r_objSel=[4, 5, 6]
l_earlyVent=[14, 15, 16]
l_earlyDors=[19, 20, 21]
l_parietal=[ 22, 23, 24]
l_objSel=[ 17, 18]
print 'Early Ventral rois: '+ str(roiNames[r_earlyVent]) + str(roiNames[l_earlyVent])
print 'Early Dorsal rois: ' + str(roiNames[r_earlyDors])+ str(roiNames[l_earlyDors])
print 'Parietal rois: '+ str(roiNames[r_parietal]) +str(roiNames[l_parietal])
print 'Object rois: '+ str(roiNames[r_objSel])+ str(roiNames[l_objSel])
networkAvg= corrAll_t# Correlation (corrAll_t) or coherence (coherAll_t)
analType='Correlation'
# Get network averages
r_earlyVentCoher=getNetworkWithin(networkAvg, r_earlyVent)
r_earlyDorsCoher=getNetworkWithin(networkAvg, r_earlyDors)
r_parietalCoher=getNetworkWithin(networkAvg, r_parietal)
r_objSelCoher=getNetworkWithin(networkAvg, r_objSel)
l_earlyVentCoher=getNetworkWithin(networkAvg, l_earlyVent)
l_earlyDorsCoher=getNetworkWithin(networkAvg, l_earlyDors)
l_parietalCoher=getNetworkWithin(networkAvg, l_parietal)
l_objSelCoher=getNetworkWithin(networkAvg, l_objSel)
#Average over last two dimensions....
r_earlyVentCoher_mean=stats.nanmean(r_earlyVentCoher.reshape([numRuns,len(r_earlyVent)*len(r_earlyVent)]), axis=1)
r_earlyDorsCoher_mean=stats.nanmean(r_earlyDorsCoher.reshape([numRuns,len(r_earlyDors)*len(r_earlyDors)]), axis=1)
r_parietalCoher_mean=stats.nanmean(r_parietalCoher.reshape([numRuns,len(r_parietal)*len(r_parietal)]), axis=1)
r_objSelCoher_mean=stats.nanmean(r_objSelCoher.reshape([numRuns,len(r_objSel)*len(r_objSel)]), axis=1)
l_earlyVentCoher_mean=stats.nanmean(l_earlyVentCoher.reshape([numRuns,len(l_earlyVent)*len(l_earlyVent)]), axis=1)
l_earlyDorsCoher_mean=stats.nanmean(l_earlyDorsCoher.reshape([numRuns,len(l_earlyDors)*len(l_earlyDors)]), axis=1)
#dorsal=[ 'L_V3A_0.25', 'L_MT_al_.5_0.25', 'L_IPS0_0.25', 'L_IPS1_0.25', 'L_IPS2_0.25', 'L_IPS3_0.25', 'L_IPS4_0.25', 'L_IPS5_0.25']
#dorsal=['R_V3A_0.25', 'R_MT_al_.5_0.25', 'R_IPS0_0.25', 'R_IPS1_0.25', 'R_IPS2_0.25', 'R_IPS3_0.25', 'R_IPS4_0.25', 'R_IPS5_0.25',
# 'L_V3A_0.25', 'L_MT_al_.5_0.25', 'L_IPS0_0.25', 'L_IPS1_0.25', 'L_IPS2_0.25', 'L_IPS3_0.25', 'L_IPS4_0.25', 'L_IPS5_0.25']
ventralIndx=np.where(np.in1d(roiNames, ventral))[0]
dorsalIndx=np.where(np.in1d(roiNames, dorsal))[0]
print 'Ventral rois: '+ str(roiNames[ventralIndx])
print 'Dorsal rois: ' + str(roiNames[dorsalIndx])
# Do Network analysis
networkAvg= corrAll_t # Correlation (corrAll_t) or coherence (coherAll_t)
analType='Correlation'
# Get network averages
ventCoher=getNetworkWithin(networkAvg, ventralIndx)
dorsCoher=getNetworkWithin(networkAvg, dorsalIndx)
#Average over last two dimensions....
ventCoher_mean=stats.nanmean(ventCoher.reshape([numRuns,len(ventralIndx)*len(ventralIndx)]), axis=1)
dorsCoher_mean=stats.nanmean(dorsCoher.reshape([numRuns,len(dorsalIndx)*len(dorsalIndx)]), axis=1)
#Correlation means and STDs across all RUNs correlation/coherence values.
allMeansWithin= (stats.nanmean(ventCoher_mean), stats.nanmean(dorsCoher_mean))
allSTDWithin=(stats.nanstd(ventCoher_mean), stats.nanstd(dorsCoher_mean))
######
# Get network btw
#Early Visual
EVbtwAllavg, EVbtwAllstd=getNetworkMeansBtw(networkAvg, ventralIndx, [dorsalIndx], numRuns)
ventralLHIndx=np.where(np.in1d(roiNames, ventralLH))[0]
dorsalRHIndx=np.where(np.in1d(roiNames, dorsalRH))[0]
dorsalLHIndx=np.where(np.in1d(roiNames, dorsalLH))[0]
print 'Ventral RH rois: '+ str(roiNames[ventralRHIndx])
print 'Ventral LH rois: '+ str(roiNames[ventralLHIndx])
print 'Dorsal RH rois: ' + str(roiNames[dorsalRHIndx])
print 'Dorsal LH rois: ' + str(roiNames[dorsalLHIndx])
# Do Network analysis
networkAvg= corrAll_t # Correlation (corrAll_t) or coherence (coherAll_t)
allNetworkAvg[condition[ii]]=networkAvg
analType='Correlation'
# Get network averages
ventCoherRH=getNetworkWithin(networkAvg, ventralRHIndx)
ventCoherLH=getNetworkWithin(networkAvg, ventralLHIndx)
dorsCoherRH=getNetworkWithin(networkAvg, dorsalRHIndx)
dorsCoherLH=getNetworkWithin(networkAvg, dorsalLHIndx)
#Average over last two dimensions....
ventCoherRH_mean=stats.nanmean(ventCoherRH.reshape([numRuns,len(ventralRHIndx)*len(ventralRHIndx)]), axis=1)
dorsCoherRH_mean=stats.nanmean(dorsCoherRH.reshape([numRuns,len(dorsalRHIndx)*len(dorsalRHIndx)]), axis=1)
ventCoherLH_mean=stats.nanmean(ventCoherLH.reshape([numRuns,len(ventralLHIndx)*len(ventralLHIndx)]), axis=1)
dorsCoherLH_mean=stats.nanmean(dorsCoherLH.reshape([numRuns,len(dorsalLHIndx)*len(dorsalLHIndx)]), axis=1)
#Correlation means and STDs across all RUNs correlation/coherence values.
allMeansWithin= (stats.nanmean(ventCoherRH_mean), stats.nanmean(ventCoherLH_mean), stats.nanmean(dorsCoherRH_mean), stats.nanmean(dorsCoherLH_mean))
allSTDWithin=(stats.nanstd(ventCoherRH_mean), stats.nanstd(ventCoherLH_mean), stats.nanstd(dorsCoherRH_mean), stats.nanstd(dorsCoherLH_mean))
######
#Define the streams
earlyVent=[1, 2, 3, 14, 15, 16]
earlyDors=[7, 8, 9, 19, 20, 21]
parietal=[10, 11, 12, 22, 23, 24]
objSel=[4, 5, 6, 17, 18]
print 'Early Ventral rois: '+ str(roiNames[earlyVent])
print 'Early Dorsal rois: ' + str(roiNames[earlyDors])
print 'Parietal rois: '+ str(roiNames[parietal])
print 'Object rois: '+ str(roiNames[objSel])
networkAvg= coherAll_t# Correlation (corrAll_t) or coherence (coherAll_t)
analType='Coherence'
# Get network averages
earlyVentCoher=getNetworkWithin(networkAvg, earlyVent)
earlyDorsCoher=getNetworkWithin(networkAvg, earlyDors)
parietalCoher=getNetworkWithin(networkAvg, parietal)
objSelCoher=getNetworkWithin(networkAvg, objSel)
#Average over last two dimensions....
earlyVentCoher_mean=stats.nanmean(earlyVentCoher.reshape([numRuns,len(earlyVent)*len(earlyVent)]), axis=1)
earlyDorsCoher_mean=stats.nanmean(earlyDorsCoher.reshape([numRuns,len(earlyDors)*len(earlyDors)]), axis=1)
parietalCoher_mean=stats.nanmean(parietalCoher.reshape([numRuns,len(parietal)*len(parietal)]), axis=1)
objSelCoher_mean=stats.nanmean(objSelCoher.reshape([numRuns,len(objSel)*len(objSel)]), axis=1)
#Correlation means and STDs across all RUNs correlation/coherence values.
allMeansWithin= (stats.nanmean(earlyVentCoher_mean), stats.nanmean(earlyDorsCoher_mean), stats.nanmean(parietalCoher_mean), stats.nanmean(objSelCoher_mean))
allSTDWithin=(stats.nanstd(earlyVentCoher_mean), stats.nanstd(earlyDorsCoher_mean), stats.nanstd(parietalCoher_mean), stats.nanstd(objSelCoher_mean))
