- Add timer for LNP model

- Iterative plotting for STC recovered filters

LNP_model

@@ -8,8 +8,11 @@ Created on Tue May  9 18:11:51 2017
 # %%
 import numpy as np
 from scipy.stats.mstats import mquantiles
+from datetime import datetime
 
-total_frames = 400000
+execution_timer = datetime.now()
+
+total_frames = 100000
 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
@@ -21,10 +24,10 @@ cweight = .5  # The weight of combination for the two filters
 def make_noise():  # Generate gaussian noise for stimulus
     return np.random.normal(0, 1, total_frames)
 
-stimulus = make_noise()
+#stimulus = make_noise()
 
-filter_index1 = 3   # Change filter type here
-filter_index2 = 1
+filter_index1 = 4  # Change filter type here
+filter_index2 = 3
 
 
 def linear_filter(t, filter_index):    # Define filter according to choice
@@ -49,8 +52,8 @@ filtered2 = np.convolve(filter_kernel2, stimulus,
                         mode='full')[:-filter_length+1]
 
 k = np.linspace(-5, 5, 1001)
-nlt_index1 = 2
-nlt_index2 = 4
+nlt_index1 = 6
+nlt_index2 = 6
 
 # %%
 def nlt(k, nlt_index):
@@ -123,3 +126,6 @@ for i in range(bin_nr):  # Sorts values into bins
                                                       (bindices == i)])))
 
 runfile('/Users/ycan/Documents/official/gottingen/lab rotations/LR3 Gollisch/scripts/plotLNP.py', wdir='/Users/ycan/Documents/python')
+
+runtime = str(datetime.now()-execution_timer).split('.')[0]
+print('Duration: {}'.format(runtime))

plotLNP.py

@@ -15,11 +15,11 @@
 fig = plt.figure(figsize=(8, 8.5))
 
 plt.subplot(rows, columns, 1)
-plt.plot(filter_kernel1, alpha=.2)
+plt.plot(filter_kernel1, alpha=.4)
 #plt.title()
 
 plt.subplot(rows, columns, 1)
-plt.plot(filter_kernel2, alpha=.2)
+plt.plot(filter_kernel2, alpha=.4)
 
 plt.subplot(rows, columns, 1)
 plt.plot(cweight*filter_kernel1+(1-cweight)*filter_kernel2, alpha=.6)

stc.py

@@ -7,10 +7,8 @@
 
 Spike-triggered covariance
 """
-from datetime import datetime
 import numpy as np
 import matplotlib.pyplot as plt
-execution_timer = datetime.now()
 
 sta_temp = sta(spikes, stimulus, filter_length)
 
@@ -21,14 +19,12 @@ def stc(spikes, stimulus, filter_length, sta_temp):
         if spikes[i] != 0:
             snippet = stimulus[i:i-filter_length:-1]
             # Snippets are inverted before being added
-            snpta = np.array(snippet-sta_temp)[np.newaxis,:]
+            snpta = np.array(snippet-sta_temp)[np.newaxis, :]
             covariance = covariance+np.dot(snpta.T, snpta)*spikes[i]
     return covariance/(sum(spikes)-1)
 
 recovered_stc = stc(spikes, stimulus, filter_length,
                     sta(spikes, stimulus, filter_length))
-runtime = str(datetime.now()-execution_timer).split('.')[0]
-print('Duration: {}'.format(runtime))
 
 # %%
 w, v = np.linalg.eig(recovered_stc)
@@ -44,11 +40,16 @@ def stc(spikes, stimulus, filter_length, sta_temp):
 plt.xlabel('Eigenvalue index')
 plt.ylabel('Variance')
 
+eigen_indices = [0, 1, 2]
+eigen_legends = []
+
 plt.subplot(1, 2, 2)
-plt.plot(v[:, 0])
-plt.plot(v[:, 1])
-plt.plot(recovered_kernel)
-plt.legend(['Eigenvector 0', 'Eigenvector 1', 'STA'], fontsize='x-small')
+for i in 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('?')

zoooftransformations

@@ -11,9 +11,9 @@ Zoo of linear filters and non-linear transformations
 import matplotlib.pyplot as plt
 import matplotlib
 
-rows = 1
-columns = 2
-fig = plt.figure(figsize=(12, 4))
+rows = 2
+columns = 1
+fig = plt.figure(figsize=(6, 8))
 
 plt.subplot(rows, columns, 1)
 filter_legends = []