def __poolfeature__(feature,ylabel,title,fname,cr,info,path):
if not os.path.exists(path):
os.makedirs(path)
cut = getballistictrials(cr)
m = cut.groupby(level=['subject','session'])[feature].mean()
e = cut.groupby(level=['subject','session'])[feature].std()
d = activitytables.groupbylesionvolumes(pd.concat([m,e],axis=1),info)
fig = plt.figure()
ax = plt.gca()
activityplots.sessionmetric(d,ax=ax,colorcycle=colorcycle)
plt.ylabel(ylabel)
plt.title(title)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def __poolfeature__(feature, ylabel, title, fname, cr, info, path):
if not os.path.exists(path):
os.makedirs(path)
cut = getballistictrials(cr)
m = cut.groupby(level=['subject', 'session'])[feature].mean()
e = cut.groupby(level=['subject', 'session'])[feature].std()
d = activitytables.groupbylesionvolumes(pd.concat([m, e], axis=1), info)
fig = plt.figure()
ax = plt.gca()
activityplots.sessionmetric(d, ax=ax, colorcycle=colorcycle)
plt.ylabel(ylabel)
plt.title(title)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
def __poolfeatureconditions__(feature,conditions,ylabel,title,fname,cr,info,path):
if not os.path.exists(path):
os.makedirs(path)
labels = range(len(conditions))
cr = resetsessionindex(cr,labels,labels[-1])
info = resetsessionindex(info,labels,labels[-1])
cut = getballistictrials(cr)
m = cut.groupby(level=['subject','session'])[feature].mean()
e = cut.groupby(level=['subject','session'])[feature].std()
d = activitytables.groupbylesionvolumes(pd.concat([m,e],axis=1),info)
fig = plt.figure()
ax = plt.gca()
activityplots.sessionmetric(d,ax=ax,colorcycle=colorcycle,connect=False)
plt.xlabel('')
plt.xticks(labels,conditions)
plt.ylabel(ylabel)
plt.title(title)
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def __poolfeatureconditions__(feature, conditions, ylabel, title, fname, cr,
info, path):
if not os.path.exists(path):
os.makedirs(path)
labels = range(len(conditions))
cr = resetsessionindex(cr, labels, labels[-1])
info = resetsessionindex(info, labels, labels[-1])
cut = getballistictrials(cr)
m = cut.groupby(level=['subject', 'session'])[feature].mean()
e = cut.groupby(level=['subject', 'session'])[feature].std()
d = activitytables.groupbylesionvolumes(pd.concat([m, e], axis=1), info)
fig = plt.figure()
ax = plt.gca()
activityplots.sessionmetric(d, ax=ax, colorcycle=colorcycle, connect=False)
plt.xlabel('')
plt.xticks(labels, conditions)
plt.ylabel(ylabel)
plt.title(title)
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
def figure1b(rr,info,path):
if not os.path.exists(path):
os.makedirs(path)
rr = rr.query('session > 0')
info = info.query('session > 0')
rrdiff = rr.groupby(level=[0,1]).diff()
nulldiff = rrdiff.time.isnull()
firstrr = rr.time[nulldiff] - info.starttime
rrdiff.time[nulldiff] = firstrr
rrsec = rrdiff.time.map(lambda x:x / np.timedelta64(1, 's'))
rrdata = rrsec.groupby(level=[0,1]).mean()
rryerr = rrsec.groupby(level=[0,1]).std()
rrgdata = activitytables.groupbylesionvolumes(pd.concat([rrdata,rryerr],axis=1),info)
fig = plt.figure()
activityplots.sessionmetric(rrgdata)
plt.ylabel('time between rewards (s)')
plt.title('performance curve')
fname = 'performance_curve.png'
fpath = os.path.join(path,fname)
plt.savefig(fpath)
plt.close(fig)
def figure1b(rr, info, path):
if not os.path.exists(path):
os.makedirs(path)
rr = rr.query('session > 0')
info = info.query('session > 0')
rrdiff = rr.groupby(level=[0, 1]).diff()
nulldiff = rrdiff.time.isnull()
firstrr = rr.time[nulldiff] - info.starttime
rrdiff.time[nulldiff] = firstrr
rrsec = rrdiff.time.map(lambda x: x / np.timedelta64(1, 's'))
rrdata = rrsec.groupby(level=[0, 1]).mean()
rryerr = rrsec.groupby(level=[0, 1]).std()
rrgdata = activitytables.groupbylesionvolumes(
pd.concat([rrdata, rryerr], axis=1), info)
fig = plt.figure()
activityplots.sessionmetric(rrgdata)
plt.ylabel('time between rewards (s)')
plt.title('performance curve')
fname = 'performance_curve.png'
fpath = os.path.join(path, fname)
plt.savefig(fpath)
plt.close(fig)
