changed for multiple repros

analysis_graphs.py

@@ -10,7 +10,7 @@ import matplotlib.pyplot as plt
 from IPython import embed
 import sys
 from icr_analysis import *
-from open_nixio import *
+from open_nixio_new import *
 
 #  Parameters
 sampling_rate = 20000
@@ -19,7 +19,7 @@ delay = 1.5  # delay in seconds after comb reaches one end, before commencing mo
 cell_name = sys.argv[1].split('/')[-2]
 
 #  Open Nixio File
-curr_comb, intervals_dict = open_nixio(sys.argv[1], sys.argv[2])
+curr_comb, intervals_dict = open_nixio_new(sys.argv[1], sys.argv[2])
 
 #  Kernel Density estimator: gaussian fit
 t = np.arange(-sigma*4, sigma*4, 1/sampling_rate)
@@ -53,7 +53,7 @@ if sys.argv[2] == 'average':
             plt.close(fig)
 
 elif sys.argv[2] == 'nonaverage':
-    for (speed, direct, pos, comb) in intervals_dict:
+    for (repro, speed, direct, pos, comb) in intervals_dict:
         spike_train = intervals_dict[speed, direct, pos, comb]
         avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
         p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
@@ -73,7 +73,7 @@ elif sys.argv[2] == 'nonaverage':
         ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
         # ax2.axvline(x=max_four,linestyle='--', color='green')
 
-        plt.savefig(('nonavg_' + '_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
+        plt.savefig(('nonavg_' + str(repro) +'_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
                      + '_' + str(comb) + '_' + str(direct) + '.png'))
         plt.close(fig)
 

analysis_graphs_new.py

@@ -0,0 +1,53 @@
+#  ---------------------------------------------------------------------------------------------------------------------
+# Name:        Firing Rate and Fourier Script (moving comb repro)
+# Purpose:     Takes nixio spike data from moving comb repro and plots firing rate and power spectrum density graph
+# Usage:	   python3 analysis_graphs.py average
+# Author:      Carolin Sachgau, University of Tuebingen
+# Created:     20/09/2018
+#  ---------------------------------------------------------------------------------------------------------------------
+
+import matplotlib.pyplot as plt
+from IPython import embed
+import sys
+from icr_analysis import *
+from open_nixio_new import *
+
+#  Parameters
+sampling_rate = 20000
+sigma = 0.01  # for Gaussian
+delay = 1.5  # delay in seconds after comb reaches one end, before commencing movement again
+cell_name = sys.argv[1].split('/')[-2]
+
+#  Open Nixio File
+intervals_dict = open_nixio_new(sys.argv[1])
+
+#  Kernel Density estimator: gaussian fit
+t = np.arange(-sigma*4, sigma*4, 1/sampling_rate)
+fxn = np.exp(-0.5*(t/sigma)**2) / np.sqrt(2*np.pi) / sigma  # gaussian function
+
+
+for (repro, speed, direct, pos, comb) in intervals_dict:
+    spike_train = intervals_dict[speed, direct, pos, comb]
+    avg_convolve_spikes = gaussian_convolve(spike_train, fxn, sampling_rate)
+    p, freq, std_four, mn_four = fourier_psd(avg_convolve_spikes, sampling_rate)
+
+    #  Graphing
+    fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1)
+
+    #  Firing Rate Graph
+    firing_times = np.arange(0, len(avg_convolve_spikes))
+    ax1.plot((firing_times / sampling_rate), avg_convolve_spikes)
+    ax1.set_title('Firing Rate of trial ' + str((speed, pos)) + ' comb = ' + str(comb) + '\n')
+    ax1.set_xlabel('Time (s)')
+    ax1.set_ylabel('Firing rate (Hz)')
+
+    #  Fourier Graph
+    ax2.semilogy(freq[freq < 200], p[freq < 200])
+    ax2.axhline(y=(mn_four+std_four), xmin=0, xmax=1, linestyle='--', color='red')
+    # ax2.axvline(x=max_four,linestyle='--', color='green')
+
+    plt.savefig(('nonavg_' + str(repro) +'_' + str(cell_name) + '_' + str(speed) + '_' + str(pos)
+                 + '_' + str(comb) + '_' + str(direct) + '.png'))
+    plt.close(fig)
+
+#  ---------------------------------------------------------------------------------------------------------------------

open_nixio.py

@@ -39,6 +39,5 @@ def open_nixio(nix_file, avg_opt):
 
     #  Spike data at baseline
     # comb_baseline = spikes[(spikes < comb_pos[0])]
-    embed()
+
-    quit()
     return curr_comb, intervals_dict

open_nixio_new.py

@@ -3,24 +3,24 @@ from IPython import embed
 from collections import defaultdict
 import numpy as np
 
-nix_file = '/home/sachgau/Documents/combproject/comb_data/2018-11-16-ag/2018-11-16-ag.nix'
 
+def open_nixio_new(nix_file):
 
-f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
+    f = nix.File.open(nix_file, nix.FileMode.ReadOnly)
-b = f.blocks[0]  # first block
+    b = f.blocks[0]  # first block
-mt = b.multi_tags['moving object-1']
+    mt = b.multi_tags['moving object-1']
-comb_pos = mt.positions[:]
+    comb_pos = mt.positions[:]
-comb_ext = np.array(mt.extents[:])
+    comb_ext = np.array(mt.extents[:])
-spikes = b.data_arrays["Spikes-1"][:]
+    spikes = b.data_arrays["Spikes-1"][:]
 
-feature_dict = {}
+    feature_dict = {}
-for feat in mt.features:
+    for feat in mt.features:
         feature_dict.update({feat.data.name[16:]: mt.features[feat.data.name].data[:]})
 
-tags = b.tags
+    tags = b.tags
-intervals_dict = defaultdict(list)
+    intervals_dict = defaultdict(list)
 
-for tag in tags:
+    for tag in tags:
         if tag.name.startswith('Baseline'):
             continue
         curr_comb = tag.metadata["RePro-Info"]["settings"]["object"]
@@ -38,5 +38,5 @@ for tag in tags:
             curr_dir = feature_dict['direction'][idx]
             curr_spikes = spikes[(spikes < comb_pos[idx + 1]) & (spikes > comb_pos[idx])]
             intervals_dict.update({(tag.name, curr_speed, curr_dir, curr_pos, curr_comb): curr_spikes})
-embed()
+
-quit()
+    return intervals_dict