def slowdownsummary(crossings, ax=None, regress=True):
if ax is None:
ax = plt.gca()
entryspeed = crossings.entryspeed
exitspeed = crossings.exitspeed
ax.plot(entryspeed, exitspeed, ".")
if regress:
pltutils.regressionline(entryspeed, exitspeed, ax, color="k")
ax.set_title("slowdown")
ax.set_xlabel("entry speed (cm / s)")
ax.set_ylabel("exit speed (cm / s)")
def slowdownsummary(crossings, ax=None, regress=True):
if ax is None:
ax = plt.gca()
entryspeed = crossings.entryspeed
exitspeed = crossings.exitspeed
ax.plot(entryspeed, exitspeed, '.')
if regress:
pltutils.regressionline(entryspeed, exitspeed, ax, color='k')
ax.set_title('slowdown')
ax.set_xlabel('entry speed (cm / s)')
ax.set_ylabel('exit speed (cm / s)')
def figure1h(cr,path):
if not os.path.exists(path):
os.makedirs(path)
cut = getballistictrials(cr)
conditions = cut[cut.trial > 0].groupby(level=['subject'])
for subject,group in conditions:
fig = plt.figure()
entryspeed = group.entryspeed
crossingspeed = group.crossingspeed
plt.plot(entryspeed,crossingspeed,'.')
pltutils.regressionline(entryspeed,crossingspeed,color='k')
plt.xlabel('entry speed (cm / s)')
plt.ylabel('crossing speed (cm / s)')
plt.title('slowdown')
fname = str.format("{0}_slowdown_stable.png",subject)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def figure1h(cr, path):
if not os.path.exists(path):
os.makedirs(path)
cut = getballistictrials(cr)
conditions = cut[cut.trial > 0].groupby(level=['subject'])
for subject, group in conditions:
fig = plt.figure()
entryspeed = group.entryspeed
crossingspeed = group.crossingspeed
plt.plot(entryspeed, crossingspeed, '.')
pltutils.regressionline(entryspeed, crossingspeed, color='k')
plt.xlabel('entry speed (cm / s)')
plt.ylabel('crossing speed (cm / s)')
plt.title('slowdown')
fname = str.format("{0}_slowdown_stable.png", subject)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
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