'useRank': 0, # use the rank transformed version od neural data
}
###############################################
#
# run PCA and store results
#
##############################################
#%%
print 'running PCA without rank'
for kindex, key in enumerate(keyList):
resultDict[key]['PCA'] = dr.runPCANormal(dataSets[key], pars)
# overview of PCA results and weights
mp.plotPCAresults(dataSets, resultDict, keyList, pars, flag='PCA')
# correlate PCA with all sorts of stuff
mp.plotPCAcorrelates(dataSets, resultDict, keyList, pars, flag='PCA')
plt.show()
# plot 3D trajectory of PCA
#
#mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col = 'phase')
#plt.show()
mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col='velocity')
mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col='turns')
#plt.show()
# save the most prominent 3 neurons for each PCA axis
pars['useRank'] = 1
print 'running PCA with rank'
#plt.show()
labelsPCA = ['GCamp6s', 'GFP', 'GCamp6s immobilized']
colorsPCA = ['#007398', 'k', '#24c8ff']
res, keys = [resultDict, resultDictCtrl,
resultDictimm], [keyList, keyListCtrl, keyListimm]
mp.averageResultsPCA(res, keys, labelsPCA, colorsPCA, fitmethod="PCA")
plt.show()
###############################################
#
# use svm to predict discrete behaviors
#
##############################################
if svm:
# overview of SVM results and weights
mp.plotPCAresults(dataSets, resultDict, keyList, pars, flag='SVM')
plt.show()
# plot 3D trajectory of SVM
mp.plotPCAresults3D(dataSets,
resultDict,
keyList,
pars,
col='etho',
flag='SVM')
plt.show()
# mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col = 'time', flag = 'SVM')
# plt.show()
# mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col = 'velocity', flag = 'SVM')
# plt.show()
# mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col = 'turns', flag = 'SVM')
# plt.show()
###############################################
#
# run PCA and store results
#
##############################################
#%%
if pca:
print 'running PCA'
for kindex, key in enumerate(keyList):
resultDict[key]['PCA'] = dr.runPCANormal(dataSets[key],
pars,
whichPC=0)
# overview of data ordered by PCA
mp.plotDataOverview2(dataSets, keyList, resultDict)
# overview of PCA results and weights
mp.plotPCAresults(dataSets, resultDict, keyList, pars)
plt.show()
mp.plotPCAresults3D(dataSets, resultDict, keyList, pars, col='time')
plt.show()
# color by before and after
colorBy = np.zeros(dataSets[key]['Neurons']['Activity'].shape[1])
colorBy[:int(dataSets[key]['Neurons']['Activity'].shape[1] / 2.)] = 1
mp.plotPCAresults3D(dataSets,
resultDict,
keyList,
pars,
col='Immobilization',
colorBy=colorBy)
plt.show()
plt.show(block=True)
print 'running periodogram(s)'
for kindex, key in enumerate(keyList):
resultDict[key]['Period'] = dr.runPeriodogram(dataSets[key],
pars,
testset=None)
###############################################
#
# use behavior triggered averaging to create non-othogonal axes
#
##############################################
if bta:
for kindex, key in enumerate(keyList):
print 'running BTA'
resultDict[key]['BTA'] = dr.runBehaviorTriggeredAverage(
dataSets[key], pars)
mp.plotPCAresults(dataSets, resultDict, keyList, pars, flag='BTA')
plt.show()
mp.plotPCAresults3D(dataSets,
resultDict,
keyList,
pars,
col='etho',
flag='BTA')
plt.show()
###############################################
#
# use svm to predict discrete behaviors
#
##############################################
if svm:
for kindex, key in enumerate(keyList):