averagetimeseries(sb_U,
column='yhead',
color=colors[1],
ax=ax2,
alpha=0.25)
averagetimeseries(ub_S,
column='yhead',
color=colors[2],
ax=ax2,
alpha=0.25)
averagetimeseries(ub_U,
column='yhead',
color=colors[3],
ax=ax2,
alpha=0.25)
proxylegend(colors, [
'stable bias [stable]', 'stable bias [unstable]',
'unstable bias [stable]', 'unstable bias [unstable]'
],
ax=ax2,
loc='upper left')
ax2.set_title('average nose trajectory')
ax2.set_ylabel('nose height (cm)')
ax2.set_ylim(1, 4)
fig.suptitle(
str.format('{0} (n = {1} trials)', name,
len(stablebias) + len(unstablebias)))
plt.show()
# Save plot
ucract = cract.join(unstablebias, how='inner', rsuffix='R')
scract.xhead = flipleftwards(scract.xhead, scract.side)
ucract.xhead = flipleftwards(ucract.xhead, ucract.side)
sb_S = scract.query('stepstate3')
sb_U = scract.query('not stepstate3')
ub_S = ucract.query('stepstate3')
ub_U = ucract.query('not stepstate3')
# Plot data
name = namemap[name]
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
averagetimetrajectory(sb_S, color='b', ax=ax, alpha=0.5)
averagetimetrajectory(sb_U, color='cyan', ax=ax, alpha=0.5)
averagetimetrajectory(ub_S, color='orange', ax=ax, alpha=0.5)
averagetimetrajectory(ub_U, color='r', ax=ax, alpha=0.5)
proxylegend(['b', 'cyan', 'orange', 'r'], [
'stable bias [stable]', 'stable bias [unstable]',
'unstable bias [stable]', 'unstable bias [unstable]'
],
ax=ax,
loc='upper left')
# ax.set_ylim(0,3.5)
ax.set_title(
str.format('{0} (n = {1} trials)', name,
len(stablebias) + len(unstablebias)))
#plt.title(str.format('stable (n = {0} trials)',len(cr.query(stable))))
plt.show()
# Save plot
cr = cr.query(selected).join(info)
cr.protocol[cr.eval(stable)] = 'stable'
cr.protocol[cr.eval(unstable)] = 'unstable'
cr.protocol[cr.eval(restable)] = 'restable'
sm = skipmeasure(cr, column)
# Custom-sort by protocol
protocolorder = {'stable': 0, 'unstable': 1, 'restable': 2}
sm['rank'] = sm['protocol'].map(protocolorder)
sm.sort(columns='rank', inplace=True)
sm.drop(labels='rank', axis=1)
sm = sm.groupby(['subject', 'protocol', 'skip'], sort=True)[column]
ms = [d for g, d in sm]
# Plot data
f, ax = plt.subplots(1, 1)
groupcomparison(ms, ['b', 'b', 'g', 'g', 'r', 'r'] * 100,
['o', 'x', 'o', 'x', 'o', 'x'] * 100,
['b', 'none', 'g', 'none', 'r', 'none'] * 100,
s=10,
ax=ax)
proxylegend(['b', 'g', 'r'], ['stable', 'restable', 'unstable'],
loc='upper left')
ax.set_xticks(np.arange(2.5, len(ms) + 2.5, 6))
ax.set_xticklabels([namemap[name] for name in names(info)[1:]])
plt.ylabel('mean speed (cm/s)')
plt.title('average speed on entry (1st third of assay)')
plt.show()
# Save plot
# Plot data
alpha = 0.25
baseline = slice(0, 28)
xpoints = createspaceaxis()
fig, (ax1, ax2) = plt.subplots(1, 2)
st, sterr = spatialaverage(xpoints,
pd.concat([sb_S, ub_S]),
'xhead_speed',
baseline=baseline)
ut, uterr = spatialaverage(xpoints,
pd.concat([sb_U, ub_U]),
'xhead_speed',
baseline=baseline)
boundedcurve(xpoints, st, sterr, color='b', ax=ax1, alpha=alpha)
boundedcurve(xpoints, ut, uterr, color='r', ax=ax1, alpha=alpha)
proxylegend(['b', 'r'], ['stable', 'unstable'], ax=ax1, loc='upper left')
ax1.set_title('average speed across space')
ax1.set_xlabel('x (cm)')
ax1.set_ylabel('speed (cm/s)')
ax1.set_xlim(-15, 25)
ax1.set_ylim(-20, 30)
sts.append(st)
uts.append(ut)
averagetimeseries(sb_S,
'xhead_speed',
baseline=baseline,
ax=ax2,
color='b',
alpha=alpha)
averagetimeseries(ub_S,
for i in range(21,22):
offact = crossingoffset(cract,steps,i)
offact.set_index(['subject','session'],inplace=True)
normalizeposition(offact,inplace=True)
stablebias,unstablebias = posturebias(offact,n=2)
fig, (ax1,ax2) = plt.subplots(1,2)
colors = ['b','cyan','orange','r']
grouplabels = ['controls','small\nlesions','lesions','matched\ncontrols']
conditions = [offact.query('stepstate3'),
offact.query('not stepstate3')]
conditioncomparison('yhead',
[controls,small,lesions,matched],
conditions,colors=[colors[0],colors[-1]],
ax=ax1)
proxylegend([colors[0],colors[-1]],['stable','unstable'],ax=ax1)
ax1.set_xticklabels(grouplabels)
ax1.set_ylim(-1,1)
conditions = [stablebias.query('stepstate3'),
stablebias.query('not stepstate3'),
unstablebias.query('stepstate3'),
unstablebias.query('not stepstate3')]
conditioncomparison('yhead',
[controls,small,lesions,matched],
conditions,colors=colors,
ax=ax2)
proxylegend(colors,['stable [R]',
'stable [W]',
'unstable [W]',
'unstable [R]'],
ax=ax2)
cr = pd.read_hdf(lesionshamcache,crossings_key)
cr = getballistictrials(cr)
# Select data
info['lesionvolume'] = lesionvolume(info)
cr = cr.query(selected).join(info)
cr.protocol[cr.eval(stable)] = 'stable'
cr.protocol[cr.eval(unstable)] = 'unstable'
cr.protocol[cr.eval(restable)] = 'restable'
# Plot data
sp = skipprobability(cr)
sp['category'] = None
sp.category[sp.eval(jumpers)] = 'jumper'
sp.category[sp.eval(nonjumpers)] = 'nonjumper'
sp.reset_index('subject',inplace=True)
f,ax = plt.subplots(1,1)
barcomparison(sp.query("protocol == 'stable'"),['b','r'],
by='category',column='frequency',ax=ax)
barcomparison(sp.query("protocol == 'unstable'"),['b','r'],
left=3,by='category',column='frequency',ax=ax)
barcomparison(sp.query("protocol == 'restable'"),['b','r'],
left=6,by='category',column='frequency',ax=ax)
proxylegend(['b','r'],['nonjumpers','jumpers'])
ax.set_ylabel('p (skip middle steps)')
ax.set_xticks([0.9,3.9,6.9])
ax.set_xticklabels(['stable','unstable','restable'])
plt.show()
# Save plot
# Select data
cr = cr.query(selected).join(info)
cr.protocol[cr.eval(unstable)] = 'unstable'
sp = skipprobability(cr)
sinfo = info.query('session == 0').reset_index('session')
sp.reset_index('protocol', drop=True, inplace=True)
sp = sp.join(sinfo)
sp.age /= np.timedelta64(1, 'D')
# Plot data
f, ax = plt.subplots(1, 1)
control(sp).plot(x='weight',
y='frequency',
color='b',
kind='scatter',
grid=False,
ax=ax)
anylesion(sp).plot(x='weight',
y='frequency',
color='r',
kind='scatter',
grid=False,
ax=ax)
ax.set_xlabel('weight (g)')
ax.set_ylabel('p (skip middle steps)')
ax.set_title('correlation of jumping with weight')
proxylegend(['b', 'r'], ['control', 'lesion'])
plt.show()
# Save plot
ubtimes['duration'] = (ubtimes.timeend - ubtimes.time) / np.timedelta64(1, 's')
sbtimes['bias'] = 'Z'
ubtimes['bias'] = 'U'
sbtimes.reset_index(inplace=True)
ubtimes.reset_index(inplace=True)
sbtimes.subject = sbtimes.subject.apply(lambda x: namemap[x])
ubtimes.subject = ubtimes.subject.apply(lambda x: namemap[x])
# Plot data
names = sbtimes.subject.unique()
names.sort()
data = pd.concat([sbtimes, ubtimes])
box = data.boxplot('duration',
by=['subject', 'bias'],
grid=False,
patch_artist=True,
return_type='dict')
boxes = box['duration']['boxes']
numboxes = len(boxes)
colors = ['orange' if i % 2 == 0 else 'dodgerblue' for i in range(numboxes)]
for patch, color in zip(boxes, colors):
patch.set_facecolor(color)
plt.ylabel('time (s)')
plt.xticks(np.arange(1.5, len(names) * 2, 2), names)
proxylegend(['orange', 'dodgerblue'], ['unstable bias', 'stable bias'])
plt.title('duration of trial after learning about stable state')
plt.ylim(0, 5)
plt.show()
# Save plot
unstable = '(9 <= session < 11)'
restable = '(11 <= session < 13)'
removed = "subject not in ['JPAK_20']"
selected = str.format("({0} or {1} or {2}) and {3} and trial > 0", stable,
unstable, restable, removed)
nonjumpers = str.format("subject not in {0}", jumpers)
jumpers = str.format("subject in {0}", jumpers)
cr = pd.read_hdf(lesionshamcache, crossings_key)
cr = getballistictrials(cr).query(selected)
# Plot data
f, (sx, ux, rx) = plt.subplots(1, 3)
cract = pd.read_hdf(lesionshamcache, crossingactivity_stable_key)
averagetrajectory(cract, cr.query(stable).query(nonjumpers), color='b', ax=sx)
averagetrajectory(cract, cr.query(stable).query(jumpers), color='r', ax=sx)
proxylegend(['b', 'r'], ['nonjumper', 'jumper'], ax=sx)
sx.set_title(str.format('stable (n = {0} trials)', len(cr.query(stable))))
sx.set_ylabel('y (cm)')
sx.set_ylim(0, 6)
cract = pd.read_hdf(lesionshamcache, crossingactivity_unstable_key)
averagetrajectory(cract,
cr.query(unstable).query(nonjumpers),
color='b',
ax=ux)
averagetrajectory(cract, cr.query(unstable).query(jumpers), color='r', ax=ux)
ux.set_title(str.format('unstable (n = {0} trials)', len(cr.query(unstable))))
ux.set_ylabel('y (cm)')
ux.set_ylim(0, 6)
cract = pd.read_hdf(lesionshamcache, crossingactivity_restable_key)
stable = '(3 <= session < 5)'
unstable = '(9 <= session < 11)'
info = pd.read_hdf(lesionshamcache, info_key)
lesionmap = lesionordermap(info)
cr = pd.read_hdf(lesionshamcache, crossings_key).query('trial > 0')
scract = pd.read_hdf(lesionshamcache, crossingactivity_stable_key)
ucract = pd.read_hdf(lesionshamcache, crossingactivity_unstable_key)
cract = pd.concat([scract, ucract])
cract.reset_index(['subject', 'session', 'crossing'], inplace=True)
# Plot data
def plotaverage(subject, ax):
cr1 = cr.query(str.format("subject == '{0}' and {1}", subject, unstable))
cr2 = cr.query(str.format("subject == '{0}' and {1}", subject, stable))
averageposturecomparison(cract, info, cr1, cr2, ax=ax)
ax.set_title(lesionmap[subject])
f, (ex1, ex2) = plt.subplots(1, 2)
plotaverage('JPAK_21', ex1)
plotaverage('JPAK_24', ex2)
proxylegend(['g', 'r'], ['stable', 'unstable'],
ax=ex1,
loc='lower left',
bbox_to_anchor=(0.8, 1))
plt.tight_layout()
plt.show()
# Save plot
uts.append(ut)
# Plot data
if namemap[selected] == 'Ca':
ax = ax1
elif namemap[selected] == 'Lb':
ax = ax2
else:
ax = None
if ax is not None:
alpha = 0.25
boundedcurve(xpoints, st, sterr, color='b', ax=ax, alpha=alpha)
boundedcurve(xpoints, ut, uterr, color='r', ax=ax, alpha=alpha)
proxylegend(['b', 'r'], ['stable', 'unstable'],
ax=ax,
loc='upper left')
ax.set_title('average speed across space')
ax.set_xlabel('x (cm)')
ax.set_ylabel('speed (cm/s)')
ax.set_xlim(-15, 25)
ax.set_ylim(-20, 30)
# Create data summary
names = [namemap[name] for name in group]
speedup = pd.concat([
pd.DataFrame.from_items([('speedup', ut - st), ('subject', name)])
for st, ut, name in zip(sts, uts, names)
])
speedup = speedup.groupby('subject').sum()
names = speedup.index.to_series()
# Select data
bias = 2
group = list(names(info))
random = '(session == 13 and trial > 20) or (14 <= session < 17)'
cr = cr.query(random)
for name in group:
selection = str.format("subject in {0}", [name])
try:
scr = cr.query(selection)
stablebias, unstablebias = posturebias(scr, n=bias)
stablebias = getballistictrials(stablebias)
unstablebias = getballistictrials(unstablebias)
except Exception:
continue
# Plot data
fig = plt.figure()
ax = fig.gca()
ss = stablebias.query('not stepstate3')
us = unstablebias.query('not stepstate3')
averagetrajectory(cract, ss, color='b', ax=ax)
averagetrajectory(cract, us, color='r', ax=ax)
ax.set_title(str.format('{0} (n = {1} trials)', name, len(ss) + len(us)))
proxylegend(['b', 'r'], ['stable bias', 'unstable bias'], ax=ax)
ax.set_ylabel('y (cm)')
ax.set_ylim(0, 6)
plt.show()
# Save plot
