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
#p = [plt.plot(t[:,0],t[:,1]) for t in traj.tolist()]
# Load video time
vtimepath = os.path.join(path, 'Analysis/videotime.csv')
vtime = np.genfromtxt(vtimepath)
# Filter trajectories by height
ftraj = trajectories.heightfilter(traj, 0, 5)
print ftraj.slices.shape
# Filter trajectories by step activity
#steps = np.genfromtxt(os.path.join(path,'Analysis\step_activity.csv'))
# Compute speed and mirror values for left trials
sp = trajectories.speed(ftraj, vtime)
mtraj = trajectories.mirrorleft(ftraj)
for s in mtraj.slices:
if s.start < s.stop: # right
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)
#p = [plt.plot(t[:,0],t[:,1]) for t in traj.tolist()]
# Load video time
vtimepath = os.path.join(path, 'Analysis/videotime.csv')
vtime = np.genfromtxt(vtimepath)
# Filter trajectories by height
ftraj = trajectories.heightfilter(traj,0,5)
print ftraj.slices.shape
# Filter trajectories by step activity
#steps = np.genfromtxt(os.path.join(path,'Analysis\step_activity.csv'))
# Compute speed and mirror values for left trials
sp = trajectories.speed(ftraj,vtime)
mtraj = trajectories.mirrorleft(ftraj)
for s in mtraj.slices:
if s.start < s.stop: # right
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)