- Add axis labels for all graphs

- Fix orientation for STA graphs

LNP_model

@@ -9,7 +9,7 @@ Created on Tue May  9 18:11:51 2017
 import numpy as np
 from scipy.stats.mstats import mquantiles
 
-total_frames = 200000
+total_frames = 400000
 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

@@ -11,8 +11,8 @@
 plt.style.use('default')
 matplotlib.rcParams['grid.alpha'] = 0.1
 rows = 2
-columns = 2
-fig = plt.figure(figsize=(12, 8))
+columns = 1
+fig = plt.figure(figsize=(8, 8.5))
 
 plt.subplot(rows, columns, 1)
 plt.plot(filter_kernel1, alpha=.2)
@@ -31,11 +31,12 @@
             'Filter {}'.format(filter_index2),
             '{}*Filter {}+{}*Filter {}'.format(cweight, filter_index1, 
              np.round(1-cweight,2),filter_index2),
-             'Spike triggered average (STA)'],
+            'Spike triggered average (STA)'],
             fontsize='x-small')
 plt.grid()
-plt.title('Linear transformation')
-plt.xlabel('Time [ms]')
+plt.title('Linear filters')
+plt.xlabel('?')
+plt.ylabel('?')
 
 
 plt.subplot(rows,columns,2)
@@ -59,6 +60,8 @@
             'Recovered using quantiles'],
             fontsize='x-small')
 plt.title('Non-linear transformation')
+plt.xlabel('?')
+plt.ylabel('?')
 
 plt.show()
 print('{} seconds were simulated with {} s time steps.'

stc.py

@@ -37,17 +37,21 @@ def stc(spikes, stimulus, filter_length, sta_temp):
 w = w[sorted_eig]
 v = v[:, sorted_eig]
 
-fig=plt.figure(figsize=(12, 4))
+fig = plt.figure(figsize=(12, 4))
 
-plt.subplot(1,2,1)
+plt.subplot(1, 2, 1)
 plt.plot(w, 'o', markersize=2)
+plt.xlabel('Eigenvalue index')
+plt.ylabel('Variance')
 
-plt.subplot(1,2,2)
+plt.subplot(1, 2, 2)
 plt.plot(v[:, 0])
 plt.plot(v[:, 1])
 plt.plot(recovered_kernel)
-plt.legend(['0', '1', 'STA'], fontsize='x-small')
-
+plt.legend(['Eigenvector 0', 'Eigenvector 1', 'STA'], fontsize='x-small')
+plt.title('Filters recovered by STC')
+plt.xlabel('?')
+plt.ylabel('?')
 
 #plt.plot(v[:, -1])
 #plt.plot(v[:, -2])