Implement automatic eigenvalue selection

LNP_model

@@ -12,7 +12,7 @@ from datetime import datetime
 
 execution_timer = datetime.now()
 
-total_frames = 4000000
+total_frames = 10000000
 dt = 0.01   # Time step
 t = np.arange(0, total_frames*dt, dt)  # Time vector
 filter_time = .6  # The longest feature RGCs respond to is ~600ms

plotLNP.py

@@ -15,7 +15,7 @@
 
 rows = 2
 columns = 1
-fig = plt.figure(figsize=(8, 8.5))
+fig = plt.figure(figsize=(8, 10))
 
 plt.subplot(rows, columns, 1)
 plt.plot(filter_kernel1, alpha=.4)

stc.py

@@ -44,11 +44,12 @@ def stc(spikes, stimulus, filter_length, sta_temp):
 plt.xlabel('Eigenvalue index')
 plt.ylabel('Variance')
 
-eigen_indices = [0, 1]
+interesting_eigen_indices=np.where(np.abs(w-1)>.05)[0]
+eigen_indices = [0, -1]
 eigen_legends = []
 
 plt.subplot(1, 2, 2)
-for i in eigen_indices:
+for i in interesting_eigen_indices:
     plt.plot(v[:, i])
     eigen_legends.append(str('Eigenvector '+str(i)))
 plt.plot(recovered_kernel,':')