def plot_comparison_measure(group_order,group_cycle,mclesionsham,bracket_offset=5,bracket_tickheight=1):
scale = 46
offsetscale = 2
plt.close('all')
fig = plt.figure('stable sessions average trial times',figsize=(4,7))
ax = fig.gca()
ax.set_color_cycle(group_cycle)
measures = [plot_average_trial_times('stable sessions',mclesionsham[group_order[i]][1:5],label='lesion' if group_cycle[i] == 'r' else 'control',offset=i*2,scale=46) for i in range(14)]
plt.ylabel('time between rewards (s)')
plt.xlabel('sessions')
lesionoffset = 3*offsetscale
lesioncenter = [scale*i+lesionoffset for i in range(4)]
lesion_times = [[get_average_trial_time(mclesionsham[i][s]) for i in group_order[0:7]] for s in range(1,5)]
lesion_mean = [np.mean(x) for x in lesion_times]
lesion_std = [np.std(x) for x in lesion_times]
plt.errorbar(lesioncenter,lesion_mean,lesion_std,fmt='--o',color=group_cycle[0],ecolor='k',linewidth=2,capthick=2,markersize=0)
controloffset = 10*offsetscale
controlcenter = [scale*i+controloffset for i in range(4)]
control_times = [[get_average_trial_time(mclesionsham[i][s]) for i in group_order[7:14]] for s in range(1,5)]
control_mean = [np.mean(x) for x in control_times]
control_std = [np.std(x) for x in control_times]
plt.errorbar(controlcenter,control_mean,control_std,fmt='--o',color=group_cycle[7],ecolor='k',linewidth=2,capthick=2,markersize=0)
pltutils.fix_font_size()
handles, labels = ax.get_legend_handles_labels()
plt.legend((handles[0],handles[7]),(labels[0],labels[7]))
tickoffset = 6.5*offsetscale
xticks = [scale*i+tickoffset for i in range(4)]
ax.set_xticks(xticks)
ax.set_xticklabels([1,2,3,4])
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.05
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(lesion_times[i],control_times[i])[1]
print sigtest,"groups"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
maxstd.append(get_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd.append(get_negative_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd = min(minstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],minstd-bracket_offset,1.4,label=testlabel,tickheight=-10*bracket_tickheight)
#################################
plt.draw()
def plot_comparison_measure(group_order,group_cycle,mclesionsham,bracket_offset=5,bracket_tickheight=1):
scale = 46
offsetscale = 2
plt.close('all')
fig = plt.figure('stable sessions average trial times',figsize=(4,7))
ax = fig.gca()
ax.set_color_cycle(group_cycle)
measures = [plot_average_trial_times('stable sessions',mclesionsham[group_order[i]][1:5],label='lesion' if group_cycle[i] == 'r' else 'control',offset=i*2,scale=46) for i in range(14)]
plt.ylabel('time between rewards (s)')
plt.xlabel('sessions')
lesionoffset = 3*offsetscale
lesioncenter = [scale*i+lesionoffset for i in range(4)]
lesion_times = [[get_average_trial_time(mclesionsham[i][s]) for i in group_order[0:7]] for s in range(1,5)]
lesion_mean = [np.mean(x) for x in lesion_times]
lesion_std = [np.std(x) for x in lesion_times]
plt.errorbar(lesioncenter,lesion_mean,lesion_std,fmt='--o',color=group_cycle[0],ecolor='k',linewidth=2,capthick=2,markersize=0)
controloffset = 10*offsetscale
controlcenter = [scale*i+controloffset for i in range(4)]
control_times = [[get_average_trial_time(mclesionsham[i][s]) for i in group_order[7:14]] for s in range(1,5)]
control_mean = [np.mean(x) for x in control_times]
control_std = [np.std(x) for x in control_times]
plt.errorbar(controlcenter,control_mean,control_std,fmt='--o',color=group_cycle[7],ecolor='k',linewidth=2,capthick=2,markersize=0)
pltutils.fix_font_size()
handles, labels = ax.get_legend_handles_labels()
plt.legend((handles[0],handles[7]),(labels[0],labels[7]))
tickoffset = 6.5*offsetscale
xticks = [scale*i+tickoffset for i in range(4)]
ax.set_xticks(xticks)
ax.set_xticklabels([1,2,3,4])
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.05
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(lesion_times[i],control_times[i])[1]
print sigtest,"groups"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
maxstd.append(get_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd.append(get_negative_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd = min(minstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],minstd-bracket_offset,1.4,label=testlabel,tickheight=-10*bracket_tickheight)
#################################
plt.draw()
def plot_trial_measures(group_cycle,group_order,trial_measures,bracket_offset=0.5,bracket_tickheight=1,ylims=None,xticklabels=None):
scale = 46
offsetscale = 2
num_sessions = len(trial_measures[0])
ax = plt.gca()
ax.set_color_cycle(group_cycle)
measures = [[trial_measures[group_order[i]][s] for s in (range(num_sessions) if i != 7 else [0])] for i in range(len(trial_measures))]
[plot_epoch_average(measures[i],label='lesion' if group_cycle[i] == 'r' else 'control',offset=i*offsetscale,scale=scale) for i in range(len(trial_measures))]
plt.ylabel('time to cross obstacles (s)')
plt.xlabel('x')
offset1 = 3*offsetscale
center1 = [scale*i+offset1 for i in range(num_sessions)]
group1_times = [[np.mean(trial_measures[i][s]) for i in group_order[0:7]] for s in range(num_sessions)]
group1_mean = [np.mean(x) for x in group1_times]
group1_std = [np.std(x) for x in group1_times]
plt.errorbar(center1,group1_mean,group1_std,fmt='o',color=group_cycle[0],ecolor='k',linewidth=2,capthick=2,markersize=0)
offset2 = 10*offsetscale
center2 = [scale*i+offset2 for i in range(num_sessions)]
group2_times = [[np.mean(trial_measures[i][s]) for i in (group_order[7:14] if s < 1 else group_order[8:14])] for s in range(num_sessions)]
group2_mean = [np.mean(x) for x in group2_times]
group2_std = [np.std(x) for x in group2_times]
plt.errorbar(center2,group2_mean,group2_std,fmt='o',color=group_cycle[7],ecolor='k',linewidth=2,capthick=2,markersize=0)
pltutils.fix_font_size()
#handles, labels = ax.get_legend_handles_labels()
#plt.legend((handles[0],handles[7]),(labels[0],labels[7]))
tickoffset = 6.5*offsetscale
xticks = [scale*i+tickoffset for i in range(num_sessions)]
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.01
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(group1_times[i],group2_times[i])[1]
print sigtest,"groups"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
maxstd.append(get_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd.append(get_negative_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
maxstd = max(maxstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],maxstd+bracket_offset,2,label=testlabel,tickheight=bracket_tickheight)
##################################
### BRACKETS (BETWEEN CONDITIONS) ###
minstd = min(minstd)
sigtest = stats.ttest_ind(group1_times[0]+group2_times[0],group1_times[-1]+group2_times[-1])[1]
print sigtest,"conditions"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
#pltutils.hbracket(xticks[1],minstd-bracket_offset,5.5,label=testlabel,tickheight=-1.5*bracket_tickheight)
pltutils.hbracket((xticks[-1]-xticks[0])/2+xticks[0],minstd-bracket_offset,9.5,label=testlabel,tickheight=-1.5*bracket_tickheight)
#####################################
### SEPARATORS ###
ylims = plt.ylim() if ylims is None else ylims
for i in range(len(xticks)-1):
separatorxi = (xticks[i]+xticks[i+1])/2
ax.plot((separatorxi,separatorxi),ylims,'k--')
plt.ylim(ylims)
##################
plt.xlabel('')
plt.draw()
############# FIGURE 3 ##############################
#### Get contact step activation ###
#def get_contact_step_activity(session,i):
# activity = session.steps[i]
# step_index = 3 if session.labels[i]['direction'] == 'right' else 4
# return activity[:,step_index]
#
#### Plot contact maximum activation distribution ###
#def plot_contact_distribution(session):
# max_contacts = [np.max(get_contact_step_activity(session,i)) for i in range(len(session.steps))]
# maxStContacts = [mc for state,mc in zip(session.labels,max_contacts) if state['state'] == 'stable']
# maxUnContacts = [mc for state,mc in zip(session.labels,max_contacts) if state['state'] == 'unstable']
# plt.plot(maxStContacts,'b.')
# plt.plot(maxUnContacts,'r.')
#
## Time Difference in microseconds
#def time_diff(t):
# numTimes = np.size(t)
# dT = np.zeros(numTimes-1)
# for i in range(numTimes-1):
# dT[i] = (t[i+1]-t[i]).microseconds
#
# return dT
#
#def get_randomized_speed_profiles(experiment,a,sessions,filterpath):
# avgSpeeds_allsess = []
# trialTypes_allsess = []
#
# # Select Session
# for s in sessions:
#
# # Load Valid Trial Filter (manually sorted)
# validFilename = filterpath + r'\valid_a' + str(a) + '_s' + str(s) + '.pickle'
# valid_trials = load_data.load_pickle(validFilename)
# numTrials = np.size(valid_trials)
#
# # There is some misalignment for sessions on the last 4 animals..there session 0 is other session 1
# if a >= 10:
# s = s-1
#
# # Set Trial Lables
# labelfilter1 = {'state':'stable'}
# labelfilter2 = {'state':'unstable'}
# labelFilters = [labelfilter1, labelfilter2]
#
# # Look at all Trajectories
# trajectories = experiment[a][s].trajectories
# times = experiment[a][s].time
# slices = experiment[a][s].slices
# labels = experiment[a][s].labels
# steps = experiment[a][s].steps
# speeds = experiment[a][s].speeds
#
# print str.format('a:s {0}:{1} {2} {3}', a, s, numTrials, len(slices))
#
# # Set Valid Trials (No exploration or tracking errors)
# crossings = valid_trials
#
# # 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 = trajectories[slices[c],0]
# if utils.is_dict_subset({'direction':'left'},labels[c]):
# # 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 = trajectories[slices[c],1]
#
# # Load and Parse Times
# trialTstrings = times[slices[c]]
# trialT = np.array([dateutil.parser.parse(timeString) for timeString in trialTstrings])
#
# # Measure Progression Speed
# diffX = np.diff(trialX)
# diffT = time_diff(trialT)/1000000 # 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
# label = labels[c]
#
# if utils.is_dict_subset({'state':'stable'},label):
# 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 plot_randomized_speed_profiles(avgSpeeds,trialTypes):
# # Set Plotting Attributes
# color1 = (0.0, 0.0, 0.0, 0.1)
# color2 = (1.0, 0.6, 0.0, 0.1)
# color1b = (0.0, 0.0, 0.0, 1.0)
# color2b = (1.0, 0.6, 0.0, 1.0)
#
# traceColors = [color1, color2]
# boldColors = [color1b, color2b]
#
# # Plot Average Speeds in bins
# plt.figure()
# numTrials = np.size(trialTypes)
# for t in range(0,numTrials):
# if trialTypes[t] == 0:
# plt.plot(avgSpeeds[t,:], color=color1)
# else:
# plt.plot(avgSpeeds[t,:], color=color2)
#
# stableTrials = np.where(trialTypes == 0)
# unstableTrials = np.where(trialTypes == 1)
# mSt = stats.nanmean(avgSpeeds[stableTrials, :], 1)
# mUn = stats.nanmean(avgSpeeds[unstableTrials, :], 1)
# eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)/np.sqrt(np.size(stableTrials)-1)
# eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)/np.sqrt(np.size(unstableTrials)-1)
#
## eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)
## eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)
#
#
# mSt = mSt[0];
# mUn = mUn[0];
# eSt = eSt[0];
# eUn = eUn[0];
#
# plt.plot(mUn, color=color2b, linewidth = 7)
# plt.plot(mSt, color=color1b, linewidth = 7)
#
## plt.plot(mSt + eSt, color=color1b, linewidth = 0.5)
## plt.plot(mSt - eSt, color=color1b, linewidth = 0.5)
## plt.plot(mUn + eUn, color=color2b, linewidth = 0.5)
## plt.plot(mUn - eUn, color=color2b, linewidth = 0.5)
# #pltutils.fix_font_size()
# plt.xlabel('crossing extent (cm)')
# plt.ylabel('normalized horizontal speed')
# pltutils.fix_font_size()
# plt.axis([0, 39, 0, 3])
#
#
##### Figure 3b ####
#
#def get_randomized_group_average_speed_profiles(profiles):
# stAvg = []
# unAvg = []
# stErr = []
# unErr = []
#
# for avgSpeeds,trialTypes in profiles:
# # Plot Average Speeds in bins
# stableTrials = np.where(trialTypes == 0)
# unstableTrials = np.where(trialTypes == 1)
#
# mSt = stats.nanmean(avgSpeeds[stableTrials, :], 1)
# mUn = stats.nanmean(avgSpeeds[unstableTrials, :], 1)
# eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)/np.sqrt(np.size(stableTrials)-1)
# eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)/np.sqrt(np.size(unstableTrials)-1)
#
# mSt = mSt[0]
# mUn = mUn[0]
# eSt = eSt[0]
# eUn = eUn[0]
#
# stAvg.append(mSt)
# unAvg.append(mUn)
# stErr.append(eSt)
# unErr.append(eUn)
# return (stAvg,stErr),(unAvg,unErr)
#
#def get_randomized_group_speed_profile_difference(avgProfiles):
# diffs = []
# errors = []
#
# # Unpack average profile structure
# (stAvg,stErr),(unAvg,unErr) = avgProfiles
#
# for mSt,eSt,mUn,eUn in zip(stAvg,stErr,unAvg,unErr):
# # Compute Difference Speed between Stable and Unstable Trials
# mDiff = mUn-mSt
# eDiff = np.sqrt((eSt*eSt) + (eUn*eUn))
# diffs.append(mDiff)
# errors.append(eDiff)
#
# diffs = np.array(diffs)
# errors = np.array(errors)
# return diffs,errors,np.mean(diffs[:, 20:], 1)
#
#def plot_randomized_group_average_speed_profiles(avgProfiles,labelx=True,labely=True,legend=True,title=None):
# # Set Plotting Attributes
# color1b = (0.0, 0.0, 0.0, 1.0)
# color2b = (1.0, 0.6, 0.0, 1.0)
#
# # Unpack average profile structure
# (stAvg,stErr),(unAvg,unErr) = avgProfiles
#
# # Prepare Bulk Arrays
# stAvg = np.array(stAvg)
# unAvg = np.array(unAvg)
# stErr = np.array(stErr)
# unErr = np.array(unErr)
#
# # Plot Averge Speed Profiles
# a1 = plt.plot(np.mean(unAvg,0), color = color2b, linewidth = 3,label='unstable')
# a2 = plt.plot(np.mean(stAvg,0), color = color1b, linewidth = 3,label='stable')
# if labelx:
# plt.xlabel('crossing extent (cm)')
# if labely:
# plt.ylabel('normalized horizontal speed')
# pltutils.fix_font_size()
# plt.axis([0, numBins, 0.5, 2.0])
# plt.yticks([0.75,1.0,1.25,1.5,1.75])
# if legend:
# plt.legend((a2[0],a1[0]),('stable','unstable'),loc='upper left')
#
# if title is not None:
# ax = plt.gca()
# ax.text(0.5,0.9,title,horizontalalignment='center',transform=ax.transAxes)
#
#def plot_randomized_speed_profile_difference_comparison(controls,lesions):
# controlMeans = np.mean(controls, 0)
# controlMeanAll = np.mean(controlMeans)
# controlError = np.std(controlMeans)/np.sqrt(7-1)
#
# lesionMeans = np.mean(lesions, 0)
# lesionMeanAll = np.mean(lesionMeans)
# lesionlError = np.std(lesionMeans)/np.sqrt(6-1)
#
# significance = 0.05
# sigtest = stats.ttest_ind(controlMeans, lesionMeans)[1]
# testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
# print sigtest
#
# cX = [1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]
# lX = [1.9, 1.9, 1.9, 1.9, 1.9, 1.9]
#
# plt.plot([0,3], [0,0], color=[0.25,0.25,0.25,1], linewidth = 1)
# plt.plot(cX, controlMeans, 'bo')
# plt.plot(lX, lesionMeans, 'ro')
# plt.plot(1.9, lesionMeans[5], 'o', color = [1.0, 0.75, 0.75, 1.0])
#
# #plt.bar(1.2, controlMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
# plt.errorbar(1.3, controlMeanAll, controlError, marker='s', mfc='blue', ecolor = 'black', mec='black', ms=1, mew=1, capsize=5, elinewidth=2)
# plt.plot([1.0,1.4], [controlMeanAll,controlMeanAll], color=[0.25,0.25,1.0,1], linewidth = 2)
#
# #plt.bar(1.6, lesionMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
# plt.errorbar(1.7, lesionMeanAll, lesionlError, marker='s', mfc='red', ecolor = 'black', mec='black', ms=1, mew=1, capsize=5, elinewidth=2)
# plt.plot([1.6,2.0], [lesionMeanAll,lesionMeanAll], color=[1.00,0.25,0.25,1], linewidth = 2)
# pltutils.hbracket(1.5,0.17,4.5,label=testlabel,tickheight=0.01)
#
# ax = plt.gca()
# ax.set_xticks([1.1,1.9])
# ax.set_xticklabels(['controls','lesions'])
# plt.ylabel('normalized speed difference')
#
# pltutils.fix_font_size()
# plt.axis([0.75,2.25,-0.2,0.2])
def plot_trial_measures(group_cycle,group_order,trial_measures,bracket_offset=0.5,bracket_tickheight=1,ylims=None,xticklabels=None):
scale = 46
offsetscale = 2
num_sessions = len(trial_measures[0])
ax = plt.gca()
ax.set_color_cycle(group_cycle)
measures = [[trial_measures[group_order[i]][s] for s in (range(num_sessions) if i != 7 else [0])] for i in range(len(trial_measures))]
[plot_epoch_average(measures[i],label='lesion' if group_cycle[i] == 'r' else 'control',offset=i*offsetscale,scale=scale) for i in range(len(trial_measures))]
plt.ylabel('time to cross obstacles (s)')
plt.xlabel('x')
offset1 = 3*offsetscale
center1 = [scale*i+offset1 for i in range(num_sessions)]
group1_times = [[np.mean(trial_measures[i][s]) for i in group_order[0:7]] for s in range(num_sessions)]
group1_mean = [np.mean(x) for x in group1_times]
group1_std = [np.std(x) for x in group1_times]
plt.errorbar(center1,group1_mean,group1_std,fmt='o',color=group_cycle[0],ecolor='k',linewidth=2,capthick=2,markersize=0)
offset2 = 10*offsetscale
center2 = [scale*i+offset2 for i in range(num_sessions)]
group2_times = [[np.mean(trial_measures[i][s]) for i in (group_order[7:14] if s < 1 else group_order[8:14])] for s in range(num_sessions)]
group2_mean = [np.mean(x) for x in group2_times]
group2_std = [np.std(x) for x in group2_times]
plt.errorbar(center2,group2_mean,group2_std,fmt='o',color=group_cycle[7],ecolor='k',linewidth=2,capthick=2,markersize=0)
pltutils.fix_font_size()
#handles, labels = ax.get_legend_handles_labels()
#plt.legend((handles[0],handles[7]),(labels[0],labels[7]))
tickoffset = 6.5*offsetscale
xticks = [scale*i+tickoffset for i in range(num_sessions)]
ax.set_xticks(xticks)
ax.set_xticklabels(xticklabels)
### BRACKETS (BETWEEN GROUPS) ###
maxstd = []
minstd = []
significance = 0.01
maxrange = len(measures)
for i in range(len(xticks)):
sigtest = stats.ttest_ind(group1_times[i],group2_times[i])[1]
print sigtest,"groups"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
maxstd.append(get_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
minstd.append(get_negative_bracket_y(measures,range(maxrange) if i < 1 else range(0,7)+range(8,14),i))
maxstd = max(maxstd)
for i in range(len(xticks)):
pltutils.hbracket(xticks[i],maxstd+bracket_offset,2,label=testlabel,tickheight=bracket_tickheight)
##################################
### BRACKETS (BETWEEN CONDITIONS) ###
minstd = min(minstd)
sigtest = stats.ttest_ind(group1_times[0]+group2_times[0],group1_times[-1]+group2_times[-1])[1]
print sigtest,"conditions"
testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
#pltutils.hbracket(xticks[1],minstd-bracket_offset,5.5,label=testlabel,tickheight=-1.5*bracket_tickheight)
pltutils.hbracket((xticks[-1]-xticks[0])/2+xticks[0],minstd-bracket_offset,9.5,label=testlabel,tickheight=-1.5*bracket_tickheight)
#####################################
### SEPARATORS ###
ylims = plt.ylim() if ylims is None else ylims
for i in range(len(xticks)-1):
separatorxi = (xticks[i]+xticks[i+1])/2
ax.plot((separatorxi,separatorxi),ylims,'k--')
plt.ylim(ylims)
##################
plt.xlabel('')
plt.draw()
############# FIGURE 3 ##############################
#### Get contact step activation ###
#def get_contact_step_activity(session,i):
# activity = session.steps[i]
# step_index = 3 if session.labels[i]['direction'] == 'right' else 4
# return activity[:,step_index]
#
#### Plot contact maximum activation distribution ###
#def plot_contact_distribution(session):
# max_contacts = [np.max(get_contact_step_activity(session,i)) for i in range(len(session.steps))]
# maxStContacts = [mc for state,mc in zip(session.labels,max_contacts) if state['state'] == 'stable']
# maxUnContacts = [mc for state,mc in zip(session.labels,max_contacts) if state['state'] == 'unstable']
# plt.plot(maxStContacts,'b.')
# plt.plot(maxUnContacts,'r.')
#
## Time Difference in microseconds
#def time_diff(t):
# numTimes = np.size(t)
# dT = np.zeros(numTimes-1)
# for i in range(numTimes-1):
# dT[i] = (t[i+1]-t[i]).microseconds
#
# return dT
#
#def get_randomized_speed_profiles(experiment,a,sessions,filterpath):
# avgSpeeds_allsess = []
# trialTypes_allsess = []
#
# # Select Session
# for s in sessions:
#
# # Load Valid Trial Filter (manually sorted)
# validFilename = filterpath + r'\valid_a' + str(a) + '_s' + str(s) + '.pickle'
# valid_trials = load_data.load_pickle(validFilename)
# numTrials = np.size(valid_trials)
#
# # There is some misalignment for sessions on the last 4 animals..there session 0 is other session 1
# if a >= 10:
# s = s-1
#
# # Set Trial Lables
# labelfilter1 = {'state':'stable'}
# labelfilter2 = {'state':'unstable'}
# labelFilters = [labelfilter1, labelfilter2]
#
# # Look at all Trajectories
# trajectories = experiment[a][s].trajectories
# times = experiment[a][s].time
# slices = experiment[a][s].slices
# labels = experiment[a][s].labels
# steps = experiment[a][s].steps
# speeds = experiment[a][s].speeds
#
# print str.format('a:s {0}:{1} {2} {3}', a, s, numTrials, len(slices))
#
# # Set Valid Trials (No exploration or tracking errors)
# crossings = valid_trials
#
# # 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 = trajectories[slices[c],0]
# if utils.is_dict_subset({'direction':'left'},labels[c]):
# # 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 = trajectories[slices[c],1]
#
# # Load and Parse Times
# trialTstrings = times[slices[c]]
# trialT = np.array([dateutil.parser.parse(timeString) for timeString in trialTstrings])
#
# # Measure Progression Speed
# diffX = np.diff(trialX)
# diffT = time_diff(trialT)/1000000 # 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
# label = labels[c]
#
# if utils.is_dict_subset({'state':'stable'},label):
# 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 plot_randomized_speed_profiles(avgSpeeds,trialTypes):
# # Set Plotting Attributes
# color1 = (0.0, 0.0, 0.0, 0.1)
# color2 = (1.0, 0.6, 0.0, 0.1)
# color1b = (0.0, 0.0, 0.0, 1.0)
# color2b = (1.0, 0.6, 0.0, 1.0)
#
# traceColors = [color1, color2]
# boldColors = [color1b, color2b]
#
# # Plot Average Speeds in bins
# plt.figure()
# numTrials = np.size(trialTypes)
# for t in range(0,numTrials):
# if trialTypes[t] == 0:
# plt.plot(avgSpeeds[t,:], color=color1)
# else:
# plt.plot(avgSpeeds[t,:], color=color2)
#
# stableTrials = np.where(trialTypes == 0)
# unstableTrials = np.where(trialTypes == 1)
# mSt = stats.nanmean(avgSpeeds[stableTrials, :], 1)
# mUn = stats.nanmean(avgSpeeds[unstableTrials, :], 1)
# eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)/np.sqrt(np.size(stableTrials)-1)
# eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)/np.sqrt(np.size(unstableTrials)-1)
#
## eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)
## eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)
#
#
# mSt = mSt[0];
# mUn = mUn[0];
# eSt = eSt[0];
# eUn = eUn[0];
#
# plt.plot(mUn, color=color2b, linewidth = 7)
# plt.plot(mSt, color=color1b, linewidth = 7)
#
## plt.plot(mSt + eSt, color=color1b, linewidth = 0.5)
## plt.plot(mSt - eSt, color=color1b, linewidth = 0.5)
## plt.plot(mUn + eUn, color=color2b, linewidth = 0.5)
## plt.plot(mUn - eUn, color=color2b, linewidth = 0.5)
# #pltutils.fix_font_size()
# plt.xlabel('crossing extent (cm)')
# plt.ylabel('normalized horizontal speed')
# pltutils.fix_font_size()
# plt.axis([0, 39, 0, 3])
#
#
##### Figure 3b ####
#
#def get_randomized_group_average_speed_profiles(profiles):
# stAvg = []
# unAvg = []
# stErr = []
# unErr = []
#
# for avgSpeeds,trialTypes in profiles:
# # Plot Average Speeds in bins
# stableTrials = np.where(trialTypes == 0)
# unstableTrials = np.where(trialTypes == 1)
#
# mSt = stats.nanmean(avgSpeeds[stableTrials, :], 1)
# mUn = stats.nanmean(avgSpeeds[unstableTrials, :], 1)
# eSt = stats.nanstd(avgSpeeds[stableTrials, :], 1)/np.sqrt(np.size(stableTrials)-1)
# eUn = stats.nanstd(avgSpeeds[unstableTrials, :], 1)/np.sqrt(np.size(unstableTrials)-1)
#
# mSt = mSt[0]
# mUn = mUn[0]
# eSt = eSt[0]
# eUn = eUn[0]
#
# stAvg.append(mSt)
# unAvg.append(mUn)
# stErr.append(eSt)
# unErr.append(eUn)
# return (stAvg,stErr),(unAvg,unErr)
#
#def get_randomized_group_speed_profile_difference(avgProfiles):
# diffs = []
# errors = []
#
# # Unpack average profile structure
# (stAvg,stErr),(unAvg,unErr) = avgProfiles
#
# for mSt,eSt,mUn,eUn in zip(stAvg,stErr,unAvg,unErr):
# # Compute Difference Speed between Stable and Unstable Trials
# mDiff = mUn-mSt
# eDiff = np.sqrt((eSt*eSt) + (eUn*eUn))
# diffs.append(mDiff)
# errors.append(eDiff)
#
# diffs = np.array(diffs)
# errors = np.array(errors)
# return diffs,errors,np.mean(diffs[:, 20:], 1)
#
#def plot_randomized_group_average_speed_profiles(avgProfiles,labelx=True,labely=True,legend=True,title=None):
# # Set Plotting Attributes
# color1b = (0.0, 0.0, 0.0, 1.0)
# color2b = (1.0, 0.6, 0.0, 1.0)
#
# # Unpack average profile structure
# (stAvg,stErr),(unAvg,unErr) = avgProfiles
#
# # Prepare Bulk Arrays
# stAvg = np.array(stAvg)
# unAvg = np.array(unAvg)
# stErr = np.array(stErr)
# unErr = np.array(unErr)
#
# # Plot Averge Speed Profiles
# a1 = plt.plot(np.mean(unAvg,0), color = color2b, linewidth = 3,label='unstable')
# a2 = plt.plot(np.mean(stAvg,0), color = color1b, linewidth = 3,label='stable')
# if labelx:
# plt.xlabel('crossing extent (cm)')
# if labely:
# plt.ylabel('normalized horizontal speed')
# pltutils.fix_font_size()
# plt.axis([0, numBins, 0.5, 2.0])
# plt.yticks([0.75,1.0,1.25,1.5,1.75])
# if legend:
# plt.legend((a2[0],a1[0]),('stable','unstable'),loc='upper left')
#
# if title is not None:
# ax = plt.gca()
# ax.text(0.5,0.9,title,horizontalalignment='center',transform=ax.transAxes)
#
#def plot_randomized_speed_profile_difference_comparison(controls,lesions):
# controlMeans = np.mean(controls, 0)
# controlMeanAll = np.mean(controlMeans)
# controlError = np.std(controlMeans)/np.sqrt(7-1)
#
# lesionMeans = np.mean(lesions, 0)
# lesionMeanAll = np.mean(lesionMeans)
# lesionlError = np.std(lesionMeans)/np.sqrt(6-1)
#
# significance = 0.05
# sigtest = stats.ttest_ind(controlMeans, lesionMeans)[1]
# testlabel = str.format("*",sigtest) if sigtest < significance else 'n.s.'
# print sigtest
#
# cX = [1.1, 1.1, 1.1, 1.1, 1.1, 1.1, 1.1]
# lX = [1.9, 1.9, 1.9, 1.9, 1.9, 1.9]
#
# plt.plot([0,3], [0,0], color=[0.25,0.25,0.25,1], linewidth = 1)
# plt.plot(cX, controlMeans, 'bo')
# plt.plot(lX, lesionMeans, 'ro')
# plt.plot(1.9, lesionMeans[5], 'o', color = [1.0, 0.75, 0.75, 1.0])
#
# #plt.bar(1.2, controlMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
# plt.errorbar(1.3, controlMeanAll, controlError, marker='s', mfc='blue', ecolor = 'black', mec='black', ms=1, mew=1, capsize=5, elinewidth=2)
# plt.plot([1.0,1.4], [controlMeanAll,controlMeanAll], color=[0.25,0.25,1.0,1], linewidth = 2)
#
# #plt.bar(1.6, lesionMeanAll, 0.2, color=[1.0,1.0,1.0,0.0])
# plt.errorbar(1.7, lesionMeanAll, lesionlError, marker='s', mfc='red', ecolor = 'black', mec='black', ms=1, mew=1, capsize=5, elinewidth=2)
# plt.plot([1.6,2.0], [lesionMeanAll,lesionMeanAll], color=[1.00,0.25,0.25,1], linewidth = 2)
# pltutils.hbracket(1.5,0.17,4.5,label=testlabel,tickheight=0.01)
#
# ax = plt.gca()
# ax.set_xticks([1.1,1.9])
# ax.set_xticklabels(['controls','lesions'])
# plt.ylabel('normalized speed difference')
#
# pltutils.fix_font_size()
# plt.axis([0.75,2.25,-0.2,0.2])
