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')