def proto_avgRange(theABF, m1=1.0, m2=1.1):
"""experiment: generic VC time course experiment."""
abf = ABF(theABF)
abf.log.info("analyzing as a fast IV")
plot = ABFplot(abf)
plt.figure(figsize=(SQUARESIZE * 2, SQUARESIZE / 2))
plt.subplot(121)
plot.title = "first sweep"
plot.figure_sweep()
plt.axvspan(m1, m2, color='r', ec=None, alpha=.1)
plt.subplot(122)
plt.grid(alpha=.5)
Ts = np.arange(abf.sweeps) * abf.sweepInterval
Ys = np.empty(abf.sweeps) * np.nan
for sweep in range(abf.sweeps):
Ys[sweep] = abf.average(m1, m2, setsweep=sweep)
for i, t in enumerate(abf.comment_times):
plt.axvline(t / 60, color='r', alpha=.5, lw=2, ls='--')
plt.plot(Ts / 60, Ys, '.')
plt.title(str(abf.comment_tags))
plt.ylabel(abf.units2)
plt.xlabel("minutes")
plt.tight_layout()
frameAndSave(abf, "sweep vs average", "experiment")
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_avgRange(theABF, m1=None, m2=None):
"""experiment: generic VC time course experiment."""
abf = ABF(theABF)
abf.log.info("analyzing as a fast IV")
if m1 is None:
m1 = abf.sweepLength
if m2 is None:
m2 = abf.sweepLength
I1 = int(abf.pointsPerSec * m1)
I2 = int(abf.pointsPerSec * m2)
Ts = np.arange(abf.sweeps) * abf.sweepInterval
Yav = np.empty(abf.sweeps) * np.nan # average
Ysd = np.empty(abf.sweeps) * np.nan # standard deviation
#Yar=np.empty(abf.sweeps)*np.nan # area
for sweep in abf.setsweeps():
Yav[sweep] = np.average(abf.sweepY[I1:I2])
Ysd[sweep] = np.std(abf.sweepY[I1:I2])
#Yar[sweep]=np.sum(abf.sweepY[I1:I2])/(I2*I1)-Yav[sweep]
plot = ABFplot(abf)
plt.figure(figsize=(SQUARESIZE * 2, SQUARESIZE / 2))
plt.subplot(131)
plot.title = "first sweep"
plot.figure_sweep(0)
plt.title("First Sweep\n(shaded measurement range)")
plt.axvspan(m1, m2, color='r', ec=None, alpha=.1)
plt.subplot(132)
plt.grid(alpha=.5)
for i, t in enumerate(abf.comment_times):
plt.axvline(t / 60, color='r', alpha=.5, lw=2, ls='--')
plt.plot(Ts / 60, Yav, '.', alpha=.75)
plt.title("Range Average\nTAGS: %s" % (", ".join(abf.comment_tags)))
plt.ylabel(abf.units2)
plt.xlabel("minutes")
plt.margins(0, .1)
plt.subplot(133)
plt.grid(alpha=.5)
for i, t in enumerate(abf.comment_times):
plt.axvline(t / 60, color='r', alpha=.5, lw=2, ls='--')
plt.plot(Ts / 60, Ysd, '.', alpha=.5, color='g', ms=15, mew=0)
#plt.fill_between(Ts/60,Ysd*0,Ysd,lw=0,alpha=.5,color='g')
plt.title("Range Standard Deviation\nTAGS: %s" %
(", ".join(abf.comment_tags)))
plt.ylabel(abf.units2)
plt.xlabel("minutes")
plt.margins(0, .1)
plt.axis([None, None, 0, np.percentile(Ysd, 99) * 1.25])
plt.tight_layout()
frameAndSave(abf, "sweep vs average", "experiment")
plt.close('all')
def proto_0222(theABF):
"""protocol: VC sine sweep."""
abf = ABF(theABF)
abf.log.info("analyzing as VC sine sweep")
plot = ABFplot(abf)
plot.figure_height, plot.figure_width = SQUARESIZE / 2, SQUARESIZE / 2
plot.figure_sweeps()
plt.tight_layout()
frameAndSave(abf, "VC sine sweep")
plt.close('all')
def proto_gain(theABF, stepSize=25, startAt=-100):
"""protocol: gain function of some sort. step size and start at are pA."""
abf = ABF(theABF)
abf.log.info("analyzing as an IC ramp")
plot = ABFplot(abf)
plot.kwargs["lw"] = .5
plot.title = ""
currents = np.arange(abf.sweeps) * stepSize - startAt
# AP detection
ap = AP(abf)
ap.detect_time1 = .1
ap.detect_time2 = .7
ap.detect()
# stacked plot
plt.figure(figsize=(SQUARESIZE, SQUARESIZE))
ax1 = plt.subplot(221)
plot.figure_sweeps()
ax2 = plt.subplot(222)
ax2.get_yaxis().set_visible(False)
plot.figure_sweeps(offsetY=150)
# add vertical marks to graphs:
for ax in [ax1, ax2]:
for limit in [ap.detect_time1, ap.detect_time2]:
ax.axvline(limit, color='r', ls='--', alpha=.5, lw=2)
# make stacked gain function
ax4 = plt.subplot(223)
plt.ylabel("frequency (Hz)")
plt.ylabel("seconds")
plt.grid(alpha=.5)
freqs = ap.get_bySweep("freqs")
times = ap.get_bySweep("times")
for i in range(abf.sweeps):
if len(freqs[i]):
plt.plot(times[i][:-1],
freqs[i],
'-',
alpha=.5,
lw=2,
color=plot.getColor(i / abf.sweeps))
# make gain function graph
ax4 = plt.subplot(224)
ax4.grid(alpha=.5)
plt.plot(currents, ap.get_bySweep("median"), 'b.-', label="median")
plt.plot(currents, ap.get_bySweep("firsts"), 'g.-', label="first")
plt.xlabel("applied current (pA)")
plt.legend(loc=2, fontsize=10)
plt.axhline(40, color='r', alpha=.5, ls="--", lw=2)
plt.margins(.02, .1)
# save it
plt.tight_layout()
frameAndSave(abf, "AP Gain %d_%d" % (startAt, stepSize))
plt.close('all')
def proto_0201(theABF):
"""protocol: membrane test."""
abf = ABF(theABF)
abf.log.info("analyzing as a membrane test")
plot = ABFplot(abf)
plot.figure_height, plot.figure_width = SQUARESIZE / 2, SQUARESIZE / 2
plot.figure_sweeps()
# save it
plt.tight_layout()
frameAndSave(abf, "membrane test")
plt.close('all')
def proto_unknown(theABF):
"""protocol: unknown."""
abf = ABF(theABF)
abf.log.info("analyzing as an unknown protocol")
plot = ABFplot(abf)
plot.rainbow = False
plot.title = None
plot.figure_height, plot.figure_width = SQUARESIZE, SQUARESIZE
plot.kwargs["lw"] = .5
plot.figure_chronological()
plt.gca().set_axis_bgcolor(
'#AAAAAA') # different background if unknown protocol
frameAndSave(abf, "UNKNOWN")
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_gain(theABF, stepSize=25, startAt=-100):
"""protocol: gain function of some sort. step size and start at are pA."""
abf = ABF(theABF)
abf.log.info("analyzing as an IC ramp")
plot = ABFplot(abf)
plot.kwargs["lw"] = .5
plot.title = ""
currents = np.arange(abf.sweeps) * stepSize - startAt
# AP detection
ap = AP(abf)
ap.detect_time1 = .1
ap.detect_time2 = .7
ap.detect()
# stacked plot
plt.figure(figsize=(SQUARESIZE, SQUARESIZE))
ax1 = plt.subplot(221)
plot.figure_sweeps()
ax2 = plt.subplot(222)
ax2.get_yaxis().set_visible(False)
plot.figure_sweeps(offsetY=150)
# add vertical marks to graphs:
for ax in [ax1, ax2]:
for limit in [ap.detect_time1, ap.detect_time2]:
ax.axvline(limit, color='r', ls='--', alpha=.5, lw=2)
# make stacked gain function
ax4 = plt.subplot(223)
plt.ylabel("frequency (Hz)")
plt.ylabel("seconds")
plt.grid(alpha=.5)
freqs = ap.get_bySweep("freqs")
times = ap.get_bySweep("times")
for i in range(abf.sweeps):
if len(freqs[i]):
plt.plot(times[i][:-1],
freqs[i],
'-',
alpha=.5,
lw=2,
color=plot.getColor(i / abf.sweeps))
# make gain function graph
ax4 = plt.subplot(224)
ax4.grid(alpha=.5)
plt.plot(currents, ap.get_bySweep("median"), 'b.-', label="median")
plt.plot(currents, ap.get_bySweep("firsts"), 'g.-', label="first")
plt.xlabel("applied current (pA)")
plt.legend(loc=2, fontsize=10)
plt.axhline(40, color='r', alpha=.5, ls="--", lw=2)
plt.margins(.02, .1)
# save it
plt.tight_layout()
frameAndSave(abf, "AP Gain %d_%d" % (startAt, stepSize))
plt.close('all')
# make a second figure that just shows every sweep up to the first AP
plt.figure(figsize=(SQUARESIZE, SQUARESIZE))
plt.grid(alpha=.5)
plt.ylabel("Membrane Potential (mV)")
plt.xlabel("Time (seconds)")
for sweep in abf.setsweeps():
plt.plot(abf.sweepX2, abf.sweepY, color='b', alpha=.5)
if np.max(abf.sweepY > 0):
break
plt.tight_layout()
plt.margins(0, .1)
plt.axis([0, 1, None, None])
plt.title("%d pA Steps from Rest" % stepSize)
frameAndSave(abf, "voltage response fromRest", closeWhenDone=False)
plt.axis([1.5, 2.5, None, None])
plt.title("%d pA Steps from %d pA" % (stepSize, startAt))
frameAndSave(abf, "voltage response hyperpol", closeWhenDone=False)
plt.close('all')