Add NLT recovery for STC and restructure plotting

LNP_model

@@ -105,14 +105,13 @@ def log_nlt_recovery(spikes, filtered_recovery, bin_nr, k):
     logbins = -logbins[::-1]+logbins
     logbindices = np.digitize(filtered_recovery, logbins)
     spikecount_in_logbins = np.array([])
-    for i in range(log_bin_nr):
+    for i in range(bin_nr):
         spikecount_in_logbins = np.append(spikecount_in_logbins,
                                           (np.average(spikes[np.where
                                                              (logbindices == i)]))/dt)
     return logbins, spikecount_in_logbins
 
-logbins,spikecount_in_logbins = log_nlt_recovery(spikes,
-                                                 filtered_recovery, 60, k)
+
 # %% Using mquantiles
 def q_nlt_recovery(spikes,filtered_recovery, bin_nr):
     quantiles = mquantiles(filtered_recovery,
@@ -126,7 +125,13 @@ def q_nlt_recovery(spikes,filtered_recovery, bin_nr):
                                                           (bindices == i)])/dt))
     return quantiles,spikecount_in_bins 
 
-quantiles,spikecount_in_bins = q_nlt_recovery(spikes, filtered_recovery,100)    
+logbins_sta,spikecount_in_logbins_sta = log_nlt_recovery(spikes,
+                                                 filtered_recovery, 60, k)
+
+quantiles_sta,spikecount_in_bins_sta = q_nlt_recovery(spikes, 
+                                                      filtered_recovery,100)    
+
+runfile('/Users/ycan/Documents/official/gottingen/lab rotations/LR3 Gollisch/scripts/stc.py', wdir='/Users/ycan/Documents/python')
 
 runfile('/Users/ycan/Documents/official/gottingen/lab rotations/LR3 Gollisch/scripts/plotLNP.py', wdir='/Users/ycan/Documents/python')
 

plotLNP.py

@@ -51,10 +51,10 @@
 plt.plot(k,cweight*nlt(k,nlt_index1)+(1-cweight)*nlt(k,nlt_index2),alpha=.6)
 
 plt.subplot(rows,columns,2)
-plt.scatter(logbins,spikecount_in_logbins,s=6,alpha=.6)
+plt.scatter(logbins_sta,spikecount_in_logbins_sta,s=6,alpha=.6)
 
 plt.subplot(rows,columns,2)
-plt.scatter(quantiles,spikecount_in_bins,s=6,alpha=.6)
+plt.scatter(quantiles_sta,spikecount_in_bins_sta,s=6,alpha=.6)
 plt.legend(['Non-linear transformation {}'.format(nlt_index1),
             'Non-linear transformation {}'.format(nlt_index2),
             '{}*NLT{}+{}*NLT{}'.format(cweight,nlt_index1,
@@ -71,3 +71,28 @@
       .format(np.round(np.max(t)),dt))
 print('{} spikes generated.'.format(int(sum(spikes)))
        )
+
+# %% Plotting STC
+
+fig = plt.figure(figsize=(12, 4))
+
+plt.subplot(1, 2, 1)
+plt.plot(w, 'o', markersize=2)
+plt.xlabel('Eigenvalue index')
+plt.ylabel('Variance')
+
+
+eigen_legends = []
+
+plt.subplot(1, 2, 2)
+for i in interesting_eigen_indices:
+    plt.plot(v[:, i])
+    eigen_legends.append(str('Eigenvector '+str(i)))
+plt.plot(recovered_kernel,':')
+eigen_legends.append('STA')
+plt.legend(eigen_legends, fontsize='x-small')
+plt.title('Filters recovered by STC')
+plt.xlabel('?')
+plt.ylabel('?')
+plt.show()
+

stc.py

@@ -37,30 +37,14 @@ def stc(spikes, stimulus, filter_length, sta_temp):
 w = w[sorted_eig]
 v = v[:, sorted_eig]
 
-fig = plt.figure(figsize=(12, 4))
-
-plt.subplot(1, 2, 1)
-plt.plot(w, 'o', markersize=2)
-plt.xlabel('Eigenvalue index')
-plt.ylabel('Variance')
-
 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 interesting_eigen_indices:
-    plt.plot(v[:, i])
-    eigen_legends.append(str('Eigenvector '+str(i)))
-plt.plot(recovered_kernel,':')
-eigen_legends.append('STA')
-plt.legend(eigen_legends, fontsize='x-small')
-plt.title('Filters recovered by STC')
-plt.xlabel('?')
-plt.ylabel('?')
+logbins_stc1,spikecount_in_logbins_stc1 = log_nlt_recovery(spikes,
+                                                 v[:,eigen_indices[0]], 60, k)   
+#quantiles_stc1,spikecount_in_bins_stc1 = q_nlt_recovery(spikes, filtered_recovery,100)    
+logbins_stc2,spikecount_in_logbins_stc2 = log_nlt_recovery(spikes,
+                                                 v[:,eigen_indices[1]], 60, k)     
+
 
-#plt.plot(v[:, -1])
-#plt.plot(v[:, -2])
-#plt.legend(['1', '2', '-1', '-2'], fontsize='x-small')
-plt.show()