path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_21/2013_04_16-11_35'
path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_23/2013_04_23-14_49'
#path = r'D:/Random/Data/JPAK_21/2013_04_16-11_35'
preprocess.make_videoanalysis(os.path.join(path, 'Analysis'))
traj = trajectories.genfromtxt(path)
traj = trajectories.scale(traj)
# Plot trajectories
#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
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
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
path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_21/2013_04_16-11_35'
path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_23/2013_04_23-14_49'
#path = r'D:/Random/Data/JPAK_21/2013_04_16-11_35'
preprocess.make_videoanalysis(os.path.join(path,'Analysis'))
traj = trajectories.genfromtxt(path)
traj = trajectories.scale(traj)
# Plot trajectories
#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
