def summarize(path):
fig = plt.figure()
# Frame rate
ax = fig.add_subplot(331)
vidtime = np.genfromtxt(os.path.join(path, 'Analysis/videotime.csv'))
ax.hist(1.0 / np.diff(vidtime), 100, normed=True)
ax.set_title('frame rate')
ax.set_xlabel('fps')
# Trajectories
ax = fig.add_subplot(332)
rawtraj = trajectories.genfromtxt(path)
rawtraj = trajectories.trajectories(rawtraj.data, rawtraj.slices[1:])
rawtraj = trajectories.scale(rawtraj)
croph = [
200 * trajectories.width_pixel_to_cm,
1000 * trajectories.width_pixel_to_cm
]
traj = trajectories.crop(rawtraj, croph)
for t in traj.tolist():
ax.plot(t[:, 0], t[:, 1], 'k', alpha=0.2)
ax.set_title('trajectories')
ax.set_xlabel('x (cm)')
ax.set_ylabel('y (cm)')
trajax = ax
# Draw trial selection
selecthandles = []
trajfilter = []
conditionfilter = []
def updateselection():
if len(trajfilter) > 0:
ind = trajfilter
if len(conditionfilter) > 0:
ind = list(set(ind).intersection(conditionfilter))
else:
ind = conditionfilter
selector.ind[:] = ind
sselector.ind[:] = ind
selector.updateselection()
sselector.updateselection()
while len(selecthandles) > 0:
handle = selecthandles.pop()
if np.iterable(handle):
for l in handle:
l.remove()
del l
del handle
for s in traj.slices[ind]:
h1 = trajax.plot(traj.data[s, 0], traj.data[s, 1], 'r', alpha=0.5)
h2 = activityax.plot(trialsteps_x[ind, :], trialsteps[ind, :],
'r.')
h3 = slipax.plot(trialslips_x[ind, :], trialslips[ind, :], 'rx')
selecthandles.append(h1)
selecthandles.append(h2)
selecthandles.append(h3)
def onselect(ind):
trajfilter[:] = ind
updateselection()
# Trajectory features
ax = fig.add_subplot(333)
selector = trajectoryfeatures(traj, vidtime, ax, onselect)
# Reward rate
ax = fig.add_subplot(334)
lr = np.atleast_1d(
np.genfromtxt(os.path.join(path, 'left_rewards.csv'), dtype=str))
rr = np.atleast_1d(
np.genfromtxt(os.path.join(path, 'right_rewards.csv'), dtype=str))
rewards = np.sort(np.concatenate((lr, rr)))
rewardtimes = [dateutil.parser.parse(t) for t in rewards]
rewardinterval = np.array(
[d.total_seconds() for d in np.diff(rewardtimes)])
ax.plot(rewardinterval)
ax.set_title('reward rate')
ax.set_xlabel('trials')
ax.set_ylabel('inter-reward interval (s)')
# Summary statistics (Top)
ax = fig.add_subplot(335)
avgsdvheight = np.array([(np.mean(t[:, 1]), np.std(t[:, 1]))
for t in traj.tolist()])
ax.boxplot(avgsdvheight)
ax.set_ylabel('height (cm)')
# Slowdown
ax = fig.add_subplot(336)
speed = trajectories.speed(traj, vidtime)
midbounds = np.array([425, 850]) * trajectories.width_pixel_to_cm
midpoints = [
traj.data[s, 0] > midbounds[0]
if traj.data[s.stop, 0] > traj.data[s.start, 0] else
traj.data[s, 0] < midbounds[1] for s in traj.slices
]
# midpoints = [(t[:,0] > midbounds[0]) & (t[:,0] < midbounds[1])
# for t in traj.tolist()]
midspeed = np.array(
[np.abs(np.mean(t[v, 0])) for t, v in zip(speed.tolist(), midpoints)])
edgespeed = np.array(
[np.abs(np.mean(t[~v, 0])) for t, v in zip(speed.tolist(), midpoints)])
print edgespeed
pts = ax.scatter(edgespeed,
midspeed,
s=10,
marker='D',
facecolors='b',
edgecolors='none')
pltutils.regressionline(edgespeed, midspeed, ax, color='k')
sselector = CollectionSelector(ax,
pts,
color_other='r',
onselection=onselect)
ax.set_title('slowdown')
ax.set_xlabel('entry speed (cm / s)')
ax.set_ylabel('exit speed (cm / s)')
# Summary statistics (Bottom)
# ax = fig.add_subplot(338)
# speed = trajectories.speed(traj,vidtime)
# avgsdvspeed = np.array([(np.abs(np.mean(t[:,0])),np.std(t[:,0])) for t in speed.tolist()])
# ax.boxplot(avgsdvspeed)
# ax.set_ylabel('speed (cm / s)')
# Step activity
ax = fig.add_subplot(337)
stepactivity = np.genfromtxt(
os.path.join(path, 'Analysis/step_activity.csv'))
trialsteps = np.array(
[np.sum(stepactivity[s, :], axis=0) for s in traj.slices])
stepnumbers = np.arange(8) + 1
trialsteps_x = np.tile(stepnumbers, (trialsteps.shape[0], 1))
ax.plot(trialsteps_x, trialsteps, 'k.')
ax.set_title('stepping')
ax.set_xlabel('step')
ax.set_xlim(0, len(stepnumbers) + 1)
ax.set_xticks(stepnumbers)
activityax = ax
# Slips
ax = fig.add_subplot(338)
slipactivity = np.genfromtxt(
os.path.join(path, 'Analysis/slip_activity.csv'))
trialslips = np.array(
[np.sum(slipactivity[s, :], axis=0) for s in traj.slices])
gapnumbers = np.arange(7) + 1
trialslips_x = np.tile(gapnumbers, (trialslips.shape[0], 1))
ax.plot(trialslips_x, trialslips, 'kx')
ax.set_title('slips')
ax.set_xlabel('gap')
ax.set_xlim(0, len(gapnumbers) + 1)
ax.set_xticks(gapnumbers)
slipax = ax
# Trial conditions (WARNING! CONSIDER STEP STATE WRAPAROUND!!)
ax = fig.add_subplot(339)
width = 0.39
trialindices = trials.gettrialindices(path)
steptrialpath = os.path.join(path, 'step{0}_trials.csv')
stepstates = np.array([
trials.gettrialstate(str.format(steptrialpath, i), trialindices)
for i in xrange(1, 7)
]).T
steptrialstate = np.array([stepstates[s.start, :] for s in traj.slices])
fractionstabletrials = np.sum(steptrialstate, axis=0) / float(
steptrialstate.shape[0])
fractionstabletrials = np.insert(fractionstabletrials,
[0, stepstates.shape[1]], 1)
unstablebars = ax.bar(stepnumbers,
np.ones(stepnumbers.shape),
width,
color='r')
stablebars = ax.bar(stepnumbers, fractionstabletrials, width, color='g')
ax.set_title('manipulations')
ax.set_xlabel('step')
ax.set_xlim(0 + width / 2., len(stepnumbers) + 1 + width / 2.)
ax.set_xticks(stepnumbers + width / 2.)
ax.set_xticklabels(stepnumbers)
conditionax = ax
# Select trial conditions
def selectstepindices(evt, bars, trialstate):
try:
step = next(i - 1 for i, bar in enumerate(bars)
if bar.contains(evt)[0])
except StopIteration:
return []
if step >= 0 and step < trialstate.shape[1]:
ind = np.nonzero(trialstate[:, step])[0]
else:
ind = range(trialstate.shape[0])
return ind
notsteptrialstate = np.bitwise_not(steptrialstate)
def onbuttonpress(evt):
if evt.inaxes == conditionax:
ind = selectstepindices(evt, stablebars, steptrialstate)
if len(ind) == 0:
ind = selectstepindices(evt, unstablebars, notsteptrialstate)
conditionfilter[:] = ind
updateselection()
ax.figure.canvas.mpl_connect('button_press_event', onbuttonpress)
plt.tight_layout(pad=0.5)
return rawtraj, traj, vidtime, stepactivity, selector, sselector
def summarize(path):
fig = plt.figure()
# Frame rate
ax = fig.add_subplot(331)
vidtime = np.genfromtxt(os.path.join(path, 'Analysis/videotime.csv'))
ax.hist(1.0 / np.diff(vidtime),100,normed=True)
ax.set_title('frame rate')
ax.set_xlabel('fps')
# Trajectories
ax = fig.add_subplot(332)
rawtraj = trajectories.genfromtxt(path)
rawtraj = trajectories.trajectories(rawtraj.data,rawtraj.slices[1:])
rawtraj = trajectories.scale(rawtraj)
croph = [200*trajectories.width_pixel_to_cm,1000*trajectories.width_pixel_to_cm]
traj = trajectories.crop(rawtraj,croph)
for t in traj.tolist():
ax.plot(t[:,0],t[:,1],'k',alpha=0.2)
ax.set_title('trajectories')
ax.set_xlabel('x (cm)')
ax.set_ylabel('y (cm)')
trajax = ax
# Draw trial selection
selecthandles = []
trajfilter = []
conditionfilter = []
def updateselection():
if len(trajfilter) > 0:
ind = trajfilter
if len(conditionfilter) > 0:
ind = list(set(ind).intersection(conditionfilter))
else:
ind = conditionfilter
selector.ind[:] = ind
sselector.ind[:] = ind
selector.updateselection()
sselector.updateselection()
while len(selecthandles) > 0:
handle = selecthandles.pop()
if np.iterable(handle):
for l in handle:
l.remove()
del l
del handle
for s in traj.slices[ind]:
h1 = trajax.plot(traj.data[s,0],traj.data[s,1],'r',alpha=0.5)
h2 = activityax.plot(trialsteps_x[ind,:],trialsteps[ind,:],'r.')
h3 = slipax.plot(trialslips_x[ind,:],trialslips[ind,:],'rx')
selecthandles.append(h1)
selecthandles.append(h2)
selecthandles.append(h3)
def onselect(ind):
trajfilter[:] = ind
updateselection()
# Trajectory features
ax = fig.add_subplot(333)
selector = trajectoryfeatures(traj,vidtime,ax,onselect)
# Reward rate
ax = fig.add_subplot(334)
lr = np.atleast_1d(np.genfromtxt(os.path.join(path, 'left_rewards.csv'), dtype=str))
rr = np.atleast_1d(np.genfromtxt(os.path.join(path, 'right_rewards.csv'), dtype=str))
rewards = np.sort(np.concatenate((lr,rr)))
rewardtimes = [dateutil.parser.parse(t) for t in rewards]
rewardinterval = np.array([d.total_seconds() for d in np.diff(rewardtimes)])
ax.plot(rewardinterval)
ax.set_title('reward rate')
ax.set_xlabel('trials')
ax.set_ylabel('inter-reward interval (s)')
# Summary statistics (Top)
ax = fig.add_subplot(335)
avgsdvheight = np.array([(np.mean(t[:,1]),np.std(t[:,1])) for t in traj.tolist()])
ax.boxplot(avgsdvheight)
ax.set_ylabel('height (cm)')
# Slowdown
ax = fig.add_subplot(336)
speed = trajectories.speed(traj,vidtime)
midbounds = np.array([425,850]) * trajectories.width_pixel_to_cm
midpoints = [traj.data[s,0] > midbounds[0]
if traj.data[s.stop,0] > traj.data[s.start,0]
else traj.data[s,0] < midbounds[1]
for s in traj.slices]
# midpoints = [(t[:,0] > midbounds[0]) & (t[:,0] < midbounds[1])
# for t in traj.tolist()]
midspeed = np.array([np.abs(np.mean(t[v,0]))
for t,v in zip(speed.tolist(),midpoints)])
edgespeed = np.array([np.abs(np.mean(t[~v,0]))
for t,v in zip(speed.tolist(),midpoints)])
print edgespeed
pts = ax.scatter(edgespeed,midspeed,s=10,marker='D',
facecolors='b',edgecolors='none')
pltutils.regressionline(edgespeed,midspeed,ax,color='k')
sselector = CollectionSelector(ax,pts,color_other='r',onselection=onselect)
ax.set_title('slowdown')
ax.set_xlabel('entry speed (cm / s)')
ax.set_ylabel('exit speed (cm / s)')
# Summary statistics (Bottom)
# ax = fig.add_subplot(338)
# speed = trajectories.speed(traj,vidtime)
# avgsdvspeed = np.array([(np.abs(np.mean(t[:,0])),np.std(t[:,0])) for t in speed.tolist()])
# ax.boxplot(avgsdvspeed)
# ax.set_ylabel('speed (cm / s)')
# Step activity
ax = fig.add_subplot(337)
stepactivity = np.genfromtxt(os.path.join(path, 'Analysis/step_activity.csv'))
trialsteps = np.array([np.sum(stepactivity[s,:],axis=0) for s in traj.slices])
stepnumbers = np.arange(8) + 1
trialsteps_x = np.tile(stepnumbers,(trialsteps.shape[0],1))
ax.plot(trialsteps_x,trialsteps,'k.')
ax.set_title('stepping')
ax.set_xlabel('step')
ax.set_xlim(0,len(stepnumbers)+1)
ax.set_xticks(stepnumbers)
activityax = ax
# Slips
ax = fig.add_subplot(338)
slipactivity = np.genfromtxt(os.path.join(path, 'Analysis/slip_activity.csv'))
trialslips = np.array([np.sum(slipactivity[s,:],axis=0) for s in traj.slices])
gapnumbers = np.arange(7) + 1
trialslips_x = np.tile(gapnumbers,(trialslips.shape[0],1))
ax.plot(trialslips_x,trialslips,'kx')
ax.set_title('slips')
ax.set_xlabel('gap')
ax.set_xlim(0,len(gapnumbers)+1)
ax.set_xticks(gapnumbers)
slipax = ax
# Trial conditions (WARNING! CONSIDER STEP STATE WRAPAROUND!!)
ax = fig.add_subplot(339)
width = 0.39
trialindices = trials.gettrialindices(path)
steptrialpath = os.path.join(path, 'step{0}_trials.csv')
stepstates = np.array([trials.gettrialstate(str.format(steptrialpath,i), trialindices)
for i in xrange(1,7)]).T
steptrialstate = np.array([stepstates[s.start,:] for s in traj.slices])
fractionstabletrials = np.sum(steptrialstate,axis=0)/float(steptrialstate.shape[0])
fractionstabletrials = np.insert(fractionstabletrials,[0,stepstates.shape[1]],1)
unstablebars = ax.bar(stepnumbers,np.ones(stepnumbers.shape),width,color='r')
stablebars = ax.bar(stepnumbers,fractionstabletrials,width,color='g')
ax.set_title('manipulations')
ax.set_xlabel('step')
ax.set_xlim(0+width/2.,len(stepnumbers)+1+width/2.)
ax.set_xticks(stepnumbers+width/2.)
ax.set_xticklabels(stepnumbers)
conditionax = ax
# Select trial conditions
def selectstepindices(evt,bars,trialstate):
try:
step = next(
i-1 for i,bar in enumerate(bars)
if bar.contains(evt)[0])
except StopIteration:
return []
if step >= 0 and step < trialstate.shape[1]:
ind = np.nonzero(trialstate[:,step])[0]
else:
ind = range(trialstate.shape[0])
return ind
notsteptrialstate = np.bitwise_not(steptrialstate)
def onbuttonpress(evt):
if evt.inaxes == conditionax:
ind = selectstepindices(evt,stablebars,steptrialstate)
if len(ind) == 0:
ind = selectstepindices(evt,unstablebars,notsteptrialstate)
conditionfilter[:] = ind
updateselection()
ax.figure.canvas.mpl_connect('button_press_event',onbuttonpress)
plt.tight_layout(pad=0.5)
return rawtraj,traj,vidtime,stepactivity,selector,sselector
y = mtraj.data[s, 1]
else:
x = mtraj.data[s, 0] - 7 * trajectories.width_pixel_to_cm
y = mtraj.data[s, 1]
plt.plot(x, y, 'k', alpha=0.1)
bins = np.linspace(0, 40, 40)
rawspeedbins = np.array(trajectories.speedbins(mtraj, sp, bins))
# Compute baseline speed in 1st third of assay and subtract
baselinesp = np.nanmean(rawspeedbins[:, 0:rawspeedbins.shape[1] / 3], axis=1)
speedbins = rawspeedbins - baselinesp.reshape((len(baselinesp), 1))
# Get step state
trajtrials = trials.gettrialindices(path)
stepstate = trials.gettrialstate(os.path.join(path, 'step3_trials.csv'),
trajtrials)
# Split trials into stable/unstable
validtrials = mtraj.slices
stabletrials = [i for i, s in enumerate(validtrials) if stepstate[s.start]]
unstabletrials = [
i for i, s in enumerate(validtrials) if not stepstate[s.start]
]
plt.figure()
p = [plt.plot(t, 'g', alpha=0.1) for t in speedbins[stabletrials]]
p = [plt.plot(t, 'r', alpha=0.1) for t in speedbins[unstabletrials]]
m = np.nanmean(speedbins[stabletrials], axis=0)
plt.plot(m, 'g')
m = np.nanmean(speedbins[unstabletrials], axis=0)
plt.plot(m, 'r')
def get_randomized_speed_profiles(sessions):
avgSpeeds_allsess = []
trialTypes_allsess = []
# Select Session
for s in sessions:
# Load trials
rawtrials = trajectories.genfromtxt(s)
validtrials = trajectories.heightfilter(rawtrials,430,590)
[plt.plot(t[:,0],t[:,1],'k',alpha=0.1) for t in validtrials.tolist()]
plt.gca().invert_yaxis()
numTrials = validtrials.slices.shape[0]
print numTrials
# There is some misalignment for sessions on the last 4 animals..there session 0 is other session 1
# if a >= 10:
# s = s-1
# Look at all Trajectories
traj = validtrials
preprocess.make_videoanalysis(os.path.join(s,'Analysis'))
vtimepath = os.path.join(s, 'Analysis/videotime.csv')
vtime = np.genfromtxt(vtimepath)
trajtrials = trials.gettrialindices(s)
stepstate = trials.gettrialstate(os.path.join(s,'step3_trials.csv'),trajtrials)
#print str.format('a:s {0}:{1} {2} {3}', a, s, numTrials, len(validtrials.slices))
# Set Valid Trials (No exploration or tracking errors)
crossings = traj
# Set Binning and Range
avgSpeeds = np.zeros((numTrials, numBins))
trialTypes = np.zeros((numTrials, 1))
for t in range(0,numTrials):
#label_indices = np.array(pt.get_labeled_indices(labels,labelFilters[l]))
#c = crossings[t]
# Load X Trajectories and flip all of 'Left'
trialX = crossings.data[crossings.slices[t],0]
if trialX[0] > trialX[-1]:
# ALign on 2 important rails (the center of rail 3 is 550)
# and the centr of rail 4 is 737, therefore, the first encounter
# is at 550 going "right", and when flipped, (1280-737 = 543)
# going "left"...therefore, to correct for the shift, I subteact 1273
# and align the left and right trials
trialX = np.abs(trialX-1273)
# Load Y Trajectories
trialY = crossings.data[crossings.slices[t],1]
# Load and Parse Times
trialT = vtime[crossings.slices[t]]
# Measure Progression Speed
diffX = np.diff(trialX)
diffT = np.diff(trialT) # Time interval in seconds
speedX = np.concatenate((np.zeros(1) , diffX/diffT))
# Find enter/exit and crop trials
# indR = np.where(trialX > 1200)
# indL = np.where(trialX < 150)
# if (np.size(indR) > 0) and (np.size(indL) > 0):
# exitInd = indR[0][0]+1
# enterInd = indL[0][-1]
#
# trialX = trialX[enterInd:exitInd]
# trialY = trialY[enterInd:exitInd]
# speedX = speedX[enterInd:exitInd]
# Bin (progrssion - X) Speed Profiles (from position 200 to 1200)
for b in range(0,numBins):
bins = np.where((trialX >= (200+(b*binSize))) & (trialX < (200+(b*binSize)+binSize)))
if np.size(bins) > 0:
avgSpeeds[t, b] = np.mean(speedX[bins])
else:
avgSpeeds[t, b] = np.NaN
# Correct for starting speed - - first Third of assay
baseSpeed = stats.nanmean(avgSpeeds[t, 0:14])
avgSpeeds[t,:] = avgSpeeds[t,:]/baseSpeed
# Get Lables
if stepstate[crossings.slices[t].start]:
trialTypes[t] = 0
else:
trialTypes[t] = 1
# Pool All Average Speeds/TrialTypes Across Sessions
avgSpeeds_allsess.append(avgSpeeds)
trialTypes_allsess.append(trialTypes)
avgSpeeds = np.concatenate(avgSpeeds_allsess)
trialTypes = np.concatenate(trialTypes_allsess)
return avgSpeeds,trialTypes
def figure3(paths):
stable = []
unstable = []
for path in paths:
traj = trajectories.genfromtxt(path)
steps = np.genfromtxt(os.path.join(path,'Analysis\step_activity.csv'))
time = np.genfromtxt(os.path.join(path,'front_video.csv'),dtype=str)
trajtrials = trials.gettrialindices(path)
stepstate = trials.gettrialstate(os.path.join(path,'step3_trials.csv'),trajtrials)
ftraj = trajectories.heightfilter(trajectories.lengthfilter(traj,0,500),0,6)
# plt.figure()
# for t in traj.tolist():
# plt.plot(len(t),max(t[:,1]),'k.')
# for t in ftraj.tolist():
# plt.plot(len(t),max(t[:,1]),'r.')
traj = trajectories.mirrorleft(ftraj)
#activesteptrials = [sum(steps[s,3 if s.step < 0 else 2]) / 255 for s in traj.slices]
activesteptrials = [s for s in traj.slices
if (sum(steps[s,3 if s.step < 0 else 2]) / 255) > 500]
traj = trajectories.trajectories(traj.data,activesteptrials)
# plt.figure()
# for t in traj.tolist():
# plt.plot(t[:,0],t[:,1],'k',alpha=0.1)
speed = [np.insert(np.diff(traj.data[s,0])/timedelta(time[s]),0,0)
for s in traj.slices]
validtrials = [traj.slices[i] for i,s in enumerate(speed) if np.mean(s) > 0]
traj = trajectories.trajectories(traj.data,validtrials)
speed = [s for s in speed if np.mean(s) > 0]
# plt.figure()
# for sp in speed:
# plt.plot(sp,'k',alpha=0.1)
# Bin (progrssion - X) Speed Profiles (from position 200 to 1200)
numBins = 25
binSize = 50 / numBins
bins = range(0,50,binSize)
speedbins = np.zeros((len(traj.slices),numBins))
for i,t in enumerate(traj.tolist()):
xs = t[:,0]
binindices = np.digitize(xs,bins)
for b in range(numBins):
speedbin = speed[i][binindices == b]
speedbins[i,b] = np.mean(speedbin) if speedbin.size > 0 else np.nan
basespeed = stats.nanmean(speedbins[i,0:numBins/3])
speedbins[i,:] /= basespeed
stabletrials = [i for i,s in enumerate(validtrials) if stepstate[s.start]]
unstabletrials = [i for i,s in enumerate(validtrials) if not stepstate[s.start]]
stablespeeds = speedbins[stabletrials,:]
unstablespeeds = speedbins[unstabletrials,:]
stable.append(stablespeeds)
unstable.append(unstablespeeds)
def plotcurve(speeds,color):
x = np.arange(numBins) * binSize
mu = np.mean(speeds,axis=0)
sd = np.std(speeds,axis=0)
plt.plot(x,mu,color,linewidth=3)
plt.plot(x,mu-sd,'k--')
plt.plot(x,mu+sd,'k--')
plt.fill_between(x,mu-sd,mu+sd,alpha=0.1,color=color)
plt.figure()
stablespeeds = np.concatenate(stable,0)
unstablespeeds = np.concatenate(unstable,0)
plotcurve(stablespeeds,'g')
plotcurve(unstablespeeds,'r')
return stablespeeds,unstablespeeds
x = mtraj.data[s,0]
y = mtraj.data[s,1]
else:
x = mtraj.data[s,0]-7*trajectories.width_pixel_to_cm
y = mtraj.data[s,1]
plt.plot(x,y,'k',alpha=0.1)
bins = np.linspace(0,40,40)
rawspeedbins = np.array(trajectories.speedbins(mtraj,sp,bins))
# Compute baseline speed in 1st third of assay and subtract
baselinesp = np.nanmean(rawspeedbins[:,0:rawspeedbins.shape[1]/3],axis=1)
speedbins = rawspeedbins-baselinesp.reshape((len(baselinesp),1))
# Get step state
trajtrials = trials.gettrialindices(path)
stepstate = trials.gettrialstate(os.path.join(path,'step3_trials.csv'),trajtrials)
# Split trials into stable/unstable
validtrials = mtraj.slices
stabletrials = [i for i,s in enumerate(validtrials) if stepstate[s.start]]
unstabletrials = [i for i,s in enumerate(validtrials) if not stepstate[s.start]]
plt.figure()
p = [plt.plot(t,'g',alpha=0.1) for t in speedbins[stabletrials]]
p = [plt.plot(t,'r',alpha=0.1) for t in speedbins[unstabletrials]]
m = np.nanmean(speedbins[stabletrials],axis=0)
plt.plot(m,'g')
m = np.nanmean(speedbins[unstabletrials],axis=0)
plt.plot(m,'r')
plt.show()
