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
""" import os import trials import preprocess import trajectories import numpy as np import matplotlib.pyplot as plt # Load trajectories path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_20/2013_04_04-11_40' path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_21/2013_04_04-12_14' 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
""" import os import trials import preprocess import trajectories import numpy as np import matplotlib.pyplot as plt # Load trajectories path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_20/2013_04_04-11_40' path = r'D:/Protocols/Behavior/Shuttling/LightDarkServoStable/Data/JPAK_21/2013_04_04-12_14' 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
