eigRes = np.transpose(eigRes)
# eigRes contain responses of 'Eigen neurons' - Neuron response in eigen space
eigCellData = np.zeros((numEigNeurons, stimuliSeq.size, 121))
eigSpikeRate = np.zeros((numEigNeurons, stimuliSeq.size, 2))
eigCirvar = np.zeros((numEigNeurons), dtype=np.complex64)
eigDircirvar = np.zeros((numEigNeurons), dtype=np.complex64)
for i in xrange(numEigNeurons):
for j in xrange(stimuliSeq.size):
theta = stimuliSeq[j]
eigCellData[i, j] = np.append(eigRes[i, 120*j: 120*(j+1)], np.radians(stimuliSeq[j]) )
# Calculate spike rate response of each neuron as a real number
eigSpikeRate[i] = osi.calculateSpikeRate(eigCellData[i])
eigSpikeRate[i] = preprocessing.scale(eigSpikeRate[i])
eigCirvar[i], eigDircirvar[i], _, _ = osi.computeAll(eigSpikeRate[i])
# ************************ OSI analysis ends ****************************
# scatter plot preferred orientation of each eigen neurons
if False:
x = np.angle(eigCirvar)
y = numEigNeurons*np.ones(x.shape)
if 'figPrefOri' not in locals():
figPrefOri, axPrefOri = plt.subplots()
axPrefOri.set_xlabel('Orientation')
axPrefOri.set_ylabel('Number of principal components')
axPrefOri.set_title('Orientation of eigen neurons for different components')
axPrefOri.scatter(x, y, s=30, color='red')
if numEigNeurons == numComps[-1]:
now = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M_%S")
OSI_ref = data['OSI'][0,0].reshape((65,))
cellData = np.zeros((smoothData.shape[0], stimuliSeq.size, 121))
spikeRate = np.zeros((smoothData.shape[0], stimuliSeq.size, 2))
OSI = np.zeros(smoothData.shape[0])
DSI = np.zeros(smoothData.shape[0])
cirvar = np.zeros((smoothData.shape[0]), dtype=np.complex64)
dircirvar = np.zeros((smoothData.shape[0]), dtype=np.complex64)
for i in xrange(smoothData.shape[0]):
for j in xrange(stimuliSeq.size):
cellData[i, j] = np.append(smoothData[i, 120*j: 120*(j+1)], np.radians(stimuliSeq[j]) )
# sort the array
cellData[i] = cellData[i, np.argsort(cellData[i, :, -1])]
# Calculate spike rate response of each neuron as a real number
spikeRate[i] = osi.calculateSpikeRate(cellData[i])
cirvar[i], dircirvar[i], OSI[i], DSI[i] = osi.computeAll(spikeRate[i])
# ==================== Verify results ===================
if False:
print(np.corrcoef(np.abs(cirvar), OSI_ref)) # Higher correlation required
fig, ax = plt.subplots()
x = np.array(xrange(OSI.size))
ax.plot(x, OSI_ref, linewidth=2, label='reference')
ax.plot(x, np.abs(cirvar), linewidth=2, label='estimated')
# % ----------------------- k-means clustering ----------
if False:
featureVec = np.vstack((np.abs(cirvar), np.abs(dircirvar)))
featureVec = np.transpose(featureVec)
whiteFeatureVec = whiten(featureVec)
numClusters = 3