def __init__(self, abf: pyabf.ABF, channel: int = 0):
"""
This object contains passive cell membrane properties calculated from
each sweep an episodic protocol containing a voltage-clamp step.
WARNING: These calculations are not optimized for speed or accuracy.
This module is experimental and included only for backward compatibility
purposes. Users are highly encouraged to write their own analysis code.
"""
assert isinstance(abf, pyabf.ABF)
self.sweepCount = abf.sweepCount
self.TimeSec = np.arange(abf.sweepCount) * abf.sweepIntervalSec
self.TimeMin = self.TimeSec / 60.0
Result = pyabf.tools.sweep.SweepMeasurement
self.Ih = Result(abf.sweepCount, "Holding Current", "Ih", "pA")
self.Rm = Result(abf.sweepCount, "Membrane Resistance", "Rm", "MOhm")
self.Ra = Result(abf.sweepCount, "Access Resistance", "Ra", "MOhm")
self.CmStep = Result(abf.sweepCount, "Capacitance (Step)", "Cm", "pF")
self.CmRamp = Result(abf.sweepCount, "Capacitance (Ramp)", "Cm", "pF")
for sweepNumber in abf.sweepList:
abf.setSweep(sweepNumber, channel)
# square step memtest
Ih, Rm, Ra, CmStep = pyabf.tools.memtestMath.currentSweepStep(abf)
self.Ih.values[sweepNumber] = Ih
self.Rm.values[sweepNumber] = Rm
self.Ra.values[sweepNumber] = Ra
self.CmStep.values[sweepNumber] = CmStep
# ramp memtest
CmRamp = pyabf.tools.memtestMath.currentSweepRamp(abf)
self.CmRamp.values[sweepNumber] = CmRamp
def restPotential(abf: pyabf.ABF, fig: pyABFauto.figure.Figure):
pyabf.filter.gaussian(abf, 1)
xs = abf.getAllXs()
ys = abf.getAllYs()
rmp = np.nanmean(ys)
plt.grid(alpha=.5, ls='--')
plt.plot(xs, ys, alpha=.7)
plt.axhline(rmp, lw=3, color='r', ls='--')
t = plt.gca().text(.97,
.97,
f"RMP = {rmp:.02f} mV",
transform=plt.gca().transAxes,
verticalalignment='top',
horizontalalignment='right',
fontsize=22,
family='monospace',
color='k')
plt.title(abf.abfID + ".abf")
plt.ylabel("Membrane Potential (mV)")
plt.xlabel("Time (seconds)")
plt.margins(0, .1)
plt.tight_layout()
def plotFullAbf(abf: pyabf.ABF, ax: matplotlib.axes.Axes):
for sweep in range(abf.sweepCount):
abf.setSweep(sweep, absoluteTime=True)
ax.plot(abf.sweepX / 60, abf.sweepY, 'b-')
ax.margins(0, .1)
ax.set_ylabel("Current (pA)")
ax.set_xlabel("Time (minutes)")
addTagLines(abf, ax)
def measureTau(abf: pyabf.ABF,
sweep: int,
epoch: int = 3,
percentile: float = .05,
ax: plt.Axes = None):
abf.setSweep(sweep)
# use epoch table to determine puff times
puffTimeStart = abf.sweepEpochs.p1s[epoch] / abf.sampleRate
puffTimeEnd = abf.sweepEpochs.p2s[epoch] / abf.sampleRate
# calculate baseline level
baselineStart = puffTimeStart - .1
baselineEnd = puffTimeStart
baselineMean = getMean(abf, baselineStart, baselineEnd)
# find antipeak
antipeakIndex = getAntiPeakIndex(abf.sweepY, abf.sampleRate, puffTimeStart,
puffTimeStart + .5)
antipeakLevel = abf.sweepY[antipeakIndex]
# find portion of curve to fit
curveIndex1, curveIndex2 = getCurveIndexes(abf.sweepY, antipeakIndex,
baselineMean, percentile)
curveYs = -abf.sweepY[curveIndex1:curveIndex2]
curveXs = np.arange(len(curveYs)) / abf.sampleRate
try:
p0 = (500, 15, 0) # start with values near those we expect
params, cv = scipy.optimize.curve_fit(monoExp, curveXs, curveYs, p0)
except:
print(f"FIT FAILED (sweep {sweep})")
return None
m, t, b = params
curveYsIdeal = monoExp(curveXs, m, t, b)
tauMS = 1000 / t
if (tauMS < 0):
return None
if ax:
yPad = abs(antipeakLevel - baselineMean) * .1
ax.plot(abf.sweepX, abf.sweepY, alpha=.5)
ax.grid(alpha=.5, ls='--')
ax.axhline(baselineMean, ls='--', color='k')
ax.plot(abf.sweepX[curveIndex1:curveIndex2], -curveYsIdeal, color='k')
ax.set(title=f"first sweep tau = {tauMS:.02f} ms")
ax.axis([
baselineStart - .1, baselineStart + 1, antipeakLevel - yPad,
baselineMean + yPad
])
ax.axvspan(puffTimeEnd, puffTimeEnd + .5, color='g', alpha=.1)
ax.axvspan(puffTimeEnd + .5, puffTimeEnd + .6, color='m', alpha=.1)
return tauMS
def plotPeakBySweep(abf: pyabf.ABF, ax: matplotlib.axes.Axes, epoch: int = 3):
puffTimeStart = abf.sweepEpochs.p1s[epoch] / abf.sampleRate
puffTimeEnd = abf.sweepEpochs.p2s[epoch] / abf.sampleRate
values = []
for i in range(abf.sweepCount):
abf.setSweep(i)
baselineStart = puffTimeStart - .1
baselineEnd = puffTimeStart
baselineMean = getMean(abf, baselineStart, baselineEnd)
antipeak = getMin(abf, puffTimeEnd, puffTimeEnd + .5)
values.append(abs(antipeak - baselineMean))
ax.plot(abf.sweepTimesMin, values, '.-', color='k')
ax.set_ylabel("Peak Response (pA)")
ax.set_xlabel("Time (minutes)")
ax.grid(alpha=.5, ls='--')
addTagLines(abf, ax)
def plotTimeAfterBySweep(abf: pyabf.ABF,
ax: matplotlib.axes.Axes,
epoch: int = 3):
puffTimeStart = abf.sweepEpochs.p1s[epoch] / abf.sampleRate
puffTimeEnd = abf.sweepEpochs.p2s[epoch] / abf.sampleRate
values = []
for i in range(abf.sweepCount):
abf.setSweep(i)
baselineStart = puffTimeStart - .1
baselineEnd = puffTimeStart
baselineMean = getMean(abf, baselineStart, baselineEnd)
mean = getMean(abf, puffTimeEnd + .5, puffTimeEnd + .6) - baselineMean
values.append(mean)
ax.plot(abf.sweepTimesMin, values, '.-', color='m')
ax.set_ylabel("pA after 1s")
ax.set_xlabel("Time (minutes)")
ax.grid(alpha=.5, ls='--')
addTagLines(abf, ax)
def getFullTrace(abf: pyabf.ABF):
sweeps = [None] * abf.sweepCount
for sweepIndex in range(abf.sweepCount):
abf.setSweep(sweepIndex)
sweeps[sweepIndex] = abf.sweepY
return np.concatenate(sweeps)
"""
Created on Aug 14, 2018
@author: jwhite
"""
from pyabf import ABF
if __name__ == '__main__':
a = ABF(
abfFilePath=
'/Volumes/external/hnl/incoming/PD_surgery_data/2017_08_25/2017_08_25_0004.abf',
loadData=False)