def figure1j(info, path):
if not os.path.exists(path):
os.makedirs(path)
fig = plt.figure()
sessions = info.groupby(level=['subject'])
xticks = []
xticklabels = []
barplotwidth = 4
for i, (subject, sinfo) in enumerate(sessions):
subjectpath = os.path.join(activitymovies.datafolder, subject)
days = sinfo.index.levels[1][sinfo.index.labels[1]]
act = activitytables.read_subjects(subjectpath, days=days)
cr = activitytables.read_subjects(subjectpath,
days=days,
selector=activitytables.crossings)
offset = i * barplotwidth
stact = act[act.stepstate3]
stcr = cr[cr.stepstate3]
stfeatures = activitytables.stepfeature(stact, stcr, 4, 3)
stskipfrequency = 1 - float(len(stfeatures)) / len(stcr)
uact = act[~act.stepstate3]
ucr = cr[~cr.stepstate3]
ufeatures = activitytables.stepfeature(uact, ucr, 4, 3)
uskipfrequency = 1 - float(len(ufeatures)) / len(ucr)
plt.bar((-1 + offset, offset), (stskipfrequency, uskipfrequency),
color=['b', 'r'],
width=1,
hold=True)
xticks.append(offset)
xticklabels.append(subject.replace('JPAK_', ''))
p1 = plt.Rectangle((0, 0), 1, 1, fc="b")
p2 = plt.Rectangle((0, 0), 1, 1, fc="r")
plt.legend([p1, p2], ['stable', 'unstable'],
bbox_to_anchor=(0., 1.02, 1., .102),
loc=3,
ncol=2,
mode="expand",
borderaxespad=0.)
plt.xlabel('subject (JPAK)')
plt.ylabel('p(skipping)')
plt.xticks(xticks, xticklabels)
plt.xlim(-2, len(sessions) * barplotwidth - 1)
#plt.title(str.format('4th step contact frequency',subject))
fname = str.format("all_animals_4thstep_frequency.png", subject)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
def figure1l(info,path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject'])
for subject,sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder,subject)
days = sinfo.index.levels[1][sinfo.index.labels[1]]
act = activitytables.read_subjects(subjectpath,days=days)
cr = activitytables.read_subjects(subjectpath,days=days,
selector=activitytables.crossings)
axScatter = plt.subplot2grid((3,3),(1,0),rowspan=2,colspan=2)
axHistx = plt.subplot2grid((3,3),(0,0),colspan=2)
axHisty = plt.subplot2grid((3,3),(1,2),rowspan=2)
axes = (axScatter,axHistx,axHisty)
stepoffset = stepcenter_cm[3][1]
bins = 50
xlim = (20-stepoffset,30-stepoffset)
ylim = (-1,9)
alpha = 0.75
stact = act[act.stepstate3]
stcr = cr[cr.stepstate3]
stfeatures = activitytables.stepfeature(stact,stcr,4,3)
leftwards = stfeatures.side == 'leftwards'
stfeatures.xhead[leftwards] = max_width_cm - stfeatures.xhead[leftwards]
stfeatures.xhead -= stepoffset
activityplots.scatterhist(stfeatures.xhead,stfeatures.yhead,color='b',
bins=bins,axes=axes,xlim=xlim,ylim=ylim,
histalpha=alpha)
uact = act[~act.stepstate3]
ucr = cr[~cr.stepstate3]
ufeatures = activitytables.stepfeature(uact,ucr,4,3)
leftwards = ufeatures.side == 'leftwards'
ufeatures.xhead[leftwards] = max_width_cm - ufeatures.xhead[leftwards]
ufeatures.xhead -= stepoffset
activityplots.scatterhist(ufeatures.xhead,ufeatures.yhead,color='r',
bins=bins,axes=axes,xlim=xlim,ylim=ylim,
histalpha=alpha)
axScatter.set_xlabel('x (cm)')
axScatter.set_ylabel('y (cm)')
axScatter.legend(['stable', 'unstable'],loc=2)
axHistx.set_title(str.format('{0} nose position on contact',subject))
fname = str.format("{0}_nose_stable_unstable.png",subject)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def figure1k1(info,path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject','session'])
for (subject,session),sinfo in sessions:
subjectpath = os.path.join(activitymovies.datafolder,subject)
sact = activitytables.read_subjects(subjectpath,days=[session])
scr = activitytables.read_subjects(subjectpath,days=[session],
selector = activitytables.crossings)
features = activitytables.stepfeature(sact,scr,4,3)
leftwards = features.side == 'leftwards'
features.xhead[leftwards] = max_width_cm - features.xhead[leftwards]
median = np.sort(features.xhead)[len(features)/2+1]
median = features[features.xhead == median].iloc[-1,:]
vidpaths = activitymovies.getmoviepath(sinfo)
timepaths = activitymovies.gettimepath(info)
movie = [video.video(mpath,mtimepath)
for mpath,mtimepath in zip(vidpaths,timepaths)][0]
medianframe = movie.frame(sact.index.get_loc(median.name))
flip = median.side == 'rightwards'
stepindex = 3 if flip else 4
medianframe = activitytables.cropstep(medianframe,stepindex,flip=flip)
fname = str.format("{0}_session_{1}_median_step_posture.png",
subject, session)
fpath = os.path.join(path,subject)
if not os.path.exists(fpath):
os.makedirs(fpath)
fpath = os.path.join(fpath,fname)
cv2.imwrite(fpath,medianframe)
def figure1j(info,path):
if not os.path.exists(path):
os.makedirs(path)
fig = plt.figure()
sessions = info.groupby(level=['subject'])
xticks = []
xticklabels = []
barplotwidth = 4
for i,(subject,sinfo) in enumerate(sessions):
subjectpath = os.path.join(activitymovies.datafolder,subject)
days = sinfo.index.levels[1][sinfo.index.labels[1]]
act = activitytables.read_subjects(subjectpath,days=days)
cr = activitytables.read_subjects(subjectpath,days=days,
selector=activitytables.crossings)
offset = i * barplotwidth
stact = act[act.stepstate3]
stcr = cr[cr.stepstate3]
stfeatures = activitytables.stepfeature(stact,stcr,4,3)
stskipfrequency = 1 - float(len(stfeatures)) / len(stcr)
uact = act[~act.stepstate3]
ucr = cr[~cr.stepstate3]
ufeatures = activitytables.stepfeature(uact,ucr,4,3)
uskipfrequency = 1 - float(len(ufeatures)) / len(ucr)
plt.bar((-1 + offset,offset),(stskipfrequency,uskipfrequency),
color=['b','r'],width=1,hold=True)
xticks.append(offset)
xticklabels.append(subject.replace('JPAK_',''))
p1 = plt.Rectangle((0, 0), 1, 1, fc="b")
p2 = plt.Rectangle((0, 0), 1, 1, fc="r")
plt.legend([p1,p2],['stable', 'unstable'],
bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
ncol=2, mode="expand", borderaxespad=0.)
plt.xlabel('subject (JPAK)')
plt.ylabel('p(skipping)')
plt.xticks(xticks,xticklabels)
plt.xlim(-2,len(sessions) * barplotwidth - 1)
#plt.title(str.format('4th step contact frequency',subject))
fname = str.format("all_animals_4thstep_frequency.png",subject)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def __getclips__(act,cr,info,rowidx=slice(None)):
features = []
moviepath = activitymovies.getmoviepath(info)
for (subject,session),group in cr.groupby(level=['subject','session']):
steps = activitytables.stepfeature(act,group,4,3).iloc[rowidx,:]
steps['subject'] = subject
steps['session'] = session
steps.reset_index(inplace=True)
steps.set_index(['subject','session'],inplace=True)
features.append(steps[['frame','side']].join(moviepath))
return pd.concat(features)
def __getclips__(act, cr, info, rowidx=slice(None)):
features = []
moviepath = activitymovies.getmoviepath(info)
for (subject, session), group in cr.groupby(level=['subject', 'session']):
steps = activitytables.stepfeature(act, group, 4, 3).iloc[rowidx, :]
steps['subject'] = subject
steps['session'] = session
steps.reset_index(inplace=True)
steps.set_index(['subject', 'session'], inplace=True)
features.append(steps[['frame', 'side']].join(moviepath))
return pd.concat(features)
def figure2e(act, cr, rr, info, path):
if not os.path.exists(path):
os.makedirs(path)
features = []
cr = cr[cr.stepstate3 == False]
for (subject, session), group in cr.groupby(level=['subject', 'session']):
#step = activitytables.stepframeindices(act,group,4,3)
step = activitytables.stepfeature(act, group, 4, 3).iloc[0:1, :]
step['subject'] = subject
step['session'] = session
step.reset_index(inplace=True)
step.set_index(['subject', 'session'], inplace=True)
steprr = step.join(rr, rsuffix='reward')
timetoreward = steprr.timereward - steprr.time
timetoreward = timetoreward[timetoreward > 0]
features.append((subject, timetoreward.min()))
fig = plt.figure()
n = [(m[1].values[0] /
np.timedelta64(1, 's') if m[1] is not np.nan else np.nan, m[0], 5)
for m in features]
data = pd.DataFrame(n, columns=['timetoreward', 'subject', 'session'])
data.set_index(['subject', 'session'], inplace=True)
result = activitytables.groupbylesionvolumes(data, info)
ncontrols = sum((1 for m in result.index.labels[1] if m == 0))
nlesions = sum((1 for m in result.index.labels[1] if m == 1))
clabels = ['C' + chr(ord('a') + i) for i in range(ncontrols)]
llabels = ['L' + chr(ord('a') + i) for i in range(nlesions)]
plt.bar(range(len(result)),
result.timetoreward,
color=['b'] * 11 + ['r'] * 11)
plt.xlim(-1, len(result) + 1)
plt.xticks(np.arange(len(result)) + 0.4, clabels + llabels)
plt.xlabel('subject')
plt.ylabel('time to reward (s)')
plt.title('time to first reward after manipulation')
p1 = plt.Rectangle((0, 0), 1, 1, fc="b")
p2 = plt.Rectangle((0, 0), 1, 1, fc="r")
plt.legend([p1, p2], ['control', 'lesion'])
fname = str.format("manipulation_first_reward_time.png", subject)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
def figure2e(act,cr,rr,info,path):
if not os.path.exists(path):
os.makedirs(path)
features = []
cr = cr[cr.stepstate3 == False]
for (subject,session),group in cr.groupby(level=['subject','session']):
#step = activitytables.stepframeindices(act,group,4,3)
step = activitytables.stepfeature(act,group,4,3).iloc[0:1,:]
step['subject'] = subject
step['session'] = session
step.reset_index(inplace=True)
step.set_index(['subject','session'],inplace=True)
steprr = step.join(rr,rsuffix='reward')
timetoreward = steprr.timereward - steprr.time
timetoreward = timetoreward[timetoreward > 0]
features.append((subject,timetoreward.min()))
fig = plt.figure()
n = [(m[1].values[0]/np.timedelta64(1,'s') if m[1] is not np.nan else np.nan,
m[0],5) for m in features]
data = pd.DataFrame(n,columns=['timetoreward','subject','session'])
data.set_index(['subject','session'],inplace=True)
result = activitytables.groupbylesionvolumes(data,info)
ncontrols = sum((1 for m in result.index.labels[1] if m == 0))
nlesions = sum((1 for m in result.index.labels[1] if m == 1))
clabels = ['C' + chr(ord('a') + i) for i in range(ncontrols)]
llabels = ['L' + chr(ord('a') + i) for i in range(nlesions)]
plt.bar(range(len(result)),result.timetoreward,color=['b']*11 + ['r']*11)
plt.xlim(-1,len(result) + 1)
plt.xticks(np.arange(len(result)) + 0.4,clabels+llabels)
plt.xlabel('subject')
plt.ylabel('time to reward (s)')
plt.title('time to first reward after manipulation')
p1 = plt.Rectangle((0, 0), 1, 1, fc="b")
p2 = plt.Rectangle((0, 0), 1, 1, fc="r")
plt.legend([p1,p2],['control', 'lesion'])
fname = str.format("manipulation_first_reward_time.png",subject)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def figure1k1(info, path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject', 'session'])
for (subject, session), sinfo in sessions:
subjectpath = os.path.join(activitymovies.datafolder, subject)
sact = activitytables.read_subjects(subjectpath, days=[session])
scr = activitytables.read_subjects(subjectpath,
days=[session],
selector=activitytables.crossings)
features = activitytables.stepfeature(sact, scr, 4, 3)
leftwards = features.side == 'leftwards'
features.xhead[leftwards] = max_width_cm - features.xhead[leftwards]
median = np.sort(features.xhead)[len(features) / 2 + 1]
median = features[features.xhead == median].iloc[-1, :]
vidpaths = activitymovies.getmoviepath(sinfo)
timepaths = activitymovies.gettimepath(info)
movie = [
video.video(mpath, mtimepath)
for mpath, mtimepath in zip(vidpaths, timepaths)
][0]
medianframe = movie.frame(sact.index.get_loc(median.name))
flip = median.side == 'rightwards'
stepindex = 3 if flip else 4
medianframe = activitytables.cropstep(medianframe,
stepindex,
flip=flip)
fname = str.format("{0}_session_{1}_median_step_posture.png", subject,
session)
fpath = os.path.join(path, subject)
if not os.path.exists(fpath):
os.makedirs(fpath)
fpath = os.path.join(fpath, fname)
cv2.imwrite(fpath, medianframe)
def figure1l(info, path):
if not os.path.exists(path):
os.makedirs(path)
sessions = info.groupby(level=['subject'])
for subject, sinfo in sessions:
fig = plt.figure()
subjectpath = os.path.join(activitymovies.datafolder, subject)
days = sinfo.index.levels[1][sinfo.index.labels[1]]
act = activitytables.read_subjects(subjectpath, days=days)
cr = activitytables.read_subjects(subjectpath,
days=days,
selector=activitytables.crossings)
axScatter = plt.subplot2grid((3, 3), (1, 0), rowspan=2, colspan=2)
axHistx = plt.subplot2grid((3, 3), (0, 0), colspan=2)
axHisty = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
axes = (axScatter, axHistx, axHisty)
stepoffset = stepcenter_cm[3][1]
bins = 50
xlim = (20 - stepoffset, 30 - stepoffset)
ylim = (-1, 9)
alpha = 0.75
stact = act[act.stepstate3]
stcr = cr[cr.stepstate3]
stfeatures = activitytables.stepfeature(stact, stcr, 4, 3)
leftwards = stfeatures.side == 'leftwards'
stfeatures.xhead[
leftwards] = max_width_cm - stfeatures.xhead[leftwards]
stfeatures.xhead -= stepoffset
activityplots.scatterhist(stfeatures.xhead,
stfeatures.yhead,
color='b',
bins=bins,
axes=axes,
xlim=xlim,
ylim=ylim,
histalpha=alpha)
uact = act[~act.stepstate3]
ucr = cr[~cr.stepstate3]
ufeatures = activitytables.stepfeature(uact, ucr, 4, 3)
leftwards = ufeatures.side == 'leftwards'
ufeatures.xhead[leftwards] = max_width_cm - ufeatures.xhead[leftwards]
ufeatures.xhead -= stepoffset
activityplots.scatterhist(ufeatures.xhead,
ufeatures.yhead,
color='r',
bins=bins,
axes=axes,
xlim=xlim,
ylim=ylim,
histalpha=alpha)
axScatter.set_xlabel('x (cm)')
axScatter.set_ylabel('y (cm)')
axScatter.legend(['stable', 'unstable'], loc=2)
axHistx.set_title(str.format('{0} nose position on contact', subject))
fname = str.format("{0}_nose_stable_unstable.png", subject)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
