth,
'-',
color=color,
lw=5,
alpha=.5,
label=label)
return
def analyzeSweep(abf):
Y = abf.sweepYsmartbase()[abf.pointsPerSec * .5:]
#Y=abf.sweepY[abf.pointsPerSec*.5:]
AV, SD = np.average(Y), np.std(Y)
dev = 5 # number of stdevs from the avg to set the range
R1, R2 = [(AV - SD) * dev, (AV + SD) * dev]
nBins = 1000
hist, bins = np.histogram(Y, bins=nBins, range=[R1, R2], density=True)
histSmooth = abf.convolve(hist, cm.kernel_gaussian(nBins / 5))
peakI = np.where(histSmooth == max(histSmooth))[0][0]
# center the peak at 0 pA
hist = np.roll(hist, int(nBins / 2 - peakI))
histSmooth = np.roll(histSmooth, int(nBins / 2 - peakI))
# normalize height to 1
hist, histSmooth = hist / max(histSmooth), histSmooth / max(histSmooth)
# separate EPSC and IPSC by splitting data at the peak
EPSC, IPSC = np.split(histSmooth, 2)
return np.sum(EPSC), np.sum(IPSC), np.sum(EPSC) / np.sum(IPSC)