def proto_0101(theABF):
abf = ABF(theABF)
abf.log.info("analyzing as an IC tau")
#plot=ABFplot(abf)
plt.figure(figsize=(SQUARESIZE / 2, SQUARESIZE / 2))
plt.grid()
plt.ylabel("relative potential (mV)")
plt.xlabel("time (sec)")
m1, m2 = [.05, .1]
for sweep in range(abf.sweeps):
abf.setsweep(sweep)
plt.plot(abf.sweepX2,
abf.sweepY - abf.average(m1, m2),
alpha=.2,
color='#AAAAFF')
average = abf.averageSweep()
average -= np.average(
average[int(m1**abf.pointsPerSec):int(m2 * abf.pointsPerSec)])
plt.plot(abf.sweepX2, average, color='b', lw=2, alpha=.5)
plt.axvspan(m1, m2, color='r', ec=None, alpha=.1)
plt.axhline(0, color='r', ls="--", alpha=.5, lw=2)
plt.margins(0, .1)
# save it
plt.tight_layout()
frameAndSave(abf, "IC tau")
plt.close('all')
def proto_0203(theABF):
"""protocol: vast IV."""
abf = ABF(theABF)
abf.log.info("analyzing as a fast IV")
plot = ABFplot(abf)
plot.title = ""
m1, m2 = .7, 1
plt.figure(figsize=(SQUARESIZE, SQUARESIZE / 2))
plt.subplot(121)
plot.figure_sweeps()
plt.axvspan(m1, m2, color='r', ec=None, alpha=.1)
plt.subplot(122)
plt.grid(alpha=.5)
Xs = np.arange(abf.sweeps) * 5 - 110
Ys = []
for sweep in range(abf.sweeps):
abf.setsweep(sweep)
Ys.append(abf.average(m1, m2))
plt.plot(Xs, Ys, '.-', ms=10)
plt.axvline(-70, color='r', ls='--', lw=2, alpha=.5)
plt.axhline(0, color='r', ls='--', lw=2, alpha=.5)
plt.margins(.1, .1)
plt.xlabel("membrane potential (mV)")
# save it
plt.tight_layout()
frameAndSave(abf, "fast IV")
plt.close('all')
def proto_0111(theABF):
"""protocol: IC ramp for AP shape analysis."""
abf = ABF(theABF)
abf.log.info("analyzing as an IC ramp")
# AP detection
ap = AP(abf)
ap.detect()
firstAP = ap.APs[0]["T"]
# also calculate derivative for each sweep
abf.derivative = True
# create the multi-plot figure
plt.figure(figsize=(SQUARESIZE, SQUARESIZE))
ax1 = plt.subplot(221)
plt.ylabel(abf.units2)
ax2 = plt.subplot(222, sharey=ax1)
ax3 = plt.subplot(223)
plt.ylabel(abf.unitsD2)
ax4 = plt.subplot(224, sharey=ax3)
# put data in each subplot
for sweep in range(abf.sweeps):
abf.setsweep(sweep)
ax1.plot(abf.sweepX, abf.sweepY, color='b', lw=.25)
ax2.plot(abf.sweepX, abf.sweepY, color='b')
ax3.plot(abf.sweepX, abf.sweepD, color='r', lw=.25)
ax4.plot(abf.sweepX, abf.sweepD, color='r')
# modify axis
for ax in [ax1, ax2, ax3, ax4]: # everything
ax.margins(0, .1)
ax.grid(alpha=.5)
for ax in [ax3, ax4]: # only derivative APs
ax.axhline(-100, color='r', alpha=.5, ls="--", lw=2)
for ax in [ax2, ax4]: # only zoomed in APs
ax.get_yaxis().set_visible(False)
ax2.axis([firstAP - .25, firstAP + .25, None, None])
ax4.axis([firstAP - .01, firstAP + .01, None, None])
# show message from first AP
firstAP = ap.APs[0]
msg = "\n".join([
"%s = %s" % (x, str(firstAP[x])) for x in sorted(firstAP.keys())
if not "I" in x[-2:]
])
plt.subplot(221)
plt.gca().text(0.02,
0.98,
msg,
transform=plt.gca().transAxes,
fontsize=10,
verticalalignment='top',
family='monospace')
# save it
plt.tight_layout()
frameAndSave(abf, "AP shape")
plt.close('all')
def proto_0911(theABF):
abf = ABF(theABF)
abf.log.info(
"analyzing as paired pulse stimulation with various increasing ISIs")
plt.figure(figsize=(8, 8))
M1, M2 = 2.2, 2.4
I1, I2 = int(M1 * abf.pointsPerSec), int(M2 * abf.pointsPerSec)
Ip1 = int(2.23440 * abf.pointsPerSec) # time of first pulse in the sweep
pw = int(1.5 / 1000 * abf.pointsPerSec) # pulse width in ms
B1, B2 = 1, 2 # baseline time in seconds
plt.axhline(0, alpha=.5, ls='--', lw=2, color='k')
for sweep in range(abf.sweeps):
abf.setsweep(sweep)
Xs = abf.sweepX2[I1:I2] * 1000
baseline = np.average(
abf.sweepY[int(B1 * abf.pointsPerSec):int(B2 * abf.pointsPerSec)])
Ys = abf.sweepY[I1:I2] - baseline
Ys[Ip1 - I1:Ip1 - I1 + pw] = np.nan # erase the first pulse
isi = int(10 + sweep * 10) # interspike interval (ms)
Ip2d = int(isi / 1000 * abf.pointsPerSec)
Ys[Ip1 - I1 + Ip2d:Ip1 - I1 + pw +
Ip2d] = np.nan # erase the second pulse
plt.plot(Xs - Xs[0], Ys, alpha=.8, label="%d ms" % isi)
plt.margins(0, .1)
plt.legend()
plt.grid(alpha=.4, ls='--')
plt.title("Paired Pulse Stimuation (varied ISIs)")
plt.ylabel("clamp current (pA) [artifacts removed]")
plt.xlabel("time (ms) [offset by %.02f s]" % M1)
plt.tight_layout()
frameAndSave(abf, "pp_varied")
plt.close('all')
def proto_0202(theABF):
"""protocol: MTIV."""
abf = ABF(theABF)
abf.log.info("analyzing as MTIV")
plot = ABFplot(abf)
plot.figure_height, plot.figure_width = SQUARESIZE, SQUARESIZE
plot.title = ""
plot.kwargs["alpha"] = .6
plot.figure_sweeps()
# frame to uppwer/lower bounds, ignoring peaks from capacitive transients
abf.setsweep(0)
plt.axis([None, None, abf.average(.9, 1) - 100, None])
abf.setsweep(-1)
plt.axis([None, None, None, abf.average(.9, 1) + 100])
# save it
plt.tight_layout()
frameAndSave(abf, "MTIV")
plt.close('all')
def proto_0912(theABF):
abf = ABF(theABF)
abf.log.info("analyzing as 40ms PPS experiment")
BL1, BL2 = 1, 2 # area for baseline
ISI = 40 # inter-stimulus-interval in ms
Ip1 = int(2.31255 * abf.pointsPerSec) # time of first pulse in the sweep
Ip2 = int(Ip1 +
(ISI / 1000) * abf.pointsPerSec) # second pulse is 40ms later
Ip3 = Ip2 + (Ip2 - Ip1) # distance after Ip2 to scan for the second peak
pw = int(3 / 1000 * abf.pointsPerSec) # pulse width in ms
peakTimes,peak1heights,peak2heights,peakRatios,baselines,peakTransient=[],[],[],[],[],[]
ROI = None
ROIpad = int(.02 * abf.pointsPerSec)
# calculate peak ratios and all that
for sweep in range(abf.sweeps):
abf.setsweep(sweep)
baseline = np.average(
abf.sweepY[int(BL1 * abf.pointsPerSec):int(BL2 *
abf.pointsPerSec)])
Ra = np.max(abf.sweepY[int(.51 *
abf.pointsPerSec):int(.52 *
abf.pointsPerSec)])
peakTransient.append(Ra - baseline)
abf.sweepY = abf.sweepY - baseline
for I in [Ip1, Ip2]:
abf.sweepY[I:I + pw] = np.nan # blank out each pulse
abf.sweepY[:Ip1 -
int(.05 *
abf.pointsPerSec)] = np.nan #blank out 50ms before P1
abf.sweepY[Ip1 +
int(.15 *
abf.pointsPerSec):] = np.nan #blank out 15ms after P1
peak1 = abf.sweepY[int(Ip1):int(Ip2)]
peak2 = abf.sweepY[int(Ip2):int(Ip3)]
peakTimes.append(abf.sweepStart)
peak1heights.append(np.nanmin(peak1))
peak2heights.append(np.nanmin(peak2))
baselines.append(baseline)
peakRatios.append(peak2heights[-1] / peak1heights[-1])
thisROI = abf.sweepY[int(Ip1) - ROIpad:int(Ip3) + ROIpad]
if ROI is None:
ROI = thisROI.flatten()
else:
ROI = np.vstack((ROI, thisROI.flatten()))
peakTimes = np.array(peakTimes) / 60 # seconds to minutes
# figure showing the averaged evoked response for certain time ranges
avgSweeps = 3 * 5 # 3 sweeps/minute, 5 minutes
avgLocations = [10 * 3, 20 * 3,
30 * 3] # the end of the area for averaging
plt.figure(figsize=(8, 8))
plt.grid(alpha=.4, ls='--')
plt.axhline(0, alpha=.5, ls='--', lw=2, color='k')
for S2 in avgLocations:
S1 = S2 - avgSweeps
AV = np.average(ROI[S1:S2], axis=0)
ER = np.std(ROI[S1:S2], axis=0) #/np.sqrt(len(ROI))
Xs = abf.sweepX[:len(AV)]
plt.fill_between(Xs, AV - ER, AV + ER, alpha=.1)
plt.plot(Xs, AV, label="sweeps %d-%d" % (S1, S2))
plt.legend()
plt.tight_layout()
frameAndSave(abf, "pp_avg")
plt.close('all')
# figure showing peak height and ratio over time
plt.figure(figsize=(8, 8))
plt.subplot(211)
comment_lines(abf)
plt.grid(alpha=.4, ls='--')
plt.plot(peakTimes, peak1heights, 'g.', ms=15, alpha=.6, label='pulse1')
plt.plot(peakTimes, peak2heights, 'm.', ms=15, alpha=.6, label='pulse2')
plt.axis([None, None, None, 0])
plt.legend()
plt.title("Paired Pulse Stimuation")
plt.ylabel("Peak Amplitude (pA)")
plt.subplot(212)
comment_lines(abf)
plt.grid(alpha=.4, ls='--')
plt.axhline(100, alpha=.5, ls='--', lw=2, color='k')
plt.plot(peakTimes, np.array(peakRatios) * 100, 'r.', ms=15, alpha=.6)
plt.axis([None, None, 0, None])
plt.ylabel("Paired Pulse Ratio (%)")
plt.xlabel("Experiment Duration (minutes)")
plt.tight_layout()
frameAndSave(abf, "pp_experiment")
plt.close('all')
# figure showing baseline over time (Ih)
plt.figure(figsize=(8, 8))
plt.subplot(211)
plt.grid(alpha=.4, ls='--')
plt.plot(peakTimes, baselines, 'b.', ms=15, alpha=.6)
plt.margins(0, .1)
plt.axis([None, None, plt.axis()[2] - 100, plt.axis()[3] + 100])
comment_lines(abf)
plt.title("Holding Current (pulse baseline)")
plt.ylabel("Clamp Current (pA)")
#plt.xlabel("Experiment Duration (minutes)")
plt.subplot(212)
plt.grid(alpha=.4, ls='--')
access = np.array(peakTransient) / peakTransient[0] * 100
plt.plot(peakTimes, access, 'r.', ms=15, alpha=.6)
plt.axhspan(75, 125, alpha=.1, color='k', label='+/- 25%')
plt.margins(0, .5)
plt.axis([None, None, 0, None])
comment_lines(abf)
plt.title("Access Resistance")
plt.ylabel("Peak Transient Current (% of first)")
plt.xlabel("Experiment Duration (minutes)")
plt.tight_layout()
frameAndSave(abf, "pp_baselines")
plt.close('all')
