Change plotting for report, add list of interesting cells

figure_plotter.py

@@ -12,19 +12,112 @@
 import matplotlib.pyplot as plt
 
 
-plotthis = [['2017_02_14', '901', 'On cell'],
-            ['2017_01_17', '201', 'Off cell']]
+#           Exp date      Cluster Label Flipped
+plotthis = [['2017_02_14', '901', 'On cell', False],
+            ['2017_01_31', '8401', 'Off cell', True],
+            ['2017_01_17', '201',  'On off 1', True]]
 
 for i in plotthis:
 
     main_dir = '/Users/ycan/Documents/official/gottingen/lab rotations/\
 LR3 Gollisch/data/Experiments/Mouse/'
-    
+
     currentfile_c = main_dir + i[0] +\
-                     '/analyzed/2_SP_C' + i[1] + '.npz'
+                    '/analyzed/2_SP_C' + i[1] + '.npz'
     currentfile_f = main_dir + i[0] +\
-                     '/analyzed/3_SP_C' + i[1] + '.npz'
-
-
+                    '/analyzed/3_SP_C' + i[1] + '.npz'
+
     c = np.load(str(currentfile_c))
     f = np.load(str(currentfile_f))
+
+    savepath = '/Users/ycan/Documents/official/gottingen/lab rotations/\
+LR3 Gollisch/figures' + i[2]
+
+    flipper = 1
+    if i[3]:
+        flipper = -1
+
+    # %% plot all
+    plt.figure(figsize=(12, 12), dpi=200)
+    plt.suptitle(str(' '.join(str(c['spike_path'])
+                 .split('rasters')[0].split('Experiments')[1]
+                 .split('/'))+str(i[1])))
+    plt.subplot(3, 3, 1)
+    plt.plot(f['sta']*flipper)
+    plt.plot(f['v'][:, 0]*flipper)
+    plt.title('Filters')
+    plt.legend(['STA', 'Eigenvalue 0'], fontsize='small')
+    plt.xticks(np.linspace(0, 20, 3), np.linspace(0, 600, 3).astype(int))
+    plt.ylabel('Full field flicker\n$\\regular_{Linear\,\,\,output}$',
+               fontsize=16)
+    plt.xlabel('Time [ms]')
+
+    ax = plt.subplot(3, 3, 2)
+    plt.plot(f['bins_sta'], f['spikecount_sta'][::flipper], '-')
+    plt.plot(f['bins_stc'], f['spikecount_stc'][::flipper], '-')
+    plt.text(.5, .99, 'On-Off Bias: {:2.2f}\nTotal spikes: {}'
+             .format(float(f['onoffindex']), f['total_spikes']),
+             horizontalalignment='center',
+             verticalalignment='top',
+             transform=ax.transAxes)
+    plt.title('Non-linearities')
+    plt.ylabel('Firing rate')
+    plt.xlabel('Linear output')
+
+    plt.subplot(3, 3, 3)
+    plt.plot(f['w'], 'o')
+    plt.title('Eigenvalues of covariance matrix')
+    plt.xticks(np.linspace(0, 20, 3))
+    plt.xlabel('Eigenvalue index')
+    plt.ylabel('Variance')
+
+    plt.subplot(3, 3, 4)
+    plt.plot(c['sta_weighted']*flipper)
+    plt.plot(c['v'][:, 0]*flipper)
+    plt.plot(c['temporal'])
+    plt.title('Filters')
+    plt.ylabel('Checkerflicker\n$\\regular_{Linear\,\,\,output}$', fontsize=16)
+    plt.xlabel('Time [ms]')
+    plt.xticks(np.linspace(0, 20, 3), np.linspace(0, 600, 3).astype(int))
+    plt.legend(['Weighted stimulus', 'Eigenvalue 0', 'Brightest pixel'],
+               fontsize='small')
+
+    ax = plt.subplot(3, 3, 5)
+    for j in range(len(c['bins'])):
+        plt.plot(c['bins'][j], c['spike_counts_in_bins'][j][::flipper], '-')
+    plt.text(.5, .99, 'On-Off Bias: {:2.2f}\nTotal spikes: {}'
+             .format(float(c['onoffindex']), c['total_spikes']),
+             horizontalalignment='center',
+             verticalalignment='top',
+             transform=ax.transAxes)
+    plt.title('Non-linearities')
+    plt.xlabel('Linear output')
+    plt.ylabel('Firing rate')
+
+    plt.subplot(3, 3, 6)
+    plt.plot(c['w'], 'o')
+    plt.title('Eigenvalues of covariance matrix')
+    plt.xticks(np.linspace(0, 20, 3))
+    plt.xlabel('Eigenvalue index')
+    plt.ylabel('Variance')
+
+    plt.subplot(3, 3, 7)
+    plt.imshow(c['sta_unscaled'][:, :, c['max_i'][2]].reshape((60, 80,)),
+               cmap='Greys',
+               vmin=np.min(c['sta_unscaled']),
+               vmax=np.max(c['sta_unscaled']))
+    plt.title('Receptive field')
+
+    plt.subplot(3, 3, 8)
+    f_size = 5
+    plt.imshow(c['sta_unscaled'][c['max_i'][0]-f_size:c['max_i'][0]+f_size+1,
+                                 c['max_i'][1]-f_size:c['max_i'][1]+f_size+1,
+                                 int(c['max_i'][2])],
+               cmap='Greys',
+               vmin=np.min(c['sta_unscaled']),
+               vmax=np.max(c['sta_unscaled']))
+    plt.title('Brightest pixel: {}'.format(c['max_i']))
+    plt.tight_layout(pad=5, h_pad=1, w_pad=1.8)
+    plt.show()
+#    plt.savefig(savepath, dpi=200, bbox_inches='tight')
+    plt.close()

interesting_cells.txt

@@ -0,0 +1,32 @@
+2016-10-21
+3002	exponential for both
+3201	quadratic for checker
+4403	exponential for both
+4704	quadratic for checker
+9702	exponential
+11101	upside down temporal fff filter
+23803	flips nlt from off to on
+
+2017-01-17
+103	exponential for both, higher ooi for onoffsteps
+703	onoff for checker, exponential FFF
+902	quadratic for FF, exponential checker
+3101	quadratic to exponential
+3307	quadratic to exponential, interesting RF
+4105	flips, quadratic to exponential
+6205	flips, sigmoid to exponential, interesting RF
+7703	flips for onoffsteps, exponential otherwise
+9502	flips, both exponential
+14103	typical onoff cell?
+23101 	flips for onoffsteps, linear otherwise
+
+2017-01-31 small number of cells
+301	extreme exponential, flips for onoffsteps
+1101	flips, linear for both
+8401	typical off cell
+8501	typical on cell
+
+2017-02-14
+901 	typical on cell
+20601	typical off cell but onoff with onoffsteps
+22302 	flips from on to off